i.e. boundaries between individuals occurring at the same time, or « physical » boundaries -as opposed to diachronic boundaries, that occur between successive generations. on the long term, of individual dispersal events. Whereas dispersal is an individual-and generation-centred phenomenon that may be observed directly, migration is a time-averaged, population-centred event that is only detectable through indirect methods, such as gene ow reconstruction. us, while dispersal properly belongs to the life-history theory 1 , and is expected to be 1.

In a word, the life history theory reconsiders traits of anatomy, physiology and behaviour in the light of « life history traits », i.e. reproductive, demographic or foraging strategies (see Stearns 1992 for details).

gian Sequencing Centre team's creativity and skill made this work possible: I am extremely grateful to Morten Skage's ideas and hard work, Marianne Selander Hansen's skill and optimism, and Ave Tooming Klunderud's support, as well as to Lex Nederbragt's experience and help. is work was nally achieved thanks to the support of Pr Denis Allemand and the Centre Scienti que de Monaco, who welcomed me for the nal months of this work, and provided continued collaboration and support for its whole duration. I thank the CSM team for its warm welcome -and particularly Leila Ezzat for proofreading this thesis.

I happily thank Pr Guillermo Luna Jorquera and the merry team of the EDAM, at the Universidad Catolica del Norte, in Coquimbo -Claudia Fernandez Zamorra, Diego Miranda Urbina, and Nicole Licuime Castillo for their invaluable help on the eld, Nicolas Gouin and Rasme Hereme from the CEAZA in the lab, and Paula Plaza and Maritza Cortes Labra for their ideas and experience.

I am sincerely grateful to Dr John Davey and Dr Grégory Beaugrand who took the time to review in great depth this hefty manuscript, to Dr Serge Potier who also presided the jury during its defense, and to Dr Giorgio Bertorelle, after contributing largely to this work, travelled a long way to discuss it at lenght. Defending this thesis in front of such a jury has been an honour -but also a great and unexpected pleasure.

Finally, although a PhD thesis is a rather dreary thing to dedicate, all my thanks go to my family -and in particular to my parents, to Cécile, who gave me the empirical demonstration of how to defend a PhD, and to Hélène, who is nearly there too -and to the crew of the schooner La Sonate -Norith Eckbo, Jean-Christophe Gairard, Florian Maury and Marc Danger -for opening the windows of the world for a breath of fresh air after all this exhausting work.

/ 337

Table of contents

Index of figures

Articles and communications. e following articles are included in this thesis:

-

Foreword

e work we present here lies in-between two poles of biological sciences -ecology and evolution. Increasingly well explored from a theoretical and laboratory perspective, the practical interface of these two disciplines, when applied to wild species and natural ecosystems, is at the core of intense contemporary investigation: for this reason, as will appear to the reader, the methods we use matter nearly as much as the conclusions we reach. To that aim, we will examine the particular stories of three seabirds of the Southern Seas, the Emperor penguin (Aptenodytes forsteri),

the King penguin (Aptenodytes patagonicus), and the Peruvian diving-petrel (Pelecanoides garnotii), each of them confronted to environmental change in its own manner. Our primary line of investigation will be the way genetic data -an evolutionary exploration tool by excellence -can be integrated in the framework of ecological models, and in particular in numeric representations of climate.

is thesis is divided into eight main sections. A rst introductory chapter will present the main concepts around which our work revolves, as well as its climatic and oceanographic framework. A second chapter will give an overview the methodological concepts that will be deployed in this particular study. e third and fourth chapters will focus on the King penguin, and its interactions with its habitat both at local, and global scale. e fth and sixth chapters will repeat this exercise for the Emperor penguin. e seventh chapter, that presents the particular case of the little-known Peruvian diving-petrel, will showcase how these methods can be used as an exploratory tools in remote species where eld data is scarce. Finally, an eighth and last chapter will sum-

1. e « un-divisible », by etymology.

2.

Fitness is rst de ned by Darwin 1859, together with the concept of natural selection, as the idea that « individuals having any advantage, however slight, over others, [...] have the best chance of surviving and of procreating their kind. On the other hand, we may feel sure that any variation in the least degree injurious would be rigidly destroyed. is preservation of favourable variations, and the destruction of injurious variations, I call Natural Selection, or the Survival of the Fittest » -tness being here loosely conceived as the advantage conferred to an individual by its particular characteristics. As further noted by [START_REF] Barker | Adaptation and Fitness in Animal Populations: Evolutionary and Breeding Perspectives on Genetic Resource Management[END_REF], « tness may refer to a marise the main ndings of this work, and propose some conclusions, both for our three focal species, and for the development of more accurate investigation methods.

As already appears from its organisation, the question of scales -both spatial, and temporal -will be a structuring one throughout this manuscript. e complexity of temporal scales in speciation processes is still at the core of evolutionary investigations (see e.g. [START_REF] Gould | Punctuated equilibria: an alternative to phyletic gradualism. 283. Gouretski V[END_REF] -and this complexity only increases when considering ecological processes together with evolutionary ones. When focusing on a metazoan organism (a simple case compared to the bewildering organisation of plants), a point of relatively atomic singularity is reached with the individual 1 . Above that scale, the longer-lasting populations and species are increasingly complex ensembles obeying to stochastic processes; while below that scale, the rapid and minute scales of physiology bring us into yet another stochastic realm of populations, of cells or molecules this time: thus, going below or above the individual indistinctly leads us back from the most singular towards an increasing form of universality.

e nested temporal scales of individuals, populations and species also need to be thought of in the complex temporality of their environment. Metazoan life is directly characterised by the instantaneous interface between the individual, and the immediate conditions of its environment. At an instantaneous scale, this interface is highly variable, both externally (through the presence or absence of food, conspeci cs, c ompetitors, o r t he s tate o f t he w eather, e tc) a nd i nternally (i.e.

through the physiological state of the individual), and leads to a wide variation of « instantaneous tness » -or, more simply, immediate well-being. Yet this stochastic variation is averaged throughout the lifespan of the individual to a range of external and internal conditions, that results in a individual general tness 2 . Further on, the large-scale uctuations in average weather, or climate, together with the associated changes in biological communities 1 , impose long-term patterns on the average relationship of large numbers of individuals to their changing environment -and, ultimately, evolution. Finally, in-between the immediate contact of the individual and the instant, and the slow change of the relationship of the species and the state of the world, lie the multiple facets of intermediate variations. e oscillating climatic phenomena that operate on a few years scale, the centennial variations of the Southern Annular Mode, or the slower pulse of Milanković cycles and Pleistocene glaciations, all blur any direct, linear extension of short-term phenomena to long-time scales. is fractal conception of the scales on which chronological processes operate is mirrored in space, through the local heterogeneity both of individuals and of environment: local adaptation or local extinction often occur at cross-current with the general stream of evolution.

e theory of population genetics seems to take all its sense at the light of this complex structure of time and space -with the curious meaning that time and space take there. A gene (this very material, real and transient fragment of a DNA molecule bounded both in space and in time) is progressively abstracted in the population genetics framework until it becomes a continuous timeobject -a sequence. In this framework, the diversity of DNA molecules is relieved both from its synchronic and diachronic boundaries, as formalised through the approximations of the continuous-time coalescent and the di usion equations of drift (see later on in §39 p. 99 and §40 p. 101).

e resulting generalised object now transforms slowly and continuously over the evolution of the species, in an essentially Braudelian longue durée conception 2 . Yet this approximation requires the sacri ce of the most obviously discrete aspects of metazoan life: the individual and the generation.

genotype, an individual, a population or a species », but may be broadly de ned as « the ability of organisms to pass on the genes they carry ». 1.

By « biological community », or biocoenosis, we refer to the ensemble of species that share a single habitat and interact within it (see Möbius 1877).

2.

See Braudel 1958, and §16 p. 60. In parallel, however, a fundamental postulate of the modern synthesis is that evolution operates precisely at the level of the individual [START_REF] Mayr | The evolutionary synthesis: perspectives on the unification of biology[END_REF][START_REF] Schlosser | Evidence for gene flow differs from observed dispersal patterns in the Humboldt penguin, Spheniscus humboldti[END_REF]. At this level, genetic material is once again highly bounded: within the space of the individual's body, it is classically (although simplistically) considered one, and collectively subjected to tness selection at the interface of the body and the environment. e s anction o f t his tness se lection on the space of the body is the shift of its boundaries in time: the frequency of beginning of a new organism carrying these genes, or breeding, and the delayed demise of that ensemble (or death): more beginnings and later demises are the way evolutionary success is sanctioned. Breeding and death have a position in time, yet they are also spatial boundaries, as they describe the beginning and end of the body, that embodiment of the abstract sequence along its continuous tree-like evolution.

And so, a mise en abyme occurs for the gene too: the processes that apply on the individual (a collection of identical DNA molecules) as tness selection apply on the family group (an ensemble of highly similar molecules) as kinship selection, and on the species as evolution. And we are left wondering whether the variations in tness that we document at the level of the individual (the variation in individual « quality » and life-history choices, and their direct consequences on that individual's life) are of the same nature as the broad trends in the adaptation of the species to an environment and a state of the climate, or whether the tness of an individual is an essentially di erent concept from the tness of a population, the one involving a stochastic, particular association of alleles, and the other a collective distribution of allele frequencies and expected tness selected through repeated, but inaccurate, individual trajectories and accidents.

In ne, this amounts to asking how prevalent the individual should be in the study of the collective, and whether there is any sense at all in disregarding, even for theoretical purposes, that intuitively central gure when considering the fate of species. Although here purely methodological, this question ultimately converges towards ethical perspectives: indeed, the relative importance of the individual and the species is a di cult intuition, or decision, in our relationship to nature as a whole. Conservation biology is mostly concerned with species, at the cost of disregarding individual trajectories: yet it may sometimes appear that this conception fails to capture the true complexity of our surroundings, by reducing to a handful of taxonomic categories what is really a rich spectrum of individual consciousnesses 1 . And paradoxically, despite its near-complete disappearance throughout the present manuscript, I nish with a clear intuition that only the individual makes absolute sense, and that the ultimate goal of my study was to better understand what exactly is the experience of the singular seabird who, against larger population trajectories and environmental changes, « ever stands forth his own inexorable self » 2 . However, these considerations must remain the salt of this work, and should not interfere with the detachment that is required by all sound scienti c endeavours.

umbing through this manuscript, it will be evident that the diversity of methods is beyond a single person's work. e contributions of Dr Céline Le Bohec on the ecological side, and of Dr Emiliano Trucchi on the evolutionary side, have been determinant at every single step of this study. No less important was the expertise of Dr Giorgio Bertorelle, Dr Xiaoming Liu, and Dr Virginie Raybaud, in giving shape and soundness to the di erent parts of this manuscript: I want to thank them here once again. 1.

« Yet what is consciousness? You can imagine that I will not de ne something so concrete, to constantly present to the experience of each of us. But without giving of consciousness a de nition that would be less clear than consciousness itself, I can characterise it through its most prominent feature: consciousness means memory » [START_REF] Bergson | Life and consciousness[END_REF]). 2.

To quote from Melville's Moby Dick.

Introduction - §1

Chapter 1: Introduction

Populations, philopatry and dispersal §-1 Species: type and repetition. e s ystematic d escription o f s pecies c an b e s een a s a n extreme mediation of the individual and the collective. A single sample, the holotype, is taken as the explicit reference for bestowing the species' name -and the rest is essentially a repetition, in time and in space, of that individual type. at r epetition, h owever, c annot b e p erfect [START_REF] Kierkegaard | Gjentagelsen. Et Forsøg i den experimenterende Psychologi af Constantin Constantius[END_REF]):

variation, or diversity, plays a central role, both in time and in space [START_REF] Mayr | The growth of biological thought: diversity, evolution, and inheritance[END_REF]. Intra-speci c phenotypic variation only became the focus of biological sciences as « individual di erences » with the works of Darwin, who famously understood them to « a ord materials for natural selection to act on and accumulate » (Darwin 1859): the genetic basis of this diversity is now at the centre of the modern evolutionary synthesis [START_REF] Mayr | The evolutionary synthesis: perspectives on the unification of biology[END_REF][START_REF] Schlosser | Evidence for gene flow differs from observed dispersal patterns in the Humboldt penguin, Spheniscus humboldti[END_REF]. While not serving any pre-de ned purpose, variation may a posteriori be organised along a spectrum ranging from adaptive to neutral diversity [START_REF] Kimura | Solution of a process of random genetic drift with a continuous model[END_REF][START_REF] Wagner | Neutralism and selectionism: a network-based reconciliation[END_REF]. At the molecular level, adaptive variation can be de ned as the appearance of mutations with a bene cial phenotypic e ect, that are positively selected in the population [START_REF] Mayr | Populations, species, and evolution: an abridgment of animal species and evolution[END_REF], while neutral variation is the appearance of mutations o ering virtually no hold to natural selection [START_REF] Kimura | Solution of a process of random genetic drift with a continuous model[END_REF]. Without taking position as to the relative importance of neutral and adaptive variation in molecular evolution (a still debated topic, see e.g. [START_REF] Wagner | Neutralism and selectionism: a network-based reconciliation[END_REF], the present work will mostly focus on neutral variation, as an insight into non-molecular ecological processes.

Introduction - §1
e organisation of molecular variation in space and time is the foundation of population genetics [START_REF] Wolff | The interpretation of population structure by F-statistics with special regard to systems of mating[END_REF]Kingman 2000), which can be de ned as the study of genetic diversity within the conventional spatio-temporal bounds of a species -those « merely arti cial combinations made for convenience » in the midst of « intermediate gradations » [START_REF] Darwin | On the origin of species[END_REF]). e extent of this diversity and its organisation re ect b asic c haracteristics o f t he s pecies: g enetic d iversity i tself has been linked to a variety of parameters, ranging from organismal complexity [START_REF] Lynch | The origins of genome complexity[END_REF] to population size and history [START_REF] Amos | Factors affecting levels of genetic diversity in natural populations[END_REF] or life-history strategies (Romiguier et al. 2014), while its organisation in space and time is largely a consequence of its demographic and migratory history (a point introduced in §41 p. 103). erefore, studying species as a population-scaled system allows us to capture not only their intrinsic taxonomic diversity, but also the variety their ecological and evolutionary dynamics.

A most remarkable feature of the population genetic approach to within-species diversity, that has important methodological consequences, is (as far as metazoans are concerned) its necessarily ambivalent conception of the individual. Population genetics are, in a sense, pervaded with an overwhelming notion of singularity: as Ernst Mayr put it, « wherever we look, we nd uniqueness, and uniqueness spells diversity » [START_REF] Mayr | The growth of biological thought: diversity, evolution, and inheritance[END_REF]). e i ndividual, a s a s ingular ( and l ikely unique) combination of alleles, is the only possible form upon which tness selection may operate, and thus the only e ective shape of genetic material -in that sense, a population, or a gene pool, is nothing else than an abstract representation of what is really only an aggregation of singular individuals. Although the concepts of species or populations (see §2 p. 25) are essential tools for understanding behavioural, demographic or evolutionary processes, the only observable, uninterpreted atomic unit of metazoan life is the individual. Hence, any empirical study that relies on the sampling of groups or populations really relies, in fact, on the sampling of a collection of unique individuals, the assignment to a group being a posterior decision of the observer (which poses very concrete questions of sampling design, see Fine-scale colony structure, p. 135).

And yet the individual is dissolved either vertically, or transversally, throughout the theoretical framework of population genetics. e central notion of allele frequency (see §45 p. 117), for example, exists only through the transversal analysis (i.e. at a single point of time) of a large number of chromosomes at a single genomic position, without retaining any information as to the particular associations of alleles within individuals -it can thus be conceived as a removal of the synchronic 1 boundaries between individuals -an idea that reaches its mathematical consecration in the di usion equations approximating genetic drift (see §39 p. [START_REF] Bouzat | Regional genetic structure in the Magellanic Penguin (Spheniscus magellanicus) suggests metapopulation dynamics[END_REF]). e whole structure of the coalescent (see §40 p. 101 sqq.), on the other hand, relies on the vertical (i.e. cross-generation) analysis of sequences, throughout which the advent and demise of the individual (i.e. the generation) is an accident, and a mere unit of time: thus, the coalescent concept itself involves the dissolution of the diachronic separation between individuals -a conception that is formalised in the continuous-time representation of the coalescent, that completely discards the very notion of generation or individual (see §45 p. 117).

is paradoxical representation of singularity in the population genetic framework is in stark contrast with the prevalent concept of the individual both in the systematic framework (where singularity is reduced to dispersion around the holotype in morphospace), and in the ecological framework -where diversity is either replaced by homogeneous population parameters such as density [START_REF] Bolnick | Why intraspecific trait variation matters in community ecology[END_REF], or on the contrary emphasised through attention to singular lifehistory choices (e.g. Le Bohec 2008;[START_REF] Weimerskirch | Seabirds-individuals in colonies[END_REF]. One of the challenges of the modern evolutionary synthesis therefore remains the reconciliation of these di erent paradigms despite widely divergent tenets. In particular, the di erence between the concepts of population size or migration rate in the ecological and the population genetic (henceforth « evolutionary ») paradigms (see §41 p. 103 and Fig. 1 p. 24) makes the integration of cross-disciplinary sources dicult (see e Emperor synnome, p. 211) -and so do the very di erent time scales on which these

Introduction - §1
di erent frameworks are based: in many respects, the population genetics framework occupies an intermediate position between systematics (that operates on the speciation time scale) and ecology (that focuses on the discrete lifespans of individuals). While the synthesis of these di erent modes is, of course, beyond the scope of this work, the questions addressed here, being at the crossroads of ecology and population genetics, will be best understood through this unavoidable ambivalence.

Introduction - §1

Figure 1 | Demography in the Ecological and the Genetic paradigms. In the ecological paradigm, a subset of individuals is used to infer population-wide demographic parameters, and these de ne a general population model, normally in units of number of breeders and at the decadal scale. In the genetic paradigm, a subset of

Introduction - §2
chromosomes is sampled, that integrates diachronic aspects of the population: this subset is used to t a genome evolution model, that further de nes a general demographic model, in units of e ective breeders (see §41 p. 103) and usually at the scales of several thousand years. §-2 Population, deme, colony. Just as the distribution of individual positions in the morphological, behavioural or genetic space de nes a species' patterns of spatial and temporal diversity, the extent of the overlap between an individual's movement range on the one hand and the species' range on the other determines a spectrum of spatial structures, with homogeneous, panmictic ensembles on the one end, and highly heterogeneous and fragmented systems on the other. e particular architecture of a species integrates processes occurring on very di erent scales: and its description will change according to the framework in which analysis is conducted (Esler et al. 2006). e de nition of a population -a central concept in a eld such as population genomicsthus di ers widely according to whether we place ourselves in a genetic, or an ecological paradigm [START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. In the ecological paradigm, the criteria retained are usually geography (individuals occurring at the same place, e.g. Krebs 2013), interactions between individuals (such as sharing food resources, e.g. Hu aker & Gutierrez 1984), or, importantly, demographic independence (i.e. migration between groups is not su cient to force them into similar demographic trajectories, or in uence extinction risk of one group, e.g. [START_REF] Mccauley | Divergent trends in land and ocean temperature in the Southern Ocean over the past 18,000 years[END_REF]. Waples

and Gaggiotti [START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF] summarise the ecological de nition as « a group of individuals of the same species that co-occur in space and time and have an opportunity to interact with each other » -a de nition that relies on the examination of a species distribution, demography and behaviour at an observable timescale, i.e. in the order of magnitude of the observer's own lifetime.

In the genetic or « evolutionary » paradigm [START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF], on the other hand, a population is rather de ned a s a b reeding c ommunity, i .e. a g roup o f i ndividuals t hat h ave a higher probability of breeding with each other, than with an individual from another group [START_REF] Hartl | Principles of population genetics[END_REF], and consequently as a group of individuals with correlated genotypes. e

Introduction - §2
quantitative aspect of the minimum correlation needed to achieve population status is debated: but whenever a standard criterion is proposed, it relies on a comparison of the relative in uences of generic drift and migration in shaping a group's genetic diversity (see §39 p. 99 and §41 p. 103). For example, a threshold of one e ective migrant per generation has been used in empirical studies [START_REF] Mesak | Hundreds of SNPs vs. dozens of SSRs: which dataset better characterizes natural clonal lineages in a self-fertilizing fish[END_REF]Vucetich & Waite 2000;Wang 2004): if two groups exchange more than one e ective migrant per generation, they are not considered separate. Higher thresholds have been proposede.g. a maximum of 5, or 25 e ective migrants per generation (see [START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF] for a review). As these criteria are concerned with per-generation parameters, they can only apply when averaged over several generationsi.e. on a much longer time frame than the ecological de nition.

e relationship between the concepts of population in the ecological and the evolutionary paradigms is not a trivial one. Although it would seem that behavioural and geographical isolation of a group of individuals naturally leads to reduced gene ow and genetic isolation, it is not clear how far the patterns observed on the decadal scale also apply over longer periods, as group cohesion may be heterogeneous over time or space, and lead to the apparent paradox of behaviourally very structured, yet genetically fully admixed species (e.g. Harlequin ducks, Esler et al. 2006, or Wandering albatrosses, [START_REF] Milot | The seabird paradox: dispersal, genetic structure and population dynamics in a highly mobile, but philopatric albatross species[END_REF]. In order to clearly distinguish between these di erent paradigms, in the present work, we will mainly use three related -but not equivalent -terms to describe the architecture of species: a deme, a population, and a colony.

A deme is a fully genetic de nition o f a g roup o f i ndividuals. I t m ay b e s ummarised a s « the largest area or collection of individuals where mating is (on average) random » (Hamilton 2011). is de nition does not necessarily include a spatial component, as several groups of random-mating individuals may occur in sympatry (e.g. Killer whales, [START_REF] Riesch | Cultural traditions and the evolution of reproductive isolation: ecological speciation in killer whales[END_REF]. In particular, demes do not need to have boundaries (neither geographical nor behavioural), and may be de ned i n a « sliding window » fashion, e.g. in a classical isolation-by-distance system, where each individual may be taken as the focal point of a group or « genetic neighbourhood » [START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF] within

Introduction - §2
which alleles are highly correlated, and which decays gradually with distance. In that case the deme might be seen as the local approximation of a broader spatial autocorrelation system [START_REF] Double | Dispersal, philopatry, and infidelity: dissecting local genetic structure in superb fairy-wrens (Malurus cyaneus)[END_REF]Epperson 2005). A threshold of 5% probability of mating has been proposed to de ne t he l imits o f a d eme ( Hamilton 2 011) -b ut t his i s o bviously a rbitrary, a s t he concept makes most intuitive sense in discretely distributed species, with strong discontinuities in mating probability. When de ning a deme, we therefore assume that within the ensemble under scrutiny, mutation and drift, as opposed to migration, are the major contributors to genetic diversity.

A colony -especially for seabirds, see §18 p. 65 -is the conceptual counterpart of a deme: it is a completely geographical and behavioural concept, describing a spatial aggregation of breeding individuals over a period of time, and does not make any assumption as to the underlying genetic structure or evolutionary processes. e t erm i s w idely u sed i n a v ariety o f o rganisms, ranging from polyps (e.g. corals or siphonophores) to insects (e.g. ants, [START_REF] Gaspar | Comparison of behaviour, body mass, haematocrit level, site fidelity and survival between infested and non-infested king penguin Aptenodytes patagonicus by ticks Ixodes uriae[END_REF], each time with a di erent set of characteristics (in particular as to functional integration, which is extreme in siphonophores, see e.g. [START_REF] Dugger | Complex colony level organization of the deep sea siphonophore Bargmannia elongata (Cnidaria, Hydrozoa) is directionally asymmetric and arises by the subdivision of pro Annexes - §186 drial DNA restriction data[END_REF], but minimal in corals, see e.g. [START_REF] Soong | C'est en particulier le cas des oiseaux de mer (voir §18) qui passent le plus clair de leur temps à s'alimenter au large, en petits groupes, mais se rassemblent en grandes colonies sur la terre ferme au début de la saison de reproduction[END_REF].

Coloniality is also well-spread in terrestrial vertebrates, whether in mammals (such as spermophiles, [START_REF] Armitage | Sociality as a life-history tactic of ground squirrels[END_REF][START_REF] Johnson | Social organization in a colony of rock squirrels (Spermophilus variegatus)[END_REF], or in birds [START_REF] Rolland | The evolution of coloniality in birds in relation to food, habitat, predation, and life-history traits: a comparative analysis[END_REF]. While some bird species' colonial behaviour extends to all aspects of their life (e.g. the well organised European starling Sturnus vulgaris colonies), most only exhibit colonial behaviour during reproduction [START_REF] Lack | Ecological adaptations for breeding in birds[END_REF]. is is especially the case in seabirds (see §18 p. 65), that may spend most of the year foraging alone or in small groups, out at sea, but gather in large colonies on land at the start of the breeding season. Paradoxically, philopatry is high in almost all seabirds (see §5 p. 32): although individuals roam far and wide during their foraging season, and have virtually no need for the shore, they keep a strong inner link to a particular patch of land, and return there regularly to breed. For some species with a limited foraging range, this may be the only available breeding grounds in the area (e.g. for the Black guillemot Cepphus grylle, see Ewins 1986). Yet in the widest-ranging seabird species, such as the great albatrosses (Diomedea sp.), or the Aptenodytes

Introduction - §3
penguins, colonial behaviour becomes a truly abstract conception: non-breeding foraging range overlaps widely between breeding colonies (and may in reality extent all around the world, see [START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF], while individuals will return to their birth-place with astounding regularity.

Individual learning, and horizontal, or « cultural » (sensu [START_REF] Whitehead | The cultural lives of whales and dolphins[END_REF]) transfer of information between birds breeding at the same place, may even lead to colony-speci c idiosyncrasies, for example di erent foraging grounds for nearby colonies of the same species [START_REF] Weimerskirch | Seabirds-individuals in colonies[END_REF]. us, the colony can be seen as the most extreme representation of the population concept in the ecological paradigm: a spatially and temporally stable aggregation of individuals, that can be easily observed, de ned and bounded (although not without conceptual choices, see §41 p. 103), but without assumed genetic characteristics.

Our use of population is at the intersection of deme and colony. In this work, a population will be de ned as an ensemble that is bounded at the same time in the geographical, the demographical and the evolutionary spaces -in other words, a colony, or group of colonies, that also happens to be a deme. Rather than an anecdotical coincidence, speaking of a population implies that the processes that can be observed at the ecological scale (i.e. geographical clustering of individuals, use of common resources, or philopatric behaviour) are strong and stable enough through time to have an in uence on evolutionary mechanisms, so that both the ecological and the genetic paradigms overlap. us, as opposed to a deme, our de nition of population requires a geographical component -and as opposed to a colony, it also requires an evolutionary criterion.

Two antagonistic forces thus interact in shaping the wide variety of possible species architectures:

dispersal, or the active or passive ability of individuals to move across their habitat, and philopatry, or the tendency for individuals to stay, or return, to their birth place (when fully passive, this may more accurately be called inertia). Taken together, these two forces may respectively be considered as the speci c uidity and viscosity of a species. §-3 What is a Synnome? e p articular a rchitecture o f t he t wo Aptenodytes p enguin s pecies falls short of any existing concept. As will be exposed in detail in the following chapters (especially e King synnome, p. 163 and e Emperor synnome, p. 211), Aptenodytes penguins display a startling tension between the ecological and the evolutionary de nitions of a population. Colony structure is brought to an extreme, with highly discrete aggregations of individuals that appear fairly stable through time, at least at the centennial scale. Philopatry is high (see §19 p. 68), and is thought to extend not only to the colony, but also regularly to the very nesting site within that colony (see Fine-scale colony structure, p. 135 for details). Colonies have a high degree of demographic independence, as shown for example by the contrasting trajectories of the ve rookeries on Possession Island, in Crozet Archipelago [START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]). However, none of the genetic criteria are met, and the long-term uidity of the species appears to fully counteract any incipient genetic structuring, with migration asserting itself as a major evolutionary force in these species.

Such a structure is distinct from a metapopulation system (see e.g. [START_REF] Handrich | Ice-sheet mass balance and climate change[END_REF], in which subunits are, to some extent, genetically separated -but it is also distinct from a panmictic system, since the extremely clustered spatial distribution of individuals, and high philopatry, contradicts the assumption of random mating at the generation-scale. Although extensive research remains to be done as to the underlying mechanisms, it is possible that heterogeneous demographic processes are involved -for example pulsatile dispersal, in which catastrophic local events would prompt massive dispersal during some generations, and no dispersal at all some others (see Empirical evidence of heterogeneous dispersal, p. 259 for a possible instance of this phenomenon). In order to describe this paradoxical structure, in this work, we use the term synnome, that we derive from the Greek σύννομος, « a common grazing of ocks », that has in particular been used for gathering ocks of birds 1 , and which was commonly used by extension for « reunions », « gatherings », and even « kindred » (see §123 p. 236). e pivotal concept of grazing together (or συννομός, synnomos, « shared pasture ») accurately renders the importance of central place foraging in the

Introduction - §3 1.
See for example Aristophanes' Birds (v. 1755 sqq.): « ἔπεσθε νῦν γάμοισιν ὦ | φῦλα πάντα συννόμων | πτεροφόρ᾽ ἐπὶ δάπεδον Διὸς | καὶ λέχος γαμήλιον. » -« let them now gather, all the feathered ocks [synnómōn], and follow the brides to the houses of Zeus and to their wedding bed ».

Introduction - §4
colony structure of seabird species, while distinguishing it from more deeply fragmented metapopulation systems. ese d i erent m eanings t ogether c onvey t he p articularity o f t he observed structure, that we here de ne as « one single, nearly homogeneous pool of individuals is that distributed in a highly discrete way throughout its range. Local concentrations, or colonies, are highly consistent on the scale of a few generations, at which scale philopatry may be the norm. Yet, migration between these areas is high enough to maintain total homogeneity of the species' gene pool, so that, viewed on a micro-evolutionary time-scale, the only relevant unit is the species as a whole. » §-4 Dispersal and migration. Populations may be de ned, as we exposed earlier, under two divergent paradigms -the ecological and the evolutionary ones (see §2 p. 25). Across both paradigms, a population is a bounded entity: either geographically or genetically, it is characterised by its autarkic aspect. Any kind of movement amongst groups works against the constitution of population units. Demographically, movement promotes the coupling of population trajectories, and counters the evolution of demographic independence. Genetically, movement provokes the transfer of alleles between demes, and works towards homogenising genetic variation and allele frequencies. However, the distinction between the ecological and the evolutionary paradigms also apply to inter-group movements, and, here again, prompted the choice of di erent terms. An unfortunate problem arises, however, because of the convergent choice of « migration » to describe very di erent phenomena, that we will clarify here.

e most common sense of migration, especially in a bird-biology context, is the seasonal movements of groups of birds (often colonies) between distinct breeding and overwintering grounds.

Migratory species, in that sense, have evolved particular adaptations (see e.g. [START_REF] Berthold | Genetic control of migratory behaviour in birds[END_REF]) that allow them to achieve well-timed departure and arrivals to track the most bene cial environments year-round. Interestingly, migratory behaviour is a challenge to the de nition of populations in the ecological paradigm, since groups may have di erent geographical and demographic boundaries in their summer, and winter ranges. However, migratory behaviour in that sense will not be examined in this work, mainly because it is not classically observable in our focal system (although the inter-breeding foraging trips of penguins may arguably be related to migratory behaviour).

A more simple, one-way movement is the dispersal of individuals out of their original group. Dispersal was originally described by Howard 1960 as « the movement the animal makes from its point of origin to the place where it reproduces ». From each individual's perspective, it is « the greatest distance its genetic characteristics are transmitted, rather than the greatest distance the animal may have migrated or otherwise travelled away from the place it was conceived, hatched or born ». Dispersal can be de ned both in the ecological and in the genetic paradigms. Ecologically, dispersal is the force working towards demographic linkage of populations, as an excess of juveniles in one location can compensate a low recruitment in another through direct displacement of individuals. Genetically, a dispersal event is the « unit » of gene ow, with each dispersing individual bringing a set of alleles from one location to another.

e second sense of migration, and the one we will use throughout this work, has been described by Dingle and Drake [START_REF] Dingle | What is migration[END_REF] in a biogeographical context as « range expansions of faunas or individual species », such as « the northward extension of ranges following the retreat of glaciers at the end of the ice ages ». More speci cally, in a population genetic context, the migration parameter M has been de ned by the same authors as « the exchange of genes among populations by whatever means, including but not limited to migration as we consider it here ». It is used in that sense in the coalescent framework, in particular by Beerli and colleagues [START_REF] Beerli | Comparison of Bayesian and maximum-likelihood infer-Annexes - §186 ence of population genetic parameters[END_REF]Beerli & Palczewski 2010, see more details in §41 p. 103). In this sense, migration is the averaged extent,

Introduction - §4 Introduction - §5
highly variable depending on individual characteristics and temporary environmental conditions, migration is a structural characteristic of the system, and is only expected to vary slowly according to broad changes in the organisation of a species. §-5 Philopatry and site delity. Philopatry has rst been described as Ortstreue (« site delity ») by von Haartman (Von Haartman 1949), as the tendency of the pied y-catcher Ficedula hypoleuca to breed near its birth place, and has been generalised under the term philopatry by Huntington (Huntington 1951) as « the tendency of an animal to return to its birthplace or breeding place, particularly for breeding ». us, although the term has occasionally been used to describe adultdelity to its breeding spot [START_REF] Anderson | Philopatry, dispersal, and the genetic structure of waterfowl populations[END_REF], it describes, in the strict sense, the delity of adults to their birth place, or « natal philopatry », and is only properly used in that sense [START_REF] Peakall | Site fidelity is an inconsistent determinant of population structure in the hooded merganser (Lophodytes cucullatus): evidence from genetic, mark-recapture, and comparative data[END_REF]. Philopatry sensu stricto has important implications for the genetic processes that shape a species' architecture. e tendency to breed near one's birth place implies a signi cant departure from the panmixia hypothesis, since the probability of mating, for two randomly chosen individuals, becomes inversely proportional to their distance at birth. At a low level, this generates patterns of isolation by distance, where relatedness between individuals is a function of their geographical separation [START_REF] Wolff | The interpretation of population structure by F-statistics with special regard to systems of mating[END_REF]Wright 1946); at higher intensity, this may lead inbreeding and population fragmentation [START_REF] Mayr | Populations, species, and evolution: an abridgment of animal species and evolution[END_REF][START_REF] Avise | Phylogeography of colonially nesting seabirds, with special reference to global matrilineal patterns in the sooty tern (Sterna fuscata)[END_REF]. However, the bene ts of philopatry may be considerable. ey m ay b e b ehavioural, s uch a s i ncreased k nowledge o f t he higher quality breeding spots or partners [START_REF] Wheelwright | Philopatry, natal dispersal, and inbreeding avoidance in an island population of Savannah Sparrows[END_REF][START_REF] He | Habitat saturation, benefits of philopatry, relatedness, and the extent of cooperative breeding in a cichlid[END_REF][START_REF] Arnaud | Philopatry and within-colony movements in Columbian ground squirrels[END_REF], or enhanced selective value of proximal defensive behaviour [START_REF] Dugger | Complex colony level organization of the deep sea siphonophore Bargmannia elongata (Cnidaria, Hydrozoa) is directionally asymmetric and arises by the subdivision of pro Annexes - §186 drial DNA restriction data[END_REF] and allofeeding [START_REF] Lecomte | Alloparental feeding in the king penguin[END_REF]) through kinship selection; or they may be genetic, e.g. through the promotion of local micro-adaptation [START_REF] Richardson | Microgeographic adaptation and the spatial scale of evolution[END_REF]) -which may apply either on land, or on the associated foraging areas, which are often speci c to a colony (see [START_REF] Weimerskirch | Seabirds-individuals in colonies[END_REF].

Philopatric behaviour is considered to be the basis of coloniality [START_REF] Bowler | Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics[END_REF]. However, there is an important discrepancy between the genetic and ecological time scales. On an ecological scale, the mean return rate of juveniles to their natal colony over a few generations is

Introduction - §6
generally taken as an estimator for the strength of philopatry. In several species, in particular seabirds, very high observed return rates seem to be inconsistent with the lack of genetic structure between colonies -the so-called seabird paradox identi ed b y M ilot e t a l. ( Milot e t a l. 2008).

However, based on simulation approaches, comparatively low (5% to 10%) migration rates have been shown to be su cient to counterbalance the e ects of local genetic drift on a longer time scale [START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. Moreover, the assumption that philopatry is a stationary process in colonial species, and that the mean return rate should converge over time to the species philopatry, does not take into account the possibility of pulsatile mass-dispersal events, that may reshu e gene pools entirely amongst several populations. Philopatry, as opposed to the instantaneous probability of delity ( F ) o r d ispersal ( 1 -F ) , i s n ot a d irectly e stimable parameter [START_REF] Kendall | On the estimation of dispersal and movement of birds[END_REF][START_REF] Peakall | Site fidelity is an inconsistent determinant of population structure in the hooded merganser (Lophodytes cucullatus): evidence from genetic, mark-recapture, and comparative data[END_REF], but rather a long-term behavioural trait of the species, that may or may not lead to a choice of site delity, d epending o n t he a ctual environmental conditions. §-6 Philopatry and dispersal in oceanic systems. e dynamics of oceanic structures di er dramatically from terrestrial ones when it comes to dispersal [START_REF] Steele | Testing for effects of climate change on competitive relationships and coexistence between two bird species[END_REF]. Although at the lower levels of the trophic system, passive long-distance dispersal is equally present in terrestrial and marine environments, as atmospheric currents perform the same role as sea currents for seed or propagule dispersal [START_REF] Cain | Long-distance seed dispersal in plant populations[END_REF][START_REF] Nathan | Spatial patterns of seed dispersal, their determinants and consequences for recruitment[END_REF][START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF][START_REF] Nathan | Long-distance dispersal of plants[END_REF][START_REF] Nathan | Mechanisms of long-distance seed dispersal[END_REF][START_REF] Nikula | Passive rafting is a powerful driver of transoceanic gene flow[END_REF], the di erence in spatial organisation and structure becomes more important for larger organisms, especially vertebrates [START_REF] Cariou | Comparing marine and terrestrial ecosys-Annexes - §186 tems: implications for the design of coastal marine reserves[END_REF]). Terrestrial ecosystems have typically higher habitat fragmentation, less dispersal and gene ow, and globally more closed systems [START_REF] Warren | Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss[END_REF]Turchin 1998;[START_REF] Cariou | Comparing marine and terrestrial ecosys-Annexes - §186 tems: implications for the design of coastal marine reserves[END_REF]): the interaction between latitudinal and altitudinal gradients results in very complex habitat distributions that contrast with the mostly zonal pelagic habitats [START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF]. On the other hand, oceans may be considered as a uid landscape in which both active and passive dispersal occurs on very large scales [START_REF] Queiroz | Origin and evolution of the sub-Antarctic islands: the foundation[END_REF][START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF][START_REF] Nikula | Passive rafting is a powerful driver of transoceanic gene flow[END_REF]. Habitat fragmentation, al-though documented [START_REF] Acosta | Benthic dispersal of Caribbean spiny lobsters among insular habitats: implications for the conservation of exploited marine species[END_REF][START_REF] Gómez-Díaz | Genetic relatedness does not retain spatial pattern across multiple spatial scales: dispersal and colonization in the coral, Pocillopora damicornis[END_REF], is usually restricted to coastal or benthic species with extremely specialised niches [START_REF] Reich | Global-scale latitudinal patterns of species diversity in the deep-sea benthos[END_REF][START_REF] Whitlatch | Scale-dependent benthic recolonization dynamics: life stage-based dispersal and demographic consequences[END_REF][START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF]). Long distance passive (i.e. current-driven) dispersal has been observed in organisms ranging from planktonic species (e.g. vagile stages of kelp, or invertebrates, see [START_REF] Fraser | Oceanic rafting by a coastal community[END_REF][START_REF] Nikula | Passive rafting is a powerful driver of transoceanic gene flow[END_REF] to larger vertebrates, especially at early life stages (e.g. turtles, see [START_REF] Gaspar | Comparison of behaviour, body mass, haematocrit level, site fidelity and survival between infested and non-infested king penguin Aptenodytes patagonicus by ticks Ixodes uriae[END_REF]. Active dispersal by vertebrate species is common at transoceanic scales (Bowen & Sini 1999;[START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF].

In oceanic systems dominated by strong currents (such as, in this study, the Antarctic Circumpolar Current system (ACC -see §7 p. 35, or the Humboldt Current system -see §147 p. 276), passive and active dispersal are general features of communities. In the ACC, it has been evidenced for species ranging from algae and small invertebrates [START_REF] Nikula | Circumpolar dispersal by rafting in two subantarctic kelp-dwelling crustaceans[END_REF], to benthic species [START_REF] Arango | Genetic differentiation in the circum-Antarctic sea spider Nymphon australe[END_REF]) and sh [START_REF] Matschiner | Gene flow by larval dispersal in the Antarctic notothenioid fish Gobionotothen gibberifrons[END_REF][START_REF] Damerau | Comparative population genetics of seven notothenioid fish species reveals high levels of gene flow along ocean currents in the southern Scotia Arc, Antarctica[END_REF]. However, the extent of genetic diversity along circumpolar ranges remains variable amongst taxa, and appears to depend largely on speci c dispersal potential and life-history traits (Rogers 2007). In many cases, discontinuities such as the Antarctic Peninsula region (which extends North to almost 62°S) break these homogeneous dispersal systems, and stand out as distinct provinces, both in terms of climate [START_REF] Mulvaney | Oceanic birds of South America: a study of species of the related coasts and seas, including the American quadrant of Antarctica, based upon the Brewster[END_REF]) and of biogeography 1 [START_REF] Terauds | Conservation biogeography of the Antarctic[END_REF]).

e active dispersal power of vertebrates has, however, one paradoxical consequence: their higher ability to move around oceanic systems allows them to not disperse along the currents. While sessile species resist to dispersal by sheer inertia, active swimmers and yers exhibit philopatric be-

Introduction - §6 Introduction - §7
whales, see Hoelzel 1998), or colonies (as in most seabirds, see Friesen et al. 2007). Indeed, in most cases, natal philopatric behaviour is strong enough to promote genetic di erentiation even between populations that largely overlap in their foraging or wandering areas [START_REF] De Dinechin | Speciation chronology of rockhopper penguins inferred from molecular, geological and palaeoceanographic data[END_REF]Friesen et al. 2007;[START_REF] Smagulova | Global phylogeography of the band-rumped storm-petrel (Oceanodroma castro; Procellariiformes: Hydrobatidae)[END_REF]). Such patterns have been observed in marine mammals (both cetaceans and pinnipeds, see Hoelzel 1998; [START_REF] Baker | Hierarchical structure of mitochondrial DNA gene flow among humpback whales Megaptera novaeangliae, world-wide[END_REF][START_REF] Bonin | Unexpected genetic differentiation between recently recolonized populations of a long-lived and highly vagile marine mammal[END_REF]), in turtles (Molfetti et al. 2013), and in most seabirds (Friesen et al. 2007;[START_REF] Bicknell | Population genetic structure and long-distance dispersal among seabird populations: Implications for colony persistence[END_REF].

us, t he p ossibility o f f ast a nd u nhindered m ovements i n o ceanic s ystem h as t he unexpected consequence of allowing high return rates to particular breeding locations in most vertebrates, rather than promoting complete genetic mixing. Antarctica. e resulting atmospheric circulation is the major cause of climate variability in the Southern Ocean [START_REF] Mackintosh | Seabirds as indicators of aquatic ecosystem conditions: A case for gathering multiple proxies of seabird health[END_REF][START_REF] Abram | Evolution of the Southern Annular Mode during the past millennium[END_REF], with important decadal-scale cyclicality, and broader centennial-scale oscillations [START_REF] Abram | Evolution of the Southern Annular Mode during the past millennium[END_REF]. SAM has opposite e ects in the polar and subtropical zones. During positive SAM events, the poleward shift of the westerlies strengthens the Ekman transport in the ACC, resulting in an equatorward shift of the APF, increased upwelling activity south of the APF, and increased marine productivity in the polar frontal zone [START_REF] Lovenduski | Impact of the Southern Annular Mode on Southern Ocean circulation and biology[END_REF] -while in the subtropical zone, warm SST anomalies result in decreased productivity. Conversely, negative SAM events (i.e. northwards expansion of the Westerlies) have a strong e ect on the whole food chain, up to top predators [START_REF] Forcada | Penguin responses to climate change in the Southern Ocean[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF].

Antarctica and the Southern Ocean

While the impact of the SAM is essentially felt all around the Southern Ocean, each ocean basin is also subject to the more local (although tele-connected) e ects of the dipole events. While the El Niño phenomenon di ers qui signi cantly from the South Indian and Atlantic dipoles, its eir link with the ENSO, and their global connections, however, still remain to be fully investigated [START_REF] Saji | Possible impacts of Indian Ocean dipole mode events on global climate[END_REF][START_REF] Ashok | Individual and combined influences of ENSO and the Indian Ocean dipole on the Indian summer monsoon[END_REF][START_REF] Abram | Recent intensification of tropical climate variability in the Indian Ocean[END_REF]. Together, the SAM, ENSO and dipole events also contribute to the variability in sea ice extent around Antarctica [START_REF] Simmonds | Relationships between the interannual variability of Antarctic sea ice and the Southern Oscillation[END_REF][START_REF] Lefebvre | Influence of the Southern Annular Mode on the sea ice-ocean system[END_REF][START_REF] Stammerjohn | [END_REF]).

e integrated e ect of the SAM, the ENSO, and the dipole events result in combined modes of variability in the Southern Ocean (Fogt & Bromwich 2006;Ciasto & ompson 2008;[START_REF] Fogt | son aspect actuel au début du Pliocène (il y a ~5.2 Ma). La séparation physique entre l'Antarctique et l'Amérique Annexes - §9 (traduction française) du Sud se produit entre l'Eocène moyen[END_REF], that have deep impact on biological communities. Primary productivity in the Southern Ocean responds both to the SAM [START_REF] Lovenduski | Impact of the Southern Annular Mode on Southern Ocean circulation and biology[END_REF] and to ENSO [START_REF] Beaufort | ENSO-like forcing on oceanic primary production during the late Pleistocene[END_REF][START_REF] Behrenfeld | Biospheric primary production during an ENSO transition[END_REF], mostly mediated through changes in surface water temperature, sea ice, and nutrient availability. Both changes in the physical properties of surface waters and in trophic interactions also have an impact at trophic levels above phytoplankton, e.g. on the Antarctic krill (Murphy et al. 2007b): in turn, these e ects are passed on to top-level predator species such as right whales [START_REF] Leaper | Global climate drives southern right whale (Eubalaena australis) population dynamics[END_REF], elephant seals [START_REF] Mcmahon | Climate change and seal survival: evidence for environmentally mediated changes in elephant seal, Mirounga leonina, pup survival[END_REF] or penguins [START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. Although climate variability is a perfectly natural phenomenon, and should not threaten species resilience, the highly integrated character of the Southern Ocean climate makes it liable to entering large feedback mechanisms, thereby amplifying the e ects of cli-

Introduction - §8
mate change (see §12 p. 52). It is therefore of the utmost importance to gain a good understanding both of the climatic processes themselves and of their e ects on biological communities. together with the slow onset of icehouse-earth conditions -the opening of the Drake passage itself having possibly contributed to the global temperature decrease [START_REF] Nong | Simulation of ocean temperature change due to the opening of Drake Passage[END_REF][START_REF] Livermore | Shackleton Fracture Zone: no barrier to early circumpolar ocean circulation[END_REF]). e Antarctic Circumpolar Current (see §7 p. Miocene marks the transition from greenhouse-earth conditions to present-day-like icehouse conditions. e Antarctic Ice Sheet is thought to have taken over the prevailing tundra environment by the early Miocene [START_REF] Raine | Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees[END_REF], and developed to reach its current extent by the end of the period [START_REF] Westerhold | Middle to late Miocene oxygen isotope stratigraphy of ODP site 1085 (SE Atlantic): new constrains on Miocene cli-mate variability and sea-level fluctuations[END_REF]. Despite high (and still not fully understood) variability, prevalent marine conditions were warmer during the early Pliocene than today [START_REF] Haywood | Middle Miocene to Pliocene history of Antarctica and the Southern Ocean[END_REF]) -although with high marine productivity. e SST was about 5°C warmer than today throughout the Southern Ocean [START_REF] Whitehead | Pliocene summer sea surface temperature reconstruction using silicoflagellates from Southern Ocean ODP Site 1165[END_REF], and the APF lay about 6° South of its present position in the mid-Pliocene [START_REF] Barron | Diatom constraints on the position of the Antarctic Polar Front in the middle part of the Pliocene[END_REF], while the global thermohaline circulation may have been stronger than observed now [START_REF] Haywood | Middle Miocene to Pliocene history of Antarctica and the Southern Ocean[END_REF]. By the late Pliocene (~2.5

Introduction

Myr), however, Antarctic cooling strengthened, with increased Westerlies and equatorward migration of the APF, and possibly a reduction in the thermohaline circulation [START_REF] Mckay | Antarctic and Southern Ocean influences on Late Pliocene global cooling[END_REF].

Most of the Subantarctic islands came to existence during that period. Apart from the continental-shelf South Georgia and Falklands (that are remnants of Gondwana, and as old as neighbouring South America), and sub-continental Kerguelen islands (that were formed during the Oligocene), the subantarctic islands are mostly young, volcanic mounds raised from the sea-oor.

Crozet, Heard and Macquarie were thus formed during the late Miocene, and Prince Edward, Bouvet, Gough, etc. appeared between the Pliocene and the Pleistocene -and as late as 0. in [START_REF] Kemp | Des indicateurs chimiques peuvent être extraits de carottes de glace comme de sédiment. Ces di érents moyens sont utilisés soit directement, soit comme conditions initiales pour un modèle numérique de climat, pour reconstituer les condi-Annexes - §9[END_REF] or the extent of sea ice (e.g. Hodell et al. 2001;Gersonde et al. 2005). Fossil pollen in terrestrial or marine cores provides direct evidence as to the prevailing climatic conditions (e.g. [START_REF] Mccauley | Divergent trends in land and ocean temperature in the Southern Ocean over the past 18,000 years[END_REF]. Chemical indices can be extracted from ice cores 1 and marine sediment cores 2 . ese multiple proxies are used either directly, or as an input in Atmosphere-1.

Considering they have been deposited at the core site as wind-borne aerosols, this requires hypotheses as to the atmospheric circulation at the core site (see Wol et al. 2006): these normally include methanesulphonic acid (MSA, as a proxy for marine dimethyl sulphide, a biomarker that re ects algal growth and thence marine productivity -see [START_REF] Legrand | Ice-core record of oceanic emissions of dimethylsulphide during the last climate cycle[END_REF], sea salt sodium (freed as high-salt brine at the surface of sea ice, and thus indicating sea ice extent, see Wol et al. 2003), iron and calcium (transported as dust, and revealing atmospheric circulation, see Wol et al. 2006). Deuterium/hydrogen ratio (δD = 2 H/ 1 H) is commonly used as a proxy for temperature anomaly [START_REF] Augustin | Eight glacial cycles from an Antarctic ice core[END_REF]Wol et al. 2006), and can be combined with the record of greenhouse gases (CO 2 and CH 4 mostly, see [START_REF] Fischer | Decadal variability of the ENSO teleconnection to the high-latitude South Pacific governed by coupling with the Southern Annular Mode[END_REF][START_REF] Petit | Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica[END_REF][START_REF] Augustin | Eight glacial cycles from an Antarctic ice core[END_REF]).

2.

Marine sediment cores, besides isotopic signatures (δ 18 O, see Lisiecki & Raymo 2005) and chemical signatures such as iron ux (from continental input, as a marker of marine productivity, see Martínez [START_REF] Garcia | Les assemblages de diatomées et de foraminifères dans le sédiment marin o rent un indice précis de l'état des masses d'eau de surface, et peuvent être utilisés pour identi er la position de l'APF[END_REF], also provide biomarker ev-

Introduction - §9 Introduction - §9
Despite local variations and alternating conditions in the di erent oceanic basins, we can separate four major periods in the Southern Ocean recent history, that appear consistent at the global scale: (i) Quaternary conditions (mid-Pleistocene to 22 Kyr BP), (ii) LGM conditions Kyr BP), (iii) Pleistocene glacial retreat and early holocene optimum , (iv)

Holocene hypsithermal and neoglacial conditions (8-0 Kyr BP).

(i) Quaternary conditions are characterised by the slow onset of glacial conditions, in the continuity of the Miocene-Pliocene icehouse trend [START_REF] Haywood | Middle Miocene to Pliocene history of Antarctica and the Southern Ocean[END_REF], although with marked glacial-interglacial cycles with a phase of ~100 Kyr [START_REF] Petit | Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica[END_REF][START_REF] Augustin | Eight glacial cycles from an Antarctic ice core[END_REF]Wol et al. 2006), during which the APF exhibits important latitudinal variations (see [START_REF] Kemp | Des indicateurs chimiques peuvent être extraits de carottes de glace comme de sédiment. Ces di érents moyens sont utilisés soit directement, soit comme conditions initiales pour un modèle numérique de climat, pour reconstituer les condi-Annexes - §9[END_REF]. Winter sea ice cover already reached as far as ~56°S in the Paci c during the period (Gersonde et al. 2005). Little is known of land ice throughout the period, as the following LGM glacier growth obliterated most of the direct evidence [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF].

(ii) Last Glacial Maximum (LGM, has been the focus of much research, in particular regarding the Antarctic ice sheet (e.g.

2013

). Precise dynamics of the APF still remain to be understood, however, as the strong bathymetric constraints at several locations (the Campbell Plateau, the Kerguelen Plateau, or the Drake passage) are thought to have prevented equatorward frontal displacement [START_REF] Rolland | The evolution of coloniality in birds in relation to food, habitat, predation, and life-history traits: a comparative analysis[END_REF]Neil et al. 2004;[START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]. Westerly wind change reconstruction is still uncertain, but available data point to strengthened circulation and equatorward widening of the westerlies [START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]. Winter sea ice expanded greatly during the period, reaching at least

Introduction - §9
50°S in winter [START_REF] Clarke | Paleogene equatorial penguins challenge the proposed relationship between biogeography, diversity, and Cenozoic climate change[END_REF]Gersonde et al. 2005 -with variation between the icy Atlantic and Indian oceans, and the more ice-free Paci c). A s hift i n m arine p roductivity h as b een inferred from the Antarctic to the Subantarctic region [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF], without however a global reduction in biomass (Wol et al. 2006). e islands of Heard, Crozet, Marion, and the Drake Arc were entirely covered by ice, while Kerguelen and South Georgia may have had some ice-free areas, and the Falklands and Macquarie underwent periglacial conditions, with a harsh tundra environment [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]. Like in the Northern Hemisphere, the prevailing glacial conditions pushed the higher latitude terrestrial or semi-terrestrial biodiversity into a handful of refugia, mostly located in the now subtropical (but then subantarctic) islands of Gough and StHelena in the Atlantic, Amsterdam in the Indian Ocean, and Auckland and Campbell in the Paci c. e Falklands and the Patagonian Shelf area (where swathes of land emerged from the lower sea level) may also have granted ice-free refugia (see [START_REF] Fraser | Kelp genes reveal effects of subantarctic sea ice during the Last Glacial Maximum[END_REF][START_REF] Fraser | Poleward bound: biological impacts of Southern Hemisphere glaciation[END_REF]. Marine diversity, on the other hand, was not severely a ected (see e.g. [START_REF] Morin | Geographic and temporal dynamics of a global radiation and diversification in the killer whale[END_REF].

(iii) Pleistocene glacial retreat and early Holocene optimum is a period of global thawing and warming following the LGM. In the Southern Ocean, the deglaciation has very contrasting chronologies, in particular in relation to the local topography and bathymetry: while the dry land expanses of Kerguelen and South Georgia, and the shelf-less archipelagos of the Scotia Arc, saw ice retreating early, the shelves of Crozet and Prince Edward retained extensive land ice caps as late as the early Holocene [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]. Sea ice retreated slowly during the late Pleistocene, with an important regain during the Antarctic Cold Reversal, around 14.7 Kyr BP (likely due to the massive in ux of meltwater from the Antarctic Ice Sheet, see Weber et al.

2014

). e Younger Dryas Northern hemisphere cold reversal corresponds, on the other hand, to a warm period in the Southern Ocean [START_REF] Kanno | Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks[END_REF]. Oceanic fronts moved poleward and reached their current position by the beginning of the Holocene, during the so-called Holocene Climatic Optimum [START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]. Around ~9 Kyr BP, a second cold reversal in the

Introduction - §10
Antarctic waters led to a slight regain of sea ice [START_REF] Nielsen | Holocene climate in the Atlantic sector of the Southern Ocean: Controlled by insolation or oceanic circulation[END_REF]) -although the state of the Southern Ocean may not have been homogeneous between ocean basins.

(iv) Holocene hypsithermal and neoglacial conditions (8-0 Kyr BP) mark the advent of the modern (pre-industrial) conditions throughout the Southern Ocean. Sea surface temperature reaches a maximum around 7.5 Kyr BP [START_REF] Calvo | Antarctic deglacial pattern in a 30 kyr record of sea surface temperature offshore South Australia[END_REF], with subantarctic ice-free conditions at ~50°S around 6-5 Kyr BP (Hodell et al. 2001;[START_REF] Nielsen | Holocene climate in the Atlantic sector of the Southern Ocean: Controlled by insolation or oceanic circulation[END_REF]. Land ice conditions are then similar to the present, with complete deglaciation on Crozet and Prince Edward [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF].

ere warmer hypsithermal conditions are followed by colder conditions as early as 5 Kyr BP in East Antarctica, and 3 Kyr BP in West Antarctica [START_REF] Garcia | Les assemblages de diatomées et de foraminifères dans le sédiment marin o rent un indice précis de l'état des masses d'eau de surface, et peuvent être utilisés pour identi er la position de l'APF[END_REF]. However, the oceanographic landscape is not thought to have changed much, let aside minor variation in sea ice, and species distribution is thought to have been very similar to pre-industrial ranges. §-10 Biogeography of the Southern Ocean. e p resent-day S outhern O cean h as a r ather simple biogeography, made of broad, often zonally bounded systems, but that host a considerable diversity of marine life [START_REF] Smetacek | -le changement climatique est-il spécial en lui même dans les régions polaires, au delà de son rythme accéléré ; ou ses e ets sur les communautés biologiques sont-ils surtout le résultat de la relative intégrité de ces communautés encore peu anthropisées ? §-13 Réponse des espèces au changement climatique[END_REF][START_REF] Chown | et Fig. 6), et il est probable que la particularité apparente des régions polaires ne soit dûe à rien d'autre qu'à leur destruction plus tardive, garantie par leur inaccessiblité -et non à quelque caractéristique particulière qui y favoriserait la mégafaune[END_REF]. Four distinct areas may be distinguished: (i) the Subantarctic zone, which is outside the Southern Ocean proper, but biogeographically bound to it, is bounded by the Subtropical convergence to the North, and the APF to the South, (ii) the Antarctic Ocean zone, south of the Subantarctic zone, and bounded to the South by the Northern Limit of the Circumpolar Current, (iii) the Coastal Antarctic zone, which includes the Antarctic coastal countercurrent and shelf area, as well as the coastal ice area, and

(iv) the Antarctic peninsula [START_REF] Convey | Antarctic Ecosystems: An extreme environment in a changing world[END_REF][START_REF] Terauds | Conservation biogeography of the Antarctic[END_REF]. In this study, we are mostly concerned with the rst three concentric zones. e Subantarctic area is characterised by mild air temperatures, high precipitation, absent sea ice cover, and prevailing Westerlies. Foraging opportunities stem mostly from two sources. As mentioned earlier (see §7 p. 35), the tremendous upwelling areas associated with the APF are a major contributor to the world's marine productivity [START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF][START_REF] Bathmann | Spring development of phytoplankton biomass and composition in major water masses of the Atlantic sector of the Southern Ocean[END_REF][START_REF] Carr | A comparison of global estimates of marine primary production from ocean color[END_REF], and attract long-distance central-place foragers such as the King penguin [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF]). e pack-ice area in the Antarctic and Coastal Antarctic areas also concentrate massive primary productivity [START_REF] Arrigo | Primary production in Antarctic sea ice[END_REF][START_REF] Arrigo | Primary productivity of near surface communities within Antarctic pack ice[END_REF], and are an important food resource both for Antarctic

Coastal foragers such as the Snow Petrel Pagodroma nivea (Barbraud & Weimerskirch 2001b) or the Adélie penguin [START_REF] Wienecke | Adélie penguin foraging behaviour and krill abundance along the Wilkes and Adélie land coasts, Antarctica[END_REF].

In particular, open-water and upwelling conditions around polynyas along the coast (see §18 p. 65 and Fig. 4 p. 42) concentrate sea-ice-linked productivity, and are critical foraging areas for several species [START_REF] Ancel | Foraging behaviour of emperor penguins as a resource detector in winter and summer[END_REF][START_REF] Ancel | Exploration biologique de la polynie de Mertz[END_REF]). e most salient feature of the Southern Ocean biogeography is therefore its almost direct link to very broad climatic features. Unlike terrestrial environments, where the complex interac- 2008; [START_REF] Blois | Climate change and the past, present, and future of biotic interactions[END_REF][START_REF] Moritz | The future of species under climate change: resilience or decline[END_REF]). e diverse mechanisms through which these changes occur in ecosystems make them particularly di cult to predict. ey range from the disruption of developmental processes in lower-trophic-level organisms by higher sea surface temperatures [START_REF] Gould | Punctuated equilibria: an alternative to phyletic gradualism. 283. Gouretski V[END_REF][START_REF] Behrenfeld | Climate-driven trends in contemporary ocean productivity[END_REF] or water acidi cation [START_REF] Harvey | Meta analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming[END_REF], with cascading trophic-web e ects (e.g. [START_REF] Mcmahon | Climate change and seal survival: evidence for environmentally mediated changes in elephant seal, Mirounga leonina, pup survival[END_REF], to shifts in species distribution and associated biodiversity loss (e.g. in Arctic copepods, see [START_REF] Beaugrand | Reorganization of North Atlantic marine copepod biodiversity and climate[END_REF]. Marine ecosystems, just as terrestrial ones, are already severely a ected by human exploitation, which diminishes their resilience to climate change (see [START_REF] Hackett | BioEdit: a userfriendly biological sequence alignment editor and analysis program for Windows 95/98/NT[END_REF][START_REF] Watson | en particulier pour les prédateurs supérieurs, qui déterminent dans une large mesure les propriétés de résilience des écosystèmes[END_REF]Mc-Cauley et al. 2015). us, expected biodiversity collapse under the darker scenarios (see §50 p.

Introduction - §12 130
) is projected to a ect 50% to 70% of the oceans to a greater extent than all the changes that took place in the Pleistocene and Holocene [START_REF] Beaugrand | Future vulnerability of marine biodiversity compared with contemporary and past changes[END_REF].

Concurrently, a several studies have investigated the e ects of climate change on species through the cyclic variability of climate, which is especially prevalent in oceanic systems (see for example §8 p. 40 [START_REF] Frederiksen | Scale dependent climate signals drive breeding phenology of three seabird species[END_REF]. While this is often a reasonable approximation -and importantly the only proxy available anyway -these meteorological changes should not be fully equated to climate change. Weather, which is mostly at stake there, is only one interface between climate change and species. Warm phases of the ENSO may accurately reproduce warm-earth conditions in the Southern Ocean, but not additional coupling mechanisms with, e.g., the Northern Hemispheredespite the fact that Arctic-Antarctic couplings have been shown to be central to climate change stricto sensu [START_REF] Barbante | One-to-one coupling of glacial climate variability in Greenland and Antarctica[END_REF]. While the approximation is often a methodological necessity,

meteorological

Introduction - §12

However, the biogeography of the Southern Ocean (see §10 p. 48) also contributes to aggravated e ects of climate change. While habitats are generally expected to shift poleward in response to climate warming (see [START_REF] Williams | Projected distributions of novel and disappearing climates by 2100 AD[END_REF]Fraser et al. 2012, and §15 p. 57) As a result, the seemingly pristine polar regions have largely come to concentrate the e ects of climate change. For a long time, it has been thought that polar ecosystems were simpler, and with shorter trophic chains, than lower-latitude systems, thus leading to more easily understandable shifts. However, it is now recognised that both taxonomic diversity and trophic network complexity is just as high in the high latitudes than in the temperate regions [START_REF] Smetacek | -le changement climatique est-il spécial en lui même dans les régions polaires, au delà de son rythme accéléré ; ou ses e ets sur les communautés biologiques sont-ils surtout le résultat de la relative intégrité de ces communautés encore peu anthropisées ? §-13 Réponse des espèces au changement climatique[END_REF]Chown et al. 2015 and Fig. 6 p. 53), and it has been suggested that the apparent speci cities of the polar oceans are due more to the delayed human impact due to sheer inaccessibility, than to a unique character favouring, e.g., marine macrofauna: indeed, [START_REF] Smetacek | -le changement climatique est-il spécial en lui même dans les régions polaires, au delà de son rythme accéléré ; ou ses e ets sur les communautés biologiques sont-ils surtout le résultat de la relative intégrité de ces communautés encore peu anthropisées ? §-13 Réponse des espèces au changement climatique[END_REF] have proposed that these regions rather represent a vestigium of the pre-historical global marine ecosystems -in their words, a « Serengetis of the sea ». us, we may be currently witnessing in the high latitudes what happened already long ago closer to the equator and what is also happening in continental Africa, with the demise of mammalian macrofauna and its replacement by ubiquitous human exploitation. Whether climate change is really particular per se in the polar regions beyond its physically increased pace, or whether the nature of its e ects on biotic communities is

Introduction - §14
rather a consequence of the relative integrity of these communities, is a question that may be ur- 2003). Most studies, however, agree on the idea that three fundamental mechanisms interact in determining response to climate change at the species level: (i) adaptive microevolution through tness selection [START_REF] Bradshaw | La Méditerranée et le monde méditerranéen à l 'époque de Philippe II. 108. Braudel F[END_REF][START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF], (ii) phenotypic plasticity and in particular changes in phenology [START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF][START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF], and

(iii) range shift to track habitat isolines [START_REF] Walther | Ecological responses to recent climate change[END_REF][START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF][START_REF] Vanderwal | Focus on poleward shifts in species' distribution underestimates the fingerprint of climate change[END_REF]. §-14 Micro-evolution and phenotypic plasticity. Micro-evolution and phenotypic plasticity share a common structure: in order to accommodate its changing environment, a species changes its physiological, phenological, or generally behavioural characteristics [START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF]).

e mechanisms involved, however, are very di erent. Micro-evolutionary response happens on a multi-generation scale, through selection of the ttest heritable features (either genetic or epigenetic): thus, it is a population-scale process. Phenotypic plasticity, on the other hand, is an individual-based phenomenon. Either through developmental, or through purely behavioural modications, each individual optimises its tness under the prevailing conditions -but its new state is

Introduction - §14
not heritable, if we let aside the comparatively rare cultural transmission of behaviour [START_REF] Whitehead | The cultural lives of whales and dolphins[END_REF].

Evolutionary responses to anthropogenic climate change have already been evidence in several very short-lived species, such as mosquitoes [START_REF] Bauer | The relevance of environmental conditions for departure decision changes en route in migrating geese[END_REF][START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF].

However, all studies conducted in vertebrates have up to now failed to detect any clearly evolutionary process (see Møller et al. 2010 for a complete review of the subject in birds). e mismatch between the very fast pace of climate change, the longer generation time of vertebrates, and their generally K-like reproductive strategies 1 , makes true evolution di cult to achieve.

Plasticity, on the other hand, has been shown to play an important part in several species' shortterm response [START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF][START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF]. It is primarily known to a ect phenology in order to match the meteorological conditions and most importantly the peak of nutrient availability, as has been evidenced in birds such as the Great tit (Parus major [START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF][START_REF] Husby | Contrasting patterns of phenotypic plasticity in reproductive traits in two great tit (Parus Annexes - §186 major) populations[END_REF] or the Collared ycatcher (Ficedula hypoleuca, Dunn et al.

2010

), but also in plants (Anderson et al.

2) -but is may also apply to metabolic traits [START_REF] Mckay | Antarctic and Southern Ocean influences on Late Pliocene global cooling[END_REF][START_REF] Nicotra | La structure hautement dynamique de l'Océan Austral fait de la phénologie un mécanisme central pour al survie de la plupart des espèces[END_REF] or morphology [START_REF] Przybylo | Climatic effects on breeding and morphology: evidence for phenotypic plasticity[END_REF]. e highly dynamic nature of the Southern Ocean system makes phenology a crucial condition of survival for most species. e seasonal temperature cycle brings about a latitudinal variation in the position of the Polar Front (see §7 p. 35), thus potentially changing the distance between foraging grounds and breeding locations along the breeding season for several species (e.g. [START_REF] Massom | Fast ice distribution in Adélie Land, East Antarctica: interannual variability and implications for emperor penguins Aptenodytes forsteri[END_REF]Bost et al. 2015), or drives the strength of the upwelling activity, and thus the productivity, in the Humboldt Current System ( iel et al. 2007). us, just as the timing of the insect abundance peak is a critical factor for several bird species [START_REF] Dunn | Mais elle a ecte aussi les traits métaboliques[END_REF], the seasonal variation of foraging areas in oceanic systems may be a major constraint on their phenology, and its evolution is expected to

Introduction - §15
force a plastic shift in the phenology of several species. Such shifts, however, have a high tness cost [START_REF] Van Buskirk | The fitness costs of developmental canalization and plasticity[END_REF], as they increase the likelihood of a mismatch between di erent ecological constraints [START_REF] Both | Climate change and population declines in a long-distance migratory bird[END_REF], and may therefore rapidly reach their limits [START_REF] Viblanc | Stress hor-mones in relation to breeding status and territory location in colonial king penguin: a role for social density?[END_REF][START_REF] Both | Climate change and population declines in a long-distance migratory bird[END_REF][START_REF] Møller | Populations of migratory bird species that did not show a phenological response to climate change are declining[END_REF].

Microevolutionary response, since it relies on inter-generational selection in pre-existing varia- ), all the more as these two traits tend to be highly correlated (Romiguier et al. 2014).

Micro-evolution and plasticity are, however, closely interwoven: only the strength of heritability may allow to distinguish between both [START_REF] Raymond | GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism[END_REF], and more importantly, plasticity itself is increasingly recognised as an evolving trait [START_REF] Nussey | Selection on heritable phenotypic plasticity in a wild bird population[END_REF][START_REF] Pelletier | Selection on heritable seasonal phenotypic plasticity of body mass[END_REF].

In this study, however, both the pace of current climate change and the life-history of long-lived seabirds exclude, a priori, any signi cant evolutionary response. A plastic response, on the other hand, is possible, and even to be hoped for. It is however beyond the scope of our work, as crossgenerational data is required in order to build a clear picture of such a process. Up to now, evidence has been presented in several short-generation-time species (see e.g. Charmantier et al. 2008, etc.) -but several decades of phenological and behavioural observations would be necessary to build a clear picture given the long generation time of most seabirds. §-15 Habitat displacement and range shift. Range shift is increasingly recognised as a major feature of short-term response to climate change [START_REF] Davis | Making mistakes when predicting shifts in species range in response to global warming[END_REF][START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF]Burrows et al. 2014, etc.). While the main expression of plastic response concentrates on the phenology (see §14 p. 55), that is to say on a temporal frame shift in the breeding cycle of the species or population, range shift is a spatial frame shift in the distribution of the organism. As opposed to micro-

Introduction - §15
evolution and plasticity, it exclusively involves a change in geographical, and not temporal, physiological or morphological space -although cases of « pure » range shifts, not associated with phenological shifts, are scarce (Møller et al. 2010). Range shift can be conceived as the expression of di erent individual-and species-scale factors. On a functional level, niche conservatism can be dened as the tendency, for an individual, to remain within its physiologically and ecologically dened comfort zone [START_REF] Wiens | Niche conservatism as an emerging principle in ecology and conservation biology[END_REF]. Depending on the width of that niche, and on the evolvability of the species, niche conservatism may apply on very long time scales. For example, New

World bird taxa exhibit a high degree of evolutionary-level niche conservatism, with the more derived species occupying the most specialised environments [START_REF] Harvey | Meta analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming[END_REF], while similar structures have been identi ed i n s tream sh (C omte et al . 20 14) or la nd ma mmals (Martínez Meyer et al. 2004). is slow-time niche conservatism means that habitat preference is not a fastevolving trait, and may be phylogenetically inherited within taxa, slowing down the colonisation of new habitats.

On shorter ecological time scales, on the contrary, niche conservatism is the main force that prompts individuals to follow their habitat rather than adapt to new conditions. As such, it results in habitat tracking, a movement by which a population will be displaced together with environmental conditions. Habitat tracking, when observed from a human perspective, results in range shift, a global modi cation of the species geographical distribution in response to trends in habitat change. Global warming, for example, typically (and schematically) results in a displacement of habitat and species towards the poles, or the higher altitude regions (Jump et al. 2009;[START_REF] Fraser | Poleward bound: biological impacts of Southern Hemisphere glaciation[END_REF].

In an ideal system, species may be expected to follow habitat displacement smoothly (Sche er et al. 2001). However, the anisotropic character of biozones implies that habitats do not transform in a homogeneous manner (Di enbaugh & Field 2013), and that changes are likely to be doubly heterogeneous -between ecosystems and between species within ecosystems. A recent study on Australian birds [START_REF] Vanderwal | Focus on poleward shifts in species' distribution underestimates the fingerprint of climate change[END_REF], for instance, has shown that during the past 60 years,

Introduction - §15
species' range shifts had followed mostly idiosyncratic trajectories on the whole continent, only partly explained by habitat displacement. Such an example stresses the importance of biotic interactions in the determination of a species' range and range displacement, as several interspeci c relationships can be a ected simultaneously by climate change (e.g. 2006;[START_REF] Turner | Antarctic climate change and the environment: an update[END_REF][START_REF] Chown | et Fig. 6), et il est probable que la particularité apparente des régions polaires ne soit dûe à rien d'autre qu'à leur destruction plus tardive, garantie par leur inaccessiblité -et non à quelque caractéristique particulière qui y favoriserait la mégafaune[END_REF] and have an extremely limited margin for habitat shift [START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF]), are an extreme example of problematic habitat displacements [START_REF] Williams | Projected distributions of novel and disappearing climates by 2100 AD[END_REF]. Detecting lagged response to ongoing environmental changes before the tipping-point is therefore critical if we are to develop appropriate and e cient management and conservation strategies and to accurately forecast the actual distribution of future critical refugial areas [START_REF] Keppel | Refugia: identifying and understanding safe havens for biodiversity under climate change[END_REF]Hope et al. 2013;[START_REF] Watson | en particulier pour les prédateurs supérieurs, qui déterminent dans une large mesure les propriétés de résilience des écosystèmes[END_REF], in particular for top-level predators that largely determine ecosystem resilience [START_REF] Soong | Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño-Southern Oscillation and Southern Annular Mode variability[END_REF][START_REF] Heithaus | donc de comprendre comment la distribution de chaque espèce s'organise, à des échelles allant de la diversité intra-coloniale, à l'île[END_REF]) in order to develop appropriate and e cient management and conservation strategies. us, range shift predictions are becoming a major challenge in ecological modelling. ) is taken as a proxy for anthropogenic events, or the very short time, in which meteorological stochasticity and cyclic phenomena (for example the North Atlantic Oscillation) are used to the same purpose 1 . In that context, one of the di culties of understanding contemporary cli-

Aims of this work

Introduction - §16
1.

An opposition theorised by Fernand Braudel in his study of the History of the Mediterranean, as the distinction between the « longue durée » (in which the interaction of humans with their geography and habitat prevail) and the « short, fast and nervous oscillations » of « event history » [START_REF] Bradshaw | La Méditerranée et le monde méditerranéen à l 'époque de Philippe II. 108. Braudel F[END_REF][START_REF] Bradshaw | La Méditerranée et le monde méditerranéen à l 'époque de Philippe II. 108. Braudel F[END_REF]. Along this spectrum, Braudel stresses the « increasingly precise notion of the multiplicity of time, and of the exceptional value of long time » (Braudel 1958). us, « every historical work decomposes past time, chooses between its chronological realities, according to more or less conscious preferences and exclusions. Traditional history, which is focused on the swift time, the individual, the event, has long since accustomed us to its precipitated, dramatic, gasping narrative. e newer economic and social history emphasises its search for cyclic oscillations and has tuned itself to them: it became entangled in the illusion, but also in the reality of the cyclical raises and falls of prices. us there is, next to the narrative (...), a recitation of the conjuncture that examines the past by thick slices: decades, scores and fties. But well beyond that second recitation lies a history of a steadier breath still, and now of centennial scale: the long, longue durée history » [START_REF] Bradshaw | La Méditerranée et le monde méditerranéen à l 'époque de Philippe II. 108. Braudel F[END_REF]. is essential decomposition of history into layers of causes and events -structural, conjunctural and proximal -is now a tenet of modern history. Yet, it is hardly theorised in biological sciences, where it only lives in the often unreconciled opposition of evolutionary and ecological paradigms ( §1 p. 21).

Introduction - §16

mate change is that it operates on a scale for which we have little or no concepts, because it has no known precedent in the history of the earth, and that requires us to nd a middle ground between ecological and evolutionary times. And so, a central question is whether the observations we extract from very long term (i.e. palaedemographic) studies are of the same nature as the ones we extract from short term (typically monitoring) studies -and whether any of these may explain and predict the events to be expected under anthropogenic climate change. In many respects, it may appear that the nearly instantaneous observations that we make on monitored populations, and that span a few generations at best, are dominated by several layers of stochasticity and mixed forcings -the stochasticity and cycles of meteorology around the trends of climate and its nested cycles, the stochasticity of demography around the cycles and trajectories of populations and species, the slow but chaotic restructuring of ecosystems when its parts move in di erent directions -and that these contradictory signals may hide for some time the inevitably directional trends of global change. At the other end of the spectrum, however, palaeodemography, by its evening out of this very stochasticity and these high-frequency variations, retains only broad patterns of change, such as the slow pulse of Pleistocene glaciations, that leave time for evolution to occur. It may, however, totally fail to capture the now comparatively swift movement of manmade change.

For these reasons, what really occurs in a situation such as contemporary anthropogenic climate change can be conceived as a disruption of the pace and manner in which these nested scales are integrated, and an increasingly likely mismatch between layers of spatial and temporal variation.

e duration of the generation, for example, may be simplistically considered as the time lapse (i) e comparative biogeography of these three species is the foundation of this study. A rst task will be to understand how range-wide distributions are organised, at scales ranging from intra-colonial diversity to island and range-wide levels, and to establish what seems to be the relevant demographic unit for studying past, current and projected demographic trends. is will Nevertheless, achieving a better understanding of seabird population dynamics requires a critical examination of these predictions, and an integration of the methods that prompted them. We address this issue using the total available evidence for the best known of these three species, the King penguin, and propose a possible re-interpretation of the Emperor penguin's response to Southern Ocean warming. In both cases, we try to propose a likely consensus projection based both on long-and short-time observations, as a temptative framework to solve the di cult problem of ecological and evolutionary integration in the context of climate change.

Introduction - §17
Introduction - §17

1.

As will be exposed in details in Concepts and methods (p. 65), di erent reasons prompted these three projections: the observed dependency on sea ice, and the modelled evolution of that same sea ice for the Emperor penguin, the correlation between foraging area and sea surface temperature for the King penguin, and the observed and partly unexplained (but likely only marginally climate-related) population decline for the Peruvian diving-petrel. (from the primary phytoplankton production to the upper sh or krill biomass) will ultimately a ect the availability of their resource [START_REF] Bakun | Wasp-waist populations and marine ecosystem dynamics: navigating the predator pit topographies[END_REF][START_REF] Frederiksen | From plankton to top predators: bottom up control of a marine food web across four trophic levels[END_REF][START_REF] Hunt | The impact of avian predator-scavengers on King Penguin Aptenodytes patagonicus chicks at Marion Island[END_REF][START_REF] Ainley | Paradigm lost, or is top-down forcing no longer significant in the Antarctic marine ecosystem[END_REF] teristics). us, their ability to cover very large areas during their foraging trips is in contrast with the scarcity of available breeding habitat, and a strong tendency to return to the same location to breed year after year -although most of species have the ability to disperse well beyond the borders of their local grounds, and most have been observed to do so (e.g. [START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF]Milot et al. 2008, etc.) [START_REF] Freer | Limited genetic differentiation among chinstrap penguin (Pygoscelis antarctica) colonies in the Scotia Arc and Western Antarctic Peninsula[END_REF], have been shown to exhibit very weak or absent isolation patterns amongst colonies. On the other hand, the Little penguin Eudyptula minor [START_REF] Overeem | Contrasting genetic structuring between colonies of the World's smallest penguin, Eudyptula minor (Aves: Spheniscidae)[END_REF][START_REF] Peucker | Range-wide phylogeography of the little penguin (Eudyptula minor): evidence of long-distance dispersal[END_REF] or the Yellow-eyed penguin

Introduction

Megadyptes antipodes (Boessenkool et al. 2009a;Boessenkool et al. 2009b) show signi cant isolation patterns, while the Gentoo penguin Pygoscelis papua [START_REF] De Dinechin | Speciation chronology of rockhopper penguins inferred from molecular, geological and palaeoceanographic data[END_REF], and the Rockhopper penguin Eudyptes chrysocome [START_REF] Banks | Genetic evidence for three species of rockhopper penguins, Eudyptes chrysocome[END_REF][START_REF] Jouventin | Genetic isolation and divergence in sexual traits: evidence for the northern rockhopper penguin Eudyptes moseleyi being a sibling species[END_REF][START_REF] De Dinechin | Speciation chronology of rockhopper penguins inferred from molecular, geological and palaeoceanographic data[END_REF]) are thought to be engaged in a speciation process between isolated oceanic provinces.

While morphological evidence points towards a certain degree of homogeneity in the Emperor penguin, the King penguin's alleged subspecies structure [START_REF] Mathews | Fitchi: Haplotype genealogies based on Fitch distances[END_REF]Barrat 1976 and §21 p. 72), points to much reduced gene ow -a conclusion supported by an early study [START_REF] Viblanc | Stress hor-mones in relation to breeding status and territory location in colonial king penguin: a role for social density?[END_REF]. While the heterogeneity of methods and sampling designs used in these di erent studies prevents a systematic comparison of population structures, it appears that genetic homogeneity is higher in the pelagic-foraging species such as the Adélie and the Chinstrap, and that population isolation is characteristic of more coastal species such as the Gentoo or the Rockhopper.

However, the genetic structure of populations is not a direct re ection of their philopatric behaviour, as it also integrates other traits such as generation time, substitution rate [START_REF] Jarvis | Whole-genome analyses resolve early branches in the tree of life of modern birds[END_REF]. All the species in these groups share some either plesiomorphic, or convergent traits. ey rely on marine productivity for food, either directly preying on sh, crustaceans or mollusks, or foraging in the marine debris on the shore. eir plumage evolved advanced insulation and waterproo ng properties. eir feet evolved interdigital webbing to facilitate paddling or steering. ey are not usually territorial in their foraging areas, as opposed to most raptor or passerine species, and most of them breed colonially. However, their exploitation of the marine environment is highly diverse. Some species are mostly restricted to the shoreline (e.g. plovers or herons), and most usually occupy only the coastal waters (e.g. seagulls, pelicans, cormorans, etc).

Few species are truly pelagic, and most of these belong to the Procellariimorphae or « storm-birds » -often called seabirds stricto sensu. Paradoxically, within that group, the colonisation of the high seas followed two opposite paths: (i) some species, within the procellariiforme family, developed 1.

Both recent and questionable compounds of Latin and Greek (aequor, the « high seas » and ὄρνις, « bird » for the rst, and procella, « storm », and μορφή, « shape » for the second).

Concepts and methods - §20 extraordinary gliding abilities, and started using the strong winds prevailing at sea to dispense with the metabolic costs of active ight -these are the petrels, fulmars, shearwaters, and above all albatrosses, that may circle the entire planet during a foraging voyage [START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF]. (ii) Some others, however, developped swimming abilitites instead, at the cost of reduction, or complete loss, of ight: these are rst and formost the Sphenisciformes (the penguins), the Alciformes (the auks, pu ns, mures and guillemots), and the Pelecanoididae or diving-petrels (a part of the mostly gliding Procellariiformes). Morphological resemblances between these three families are due true convergence (see Grassé 1950 for details), and the most remarkable is the modi cation of the wings to facilitate underwater swimming: since the resistance of the water is much stronger than that of the air, wings have become shorter, more rigid, and the pectoral muscles have developed accordingly. e trade-o for this adaptation has been the complete loss of ight in modern penguins -as well as in at least one alcid, the now-destroyed Great Auk (Alca impennis) -and a costly and laborious apped ight in the remaining Alcidae, and in Pelecanoididae. Posterior displacement of the hind limbs also accurred in all three groups, with an increasingly upright stand- ese common characteristics set apart the Northern Hemisphere Alcidae, and the Southern Hemisphere Spheniscidae and Pelecanoididae, from the rest of the seabirds. is work will mainly focus on Spheniscidae, and in particular on the two Aptenodytes penguin species. e Pelecanoididae, due to their several convergence points with the penguins, will also be used as a reference outgroup in the seventh chapter, Unexpected philopatry in an insular seabird, the Peruvian divingpetrel, p. 275.

1.

Diving abilities being a direct function of myoglobin availability, and hence body mass.

Concepts and methods - §20

Concepts and methods - §21 §-21 Origin, evolution and radiation of sphenisciforms. Although penguins bene t f rom a n outstandingly rich fossil record [START_REF] Simmonds | Relationships between the interannual variability of Antarctic sea ice and the Southern Oscillation[END_REF][START_REF] Myrcha | Eocene penguins of Seymour Island, Antarctica: Taxonomy[END_REF][START_REF] Clarke | Paleogene equatorial penguins challenge the proposed relationship between biogeography, diversity, and Cenozoic climate change[END_REF], their evolutionary history presents considerable uncertainties. e e arliest s tem p enguin r adiation i s generally thought to have occurred just after [START_REF] Baker | Multiple gene evidence for expansion of extant penguins out of Antarctica due to global cooling[END_REF][START_REF] Clarke | Paleogene equatorial penguins challenge the proposed relationship between biogeography, diversity, and Cenozoic climate change[END_REF], or just before [START_REF] Slack | Early penguin fossils, plus mitochondrial genomes, calibrate avian evolution[END_REF] Gondwanaland [START_REF] Baker | Hierarchical structure of mitochondrial DNA gene flow among humpback whales Megaptera novaeangliae, world-wide[END_REF]. Early apomorphies that allowed for adaptation to cold waters, and in particular the humeral arterial plexus counter-current heat exchange structure ( omas et al. 2011), are thought to have evolved in the Cenozoic greenhouse-earth, at lower latitudes, and independently from the current extreme-cold adaptation. Although theory predicts that larger taxa should be better adapted to cold climate due to a favourable body surface-to-volume ratio, only smaller taxa survived through the late Tertiary icehouse earth transition, which lead to suggest that i) giant penguin diversi cation was linked to increased upwelling activity and marine productivity during the Cenozoic [START_REF] Clarke | Paleogene equatorial penguins challenge the proposed relationship between biogeography, diversity, and Cenozoic climate change[END_REF]) and that ii) their demise was linked to the appearance of competing sh-eating s mall c etacean t axa ( Fordyce e t a l. 1 990; Williams 1995).

Concepts and methods - §21

Crown penguins are thought to have originated at higher, subantarctic latitudes, and to have radiated back to lower subtropical and tropical latitudes in the recent past [START_REF] Baker | Multiple gene evidence for expansion of extant penguins out of Antarctica due to global cooling[END_REF] e most remarkable features of the known evolutionary history of the penguins are thus that i) the stem group is ancient and separated early from other neornithes species (Gavryushkina et al.

2015)

; ii) early penguin diversity is not associated with polar or otherwise cold environment, but rather with greenhouse-earth temperate habitats [START_REF] Baker | Multiple gene evidence for expansion of extant penguins out of Antarctica due to global cooling[END_REF]omas et al. 2011); iii) the giant penguin fossil abundance and the co-occurrence of their demise with the radiation of small-Concepts and methods - §22 er sh-eating cetaceans makes it likely that they occupied a central top-predator role in the Cenozoic marine food-web [START_REF] Forcada | Penguin responses to climate change in the Southern Ocean[END_REF][START_REF] Williams | Projected distributions of novel and disappearing climates by 2100 AD[END_REF][START_REF] Baker | Multiple gene evidence for expansion of extant penguins out of Antarctica due to global cooling[END_REF]; iv) the adaptation of crown penguins to extreme cold conditions is secondary, and builds upon pre-adaptations

( omas e t a l. rat 1976). Yet, however populous they may have been, King penguins populations were massacred at industrial scales through most of their range by the whaling and sealing expeditions: the population on Macquarie was thus reduced to ca 3,400 birds (~0.7% of its current size) by the early 20 th century (Rounsevell & Copson 1982), while most colonies were driven to near-extinction throughout the species range [START_REF] Mulvaney | Oceanic birds of South America: a study of species of the related coasts and seas, including the American quadrant of Antarctica, based upon the Brewster[END_REF][START_REF] Mulvaney | Oceanic birds of South America: a study of species of the related coasts and seas, including the American quadrant of Antarctica, based upon the Brewster[END_REF][START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF][START_REF] Lenormand | The distribution and size of king penguin rookeries on South Georgia[END_REF][START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF][START_REF] Pistorius | Population development and historical occurrence of king penguins at the Falkland Islands[END_REF], and to complete extinction in Heard and Tierra del Fuego [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF][START_REF] Gales | Recovery of the king penguin, Aptenodytespatagonicus, population on Heard Island[END_REF] -although is di cult to ascertain the global extent of the reduction in population size. Following the end of slaughters in the mid-20th century, the King penguin population rebounded [START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF][START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF][START_REF] Hellenthal | King penguin population on Macquarie Island recovers ancient DNA diversity after heavy exploitation in historic times[END_REF] -Garrard 1922). Eggs were also collected for study during Shackleton's last expedition (1914( -17, as narrated in Shackleton 1919)).

Discovery of breeding colonies was slow and di cult, owing to the technical di culties of winter expeditions in the most remote areas of Antarctica [START_REF] Wienecke | The history of the discovery of emperor penguin colonies[END_REF], and has been substantially

helped by satellite exploration [START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF][START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF].

However, it is believed that several colonies are still undiscovered [START_REF] Ancel | Emperors in hiding: when icebreakers and satellites complement each other in Antarctic exploration[END_REF]. In contrast with the large-scale massacres of king penguins, the direct impact of humans on Emperor penguins has been relatively small, a fact owing unfortunately more to the remoteness of its habitat than to any particular benevolence towards this remarkable species. However, an impact of re- Concepts and methods - §22 Antarctic Polar Front, Subantarctic convergence and Subtropical convergence, approximate mean position (Australian Antarctic Division dataset, after [START_REF] Orsi | On the meridional extent and fronts of the Antarctic Circumpolar Current[END_REF].

Study sites and species

Concepts and methods - §23 §-23 e Emperor penguin. e Emperor penguin (Aptenodytes forsteri) is the largest extant penguin species, measuring up to 120 cm and weighting up to 45 kg [START_REF] Stonehouse | The Emperor Penguin, Aptenodytes Forsteri[END_REF]. It breeds during winter along the shoreline of mainland Antarctica, at latitudes comprised between 64°S and 77°S [START_REF] Wilson | Distribution and abundance of Antarctic and sub-Antarctic penguins: a synthesis of current knowledge[END_REF]. All across this range, it reproduces in well-separated colonies that gather on the sea-ice at the beginning of the breeding season, in the rst d ays of winter, and that dissolve again completely during the austral summer, as the chicks edge and their breeding ground breaks up, only to gather again at the beginning of the next winter. e size of these colonies usually ranges from a few hundred, to dozens of thousands of breeding pairs [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]. Despite their cyclical existence, however, each colony is considered to be a very stable unit, both in terms of location, and of individual identity: the Emperor penguin is thought to be a strongly philopatric species, both as a juvenile, and as an adult [START_REF] Stonehouse | The Emperor Penguin, Aptenodytes Forsteri[END_REF][START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF][START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF][START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF][START_REF] Isenmann | Ecoéthologie du Manchot empereur (Aptenodytes forsteri) et comparaison avec le Manchot Adélie (Pygoscelis adeliae) et le Manchot royal (Aptenodytes paragonica)[END_REF]Isenmann 1971;[START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF]Kooyman 1993;[START_REF] Ancel | The different breeding strategies of penguins: a review[END_REF]. Recent satellite surveys have shown that its present-day breeding range is made up of 52 colonies, distributed along most of the Antarctic shoreline [START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF][START_REF] Ancel | Emperors in hiding: when icebreakers and satellites complement each other in Antarctic exploration[END_REF]). e only signi cant interruption in this distribution is the Antarctic peninsula (see §10 p. 48), which extends North to almost 62°S, at the very limit of the specie's breeding habitat: indeed, the rst documented loss of an Emperor colony happened at the northern end of the species' range, in Dion Island, as recently as the early 21 st century, and has been directly associated with the region's important warming trend [START_REF] Tomczak | First recorded loss of an emperor penguin colony in the recent period of Antarctic regional warming: implications for other colonies[END_REF]. However, the species' high dependence on sea ice makes it a di cult task to accurately predict its future response to climate change (see §18 p. 65), the dynamics of which we still poorly understand in Antarctica [START_REF] Turner | Ecological importance of the southern boundary of the Antarctic Circumpolar Current[END_REF][START_REF] Shu | Assessment of sea ice simulations in the CMIP5 models[END_REF].

e Emperor penguin's complex breeding system poses particular challenges to population modelling. is long-lived bird (the current lifespan estimate is unknown -see (16 years, Jenouvrier et al. 2014), which implies that generations are largely overlapping, in contradiction with the assumptions of the Wright-Fisher population model (Wright 1931 and §39 p. 99). Although divorce rate is high in this serially monogamous bird (ca. 85%, Isenmann 1971), mating is not likely to be fully random [START_REF] Bried | Why do Aptenodytes penguins have high divorce rates[END_REF]. Finally, the species' particular spatial organisation during breeding make capture-mark-recapture technically challenging, so that even instantaneous demographic trends are not trivial to infer. §-24 e Pointe Géologie colony and its regional and continental ties. e Pointe Géologie Emperor penguin colony is by far the best-studied one in Antarctica. Its presence was one of the reasons for the choice of location for the Dumont d'Urville research station, in 1952, and it has been monitored continuously ever since [START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF][START_REF] Isenmann | Ecoéthologie du Manchot empereur (Aptenodytes forsteri) et comparaison avec le Manchot Adélie (Pygoscelis adeliae) et le Manchot royal (Aptenodytes paragonica)[END_REF]Isenmann 1971;Jouventin 1971a;Jouventin 1971b;Jouventin 1972b;[START_REF] Jouventin | Mortality parameters in emperor penguins Aptenodytes forsteri[END_REF][START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF]Barbraud & Weimerskirch 2001a;[START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF][START_REF] Ancel | Emperor penguin mates: keeping together in the crowd[END_REF][START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF]). As it is also the most accessible colony during the winter breeding season, the breeding behaviour of the Emperor penguin is mostly known through observations made on this colony (see references above). e corollary is that our knowledge of the species is often restricted to local observations, and that we may lack a global perspective on behavioural, phenological or demographic traits (see e Emperor synnome, p. 211).

Of particular interest, for example, is the sudden decline in the number of breeders on the Pointe Géologie colony, that abruptly dropped from ca 6,000 to ca 3,000 breeding pairs between 1975 and 1985 (Barbraud & Weimerskirch 2001a). Although it has been alternatively linked to local e ects of climate change (Barbraud & Weimerskirch 2001a;[START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF][START_REF] Barbraud | Comparison of emperor penguin declines between Pointe Géologie and Haswell Island over the past 50 years[END_REF], or to increased predation pressure by killer whale as a consequence of the decline of their usual prey, the minke whale, under whaling industry pressure (Ainley et al. 2010b), the period also corresponds to heavy logistic and scienti c activity on the colony, in particular large-

Concepts and methods - §25

scale ipper b anding ( Jenouvrier 2 004) w hich h as b een u nambiguously s hown t o negatively a ect survival [START_REF] Gaspar | Comparison of behaviour, body mass, haematocrit level, site fidelity and survival between infested and non-infested king penguin Aptenodytes patagonicus by ticks Ixodes uriae[END_REF][START_REF] Dugger | Complex colony level organization of the deep sea siphonophore Bargmannia elongata (Cnidaria, Hydrozoa) is directionally asymmetric and arises by the subdivision of pro Annexes - §186 drial DNA restriction data[END_REF]Le Maho et al. 2011;Saraux et al. 2011a). Interestingly, the decline of the Pointe Géologie Emperor penguin study colony has a parallel on Crozet archipelago, where the King penguin study colony in the Baie du Marin (see §26 p. 81), also subject to human disturbance and heavy ipper-banding [START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]Saraux et al. 2011a), is the only one to have declined, while the four other colonies of the same island were undergoing very rapid growth [START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]) -thus leaving little doubt as to the e ects of human activity on colony demographic trajectory.

But the decline of the Pointe Géologie colony also raised the question of the fate of the missing penguins. While their disappearance has usually been explained by increased mortality, we suggest that dispersal to other colonies may have played an important role (see Empirical evidence of heterogeneous dispersal, p. 259 and §135 p. 250) -a possibility that would change our interpretation of the global demographic trajectory of the species. However, the lack of both of direct evidence for dispersal, and of a broad comparative perspective in the Emperor penguin makes a global assessment of the species' status di cult, and will require an increased methodological e ort (see e Emperor synnome, p. 211 and Empirical evidence of heterogeneous dispersal, p. 259).

In the meantime, it is still unclear whether the Pointe Géologie colony can be taken as a reliable indicator of worldwide population trend or whether its importance is rather on a regional scale. §- [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF], and behaves there in a remarkably territorial way, although it does not build a nest and incubates its egg on its feet in the same manner as its sister species. e King's breeding system also extends over the winter, but egg-laying, hatching and brooding occur during summer, and chicks fast unguarded in « crèches » over the winter [START_REF] Stonehouse | The King penguin: Aptenodytes patagonica, of South Georgia[END_REF][START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF]). During the early chick rearing period, king penguins forage mostly on the APF (see §18 p. 65), which becomes a critical food resource during the months of January and February [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF][START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF].

Like the Emperor (see §23 p. 78), the King penguin presents interesting challenges to demographic modelling. is e qually l ong-lived b ird h as a s lightly s horter g eneration t ime (~10. [START_REF] Liu | Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure[END_REF]. However, the number of markers available in a typical SNP experiment is several hundred times higher than what can reasonably be achieved with microsatellite markers, and it has been shown that an increased number of markers contributed highly to detection power for several genetic patterns [START_REF] Liu | Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure[END_REF]Hoban et al. 2013b). Direct comparisons

Concepts and methods - §28

« Chincha Islands war » of 1864-1866 between Spain and a Peru-Chile alliance for the possession of the Chincha guano islands.

1.

Brownian motion of particles is used as an approximation of the stepwise mutation in microsatellites: an n-mer allele can randomly mutate to n+1 or to n-1 at each mutation even, ultimately leading to an increasingly dispersed distribution of fragment lengths.

Concepts and methods - §29

informed by empirical data, however, have alternatively concluded in an increased power (Ho man et al. 2014) or an increased noise [START_REF] Mesak | Hundreds of SNPs vs. dozens of SSRs: which dataset better characterizes natural clonal lineages in a self-fertilizing fish[END_REF] From DNA libraries to polymorphism data. §-31 Particularity of RAD data for analysis. An important strength of the RAD-sequencing approach (see §29 p. 85) is the possibility it o ers to work on a genome-wide scale in non-model species for which no reference genome is available [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]. As every RAD locus is constrained by the location of cut-sites, rst-in-pair read overlap is complete at each particular location. It is thus possible to match the multiple reads covering a locus without any information about its position in the genome. But even in the presence of a reference genome, the highly discretised character of the RAD-sequencing data changes substantially the analysis paradigm.

Concepts and methods - §31

e « natural » structure of genomic data is shaped by two opposite processescoalescence and recombination. e coalescence process can be described as the down-time branching of sequences through the occurrence of new mutations (see §40 p. 101). e recombination process, on the contrary, is the merging of sequences with di erent coalescent histories, through physical crossing-over during meiosis. us, a coalescent event can be seen as the fusion of sequences when going backward in time, a recombination event is, on the opposite, a fusion of two sequences going forward in time (see §42 p. 107). e direct consequence of a shared coalescence history is the linkage between di erent polymorphic sites: in the absence of recombination, and with an innite-sites model, co-occurrence of derived alleles re ects the coalescent history of the sequences.

Recombination, on the other hand, breaks this linkage, by physically linking derived alleles that did not arise in the same lineages in the rst place. Since recombination is a relatively random process, the chances that it would happen between two given loci increases with the distance between these loci. us, two polymorphic nucleotide positions separated by only a few base-pairs are expected to keep their linkage much longer than two loci separated by several thousands of base-pairs. In continuous genomic data, this means that linkage between markers will decay continuously with increasing distance, until a point where distant loci may be considered e ectively independent. Between very close and absolutely linked loci, and distant and e ectively independent loci, virtually all states of linkage disequilibrium can be found.

RAD-sequencing data, on the other hand, has a strongly bimodal structure. Since only short fragments (95 to 125 bp) of DNA are sequenced, and since these are assumed to be evenly interspersed in the genome, two polymorphic positions that belong to the same locus can be considered non-recombining, and their allele distribution is solely the consequence of the coalescent history of the locus, while polymorphic positions belonging to di erent loci are e ectively independent, and their co-occurrence on a chromosome is mainly driven by the recombination process. us, while the « classical » polymorphism calling framework assumes continuity in the data, and therefore works on a per-base rationale, RAD data can also be handled in a discrete

Concepts and methods - §32

way, rst identifying reads belonging to the same RAD locus, and then treating these loci as nonrecombining units. §-32 e S tacks p ipeline. is la tter ap proach is im plemented in th e po pular St acks pipeline [START_REF] Catchen | Stacks: building and genotyping loci de novo from short-read sequences[END_REF][START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF]. Stacks builds upon the discretised RAD paradigm, and makes the RAD locus a self-standing unit. e a nalysis p ipeline r uns i n t hree s teps: (i)

identifying exactly matching reads, that come from a single genomic location and a single allele (a « stack » in the program's lexicon), and match those stacks within individuals to identify polymorphic loci, and (ii) match loci across individuals to build a common catalog, and (iii) reference all data to this common catalog.

e rst step can be achieved in two di erent ways: either (a) by direct local de novo assembly, without a reference genome (using the ustacks program), by matching the reads against each other, or (b) by mapping the raw reads against a reference genome (using an independent mapping program, in our case Bowtie2, Langmead & Salzberg 2012), and collating reads that map at the same genomic position (using the pstacks program). While both approach are valid, mapping reads to a reference genome is preferable whenever possible, rst b ecause i t a llows for polymorphism data to be compared between Stacks and other methods through the mapping coordinates, and second because it guarantees that sequenced contaminants (bacterial DNA, etc) are not retained in the analysis, as these are not expected to align to the reference genome. It is this second approach that we used in this work, using as a reference the published Emperor penguin genome (Zhang et al. 2011b), both for the Emperor and for the King penguin sequencing data.

e second and third steps are done using the cstacks and sstacks programs, and allow the building of a MySQL database containing RAD loci (as opposed to polymorphic sites) as individual entries. A particularity of this approach is that polymorphic sites are identi ed e arly i n the pipeline (at step (i)), within individuals, when matching stacks to form diploid loci. us, SNP calling does not integrate information from the whole population as a basis for the likelihood

Concepts and methods - §33

function -therefore there is an increased probability of mistakenly scoring sequencing errors as legitimate polymorphisms in low-coverage data. A nal correction has been implemented in the rxstacks program, that correct genotype calls based on information from the whole dataset. However, this two-steps procedure only re-evaluate genotypes at already identi ed S NP l oci, a s opposed to the SNP positions themselves, and is therefore more error-prone than a native joint SNP and genotype calling algorithm such as Samtools' mpileup (Li et al. 2009) or GATK's Hap-lotypeCaller [START_REF] Depristo | The annual breeding cycle of unbanded king penguins Aptenodytes patagonicus on Possession Island (Crozet)[END_REF] pipelines.

e strength of the Stacks approach, however, remains the rapidity and ease of querying and ltering o ered by the MySQL database structure, that allows for e cient data mining, before deploying more robust and computationally-intensive analysis methods. §-33 SNP-calling approach. Although the locus-based paradigm o ered by the Stacks framework (see §32 p. 89) is an e cient representation of RAD data structure, it is also possible to handle RAD-sequencing data as conventional high-throughput sequencing data, provided the bimodal linkage structure is taken into account in downstream analysis. is approach involves three successive steps: (i) raw read mapping and ltering, ( ii) S NP a nd g enotype c alling, a nd ( iii) data ltering.

For the initial read mapping, we use Bowtie2 [START_REF] Depristo | The annual breeding cycle of unbanded king penguins Aptenodytes patagonicus on Possession Island (Crozet)[END_REF], allowing only for fully mapped read pairs, respecting the observed length of our paired-end libraries (250 to 1,000 bp).

While other tools, such as the Burrow-Wheeler transform alignment (BWA, [START_REF] Li | Fast and accurate short read alignment with Burrows-Wheeler transform[END_REF], perform a comparable task, not all support end-to-end read mapping: BWA, for example, allows

for missing bases at both ends of the raw reads. is is suitable for most short-read mapping situations, but not for RAD sequencing, which relies on the presence of a cut-site, and thus puts a strong constraint on the correct alignment of the rst six (in our case) bases of the raw reads. Using Bowtie2, we have typically high alignment rates (75% to 85% then scanned all together on a per-site basis by the GenotypeGVCF program, that makes use of individual depth, base quality, variant frequency, and variant phasing information to call genotypes in a maximum-likelihood framework. e main drawback of this approach is its extremely time-consuming character, as computation time grows exponentially with the number of included individuals: thus, on our platform, genotyping ~200 individuals for the whole RADome on 48 threads can take up to 10 days. Another faster, and equally accurate method is the Samtools mpileup/bcftools pipeline (Li et al. 2009), which is also deployed in two steps: rst the collating of all alignment les into a single « p i l e u p » format, and then a genotype calling and ltering step, also in a maximum-likelihood framework. While the Samtools pipeline can not be multithreaded, it can easily be split in chunks along the genome, and the results merged after genotype calling.

e nal ltering of called genotypes is mostly done in VCFtools [START_REF] Danecek | The variant call format and VCFtools[END_REF]). e details of ltering greatly depends on the analysis for which data is prepared, although some aspects

Concepts and methods - §34

are invariant. For one, a form of ensemble calling may be highly desirable. All maximum-likelihood genotype calling algorithms are model-based: they implement an explicit model describing the likelihood of a particular genotype given the observed data. Yet these models di er between algorithms: while they overlap very closely in the « central » part of the genotype calling problem, they may di er quite widely at its limits -for example, under very high or very low coverage, or when both alleles are represented with very asymmetric depth. However, while it is possible to assess the accuracy of genotyping using an ascertained SNP panel in model data (e.g. human genomic material), there is no straightforward method for ranking genotype models in more exotic organisms. A robust method to address the potential lack of agreement between SNP models is to retain only consensus SNPs, i.e. loci and genotypes called the same way by a panel of models, as the most reliable set of loci (Greminger et al. 2014).

Within this consensus SNP set, further ltering may be necessary, relative, this time, not to the model after which genotypes have been called, but rather to the structure of the available population dataset. Some analyses may be overly sensitive to missing data (e.g. non-assignment bias in principal component analysis), in which case only loci genotyped in a high proportion of individuals should be retained. Some analyses, on the other hand, are very sensitive to genotype mis-assignment (e.g. erroneously calling a homozygous site, where the true underlying genotype is heterozygous), in which case it is critical to impose a minimum depth, e.g. 10x, over retained genotypes (see §35 p. 93) in order to obtain reliable results. However, all such ltering always comes at the cost of reducing the number of loci available for analysis: therefore, a tradeo often needs to be made between the quality of the data necessary for the analysis, and the quantity of data required for robust results (see §36 p. 95). §-34 Genotype-free approach. Most current model-based calling algorithms implement a maximum-likelihood framework (see §33 p. 90). Genotype calling thus commonly happens in two steps: rst, at each position, a likelihood score is calculated for all 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, and TT) -this score being de ned as the probability of the se-

Concepts and methods - §35

quencing data given each possible genotype (Korneliussen et al. 2014). en, the genotype with the best likelihood score is retained as the « true » genotype, provided the contrast with the second-best likelihood score is steep enough. However, this second step, by choosing one single genotype, suppresses the information contained in the genotype likelihood distribution [START_REF] Nielsen | Genotype and SNP calling from next-generation sequencing data[END_REF][START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]. In many cases, this gives a false sense of certainty to data that did not, in reality, support any positive genotype call: thus, it has been proposed that analysis may often be more accurately performed by focusing on the genotype likelihood distribution, rather than on called genotypes [START_REF] Nielsen | Genotype and SNP calling from next-generation sequencing data[END_REF][START_REF] Nielsen | Genotype and SNP calling from next-generation sequencing data[END_REF]Nielsen et al. 2012;[START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]. is approach has recently been implemented in the ANGSD program (Korneliussen et al. 2014). is a pproach i s p articularly p owerful f or l ow-coverage d ata, w here the genotype likelihood distribution tends to make little di erence between homozygous and heterozygous calls. erefore, i t w as p referred f or o ur R AD s equencing d ata, w henever suitable analysis algorithms were implemented for downstream data treatment (e.g. principal component in particular of Illumina type, are (i) the random sampling process that occurs during fragment binding and cluster generation and (ii) the relatively high error rate, with possibly more than 0.1% erroneous base calls. us, every analysed fragment must be sequenced several times independently, in order to ensure that (i) both alleles have been sampled, and (ii) sequencing errors can be accurately ltered out.

Genotype under-sampling occurs when, at a heterozygote position, only one of the two possible bases is sampled across the whole sequencing coverage (Nielsen et al. 2012). At a sequencing depth of one, this is necessarily the case. At a depth of n, this occurs with a probability of ( 1 /

Concepts and methods - §35

2 (n-1) ). us, at a depth of 5, the probability of under-sampling the true genotype is 0.0625 (in other words, out of a very large number of heterozygous positions, ~6.25% will erroneously be called as homozygous); at a depth of 10 that probability falls to 9.77e -4 . is, however, refers to « e ective » or « true » sequencing coverage, where 5X means that 5 independent genomic copies of a DNA region have been sequenced in the same individual. Yet observed coverage may be signi cantly overestimated compared to e ective coverage.

Indeed, in case of sub-optimal library preparation e ciency, too few individual DNA fragments are prepared compared to the ow cell capacity. During PCR library enrichment, multiple clonal copies of these fragments are generated. Normally, the random sampling process occurring during ow cell clustering would ensure that ~1 copy of each fragment only is represented in the sequenced library. However, in case of a low-diversity library, several PCR-copies of each fragment will be sequenced. Although these fragments, or « PCR duplicates », contribute to the total sequencing depth, they do not stem from independent genomic copies. us, if each independent fragment is, on average, sequenced along with 1 duplicate (50% of the library is thus made up of duplicates -or 50% duplication rate), an observed coverage of 10X will, in reality, only give information as to 5 independent genomic copies: its e ective depth is 5X. e probability of under-sampling genotypes will therefore be 1 / (2 (5-1) ) = 0.0625, and not 1 / (2 (10-1) ) ≈ 9.77e -4 . is problem can become more important in case of drastic scenarios of PCR duplication, where the rate can reach up to 90% (a coverage of 10X will represent only one genomic copy, and will not be enough to identify a heterozygote).

e most visible consequence of genotype under-sampling will be the loss of heterozygosity despite acceptable coverage (10 to 20X, see Ho man et al. 2014). Under ideal conditions, observed heterozygosity increases with depth of sequencing at low coverage, and converges towards true heterozygosity at 15 to 20X (when virtually every site has a nearly-null probability of having been under-sampled). However, if a large number of duplicates are present, correlation between heterozygosity and depth of coverage is heterogeneous, or absent.

If left un ltered, PCR duplicates are likely to introduce a strong bias in several analyses, by leading to overly con dent genotype calls where, in reality, evidence is scarce. In more extreme cases, it will also lead to homozygous calls where both alleles have been sampled, by arti cially increasing the depth of coverage of one allele only, and leading to discarding the low-coverage alternate allele as a sequencing error.

Fortunately, the original RADseqprotocol allows for e cient duplicate removal. Since DNA is randomly sheared after restriction, the length of individual fragments is expected to be broadly distributed; at medium to high coverage, no two fragments are expected to have the exact same length in the same individual, at the same locus. us, if two fragments of the same individual share the same mapping coordinates both on their front-read an reverse-read, these may be considered to be duplicates, and one of them (the one with the overall lowest quality) may be removed. An approach implemented in the popular pipeline Stacks [START_REF] Catchen | Stacks: building and genotyping loci de novo from short-read sequences[END_REF][START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF]) uses as a ltering criterion the fact that both front-read and reverse-read must match exactly in order to discard the lowest-quality read pair as a duplicate. is approach, however, assumes that sequencing errors are absent, which is a very unrealistic hypothesis in the case of high-throughput sequencing data: the consequence is that all duplicate reads including sequencing errors will be kept as original genomic copies, together with genuine reads, which may lead to many false-positive SNP calls. Another approach, implemented in PicardTools (http://picard.sourceforge.net), relies instead on prior read mapping: two read pairs will be agged as duplicate only if their mapping coordinates and mapping descriptors (as symbolised in a CIGAR string 1 ) are identical. is approach has the advantage of not being sensitive to sequencing errors; however it necessitates a reference genome of some kind in order to perform read mapping. We preferred the latter approach for this study. is particular algorithm being especially sensitive to missing data, it highlights the importance of nding a g ood c ompromise b etween g enotype c ertainty a nd m issing g enotypes -t he main problem posed by PCR duplication and loss of sequencing e ciency.

Being PCR-free, our whole-genome resequencing libraries are immune from the duplication issues identi ed in our RAD-sequencing data (see §35 p. 93): therefore, the data processing steps

Concepts and methods - §37 could be greatly simpli ed. For each sample, we pooled the data from both sequencing lanes (see §30 p. 86), and mapped it to the published Emperor penguin reference genome (Zhang et al. 2011b) using Bowtie2 [START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF], allowing only for unique, concordant paired-end alignments. Output BAM les were sorted and ltered using Samtools 0.1.19 (Li et al. 2009) and PicardTools 1.113 (http://picard.sorceforge.net). §-37 Phasing of the WGS data. A particular di culty, in WGS data analysis, is the inference of the correct phase for the pre-established polymorphism. Two main methodological approaches have been proposed [START_REF] Browning | Haplotype phasing: existing methods and new developments[END_REF]): (i) an observed-linkage approach, and (ii) a computational approach. e o bserved-linkage o r r ead-based a pproach ( i) e xploits t he naturally phased base calls in the paired-end Illumina reads, in a process similar to sca olding, but using the prior positional information provided by the read mapping step. At su ciently high coverage, the overlap between reads may allow the reconstruction of long stretches of phased haplotypes. However, the process is often broken -either necessarily whenever the distance between two successive heterozygous sites exceeds the library insert length, or stochastically through variation in read overlap distribution. us, at medium coverage, read-based phasing tends to produce short blocks of phased haplotypes, separated by stretches of unphased sequence. e computational approach (ii), on the other hand, relies on a prior model of recombination to estimate the most likely local haplotypes given the observed linkage data. Ideally, a speci c recombination model is established beforehand in the form of a genetic map, or an ascertained phased reference panel is used. In a non-model species, however, computational phasing may rely on population data: across a large number of individuals, assuming a constant recombination rate, the measure or linkage disequilibrium between closely located SNPs is considered informative of their physical phasing, and used to infer the most likely underlying local haplotypes.

Ideally, both approaches should be combined, as is the case in the ShapeIt PIRs pipeline [START_REF] O 533. O'connell | Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations[END_REF]: read-based physical linkage is assessed rst in order to build local haplotypes, and the phasing between those is further assessed computationally using population data.

Concepts and methods - §39

(iii) Variant calls are produced through SNP and genotype calling algorithms (see §33 p. 90), usually stored as a VCF (Variant Call Format, or its binary version, the BCF) le, a nd usually handled using VCFtools [START_REF] Danecek | The variant call format and VCFtools[END_REF]). e VCF format removes most of the redundancy of raw and aligned formats, as each genomic position is represented by a single entry, and the number of reads covering that position is summed numerically for each individual. us, a major shift of paradigm occurs between the aligned and the called formats: the identity of the original reads is lost, and information switches from a « horizontal » (sequence) format, to a « vertical » (per-site) format. Usually only polymorphic sites are retained at that step, although the Broad Institute gVCF (genomic Variant Call Format) includes also the sequence at non-polymorphic sites, as « blocks » of sequence data. e main characteristic of VCF formats is that it is an « analysed » format, storing an interpretation of the raw genetic data, rather than the data itself: it contains information that is the intersection of a dataset, an analysis protocol, a particular variant calling model, ltering decisions, etc.

(iv) Input formats are usually particular to each analysis program. ey are, in their vast majority, a reformulation of the VCF format, with or without data loss (e.g. 0/1 coding of alleles instead of ATCG coding, etc). ey are too speci c to an individual program or version to be detailed in this work.

Genetic analysis framework

e detail of the particular analyses and models used in each chapter will be detailed in the corresponding Methods sections. Here, however, we present the conceptual framework of the methods used repeatedly throughout this work. §-39 Populations, random mating and neutral drift. As we introduced earlier (see §2 p. 25), the basis of population genetics is the deme, which, whenever it has a strong geographic component, is described as a population. A population, in the population genetic framework, is oftentimes ap-

Concepts and methods

Concepts and methods - §40

Concepts and methods - §41

Coalescence is an essentially stochastic phenomenon (Fig. 9A p.102): interestingly, although the actual coalescence events are random, the probability of two lineages coalescing (i.e. having a common ancestor) at any given point of the past is a direct function of the e ective population size. Given a diploid population of constant e ective size Ne (thus 2.Ne lineages in the gene pool at each generation), and a constant evolution rate of one substitution per generation, the probability of any two lineages sharing a common ancestor at the previous generation is ( 1 / 2.Ne ), and the probability of not sharing a common ancestor is 1 -( 1 / 2.Ne ). Expanding this to any generation t, the probability of two lineages having a common ancestor at generation t is the product of their probability of having a common ancestor given the population size at generation t, and their probability of not having already had a common ancestor at any previous generation, 1) . ( 1 tive population size also diverges widely from census size (sometimes by orders of magnitude, see

or ( 1 -( 1 / 2.Ne ) ) (t -
for example e Emperor synnome, p. 211) as soon as non-breeding individuals are an important part of the species, e.g. when sexual maturity is delayed (as in most long-lived species), or when an adult may choose to not breed if its body condition is not adequate -or when a senescent individual chooses not to die (as in humans and albatrosses). As it then becomes increasingly dicult to de ne the number of individuals contributing to the species' genetic diversity through external evidence, the e ective population size is commonly de ned as a direct index de ning the characteristics of the observed gene pool, in other words, as the size of a theoretical Wright-Fisher population of similar genetic characteristics (Hamilton 2011). As we mentioned earlier ( §40 p.

101), in a population of constant size, and with a unit substitution rate, the probability of two lineages coalescing at generation t is ( 1 -( 1 / 2.Ne ) ) (t -1) . ( 1 / 2.Ne ). Provided the sample is large enough, this probability can be approximated by the observed density of coalescence events, and allows us to estimate the e ective population size. Yet since substitution rate μ is, in reality, never equal to 1, this estimated population size is scaled by the substitution rate as ϑ = 4.Ne.μ (in a diploid organism) -with the underlying idea that a small, but slowly mutating population will undergo the same amount of genetic drift as a larger, but faster evolving one.

us, a ssuming t hat s ubstitution r ate i s c onstant t hrough t ime ( which i s r easonable a t t he intraspeci c level), the distribution of times to coalescence throughout gene trees is an estimator of the probability of coalescence throughout the history of the population -and hence of the population size through time. is is the fundamental idea of the Skyline Plot approach (Fig. 10 p.

107): in its simplest form, each coalescence interval in a single gene tree is converted by maximum-likelihood into a population size during a time interval. Of course, the stochasticity of coalescence events is directly transferred to the population size estimates (as can be seen on the rst row of Fig. 10 p. 107). Several approaches have been proposed to lter out this noise -ranging from simple ideas such as the generalised skyline plot (which imposes thresholds for taking changes into account, and lters out excessively quick « jumps » in population size), to more com-

Concepts and methods - §41

plex ones line the Bayesian Skyline Plot (which tries to estimate a general demography with a low number of epochs, that maximises the likelihood of the observed gene tree -with a set of assumptions, like the autocorrelation of the population size through time, reducing the demography space to be explored). More complex and accurate approaches have been proposed to better address the problem of estimating past population size changes (see §44 p. 112 to §47 p. 122), but most of them ultimately rely on this basic tie between coalescence rate and scaled e ective population size.

A fundamental feature of the link between coalescence and population size, as it is currently conceived, is the irrelevance of gene tree topology -reconstructed topology is only a means to accurately estimate coalescence intervals. Migration, on the other hand, mostly a ects the structure of gene trees through the topology of the coalescent. In geographically complex groups of individuals, migration (as de ned in §4 p. 30) interacts with population size in shaping the gene pool: in a given deme, genetic diversity may only arise through (i) mutation and drift (both functions of the e ective population size) or (ii) migration. Unlike population size, however, migration is not estimated absolutely from the genetic data, but rather in reference to the present-day distribution of the sequences. In the coalescent framework, if we consider two present-day demes (see §2 p. 25

for a de nition), migration can be de ned as the probability (as usual looking back in time) for a given lineage to move from its current deme to the other at the previous generationi.e. the probability of any « e ective individual » to have one of its parents being an immigrant. us, the probability of coalescence is, within each deme, the probability of a lineage merging with another from the same deme at a given generation (a function, as we exposed, of population size) -and the probability of migration is the probability of a lineage stemming back to another deme at the same period. Identifying migration through the coalescent structure across several demes has some limitations: in particular, it requires that deme identity is stable throughout species' analysed history (i.e. within ~ 4Ne generations), and that during that time gene ow i s low enough, that back-and-forth lineage migration is separated by at least one coalescence event.

Technically, accurate estimation is restricted to demes stricto sensu (see §2 p. 25), where mutation and drift are major contributors to genetic diversity, an migration a marginal contributor.

Concepts and methods - §42 coalescence event when looking backwards in time (as presented in details in §40 p. 101). How-ever, the recombination process interferes with the pure coalescence process when we consider longer sequences, and a fortiori genomic-scale data. During recombination events (usually the manifestation, at the sequence level, of the crossing-over of chromosomes during meiosis), sequences sharing di erent ancestries (and thus di erent coalescent histories) become concatenated into one new sequence. By reshaping the associations between alleles, recombination is a major driver of genome evolution [START_REF] Reich | Global-scale latitudinal patterns of species diversity in the deep-sea benthos[END_REF][START_REF] Zhang | Genome shuffling leads to rapid phenotypic improvement in bacteria[END_REF], and must be accounted for when reconstructing the evolutionary history of a set of haplotypes. In the context of sequence evolution, recombination events can be seen as the counterpart of coalescence events: looking back in time, a coalescence event is the merging of two di erent sequences into one ancestral sequence, while a recombination event is the splitting of one single sequence into two distinct ancestral sequences.

e Ancestral Recombination Graph (ARG) is the representation of the full ancestry of a set of sequences, that includes both coalescence, and recombination events [START_REF] Raine | Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees[END_REF]. Just like a classical phylogenetic tree, it is constituted of nodes, and edges -however, unlike a in phylogenetic tree, nodes can be of two di erent types: coalescent nodes, or recombination nodes.

When estimated over a very short stretch of non-recombining DNA, the ARG is essentially a coalescent tree, but its complexity increases when it is estimated over longer, recombining sequences. e full ARG of population-scale genomic data virtually encompasses all the possible information about population size changes, migration events, selection processes, etc. However, the complexity of the structure makes it extremely challenging to reconstruct and analyse, and it is mostly simpli ed down to a lower-dimensionality representation, such as the multi-dimensional allele frequency spectrum (see §45 p. 117).

e Sequentially Markovian Coalescent (SMC, Wiuf & Hein 1999 -see §47 p. 122) is a transversal interpretation of the ARG. As a set of orthologous 1 sequences is considered transversally,

1.

I.e. stemming from a single common ancestral copy.

Concepts and methods - §42

Concepts and methods - §42 the coalescent process is « pure », i.e. without recombination, at each single nucleotide position.

As long as successive nucleotides share the same ancestral history, a single coalescent tree holds.

However, a recombination event will introduce a change in the tree topology, as the ancestral relationships between sequences are shu ed -and this new topology will hold until the next recombination point. is structure can be summarised as a Markovian process: a single coalescent tree is a state in the process, a recombination event is a state change, and the topology of the coalescent tree before the state change has no bearing on the topology after the state change. One particularity of the SMC model is that it allows inference of multiple tree topology, i.e. coalescent histories, when sampling along a single set of recombining sequences.

Sampling along the SMC can be conducted in several ways in order to infer population history:

(a) A « classical » approach relies on the extreme discretisation of the genome to sample virtually independent states of the ARG. us, w hen s ampling s hort s equences s eparated b y v ery large chromosomic distances, we can assume that 1) no recombination occurs within the sequences under consideration, and 2) recombination occurred freely between the sequences, so that their coalescent histories are fully independent draws from the population's ARG. Since, in that case, sampled topologies are comparatively few and far in-between, it is possible to estimate the full coalescent tree for each locus, and to draw information from every node. is is the approach implemented in several multi-locus algorithms, for example the Extended Bayesian Skyline Plot (Heled & Drummond 2008, see §44 p. 112), or the Migrate-n algorithm [START_REF] Beerli | Comparison of Bayesian and maximum-likelihood infer-Annexes - §186 ence of population genetic parameters[END_REF][START_REF] Beerli | Unified framework to evaluate panmixia and migration direction among multiple sampling locations[END_REF].

(b) A continuous approach, on the contrary, is based on the reconstruction of the states and state changes in the hidden SMC model through transversal scanning of the set of sequences. us, a coalescent tree is reconstructed for each non-recombining interval between two state changes, decomposing the full ARG into a set of coalescent-only graphs. is is the approach implemented in the « SMC » family of models (see §47 p. 122) (c) the ARG may also be sampled indirectly,

Concepts and methods - §43

through a set of summary statistics -for example the distribution of allele frequencies , either represented as a spectrum (see §45 p. 117) or as single statistics, such as Wright's F-statistics or Tajima's D (see §43 p. 110). ese di erent approaches will be developed below. §-43 What is a summary statistic? e full complexity of population genomic data could ideally be represented without loss in a highly dimensional structure such as the Ancestral Recombination Graph (see §42 p. 107). However, such an endeavour has limitations both in means, and in aims.

Accurately reconstructing a population's ARG would require awlessly ascertained genomic datawhich we can not yet produce -and monumental computational resources (see [START_REF] Lam | The imperfect ancestral recombination graph reconstruction problem: upper bounds for recombination and homoplasy[END_REF].

On the other hand, the full ARG is hardly intelligible by itself [START_REF] Arenas | The importance and application of the ancestral recombination graph[END_REF], and its major characteristics need to be extracted in order to produce meaningful information. e need to reduce the ARG to a simpler object therefore stems both from the failure to gather exhaustive data and computational resources, and from the necessity of obtaining more easily comparable indices.

us, the common character of most population genetic approaches -several of which have been devised long before the theorisation of the ARG -is to be in some respect a reduction of the full graph into a lower-dimensionality object, such as a tree, a distribution, or a single statistic.

In the « classical » population genetics approach (see §28 p. 84), the nature of the markers used (single mtDNA sequence or low number of nuclear loci) naturally makes the ARG a very distant concept. e low information content of the data only allows for the estimation of « point » indicators, not of the shape and extent of variation in signal across the genome. us, low-dimensionality statistics have initially been devised to match the information content of the data that was possible to produce: these focus on describing the amount and distribution of polymorphism between a set of samples. As we mentioned above ( §40 p. 101 and §42 p. 107), the present-day distribution of genetic variation is a result of the history of coalescence, recombination, and migration within the species -if the sequences under scrutiny are not evolving neutrally, selection must be added to that list, but neutrality is normally veri ed, or assumed. In the Wright-Fisher « drift »

Concepts and methods - §43 framework (see §39 p. 99), on the other hand, only that nal state is really considered and described at all.

A rst and essential descriptor of an instantaneous state of genetic variation is the density of segregating sites, often called S, that is a measure of the depth of the local coalescent tree in a perfect Wright-Fisher constant-size population -and therefore an indirect estimator of ϑ, the mutationscaled population size, also described as Watterson's estimator ϑ W (Watterson 1975). However, Watterson's ϑ technically discretises allele frequencies as a binary character (« minor allele frequency > 0 »), and thus discards the obvious informative content of the observed allele frequency distribution. erefore, a m ore r e ned es timator is nu cleotide di versity, no rmally de signated as π, and de ned as the average number of pairwise di erences in a set of sequencesi.e. the mean value of the mismatch distribution [START_REF] Nei | Thermal isolation of Campbell Plateau, New Zealand, by the Antarctic Circumpolar Current over the past 130 kyr[END_REF]. Shifting from the polymorphism in a full set of sequences to the average polymorphism in pairs of sequences sampled from that set, π retains information as to the allele frequency distribution in the sample, and is also an estimator of ϑ under a neutral model, as ϑ π .

ese two estimators of ϑ become inaccurate when the true population departs from the Wright-Fisher model: interestingly, however, the inaccuracy of these indices does not arise in the same way, and is in itself informative. ϑ π is sensitive to the frequency of alleles, and intermediate-frequency variants will weight most in the estimator: thus, histories with a high density of ancient coalescence events will lead to higher values of ϑ π . ϑ W , on the other hand, since it handles allele frequencies in a binary way (i.e. « polymorphic » or « monomorphic »), is not sensitive to the depth of pairwise coalescence of sequences, and hence is not a ected by the structure of the coalescent: it only re ects the depth of the full local coalescent. us, the di erence of ϑ π and ϑ W is an indicator of the neutrality of the coalescence process, as proposed by Tajima 1989: if both estimators are similar, a neutral constant-size coalescent process cannot be rejected, but if ϑ π is larger than ϑ W , we can infer an excess of ancient coalescence events, and therefore population growth.

Other indicators (Fu's F S , Fay and Wu's H, etc.) have been devised with a similar idea, all re ect-

Concepts and methods

- §43
ing, in some way or some other, skews in the coalescent structure, and consequently directional departures from the neutral population model.

Another central (and still very much in use) application of point-value summaries of the ARG is the quanti cation o f p opulation s tructure, o r, i n o ther w ords, o f t he a mount o f d i erence between allele frequencies in distinct sets of samples. e most common approach is the family of « F-statistics » or xation indices [START_REF] Wolff | The interpretation of population structure by F-statistics with special regard to systems of mating[END_REF], initially described in the synchronic drift framework (see §39 p. 99), but that can be re-thought in the coalescent framework. In the original formulation, the xation index idea relies on the Wahlund e ect [START_REF] Wahlund | Zusammensetzung von Populationen und Korrelationserscheinungen vom Standpunkt der Vererbungslehre aus betrachtet[END_REF] [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]. RAD sequencing (see §29 p. 85) allows the production of a large number of short sequenced loci that are widely interspersed in the genome, so that they can be assumed unlinked. Within loci, recombination rate can also be assumed null, and the random sampling process in the genome allow us to consider the loci as neutrally-evolving (a condition rarely met in traditional multilocus approaches, that often rely on gene sequencing). us, the three main assumption of EBSP are a priori veri ed by RAD data. However, two characteristics of RAD sequencing require some adaptation: (i) the sequencing process makes locus-and allele-dropout a signi cant problem and (ii) short loci necessarily have less segregating sites than longer loci, so that a selection must be made in order to base the analysis on informative data.

Allele dropout happens either as a result of cut-site mutation, that prevents one of the alleles to be sampled during the RAD library preparation (see §29 p. 85), or of under-sequencing, that causes one allele only to be sampled during the sequencing process (see §35 p. 93). e direct consequence of allele dropout is a mis-representation of true haplotype frequencies (see §35 p. [START_REF] Borboroglu | Penguins: natural history and conservation[END_REF], that is likely to bias the reconstruction of the coalescent history of the population, through an over-representation of null branch lengths in the gene tree topologies. A simple workaround is to sample only one haplotype per individual: while halving the dataset, this guarantees that the distribution of coalescence events will be faithfully reproduced. Locus dropout is a di erent issue, that is more easily solved: both during library preparation and during sequencing, loci may be lost in some individuals. us, it may be di cult to nd a su cient number of informative loci that are sequenced in the same set of individuals. However, coalescent reconstructions are not individual-, but rather population-centred: as long as panmixia is veri ed i n t he p opulation, the identity of the individuals from which haplotypes were sampled has no bearing on the sampling of the haplotype pool. In order to avoid potential skews due to, for example, the sampling of related individuals (i.e. individuals with correlated haplotypes at all loci), it may actually be desir-able to choose haplotypes randomly from the full (haploid) population for each locus, rather than to choose individuals.

e information content of loci is a di erent problem. Since RAD loci as considered neutrally evolving, we suppose that the general genome background substitution rate applies to the RADome. However, since substitution is essentially a rare, stochastic process, we expect the actual number of substitutions occurring in a given genomic interval to follow a Poisson distribution, with a parameter λ equal to the genome-wide background substitution rate (see §48 p. 124). Given the low substitution rate in long-lived vertebrates, most short (here 95 bp) loci are expected to contain zero or 1 SNP. However, the general Skyline Plot rationale implies that several coalescence events must be reconstructed in order to obtain information on the variation of coalescence probability (i.e. population size) through time. Indeed, in the absence of recombination, the number of coalescence events is, at most, equal to the number of polymorphic sites: a locus with 3 SNPs is, at best, informative as to 3 coalescent nodes (less if multiple substitution occurred on the same branch). In our experience, loci with less than 4 SNPs are not informative enough to robustly infer population history. In the same population, the number of SNPs observed at a locus is a function of the number of haplotypes sampled and of the haplotype frequency distribution:

generally, sampling more individuals will increase the number of polymorphic sites discovered, until all extant alleles have been represented. However, loci with excessive diversity are also suspicious: in a Poisson process, the probability drops rapidly towards the upper tail of the distribution -in our case, very few loci are expected to have more than 7 or 8 SNPs. Processing artefacts, on the other hand, such as a low-quality read, or collapsed paralogous loci 1 , can have the e ect of increasing the observed number of SNPs at a given locus. For that reason, we restrict the analysis to loci that have from 4 to 6 SNPs amongst 50 randomly sampled haplotypes. As this selection is only made amongst neutrally evolving loci, the underlying substitution and diversi cation

Concepts and methods

- §44 1.
at is, regions of the genome that stem from a common ancestral copy through a duplication event. Each paralogous copy further evolves separately, in a generally tree-like way. If the duplication event is recent, paralogs may retain enough similarity to be merged or confused during analysis, resulting in a chimaeric, non-tree-like ensemble.

processes are not di erent from those at work in the rest of the RADome. By doing so, we are only ltering the stochastic output of the substitution Poisson process.

is locus selection also has implications for the parametrisation of the EBSP model. EBSP, like any other model, may be easily subject to over-parametrisation: however, due to the huge computational burden of the MCMC algorithm, precisely estimating the number of necessary parameters (through, e.g. Bayes factor comparison of path-sampled alternative models' marginal likelihoods) is a di cult task. However, the structure of RAD data allows for a particularly e cient parametrisation. In Trucchi et al. [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF], an independent substitution model was de ned for each locus in the analysis: even reducing the sites model to a simple HKY+G with empirical base frequencies, a substitution rate and a transition/transversion ratio (kappa) 1 had to be de ned of each locus (or 100 parameters for a 50-locus analysis), each of them informed with very little data. On the other hand, if we assume the RADome to be a globally neutral ensemble, we may assume that a single substitution model holds for all loci: we need only, in theory, de ne one substitution rate and one kappa parameter for all 50 loci, informed this time by the full dataset. Substitution rate, however, is strongly biased in our model by the selection of loci with higher observed polymorphism: although the full RADome veri es the expectation of a Poissondistributed number of polymorphisms per locus with a single parameter, we use a truncated distribution that is not accounted for by a single substitution rate. erefore we use a single kappa parameter for all sites, but a di erent substitution rate for each class of loci (by « class », we mean whether the locus includes 4, 5 or 6 polymorphic sites). is reduced parametrisation scheme has

Concepts and methods - §45

ble to link the observed RAD substitution rates, and the whole-genome background substitution rate (see §48 p. 124).

We use this much simpli ed parametrisation in e King synnome, p. 163, and e Emperor synnome, p. 211, and nd that the slow MCMC convergence issues encountered by Trucchi et al. are mostly solved, and that repeatability is high between di erent random sets of loci. However, large improvements could be achieved by designing a RAD-speci c parametrisation for the EBSP, that would take into account the observed distribution of polymorphisms in the whole RADome, and use its λ parameter as an underlying rate for the loci included in the analysis. §-45 e Allele Frequency Spectrum approach. e Allele Frequency Spectrum (AFS) is a rich summary of the Ancestral Recombination Graph. is data structure gathers the counts (or proportions) of sites in the genome for which the derived allele has a frequency f in the population under consideration. While the « true » AFS for a population could only be compiled through exhaustive sampling, the AFS can also be estimated from a random subset of individuals sampled from a panmictic population. us, the maximum resolution of the reconstructed spectrum will be 1/(2S+1) (with S being the number of sampled diploid individuals). Since usually the sample size is much smaller than the e ective population size, only alleles that are already present in a signi cant fraction of the population (i.e. f ≳ S/Ne) are e ectively represented in the spectrum.

Under its most simple expression, the AFS can be reconstructed directly from allele counts in the sample, with the observed frequency taken as an estimator of the true frequency in the population. However, the di culty of ascertaining genotypes reliably at a large number of loci, in a large number of individuals, makes such a direct estimation rather error-prone. Maximum-likelihood estimates of the AFS, that take into account the possibility of genotype errors, and the impact of missing data, are used whenever possible, and have been shown to provide a much more robust estimate of the « true » population AFS (Nielsen et al. 2012). e ML estimate of the AFS is not a direct transformation of the observed sites counts, and should better be interpreted as the distrib-Concepts and methods - §45 ution of probabilities, for a given allele, to be found with a frequency f in the population -here, we used this latter method as implemented in ANGSD (Korneliussen et al. 2014).

Another di culty of AFS estimation lies in the correct identi cation o f a ncestral a nd derived states at each position. Accurately estimating the full AFS for a population requires that we can determine whether a given site should be considered as having a frequency f = 1/2Ne, or rather f = 1 -(1/2Ne). In order to do this, it is necessary to reconstruct the state at each position in the genome at the parent node for the taxon, thus requiring sequence and polymorphism data for a sister taxon, and for an outgroup. If this is not possible, the AFS may be folded: instead of considering the frequency of the derived allele, one considers the frequency of the minor allele (the allele with the lesser frequency -which raises additional problems when considering multiple populations). Although this approach removes the errors due to ancestral state mis-identi cation, it greatly increases the number of possible scenarios leading to the same observed AFS, thus reducing the robustness of inferences.

e AFS is highly informative as to demography and gene ow. When starting from a single ancestral sequence, each new mutation appears as single copy in the whole population, i.e. with a frequency f = 1/(2Ne). If the tness value of that new mutation is neutral, its frequency is expected to vary only due to the stochastic process associated with binomial sampling at each generation, with variations in population size being a major forcing on allele frequency drift. For example, a population bottleneck will have the immediate e ect of removing rare variants from the population, and biasing the AFS towards intermediate allele frequencies. us, the AFS for a given population re ects the successive demographic events in the population. But more informative is the N-dimensional AFS, that summarises the distribution of alleles frequencies amongst N populations, with P(i,j,k) being the probability, for a given allele, to have the frequency i in population I, the frequency j in population J, and the frequency k in population K. If populations I, J and K are three random subsets of one single panmictic population, we expect the frequencies i, j, and k to be highly correlated for all sites, since the only source of variation is the stochastic er-

Concepts and methods - §45

ror due to the sampling process. Isolation between populations, on the other hand, will manifest itself as a decay in the correlation between allele frequencies between these populations. e Ndimensional AFS (or « multi-AFS ») is therefore a result both of the populations' demographic history, and migration history (see Fig. 11 p. 120).

Whereas the ARG explicitly represents population history by integrating time as a variable in its topology, the AFS is a « temporally at » object, that only contains the outcome of demographic and migratory events. Inferring population history from the AFS therefore requires a more elaborate procedure than would be needed using an explicit coalescent history. e rst approach that has been proposed to exploit the full N-dimensional AFS is that of Recently, a di erent approach has been suggested to infer population history from the observed N-dimensional AFS, based on coalescent simulations. is approach, proposed by Exco er et al. One limitation of both methods is that they are strongly model-constrained: the parameter space has to be carefully de ned from prior knowledge. Since possible population histories can be extremely diverse, and since the AFS does not represent the temporal component of the ARG, it is necessary to de ne a demographic function, with a xed set of free parameters that will be optimised by maximum composite likelihood -these parameters including the timing of major events, the population size and growth rate before and after those events, and the migration rates Instead of explicitly modelling recombination events (as the PSMC approach does, see §47 p.

122), the STY assumes each single variable site to be independent, and to stem for a separate coalescence process (a simpli cation de ning the composite likelihood function). e probability of a site having a derived allele frequency f is a function of population size at each point of time in the past: thus, we can search for population size values (mutation-scaled as ϑ) that maximise the composite likelihood of the observed spectrum. Although the STY approach does not explicitly model the full coalescent process as EBSP does, it is able to use information from a much larger number of loci, and thus may better capture the information contained in the full dataset.

Concepts and methods - §47 However, recent history is di cult to retrieve, since fewer recombination events can be detected, which is the main shortcoming of the method. To overcome this problem, the multiple sequen- 2008). In order to infer the genome-wide evolution rate, we performed a joint analysis of mitochondrial HVR and RAD data in a multilocus Extended Bayesian Skyline Plot framework, using the HVR rate as a calibration, by setting a normally-distributed prior re ecting t he published point estimate and 95%CI interval, with a high sampling rate along the MCMC (10x the sum of sampling rates for all other substitution rates) in order to ensure free sampling of the prior.

In a coalescent framework, we need to assess the frequency of coalescence events along time:

thus, calibration must address the rate of transmitted mutations only, i.e. the species' substitution rate, as opposed to its intrinsic mutation rate (a purely physiological parameter of little relevance here). However, the germ line cell divisions are, within a taxon, independent from the generation e EBSP analysis was setup as exposed in e Emperor synnome, p. 211, and is brie y reminded here. We downsampled the data to haploid individuals, and using independent 50 loci with 50 haplotypes each, with 3 to 6 polymorphic sites, in addition to 50 randomly selected HVR haplotypes. We speci ed one independent site model for each locus class [START_REF] Abram | Evolution of the Southern Annular Mode during the past millennium[END_REF][START_REF] Ackerley | Quantifying the effects of perturbing the physics of an interactive sulfur scheme using an ensemble of GCMs on the climateprediction.net platform[END_REF]5 or 6 SNPs,and HVR). For each class, speci ed a HVR model, allowing for invariant sites for the HVR, but not for the short nuclear loci, and for gamma-distributed rate heterogeneity discretised in 4 classes.

Transition-transversion ratio kappa was linked across nuclear models. All chains were run in duplicate to check for convergence and for a su cient length to gather ESS 1 > 200 for all parameters, which necessitated 500,000,000 to 1,000,000,000 steps.

Since we parametrised each locus class separately, we expect our model to t a class-speci c substitution rate as a function of the observed number of segregating sites, rather than a common substitution rate. However, as we focus on neutrally evolving regions of the genome, we expect the number of segregating sites to follow a Poisson distribution, of parameter λ equal to the mean number of segregating sites per RAD locus. On a large number of sequences, the expected value E(λ) converges towards the "true" underlying constant mutation rate, multiplied by the total tree length for each locus. us, if we x the tree length, λ becomes an estimator of the substitution rate μ. However, under the EBSP model, the observed number of segregating sites is taken as an estimator of λ, and consequently of the substitution rate μ. erefore we expect the inferred value of μ for each locus class to be a posterior probability of the "true" substitution rate, conditional on the mean number of segregating sites observed for that class [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]. In order to retrieve the underlying common substitution rate μ, we rst tted a log-linear model to the in- e geographical extent of a species' habitat de nes its potential range -that is, the range it would occupy if biological systems were fully mechanistic. Several factors may prevent a species from occupying its whole potential range: for example, suitable habitat may occur outside of the species' dispersal range, or the species may have gone locally extinct through hunting pressure, etc. e actual range occupied by the species, which is necessarily fully nested within its potential range, is its realised range (Hutchinson 1957) -the one provided in atlases and distribution maps (e.g. Fig. 7). A mismatch between the potential and the realised range is common at the global scale, especially in continental taxa: while similar ecosystem are normally found zonally around the world, dispersal across ocean basins is a rare event for larger organisms. us, t he a bundant endemic fauna of Australia and New Zealand owes more to the lack of connectivity between similar habitats than to restricted potential ranges. Indeed, the ease and rapidity of biological invasions shows that once the (often punctual) barrier-to-dispersal problem is solved (e.g. once the ocean has been crossed in some way), whole swathes of new habitat may be colonised rapidly ( uiller e t al.

2005; [START_REF] Trucchi | Long live the alien: studying the fate of the genomic diversity along the long-term dynamics of an extremely successful invader, the crested porcupine[END_REF], bringing the realised range closer to the potential range. e dispersal problem is less drastic in marine systems (see §6 p. 33), and is most important meridionallywhile connectivity is rather large between ocean basins at the same latitudes (especially at higher latitudes were connectivity is enhanced by inter-ocean circulation, see §7 p. 35), the equatorial region may constitute an important barrier between the Arctic and the Antarctic, resulting in fully independent organisms occupying similar ecological functions and displaying convergent traits (for example the auklets and the diving-petrels, see §20 p. 71).

is convergence, in similar habitats, of species ful lling similar ecosystem functions, allows us to de ne the concept of niche (Hutchinson 1957). Whereas a habitat is a species-focused, and largely geographical concept (« is that place, given its biotic and abiotic constraints, suitable for that species ? »), a niche is a functional, ecosystem-focused concept (« is there already a species here

Concepts and methods - §49

that can eat anchovy in the coastal waters while nesting on these scree slopes ? »). Although both concepts are often confused, they are extremely di erent. Modelling a niche would rely on explicit modelling of each relevant species and their relationships in the ecosystem [START_REF] Wiens | Niches, models, and climate change: assessing the assumptions and uncertainties[END_REF] proach is that it takes the species realised range as an estimator of its potential range -in some cases, this can be just an approximation, but in some other it can be a true error [START_REF] Peakall | Site fidelity is an inconsistent determinant of population structure in the hooded merganser (Lophodytes cucullatus): evidence from genetic, mark-recapture, and comparative data[END_REF]. is becomes even worse when only abiotic habitat characteristics are included in the model (as it is the case in the 2015 Audubon society report on future North American bird distributions, for example -see [START_REF] Langham | Conservation Status of North American Birds in the Face of Future Climate Change[END_REF], since biotic constraints are expected to reduce the set of abiotically exploitable areas. us, the abiotic habitat estimated from the realised range is very likely to underestimate the range of abiotic conditions that a species can tolerate, as it confuses the abiotic limitations of the biotic interactors with the abiotic limitations of the focal species itself. For example, a species may be limited to a very little part of its otherwise potential habitat by the presence of a strong competitor: the future distribution of that species will then mostly depend on the future evolution of the competitor, and not of the local climate. If, by chance, the competitor is less tolerant to changes in abiotic conditions, then climate change may

Concepts and methods - §50 manifest itself as a range extension of our example species. If we had in the rst place modelled that species habitat by hypothesising that it can only tolerate the precise abiotic conditions in which it is found, it is very likely that this overly precise set of conditions will be disrupted by any change of climate, and that we will forecast that species' extinction. e same goes to demographic, rather than distributional, modelling: there again, observed correlations are assumed, but not shown, to imply a causal relationship between environmental parameters and the species' growth rate. However this line of thought is usually awed, as has been convincingly exempli ed A causal approach to modelling, on the other hand, will endeavour to identify parameters that have a know, direct in uence on the species' demography. In the case of the Chinstrap penguin, this would mean including krill abundance, and not sea ice extent, as an explanatory variable.

is is the approach we use in e King synnome, p. 2014) -a discrepancy that may be linked to di erent factors. First, most current GCMs treat ocean, atmosphere and sea ice as three separate models, with boundary interfaces at the surface of the ocean, and the upper and lower surfaces of the sea ice layer. Relative to its own internal dynamics, boundary conditions are thus comparatively very important for sea ice, and the current approach may lead to incorrect representation. Secondly, the spatial resolution of most CMIP5 models (usually a 1°x1° grid) may be too coarse to explicitly model latent-heat polynya, that act as sea-ice formation zones (see §118 p. 225 and [START_REF] Kusahara | Sobre la distribución del Pingüino Rey Aptenodytes Patagonicus (Aves: Spheniscidae) en Chile[END_REF]. But lastly and most importantly, the input of freshwater (with a higher freezing temperature) from the melting continental ice sheet has an important impact on the sea ice formation: however, the Antarctic ice sheet itself is not modelled in the CMIP5, but merely treated as a speci ed boundary condition, which is likely to lead to an incorrect representation of sea ice (see Bintanja et al. 2015 and §118 p. 225).

As CMIP5 models include di erent representations of climatological phenomena, their outputs may vary, both for the reconstruction of past climate, or for the prediction of future changes. A

Concepts and methods - §51

common way to handle these divergences is the ensemble approach, in which several di erent models are used jointly (Ainley et al. 2010a;[START_REF] Doak | Demographic compensation and tipping points in climate-induced range shifts[END_REF][START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF]Raybaud et al. 2013;[START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF][START_REF] Beaugrand | Future vulnerability of marine biodiversity compared with contemporary and past changes[END_REF]. us, it may be expected that the most robust features, represented similarly in all models, will be preserved, while the model-speci c and possibly artifactual divergent traits will be evened out [START_REF] Cabré | Oxygen minimum zones in the tropical Pacific across CMIP5 models: mean state differences and climate change trends[END_REF]. Of course, this « democratic » approach to model selection is not failsafe: rst, because despite the controls included in the CMIP5 validation process, not all model are of similar « quality » at a given location -the degree of precision and realism varies between models, and so does the size of the spatial grid over which phenomena are represented. Another source of imprecision is the similarity between di erent models, that may in some cases amount to pseudo-replication when several models of related architecture give similar outputs because they represent climate in the same way [START_REF] Cabré | Oxygen minimum zones in the tropical Pacific across CMIP5 models: mean state differences and climate change trends[END_REF]. In order to assess the robustness of the predicted trends to the choices in model parametrisation, an ensemble approach may be used within the framework of a single model, by using multiple equally possible parameterisations in a perturbed physics ensemble approach [START_REF] Ackerley | Quantifying the effects of perturbing the physics of an interactive sulfur scheme using an ensemble of GCMs on the climateprediction.net platform[END_REF]). But a more common approach is to use altogether di erent models when the focus it the prediction itself, and not its robustness.

Although ensembles of AO-GCMs usually perform better than single models, their aim is to represent the earth system as a whole: thus, their still may not accurately capture local aspects of certain parameters. In the case of less reliably represented areas, it is common to include only models that already accurately describe the state of climate during the historical period on a few accurately observed parameters (

Abstract

How genetic diversity is maintained in philopatric colonial systems remains unclear, and understanding the dynamic balance of philopatry and dispersal at all spatial scales is essential to the study of the evolution of coloniality. In the King penguin, Aptenodytes patagonicus, return rates of post-edging c hicks t o t heir n atal s ub-colony a re r emarkably h igh. E mpirical s tudies have shown that adults return year after year to their previous breeding territories within a radius of a few meters. Yet little reliable data are available on intra-and inter-colonial dispersal in this species. Here, we present the rst ne-scale st udy of th e ge netic st ructure in a ki ng penguin colony in the Crozet Archipelago. Samples were collected from individual chicks and analysed at 8 microsatellite loci. Precise geolocation data of hatching sites and selective pressures associated with habitat features were recorded for all sampling locations. We found that despite strong natal and breeding site delity, king penguins retain a high degree of panmixia and genetic diversity.

Fine-scale colony structure - §56

Yet, genetic structure appears markedly heterogeneous across the colony, with higher-than-expected inbreeding levels, and local inbreeding and relatedness hotspots that overlap predicted higher-quality nesting locations. is p oints t owards h eterogeneous p opulation s tructure a t the nell et al. 2012;Fernández-Chacón et al. 2013). It is known to promote gene ow [START_REF] Winters | Gene dispersal and outbreeding in a philopatric mammal[END_REF] and genetic adaptability [START_REF] Bowler | Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics[END_REF] as well as plasticity in response to habitat changes [START_REF] Travis | Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica[END_REF][START_REF] Schlosser | Evidence for gene flow differs from observed dispersal patterns in the Humboldt penguin, Spheniscus humboldti[END_REF]).

Both philopatry and dispersal occur at all spatial scales in many colonial species [START_REF] Murrell | The evolution of dispersal distance in Annexes - §186 spatially-structured populations[END_REF][START_REF] Barlow | Estimating dispersal distributions at multiple scales: within-colony and among-colony dispersal rates, distances and directions in European Shags[END_REF]), yet it has often been assumed that strong philopatric behaviour is a common trait of most pelagic seabirds [START_REF] Frederiksen | Philopatry and dispersal within a Black guillemot colony[END_REF][START_REF] Bried | Population biology of the wandering albatross (Diomedea exulans) in the Crozet and Kerguelen archipelagos, southern Indian Ocean, approached Annexes - §186 through genetic and demographic methods[END_REF][START_REF] Dugger | Complex colony level organization of the deep sea siphonophore Bargmannia elongata (Cnidaria, Hydrozoa) is directionally asymmetric and arises by the subdivision of pro Annexes - §186 drial DNA restriction data[END_REF]. Indeed, the bene ts o f p hilopatry a nd c oloniality ( Coulson 2 002), t he b arriers to dispersal (Friesen et al. 2007) and the dynamics of colony formation and extinction [START_REF] Matthiopoulos | Metapopulation consequences of site fidelity for colonially breeding mammals and birds[END_REF] have traditionally been explored under that hypothesis. us several studies have documented a lack of genetic structure amongst distant colonies, which, in this context, seemed incompatible with assumed philopatric behaviour (the so-called "seabird paradox"

of [START_REF] Milot | The seabird paradox: dispersal, genetic structure and population dynamics in a highly mobile, but philopatric albatross species[END_REF].

It is often considered that the relatively weak evidence for structure, together with the high level of genetic diversity observed in many seabird species (e.g. [START_REF] Akst | A comparison of genetic diversity between the Galápagos penguin and the Magellanic penguin[END_REF][START_REF] Bouzat | Regional genetic structure in the Magellanic Penguin (Spheniscus magellanicus) suggests metapopulation dynamics[END_REF][START_REF] Gómez-Díaz | Genetic relatedness does not retain spatial pattern across multiple spatial scales: dispersal and colonization in the coral, Pocillopora damicornis[END_REF], are mostly the consequence of the random dispersal of a few juveniles.

On the other hand, the importance of intra-colonial dispersal in tness enhancement has been does not display territorial behaviour [START_REF] Williams | Projected distributions of novel and disappearing climates by 2100 AD[END_REF], the King penguin is known to choose and defend a breeding territory within the colony, even though it does not build a nest (Bried & Jou-Fine-scale colony structure - §58 ventin 2001). Breeders of this species have been described as having a marked philopatric behaviour across reproductive seasons and have been observed to use the same breeding site, within a radius of a few meters, year after year within their colony [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF][START_REF] Bried | Population biology of the wandering albatross (Diomedea exulans) in the Crozet and Kerguelen archipelagos, southern Indian Ocean, approached Annexes - §186 through genetic and demographic methods[END_REF]. Return rates of juveniles to their natal colony are also typically high (Le Bohec et al. 2008;Saraux et al. 2011b). However, it is not yet clear whether natal philopatric behaviour also extends to the speci c birth site within the colony, and whether this behaviour may result in local structuring of populations. In order to test this, a genetic study was conducted in the "Baie du Marin" scriptors may be expected [START_REF] Wolff | The interpretation of population structure by F-statistics with special regard to systems of mating[END_REF], with an excess of homozygosity re ecting higher-thanrandom relatedness between paired individuals. However, a prior to F-statistics is that populations are in a state of equilibrium, which may be in contradiction with ne spatial and temporal scale analysis, such in our study [START_REF] Whitlock | Emperor penguin colonies in the Australian Antarctic Territory: how many are there[END_REF][START_REF] Paetkau | Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power[END_REF]. Alternatively spatial autocorrelation should be detected at the colony level, re ecting a g radual isolation-bydistance trend, though this should only hold true if the process leading to spatial structure is ho-Fine-scale colony structure - §59

colony
mogenous at the individual level [START_REF] Osborne | Non-stationarity and local approaches to modelling the distributions of wildlife[END_REF]). (ii) If, on the other hand, philopatric behaviour is a more variable trait dependent on local ecological conditions, the processes involved in colony structure are expected to belong to the second class. Local heterogeneous signatures should therefore be found, such as divergent population parameters for di erent areas of the colony [START_REF] Jones | Incubation et élevage itinérants chez les manchots empereurs de Pointe Géologie (Terre Adélie). 362. Jouventin P (1972a) Note sur l'existence et la signification d'une rythmicité des parades mutuelles. 363. Jouventin P[END_REF]). Although inbreeding is not in itself an indicator of spatial structure, higher-than-expected individual inbreeding levels may reveal a non-random mating system, favouring relatives as potential partners. In such cases, a spatially-correlated distribution of inbreeding suggests a space-driven mating system [START_REF] Rozas | Distribution of individual inbreeding coefficients, relatedness and influence of stocking on native anadromous brown trout Annexes - §186 (Salmo trutta) population structure[END_REF]Vogl et al. 2002). Pairwise relatedness, calculated across spatially-de ned w indows, i s a nother i nsight i nto t he s ame phenomenon [START_REF] Rozas | Distribution of individual inbreeding coefficients, relatedness and influence of stocking on native anadromous brown trout Annexes - §186 (Salmo trutta) population structure[END_REF][START_REF] Stevens | Inference of relationships in population data using identity-by-descent and identity-by-state[END_REF].

In this study, we use a combined approach including genetic, spatial and local ecological data to investigate the mechanisms of genetic mixing inside a seabird colony. We focus in particular on how ne-scale h abitat h eterogeneity p atterns m ay i n uence lo cal di spersal be haviour, an d how lack of genetic structuring can emerge from small-scale heterogeneity in ecological parameters. Fine-scale colony structure - §60

Materials and Methods

Handled animals were removed from the colony in order to minimize the disturbance to neighbouring birds and taken to a shelter a few meters away for manipulation. ey were hooded to reduce their stress and manipulations lasted between 5 and 10 minutes. All blood-sampling and tagging material was sterilized (either sealed, or through chemical sterilization). Moreover, Vétédine soap and alcoholic antiseptic solutions were used to disinfect the skin before bleeding and tagging. Flesh wounds did not seem infected thereafter (personal observations on a subset of recaptured birds). §-60 Study site and sampling. Our study was conducted during the summer season 2010/2011 in the king penguin colony of "La Baie du Marin", on Possession Island, Crozet Archipelago (46°25'S, 51°45'E), which comprises around 16,000 breeding pairs [START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]. Chicks born in a sub-section of the colony comprising approximately 10,000 breeding pairs (Antavia, Gendner et al. 2005) were randomly selected and captured along a peripherical 120m-transect, in order to maximise separation distance. To minimize disturbance of breeding penguins, sampling was conducted no deeper than 4-5 nests towards the center the colony. Sampling was conducted at a high spatial resolution (~2 m between successive samples, i.e. every second nest at the time of sampling) in order to assess ne-scale genetic structure of the colony (sampling map is given in Fig. 13). e sampling area was partitioned according to natural terrain discontinuities (such as tussocks, rocks, or ooded patches), in a total of 6 clusters encompassing all sampled nests (Fig. 13). 175 early-hatched chicks were thus sampled during early brooding (~ 2 week old chicks) and temporarily tagged with a small external plastic pin (Floytag®). Each chick's hatching site was geolocated with a Global Positioning System device (Garmin eTrex®), and further con rmed using visual grid markings in the eld (ca. 10x10m cells). King penguins are monogamous during a reproductive season and lay a single egg each year [START_REF] Stonehouse | The King penguin: Aptenodytes patagonica, of South Georgia[END_REF][START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF]), thus we exclude the possibility of full siblings in the study population. Blood (~ 100 µL) samples were collected from the brachial veins using heparin-coated tubes, transferred to a Whatman lter paper, dried, and later frozen at -20°C for the genetic analysis. Genotyping was performed with 10 microsatellite loci [START_REF] Akst | A comparison of genetic diversity between the Galápagos penguin and the Magellanic penguin[END_REF][START_REF] Billing | Isolation and characterization of polymorphic microsatellite loci for the study of paternity and population structure in the little penguin Eudyptula minor[END_REF][START_REF] Ahmed | Isolation and characterization of macaroni penguin (Eudyptes chrysolophus) microsatellite loci and their utility in other penguin species (Spheniscidae, AVES)[END_REF][START_REF] Hart | Characterization of microsatellite loci for utility in a range of penguin species[END_REF]. PCR was conducted using MasterMix® (Qiagen) premixed TAQ-polymerase, dNTPs and MgCL 2 , in a total reaction volume of 12.5 μL (6.75 μL Master-Mix, 2 μL of each primer pair, 0.5 μL DNA, H 2 O up to a nal volume of 12.5 μL). Ampli cation followed a touchdown protocol, with a 5-minute denaturation at 95°C, 12 ampli cation cy-cles of a 30 second denaturation at 95°C, 30 seconds of annealing beginning at 63°C in the rst cycle and decreasing one degree at each cycle until 52°C, and 30 seconds of elongation at 72°C, followed by 23 cycles of 30 seconds at 95°C, 30 seconds at 52°C, and 30 seconds at 72°C. Ampli cation was nished with 10 minutes at 72°C. e same protocol was used for all primers.

FAM and HEX uorochrome-labelled primers were used for sequencing: several microsatellite loci could be multiplexed in the same reaction (Table 1). When multiplex ampli cation failed, samples were ampli ed at each locus separately, and pooled together for electrophoresis. Sequencing was performed on an ABI3730 capillary sequencer. 1 μL of PCR product was diluted in 9 μL H2O, and 1 μL of this solution was added to 10 μL formamide and 0.2 μL GeneScan 500-ROX size standard (Applied Biosystems). Output les were analysed on GeneMapper (Applied Biosystems). A random 10% of the samples were ampli ed and genotyped a second time to check the repeatability and accuracy of the readings.

- - - - - 260 0.048 - - - - - - - - - - - - - - 265 0.015 - - - - - - - - - - - - - - 270 0.012 - - - - - - - - - - - - - - 280 0.003 - - - - - - - -
Fine-scale colony structure - §61

Fine-scale colony structure - §63 §-62 Analysis of microsatellite data. Hardy-Weinberg equilibrium was tested in adegenet [START_REF] Jombart | adegenet: a R package for the multivariate analysis of genetic markers[END_REF]) with 100,000 permutations, and in Fstat [START_REF] Goudet | Fstat: a computer program to calculate F-Statistics[END_REF] distance between samples should be observed. In order to test that prediction, spatial autocorrelation analysis was performed across the whole sampling area using Genalex [START_REF] Peakall | Site fidelity is an inconsistent determinant of population structure in the hooded merganser (Lophodytes cucullatus): evidence from genetic, mark-recapture, and comparative data[END_REF], through multivariate analysis of pairwise euclidean and square genetic distances, regardless of any spatial grouping. Signi cance o f a utocorrelation w as a ssessed t hrough r andom permutation of samples across the whole area, and subsequent bootstrapping of the samples contained within pre-de ned d istance-classes a round e ach i ndividual: p ermutation t ests a re u sed t o assess the randomness of the distribution, while bootstrapping allows for the evaluation of the weight of potential individual outliers. We used distance classes starting at 2 meters (the average minimum distance between two sampled nests), and no longer than 8 meters (the average radius of our clusters). Longer distance classes were excluded as they would give too much weight to crosscluster comparisons. 100,000 random permutations were performed.

However, heterogeneous spatial processes may not always be detected by global spatial autocorrelation analyses (Miller 2012). Heterogeneous ne-scale genetic structure was therefore assessed in Genalex using the 2D-Local Spatial Analysis algorithm (2D-LSA, [START_REF] Double | Dispersal, philopatry, and infidelity: dissecting local genetic structure in superb fairy-wrens (Malurus cyaneus)[END_REF]: each individual was tested for genetic relatedness to its n nearest neighbours in order to identify ne-scale

Fine-scale colony structure - §64 patches of lower genetic diversity. is method di ers from standard autocorrelation tests in that it completely removes further distance classes from the analysis, and gives the same weight to all individuals considered as neighbours. us, it takes into account the possibility of locally restricted areas, within which all individuals are equivalently related and therefore do not exhibit spatial autocorrelation. Once again, signi cance levels were estimated through random permutation of the samples. Analyses were performed with a number of neighbours ranging from 8 to 10 (the approximate closest 5% of the colony), and 100,000 random permutations. §-64 Inbreeding and relatedness. Individual inbreeding coe cients and pairwise relatedness were also taken as an index of local genetic structure: if potential mates are more related to each other in some areas, then o spring sampled in that area are expected to be more inbred, and potentially related to each other. Inbreeding was studied both at population and individual levels. At population level, Ballou's maximum likelihood method was preferred [START_REF] Ballou | Ancestral inbreeding only minimally affects inbreeding depression in mammalian populations[END_REF], since it has been shown to better estimate population parameters provided sample is large enough [START_REF] Weir | Estimating F-statistics for the analysis of population structure[END_REF]).

However, MLE methods are known to be sensible to small sample size, and they are therefore less suitable for estimating individual parameters. Individual inbreeding and relatedness were thus calculated using Ritland's method-of-moments estimators (Ritland 1996). Ballou's MLE index was calculated in adegenet [START_REF] Jombart | adegenet: a R package for the multivariate analysis of genetic markers[END_REF]. Individual means were extracted from a subset of 2,000 values sampled from 20,000 estimates, and were then averaged across the whole sample.

Ritland's MME estimators were computed in Coancestry [START_REF] Wang | COANCESTRY: a program for simulating, estimating and analysing relatedness and inbreeding coefficients[END_REF]. As expectations for individual inbreeding levels vary, and cannot be reliably imported from another study organism, expected inbreeding distribution was estimated through simulation. A virtual population of entirely non-related individuals (as is the expectation for a large, ideal and randomly sampled population)

was generated with the same allele frequencies and per-locus homozygosity levels as the sampled colony, and empirical data was compared to the simulated individual inbreeding distribution.

Di erence in average individual inbreeding between the observed and the simulated distribution was assessed through a bootstrapping method as implemented in Coancestry: 10 independent Fine-scale colony structure - §66 simulated datasets were generated. For each of them, both observed and simulated individuals were pooled, and 100,000 randomly redistributed partitions were generated. Group-averaged inbreeding di erence was computed for each of these partitions, and signi cance of the observed result was checked against this random distribution. Observed individual inbreeding was then mapped onto the colony to identify potential high-inbreeding clusters using Quantum GIS (Quantum GIS Development Team, 2013). §-65 Pairwise cluster analysis. If our second hypothesis (heterogeneous structuring processes)

holds true, we expect these processes to apply di erently in speci c areas of the colony depending on local characteristics. We therefore performed the following analyses on the discrete natural clusters de ned i n t he s ampling a rea ( Fig. 1 3), a s o pposed t o t he c ontinuous m odels applied above. Genetic di erentiation between clusters was tested in Genepop (exact G-test, default

Markov chain parameter values) and Fstat using chi-square assessment of genotypic frequencies.

Pairwise divergence in inbreeding and relatedness distribution between clusters was also assessed: For representation purposes, each nesting location was attributed its local tick load, chick survival score and occupancy score, and values were interpolated across the whole colony in Quantum GIS, using squared inverse distance algorithm.

In order to assess the relation and overlap of the three included ecological variables, we performed a principal component analysis (as implemented in R package FactoMineR, [START_REF] Lê | Fac-toMineR: an R package for multivariate analysis[END_REF]). Since we focus on three variables that seem to determine habitat quality on the periphery of the colony, but omit some factors that may interfere in other zones than our study area (e.g.

vulnerability to ooding, or density pressure), we restricted the PCA to the peripheral area where

Fine-scale colony structure - §67 our genetic sampling is restricted. Each variable was averaged by grid zone, across all available years. e rst principal component was plotted on the colony using Quantum GIS.

Results §-67 Microsatellite data. Of the 10 microsatellite loci tested for genotyping, only 8 were successfully ampli ed, and were used in the subsequent analyses. Ech011 ampli ed irregularly in king penguins under the tested PCR conditions, and Ech081 was repeatedly scored as more than 2 alleles per genotype. Only Ech030, Ech036, Ech051, Ech071, AM13, B3-2, Emm4 and RM3

were therefore retained for genotyping. For these, scoring repeatability was of 100% across our random replicates.

e total population was found to be in Hardy-Weinberg equilibrium. After 100,000 randomisations, no locus showed signi cant d eparture f rom H ardy-Weinberg e quilibrium (P-values: Ech030 = 1; Ech036 = 0.9997; Ech051 = 0.3880; Ech071 = 1; AM13 = 1; B3-2 = 0.4948; Emm4 = 1; RM3 = 0.0697). F IT was not found to be signi cantly d i erent f rom z ero f or the whole sample (F IT = 0.0259), although it was signi cantly higher for one locus (Ech071: F IT = 0.1275). Considering this might be a sign of weak linkage to a region under selective pressure, subsequent analyses were therefore tested both including and excluding this locus. No locus was found to be under linkage disequilibrium. Allele frequencies, counts, and genetic diversity are given in Table 1.

Population inbreeding indicator mean value across our sample was F = 0.2321 ± 0.0958 (Ballou's MLE estimator, standard error: 0.0074). At the individual level, MME inbreeding coe cients were found to deviate from a random distribution. In 80% of our simulations, the empirical data fell outside the 95% interval of the random distribution, and in all cases the di erence was clearly positive (empirical mean inbreeding clearly higher than simulated). In particular, the lower (outbred) range was strongly under-represented as compared to a random, non-related sample as simulated in Coancestry (Fig. 14b-c). Ritland's inbreeding coe cient was then mapped on the colony. Two more inbred regions appeared, at the south-east and in the centre of the sampling area (Fig. 15a), corresponding to clusters C3 and C5-C6. did not yield any signi cant structure across the sampling area (Fig. 16a), suggesting no visible decay in correlation in the rst distance classes, as would be expected if genetic diversity varied along a gradient across the colony. Intrinsic clustering of the dataset, assessed in Structure, did not yield any signi cant results either (Fig. 16b). In our 2D-LSA analyses however, a maximum of 10 individuals across our sample were found to have signi cant genetic correlation with their 9

nearest neighbours (P < 0.05). It is noteworthy that these individuals were clustered in two groups that strongly overlapped with the higher-inbreeding regions identi ed previously (clusters C3 and C5, see Fig. 15b). Genetic correlation decayed quickly when we increased the number of neighbours used in the analysis above the nearest 5% of the colony. However, pairwise F ST comparisons did not yield any signi cant result.

Heterogeneity in inbreeding distribution across di erent clusters was tested pairwise in Coancestry (bootstrapping: 10,000): cluster C1 was found to have signi cantly ( P C1 < 0.05) less inbreeding than clusters C2, C3, C5 and C6 (but not C4). Most signi cant was the comparison between C1 and C5 (Table 2). In the same way, pairwise individual relatedness was found to be signi -cantly higher in C6 than in C1 and C4 (other pairwise comparisons were non-signi cant, see Table 2). in C5 (Fig. 17). Principal component analysis results support this view: the rst axis, which accounted for 40.5% of the total variation (correlation coe cients, contributions: chick survival 0.82, 55.6%, occupancy timing 0.62, 31.6%, tick load -0.39, 12.8%), separates between the south-eastern end of the colony, of higher habitat quality, and the western end, of lower habitat quality (Fig. 18). However, neither of the individual habitat quality descriptors correlates directly with individual-level or cluster-level genetic descriptors. A possible bias due to the exclusively peripheral sampling design (required in order to minimize colony disturbance) may also be considered: indeed, the peripheral areas have generally higher tick load and later settling dates. However, central areas do su er from speci c pressure, such as higher density and associated stress levels [START_REF] Viblanc | Stress hor-mones in relation to breeding status and territory location in colonial king penguin: a role for social density?[END_REF]. Predation pressure, on the other hand, is not expected to di erentiate peripheral and central areas. Although it has been shown that immediately peripheral birds spend twice as much time interacting with predators than central birds [START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF], most of the chick mortality has been shown to happen during winter, especially through predation by the giant petrel (Macronectes giganteus, Hunter 1991), at a time when chicks have gathered in creches. Moreover, our eld observations tend to show that summer predation on eggs and small chicks by the subantartic skua (Stercorarius antarcticus) is not

Fine-scale colony structure - §73 more frequent at the periphery than at the center of the colony. Finally, the overall breeding success has been shown to be independent of central or peripheral location [START_REF] Descamps | Relating demographic performance to breeding-site location in the King Penguin[END_REF], which supports the idea that the purely geometric location in relation to the colony (such as center-to-periphery gradients) is not the main determinant of habitat quality. We can therefore assume that spatial processes driving breeding site choice identi ed on the edges of a colony should also apply at its centre.

Another possible limit to our study may arise from our choice of sampling pre-dispersal chicks, as opposed to adults, and to analyze nuclear autosomal markers (as opposed to sex-speci c ones).

Indeed, a constant bias such as sex-speci c dispersal may lead to more genetic structure in one sex than the other, a phenomenon which may be blurred by our sampling of pre-dispersal individuals of undetermined sex. Yet, although sex-speci c d ispersal i s a c ommon p henomenon i n birds [START_REF] Chevin | The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes[END_REF], on a mutli-generation scale it is only expected to in uence the structure of sex-speci c markers, and not of autosomal loci, which are subject to admixture [START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF]. us, our sampling design should only reduce our ability to detect instantaneous sex-speci c phenomenons, and not population-wide, sex-independent structures.

However, a more likely explanation of the weakness of the detected signal may lie in inter-annual habitat variability: if local philopatric maxima are conditioned by the past breeding experience of individuals, they may be subject to medium time-scale variation, that is, higher-quality breeding sites may change every few generations, depending on ooding v ariations, g round e rosion, or parasite pressures. ese very ne-scale shifts may be su cient to weaken genetic structure at the colony level on longer time scales.

Moreover, at the individual scale, it may be di cult for a breeder to retain its breeding site consistently across all breeding seasons. Indeed, the complex breeding cycle of the King penguin extends for more than a year, implying that birds are alternately early or late breeder [START_REF] Stonehouse | The King penguin: Aptenodytes patagonica, of South Georgia[END_REF][START_REF] Stonehouse | The King penguin: Aptenodytes patagonica, of South Georgia[END_REF][START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF]), regardless of individual quality [START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF].

erefore, even experienced breeders might sometimes be forced to settle in lower-quality spots we therefore believe that these lower-quality, strongly outbred areas are playing an active role in mitigating the potentially drastic e ects of strong natal philopatry on local genetic drift and loss of diversity. Our ndings i llustrate h ow i ndividual l ife-history d ecisions, s uch a s s ite-delity or dispersion, are related at the colony level with local environment features. ese c onstitute true colony processes that actively enhance higher-scale population functions such as genetic mixing and inbreeding avoidance, thereby allowing the persistance over time of philopatric colonial systems.

Appendices

We are very grateful to Magali Beaugey for her help in the lab. We also thank M. Context §-76 From local to global gene ow. In the previous chapter, we investigated the patterns of very ne-scale local dispersal within a colony of king penguins. e absence of intra-colonial genetic structure suggests that philopatry does not apply to particular areas within the colony beyond a handful of generations. However, at the colony level, philopatry is a strong and fairly well documented behaviour (see e.g. [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF][START_REF] Bried | Population biology of the wandering albatross (Diomedea exulans) in the Crozet and Kerguelen archipelagos, southern Indian Ocean, approached Annexes - §186 through genetic and demographic methods[END_REF]Saraux et al. 2011b). Juvenile return rates to the natal colony may reach up to 87% in that species (Saraux et al. 2011a). Yet, while return rate may be measured accurately, assigning the respective roles of mortality and dispersal in the non-returning fraction of the population is a challenging task. At the single-generation scale, this may be a methodological dead-end: however, a lower bound may be placed on average dispersal rates at the coalescent scale using gene ow a s a n i ndicator o f t he migration the other is essential if we are to draw global conclusions from single-colony surveys: (i) true demographic independence would imply that the main driver of colony trajectory is the local environmental conditions, while (ii) complex demographic correlation between colonies may blur the direct relationship between environmental conditions and local demographic patterns. In the rst case, climate-envelope-type model may be applied successfully, while more complex modelling procedures may be necessary in the second case. Moreover, our understanding of the demographic trajectory of the King penguin is further complicated by its recent history of near-extinction, as steep population growth in the recent past is mostly an artefact of population recovery (see §22

e King synnome - §78 p. 75 for details): thus, our knowledge both of population structure, and of demographic trends, should be rooted in a longer time frame in order to detach itself from these recent events. §-78 Integrating genetics with climate models. Evolutionary and ecological proxies of species demography are grounded in very di erent conceptual frameworks (see §2 p. 25 for an overview).

While the ecological paradigm relies on the short-term, local observation of the species in order to extract larger order parameters, evolutionary studies use the information contained in the species' coalescent structure to infer long-term demographic events. Relating these demographic histories with environmental variables (typically climate) thus also relies on very di erent princi- [START_REF] Peakall | Site fidelity is an inconsistent determinant of population structure in the hooded merganser (Lophodytes cucullatus): evidence from genetic, mark-recapture, and comparative data[END_REF]. Genetic data, however, will not usually lend itself to such an approach: demographic reconstructions are more qualitative than quantitative (in particular because both the size of population and the extent of changes are scales by an unobserved long-term substitution rate, and because of the loose relationship between e ective and true population size, see §41 p. 103), and few environmental variables are available on a comparable time scale (see §9 p. 42) -so that the lack of correlation between reconstructed demography and climate (e.g. [START_REF] Morin | Geographic and temporal dynamics of a global radiation and diversification in the killer whale[END_REF] may be either explained by the low sensibility of the species, or of the methods. e preferred approach must be robust to these shortcomings: for example, a hypothesis-testing framework may be used instead of an agnostic exploratory one. Prior conceptions about the determinants of a species' range or demographics may be examined throughout the history of the species, and their long-term validity can be assessed, without necessarily establishing a direct, quantitative relationship between them. is is the approach that we develop in this chapter. Relying on the general e King synnome - §79 characteristics of the King penguin's breeding and foraging biology, we extract traits that appear to be proximal determinants of its survival and breeding success, and broad, easily observable indices related to these traits, in order to assess their (mostly qualitative) realisation at di erent periods of time.

Abstract

Range shift is the primary short-term response of species to rapid climate change, but may be challenged by natural or anthropogenic ecosystem fragmentation.

2014

). However, recent tracking studies have revealed a southward extension in their foraging ranges as a result of climate change [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. As a result of the associated increase in energy expenditure, the Crozet population is expected to decline within the coming decades (Le Bohec et al. 2008;[START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. e continuous poleward displacement of the species' foraging grounds, combined with the discrete distribution of its breeding locations, implies that King penguin populations must undergo abrupt location shifts from one island to another to follow their habitat, thereby increasing the risk of mismatched strategies. §-81 A Synnome species. In order to predict the limits and opportunities for this species to track its fragmented habitat, we adopted here a cross-disciplinary approach, integrating information from ecology, behaviour and genomics, together with multi-proxy paleoclimate reconstructions and numerical climate models. e most striking feature of the present-day King penguin population is its worldwide panmixia, that we explain by a high inferred migration rate among colonies. Our genome-wide data, including ~35,000 independent polymorphic DNA loci generated using a RAD-sequencing approach [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF], and gathering 163 individuals from 13 di erent locations covering most of the King penguin's contemporary range (see S-0 p. 177, and Fig. 22), strongly contradict the alleged separation between the South Atlantic patagonicus and the South Indian and Paci c halli subspecies [START_REF] Mathews | Fitchi: Haplotype genealogies based on Fitch distances[END_REF][START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF], suggesting that the traits used as a basis for subspecies delineation are better explained by phenotypic plasticity than by reproductive isolation. Both classical descriptors of genetic variation and structure analysis unambiguously support a fully-panmictic worldwide population (see S-1 p. 181 for details).

Full admixture among colonies is also clear when repeating these analyses at the island level (see e King synnome - §82 S-1 p. 181). Furthermore, bio-logging experiments and empirical observations suggest that short and long distance movements are signi cant contributors to the ongoing genetic mixing. Recently reported new colonies [START_REF] Convey | Survey of marine birds and mammals of the South Sandwich Islands[END_REF][START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF][START_REF] Pistorius | Population development and historical occurrence of king penguins at the Falkland Islands[END_REF] indicate that immigration-fuelled colony establishment can occur at a decadal scale. Contrary to previous hypotheses, recapture of tagged individuals (see S-2 p. 196 for details) shows that dispersal is also strong at the generation-scale: thus, dispersal ability is not a limiting factor in the King penguin's response to environmental change, and the species can be assumed to realise its fundamental episode (Fig. 19). During the late Pleistocene and early Holocene, a period of steep population growth is followed by a long plateau. In order to assess the importance of general marine productivity variation in this response, we repeated the analysis for the high-Antarctic Emperor penguin (A. forsteri), the only other extant large penguin species, using 110 individuals and an identical sequencing approach. In contrast with the King penguin, the Emperor penguin shows no detectable demographic response to the recent Pleistocene and Holocene climatic events (Fig. 19 and S-3 p. 197), but rather a slow increase during the Pleistocene, with a possible population maximum after the Valdivian interglacial (Fig. 19). e King penguin's response to past climate change is best explained by variations in the extent of suitable habitat (see S-4 p. 200 for details). We relied on both observed and modelled paleoclimatic data to identify the extent of the King penguin's past fundamental niche, which we de ned based on three major traits that directly determine habitat suitability: (a) within foraging distance of the prey stock at the APF [START_REF] Bost | The importance of oceanographic fronts to marine birds and mammals of the southern oceans[END_REF], (b) reduced sea ice extent to allow for overwinter chick-rearing [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF], and (c) insular and ice-free land [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF]). As con rmed by the e King synnome - §82 absence of LGM impact on the Emperor penguins (Fig. 19), the overall productivity of the Southern Ocean did not change signi cantly during the Pleistocene and Holocene periods (Wol et al. 2006;[START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]). On the contrary, the location of the APF zone and the extent of land ice and winter sea ice cover exhibited important latitudinal variation over the period (Gersonde et al. 2005;[START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF][START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]. us, the location of optimal King penguin breeding areas changed vastly between warm and cold conditions. APF and foraging range predictions, based on historical period experiments from an ensemble of IPCC CMIP5 models (see S-4 p. 200 for details), closely matched both observed APF and empirical foraging distances derived from bio-logging experiments ( §103 p. 202). Our model is able to capture the full present-day range of the King penguin, and our paleohabitat reconstructions are also in close agreement with the species' reconstructed demography (Fig. 19 and Fig. 20). Under LGM conditions, the equatorward displacement of the APF and increased land and sea ice cover [START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF][START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]) reduced the King penguin's range to a fraction of its current extent (Fig. 20A), as suggested by the inferred population bottleneck (Fig. 19A).

Assuming a 700-km February foraging distance as the upper limit for successful breeding [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF], the only two possible refugial areas were found in the Falklands, and in the Campbell plateau region, a much reduced refugial range compared to the eight pre-industrial breeding areas [START_REF] Bost | King Penguin. In: Penguins, Natural history and conservation[END_REF]. By mid-Holocene, on the other hand, the King penguin already occupied most of its pre-industrial range (Fig. 20B-C). e A PF o ccupied a p osition c lose t o its present-day state at most locations, while all present-day breeding archipelagos (except for South Georgia) were free from sea ice, and land ice receded early on Kerguelen and South Georgia -although it persisted until early Holocene on Crozet and Prince Edward archipelagos [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]). e K ing p enguin r apidly e xploited t hese n ewly a vailable l ocations, a s e videnced by the steep growth and following plateau in our demographic reconstructions. us, the King penguin's response to past climate change strongly supports the idea that modi cations in the posi-tion of the APF and in the distribution of land and sea ice, by modifying the extent of available habitat, have a major impact on the species' demographic trajectory. Colony loss is likely to bring about a decrease in population size, although high dispersal ability also implies that newly available locations may be colonised rapidly. Under the "business-as-usual" rcp-8.5 scenario, 70% of the present-day 1.6 million King penguin breeding pairs [START_REF] Bost | King Penguin. In: Penguins, Natural history and conservation[END_REF]) are expected to abruptly relocate or disappear before the end of the century. Forty-nine percent of the world population are projected to lose their habitat completely by 2100 (on Crozet and Prince Edward), and ~21% may also see their habitat degrade strongly due to regularly nearlimit foraging distances (on Kerguelen, Falklands and Tierra del Fuego). ese l osses m ay be partly compensated by the predicted colonisation of Bouvet, and by a possible additional growth on Heard and South Georgia due to improved foraging conditions. ese last two locations, together with Macquarie Island, are likely to become the major refugia for the King penguin in the coming decades. Under the low-emission rcp-2.6 scenario, however, only Crozet and Falkland populations come under direct threat, while other colonies may retain good foraging conditions e King synnome - §84 (Fig. 21), and undergo minimal demographic impact: thus, our results stress the importance of immediate action to limit radiative forcing, as e cient attenuation strategies may still have a positive outcome for the Southern Ocean biodiversity. We also insist on the importance of taking preemptive conservation measures in areas of the Polar Regions, such as Bouvet, that may act as cold biodiversity refugia for the coming warm-earth conditions if we fail to limit radiative forcing in time. §-84 Uncertainties and perspectives. us, a lthough t he i mpact o f t hese c hanges o n t he global King penguin population size will largely depend upon the relative roles of dispersal, breeding failure, and mortality in colony extinctions, the forecasted re-shaping of the species' distribution is considerable. Nevertheless, our projection is likely to be an underestimate, as we only take into account the maximum foraging distance after which no successful breeding may take place.

However, increasing foraging distances even below the 700 km-limit have been shown to impact breeding success strongly, and may trigger a colony decrease well before the extinction threshold is reached (Le Bohec et al. 2008;[START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. In addition, our model does not take into account aggravating e ects of climate change, such as sea level rise [START_REF] Rahmstorf | A semi-empirical approach to projecting future sea-level rise[END_REF] e penguin example illustrates how data about habitat distribution and connectivity, dispersal abilities, trophic interactions, and past range shifts need to be integrated into atmospheric and oceanographic scenarios in order to capture the full extent of human impact on threatened ecosystems. Beyond its implications for the Southern Ocean, the King penguin's complex stepping-stone trajectory o ers a paradigmatic representation of the impact of global warming on species distributions, whenever heterogeneous environmental change leads to the uncoupling of a species' di erent critical areas (e.g. breeding, foraging, or overwintering grounds), and thus results in mismatched strategies. As we show here, mismatching between feeding and breeding strategies is strongly aggravated in fragmented habitats -a growing pressure in urban or agricultural matrices, that has been identi ed as the pivotal aspect of species extinction worldwide (Haddad et al.

2015)

. Habitat fragmentation increases the risk of divergent trends in the di erent aspects of a species' niche, while reducing corridors that may allow continuous niche tracking: by forcing species to undergo tipping-point range-shifts, it has the double e ect of aggravating the impact of environmental change, but largely masking it, placing populations in a situation of climatic debt well before the critical threshold is reached. us, the King penguin's cautionary tale should invite us to explore further the complex and sometimes paradoxical e ects of climate change, even in those ecosystems that may still appear most remote and pristine.

e King synnome - §86 N=10, Kildalkey Bay, Archway Bay "ARC", N=10, and King Penguin Bay "KPB", N=10 -all samples were edged j uveniles). W e s ampled o ne c olony f rom K erguelen a rchipelago (S49°20' E69°20' "KER", N=16 -all samples were edged juveniles), from Falkland archipelago (S51°45' W59°00' -"FLK", N=10 -all samples were breeding adults), from South Georgia (S54°15' W36°45' -"GEO", N=12 -all samples were moulting adults), and from Heard Island (S53°00' E73°30' -"HEA", N=7 -all samples were breeding adults). See Fig. 22 for the geographical location of these colonies. Blood was stored in Queen's lysis bu er at +4°C (Crozet, Marion, Kerguelen), or centrifuged, and red blood cells stored in ethanol at -20°C (Falklands, South Georgia, Heard). DNA was extracted using a spin-column protocol (Qiagen DNEasy© Blood and Tissue kit) with minor modi cations. §-86 Genome-wide Single Nucleotide Polymorphism (SNP) typing. SNP discovery and sequencing followed a single-digest RAD-sequencing protocol [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]. Genomic DNA was checked for degradation on a 1.5% agarose gel, and only samples with consistently high molecular weight were retained and quanti ed b y uorometry (L ife te chnologies™ Qu bit®). e 163 samples was retained and sequenced in 6 distinct libraries. (i) approximately 150 ng of genomic DNA per sample were digested with the restriction enzyme Sbf-I-HF (NEB); (ii) each sample was then ligated to a unique barcoded P1 adapter prior to pooling in a single library. e library was then sheared by sonication (7 cycles 30'' ON -30'' OFF); (iii) sonicated libraries were concentrated to 25 µl by DNA capture on magnetic beads (beads solution:DNA = 0.8:1), thus further reducing the carry-over of non-ligated P1 adapters, and the target size range fraction (350-650 bp) was then selected by automated gel electrophoresis (BluePippin®); (iv) capture on magnetic beads using the same beads:DNA ratio (0.8:1) was then employed in all following puri cation s teps ( after b lunt-end repairing, p oly-A t ailing, P 2 a dapter l igation a nd l ibrary enrichment by PCR). Magnetic beads were kept together with the library throughout the pre-PCR steps, and DNA was re-bound to the beads for puri cation using a PEG-8000 binding solution;

(v) PCR ampli cation was performed in 8 x 12.5 µl aliquots pooled after the ampli cation in or-e King synnome - §87 der to reduce ampli cation bias on few loci due to random drift. PCR was performed using NEB Phusion® polymerase with the following cycles: 30" denaturation at 98°C, 18 cycles of ampli cation (10" at 98°C, 30" at 65°C, and 30" at 72°C), and a nal elongation of 5' at 72°C; (vi) the library was then quanti ed by a uorimetry-based method (Life technologies™ Qubit®), and molarity was checked on an Agilent Bioanalyzer chip (Invitrogen™). A nal volume of 20 µl for each library was submitted for paired-end sequencing on an Illumina HiSeq2000 sequencer (V3 Genbank accession number JQ256379-JQ256413) con rms the idea of a single, worldwide and fully panmictic population. Pairwise Fst is low (Fst=0.032), and a haplotype network does not support any population separation between the two islands (Fig. 24B).

e King synnome - §94 were run in duplicate to check for convergence and for a su cient length to gather ESS > 200 for all parameters, which necessitated 500,000,000 to 1,000,000,000 steps.

Since we parametrised each locus class separately, we expect our model to t a class-speci c substitution rate as a function of the observed number of segregating sites, rather than a common substitution rate. However, as we focus on neutrally evolving regions of the genome, we expect the number of segregating sites to follow a Poisson distribution, of parameter λ equal to the mean number of segregating sites per RAD locus. On a large number of sequences, the expected value E(λ) converges towards the "true" underlying constant mutation rate, multiplied by the total tree length for each locus. us, if we x the tree length, λ becomes an estimator of the substitution rate μ. However, under the EBSP model, the observed number of segregating sites is taken as an estimator of λ, and consequently of the substitution rate μ. erefore we expect the inferred value of μ for each locus class to be a posterior probability of the "true" substitution rate, conditional on the mean number of segregating sites observed for that class [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]. In order to retrieve the underlying common substitution rate μ, we rst tted a log-linear model to the inferred substitution rates (μ 3 = 0.0159, μ 4 = 0.0218, μ 5 = 0.0275, μ 6 = 0.0389. time periods, relies on the accurate phasing of haplotypes, which unfortunately is not possible in a non-model species, in the absence of a large transmission or population dataset. In order to exploit unphased haplotypes, analysis must be restricted to the pairwise case, as PSMC'. However, since recombination events are treated as a Markovian process along the sequence, it is still possible to increase the likelihood of the reconstruction by concatenating several genomes together, thus increasing the independent sampling of TMRCA. We selected three high-quality samples for the King penguin, and the Emperor penguin. Libraries were prepared with a standard Illumina(c)

TruSeq™ PCR-free protocol, and multiplexed on two lanes of a HiSeq 2500 V4 sequencer at the Norwegian Sequencing Center facility, University of Oslo. Reads were mapped to the published Emperor penguin genome (Zhang et al. 2011b) with high success (unique concordant alignment rate, King penguin: ~86%, Emperor penguin: ~81%). We retained only longer sca olds (length ≥ 2 Mb, i.e. 188 sca olds making up for ~80% of the total reference length) for the analysis.

Analysis was run on all three samples from each species simultaneously, with 200 bootstrap replicates. Substitution rate and generation time were de ned a s a bove ( §95 p . 1 86- §96 p . 187).

Results (Fig. 27) are very similar to the EBSP analysis ( §96 p. 187, Fig. 26): the King penguin population grows rapidly in the late Pleistocene, while the Emperor penguin population is mostly stable. However, the resolution of the PSMC' analysis is low in the recent periods, and the last 4 to 5 time bins exhibit considerable instability when compared across reconstructions (Fig. 27A), as opposed to older time periods. us, the precise timing of the LGM bottleneck is not precisely retrieved for the King penguin: the two-step expansion since the mid-pleistocene (Fig. 19A) appears smoothed in one single growth trend. A similar behaviour can be reproduced when simulating data with two bottlenecks in a rapid succession (see §98 p. 192): thus, our PSMC' analysis is in accordance (although with much lower precision) with our general demography.

e King synnome - §98 (i) e Stairway plot retrieves the principal events in the simulation (Fig. 28A). e main di erence lies in the attenuation of the LGM bottleneck, that is mainly visible in the shape of the 95%CI. is is of importance, since it indicates that the Stairway approach may underestimate, rather than overestimate, the bottleneck signal in the data: thus, the bottleneck inferred from the empirical data is likely to be at least as deep as reconstructed. e demographic peak that is visible in the 95%CI at the beginning of the Holocene in our reconstruction from the empirical data (Fig. 19A), on the other hand, although not simulated, is also present in the simulation's 95%CI.

us, that secondary peak rather appears to be entirely artefactual.

(ii) EBSP on simulated data globally matches the expected demographic history (Fig. 28B), with the true demography nearly entirely included in the EBSP CI95% interval. However, the double bottleneck in our simulated data is smoother out as one single depression in the reconstruction, that matches neither bottleneck, but rather averages them -although additional complexity is visible in the shape of the lower CI95% interval. When comparing the empirical-data EBSP (Fig. 26), and the simulated reconstructions, CI95% overlap entirely although median e ective population size di ers, and uncertainty is much larger in the empirical EBSP. Interestingly, however, the empirical run exhibits some features of our simulated model that the simulated-data run fails e King synnome - §98 hours. Antennas were deployed in the evening, in order to record the activity peak around sunrise. In the current state of development of this system, it is impossible to assess how many individuals (tagged or not) crossed the detection zone during the deployment period: thus, recaptures can only be analysed as presence-absence data, and not as quantitative CMR results. Due to the harsh eld conditions, deployments were also in some measure opportunistic; and it is generally impossible to ascertain the status of detected individuals (breeding or moulting), except when their age excluded a breeding attempt. is data, however, provides us precious insights into the behavioural mobility of the species, since antennas were usually located well within the target colonies, and not directly at the seaside: thus, only penguins wandering into a colony distinct from their birth colony were detected.

e King synnome - §100

We performed a total of 28 12-hours deployments during the eld s easons 2011-2012, 2012-2013, 2013-2014, and 2014-2015 (Per- (Per- [START_REF] Delord | Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming[END_REF], and only two 5m-long antennas were deployed along the beach: thus, this single recapture suggests that dispersal from Crozet to Kerguelen may not be a rare event. and warmer Subantarctic surface waters, where the colder southern water masses sink below the northern water mass, at the Antarctic Polar Front (APF). is convergence is compensated by a divergence area, where upwelled deep water masses rise to the surface, creating an intense marine productivity area [START_REF] Mackintosh | Seabirds as indicators of aquatic ecosystem conditions: A case for gathering multiple proxies of seabird health[END_REF]Peterson & Whitworth 1989;Meijers 2014). is a rea is characterised by a steep surface temperature gradient, between 5°C and 3°C [START_REF] Moore | Location and dynamics of the Antarctic Polar Front from satellite sea surface temperature data[END_REF].

S-3: Palaeoclimate of the Southern

Generally, a cooling of surface waters in the Southern Ocean is re ected in a northward displacement of the APF, while a warming brings the APF southward. However, as the APF is de ned by the interaction of deep and surface water masses, it is strongly constrained by the sea bottom topography [START_REF] Moore | Location and dynamics of the Antarctic Polar Front from satellite sea surface temperature data[END_REF]. Important bathymetric features, such as the Campbell Plateau, e King synnome - §101

the Drake passage, or the Kerguelen plateau, may constrain the position or structure of the APF.

In other areas, most importantly the Southern Indian Ocean and Southern Atlantic Ocean, APF displacement is mostly free from bathymetric constraints, and exhibits the largest latitudinal variation [START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]).

e Campbell Plateau may be the best studied case of bathymetric constraint on the APF. Both ow m odels a nd s ediment c ore e vidence s howed t hat t he A PF r emained s outh o f t he plateau throughout the Pleistocene, despite important changes in sea surface temperature and frontal positions throughout the Southern Ocean. Whereas the APF is free to move south to greater depths, it is constrained to the North by the sea oor rise [START_REF] Harvey | Meta analysis reveals complex marine biological responses to the interactive effects of ocean acidification and warming[END_REF][START_REF] Mackintosh | Seabirds as indicators of aquatic ecosystem conditions: A case for gathering multiple proxies of seabird health[END_REF][START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]. Similarly, the Drake passage constrains both the northern and southern boundaries of the APF [START_REF] Moore | Location and dynamics of the Antarctic Polar Front from satellite sea surface temperature data[END_REF][START_REF] Dong | Location of the Antarctic polar front from AMSR-E satellite sea surface temperature measurements[END_REF][START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF]. Finally, the Kerguelen plateau has been shown to alter the subsurface structure of the front, with its deeper manifestations moving North of the islands, while the surface expressions move South [START_REF] Moore | Location and dynamics of the Antarctic Polar Front from satellite sea surface temperature data[END_REF][START_REF] Paradis | Polar Front around the Kerguelen Islands: An up to date determination and associated circulation of surface/subsurface waters[END_REF]. ese features, however, are now well modelled in the CMIP5 panel,

2014). (a)

Ice core data (e.g. EPICA Dome C and Vostok) provide direct evidence for chemical conditions at the core site, and indirect evidence for the oceanic source areas, provided transfer models are accurate enough (Wol et al. 2003;Wol et al. 2006). Parameters derived from ice core evidence mostly covers air temperature, sea ice extent, and marine productivity (Wol et al.

2006) (b)

Benthic sediment core provide more direct evidence for marine conditions (tempera-e King synnome - §101 ture, sea ice cover, productivity) at the core location (Gersonde et al. 2005;[START_REF] Garcia | Les assemblages de diatomées et de foraminifères dans le sédiment marin o rent un indice précis de l'état des masses d'eau de surface, et peuvent être utilisés pour identi er la position de l'APF[END_REF]. (c) Peat cores and geological evidence on the subantarctic islands and surrounding continental shelf are mostly informative for land ice cover [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]. Taken together, this evidence allows for a general palaeoclimatic reconstruction in the Southern Ocean. However, there is still much progress to be done in reconciling the di erent sources of evidence, as variabil- South Georgia may have had ice-free areas. Falklands and Macquarie underwent periglacial conditions [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]. Likely faunal refugia were the currently subtropical islands of Gough, Auckland and Campbell, as well as the Falklands and more generally the Patagonian shelf area [START_REF] Fraser | Poleward bound: biological impacts of Southern Hemisphere glaciation[END_REF].

(iii) Pleistocene glacial retreat and early holocene optimum ( 17 extensive land ice until the end of the period [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]. e e nd o f t he p eriod is marked by a rst cold reversal in the Antarctic waters and a short increase in sea ice cover, of unknown extent [START_REF] Nielsen | Holocene climate in the Atlantic sector of the Southern Ocean: Controlled by insolation or oceanic circulation[END_REF]).

(iv) Holocene hypsithermal and neoglacial conditions (8-0 Kyr BP) were characterised by a warmer climate, similar to historical conditions, interrupted by minor cold reversals. e subantarctic region is ice-free, and the northernmost islands of Gough, Auckland and Campbell are located north of the Subantarctic front [START_REF] Hodgson | soit directement au site de la carotte (comme c'est le cas pour les carottes de sédiment terrestre ou marin -voir Gersonde et al[END_REF]. Temperature reaches a maximum around ~7.5 Kyr BP in the South Paci c [START_REF] Calvo | Antarctic deglacial pattern in a 30 kyr record of sea surface temperature offshore South Australia[END_REF]. Marine conditions are warm and ice free at ~50ºS until around 6-5 ka BP (Hodell et al. 2001;[START_REF] Nielsen | Holocene climate in the Atlantic sector of the Southern Ocean: Controlled by insolation or oceanic circulation[END_REF] (Meijers 2014). We applied a multi-model ensemble approach, a common improvement over single-model projections, as only the trends present in most models are retained in the nal ensemble mean (Meijers 2014). We selected 15 AOGCMs based on the range of available outputs and their coverage of the Southern Ocean (see Table 3). All model outputs were downloaded from the ESGF nodes (pcmdi9.llnl.gov/). In our study, we used the following variables: Sea Surface Temperature (SST) and Sea-Ice Concentration (SIC). For each variable, we calculated the multi-model ensemble mean and standard deviation using the Climate Data Operators toolset (CDO 2015, available at: http://www.mpimet.mpg.de/cdo).

Reconstructions were performed under Last Glacial Maximum, mid-Holocene, and Historical conditions, and projections according to three Representative Concentration Pathways (rcp) scenarios, the rcp2.6, rcp4.5, and rcp8.5, corresponding respectively to the strong emissions reduction scenario, a moderate emissions pro le and the "business-as-usual" scenario. We excluded the rcp6.0 as too few model outputs are available yet. isotherm was used as a diagnostic of the position of the Antarctic polar front (APF, see [START_REF] Moore | Location and dynamics of the Antarctic Polar Front from satellite sea surface temperature data[END_REF] where the King penguin is known to forage [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF]). e particular breeding cycle of the King penguin makes the constraints on foraging behaviour especially strong during the early chick rearing stage, when the juveniles have not yet reached thermal independence, and need regular feeding while not being able to survive without an adult [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF], which happens around the month of February. is is supported by observed foraging trips, which show a much greater geographic constraint during the month of February [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. us, we focused our analysis to the position of the APF in February, as representative of the maximum constraint on foraging trips.

Before using SST outputs derived from AOGCMs, we assessed the accuracy of the representation of the Southern Ocean by comparing each model SST-output for historical runs to satellitemeasured SST from december 1981 to december 2005, using the NOAA Optimal Interpolation v2 SST dataset [START_REF] Reynolds | An improved in situ and satellite SST analysis for climate[END_REF]. Cell-by-cell (1°x1°) linear correlation of SST was assessed and R 2 , slope and intercept were plotted in order to assess the spatial distribution of model departure from observed values.

As modelled SST was generally found warmer than observed SST in the APF zone over the historical period, we followed the correction applied by [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF]. In order to maximise the t between observed and modelled SST for each archipelago, we de ned four oceanic sectors:

South Atlantic Ocean (45°W to 18°E), South Indian Ocean (18°E to 80°E), Macquarie (135°E to 180°E), and Falkland region (75°W to 45°W), ranging in latitude from 45°S to 55°S, but extended to 60°S in the Falkland region to account for the higher latitude of the APF around Cape Horn. For each of these sectors, we tested the linear correlation between modelled and observed SST, and we corrected the model value linearly when needed (Table 4). e 5°C SST isotherm was then calculated in GDAL (www.gdal.org), and kilometric distance between each island and the 5°C isotherm was calculated using the OGR Python library. Correctness of our model was assessed through 1) correlation the observed and modelled distances to the 5°C isotherm on the 1981-2005 period and 2) consistency between these distances and published data on King penguin foraging areas. foraging trajectories meet the APF in the higher-productivity areas on the edge of the Campbell plateau, where upwelling is increased, rather than southward along the shortest route. Finally, the predicted and observed ranges di er most strongly in the Falklands (predicted: ~640 km and observed: 300-500 km, see [START_REF] Pütz | The diving behaviour of brooding king penguins (Aptenodytes patagonicus) from the Falkland Islands: variation in dive profiles and synchronous underwater swimming provide new insights into their foraging strategies[END_REF], a discrepancy explained by the fact the small Falkland population frequently forages on the Patagonian Shelf break, and not directly on the APF [START_REF] Pütz | The diving behaviour of brooding king penguins (Aptenodytes patagonicus) from the Falkland Islands: variation in dive profiles and synchronous underwater swimming provide new insights into their foraging strategies[END_REF]. is di erent behaviour of the Falkland population makes its response to APF displacement more uncertain, as other productivity areas may remain available. However, it seems that the Patagonian Shelf could never sustain a large King penguin population [START_REF] Pistorius | Population development and historical occurrence of king penguins at the Falkland Islands[END_REF], and it is sustaining a high, and increasing, anthropogenic pressure from over shing and climate change [START_REF] Hackett | BioEdit: a userfriendly biological sequence alignment editor and analysis program for Windows 95/98/NT[END_REF]. It is therefore unlikely that the Falklands may sustain a signi cant King penguin population on a centennial time scale. §-104 Winter sea-ice concentration (SIC). Winter Sea-Ice Concentration (SIC) is known to limit the southward expansion of the King penguin's breeding range, as the species overwinter breed-

ing cycle makes open-water conditions a requisite throughout the year [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF]. Although e King synnome - §105 SIC may still be subject to biases in its representation compared to SST, it has improved since CMIP3 [START_REF] Turner | Ecological importance of the southern boundary of the Antarctic Circumpolar Current[END_REF][START_REF] Xu | Simulation of sea ice in FGOALS-g2: Climatology and late 20th century changes[END_REF][START_REF] Shu | Assessment of sea ice simulations in the CMIP5 models[END_REF]. We take the 15% concentration isoline as being representative of the e ective sea ice edge [START_REF] Turner | Ecological importance of the southern boundary of the Antarctic Circumpolar Current[END_REF]. We only consider the sea-ice concentration at their maximum, during the months of august and September. jected to become ice-free all year round by 2080 under all forcing scenarios (rcp2.6: 0.058 ±0.037, rcp4.5: 0.028 ±0.024, rcp8.5: 0.00041 ±0.00053). However, sea ice projections may not be quite as reliable as SST projections. Indeed, although geographical distribution is modelled rather accurately, CMIP5 ensemble models fail to reproduce the increase in sea ice extent observed in East Antarctica over the last decades, suggesting that some processes are not yet adequately accounted for in the current models [START_REF] Turner | Ecological importance of the southern boundary of the Antarctic Circumpolar Current[END_REF]) -in particular, the impact of the in ux of fresh meltwater from the Antarctic ice sheet on the extent of winter sea ice may still be widely underestimated. If such a bias exists, however, it underestimates the true extent of sea ice:

in that case, the King penguin's range reduction may be even more drastic than we forecast here, as Bouvet may not be ice-free and suitable for colony establishment by the end of the century. §-105 Uncertainties assessment. Although the use of a multi-model ensemble mean approach is considered to outperform the use of a single climate model, it is also essential to assess the uncertainties related to AOGCMs to evaluate the con dence that can be attached to our results. Out-e King synnome - §105 puts of the di erent AOGCMs may diverge across time and space because they are based on diverse parameterization of natural processes, downscaling approaches, spatial resolutions, etc… To assess the uncertainties associated with our projections, we calculated, for each rcp scenario, the projected foraging distance derived from each climate model separately. We followed the protocol km [START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. erefore, we considered that a location had reached its critical foraging distance when foraging distance was higher than 700 km for at least 20% of a consecutive decade. Variability between models remains relatively high, as has already been observed in previous studies [START_REF] Cabré | Oxygen minimum zones in the tropical Pacific across CMIP5 models: mean state differences and climate change trends[END_REF]Goberville et al. 2015 -see Fig. 30). At all locations, predictions overlap entirely between rcp scenarios for the rst decade of our projections, as is expected. is is still mostly the case in the middle of the century (2041)(2042)(2043)(2044)(2045)(2046)(2047)(2048)(2049)(2050). Most of the divergence between scenarios appear by the end of the century. is may take the form of (i) a strong divergence of the rcp-8.5 projections as opposed to rcp-4.5 and rcp-2.6 (as in Kerguelen and Bouvet); (ii) an increased dispersion on rcp-8.5 projections (as in Crozet and Prince Edward), or (iii) a more gradual panel of possible outcomes from rcp-2.6 to rcp-8.5 (at most other locations), or (iv) no strong di erence between scenarios in Heard Island. is contrast between scenarios is also noticeable when considering the proportion of individual models predicting a local extinction at each time period (Fig. 30). In the last decade of the century, the 'business-as-usual" rcp-8.5

scenario stands out compared to the "controlled-emissions" rcp-2.6 and rcp-4.5 in Kerguelen, Crozet, Prince Edward, Bouvet and South Georgia, while all three scenarios make up a gradient in Macquarie, South Sandwich, the Falklands, and Tierra del Fuego. Under rcp-8.5, more than 50% of the models predict extinction in Crozet, Prince Edward and the Falklands by the end of the century, and the di cult position of Kerguelen and Tierra del Fuego is con rmed by the fact that a large proportion of models predict extinction on these islands too. Overall, although intermodel variability remains high, and alternative outcomes are possible, the strong consensus both in the increasing foraging distance trend, and in the actual prediction for local extinction, stress both the very likely nature of the threats upon the Southern Ocean ecosystems under the rcp-8.5

scenario, and the possibility of yet avoiding the most destructive e ects of these threats if immediate action allows us to bring greenhouse-gas emissions closer to the rcp-2.6 forcing scenario.

Abstract

The rate of ongoing environmental change is now thought to exceed the rate at which most species [START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF], including humans [START_REF] Díaz | Separating sensitivity from exposure in assessing extinction risk from climate change[END_REF], are able to adapt, with signi cant c onsequences f or t heir r esilience a nd f or e cosystem s ustainability. I n a r ecent review, [START_REF] Chown | et Fig. 6), et il est probable que la particularité apparente des régions polaires ne soit dûe à rien d'autre qu'à leur destruction plus tardive, garantie par leur inaccessiblité -et non à quelque caractéristique particulière qui y favoriserait la mégafaune[END_REF] reported that despite the pristine appearance of Antarctica, its species and ecosystems are also under considerable threat. Investigating trends and risks in such a remote area relies mostly on a handful of bio-indicator species: the Emperor penguin (Aptenodytes forsteri) often fulfills this task, as it integrates the multiple effects of climate change (Ainley et al. 2010a). A recent study suggests that populations of this flagship Antarctic species may be at high risk continent-wide within the next 100 years [START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF].

However, predicting the dynamics of species adaptation and persistence requires a more complete understanding of the temporally and spatially complex ecological processes shaping the structure of worldwide populations, and can greatly benefit from the synergy amongst disciplines and methods. The projected decline of the Emperor penguin population [START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF]) relies on the explicit assumption, based on behavioural observations, that inter-colony dispersal is negligible. Yet, using genome-wide data from individuals belonging to several colonies encompassing the whole Antarctic continent, we reveal that Emperor penguin populations are characterised by a high level of gene flow, with migration rates reaching up to 4.2% between colonies separated by more than 8,000 km of coastline, and a shared common e Emperor synnome - §108 demography over the last quaternary climate change period. Thus, the population structure in this polar top-predator is better explained by one single global population rather than by a fragmented colonial system. By rejecting the view of the local colony as a relevant demographic unit, our results highlight that i) robust demographic projections and extinction risk estimations will only be possible by including dispersal rates in the models, and ii) population size fluctuations in single colonies should rather be taken as indicative of local stochastic events, since the species' response to global environmental change will likely follow a shared evolutionary trajectory.

Results §-108 Studying demography in the context of climate change. e importance of global warming is now a central subject of international concern, and an increasing number of studies seek to understand its impact on the world's ecosystems and to predict likely scenarios in response to climate projections ( omas e t a l. 2 004), e ither i n o rder t o s et u p m ore e cient conservation strategies, or as a means to prompt urgent political action [START_REF] Watson | en particulier pour les prédateurs supérieurs, qui déterminent dans une large mesure les propriétés de résilience des écosystèmes[END_REF][START_REF] Chown | et Fig. 6), et il est probable que la particularité apparente des régions polaires ne soit dûe à rien d'autre qu'à leur destruction plus tardive, garantie par leur inaccessiblité -et non à quelque caractéristique particulière qui y favoriserait la mégafaune[END_REF]. One of the main di culties of this task lies in the fact that only a handful of species have been monitored for more than a few decades, and, in most cases, our knowledge of their demography is limited to short-term (i.e. generation-scale) responses [START_REF] Saether | Finite metapopulation models with density dependent migration and stochastic local dynamics[END_REF]). Yet, in order to establish reliable projections, larger-scale population parameters must be integrated into demographic models. Recent development of high-throughput sequencing allows the analysis of genome-wide and population-scale data and provides the genomic signature of the important demographic parameters that can be used to accurately predict species responses to global change [START_REF] Allendorf | Genomics and the future of conservation genetics[END_REF]).

e Emperor synnome - §109

Extracting species-wide projections from time-series collected on a single population requires a precise understanding of how local events relate to species-scale demographic processes. We need for instance to establish whether the observed local extinctions or uctuations occur as a consequence of mortality peaks, massive dispersal events, or a combination of both. Adult mortality has traditionally been proposed as the primary factor (through changes in resource availability and subsequent starvation, see Barbraud & Weimerskirch 2001a, or increased predation, see Ainley et al. 2010b), and several models have been built on that basis [START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF]). Yet, these models mostly rely on the explicit assumption that movement amongst populations is negligible [START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF] from heightened energy expenditures [START_REF] Massom | Fast ice distribution in Adélie Land, East Antarctica: interannual variability and implications for emperor penguins Aptenodytes forsteri[END_REF][START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF], or removed the usual colony location [START_REF] Ancel | Emperors in hiding: when icebreakers and satellites complement each other in Antarctic exploration[END_REF]. On the other hand, recent empirical evidence increasingly points to an important e ect of dispersal in that species' respond to habitat disruption [START_REF] Forcada | Penguin responses to climate change in the Southern Ocean[END_REF]. e rapid recovery of the emperor penguin population in Coulman Island con rms this assumption and excludes a peak in adult mortality followed by re- Emperor penguin colonies representing the whole species' range (Fig. 31A). In order to assess both ne-a nd l arge-scale p rocesses, w e s ampled t hree c olonies i n a t ight c luster around Terre Adélie, in Eastern Antarctica: Eastern and Western Mertz colonies (Ancel et al. 2014 -'MZE' and 'MZW') frequency covariance between populations over variable sites, or from called genotypes using Reich's estimator (Reich et al. 2009) WSH to MZE, but with numerous migration events inferred between most colonies (Fig. 38). e Emperor synnome - §113 §-113 Continental-scale migration. In order to quantitatively assess the importance of continental dispersal, we modelled the inter-colony migration rates required to generate such a level of genetic admixture. We rst co-estimated e ective population size and bidirectional migration rates between a subset of four colonies representing our whole sampling area by simulating genetic data under a continuous-time Markovian coalescent model against the observed two-dimensional allele frequency spectra, using a composite-likelihood approach (Exco er et al. 2013, see §135 p. 250). Analysis was calibrated using a RADome mutation rate as estimated for King penguin [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]) and a generation time of 16 years [START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF]). E ective population sizes all converge to an average of ~4,000 (from 1120 to 7640) breeding individuals (detailed estimates in supplementary Table 7 and Fig. 31A), in keeping with the observed median size of extant colonies [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]. Each colony is estimated to receive, on average, between ~0.7% and ~4.2% of its e ective population size in migrants every generation. us, if we were to scale these results to present-day observed census size, a colony such as Pointe Géologie (DDU), with a count of ~7,000 breeding adults, would exchange, on average, between ~260 and ~300 migrants (3.8% to 4.2%) per generation with the rest of the continent. We further validated these results using a haplotype-based multilocus Bayesian approach as implemented in Migrate-n (Beerli & Palczewski 2010, see §134 p. 247). Model ranking using Bayes factor model choice gives clear support for a full-migration model with very high gene ow. Estimated migration rates (M) and population sizes (Θ) are highly homogeneous (mean mutation-scaled (μ) migration rate M = m / μ = 2,358 ±130, mean mutation-scaled e ective size Θ = 0.0018 ±8.2e -5 ).

Considering a conservative range of μ for our subset of loci [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF], we can estimate that each colony receives on average from 5.40% (±0.22) to 10.00% (±0.42) of its total e ective size as migrants from other colonies at each generation (see §134 p. 247). Although slightly higher, this estimate is of the same order of magnitude as the one derived from the joint allele frequency spectra. We highlight the fact that these are estimates of the migration parameters aver- [START_REF] Mayr | Populations, species, and evolution: an abridgment of animal species and evolution[END_REF], « a high dispersal ability is a necessity for occupants of temporary habitats ». Since dispersal has been the immediate response for colonies facing habitat disturbance in the recent past [START_REF] Forcada | Penguin responses to climate change in the Southern Ocean[END_REF][START_REF] Tomczak | First recorded loss of an emperor penguin colony in the recent period of Antarctic regional warming: implications for other colonies[END_REF][START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF], it may therefore be thought to play an important role by adding exibility to an otherwise rigid philopatric system, and by allowing the species to dynamically exploit the best breeding locations in rapidly changing polar environments. Yet, such demographic events are of local rather than global signi cance.

Population trends extracted from a single colony re ect the immediate quality of the focal location on a generation scale, rather than species-wide parameters (Fig. 32C). In this context, the colony is not a truly relevant demographic unit, but rather a transient aggregation of individuals at a particular point of time. In order to distinguish this structure from panmictic and metapopulation systems, we propose the term synnome for this combination of an exceptionally fragmented space and a very uid gene pool (see full de nition in Supplementary methods S-0 p. 236). §-115 Future prospects. Our ndings highlight the importance of adopting a cross-disciplinary approach, integrating population genomics and behavioural ecology, to the study of population dy-e Emperor synnome - §115

namics. Such an approach should help in the development of more robust and accurate demographic projections at the whole species level. ese re ned projections will then be more likely to allow estimation of the extent of threats to vulnerable species and identifying the proximal

Methods Summary

Samples were collected from edged chicks or frozen carcasses at the end of the breeding season.

RAD sequencing followed Baird and colleagues' original protocol [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF] at regular intervals along the shore (Ainley et al. 2010a). However, the mobility of Emperor penguin colonies imply that the distance between a particular present-day colony location and a polynya is not a hard constraint: as opposed to the King penguin whose breeding locations are xed (see e King synnome, p. 163), the Emperor is free to select a colony space that will optimise both the strength of the ice for chick rearing, and the proximity of polynyas for foraging.

e Emperor synnome - §118

Ultimately, this means that although the local icescape has a strong impact on short-term colony demography, the species as a whole is sensitive to changes in the global state of the coastal Antarctic sea-ice rather than to local conditions: as long as an equivalent breeding location is available, the expected response is relocation rather than extinction (see also [START_REF] Forcada | Penguin responses to climate change in the Southern Ocean[END_REF] on that point). For example, while an increasing distance between a subantarctic island and the Polar Front translates as an increased foraging constraint on the King penguin (see §80 p.

167), an increasing distance between a given present-day Emperor penguin rookery and the nearest polynya will most likely encourage the penguins to change their colony location accordingly to track their optimal habitat conditions, with no predictable cost for the population. us, the local, colony-based paradigm we adopted for the analysis of the King penguin's habitat in e King synnome, p. 163, and that Jenouvrier et al. 2014 deployed for predicting the Emperor penguin's future demography does not seem to be appropriate for the latter. Here, we explore an alternative approach, based on the global sea ice conditions around the coast of Antarctica. §-118 Sea ice in the CMIP5 experiments. An additional di culty in predicting the Emperor penguin's response to climate change is a technical limitation: the current CMIP5 AO-GCMs (see §50 p. 130) normally provide outputs mapped on a 1° grid, which, under the higher polar latitudes, is a rectangle of ca. 111 x 50 km -too coarse a grid to explicitly model polynyas, which are often less than 50km wide. Moreover, the current representation of Antarctic sea ice in the CMIP5 ensemble members still fails to capture important aspects of the observed conditions, such as the signi cant positive trend in sea ice extent [START_REF] Zhang | Comparative genomics reveals insights into avian genome evolution and adaptation[END_REF][START_REF] Turner | Ecological importance of the southern boundary of the Antarctic Circumpolar Current[END_REF][START_REF] Shu | Assessment of sea ice simulations in the CMIP5 models[END_REF]) -a shortcoming that may be due to the fact the Antarctic Ice Sheet is currently speci ed as a boundary in the CMIP5 ensemble, and not explicitly modelled [START_REF] Kusahara | Sobre la distribución del Pingüino Rey Aptenodytes Patagonicus (Aves: Spheniscidae) en Chile[END_REF]. Due to these constraints, it is currently impossible to accurately determine the distribution on polynya along coastal Antarctica in the CMIP5 framework.

e Emperor synnome - §119 However, our reconstructions of past population size changes give us an alternative viewpoint on the question. Although variation in habitat quality through space can be alleviated by dispersal, variations through time are rather nal, and oblige the species to adapt or decline. us, the lack of response of the Emperor penguin to Pleistocene and Holocene climate change implies that the global sea ice conditions that prevailed at these periods provided suitable habitat for the species.

Comparing the CMIP5 predictions for sea ice conditions during the Last Glacial Maximum, at midHolocene, during the historical period, and at the end of the 21st century should therefore allow us to simplify the complex spatial component of the Emperor's relationship to sea ice, and to consider several successive states of global sea ice distribution and characteristics. However, the identi ed biases in sea ice representation in CMIP5 models restricts the scope of this study: we expect it to be highly informative regarding how far CMIP5 models support a scenario in which changes in sea ice con guration b rings t he E mperor p enguin t o l arge-scale d ecline throughout the continent -but less so as to the species' actual demographic trend. §-119 Model and variable selection. Our choice of models, within the CMIP5 ensemble, is limited by the availability of outputs and the di culty of performing objective model-choice based on outputs. Only six models so far provide outputs for the PMIP3 palaeoclimate experiments, the historical period, and three RCP scenarios (RCP-2.6, RCP-4.5 and RCP-8.5 -see §102 p. 201):

the CCSM4, CNRM-CM5, the GISS-E2-R, the IPSL-CM5A-LR, the MPI-ESM-P, and the MRI-CGCM3 (see Table 5 for details). Analysis was performed on six 20-years period, during the LGM, at midHolocene, in 1980-1999 for historical runs, and 2080-2099 for the three RCP runs. Outputs were further divided into ve y early b reeding s tages ( based o n Jenouvrier e t al.

2014): interbreeding (January to March), parading and egg-laying (April-May), incubating (June-July), hatching and small-chick rearing (August-September) and rearing of the thermally independent chick (October to December). 2010a), we restricted the analysis to a zone of 3° from the coastline and o shore -which corresponds to a distance of ~330 km, largely including the maximum length of an Emperor penguin's walking trip to the nearest polynya (~100km at most before systematic breeding failure, see [START_REF] Massom | Fast ice distribution in Adélie Land, East Antarctica: interannual variability and implications for emperor penguins Aptenodytes forsteri[END_REF]. Within that zone, we considered four global descriptors of SIC: (i) the mean concentration, (ii) the variance of concentration, (iii) Moran's global index of spatial autocorrelation, and (iv) the sea ice extent within the coastal zone, de ned as the ratio of the total surface with a SIC > 15% (Ainley et al. 2010a). For each of these parameters, we calculated multi-model mean and across-model standard deviation. In order to quantify the relative importance of changes in sea ice conditions through time, compared to model uncertainties, we also calculated the coe cients of variation -either between periods (i.e. geological periods, and RCP scenarios) within models, or between models within periods.

Sea Ice ickness and Wind

Stress. ree other complementary model output variables were assessed in order to capture possible variation in the icescape that would not be re ected in SIC.

First, Sea Ice ickness (SIT) was taken as a di erent proxy for the presence of polynya. SIC describes the fragmentation of sea ice, which is normally high in polynya areas, but may be inaccurately represented in the current models. SIT is also expected to be low in polynya areas, due to the ongoing sea ice formation and o shore drift (Ainley et al. 2010a), and may not be subject to the same biases as sea ice concentration in the current models. For SIT, multi-model mean and across-model variance were calculated for the continent-wide mean thickness, standard deviation, and Moran's index. Across-models and across-periods coe cients of variations were also calculated.

O shore wind stress is the proximal cause of polynya formation (see §18 p. 65). Since sea-ice models are usually independent sub-models within the CMIP5 AO-GCMs (see e.g. Gent et al.

2011), we account for the fact that wind patterns themselves may be accurately modelled, but that the sea-ice response at expected polynya areas may be less reliably predicted. We consider two variables representative of o shore wind stress in Antarctica: the Northward surface wind speed component (VAS, in m.s -1 ) and the Surface northward downward wind stress (TAUV, in Pa). For both variables, we extracted multi-model mean and across-model variance for the continent-wide mean, standard deviation, and Moran's index for both VAS 1 and TAUV. Across-models and across-periods coe cients of variations were also calculated. All computations relied on the Climate Data Operators toolset (CDO 2015, available at: http://www.mpimet.mpg.de/cdo) and custom R scripts. §-120 Is coastal sea ice really changing? SIC is predicted to decrease in all 6 included models, while its variance increases from LGM conditions until RCP-8.5 conditions (Fig. 33A). SIT, on the other hand, shows divergent trends across models, either slightly decreasing (in CCSM4 and MRI-CGCM3), or stable from LGM to 21st century (in the other models), while its variance decreases or remains unchanged (Fig. 33B). For both variables, homogeneity of spatial distribution, 1.

VAS is not output by the CCSM4 model -for this variable, we therefore only use a 5-member ensemble.

e Emperor synnome - §120

e Emperor synnome - §120 as summarised by Moran's global index, remains unchanged throughout Pleistocene and Anthropocene -the rather high value (0.5 < I < 1) is consistent with a local clustering of low-ice areas in polynya around the continent. As expected, SIC changes most during the warmer months -in summer (interbreeding period), autumn (egg-laying) and spring (rearing). Overall, despite the known bias towards sea ice reduction in the CMIP5 models, the extent of coastal sea ice only decreases during the interbreeding period, and marginally the egg-laying period, in most models.

Wind patterns, on the other hand, are remarkably constant on the Antarctic coastal band from

LGM to RCP projections. Let aside the widely divergent GISS-E2-R model, o shore surface wind stress (TAUV) remains unchanged, in terms of absolute average value, spatial variance, and distribution (Fig. 33C). Wind speed decreases most noticeably between at the LGM-Holocene transition, but only decreases during the interbreeding period in the RCP projections (Fig. 33D).

Since o shore wind stress is the primary cause of polynya formation, the stability of coastal oshore wind patterns may be an indication that polynya dynamics did not change much during the Pleistocene and the Holocene, and may not change much in the coming decades either. is is consistent with our reconstruction of Emperor penguin population size through the past millennia (see §82 p. 169 and §112 p. 218): the lack of demographic response to the glacial maximum and following deglacial period implies that the species' habitat was not signi cantly changed, and that Emperors were able to nd a su cient number of polynya for overwinter foraging throughout the period. e Emperor synnome - §122 §-122 Can we predict Emperor penguin demography based on sea ice? Our examination of the habitat of the Emperor penguin shows the strong di erence that oppose the two Aptenodytes species.

While the King penguin's xed a nd f ragmented b reeding l ocations f orces i t t o u ndergo drastic range contractions and expansions in response to Southern Ocean chances, it appears that the Emperor penguin's ability to relocate when the local icescape changes, together with the paradoxical stability of the coastal Antarctic habitat, allowed it to maintain a constant population size throughout the main climatic events of the Pleistocene.

Although the Antarctic sea-ice area as a whole underwent large changes during the Pleistocene glacial-deglacial cycles (see §9 p. 42 and e King synnome, p. 163), the coastal zone stands out as a particular region. First, as it is bu ered by the large expanses of pack ice that shield it from the lower latitudes, changes that occur at the edges of the ice remain mostly silent in the coastal area.:

this is visible in the fact that even the projected changes in SIC are mostly visible in summer, when this bu er zone is reduced. Second, because the most important sea ice features, for the Emperor penguin, are not directly linked to the total amount of sea ice, but rather to the dynamics of its formation in the coastal wind-stress-driven divergence zones: thus, provided wind stress remains constant, an increase or decrease in total sea ice extent may have but little impact on the Emperor's ability to nd accessible foraging areas. Indeed, on very short time scales (i.e. shorter than a generation), it is possible that abrupt changes in the repartition of sea ice may result in adverse e ects for a single colony, as have been documented in Pointe Géologie (Barbraud & Weimerskirch 2001a) or in Coulman Island (Kooyman & Ponganis 2014), however, relocation to a more favourable location in the area seems to be rapid (see [START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF] and Empirical evidence of heterogeneous dispersal, p. 259). Overall, the long-term stability of the Emperor penguin population implies that local events have little in uence on the resilience of the species as a whole.

e Emperor synnome - §124 ese results do not imply that Emperor penguins will not be threatened by global warming. As we pointed out earlier (see §84 p. 174), the impacts of climate change have multiple aspects, that go well beyond the local changes in icescape. Changes in the trophic web of the Southern Ocean and an overall decrease in productivity (due to changes in upwelling activity on the Polar Front, to acidi cation, and most importantly to sheer temperature e ects on plankton communities, see e.g. [START_REF] Beaugrand | Reorganization of North Atlantic marine copepod biodiversity and climate[END_REF]) may have a much deeper impact on the Emperor penguin population than changes in polynya distribution. However, addressing these changes will require very di erent approaches, such as ecosystem modelling -a central topic, but unfortunately beyond the scope of this work.

Supporting information S-0: Main concepts in use §-123

Choice and de nition of a « synnome ». For the lack of an appropriate term that would be currently in use, we chose to designate by the word synnome the particular colonial system that can be observed in the Emperor penguin, and possibly in other species. We derive this term from the Greek σύννομος, « a common grazing of ocks », used in particular for gathering ocks of birds (e.g. Aristophanes' Aves, v. 1756-7), and which was commonly used by extension for « reunions », « gatherings », and even « kindred ». ese di erent meanings together convey the particularity of the observed structure: one single, nearly homogeneous pool of individuals is distributed in a highly discrete way throughout its range. Local concentrations, or colonies, are highly consistent on the scale of a few generations, at which scale philopatry may be the norm. Yet, migration between these areas is high enough to maintain total homogeneity of the species' gene pool, so that, viewed on a micro-evolutionary time-scale, the only relevant unit is the species as a whole. §-124 Dispersal and migration. Dispersal was originally described by Howard (Howard 1960) as « the movement the animal makes from its point of origin to the place where it reproduces ». From each e Emperor synnome - §125 individual's perspective, it is « the greatest distance its genetic characteristics are transmitted, rather than the greatest distance the animal may have migrated or otherwise travelled away from the place it was conceived, hatched or born ». Migration, on the other hand, has been described by Dingle and Drake [START_REF] Dingle | What is migration[END_REF] in a biogeographical context as « range expansions of faunas or individual species », such as « the northward extension of ranges following the retreat of glaciers at the end of the ice ages ». More speci cally, in a population genetics context, the (mutation-scaled) migration parameter M has been de ned by the same authors as « the exchange of genes among populations by whatever means, including but not limited to migration as we consider it here » [START_REF] Dingle | What is migration[END_REF]. It is used in that sense in the coalescent framework, in particular by Beerli and colleagues [START_REF] Beerli | Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations[END_REF][START_REF] Beerli | Unified framework to evaluate panmixia and migration direction among multiple sampling locations[END_REF]. us, in that context, migration is distinguished from dispersal by its larger scale: whereas dispersal is an individual-and generation-centred phenomenon that may be observed directly, migration is a time-averaged, population-centred event that is only detectable through indirect methods, such as gene ow reconstruction. N=24, HAL: N=20). DNA was extracted using a spin-column protocol (Qiagen DNEasy© Blood and Tissue kit) with minor modi cations.

S-1: Supplementary methods and analysis

e Emperor synnome - §126 §-126 Genome-wide Single Nucleotide Polymorphism (SNP) typing. SNP discovery and sequencing followed a single-digest RAD-sequencing protocol [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]. Genomic DNA was checked for degradation on a 1.5% agarose gel, and only samples with consistently high molecular weight were retained and quanti ed b y uorometry (L ife te chnologies™ Qu bit®). A to tal of 110 samples was retained and sequenced in 5 distinct libraries. (i) approximately 150 ng of genomic DNA per sample were digested with the restriction enzyme Sbf-I-HF (NEB); (ii) each sample was then ligated to a unique barcoded P1 adapter prior to pooling in a single library. e library was then sheared by sonication (7 cycles 30'' ON -30'' OFF); (iii) sonicated libraries were concentrated to 25 µl by DNA capture on magnetic beads (beads solution:DNA = 0.8:1), thus further reducing the carry-over of non-ligated P1 adapters, and the target size range fraction (350-650 bp) was then selected by automated gel electrophoresis (BluePippin®); (iv) capture on magnetic beads using the same beads:DNA ratio (0.8:1) was then employed in all following puri cation s teps ( after b lunt-end repairing, p oly-A t ailing, P 2 a dapter l igation a nd l ibrary enrichment by PCR). Magnetic beads were kept together with the library throughout the pre-PCR steps, and DNA was re-bound to the beads for puri cation using a P EG-8000 binding solution;

(v) PCR ampli cation was performed in 8 x 12.5 µl aliquots pooled after the ampli cation in order to reduce ampli cation bias on few loci due to random drift. PCR was performed using NEB Phusion® polymerase with the following cycles: 30" denaturation at 98°C, 18 cycles of ampli cation (10" at 98°C, 30" at 65°C, and 30" at 72°C), and a nal elongation of 5' at 72°C; (vi) the library was then quanti ed by a uorimetry-based method (Life technologies™ Qubit®), and molarity was checked on an Agilent Bioanalyzer chip (Invitrogen™). A nal volume of 20 µl for each library was submitted for paired-end sequencing on an Illumina HiSeq2000 sequencer (V3 chemistry, libraries 1-3), or HiSeq2500 (V4 chemistry, libraries 4-5), at the Norwegian Sequencing Centre, University of Oslo, spiked with 20% PhiX control library in order to reduce low-diversity bias.

e Emperor synnome - §127 §-127 Sequence alignment and genotyping. Data processing was performed using the following work ow: ( i) S equence d emultiplexing. R ead q uality a ssessment w as m ade i n FastQC (http:/ /www.bioinformatics.babraham.ac.uk/projects/fastqc/). Samples were de-multiplexed according to in-line barcodes using Stacks v1.20 [START_REF] Catchen | Stacks: building and genotyping loci de novo from short-read sequences[END_REF][START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF], lowquality reads were discarded, and sequences trimmed to 95 bp. (ii) Read mapping and ltering.

Demultiplexed fastq les were mapped to the published contigs of the Emperor penguin genome (Zhang et al. 2011b) using Bowtie2 2.2.3 [START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF], with standard settings, allowing only end-to-end mapping. Resulting SAM les were ltered using Samtools 0.1.19 (Li et al. 2009), PicardTools 1.113 (http://picard.sorceforge.net), and custom R and shell scripts in order to discard unpaired reads and full read pairs where at least one mate has a mapping quality score below 30. e r esulting B AM les we re th en l tered for PCR and opt ical dup licates by comparing mapping position and CIGAR string, using Picard MarkDuplicates. is process also allowed to lter out most sequencing errors, since MarkDuplicates only retains the read with the highest average Phred score in each duplicate cluster. (iii) SNP calling and genotyping. ree independent algorithms were used for SNP and genotype calling, all of them built on a maximumlikelihood framework. 1) First-in-pair reads were exported as BAM les for the maximum-likelihood-based genotype caller built in the Stacks pipeline (ref_map.pl), with a maximum of 5 mismatches allowed between alleles at a single locus (both within and between individuals), correcting genotype calls using the information from the whole dataset in the rxstacks program. 2) We used the same cleaned BAM les to simultaneously call both mismatch and indel polymorphisms in all samples using the GATK HaplotypeCaller pipeline (DePristo et al. 2011), with standard parameters, except for population heterozygosity which was set to 0.01. 3) ANGSD 0.900 [START_REF] Kim | Estimation of allele frequency and association mapping using nextgeneration sequencing data[END_REF]Nielsen et al. 2012) was used to call SNPs and genotypes using a maximum-likelihood process with the Samtools mpileup/bcftools algorithm, using the complete sample allele frequency information as a prior. Genotype calls were only retained for comparison purposes with Stacks and GATK, however, downstream analysis was performed directly on raw genotype likeli-e Emperor synnome - §127 hoods, as this approach has been shown to be much more sensitive to weak structure than classical SNP-calling analysis [START_REF] Nielsen | Genotype and SNP calling from next-generation sequencing data[END_REF][START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]. Genotype calls from all three processes were formatted into VCF format using software-speci c ( Stacks a nd G ATK) or custom scripts (ANGSD); further ltering a nd m anipulation w as d one u sing VCFtools 0.1.12 [START_REF] Danecek | The variant call format and VCFtools[END_REF]. For analyses relying on SNP calls, an additional ltering s tep w as performed by a) extracting the list of consensus loci called as SNPs by all three independent algorithms, b) discarding genotypes with a coverage under 3X for analyses sensitive to sequencing depth, c) removing loci genotyped in less than 75% of all individuals, and nally d) thinning down loci to keep only polymorphism distant of at least 1 kb, in order to minimise linkage between markers. (iv) Sex-linked marker analysis. In order to check for sex-speci c dispersal or structure patterns, we repeated analyses for each sex separately, using either autosomal loci only or Zlinked loci only. We used the published genome annotations for bird gametologs (Zhou et al.

2014

) to identify potentially Z-linked sca olds. Since females are heterogametic in birds, we expect non-pseudo-autosomal Z-linked regions to be fully homozygous in females, but to neutrally heterozygous in males. We therefore retained only sca olds that had a clearly bimodal distribution of heterozygosity, with one mode at or close to 0 (a slight tolerance was allowed to account for misalignment, sequencing errors mistyped as SNPs due to low coverage, or presence of a transposable elements with autosomal homologs). Fifteen sca olds were ultimately retained. Sca olds containing candidate Z-linked coding DNA sequences, but with no visible bimodal heterozygosity distribution, were excluded altogether from the analysis. Sex assignment was performed independently from each of the non-recombining Z sca olds, and consistency of sex calls between sca olds was checked manually. paired-end reads (±16,000) with a mean Phred score of 37, only part of which was dedicated to that project. After barcode demultiplexing and quality ltering, w e r etained a n average 147,000,000 read pairs per library (±13,000,000). Concordant alignment rate was high (70.7% ±9.1%). However, after ltering, a large proportion of the reads was identi ed as duplicates and removed from further analysis. On average, 479,000 read pairs were retained per individuals. We built an average 78,000 loci per individual (±17,000). Overlap between SNP calling methods was e Emperor synnome - §129 high: 111,686 SNPs were called by GATK (173,704 SNPs total), ANGSD (203,801 SNPs total), and Stacks (148,721 SNPs total) (Fig. 35). After ltering by coverage (minimum 3x) and missing data (minimum 75% representation), 59,037 highly con dent SNPs were retained for analysis, with a mean depth of 6.8x. Of these, 582, spread across 15 sca olds, were identi ed as unambiguously belonging to the non-recombining region of the Z chromosome. §-129 Pairwise FST, AMOVA, and summary statistics. Pairwise xation i ndex w as e stimated by two independent methods. We calculated Reich's estimator (Reich et al. 2009 -which has been shown to be most robust to small and uneven sample sizes, see [START_REF] Willing | Estimates of genetic differentiation measured by F(ST) do not necessarily require large sample sizes when using many SNP markers[END_REF]) on called genotypes, using custom R scripts. Without calling genotypes, we calculated Reynolds' estimator [START_REF] Reynolds | Estimation of the coancestry coefficient: basis for a short-term genetic distance[END_REF], taking into account uncertainty in site-allele-frequency, according to the method implemented in ngsTools [START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]. Both methods were performed on a per-site basis and averaged over 1-kb non-overlapping sliding windows, as the ratio of the windowed sum of inter-population variance over the windowed sum of total variance. For the genotype-call-free method, only sites with a probability of being variant at least equal to 0.95 were included in the estimation. Nucleotide diversity and Tajima's D were calculated on a per-locus basis. In order to avoid possible biases due to low coverage, we randomly sampled one haplotype for each individual, and performed calculations on this haploid subset. Calculations were made using adegenet and pegas packages, as well as custom R scripts. We found that the Hardy-Weinberg equilibrium holds across 466 out of 590 sca olds with all colonies assessed together (out-of-equilibrium sca olds all have less than 5 SNPs). e mean homozygosity across all individuals is low (F=0.051 ±0.094) and does not exhibit any inter-colony di erence.

e nucleotide diversity is low and highly similar across all colonies (π DDU =0.0026; π MZE =0.0023; We used SplitsTree [START_REF] Huson | Application of phylogenetic networks in evolutionary studies[END_REF] to build a neighbour-net based on pairwise Fst distances between colonies. In order to quantify the proportion of variation at each organisation level, AMOVA was performed on unlinked SNP data ( §131 p. 245) using Arlequin (Exco er et al.

2005), on a per-locus basis, with 1,000 permutations.

e Emperor synnome - §129 were calculated in PLINK v1.9 [START_REF] Purcell | Warming of global abyssal and deep South-Annexes - §186 ern Ocean waters between the 1990s and 2000s: contributions to global heat and sea level rise budgets[END_REF]. Allelic distance for pairs of individuals is rather sensitive to uneven coverage (Fig. 37, lower triangle), with lower-depth individuals appearing more similar to all other individuals than higher-depth ones. However, IBD inference, which takes into account the total genetic variance in the whole sample, appears more robust to coverage variation (Fig. 37, upper triangle). ree i ndividuals i n t he H AL c olony ( HAL13, HAL14 and HAL16, visible on Fig. 37 and Fig. 39), as well as two in the MZE colony (MZE01 and MZE04, visible on Fig. 37 and Fig. 39), appear markedly more related to each other than to the rest of the sample. Of these two clusters, only one individual was retained for inbreeding-sensitive analyses, such as principal component analysis. Pairwise IBS distances were also used to generate a neighbour-net using SplitsTree [START_REF] Huson | Application of phylogenetic networks in evolutionary studies[END_REF]). PCA was performed on called genotypes using R library adegenet [START_REF] Jombart | adegenet: a R package for the multivariate analysis of genetic markers[END_REF], and on genotype likelihoods using ngsCovar (Fumagalli et al.

2014)

. Analysis was performed either on all samples or keeping only one individual for each highly related cluster. When performed on all samples, analysis was mostly driven by the HAL relatedness cluster, with that rst P C a ccounting f or 2 .84% o f t otal v ariance. Interestingly, these outliers are also identi ed a s a s eparate g roup i n a dmixture a nalyses ( see b elow, § 132 p . 246), with high repeatability, although with only a slight gain in model t compared to a single-popu-e Emperor synnome - §132 lation model (Evanno's ΔK over 10 replicates). However, discriminant analysis of principal components [START_REF] Jombart | adegenet: a R package for the multivariate analysis of genetic markers[END_REF] performed in adegenet, contrasting these outliers and the main sample group, showed that single-locus contributions were very low and evenly spread across the genome, thus excluding any strong directional selective phenomenon. After removal of all samples but one in each high-relatedness cluster, variation did not appear to be driven by outliers anymore. Overall, the variance explained by geographical structure is extremely low. After outlier removal, the rst PCA component of variance shows a weak general pattern of isolation-by-distance along the coast. Mertz colonies stand on the one end, Pointe Géologie in the centre, and Atka Bay and Halley Bay at the other end, yet Ross sea samples do not stand out, as would be expected in case of strong, systematic distance-driven drift. However this pattern remains extremely marginal: the variance explained by the rst component barely amounts to 1.4% of the total variance, and colonies can not be distinguished on the basis of PCA. §-132 Population splits and migration topology. We used TreeMix [START_REF] Pickrell | Inference of population splits and mixtures from genome-wide allele frequency data[END_REF] to infer the topology of population splits and migrations from allele frequency variation among colonies. We produced RAD-sequencing data for the Emperor's sister species, the King penguin Aptenodytes patagonicus, and processed it with the same protocol as for our Emperor penguin samples. We genotyped 15 King penguins from the Baie du Marin colony, on Crozet archipelago, that were used as an outgroup for the analysis. We restricted our dataset to ca. 15,000 highly condent, unlinked SNPs shared between the two species. 3-population and 4-population tests did not allow us to reject a tree-like topology in our data. Analysis was performed rst using the King penguin outgroup, and bootstrapping over blocks of 500 SNPs, in order to assess the topology of relationships between our 6 Emperor penguin colonies. We used this topology to re-run TreeMix without the King penguin samples, but xing the root according to our rst results, in order to increase the resolution of the analysis (Fig. 38).

e Emperor synnome - §134

2013).

For both algorithms, only consensus polymorphic loci were retained. We tested a number of components ranging from 1 to 10, with 3 independent replicates. For fastStructure, we used a simple prior for K values ranging from 1 to 10, and a logistic prior for values from 1 to 3. Most likely number of components was chosen using fastStructure's chooseK.py script for fastStructure models, and Evanno's ΔK method (Evanno et al. 2005) for ngsAdmix models. For both algorithms, the preferred model consistently had K=1, i.e. no inferred clusters in the data. §-134 Coalescent-based analysis: Migrate-n. All methods used above provide a robust framework for identifying groups even in weakly structured populations. However, all are, to some extent, e Emperor synnome - §134 DDU, and NEU) were separated from the Weddell Sea (HAL) and the Ross Sea (WSH). Models were ordered by log Bayes factor de ned by Kass and Raftery (Kass & Raftery 1995) as lnBF = 2[ln(mL(model1))-ln(mL(model2))], with mL(model1) and mL(model2) being the marginal likelihoods for the two compared models, as calculated by thermodynamic integration.

Best support goes to the full migration matrix model (model 2). Estimated migration rates M and population sizes Θ are highly homogeneous (mean mutation-scaled migration rate m / μ = 2,358 ±130, mutation-scaled e ective sizes Θ WSH =0.0017, Θ MZE =0.0018, Θ MZW =0.0019,

Θ DDU =0.0019, Θ NEU =0.0018, Θ HAL =0.0019).
Under this model, each migration rate can also be expressed as a proportion of the receiving population's e ective size as m = 4.M.μ. Hence, the relative demographic importance of immigration for any given colony can also be expressed as Σ i =n(4.Mi .μ), with n being the total number of populations identi ed a s g ene s ources f or t he f ocal p opulation. I n o rder t o c onvert mutationscaled estimates of M and Θ, we therefore need an estimate of μ (the mean number of substitutions.site -1 .Myr -1 ) for the set of loci used in the analysis. Since the number of sites in each locus is mainly due to the stochastic nature of the mutational process, and follows a Poisson distribution of parameter λ equal to the mean SNP density per locus (see above), we can consider that a single true mutation rate (μ) applies to the whole RADome. However, since our analysis is restricted to loci containing 5 to 6 SNPs, our estimates of Θ and M are not directly scaled by μ, but rather by posterior probabilities of μ conditional on the number of SNPs in each locus class. We used the class-speci c mutation rate posterior probabilities as calculated for the King penguin [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]. As a conservative estimate, we used a range of rates tting the 3-SNPs to 6-SNPs class loci. Using a generation time of 16 years [START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF], these are μ 3snps = 1.14e -6 and μ 6snps = 2.16e -6 . Since these were estimated from a subset of 16 haplotypes, and we included 82 haplotypes in our analysis, we considered that variability was likely to be underestimated by Truc-e Emperor synnome - §135 chi and colleagues [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]) compared to our sample. us, w e d id n ot consider higher estimates than those made for 6-SNPs loci. §-135 fastsimcoal2 analysis. Joint derived-allele frequency spectra were generated in ANGSD 0.900 [START_REF] Kim | Estimation of allele frequency and association mapping using nextgeneration sequencing data[END_REF]Nielsen et al. 2012) for a subset of four colonies that encompass the whole continent (MZE, DDU, NEU and HAL). In order to polarize these spectra, we reconstructed the most likely ancestral base for all positions in the RADome. We selected 12 highquality Emperor penguin samples covering the whole species' range, as well as 12 high quality King penguin samples processed according to the same protocol (see e King synnome, p. 163).

States at all positions were determined using GATK's Haplotype Caller pipeline (DePristo et al.

2011

). We used BEDtools [START_REF] Quinlan | BED-Tools: a flexible suite of utilities for comparing genomic features[END_REF] and VCFtools' vcf-consensus script [START_REF] Danecek | The variant call format and VCFtools[END_REF] to update the published Emperor penguin genome and establish a reference RADome for both the King penguin, and the Emperor penguin, using only high-quality polymorphisms (phred-scale genotype quality ≥ 80), and including variable sites as IUPAC ambiguity codes. We aligned this RADome to the Adélie penguin genome (Pygoscelis adeliae, see Zhang et al. 2011b) using Bowtie2 [START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF], and extracted the corresponding regions. For each RAD locus, a maximum-likelihood unrooted tree was built in PhyML [START_REF] Grémillet | Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms[END_REF], and maximum-likelihood ancestral sequence for crown-Aptenodytes was reconstructed using PAML [START_REF] Yang | PAML 4: phylogenetic analysis by maximum likelihood[END_REF] and Lazarus (https://project-lazarus.googlecode.com/), using PhyML tree topology as a prior. ere ancestral states were then used to determine the ancestral and derived alleles in the Emperor penguin.

Reconstruction of population sizes and migration events was performed through composite-likelihood maximisation, by simulating joint-spectra under a continuous-time Markovian coalescent model in fastsimcoal2. 5.11 (Exco er & Foll 2011;Exco er et al. 2013). For each run, we performed a maximum of 80 ECM optimisation cycles over the 12 retained parameters (population sizes and asymmetric migration rates between the four analysed colonies), each parameter optimi-e Emperor synnome - §135 sation step requiring the generation of 100,000 simulated joint-spectra. We generated 50 nonparametric bootstrap replicates for each spectrum. For each bootstrap dataset, and for the original dataset, we ran 50 independent replicates, and retained the one with the highest log-likelihood.

We assumed a mutation rate of 2.6e-7 subsitutions.site-1.generation-1 as calculated for the King penguin [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF], and a generation time of 16 years [START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF]). Computation was performed on the high-performance Abel cluster at the University of Oslo, and required a total of ~30,000 CPU-hours.

We chose to restrict our analysis to a stepwise migration model for two main reasons. First, computational load increases rapidly with the number of estimated parameters, and higher complexity models could not be run with the necessary amount of replication. Secondly, since our model is not supposed to represent precisely the present-day state of the colonies, but rather parameters averaged over a long period of time, we do not expect the intensity of the migration ow to be much a ected by the structure of the connectivity. However, in one particular case, the estimate seems to deviate from the general pattern of migration in our analysis, and may be indicative of a very recent event, still directly linked to the present-day states of the colonies. Indeed, the gene ow b etween D DU a nd M ZE i s e stimated t o b e m uch h igher, a nd m uch m ore asymmetrical than the rest of the rates (Fig. 31, Table 7), with almost all of the gene ow being directed from DDU to MZE. Although this may be a circumstantial e ect of our sampling, it is noteworthy that the DDU colony has indeed lost nearly half its breeding population in the very recent past, with no de nitive explanation (Barbraud & Weimerskirch 2001a). Although it has been suggested that this sudden decrease may have been caused by local environmental e ects (Barbraud & Weimerskirch 2001a;[START_REF] Barbraud | Comparison of emperor penguin declines between Pointe Géologie and Haswell Island over the past 50 years[END_REF], the disturbance caused by the Dumont D'Urville research station, including its research activities on the colony, and in particular the e ect of ipper-banding of the Emperor penguins (Le Maho et al. 2011) have also been mentioned as potential causes for high emigration from the DDU colony. It is thus possible that the high gene ow inferred from DDU to MZE indicates that this decrease was at least partly caused by emigration to the nearby Mertz glacier area in response to human-induced disturbance, rather than as by environment-induced mortality. e Emperor synnome - §136 §-136 Coalescent-based analysis: BEAST2. An independent estimate of population size changes through time was performed in BEAST2 [START_REF] Bouckaert | BEAST 2: a software platform for Bayesian evolutionary analysis[END_REF]. We proceeded as above for locus selection and haplotype downsampling. In order to remain agnostic as to population structure, we performed analysis on each colony separately, as well as on the whole dataset, as the lack e Emperor synnome - §137 of strong population structure, evidenced by all other analyses, allowed us to sample haplotypes from the whole species without violating the model's assumptions. We used an extended bayesian skyline plot model [START_REF] He | Habitat saturation, benefits of philopatry, relatedness, and the extent of cooperative breeding in a cichlid[END_REF]) in order to co-estimate present-day Θ and possible past uctuations. We followed the protocol of Trucchi and colleagues [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF], but reduced the parameter space by de ning only one site-model per locus class (5 or 6 SNPs), using HKY models with empirical base frequencies, and allowing for rate variation in 4 discrete gamma categories. Kappa was linked across site models, according to our expectation for neutral variation, in order to alleviate computational load (A. Drummond, personal communication). All chains were run in duplicate to check for convergence and for a su cient length to gather ESS > 200 for all parameters, which necessitated ca. 1,000,000,000 steps on all models. Reconstruction for WSH colony is much less precise due to the very small number of haplotypes (N=4) sampled per locus. However, present-day population size estimate converges with reconstructions based on the other colonies. It is also noteworthy that our results point to a gradual increase in population size over the last ~100,000 years (with some uncertainty as to the precise dating of the beginning of the expansion, due to the di culty of precisely calibrating mutation rates in a multilocus approach), which con rms t he ndings of Li an d co lleagues (L i et al . 20 14) ba sed on a single-genome pairwise sequentially Markovian coalescent approach. §-137 mtDNA analysis. A recent study by Younger and colleagues [START_REF] Younger | Too much of a good thing: sea ice extent may have forced emperor penguins into refugia during the last glacial maximum[END_REF] focused on Emperor penguin mitochondrial DNA population structure. eir c onclusion w as that colonies from the Ross sea area are signi cantly isolated from the rest of the continent, and had a di erent demographic history. Our genome-wide SNP data does not support this view: however, our low sampling size in the Ross Sea region does not permit any de nitive conclusion. In order to assess how far this result could be reproduced from mtDNA alone, we sequenced a 792 bp fragment of mitochondrial cytochrome-B gene and a 414 bp-long HVR fragment for our RAD samples, following the same protocol as Younger et al. Primers were the following: Cyt-b forward e Emperor synnome - §137 primer 5'-GCCCCAAACCTCCGAAAATCCCA-3' and reverse primer 5'-TGTGGAG-GAGGGGGATTAGG-3'; HVR forward primer 5'-GGAACCTCCCAAAGAGTACCA-3' and reverse primer 5'-CCAACCAGATGTATCGGTGA-3'. PCR conditions were thus: for Cyt-b, 5' denaturation at 94°C, 35 cycles of ampli cation ( 30" d enaturation a t 9 4°C, 3 0" a nnealing at 57°C, and 1' elongation at 72°C), and 5' nal elongation at 72°C; for HVR, 5' denaturation at 94°C, 35 cycles of ampli cation ( 30" d enaturation a t 9 4°C, 3 0" a nnealing a t 5 9.5°C, a nd 1' elongation at 72°C), and 5' nal elongation at 72°C. PCR product was then puri ed using Illus-tra™ ExoStar™, and Sanger sequencing was performed at the ABI lab of the University of Oslo.

Quality assessment, trimming, and manual checking were performed in Geneious® v6.1.2. Unfortunately, our HVR sequences were of consistently low quality and thus could not be used reliably in analysis. Many double peaks were observed, possibly explained by HVR duplication (as previously observed in alassarche albatrosses, see [START_REF] Abbott | An unusual source of apparent mitochondrial heteroplasmy: duplicate mitochondrial control regions in Thalassarche albatrosses[END_REF], together with possible instances of polynucleotide repeat number variation. e uncertainties regarding base calls and site phasing in the case of double peaks made it impossible to extract any reliable data from these sequences.

We also re-analysed the data from Younger and colleagues (GenBank accession numbers KP644787-KP645015 and KP640645-KP640873). Considering no reliable and controlled model can account for diploid sites in mitochondrial DNA, we masked all ambiguous sites from analysis. We also considered that the higher reported similarity between Ross Sea mtDNA samples may be biased by the fact that, after masking ambiguous sites, two haplotypes are overly represented compared to the average haplotype diversity. Indeed, when concatenating HVR and Cytochrome B sequences for each individual, and putting aside the two over-represented sequences, each mitochondrial haplotype is present in an average 1.4 ±0.9 copies per colony. e two overrepresented haplotypes, on the other hand, are found in 14 samples from Cape Washington, and 15 from Cape Crozier. Not surprisingly, however, these samples are the only "shed feathers [...] collected from the Ross Sea between 2010 and 2012 [...] at least 10 m apart to minimize sam-e Emperor synnome - §137

pling the same bird" as opposed to blood samples for the other locations. It indeed may seem rather likely that the precaution was not su cient, and that the same birds were sampled multiple times, as it is common for a moulting penguin to shed feathers on a great surface. Once miscalled bases are masked, and potentially pseudo-replicate samples are removed, the resulting pattern does not exhibit the clear bipartite organisation found by Younger and colleagues, but rather a gradual di erentiation, in keeping with the general pattern we observe in genomic SNP data (Fig. 39B). As a side note, the over-representation of pseudo-replicate sequences in the Ross sea region, by violating the random-sampling assumption of coalescent reconstructions, may also account for the di erences in past demographic trends inferences found by the authors (Younger et al. gure 2).

Cytochrome-B sequences, on the other hand, showed a standard level of variation in our dataset, as assessed in DnaSP (Rozas & Rozas 1999 -10 haplotypes, gene diversity=0.634, nucleotide di-versity=0.003, Fu's Fs=-0.948, Tajima's D=-0.216, non-signi cant). Haplotype network was built based on Fitch distances between sequences, using Fitchi (Matschiner 2015), using a maximumlikelihood bifurcating tree built in RaxML (Stamatakis 2014). In keeping with the results of Younger and colleagues, Cytochrome-B sequences do not re ect geographical distribution of the samples in any way (Fig. 39A).

e p articular c ase o f t he R oss S ea a rea m ay r equire f urther a nalysis, a s m itochondrial HVR alone seems too unreliably sequenced to provide positive information. e a vailable d ata only support the extension of a low-level isolation-by-distance model to the whole continent, in keeping with our observations on genome-wide polymorphism. However, the intensity of the gene ow between the Ross Sea region and the rest of the continent has little impact on our ability to model immigration rates at the colony level. Indeed, the origin of the immigration ux is less relevant than its intensity if we are to accurately model population dynamics from colony-level data. Empirical evidence of heterogeneous dispersal - §138

local (e.g. accidental changes in the ice landscape following a particular event) and a ect only one or a few colonies at a time.

Understanding the structure of migration in time and space at such scales, however, is well beyond the power of genetic approaches. e particular synnome structure of the Aptenodytes penguins (see §3 p. 28) implies that, although colonies as geographical units may persevere for long periods (see §2 p. 25), particular lineages do not reside in the same colony for more than a few generations. us, the spatial coordinates of a lineage are to some extent anecdotal: each may be traced back from its current position to the previous one, but thence no further. us, events such as the merging of two nearby and barely isolated colonies, temporary interruption of gene ow, or the disappearance of a group dispersing into the neighbouring colonies are beyond the resolution of our analysis methods. Understanding the dynamics of dispersal thus requires direct eld observations, grounded in an ecological, rather than an evolutionary framework (see §2 p.

Abstract

Evaluating the demographic trends of marine top predators is critical to understanding the processes involved in the ongoing rapid changes in Antarctic ecosystems. However, the remoteness and logistical complexity of operating in Antarctica, especially during winter, make such an assessment di cult. Satellite imaging is increasingly recognised as a valuable method for remote animal population monitoring, yet its accuracy and reliability are still to be fully evaluated. We report here the rst ground visit of an emperor penguin colony rst discovered by satellite, but also the discovery of a second one not indicated by satellite survey at that time. Several successive remote surveys in this coastal region of East Antarctica, both before and after sudden local

Introduction

e emperor penguin (Aptenodytes forsteri), a ightless seabird endemic to Antarctica, was probably rst seen on James Cook's second voyage, in 1773-1774 Yet the rst breeding colony was only discovered in 1902 during the rst of Scott's Discovery Expeditions (Wienecke 2009b). For the colonies that could be reached from research stations, population trends usually involved ground counts [START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF][START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF]). e census of those not accessible by ground surveys was usually obtained using aerial photography [START_REF] Wilson | Adélie penguin population change in the pacific sector of Antarctica: relation to sea-ice extent and the Antarctic Circumpolar Current[END_REF]). e rst estimation from space was carried out in 2005 and 2006 for the western Ross Sea area (Barber-Meyer et al.

2007

), while the rst global survey was performed in 2009 [START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]Fretwell et al. 2012). Today, 52 colonies of emperor penguins have been identi ed ( Fretwell and Trathan, unpublished data), all of them being distributed along the coastline of Antarctica between 64°S

Empirical evidence of heterogeneous dispersal - §139 e ground survey of the Mertz Glacier area was initiated within this context and became logistically possible in November 2012. e e xistence o f a n e mperor p enguin c olony n ear t he Mertz Glacier had been suspected for almost a hundred years, and Mertz himself perished in an attempt to locate the colony. More recently, during August 1999, sightings of thousands of emperor penguins traversing the northern part of the Mertz Glacier tongue [START_REF] Ancel | Exploration biologique de la polynie de Mertz[END_REF]) con rmed that a colony must be located close to the glacier.

By using Landsat and QuickBird2 Very High Resolution Landsat imagery, con rmation of the colony site at 66°54'S, 146°37'E at the tip of the Mertz glacier tongue was obtained in November 2009 [START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]). e high resolution of the satellite imagery allowed counting 4,781 pairs at the time of image acquisition. However, before a eld survey could take place, the Mertz Glacier tongue calved in mid-February 2010 presumably inducing the colony to relocate or merge with another colony. New satellite images obtained since then suggested that the birds might attempt to breed on di erent sites depending on the year. Still, since the exact colony location and colony size appeared to change between successive observations, ground-based assessments were di cult and needed to be conducted over several seasons.

Our objective was to validate the presence of one or more emperor penguin colonies near the Mertz Glacier, to locate them, to estimate their population size (breeding adults) and breeding success (number of chicks), and to obtain other biological information. is was repeated by two di erent observers. e total number of individuals was estimated using two di erent methods to provide a probable range for each colony census: 1) under the hypothesis that inter-annual climatic e ects are largely dominant over ne-s cale l ocal e ects, we made the assumption that breeding success is approximately similar in nearby colonies for any given year. We therefore estimated the number of breeding pairs from the early November chickcounts using the population parameters deter-mined from the nearby Pointe Géologie colony (66°40'S, 140°01'E) over the whole breeding season; 2) following Budd [START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF], who proposed that, based on a census at several di erent colonies, adult counts between October and November equate to approximately one-third (26% to 40%) of the total adult population, independently of the actual breeding success. For comparison, the Pointe Géologie colony was counted at di erent times, from photographs, using the same methodology. e total number of incubating males in mid-June was taken as a proxy for the total number of breeding pairs: and chicks were counted in early November, to allow comparison with the census of the Mertz colonies.

Materials and Methods

Empirical evidence of heterogeneous dispersal - §141

To avoid potential disturbance, no low altitude ights w ere m ade o ver o r n ear t o t he Mertz colonies, and a brief landing was made ca. 1 km from the breeding sites to minimize further disturbance. Each colony was visited by 4 or 5 people on foot, and surveyed for approximately 3 hours each. No direct ground counts of chicks or adults were made during the visits due to the lack of any satisfactory vantage point. Observations focused on assessing the habitat, chick abundance and health (though no direct measures were taken in order to avoid unnecessary disturbance). Observations were also made to search for ipper-banded i ndividuals, p otentially originating from the Pointe Géologie colony, located at only ca. 250 km to the west.

To nd colony locations, satellite observations were made using freely available reduced resolution ''quicklooks'' from the com-mercial satellite provider DigitalGlobe (https://browse.digitalglobe.com/image nder/). ese images have an approximate on-the-ground resolution of ,10 m per pixel. Colonies were located visually from the images by detecting the brown signature of the bird's faecal stains on the ice. Once located, raw Very High Resolution imagery with a resolution of 0.5 m per pixel was acquired. is resolution allows areas of penguins to be di erentiated from guano using a multivariate supervised classi cation method [START_REF] Barber-Meyer | Estimating the relative abundance of emperor penguins at inaccessible colonies using satellite imagery[END_REF][START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF].

Results §-141 Habitat and Chick Census. First or eastern colony. e existence of a breeding colony of emperor penguins near the Mertz Glacier was con rmed and located at 67°19'S, 145°52'E on November 1st, 2012 (Fig. 40). e e astern c olony w as b reeding o n f ast i ce a midst g rounded icebergs, 250 km east of Dumont d'Urville research station. e colony was composed of numerous sub-groups, which appeared to have moved short distances, as con rmed by the clustered guano deposits. e topography of the breeding site was very uneven with numerous icebergs embedded in the sea ice, o ering protection from the prevailing high winds. Layers of droppings were found between layers of snow on top of the icebergs (Fig. 41), providing evidence of numerous snowfall episodes. Many icebergs, ca. 10-meter high, were also covered with guano, thus demonstrating that breeding birds had spent some time on the top of these icebergs (Fig. 41). is colony had three former incubation areas, identi ed by stained sea ice, numerous egg shells and carcasses of newly hatched chicks. During our 3-hour visit, no birds other than penguins, and no sign of predation (e.g. predated or scavenged carcasses) were found. Two Weddell seals Leptony-chotes weddelli were sighted along tidal cracks in the vicinity. Our survey yielded a count of 1,750 downy chicks, and 1,520 adults. Approximately 300 dead chicks, ranging in age from a few days (frozen) to 3-month-old (both frozen and fresh) were found. Most of them were found at the incubation sites, and on the top of small icebergs. hour survey, some of them partly buried. e ice surface was of heavily compacted and abraded snow, and it is therefore most likely that more dead chicks were buried beneath the snow surface.

Guano traces were restricted to the area currently occupied by the birds, and its direct surroundings. Due to these di cult conditions, we were unable to precisely locate the incubation area. As for the rst colony, we did not observe any individuals bearing ipper-bands. 8).

Empirical evidence of heterogeneous dispersal - §142

Moreover, a total of 1,864 individual adults were counted at the Pointe Géologie colony on October 31st, 2012, which implies a total count of ca. 2,330 to 3,580 breeding pairs according to Budd's method. is is consistent with our average 2012 winter count of ca. 3,200 breeding pairs, thus allowing us to apply this method to the Mertz Glacier census.

Adult counts from photographs were 1,520 for the eastern colony and 2,300 for the western colony, respectively (Table 8). When applying Budd's method [START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF], the estimated population size was 2,300 breeding pairs for the rst colony (ranging from 1,900 to 2,900) and 3,500

for the second (from 2,900 to 4,400), hence a total of ca. 5,800 breeding pairs for the Mertz emperor penguin population. Discussion §-143 Strength and Shortcomings of the Remote-sensing Technique. Our study con rmed sightings made 15 years ago of thousands of emperor penguins going back and forth at the northern part of the tongue of the Mertz Glacier, suggesting the presence of a colony in this area [START_REF] Ancel | Exploration biologique de la polynie de Mertz[END_REF]. Moreover, our ground visit to the two Mertz colonies demonstrates the reliability of the remote-sensing method developed by researchers [START_REF] Barber-Meyer | Estimating the relative abundance of emperor penguins at inaccessible colonies using satellite imagery[END_REF][START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]. Although the method successfully identi ed s everal p reviously k nown e mperor penguin colonies, this is the rst time that the existence of three colonies identi ed solely from satellite images have been accurately corroborated in the eld: two at the new edge of the Mertz Glacier Empirical evidence of heterogeneous dispersal - §143 in late 2012 (present study) and one on Princess Ragnhild coast (69°54'S, 27°09'E) in early 2013

(visit http://www. antarcticstation.org/for more information).

However, the locations from satellite surveys of the two colonies at the edge of the Mertz Glacier were not determined during the same year, therefore suggesting the existence of only one colony in this area. is fact suggests that further re nement of the present-day remote-sensing method may be advantageous. Only the western colony was located in December 2011, while the eastern one remained invisible. In contrast, only the eastern colony was located in 2012. Failure to identify both colonies at the same time may be attributed to di erent factors. To locate colonies, the remote-sensing method relies on guano staining on the sea-ice surface, and heavy snow cover may obliterate the signal. e western colony, which could not to be detected just a few weeks before our visit, was largely covered by snow during the last weeks of winter. Hence, almost no guano stains, abandoned eggs, or dead chicks were found. e l ocation o f t his c olony a long a high glacier edge may also have kept it hidden from the satellite, if the image was taken at too shallow an angle. In addition, a previous study (Gilbert et al. 2008) has shown that the density of emperor penguin colony can vary considerably, i.e. from 2 to 9 birds per square meter, and this within only a few hours. is means that census estimates based on colony area may be inaccurate in some circumstances due to variability in density and to inter-annual variability. is suggests that this colony was not the only one to be missed by the 2009 and 2012 satellite surveys, and that more colonies remain to be discovered in other parts of the continent. Although not always possible each season due to cloud cover and the availability of the satellite, our results suggest that satellite surveys should be conducted repeatedly and combined with eld surveys to ensure that colonies are not missed. Our paper shows that to allow con dence i n satellite observations, a multi-temporal/multi-year approach has to be used to ensure that breeding sites ed by satellite survey (Fretwell et al. 2014).

Due to the discrepancy between the coordinates given by satellites (66°54'S, 146°37'E, see [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF] and our land based ones (67°19'S, 145°52'E and 67°14'S, 145°30'E), we may assume that the colony initially detected from space in 2009 has been split into two colonies. Following the calving of the Mertz Glacier, the birds may indeed have attempted to settle in new favourable surroundings. e two new colonies are separated by 20 km along the new northern edge of the Glacier (Fig. 42). Taking into account the proximity of the two breeding sites, these colonies might however reunite in the future, especially if one or other of the sites eventually proves to be more reliable for breeding and/or foraging.

All satellite surveys conducted up to now, before and after the Mertz calving, had concluded the presence of a single emperor penguin colony, even if grouped into three close sub-colonies in 2009, in the Mertz Glacier area. It may also be that two nearby colonies already existed at the current location(s) before the 2010 calving. is is supported by the fact that the best population estimate of the Mertz Glacier colony located by [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]) was estimated to be 4,781 adults (or 5,976 pairs if we considered that 80% of the total breeding population was present during the satellite survey, see [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF], a gure which is relatively close to our assessment of 5,100 breeding pairs for the western colony alone (Table 8) but lower than the sum of the two colonies we found (7,400 breeding pairs; Table 8). In such a scenario, the second colony we discovered might be the one found before at the nearby Ninnis Glacier (Wienecke 2009a), which would have been missed in all further surveys (see [START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]. It is important to note that the two colonies appeared to di er slightly in their breeding stage. e chick-to-adult ratio was higher in the western colony than in the eastern one (1.73 vs. 1.15,respectively: Table 8). is result may be Empirical evidence of heterogeneous dispersal - §144 interpreted as a di erence in breeding success, with higher chick mortality in the eastern colony than in the western one. is hypothesis is supported by the much higher number of dead chicks in the eastern colony. Moreover, while the two colonies were visited on two consecutive days, the western colony appeared more advanced in its breeding cycle, as indicated by the generally better health and bigger size of the chicks than in the eastern colony. Finally, the western colony was much closer to the open water than the eastern one at the time of our visit, and a recent study [START_REF] Massom | Fast ice distribution in Adélie Land, East Antarctica: interannual variability and implications for emperor penguins Aptenodytes forsteri[END_REF] has shown that the distance between an emperor penguin colony and the open water may tend to correlate negatively with breeding success.

Budd [START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF] proposed that ''each rookery can be regarded as a compromise, and not always a very successful one, between the emperor's sometimes con icting requirements of safety from sea-ice breakouts, shelter, and access to food''. Finding a suitable trade-o may well be an

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §147 o ers several convergent traits with the spheniscids. Like them, its wings are derived and adapted to underwater swimming [START_REF] Luna-Jorquera | Ecofisiología de animales endotermos en un desierto cálido y un mar frío: el caso de las aves marinas de la corriente de Humboldt. Fisiología ecológica y evolutiva[END_REF] -a trait it shares with e.g. Alcids) -albeit greatly reduced, ight function is however not totally lost. is diving bird feeds mostly on sh and krill [START_REF] Jahncke | The Diet of the Peruvian Diving-Petrel at la Vieja and San Gallan, Perú (La Dieta de Pelecanoides garnotii a la Vieja y San Gallan, Perú)[END_REF]) and may reach great depths relative to its small size (routinely 30m but depths of more than 80m have been recorded, see [START_REF] Zavalaga | Velvet: algorithms for de novo short read assembly using de Bruijn graphs[END_REF]. Finally, localised upwellings seem to be a major determinant of foraging behaviour in this central-place forager (Zavalaga et al. 2010, Fernandez-Zamora in prep. and Luna Jorquera, pers. com.). However, An overview of the general behaviour of procellariimorphes makes philopatry a likely plesiomorphic trait in these species (Friesen et al. 2007), that may be counteracted by speci c constraints, such as an excessively unstable environment: we therefore expect a « stable » species such as the Peruvian diving petrel to represent the other extreme along the seabird population structure spectrum.

contrary

Abstract

Seabirds are often used as reliable sentinels of oceanic environments: however, idiosyncratic life history traits strongly in uence both their response to ecosystem changes and our capacity to assess and interpret the extent of this response. In particular, the strength of philopatric behaviour and dispersal ability are important determinants of each species' evolutionary response. Here, we

show that a representative endemic species of the Humboldt Current System, the once abundant but now critically endangered Peruvian diving petrel (Pelecanoides garnotii), exhibits extreme philopatric behaviour. Mitochondrial and genome-wide SNP data provide evidence for strong isolation and low migration at very short distance. We suggest that behavioural and morphological adaptations to coastal insular habitat and underwater foraging are the main drivers of the exceptionally strong philopatry in this aulk-like species. Importantly, range fragmentation, combined with ongoing anthropogenic pressure, are expected to put the Diving-petrel under continued high risk, as the full demographic independence between colonies is likely to preclude local population rescue through migration. e independence of each island's breeding popula-Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §147

1.

Here, it is important to note that stability is only relative to a particular species' habitat (see §49 p. 127) within a geographic or oceanic system. For example, we stated earlier that the Emperor penguin's sea ice habitat appeared as extremely unstable (see Empirical evidence of heterogeneous dispersal, p. 259): yet within the precise same area, the habitat of e.g. bottomdwelling notothenioid sh may be totally stable. us, it makes more sense to think about the stability or instability of habitats than of environments. Here, therefore, the stability of the HCS is only considered relative to the standard seabird habitat, that is heavily dependant on local upwellings and guano insular breeding locations.

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §148 tion should be a key element in the conservation strategy for this key species of the Humboldt Current System coastal archipelagos.

Introduction §-148 Seabirds as terrestrial animals. Seabirds are an important link between marine and terrestrial ecosystems [START_REF] Blais | Arctic seabirds transport marinederived contaminants[END_REF] 2000;[START_REF] Becker | Centennial decline in the trophic level of an endangered seabird after fisheries decline[END_REF][START_REF] Kanno | Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks[END_REF][START_REF] Cury | Global seabird response to forage fish depletion-one-third for the birds[END_REF][START_REF] Wagner | Effects of fisheries on seabird community ecology[END_REF] or even cause massive direct seabird mortality [START_REF] Weimerskirch | Foraging white-chinned petrels (Procellaria aequinoctialis) at risk: from the tropics to Antarctica[END_REF][START_REF] Lewison | Understanding impacts of fisheries bycatch on marine megafauna[END_REF]). Yet the often overlooked disruption of terrestrial breeding habitats can also have a very Gallán and Isla La Vieja [START_REF] Jahncke | The status of the Peruvian diving-petrel population at its main breeding areas along the coast of Peru[END_REF][START_REF] V 698 | First record of the endangered Peruvian divingpetrel Pelecanoides garnotii breeding on Corcovado island, Peru[END_REF], and one in northern Chile, between 26°S to 29°S (Islas Pan de Azúcar, with ~220 pairs, Isla Choros, with ~9,516 pairs, Isla Grande, with ~200 pairs, and Isla Pájaros II, with ~120 pairs [START_REF] Mattern | A South American marine otter Lontra felina preys upon chicks of the Peruvian diving petrel Pelecanoides garnotii[END_REF][START_REF] Simeone | Breeding distribution and abundance of seabirds on islands off northcentral Chile[END_REF]Martinez Palma 2014). Historical population sizes are little known, but all available information points to a critical decline: while 100,000 pairs bred on Isla Chañaral, Chile, in 1938(Araya Modinger & Du y 1987), this population is now completely extinct [START_REF] Simeone | Breeding distribution and abundance of seabirds on islands off northcentral Chile[END_REF]; and the now recovering Peruvian population [START_REF] Jahncke | The status of the Peruvian diving-petrel population at its main breeding areas along the coast of Peru[END_REF] was estimated to a total of ca. 4,000 individuals in 19894,000 individuals in (Hays 1989)).

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §151 dividuals have been sighted on Lobos de Afuera islands [START_REF] Figueroa | Peruvian diving petrels Pelecanoides garnotii on Lobos de afuera islands, Peru[END_REF][START_REF] Figueroa | Registros actuales e históricos del Potoyunco peruano Pelecanoides Garnotii en el Perú[END_REF]. A rapid growth of observed census size was also documented in the past decade in Perú, on La Vieja Island (IUCN Red List) and in the Chilean islands [START_REF] Simeone | Breeding distribution and abundance of seabirds on islands off northcentral Chile[END_REF][START_REF] Fernandez | Estimación de la densidad reproductiva del yunco Pelecanoides garnotii en Isla Choro[END_REF]. However, the processes underlying this possible population recovery are poorly understood, and assessing true regrowth is made di cult by our very limited knowledge of the species' life history traits. It has been suggested that the diving-petrel's poor ight abilities greatly reduces its dispersal possibilities [START_REF] Luna-Jorquera | Ecofisiología de animales endotermos en un desierto cálido y un mar frío: el caso de las aves marinas de la corriente de Humboldt. Fisiología ecológica y evolutiva[END_REF] 2009). High dispersal, possibly promoted by foraging concentrations around localised upwellings [START_REF] Jeyasingham | Specialization to cold water upwellings may facilitate gene flow in seabirds: new evidence from the Peruvian pelican Pelecanus thagus (Pelecaniformes: Pelecanidae)[END_REF], has therefore been proposed as a central underlying mechanism. It has also been suggested that high dispersal is one of the keys to the higher resilience of these species to habitat change compared to other seabirds [START_REF] Jeyasingham | Specialization to cold water upwellings may facilitate gene flow in seabirds: new evidence from the Peruvian pelican Pelecanus thagus (Pelecaniformes: Pelecanidae)[END_REF]. us, i t i s unclear whether the Peruvian diving petrel follows the expectation of high dispersal and panmictic population set by other seabirds of the Humboldt Current System, or rather the highly philopatric and low-dispersal life history strategy that its behaviour suggests.

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §153 §-152 Aims of the study. If P. garnotii exhibits the same degree of panmixia as the other seabirds of the Humboldt Current System, demographic independence of colonies may not be assumed. In that case, observed local population growth may rather be interpreted as the result of a complex process (for example, several local extinction or exodus events, anecdotally causing census increases in a handful of refugial areas through immigration). is would imply that only range-wide census may be informative as to the status of the species. Alternatively, however, if the hypothesis of high philopatry and low dispersal is veri ed, demographic independence may be expected between breeding locations throughout the species' highly fragmented range. In that case, population trends observed at single locations may re ect true demographic responses, e.g. to local conservation measures (such as reduced guano extraction in several protected islands, and creation of marine protected areas), rather than range-wide and species-scale processes. In order to understand the current demographic trends for the Peruvian diving-petrel, and the extent of the threats to the remaining populations, it is therefore necessary to establish whether observed local growth is a result of (i) intrinsic local population growth, or (ii) immigration-fueled refugial concentration. Here, we use both mitochondrial and genome-wide genetic markers to distinguish between these two hypotheses, and assess the current degree of demographic independence between diving-petrel colonies throughout its Chilean range. Analysis of molecular variance also supports signi cant di erentiation between Isla Choros and Isla Pan de Azúcar. Based on SNP data, di erence between islands account for 2.51 % of the total variation, while di erence between colonies within each island accounts for -0.099 %. According to mtDNA data, these components account respectively for 14.28 % and 0.95 % of total variation. Di erentiation between islands is signi cant according to both datasets (ΦST mtD- NA = 0.152 and ΦST SNP = 0.0251, both p-values < 0.05), but di erentiation between colonies within islands is not (ΦSC mtDNA = 0.0111, p-value = 0.297; and ΦSC SNP = -0.00101, p-value = 0.795).

Results

Principal component analysis clearly supports a two-population structure (Fig. 44A). Although the rst component only accounts for 6.62% of total variance, it discriminates widely between individuals from Isla Choros and Isla Pan de Azúcar -while the second and next components only outline inter-individual variance. e idea of a low-level but highly consistent separation is also supported by a Hamming-distance-based neighbour net (Fig. 44B): terminal edges are much longer than structural inner edges, yet sorting is complete between the two islands (a structure supported, although to a lesser extent, by a mtDNA haplotype network, see Fig. 44D). Finally, clustering analyses, whether genotype likelihood based (Fig. 44C) or K-means based strongly supports a two-population model, both over panmixia, and over higher complexity models. [1,011; 17,702]), but population is reduced strongly in Choros (before change : 95,542 [88,815; 101,124], after change:

23,832 [10,068; 36,104]).

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §156

Discussion §-156 A highly structured population. e Chilean populations of Pelecanoides garnotii stand out as remarkably isolated, despite short geographical distances and a tumultuous recent history. All available evidence supports the idea of a distinct population per island, with limited gene ow.

Genome-wide polymorphism based analyses concur on a complete sorting of individuals between Isla Choros and Isla Pan de Azúcar, albeit with limited di erentiation: the variance accounted for by the rst component of PCA (6.62%), although highly consistent with the geographical structure, is only slightly above the neutral expectation. is is fully in line with the relative lengths of internal and terminal edges, and the location sorting, in the neighbour-net (Fig. 44B). ere is a good agreement between the genome-wide nuclear and the mitochondrial signal: some aspects, however, deserve consideration. e c learer s orting o f i ndividuals o bserved f rom n uclear data compared to mtDNA (see e.g. Fig. 44B vs Fig. 44D) is most likely a consequence of the much higher resolution of the large neutral nuclear marker dataset, compared to the relatively short, and non-recombining, functional cytochrome-b gene sequence. For F-statistics based analysis (pairwise Fst, or analysis of molecular variance), on the other hand, mtDNA gives a stronger signal than nuclear markers. Although higher dispersal in males than in females has often been proposed as an explanation in similar cases, this interpretation of discrepancies between nuclear and mitochondrial signals has been questioned [START_REF] Zhao | L'ampleur et l'organisation de cette diversité re ètent certains caractères essentiels de l'espèce : la diversité génétique elle-même a été expliquée par plusieurs paramètres, tels que la complexité de l'organisme[END_REF], and the di erence in coalescence rate of haploid and diploid markers has been convincingly proposed instead. In our case, the much larger sample size used for mtDNA analysis (83 individuals) compared to nuclear analysis (21 individuals) may also account for a large part of this apparent di erence. e resolution and the strength of the signal, however, should not be confused: the weaker, but clearer nuclear signal may characterise the true isolation processes more accurately than the larger and more contrasted mitochondrial dataset (Hoban et al. 2013b).

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §157 §-157 Population size and gene ow. Reconstructed population sizes and gene ow between sampled locations are globally consistent between haplotype-based and spectrum-based approaches, although with a considerably higher precision for the spectrum-based inferences. Migrate-n and fastsimcoal2 models allow us to reject panmixia as well as full isolation between colonies, and support partial isolation, with ongoing gene ow. However, the models di er on two important points. First, Migrate-n suggests equal population sizes, with Ne ≈ 60,000 for each population, while fastsimcoal2 infers unequal (yet stable) population sizes, with N CHR ≈ 90,000 and and N AZR ≈ 1,000. Despite these contrasting results, however, both models propose an e ective population size that is considerably higher than the present-day census: the cumulated population of ~90,000 to ~120,000 e ective breeders is in stark opposition with the currently observed popula- Second, Migrate-n infers symmetric, and relatively high gene ow (~3% per generation), whereas fastsimcoal2 supports much lower, asymmetric gene ow: n orthward m igration ( from Isla Choros to Isla Pan de Azúcar) is stable and very low (~0.001% per generation), while southward migration is higher, and increases from ~0.03% to ~0.4% in the recent past. Although the historical records are too scarce to draw any de nitive conclusion, it is noteworthy that on Isla Pan de Azúcar, a thousands of decaying, unoccupied burrows can still be observed (Fernandez in prep.)a possible sign of one or several recent massive emigration events. us, generally, the haplotypebased reconstruction suggests a "homogenised" system, with population sizes averaged by higher gene ow: in contrast, the spectrum-based approach o ers a more nuanced reconstruction, that better re ects the observed state of the populations. e higher resolution of the spectrum-based approach, however, is expected. First, it is able to use the information from the full RADome,

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §158

and not only from a random subset of loci. Second, haplotype-based methods chie y capture events occurring at a substitution (i.e. millennial) scale, while spectrum-based approaches also capture drift in allele frequencies that may occur in a few generations, thus making them more suitable for the recovery of recent population history. erefore, we may more appropriately consider Migrate-n reconstructions as a general framework, that does not contradict the more precise reconstruction given by our fastsimcoal2 model. §-158 Evolution of philopatry. e h igh d i erentiation b etween t wo a ssessed s ites a t s uch close range is remarkable for seabirds in general (Friesen et al. 2007), but all the more so for the guanoseabirds ("aves guaneras") of the Humboldt Current System. e observed structure is in strong contrast with the HCS-wide panmixia of the Peruvian booby (Sula variegata, see Taylor et al.

2010

) or the Peruvian pelican (Pelecanus thagus, see [START_REF] Jeyasingham | Specialization to cold water upwellings may facilitate gene flow in seabirds: new evidence from the Peruvian pelican Pelecanus thagus (Pelecaniformes: Pelecanidae)[END_REF]. Weak genetic structure has indeed also been detected in the Humboldt penguin (Spheniscus humboldti), but only at a range of ~2,000 km [START_REF] Schlosser | Evidence for gene flow differs from observed dispersal patterns in the Humboldt penguin, Spheniscus humboldti[END_REF], as opposed to the ~350 km separating the two assessed diving-petrel breeding islands. Since both the Peruvian pelican and the Humboldt penguin share with the Peruvian diving-petrel a recent history of dramatic population decline, the contrast in population structure can hardly be explained by their recent collapse. Morphological and metabolic aptitude to ight may, on the other hand, in uence dispersal patterns -indeed, it has been proposed that the diving-petrel's adaptation to underwater wing-propelled swimming, and the necessary trade-o of poor ying abilities [START_REF] Luna-Jorquera | Ecofisiología de animales endotermos en un desierto cálido y un mar frío: el caso de las aves marinas de la corriente de Humboldt. Fisiología ecológica y evolutiva[END_REF], reduced the species' activity radius. Although no data is available for the Peruvian diving petrel, its two sister species, the Common diving petrel (P. urinatrix) and the South Georgian diving petrel (P. georgicus), have been shown to forage more locally than similar-sized, sympatric "gliding" Procellariiformes, both during the breeding season [START_REF] Navarro | Ecological segregation in space, time and trophic niche of sympatric planktivorous petrels[END_REF], and in the inter-breeding period [START_REF] Navarro | Spatial distribution and ecological niches of non-breeding planktivorous petrels[END_REF]. Further studies, however, will be necessary to decide how far the lack of dispersal ability is involved in the Peruvian diving petrel's genetic fragmentation. An alternative explanation may be purely behavioural: while philopatric behaviour is the norm in Procellariiformes (see e. solute necessity for the survival of this once extremely abundant, and now critically endangered seabird species. Indeed, despite the positive trend observed in the last decade on Isla Choros, important threats remain both for the nesting sites and for the marine habitats of diving petrels.

One of the four islands where petrels nests (Isla Pájaros II, currently with no legal protection) is still under a valid permit of guano extraction (Ministerio de Minería 1988), and ongoing extraction of guano was con rmed on the island as recently as 2003 [START_REF] Simeone | Breeding distribution and abundance of seabirds on islands off northcentral Chile[END_REF]. Additionally, future plans for mining projects include the building of a large harbour in the close vicinity of the two central breeding locations of the species, Isla Choros and Isla Grande de Atacama (Servicio de Evaluación Ambiental 2015). is could signi cantly a ect both the coastal foraging areas of the species (through increased marine transit) and nesting areas (through direct e ects of increased human intrusion). However, our results clearly show that the conservation of each single breeding location is essential for preserving the full remaining genetic diversity of this already severely reduced species, and maximizing the chances of a recovery after its massive historical decline. ampli ed for all samples using either two or four speci c primers designed from the published sequence (Nunn & Stanley 1998 -as 5'-3', yun1f: GCCCCAAACCTCCGAAAATCCCA and yun2r: GGTGATGGAGGCTAGTTGGCCG or with internal primers yun1r: GCCT-GATTCGTGAAGGAAGGTGAGG and yun2f: CCACCCTAACCCGATTCTTCGCC). Ampli cation required the following conditions: 5' at 94°C, followed by 35 cycles of 30" at 94°C, 30" at 57°C, and 1' at 72°C, and nally 5' at 72°C. PCR product was then puri ed using Illus-tra™ ExoStar™, and Sanger sequencing was performed at the ABI lab of the University of Oslo.

Material and Methods

Quality assessment, trimming and manual checking were performed in Geneious® v6.1.2. §-163 RAD library preparation and SNP typing. SNP discovery and sequencing was performed on a subset of individuals (11 from Isla Choros and 10 from Isla Pan de Azúcar) following a singledigest RAD-sequencing protocol [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]. High quality samples were selected using a 1.5% agarose gel, and quanti ed by uorometry (Life technologies™ Qubit®). (i) for each sample, 125ng of genomic DNA were digested with the restriction enzyme SbfI (NEB); (ii) each sample was ligated to a unique barcoded P1 adapter prior to pooling in a single library. e library was then sheared by sonication (7 cycles 30'' ON -30'' OFF); (iii) sonicated libraries were concentrated to 25 µl by DNA capture on magnetic beads (beads solution:DNA = 0.8:1), and the target size range fraction (300-500 bp) was then selected by gel electrophoresis and manual excision;

(iv) capture on magnetic beads using the same beads:DNA ratio (0.8:1) was then performed in all following puri cation steps (after blunt-end repairing, poly-A tailing, P2 adapter ligation and library enrichment by PCR); (vi) PCR ampli cation w as p erformed i n 8 x 1 2.5 µ l aliquots pooled after the ampli cation in order to reduce ampli cation bias on few loci due to random drift. PCR was performed using NEB Phusion® polymerase with the following cycles: 30" denat-Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §163 uration at 98°C, 21 cycles of ampli cation (10" at 98°C, 30" at 65°C and 30" at 72°C), and anal elongation of 5' at 72°C; (vii) the library was then quanti ed by uorimetry (Life technolo-gies™ Qubit®) and molarity was checked on an Agilent Bioanalyzer chip (Invitrogen™). A nal volume of 20 µl was submitted for paired-end sequencing on an ILLUMINA HiSeq2000 sequencer (V3 chemistry) at the Norwegian Sequencing Centre, University of Oslo, and spiked with 20% PhiX control library in order to reduce low-diversity bias.

As no reference genome is available for P. garnotii, data processing followed a two-step protocol.

First, loci were built de novo, using the Stacks v1.28 pipeline [START_REF] Catchen | Stacks: building and genotyping loci de novo from short-read sequences[END_REF][START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF], and a synthetic reference RADome was built. Second, this reference RADome was used to perform alignment-based SNP typing. e work ow was as follows: (i) Sequence de-multiplexing and de novo locus assembly was done according to in-line barcodes using the Stacks deno-vo_map.pl pipeline, with a maximum of 5 mismatches allowed between alleles at a single locus and genotypes in all samples, using rst reads only, with standard parameters, except for population heterozygosity which was set to 0.01, and retaining only di-allelic, non-indel sites sequenced

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §165 in at least 75% of samples. We repeated key analyses using ANGSD 0.900 [START_REF] Kim | Estimation of allele frequency and association mapping using nextgeneration sequencing data[END_REF]Korneliussen et al. 2014) to estimate allele frequency and genotype likelihoods, without hard-calling genotypes, an approach that has been shown to be much more robust for low-coverage data (Nielsen et al. 2012;Korneliussen et al. 2014). §-164 Analysis of population structure. Summary statistics were calculated in DnaSP [START_REF] Rozas | Distribution of individual inbreeding coefficients, relatedness and influence of stocking on native anadromous brown trout Annexes - §186 (Salmo trutta) population structure[END_REF]) for mtDNA, and R package adegenet [START_REF] Jombart | adegenet: a R package for the multivariate analysis of genetic markers[END_REF] and custom scripts for SNP data. Analysis of Molecular variance was performed for both datasets using Arlequin v3. et al. 2005). Second, in order to assess the impact of the HWE assumption, we used a model-free approach, as implemented in the adegenet k-means clustering [START_REF] Liu | A non-parametric approach to population structure inference using multilocus genotypes[END_REF]. §-165 Model-based estimation of population history. We relied on two separate coalescent-based approaches to estimate the amount of gene ow between the North and the South of the species Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §165 taxa). Under model ( 3), human activities have no impact on the populations, and constant gene ow is maintained between the islands until the present, with no population size changes. Under models (4), population size do not change, but gene ow is disrupted at some point during the past, presumably by the expansion of guano exploitation. Under models ( 5) and ( 6 consistently and unambiguously converging to that conclusion. Interestingly, however, in both species, principal component analysis does show a hint of sorting by distance. In the Emperor penguin (Fig. 36A), a rough gradient is visible between the colonies of the Adélie Land area and those of the Dronning Maud Land area, that may correspond to an underlying isolation-by-distance pattern. In the King penguin (Fig. 23), although no rm structure can be extracted, individuals from the same archipelago are consistently grouped together -even though groups overlap largely. Yet in both species, the variance explained by the rst p rincipal c omponents is extremely low: the rst c omponent e xplains 1 .39% o f t he t otal v ariation i n t he E mperor penguin, and 0.93% in the King penguin -in both cases, this is barely above the expectation for full homogeneity. is observation, instead of supporting the idea of structured metapopulations, Discussion - §167 ture signal is far below signi cance -and that interpreting di erences at that level may be perfectly vain.

Nevertheless, eld observations and genetic data agree at the local, and the global scales. In the King penguin, equally high return rates have been evidenced at the level of the nest [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF][START_REF] Bried | Population biology of the wandering albatross (Diomedea exulans) in the Crozet and Kerguelen archipelagos, southern Indian Ocean, approached Annexes - §186 through genetic and demographic methods[END_REF] or the colony (Le Bohec et al. 2008;Saraux et al. 2011b): yet genetic structure is equally absent at both levels. A simple explanation seems, as we proposed in §71 p.

157, the di erence in standpoint when considering return rate from an ecological or a genetic perspective. Indeed, if 90% precise local return will doubtlessly be considered a case of extreme philopatry by any eld ecologist, because of the allowance made for mortality at sea, and of the one-sided character of the implicit test, this observation may be reversed as up to 10% of yearly dispersal: a ow that is high enough to obliterate any hint of populations structure. is bias, the « seabird paradox » of [START_REF] Milot | The seabird paradox: dispersal, genetic structure and population dynamics in a highly mobile, but philopatric albatross species[END_REF], is in fact only a paradox as long as we do not really examine the bridge between the generation-scale and the evolutionary scale: a phenomenon that seems negligible when considering the lifespan of a few individuals becomes a major process on the evolutionary level. is often comes down to a subjective decision as to the side from which results will be presented. For example, even if signi cant, an Fst value or 0.01 is strongly indicative of a high gene ow: but it is more often cited as « signi cant evidence for separation ». is statement is not wrong in itself, since any given state of population connectivity is a balance between some degree of isolation, and some degree of mixing: but it is misleading, as it focusses on the minor isolation instead of the more meaningful high migration process.

2.

As we discussed in §156 p. 286, the characteristics of « classical » mitochondrial markers also makes them likely to overestimate population structure, due to their halved e ective population size, and consequently shallower coalescence time, compared to nuclear data. If this distinction is not kept in mind, mtDNA results can yield largely overestimated population structure.

Discussion - §167 plate of other penguins, or seabirds in general 1 . Our results (see §166 p. 297) bring clear evidence against both mtDNA-based studies. §-168 Synnomes and speciation. Amongst all Southern Ocean seabirds for which genetic data has been published, the two Aptenodytes species are thus, for the time being, the only two for which no relevant population structure could be identi ed. In terms of biogeography, with their full circumpolar distribution (Fig. 7), each of the Aptenodytes species should be more appropriately compared to the full Diomedea species complex: in the latter, the small population sizes and high philopatry of Wandering albatrosses has lead to full speciation between oceanic areas, whereas both large penguin species have maintained a high connectivity and full homogeneity at the same geographical scale. Indeed, preliminary analyses even emphasize the relatively low degree of diver- As a side note, the example of the evolution of laboratory and analysis methods over the past two decades should act as a twofold caution for present studies. First, conclusions drawn from less reliable material, or analysed using less robust approaches, should not systematically be taken for granted: re-analysis using more advanced methods may yield very di erent results. Second, it should also serve as a reminder to the fact that contemporary approaches may also become outdated soon enough, and that our conclusions only hold until then. We mentioned earlier how theoretical frameworks like the ARG are yet in their early development, both from a conceptual point of view, and from sheer technical limitations (e.g. the lack of an accurate and veryhigh-throughput sequencing platform, or the limits of contemporary computational power). It may well happen that further methodological and technical developments will lead us to reconsider our results -a somewhat tiresome, but absolutely unavoidable possibility.

2.

Which we already produced, but still remains to be analysed, see §30 p. 86. is observation may, in a sense, be an optimistic one, as recovery from over shing may happen faster and more fully than recovery from greenhouse-gas induced global warming (which may not happen at all on a human time scale).

Discussion

In that respect too, the Peruvian diving-petrel is an extreme case. For that species, several well identi ed direct factors have contributed to reducing large populations to vestigial colonies in a (in particular the respective roles of intrinsic growth and immigration) 1 . Finally, the precise question of the subspeci c taxonomy in the King penguin (see §25 p. 80), although we consider it as good as settled here, may require explicit testing, including morphometric measurements to match the observations of [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF].

In the Peruvian diving-petrel, on the other hand, additional sampling 2 will likely improve greatly our understanding of ongoing population processes. Our Chilean sampling shows that isolation is a major population process in this species, but an additional « outgroup » sample, distant from out sampling by ~1,500 km, should allow us to set bounds on the migratory ow, and to understand wether it is mainly driven by geographical distance (in which case we should observe a clear phylogeographic structure, driven by islotion-by-distance gradients), or by more complex mechanisms. §-174 Extending palaeodemographic reconstructions. Our nal reconstructions for population size changes in the King and the Emperor penguin use a currently state-of-the-art method [START_REF] Liu | Exploring population size changes using SNP frequency spectra[END_REF], and are a large improvement over previous studies [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]). However, coalescent-based reconstructions are inherently limited by the very structure of genetic diversity: once all examined sequences have found a single common ancestor, no further information can be retrieved. is is expected to happen, in a stable population, around 4.Ne generations back in the past (which, in Aptenodytes penguins, would be as considerable as 1 million years) -but much sooner if demographic events, such as bottlenecks, temporarily increase the probability of coalescence at given points of time. is phenomenon is easily visible when contrasting the King and the Emperor penguins (Fig. 19): although population sizes and generation times are comparable, 1.

Sampling has been done in Tierra del Fuego, in Chile, with the collaboration of Dr Juliana Vianna, but will not be processed and integrated in the present work.

2.

Sampling is currently underway in Isla San Gallán, in Peru, with the collaboration of Dr Carlos Zavalaga, but will not be nished in time to be processed and integrated in the present work.

Discussion - §174 accurately still, by estimating the joint allele-frequency spectrum of the two chronodemes 1 , and modelling allele inheritance as well as other demographic processes as unidirectional di usion parameters (from the more ancient chronodeme to most recent one). In a Stairway plot framework, this would considerably widen the parameter space (since the number of SNP classes become the product, and not the sum, of the dimensions of the two spectra), but there is no obvious impossibility there.

Technically, this would also probably require shifting to a whole-genome resequencing, rather than RAD-sequencing, approach, as enzymatic digestion is notoriously unpredictable in ancient DNA. But King penguin DNA, for example, has been successfully extracted for samples as ancient as 8,000 years old conserved in peat [START_REF] Hellenthal | King penguin population on Macquarie Island recovers ancient DNA diversity after heavy exploitation in historic times[END_REF], so that there is hope to retrieve information prior to the most ancient of our reconstructed bottlenecks, and most importantly to verify whether the oscillating population size pattern may be extended to the whole Pleistocene period. §-175 From neutral to adaptive genomics. RAD sequencing, since its original formulation in 2007 (Miller et al. 2007;[START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF][START_REF] Davey | Genome-wide genetic marker discovery and genotyping using next-generation sequencing[END_REF], has been successfully used in a wide range of genomic applications. ese have focused, as we did in this work, on the informative content of neutrally evolving regions, in particular in the elds of phylogenetics [START_REF] Cariou | Comparing marine and terrestrial ecosys-Annexes - §186 tems: implications for the design of coastal marine reserves[END_REF]Cruaud et al. 2014) case, the synnome structure also has implications on the possible realisation of processes such as positive selection (see §176 p. 320). §-176 Synnomes and gene swamping. Delving into functional and adaptive processes may also allow us to shed light on the relationship that short-and long-term processes entertain in the species' dynamics and evolution. In §95 p. 186, we reconstructed the King penguin synnome growth rate along its Pleistocene and Holocene history: despite the very large amplitude of the uctuations in population size, the growth parameter λ 1 remained very stable, ranging from a minimum λ min = 0.999 in the decline periods to a maximum of λ max = 1.002 in the growth periods. In comparison, when growth rate is extracted over a short period on a yearly basis (1999-2010, Le Bohec et al in prep.), variation is larger by one order of magnitude (λ min = 0.99 and λ max = 1.08). is is an expected observation, since growth rate is a highly stochastic parame-

1. e growth rate λ is the ratio of the population size between two successive years (in our case -otherwise any other time interval may be used). Indeed, free movement of alleles between colonies implies that migration load 1 certainly reaches Discussion - §176

Discussion

1.

Migration load can be de ned as the « decrease in mean tness of a population because of immigration. is occurs because the phenotypic mean of the population is di erent from the local optimum value » [START_REF] Lenormand | The distribution and size of king penguin rookeries on South Georgia[END_REF]. us, in the case of positive selection leading to local adaptation, migration load is the counteracting force bringing random alleles into the local gene pool through migration events, and making local adaptation less e cient. the point of gene swamping 1 (see a complete review of these concepts in [START_REF] Lenormand | The distribution and size of king penguin rookeries on South Georgia[END_REF]: for a gene to be positively selected, it needs to o er consistent selective advantage throughout the whole species' range -and, to undergo purifying selection 2 , it needs to be consistently detrimental everywhere. In short, the migration-selection equilibrium is brought to an extreme of averaged selection, where local habitat conditions are integrated over the whole distribution of the species.

Whereas individual tness selection occurs relative to the match of individual traits and local habitat characteristics, the nal selective pressure on the allele will, on the contrary, be the result of its average selective advantage under the species' total habitat. is has two important consequences: (i) even though there may be drastic local selection for certain alleles, local adaptation will be prevented by the migration load, and (ii) as long as « gene refugia » (i.e. local conditions where a gene retains a positive or neutral selective coe cient) are maintained in the species' range, xed adaptation is unlikely.

Shifting from a neutral evolution paradigm, such as the one we explored in the present work, to a functional and adaptive one, should enable us to identify such processes. Rapid local micro-adaptation, for example, should be detectable through particularly shallow coalescence times at locally selected loci, and spatial homogeneity in the patterns of selection may be an important clue on ongoing gene swamping. Comparison with highly philopatric species such as the Peruvian diving-petrel might be especially informative: in that case, the full genetic sorting between even closely neighbouring colonies makes local adaptation a likely possibility. And the tness bene t can be easily understood in the stable Atacama coast environment, where both the insular breeding grounds and the climate exhibits relatively high stability (e.g. in the ENSO cycles, see [START_REF] Moy | Variability of El Niño/ Southern Oscillation activity at millennial timescales during the Holocene epoch[END_REF]: investing on a particular local evolutionary strategy can pay o , since the is good chance that the match between adaptation and environment will be maintained for some time.

1.

Gene swamping can be used to describe « the situations where there is no signi cant response to selection because gene ow is too high » [START_REF] Lenormand | The distribution and size of king penguin rookeries on South Georgia[END_REF] -in other words, when the migration load fully counteracts the e ects of local selection.

2.

Or « negative selection »i.e. active removal of detrimental alleles from the local gene pool.

Discussion - §176 us, the contrast between high philopatry and reduced gene ow in the Diving petrel's stable environment, and the large penguins' panmictic system amidst unstable oceanic structures is double-sided: from a « neutral » point of view, unstable environments are likely to promote higher dispersal, in particular in response to local habitat destruction, as we showed in Empirical evidence of heterogeneous dispersal, p. 259, and therefore to bias population systems towards panmixia, whereas stable environments allow for perpetuation of colonial units on the long term, and consequently allow for drift and genetic isolation. But from an « adaptive » standpoint, stable environments allow species to bene t from local adaptation, and are expected to select for lower dispersal in order to reduce migration load on the e ciency of selection [START_REF] Billiard | Evolution of migration under kin selection and local adaptation[END_REF], while unstable environments will rather enhance the bene t of conserving a more generalist gene pool, that is globally suited to the range of possible conditions, although it may not be optimal in the current state of a airs -that is, ultimately, full gene swamping. Of course, distinguishing one line of explanation from the other is a lost cause, since these are probably but two ways of describing the same, uni ed phenomenon 1 .

Conclusion

Bringing together the tools of population genomics, numerical climate models and eld ecology allowed us to sketch a general picture of the e ects of climate change on three remote seabird species. And whereas the general perception of the Southern Ocean is often that of a pristine and extreme environment, one of our most remarkable conclusions may be that it is, in fact, neither.

As we exposed throughout this work, even species as rarely met by humans as the King and the Discussion - §176

1.

Or, to pursue on Tolstoy's thought, « when an apple has ripened and falls, why does it fall? Because of its attraction to the earth, because its stalk withers, because it is dried by the sun, because it grows heavier, because the wind shakes it, or because the boy standing below wants to eat it? Nothing is the cause. All this is only the coincidence of conditions in which all vital organic and elemental events occur. And the botanist who nds that the apple falls because the cellular tissue decays and so forth is equally right with the child who stands under the tree and says the apple fell because he wanted to eat it and prayed for it. Equally right or wrong is he who says that Napoleon went to Moscow because he wanted to, and perished because Alexander desired his destruction » (Tolstoy 1869).

Discussion - §176

Emperor penguin are nonetheless at the forefront of anthropogenic change. e rst and most direct e ect of these changes is, of course, the increase in temperature, both at the sea surface (which results in polar front migration and loss of foraging grounds for the King penguin) and in the atmosphere (which results in modi ed sea ice formation patterns, and potentially loss of foraging grounds too for the Emperor penguin). But, as we presented earlier (see §84 p. 174), climate change itself has other, more indirect e ects: by a ecting the physiology of plankton, it changes the global primary productivity of the Southern Ocean; by modifying sea ice patterns, it a ects the blooms of Antarctic Krill [START_REF] Brierley | Antarctic krill under sea ice: elevated abundance in a narrow band just south of ice edge[END_REF] -with cascading e ects on seabirds, see [START_REF] Nicol | Krill (Euphausia superba) abundance and Adélie penguin (Pygoscelis adeliae) breeding performance in the waters off the Béchervaise Island colony, East Antarctica in 2 years with contrasting ecological conditions[END_REF][START_REF] Chapman | The effects of variability in Antarctic krill (Euphausia superba) spawning behavior and sex/maturity stage distribution on Adélie penguin (Pygoscelis adeliae) chick growth: a modeling study[END_REF][START_REF] Travis | Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica[END_REF]; by changing the heat exchange between sea surface and atmosphere, it is likely to change the circulation patterns of water masses, with immediate e ects on ecosystem structure [START_REF] Nicol | Krill (Euphausia superba) abundance and Adélie penguin (Pygoscelis adeliae) breeding performance in the waters off the Béchervaise Island colony, East Antarctica in 2 years with contrasting ecological conditions[END_REF].

Nor does the remoteness of the Southern Ocean mean that it is isolated: directly connected to the three major ocean basins, it is at the heart of the global thermohaline circulation (see §7 p. We found that EBSP analysis is not really optimized for handling high numbers of loci (very high parametrization) and this may result in slow convergence and poor mixing of the MCMC chains.

We estimated that 3-5 billion iterations would be needed to get e ective sample size above 200 for all model parameters when analysing our dataset of 50 loci and eight individuals. Neverthe- been strongly criticized as theoretically un t to distinguish between competing population histories even if strongly simpli ed assumptions (panmixia, in nite site mutation model and neutrality) are met in the studied system [START_REF] Myers | Can one learn history from the allelic spectrum? Theoretical population biology[END_REF]. In particular, it seems that compensating events in population dynamics are impossible to detect, thus making the analysis of complex histories unreliable. In our test case, the di erent analytical approaches converged on a quite simple history of sudden population growth making it a likely suitable case for AFS analysis. Both EBSP and AFS analyses, based on independent datasets (1-SNP class versus 4-to 6-SNP classes loci), produced largely similar inferences of the time and trend of the past demography of our king penguin colony. A major di erence between results from the two methods was in the estimate of the current e ective population size (Ne = 7,000 from AFS analysis versus Ne = 170,000 from EBSP). Both estimates are inconsistent with the direct count of breeding birds on 'La Baie du Marin' colony, Possession Island (i.e. 32,000 breeding birds, see [START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]). However, it is likely that a certain degree of gene ow exists within the Possession Island, and that 'La Baie du Marin' colony is part of a metapopulation at the island level. According to the most recent estimate by direct count, the breeding population of Possession Island is ca 150,000 individuals [START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]). erefore, the population size we estimated from genomic data may re ect this larger meta-population, rather than the single colony. A study investigating the level of connectivity and gene ow among the colonies of Possession Island is currently ongoing. [START_REF] Jouzel | Orbital and millennial Antarctic climate variability over the past 800,000 years[END_REF]). e LGM period is given in green.

Annexes

In our test case, the demographic trend inferred from a single mitochondrial locus was consistent with a similar analysis carried out on 50 nuclear loci (Fig. 48 p. 339 and supplementary material, loci are genotyped, many fewer samples are needed to estimate population-level statistics and obtain demographic inferences [START_REF] Felsenstein | Accuracy of coalescent likelihood estimates: do we need more sites, more sequences, or more loci[END_REF][START_REF] Willing | Estimates of genetic differentiation measured by F(ST) do not necessarily require large sample sizes when using many SNP markers[END_REF].

anks to the inclusion of the mitochondrial marker in our genomic analyses, we were also able to propose an average substitution rate for the loci included in our RAD dataset. Our estimated rate is about one order of magnitude faster than previously reckoned in other bird species (Ellegren 2013). However, recent evidence showed that genomic substitution rates can be faster than formerly estimated from pedigree studies (O'Roak et al. 2012;Gibb & Hills 2013). Moreover, the RAD sequencing protocol may introduce biases in the genome representation: when using a m axis at the periphery of the colony, in order to maximize separation distance. A total of 140 individuals were chosen for mtDNA CR analysis, and eight of these were randomly selected for RAD sequencing analysis. Total DNA was extracted from the lter papers using standard methods and controlled for quantity and quality. §-180 Restriction site-associated DNA sequencing. Genomic DNA from eight king penguins was individually barcoded, pooled and genotyped by RAD sequencing [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF] in one library sequenced on an ILLUMINA HiSeq2000 at the Norwegian Sequencing Centre, University of Oslo. Raw reads were trimmed, demultiplexed and aligned in an unreferenced catalogue using the STACKS software pipeline [START_REF] Catchen | Stacks: building and genotyping loci de novo from short-read sequences[END_REF] running on the server facility on the ABEL cluster, University of Oslo. Further quality ltering using custom bash and python scripts was ap-

Annexes

Annexes - §186
phy employing the Bayesian Skyride plot [START_REF] Minin | Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics[END_REF], where inferred population history is bounded by credibility intervals that combine phylogenetic and coalescent uncertainties, as implemented in the BEAST 1. After quality assessment, samples showing high molecular weight and highly concentrated DNA were employed in next-generation sequencing (NGS) of RADtags [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]). e following RADseq protocol was adopted: (i) approximately 100ng of genomic DNA pers ample were digested with the restriction enzyme SbfI (NEB); (ii) each sample was then ligated to a unique barcoded P1 adapter prior to pooling in a single library. e library was then sheared by sonication, and gel electrophoresis of small library aliquots were run after the rst 5 cycles (30'' ON -30'' OFF) and then every 2-3 cycles of sonication; (iii) the target size range fraction (300-500 bp) was achieved after 8 cycles of sonication and was then selected by gel electrophoresis and manual excision; (iv) before size selection on the gel,sonicated libraries were concentrated to 25 fragments smaller than 200bp are left out. e ratio between the (theoretically salt-free) DNA solution and the binding solution that allows for proper size selection varies from batch to batch, we call it k along the protocol. k should be anywhere between 0.8 and 1.2.

-10g polyethylene-glycol (PEG-8000) -7.3g NaCl -50mL H2O

-mix until the solution is totally clear.

Annexes - §186

How to calibrate that : Dilute 50bp DNA ladder to 0.5X in EB bu er, make 12*50μL aliquots. Into each digested product, add the barcoded P1 adapter (it helps to prepare the P1 dilutions in PCR strips and use a multichannel pipette to add them to the reactions).

-n μL P1.

-mix thoroughly.

Adapters must be added before the ligation premix, in order to avoid gDNA religation.

-1 μL NEB Cutsmart bu er -0.6 μL rATP (100 nM) -0.5 μL T4 ligase (2,000,000 U/mL) -H20 qsp (7.9-n) μL Add (10-n) μL of this mix to each reaction. Mix throroughly by pipetting (no vortexing). Ligation is done overnight with a cycle protocol :

-[10°C +2°C/30" until 30°C, then -2°C/30" until 22°C] (96 cycles of 10min, 16h total) -65°C for 20' --1°C per minute down to 8°C.

is takes about 17 hours to run and should be performed overnight. e increase in e ciency compared to room temperature ligation is worth it.

STAGE 2 : Shearing and selection

Annexes - §186

Pooling: At this stage, every sample is hopefully uniquely barcoded. Aliquots from each sample can be combined in a single tube, for a total of 3 μg DNA per pool. Volume will vary depending on initial DNA concentration. e rest of the samples is better not pooled in case barcoding needs to be re-done, and can be frozen until then.

Sonication: Performed on the Bioruptor at 2°C, high power. Conditions need to be calibrated for each type of organism and gDNA quality. For penguins, 7 cycles of 30"ON/30"OFF gives optimal results. Each TPX tube can sonicate a maximum of 300 μL : if the pool is larger than this, split it across several tubes.

How to calibrate that : pick a representative sample with excess DNA, take a series of 1.5 μg DNA aliquots in a volume of 200 μL, and try a range of cycle numbers (6-9-12-15 on the rst try). Purify the product into 30 μL EB bu er and run on a 2% agarose gel. e best range should put the bulk of the DNA between 300 and 600bp. When the range is determined, repeat with ner steps, purify and run on a BioAnalyzer DNA chip. Optimal conditions give the highest molarity (not concentration !) between 300 and 600bp. Use the manual integration tool to obtain the molarity in the 300-600bp range and in the 600-900bp range. Calculate the M600-900/ M300-600 ratio. e lowest value gives the best condition possible, as it indicates the conditions that allow most fragments to be recovered in a 300-600bp gel cut. Purify and concentrate the product in a QiaQuick column. Elute in 32 μL EB bu er.

Size selection: Prepare a BluePippin 2% DF gel cassette according to instructions. Set the machine on V1 (internal) marker, with a size selection in the 300bp to 650bp range.

-Add 10 μL Blue Pippin V1 marker solution to each sample (total 40 μL).

-Run the gel (about 90 minutes).

-Run the BluePippin according to instruction sheet.

-Take out the ~40 μL eluate into a LoBind tube.

-Replace by 40 μL EB bu er. Let stand for 5 min.

Annexes - §186

-Add these 40 μL to the eluate.

-Add EB bu er qsp. 100 μL.

STAGE 3 : Fragment preparation

Initial bead puri cation: e beads we add at this stage will remain in the reaction tube until the end of the library preparation. Make sure they are at room temperature and perfectly homogeneous.

-Add 80 μL AmpureXP beads.

-Mix for 10 min on a vortex or thermomixer at maximum speed.

-Place on the magnetic rack. Let stand for 5 min.

-Remove the 180 μL supernatant and discard it.

-Add 500 μL of fresh 80% ethanol and leave for 2 min.

-Remove and replace by another 500 μL 80% ethanol, leave for 2 min.

-Remove ca. 470 μL (leave a small volume at the bottom of the tube).

-Spin the beads down. Replace on the magnet.

-Remove the remaining ethanol, dry open until bead cracking point.

-Resuspend the beads in 20 μL EB bu er.

End repair: is step converts the overhangs created by sonication into phosphorylated blunt ends. We follow exactly the NEB blunting kit protocol. e reaction is performed directly in the previous tube, with the beads in, but o the magnet. To each library, add :

-2.5 μL 10X NEB blunting bu er -2.5 μL dNTP mix (1 mM) -1 μL Blunt enzyme mix.

-Incubate at room temperature for 30min. Annexes - §186

Annexes

Now, the library should consist mostly double-stranded, properly ligated fragments ready for sequencing. ough this must be checked.

BioAnalyzer check: e nal library should be run together with its template on a BioAnalyzer DNA chip. In order to be in roughly comparable conditions, 2 μL of template should be diluted in 11.5 μL EB bu er (as this is the dilution state in the PCR reaction prior to ampli cation). On the BioAnalyzer, the library should be at roughly twice as concentrated as the template, and exhibit a clean distribution with no obvious spikes along the ampli ed s ection. Spikes indicate clonal populations of highly duplicated fragements. One should also check there is no adapter carryover, especially P1 dimers (which should have all been left out since size selection).

qPCR check: qPCR assay (Kappa dedicated SybrGreen Illumina kit) allows for the most exact measurement of ligated fragment molarity. is is the nal check before loading on the cBot. sévèrement a ectés apr l'exploitation humaine, qui diminue leur résilience aux changements climatiques [START_REF] Hackett | BioEdit: a userfriendly biological sequence alignment editor and analysis program for Windows 95/98/NT[END_REF][START_REF] Watson | en particulier pour les prédateurs supérieurs, qui déterminent dans une large mesure les propriétés de résilience des écosystèmes[END_REF][START_REF] Mccauley | Divergent trends in land and ocean temperature in the Southern Ocean over the past 18,000 years[END_REF]. L'e ondrement de la biodiversité prédit par les scénarios les plus sombres (voir §50) pourrait ainsi toucher de 50 à 70% des océans plus fortement que tous les changements qui ont eu lieu depuis le Pléistocène [START_REF] Beaugrand | Future vulnerability of marine biodiversity compared with contemporary and past changes[END_REF].

  ........................................................................................................................

  ...................................................................................................................

Figure 1 :

 1 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 2 :

 2 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 3 :

 3 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 4 :

 4 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 5 :

 5 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 6 :

 6 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 7 :

 7 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 8 :

 8 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 9 :

 9 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 10 :

 10 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 11 :

 11 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 12 :

 12 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 14 :

 14 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 15 :

 15 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 16 :

 16 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 19 :

 19 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 20 :

 20 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 24 :

 24 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 25 :

 25 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 26 :

 26 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 27 :

 27 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 28 :

 28 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 29 :

 29 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 30 :

 30 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 31 :

 31 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 32 :

 32 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 33 :

 33 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 35 :

 35 Figure 1: Demography in the Ecological and the Genetic paradigms. 24 ........................................Figure 2: Structure of water masses in the Southern Ocean. 37 .......................................................Figure 3: Primary productivity concentration on the Antarctic Polar Front. 38 ............................... Figure 4: e formation of sea ice in coastal polynyas. 42 ...............................................................Figure 5: Sea Surface Temperature changes in the Southern Ocean. 45 ...........................................Figure 6: e Food web of the Southern Ocean. 53 ........................................................................Figure 7: Distribution of the two Aptenodytes species. 77 ..............................................................Figure 8: e RAD sequencing approach. 87 ..................................................................................Figure 9: Stochasticity and demography in the coalescence process. 102 .........................................Figure 10: Coalescence and population size. 107 ............................................................................Figure 11: In uence of demographic processes on the joint AFS. 120 .............................................Figure 12: e Pairwise Sequentially Markovian Coalescent model. 122 ......................................... Figure 13: Sampling distribution. 143 ............................................................................................Figure 14: Observed individual inbreeding distribution 150 ..........................................................Figure 15: Individual inbreeding and nearest-neighbours-relatedness 151 .......................................Figure 16: Spatial correlation and Intrinsic clustering tests do not support strong structure. 152 .... Figure 17: Ecological descriptors of breeding-site quality 155 ......................................................... Figure 18: First axis of principal component analysis as a summary habitat-quality index. 156 .......Figure 19: Penguin paleodemography in response to Quaternary climate change. 170 ....................Figure 20: Past and future breeding range of the King penguin. 172 ............................................... Figure 21: Projected foraging distance under three RCP scenarios. 175 .......................................... Figure 22: Sampling design. 177 ..................................................................................................... Figure 23: Principal component analysis. 182 .................................................................................Figure 24: Neighbour-net. 183 .......................................................................................................Figure 25: Robustness of the Stairway plot method. 185 ................................................................Figure 26: Extended Bayesian Skyline Plot. 190 ..............................................................................Figure 27: Pairwise Sequentially Markovian Coalescent. 192 ..........................................................Figure 28: Validation of the demographic reconstructions through simulation. 195 ........................Figure 29: (NEXT PAGE) Foraging distance from single models. 206 ............................................Figure 30: Proportion of models predicting extinction of King penguin colonies. 209 ....................Figure 31: Drift and mixing in the Emperor penguin. 217 .............................................................Figure 32: Demography is a matter of scales. 219 ...........................................................................Figure 33: Changes in sea ice con guration along coastal Antarctica 229 ........................................ Figure 34: Uncertainty in the representation of sea ice in the CMIP5 models. 232 .........................Figure 35: Consensus SNP calling. 241 ..........................................................................................

Figure 36 :

 36 Figure 36: e imprint of geography on the Emperor synnome. .............................................. Figure 37: Pairwise genetic distance matrix. ............................................................................. Figure 38: Inference of population splits. ................................................................................. Figure 39: Haplotype network for mitochondrial sequences. ................................................... Figure 40: Western Emperor penguin colony. .......................................................................... Figure 41: Eastern Emperor penguin colony. ........................................................................... Figure 42: Satellite images of the Mertz glacier. ........................................................................ Figure 43: Full distribution of Pelecanoides garnotii. .............................................................. Figure 44: Consistent genetic structure within the Chilean range. .......................................... Figure 45: Winter sea ice extent ............................................................................................... Figure 46: Demographic inference based on the AFS. .............................................................. Figure 47: Demographic reconstructions of the Crozet king penguin colony ........................... Figure 48: Past demographic trend of the king penguin colony of 'La Baie du Marin' ............. Figure 49: Demographic reconstructions of the Crozet king penguin colony ........................... Figure 50: Bayesian Skyride Plot inferred from mitochondrial Control Region ........................

  Cristofari R, Trucchi E, Whittington JD, Vigetta S, Gachot-Neveu H, Stenseth NC, Le Maho Y, Le Bohec C (2015) Spatial heterogeneity as a genetic mixing mechanism in highly philopatric colonial seabirds. PloS one 10: e0117981. -Cristofari R, Liu X, Bonadonna F, Cherel Y, Le Maho Y, Pistorius P, Raybaud V, Stenseth NC, Le Bohec C & Trucchi E, Stepping-stone range shifts in response to climate change in the Southern Ocean. (in prep.) -Cristofari R, Bertorelle G, Ancel A, Benazzo A, Le Maho Y, Ponganis PJ, Stenseth NC, Trathan PT, Whittington JD, Zanetti E, Zitterbart DP, Le Bohec C & Trucchi E, Full circumpolar migration ensures evolutionary unity in the Emperor penguin. (Nature Communicationsin prep) -Ancel A & Cristofari R, Fretwell PT, Trathan PN , Wienecke B, Boureau M, Morinay J, Blanc S, Le Maho Y, Le Bohec C (2014) Emperors in Hiding: When Ice-Breakers and Satellites Complement Each Other in Antarctic Exploration. PloS one 9:e100404 -Cristofari R, Fernandez-Zamora F, Gouin N, Le Bohec C, Plaza P, Trucchi E, Zavalaga C, Luna Jorquera G, Unexpected population isolation in a critically endangered insular seabird, the Peruvian Diving Petrel (Pelecanoides garnotii). (in prep). e following communications also served as a basis for this work: -Le Bohec C, Whittington JD, Ancel A, Chatelain N, Cornet C, Courtecuisse J, Crenner F, Cristofari R, Marpaux S, Allemand D & Le Maho Y (2014, August) Predict changes in polar ecosystems : biological adaptation and technological innovation. Oral presentation at the XXXIIIth SCAR Open Science Conference, Auckland, New Zealand. -Cornet C, Amélineau F, Babel D, Boureau M, Courtecuisse J, Cristofari R, Descamps S, Marpaux S, Morinay J, Whittington JD, Le Maho Y. & Le Bohec C. (2014, July). Personality and environmental heterogeneity in the Adélie penguin. Oral presentation at the 15th Conference of the International Society for Behavioural Ecology, New York, USA. -Cornet C, Amélineau F, Babel D, Boureau M, Courtecuisse J, Cristofari R, Descamps S, Marpaux S, Morinay J, Saraux C, Whittington JD, Le Maho Y. & Le Bohec C. (2013, September). e adaptive capacities of Adélie penguins to face environmental variability : the role of heterogeneity within populations. Keynote presentation at the 8th International Penguin Conference, Bristol, UK. -Cristofari R, Trucchi E, Whittington JD, Vigetta S, Gachot-Neveu H, Stenseth NC & Le Bohec C (2015) Spatial heterogeneity as a genetic mixing mechanism in highly philopatric colonial seabirds. Poster presented at the XIth SCAR Biology Symposium : « Life in Antarctica : Boundaries and Gradients in a Changing Environment », Barcelona, Spain. -Cornet C, Amelineau F, Babel D, Boureau M, Courtecuisse J, Cristofari R, Descamps S, Marpaux S, Morinay J, Whittington JD, Le Maho Y. & Le Bohec C. (2013, July). Personality and its e ect on tness in the Adélie penguin. Poster presented at the XIth SCAR Biology Symposium : « Life in Antarctica : Boundaries and Gradients in a Changing Environment », Barcelona, Spain. -Le Bohec C, Cornet C, Cristofari R, Whittington JD, Courtecuisse J, Chatelain N, Crenner F, Allemand D & Le Maho Y. (2013, July). Adaptive strategies and population trends of penguins to predict changes in polar marine ecosystems. Oral presentation at the XIth SCAR Biology Symposium : « Life in Antarctica : Boundaries and Gradients in a Changing Environment », Barcelona, Spain.

§- 7 Figure 3 |

 73 Figure 3 | Primary productivity concentration on the Antarctic Polar Front. Surface chlorophyll concentration, 21 dec. 2011 to 20 mar. 2012. High chlorophyll concentration is visible all along the APF, with a local peak on the Kerguelen plateau (KP), as well as in latent heat coastal Antarctic polynyas (CP), and in the sensible heat Weddell polynyas (WP) and latent and sensible heat Ross Sea Polynya (RP) -see §18 p. 65 for details). Image courtesy of NASA, Suomi NPP VIIRS satellite program.

  general structure remains the oscillation of temperature di erences between the East and the West of the subtropical zone. A steep zonal temperature gradient guarantees strong trade winds and intense Walker circulation 1 . e periodic diminution of winds o the West coast of South America diminishes cold deep water upwelling, reduces the east-west temperature di erence across the Paci c, and, as a result, also diminishes the Walker circulation: this brings high precipitations so South America, and high sea surface temperatures across the whole Paci c[START_REF] Tomczak | First recorded loss of an emperor penguin colony in the recent period of Antarctic regional warming: implications for other colonies[END_REF][START_REF] Stuecker | A combination mode of the annual cycle and the El Niño/Southern Oscillation[END_REF]. More recently, similar cyclical events arising from zonal temperature gradients have been identi ed in the Southern Indian Ocean (the SIOD,Saji et al. 1999) and in the South Atlantic(the SASD, Taschetto & Wainer 2008;[START_REF] Wainer | Reconstruction of the South Atlantic Subtropical Dipole index for the past 12,000 years from surface temperature proxy[END_REF]).

Figure 4 |

 4 Figure 4 | e formation of sea ice in coastal polynyas. A schematic representation of coastal sea ice for- mation (light blue arrow on the ice) and local upwelling around polynya area (see §18 p. 65 for details).

  e ort is currently deployed to understand the underlying mechanisms, and possible outcomes, of ongoing climate changes (see §50 p. 130 for an overview of the Intergovernmental Panel on Climate Change) -the same can unfortunately not be said of political endeavours. e result of this scienti c e ort is a growing understanding of the articulation between three di erent domains: climate change, environmental change, and meteorology. Although deeply intertwined, these three paradigms should not be confused. Climate change alone is the object of the IPCC de nition, a nd i s l imited t o t he a tmosphere-ocean c irculation e ectsi.e. long-term changes in temperature, precipitation, wind regime, etc. ese m ay b e appropriately described and captured through physical investigation methods, such as Atmosphere-Ocean General Circulation Models (see §50 p. 130 for details), and are mostly bounded by uncertainties about the nature of physical processes at work, and the socio-economic hypotheses underlying our projections of greenhouse gas emissions. Yet while numerical climate models are increasingly successful at predicting the physical environment of the Earth in the 21st century, the response of biological communities, or « environmental change », is a more elusive topic (see §13 p. 55 sqq. for an overview), because of the lack of a prior example (although early Holocene climate change is often used as an example, its pace di ers signi cantly f rom o ngoing c hanges -s ee Petit e t al.

Figure 6 |

 6 Figure 6 | e Food web of the Southern Ocean. A simpli ed structure of the food web of the Southern Ocean. Arrows gure p redation l inks. e di versity of zo oplankton an d s h str ategies is col lapsed in our representation.

  §-16 e scales of climate change. One of the most challenging aspects of the biology of climate change is the maze of scales on which it operates. Extant studies often oscillate between two extremes: the very slow time, in which geological climate change (such as the Pleistocene-Holocene transition

  ing posture from Pelecanoididae to Spheniscidae. Finally, outstanding diving abilities have been observed in the three groups: the massive Emperor penguin may reach depths down to 500m (Kooyman & Ponganis 1998), while the intermediate-sized Uria guillemots can reach 180m (Piatt & Nettleship 1985), and the small Peruvian diving-petrel 80m (Zavalaga & Jahncke 1997) 1 .

Figure 7 |

 7 Figure 7 | Distribution of the two Aptenodytes species. Orange dots: King penguin breeding archipela- gos, blue dots: Emperor penguin breeding colonies. Orange and Blue areas, general range of the species (schematic).

Figure 8 |

 8 Figure 8 | e RAD sequencing approach. See details of the protocol in §29 p. 85.

  analysis and xation index in Fumagalli et al. 2014, admixture analysis in Skotte et al. 2013, or derived-allele frequency spectrum estimation in Korneliussen et al. 2014). §-35 Exploring the duplicate bias. Accurate estimation of SNP positions and individual genotype likelihoods is further complicated by the technical challenges of Illumina platforms, such as we use in the present study. Two important characteristics of high-throughput sequencing platforms,

  - §39 proximated through the Wright-Fisher model[START_REF] Wolff | The interpretation of population structure by F-statistics with special regard to systems of mating[END_REF] Ewens 1979), as an ensemble of randomly mating (« panmictic ») individuals, where each individual will be replaced by one o spring at the next generation, and where generations do not overlap. Although seemingly rather unrealistic, this model has repeatedly been proved to perform surprisingly well under most empirical situations(Hamilton 2011), at the only cost of the necessary re-de nition of some concepts, such as population size (see §41 p. 103 for a development).One essential feature of the Wright-Fisher model is the way the transmission of alleles is conceived. e Wright-Fisher model describes a simple Markovian process: at each generation event, allele frequencies are re-sampled from the ancestral gene pool to the o spring gene pool under a binomial law, with stochastic variation in frequencies; thus, the gene pool at time T+1 only depends on the gene pool at time T and on a stochastic sampling event, without memory from previous states. e e xpectation f or t he b inomial s ampling i s t hat, i n t he a bsence o f selection pressure, allele frequencies remain the same across generation (the Hardy-Weinberg propriety, see Edwards 2008) -an expectation marginally contradicted by the stochastic nature of the sampling, which allows frequencies to vary in a random direction at each generation. e e xtent o f this variation, or genetic drift, is a function of the size of the Wright-Fisher population: a larger population will undergo less drift than a smaller one. Drift thus becomes the main tenet of the Wright-Fisher neutral molecular evolution model: new alleles, that occur randomly and after an in nite sites model (i.e. no two mutations ever occur at the same site, see Kimura & Crow 1964 for details), can either be lost through random sampling, or on the contrary see their frequency increase in the population; so that the probability of loosing an allele through drift decreases as the frequency of the allele increases. As opposed to the coalescent framework (see §40 p. 101), the Wright-Fisher Markovian model does not integrate evolution over the history of the population: there is a succession of states, but no origin in the process. us, the discrete generation steps of the Markov chain can be approximated as a continuous process, in what is known as the di usion approximation of genetic drift Concepts and methods - §40 (Kimura 1955). Under the Kimura model, alleles are idealised as particles, their frequencies as coordinates, and drift as movement. e di usion process results in a change in the location of alleles in the frequency space, under a probability law determined by the allele's di usion coe cient -itself a re ection o f t he p opulation s ize a nd d emographic e vents t hat m ay v ary t hrough time. While the expectation for the movement of the allele remains zero for any di usion coe cient and any length of time (in keeping with the Hardy-Weinberg expectation), the variance of thenal position increases with increased di usion coe cient -in other words, over a large set of loci, a sharply clustered frequency distribution is expected to spread out over time. e di usion approximation becomes especially useful in N-dimensional frequency spaces, i.e. when the frequency of an allele is considered in several populations simultaneously, with limited di usion allowed between populations (see §45 p. 117 for applications of this propriety). Although powerful conceptual tools, the Wright-Fisher model and Kimura di usion theory have the limitation of being, by default, a site-based paradigm: alleles are represented as (usually independent) entities whose relationship is merely complementarity in the frequency space. In other words, the drift paradigm does not take the genealogical evolution of sequences into accountthus, it can not exploit one of the richest information sources in the genome structure. §-40 e C oalescent t heory. Developed b y K ingman i n t he 1 980s, t he c oalescent t heory i s a diachronic take on the previously dominantly synchronic paradigm of population genetics, and the Wright-Fisher approximations of neutral drift (Kingman 1982; Kingman 2000). As opposed to essentially atemporal descriptive statistics such as xation i ndices ( Wright 1 965) o r a nalyses of molecular variance (Exco er et al. 1992b), the coalescent explicitly models the history of the observed state of genetic diversity. When considering the tree-like history of a short DNA sequence, each new mutation appears as the branching of the ancestral lineage into two new sequences: one unchanged, and one new mutated sequence. If we happened to know the « true » history of that sequence in a population, we would witness several such branching events, as well as several extinction events, when a lineage becomes lost by the e ects of random sampling, or natural selec-tion. But considering a realistic experimental situation, we really only sample the nal state of that history, i.e. the present state. When reconstructing that history from the data contained in its present state, each branching event now appears as the merging of two lineages into one ancestral lineage -a coalescence event.

Figure 9 |

 9 Figure 9 | Stochasticity and demography in the coalescence process. Gene trees simulated under di erent demographic models using msHOT (Hellenthal & Stephens 2007). (A) Constant population: all four trees are replicates of the same simulation parameters. (B) Four di erent demographic histories.

/ 2 .

 2 Ne ). If Ne is constant and large enough, the instantaneous probability of coalescence is very small, and the cumulative probability of two lineages coalescing at or before generation t can be approximated by the exponential function, as 1e -(1 / 2 Ne ) . t . us, the coalescence theory sets an expectation for the rate of coalescence of lineages in a random sample, which follows an exponential distribution if the population size remains constant through time. As it gives a diachronic picture of the evolution of sequences in a Wright-Fisher population, it allows us to step from the earlier synchronic paradigm of population genetics, into the reconstruction of time-dependent events such as demography and migration history. §-41 Coalescence, population size and migration. e probability of coalescence at any given generation is a function of the population size at that generation (see §40 p. 101). is has two important consequences: (i) the neutral, exponential coalescent provides us with a null model against which we can assess the neutrality of observed coalescent histories, and (ii) we can further develop the coalescent framework to explicitly model population size changes through time. But an important rst step is to clarify what we mean by population size -since, as we already discussed Concepts and methods - §41

Figure 10 |

 10 Figure 10 | Coalescence and population size. Reconstruction of the past population size of two simulated populations after the original Skyline Plot model (Pybus et al. 2000). (A) Constant size population, and (B) Exponential growth population. Bottom row: simulated gene trees, generated using msHOT (Hellenthal & Stephens 2007), Middle row: length of the coalescence intervals along the tree, Upper row: direct demographic function (black line) and generalised skyline plot (dashed blue line).

  two advantages: since we use 4 instead of 100 parameters, MCMC convergence time is greatly reduced. And since we obtain a single consensus substitution rate for each locus class, it is possi-Concepts and methods- §44 1.A transversion being the replacement of a purine base (A or G) by a pyrimidine base (C or T), and a transition the replacement of a purine by the other purine, or of a pyrimidine by the other pyrimidine. us, there are 8 possible transversions, and only four possible transitions. e r atio o f t he t ransition t o t ransversion r ates, o r κ ( kappa) i s c haracteristic o f a mutation model.

Figure 11 |

 11 Figure 11 | In uence of demographic processes on the joint AFS. Four di erent pairwise situations, simulated using fastsimcoal2. Under full migration, allele frequencies are highly correlated between the two populations. With low migration, random dispersion increases around the full correlation line. With asymmetric migration, dispersion becomes skewed, and the correlation departs from the diagonal. Our last example is an asymmetric history, where the rst population( vertical marginal spectrum) underwent a strong bottleneck biasing it towards intermediate allele frequencies, while the second population remained stable.

Figure 12 |

 12 Figure 12 | e Pairwise Sequentially Markovian Coalescent model. W e represent t he independent local trees along a set of recombining sequences. Each blue arrow gures a recombination event, or state change in the hidden Markov model. e bolder lines are the two sampled chromosomes, and their pairwise coalescence is outlines in red in the local trees. e result of the pairwise sampling is a distribution of TMRCAs (see §47 p. 122 for details).

tially

  Markovian coalescent approach (MSMC, Schi els & Durbin 2014), considers more than two haplotypes at a time. us, instead of considering the TMRCA between a pair of chromosomes as an indicator of the ARG, it takes the shallowest TMRCA amongst any pair of chromosomes in the sample as its hidden Markov state. e structure of the coalescent (see §40 p. 101) implies that this TMRCA tends to become shallower as the sample size becomes larger. us, switching from PSMC to MSMC increases considerably the resolution over the most recent periods. A particularity of the MSMC algorithm, consequently, is the link between sample size and reconstruction depth. Since MSMC focuses on the rst coalescence event b etween a ny two sequences in the sample, a larger sample size will increase the amount of information about recent coalescent structure, at the cost of older events, while reducing sample size will increase accuracy for deep-time events (Schi els & Durbin 2014

  ferred substitution rates (μ 3 = 0.0159, μ 4 = 0.0218, μ 5 = 0.0275, μ 6 = 0.0389. Fitted model: intercept i = -5.02, slope s = 0.292, R 2 =0.997). A Poisson model of parameter λ equal to the mean observed number of segregating sites was a good t for the empirical distribution of number ofConcepts and methods- §48 1.ESS or E ective Sample Size is the number of uncorrelated states sampled along the Markov chain. Correlation between successive states indicates a trend in the state of the model, and hence incomplete mixing. us, the number of steps needed for the correlation between states to decay, or lag vector, is indicative of the e ciency of the mixing for the MCMC.

by

  [START_REF] Travis | Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica[END_REF] using the Chinstrap penguin Pygoscelis antarctica as a model: the penguin's breeding success is explained primarily by krill abundance, and the krill abundance is partly explained by sea ice extent. A climate-envelope model would therefore nd a rather robust correlation between penguin breeding success and sea ice extent. However a disruption of the correlation between sea ice and krill can be introduced by cetacean demography (cetaceans are important competitors, and their demography is also controlled by lower-latitude breedingground conditions) or by human exploitation (a notoriously di cult parameter to forecast in our days): thus, the observed correlation between sea ice and penguin demography is in fact fallacious, and may change abruptly whenever cetacean demography or human exploitation of krill will change regime.

  sub-colony level, in which ne-scale e nvironmental f eatures d rive l ocal p hilopatric behaviour, while lower-quality patches may act as genetic mixing mechanisms at the colony level. ese ndings show how a lack of global genetic structuring can emerge from small-scale heterogeneity in ecological parameters, as opposed to the classical model of homogeneous dispersal. Our results also emphasise the importance of sampling design for estimation of population parameters in colonial seabirds, as at high spatial resolution, basic genetic features are shown to be location-dependent. Finally, this study stresses the importance of understanding intra-colonial dispersal and genetic mixing mechanisms in order to better estimate species-wide gene ows a nd population dynamics.Introduction §-56 Coloniality and population structure. How colonial systems are maintained in non-cooperative species remains an important question in evolutionary biology[START_REF] Danchin | The evolution of coloniality: the emergence of new perspectives[END_REF][START_REF] Bowler | Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics[END_REF]. Philopatric behaviour is usually considered to be the basis of coloniality[START_REF] Bowler | Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics[END_REF]. It is thought to o er several selective advantages, such as a good knowledge of the higher quality breeding spots and of the pool of breeding partners linked to these spots[START_REF] Wheelwright | Philopatry, natal dispersal, and inbreeding avoidance in an island population of Savannah Sparrows[END_REF][START_REF] He | Habitat saturation, benefits of philopatry, relatedness, and the extent of cooperative breeding in a cichlid[END_REF][START_REF] Arnaud | Philopatry and within-colony movements in Columbian ground squirrels[END_REF], and it favours the selective value of proximal defensive behaviour[START_REF] Dugger | Complex colony level organization of the deep sea siphonophore Bargmannia elongata (Cnidaria, Hydrozoa) is directionally asymmetric and arises by the subdivision of pro Annexes - §186 drial DNA restriction data[END_REF] or allofeeding[START_REF] Lecomte | Alloparental feeding in the king penguin[END_REF]) through kinship selection. However, the drawback of such behaviour is the fragmentation of genetic diversity at the colony level[START_REF] Banks | Genetic spatial autocorrelation can readily detect sex-biased dispersal[END_REF], which leads to an increase in inbreeding within clusters of closely-related individuals, in turn potentially leading to local inbreeding depression[START_REF] Forcada | Penguin responses to climate change in the Southern Ocean[END_REF][START_REF] Forcada | Penguin responses to climate change in the Southern Ocean[END_REF]. Dispersal is therefore an essential balancing force in the conservation of colonial systems[START_REF] Schlosser | Evidence for gene flow differs from observed dispersal patterns in the Humboldt penguin, Spheniscus humboldti[END_REF][START_REF] Bowler | Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics[END_REF]; Bick-Fine-scale colony structure - §57

  studied through phenological methods(Steiner 2005), and dispersal distance, even at very ne scale, has been shown to depend on species-speci c e cological d ynamics( Murrell e t a l. 2002;[START_REF] Banks | Genetic spatial autocorrelation can readily detect sex-biased dispersal[END_REF]. However, strict correlation between dispersal, site delity, and population structure has been questioned[START_REF] Peakall | Site fidelity is an inconsistent determinant of population structure in the hooded merganser (Lophodytes cucullatus): evidence from genetic, mark-recapture, and comparative data[END_REF], and the importance of intra-colonial movements on genetic mixing has received comparatively little attention. §-57 e K ing p enguin a s a m odel. e Ki ng pe nguin, Ap tenodytes pa tagonicus, wh ich breeds in large colonies all around the sub-Antarctic area, is an ideal model to investigate these aspects of coloniality. While the King penguin's sister-species, the Emperor penguin, Aptenodytes forsteri,

  on Possession Island, Crozet Archipelago. If philopatric behaviour indeed extends to breeding territory selection, it is expected that the colony will have a non-random genetic structure, with closely related individuals distributed in spatial clusters, as has already been observed in several non-avian philopatric species (e.g.[START_REF] Ritz | Phylogeography of the southern skua complex-rapid colonization of the southern hemisphere during a glacial period and reticulate evolution[END_REF][START_REF] Kanno | Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks[END_REF]. However, even in known philopatric species, detection of genetic structure can be hindered by a variety of behaviours, such as juvenile and adult dispersion or extra-pair paternity[START_REF] Double | Dispersal, philopatry, and infidelity: dissecting local genetic structure in superb fairy-wrens (Malurus cyaneus)[END_REF]; therefore, the methods used to study it must be sensitive enough to detect even a weak signal[START_REF] Jones | Incubation et élevage itinérants chez les manchots empereurs de Pointe Géologie (Terre Adélie). 362. Jouventin P (1972a) Note sur l'existence et la signification d'une rythmicité des parades mutuelles. 363. Jouventin P[END_REF]. §-58 Expectations and hypotheses. When considering the genetic structure of a species, expected patterns may be analysed according to (i) global homogeneous processes or (ii) local heterogeneous processes whose e ect varies across the area[START_REF] Osborne | Non-stationarity and local approaches to modelling the distributions of wildlife[END_REF]. (i) e predictions associated with strong and consistent philopatric behaviour belong to the former class. Without taking spatial distribution of individuals into account, a skew in global population inbreeding de-

  §-59 Permits and ethics statement. All animals in this study were handled only once in order to mark them with a plastic tag and to conduct morphological measurements. All procedures employed during this eld work were approved by the Ethical Committee of the French Polar Institute (Institut Paul Emile Victor -IPEV) and conducted in accordance with its guidelines, also complying with French laws including those relating to conservation and welfare. Authorizations to enter the breeding site (permits n°2009-57 issued on august 26th 2009) and handle birds (permits n°2009-59 issued on august 29th 2009) were delivered rst b y t he French " Ministère de l'Aménagement du Territoire et de l'Environnement" and then by the Terres Australes et Antarctiques Françaises (TAAF).

Fine-scale colony structure - §61 Figure 13 |

 §6113 Figure 13 | Sampling distribution. Sampling was restricted to the periphery of the colony. Orange zone boundaries are marked on the ground for remote parameter assessment. Shaded clusters run from C1 (north-west) to C6 (south-east). Orange zone: extent of the colony at the peak of the breeding season.

  pairs of individuals belonging to one cluster were compared to pairs of individuals belonging to di erent clusters, and signi cance o f d istribution d ivergence w as t ested i n C oancestry[START_REF] Ainley | Paradigm lost, or is top-down forcing no longer significant in the Antarctic marine ecosystem[END_REF]000 bootstrapping samples). §-66 Site-quality descriptors analysis. To measure breeding site quality, several ecological variables were used as proxies. (i) For 3 consecutive breeding seasons(2010)(2011)(2012), chick survival rate between hatching (January/February t) and edging (November/December t) was measured as the proportion of marked chicks still alive at edging[START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF]. (ii) Parasite infestation has been shown to in uence breeding success and adult survival rate in king penguins(Mangin et al. 2003). us, for 8 consecutive years(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012), tick infestation was measured as the proportion of infested adults in a randomly-selected sample (N=50) within each colony grid zone. (iii) Finally, as early breeders lay eggs in November/December, and late breeders after January[START_REF] Brodin | Modeling the breeding cycle of longlived birds: Why do king penguins try to breed late[END_REF] Descamps et al. 2002), an estimate of the ratio of incubating and brooding birds in early March was used, as preferred nesting sites are expected to be occupied rst by Fine-scale colony structure - §66 early breeders, while late breeders will occupy remaining, lower-quality areas.For 8 consecutive years (2006For 8 consecutive years ( -2013)), site occupancy timing was thus indexed as the proportion of brooding birds among 50 randomly-chosen breeders (i.e. incubating or brooding birds, leaving non-breeders aside). is results in a score of 1 for preferred early-breeding sites, and a score of 0 for locations only occupied by late breeders.Correlation between successive year per unit of space was assessed for all three variables (Pearson's r test performed between successive years with all grid zones), and spatial autocorrelation of the data was assessed in spdep (Bivand 2013, Moran's I, two-sided test, using the closest 5%, or 8 nearest neighbours). Our underlying hypothesis being that the breeding-site location choice of an individual breeder depends on its long-term experience and knowledge of breeding-site quality, ecological data were then concatenated for all available sampling years on each space unit. Adult tick infestation on each grid-location was averaged across all 8 sampling years. Chick survival rate was spatially averaged through local spatial analysis: each sampling location was weighted with the mean survival of the closest 5% of the colony (8 nearest sampling sites) within each breeding year, and data for all years were simultaneously mapped onto the colony. Areas with generally high score therefore have a consistently high local survival rate across several breeding seasons. Spatial autocorrelation within each dataset was assessed through Moran's I test implemented in spdep R package[START_REF] Bivand | Computing the Jacobian in Gaussian spatial autoregressive models: An illustrated comparison of available methods[END_REF], with the closest 5% of the colony de ning the spatial weights.

Figure 14 Figure 15 |

 1415 Figure 14 | Observed individual inbreeding distribution deviates from expected distribution in a non-related sample. (A) Di erence between simulated and observed mean population inbreeding level (Ritland's coe cient) for 50 simulated datasets. Solid red line: observed di erence. Gray intervals: 1%, 5% and 10% con dence intervals. (B) Observed distribution in the sample. (C) Expected distribution in a non-related population, given the same parameters as the observed population.

Figure 16

 16 Figure 16 | Spatial correlation and Intrinsic clustering tests do not support strong structure. (A) Spatial correlogram. Solid red line: observed correlation, by distance classes. Dashed red lines: random expectation condence interval, determined by bootstrapping. (B) Ln probability of the genetic data under six di erent clustering situations (from 1 to 6 populations).

Figure 17 |Figure 18 |

 1718 Figure 17 | Ecological descriptors of breeding-site quality exhibit a strongly heterogeneous distribution across the colony. (A) Adult tick load, averaged for years 2005-2012. Contour lines: 0.02 steps. (B) 8-neighbour chick survival, averaged for years 2010-2012. Contour lines: 0.1 steps. (C) Site occupancy chronology, averaged for years 2006-2013. Ratio of brooding birds amongst 50 randomly selected breeders. Contour lines: 0.1 steps.

Fine-scale colony

  structure - §74 due to site-occupancy contingency at the season-level, and the output of their previous breeding season. is process could maintain a su cient rate of genetic mixing in the whole colony despite a general trend towards local structure. Finally, king penguin population genetic structure may still bear the imprint of recent demographic history. Most of the sub-Antarctic king penguin colonies su ered a drastic reduction at the turn of the nineteenth century, as sealers slaughtered them to near-extinction on most of the archipelagos for oil production. ey recovered, but recently(Rounsevell & Copson 1982;[START_REF] Gales | Recovery of the king penguin, Aptenodytespatagonicus, population on Heard Island[END_REF]. Similar colonies on Macquarie island were reportedly reduced to less than 1,600 breeding pairs in 1937 (Falla 1937). Rapid recovery followed the cessation of slaughtering(Rounsevell & Copson 1982;[START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF], and population growth rate is suggested to have neared intrinsic growth rate for three decades[START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]. is c omplex demographic history is also expected to have had an impact on colonial genetic patterns. Even under the assumption of strong, consistent processes driving structure across several generations, recent rapid growth may weaken a long-term signal in present-day colonies (Waples & Gaggiotti 2006), though in that case, the use of more markers or more individuals may increase the strength of the signal[START_REF] Banks | Genetic spatial autocorrelation can readily detect sex-biased dispersal[END_REF]. §-74 Heterogeneity as a mixing mechanism. Despite these di erent factors, the convergence of genetic and ecological indicators outlines the structural heterogeneity inherent to penguin colonies, and probably to other colonial seabirds (such as guillemots or eiders,[START_REF] Frederiksen | Philopatry and dispersal within a Black guillemot colony[END_REF][START_REF] Peakall | Site fidelity is an inconsistent determinant of population structure in the hooded merganser (Lophodytes cucullatus): evidence from genetic, mark-recapture, and comparative data[END_REF]. However, our data point towards generally transient processes, which are not homogenous and stable enough in time to impact colony structure on a large spatial and temporal frame. Local heterogeneity appears to be dependent on medium-scale factors, such as individual experience and breeding history, but also present-day site quality, and should therefore be considered as a fundamental feature of coloniality in seabirds: local philopatric hotspots are counterbalanced by active outbreeding regions, thus preserving global genetic diversity and mixing at the colony level. Instead of being an exception in an otherwise strongly philopatric system, Fine-scale colony structure - §75

  patterns of individuals. In the present chapter, we assess the extent of genetic structure between colonies of King penguins on a global scale. Based on direct observation of philopatric behaviour e King synnome - §77(Le Bohec et al. 2008; Saraux et al. 2011b) and on the parallel with other seabird species(Friesen et al. 2007), a certain degree of genetic isolation would be expected in this species. However, the high dispersal potential of the King penguin, that is known to travel considerable distances outside of its breeding season (thousands of kilometres, see[START_REF] Bost | King Penguin. In: Penguins, Natural history and conservation[END_REF], may promote long-distance gene ow and counteract the drift consequences of local philopatry: so that it is uncertain which force, of philopatry and dispersal, has the upper hand in shaping the genetic patterns in the species. §-77 Understanding the dynamics of contemporary structures. As sentinel species of the Southern Ocean (see §18 p. 65), King penguins are the object of a long-term monitoring program on Possession Island (see §26 p. 81). However, extrapolating the ndings of local surveys to the whole species requires a set of assumptions that may not be easily veri ed. e degree of demographic independence of the colonies, in particular, raises central concerns. Direct comparison of the demographic trend of the ve King penguin colonies on Possession Island showed that colonies followed di erent demographic trajectories[START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]). However, true demographic independence requires not only divergent, but fully uncorrelated trends between demes[START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. In other words, the transfer of individuals from one colony to another results in divergent demographic patterns, but also demographic dependency. Delineating the one from

  habitat e ciently, and to follow a single, uni ed demographic trajectory. §-82 Palaeodemography of the Aptenodytes penguins. e v alue o f t he K ing p enguin a s a climatic bio-indicator is con rmed b y i ts s trong d emographic r esponse t o Q uaternary c limate change, that we reconstructed using RAD-sequencing and full-genome data. We relied on a novel modelexible approach (the Stairway plot; §95 p. 186), based on the composite likelihood of the derived-allele frequency spectrum (Liu & Fu 2015), as well as on extended Bayesian skyline plot ( §96 p. 187) and pairwise sequentially Markovian coalescent ( §97 p. 190) approaches. e King penguin population experienced two bottlenecks: (a) a recent one during the Last Glacial Maximum (LGM), and (b) a more ancient one overlapping with the previous Pleistocene glacial

Figure 19 |

 19 Figure 19 | Penguin paleodemography in response to Quaternary climate change. A. Stairway plot reconstruction of population size changes for the King penguin (orange) and the Emperor penguin (blue). Solid line: median population size; shaded area: 95% con dence i nterval. B . Temperature a nomaly i n t he l ate Quaternary, as inferred from the EPICA Dome C ice core (Augustin et al. 2004). Highlighted areas: Last Glacial Maximum (LGM, ~21-19 Kyr BP) and Valdivian interglacial period (~130-115 Kyr BP). Dashed line: Pleistocene-Holocene transition (~11.7 Kyr BP).

Figure 20 |

 20 Figure 20 | Past and future breeding range of the King penguin. Reconstructed position of the Antarc- tic Polar Front in February (SST = 5°C, dashed red line), and extent of sea ice in September (SIC > 15%, light blue area) at four contrasting time periods: A. Last Glacial Maximum (21-19 Kyr BP), B. Mid-Holocene (6 Kyr BP), C. Historical period (1981-2005), D. Projection for 2100 (RCP-8.5 forcing scenario). Occupation status of the islands: orange: presence of King penguin breeding colonies, blue: sea and/or land ice preventing colony foundation, grey: too far from the Antarctic Polar Front for foraging, white: never occupied by King penguins. Islands: 1: Tierra del Fuego, 2: Falklands, 3: South Georgia, 4: South Sandwich, 5: Gough, 6: Bouvet, 7: Marion and Prince Edward, 8: Crozet, 9: Kerguelen, 10: Heard and McDonald, 11: Amsterdam, 12: Macquarie, 13: Auckland, 14: Campbell, 15: Chatham.

RCP- 4 . 5 :

 45 or decrease in ocean productivity due to ocean acidi cation(Jackson 2008) and reduction of the global thermohaline circulation[START_REF] Kuhlbrodt | An integrated assessment of changes in the thermohaline circulation[END_REF]). e a brupt n ature o f t he p redicted K ing penguin range shift may additionally accelerate the restructuring and concentration of biotic interactions (e.g. range overlap and competition with other penguin species): this in turn can generate complex feedback e ects[START_REF] Davis | Making mistakes when predicting shifts in species range in response to global warming[END_REF][START_REF] Norberg | Eco-evolutionary responses of biodiversity to climate change[END_REF], thereby decreasing the predictability of Southern Ocean biodiversity, and prompting us to adopt an explicitly precautionary approach to marine resource management. e King synnome - §84 only Crozet and Prince Edward are too far from the APF to sustain large breeding colonies by 2100, while Kerguelen retains a favourable situation. (B) Schematic representation of the di erent results of climate change in the Southern Ocean. Dark and light water masses: cold antarctic deep water and warmer subantarctic surface water (major circulation as a black arrow). APF: Antarctic Polar Front. Dashed arrows: foraging trips in 2015 and 2100. e red arrow in the egg represents the trend in breeding success.

eFigure 23 |Figure 24 |

 2324 Figure 23 | Principal component analysis. As performed on genotype likelihoods in ngsAdmix (Skotte et al. 2013), retaining only variable loci. Shaded areas re ect archipelagos.

Figure 25 |e

 25 Figure 25 | Robustness of the Stairway plot method. Stairway plot reconstructions with the same set of King penguin individuals, but based on A) the full spectrum as inferred from 140 samples, B) masking the singleton loci, C) masking both singletons and doubletons, and D) masking singletons, and using only a random subset of 70

Figure 26 |

 26 Figure 26 | Extended Bayesian Skyline Plot. Reconstruction of past population size changes for the King penguin (orange) and the Emperor penguin (blue). Solid line: median population size; shaded area: 95% con dence interval (see S2.4 for details). Dashed black line: Pleistocene-Holocene boundary. Shaded blue band: last glacial maximum.

Figure 27 |

 27 Figure 27 | Pairwise Sequentially Markovian Coalescent. Reconstruction of past population size changes for the King penguin (orange) and the Emperor penguin (blue). Each individual line represents one bootstrap replicate. Reconstruction was performed either A) for each individual separately (each shade represents one individual), or B) concatenating genomic data from all three individuals for each species. Dashed black line: Pleistocene-Holocene boundary. Shaded blue band: last glacial maximum.

Figure 28 |S- 2 :

 282 Figure 28 | Validation of the demographic reconstructions through simulation. Median e ective population size and con dence interval (in thousands of breeders) as a function of time, as reconstructed from simulated data (simulated scenario is represented by the red line on each graph). A) Stairway plot reconstruction, B)

  Ocean. §-100 De nition and constraints of the Antarctic Polar Front. e Southern Ocean is characterised by a strong circular Westerly current that ows u ninterrupted b y l and b arriers, t he Antarctic Circumpolar Current (ACC). Strong westerly winds generate important northward Ekman transport in the surface water layer, resulting in a convergence of the cold Antarctic surface waters,

  which has a much improved bathymetric resolution (Meijers 2014), and the in uence o f the Drake Passage and Campbell Plateau on the frontal structure is accurately reproduced in our reconstructions (Fig.20

  ity amongst core locations (especially benthic sediment cores) is high, and several land-sea coupling mechanisms are still poorly understood[START_REF] Mccauley | Divergent trends in land and ocean temperature in the Southern Ocean over the past 18,000 years[END_REF]). In the current state of knowledge, we can distinguish four major periods in the Southern Ocean late-Pleistocene and Holocene history: (i) Quaternary conditions, (ii) Last Glacial Maximum conditions, (iii) Pleistocene glacial retreat and early holocene optimum (17-9 Kyr BP), (iv) Holocene hypsithermal and neoglacial conditions (8-0 Kyr BP). (i) Quaternary conditions (59-22 Kyr BP) were mostly glacial-like, with slow onset of glaciation from ~35 Kyr BP, and winter sea ice cover reaching as far as ~56°S in the Paci c. Little is known of land ice throughout the period, as further glaciation obliterated most of the direct evidence. (ii) Last Glacial Maximum conditions (21-18 Kyr BP) were characterised by extensive land and sea ice cover throughout the Southern Ocean. e A ntarctic P olar F ront i s t hought t o have moved northward to 40-50°S, a movement associated with a ~5°C cooling in summer SST (Gersonde et al. 2005; MARGO 2009), although frontal movement is thought to have been constrained by bathymetry south of the Campbell plateau (Neil et al. 2004; Kohfeld et al. 2013). Winter sea ice is also thought to have reached ~50°S or further northward, or the approximate position of the present-day polar front (CLIMAP 1981) (between 47°S in the Atlantic and Indian Oceans, and 57°S in the Paci c O cean, s ee G ersonde e t a l. 2 005). M arine p roductivity is thought to have shifted from the Antarctic to the Subantarctic region (Hodgson et al. 2014), while not changing signi cantly in total biomass (Wol et al. 2006; Kohfeld et al. 2013). Islands of Heard, Crozet, Marion, and the Drake Arc were entirely covered by ice, while Kerguelen and e King synnome - §101

- 9

 9 Kyr BP) saw a gradual thawing of most land ice, with contrasting chronologies. Antarctic and subantarctic fronts retreated south to their current location (Kohfeld et al. 2013). Sea ice retreated until the Early Holocene climatic optimum (~11.5-9 Kyr BP), with an episodic increase during the Antarctic Cold Reversal around 14.5 Kyr BP, reaching its current position by ~10 Kyr BP. Kerguelen and South Georgia archipelago bear signs of early deglaciation, while Crozet and Marion islands are thought to have carried

  developed by[START_REF] Beaugrand | Future vulnerability of marine biodiversity compared with contemporary and past changes[END_REF] by calculating the density distribution of projected foraging distance for each island (i) for the current period(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), (ii) for the middle of the century (2041-2050) and (iii) for the end of the century (2091-2100) (Fig.29). In addition, for the same periods, we also calculated the percentage of models forecasting local King penguin population collapse (February foraging distance > 700 km; Fig.30), as proposed by Raybaud et al. 2013. e latitude of the APF, and therefore the duration of the King penguin's foraging trips, is subject to a high interannual variability, in particular under the in uence o f t he c yclical E l Niño Southern Oscillation and Southern Annular Mode, with year-to-year latitudinal uctuations of up to 200

Figure 29 |

 29 Figure 29 | (NEXT PAGE) Foraging distance from single models. Projected distance between subantarctic archipelagos and the Antarctic Polar Front in February extracted from IPCC CMIP5 models taken separately. Colours correspond to rcp scenarios: rcp-2.6 (green), rcp-4.5 (orange) and rcp-8.5 (red). Dashed line represents the 700-km limit. A) yearly projection, B) density distribution per rcp scenario, at three di erent time steps.

Figure 30 |

 30 Figure 30 | Proportion of models predicting extinction of King penguin colonies. Proportion of the 15 selected models pre- dicting a February foraging distance > 700 km for 20% of the decade, at three di erent time points. Colours correspond to rcp scenarios: rcp-2.6 (green), rcp-4.5 (orange) and rcp-8.5 (red).

Figure 31 |

 31 Figure 31 | Drift and mixing concur to keep a high degree of worldwide homogeneity in the Emperor penguin. (A) Estimated pairwise migration rates (outer coloured links) and Fst (inner dashed links) be- tween sampled colonies. Migrations as estimated from joint allele-frequency spectra. Colours refer to the receiving populations. (B) Neighbour-net of all sampled individuals. Colours correspond to colonies on map (A).

Finally, high geneticFigure 32 |

 32 Figure 32 | Demography is a matter of scales. (A) Demographic reconstructions (extended bayesian skyline plots), for all samples, and per-colony. Solid line: mean population size. Shaded area: 95% con dence interval. Blue area: Last Glacial Maximum (LGM). (B) Census size for the six analysed colonies (from Fretwell et al. 2012 and Ancel et al. 2014) (C) ree di erent demographic times-scales for DDU colony: a. coalescent-scale (EBSP reconstruction, see 2A), b. monitoring-scale (from Jenouvrier et al. 2006 and unpublished data), c. current catastrophic breeding failure: blue, eggs lost during brooding, gray, chicks found dead, orange, successfully edged chicks.

  causes of their decline. Our results also highlight the absolute novelty of the Emperor penguin's single fully-panmictic population. Although exceptional if compared with current knowledge about other marine predators, such as turtles (Molfetti et al. 2013), marine mammals (Hoelzel 1998), and most seabird species (Friesen et al. 2007), intense dispersal is expected to be a common evolutionary strategy in unstable high-latitude environments (Friesen et al. 2007), especially under ongoing climate warming. As a single genetic population, Emperor penguins will respond to climate change through a uni ed evolutionary trajectory. is insight thus presents new challenges to our understanding of how current climate scenarios will impact upon the future of the most cold-adapted species in the world, an iconic bio-indicator of the delicate Antarctic ecosystem.

Figure 33 |

 33 Figure 33 | (NEXT PAGE) Changes in sea ice con guration a long c oastal A ntarctica from Pleistocene to the 21st century. Mean, variance and Moran's global index for sea ice concentration in % (A)and thickness in meters (B), and o shore surface wind stress in Pa (C) and speed in m.s -1 (D). In (A), sea ice extent is additionally de ned as the ratio of sea ice surface with a concentration below 15%.

Figure 34 |

 34 Figure 34 | (NEXT PAGE) Uncertainty in the representation of sea ice in the CMIP5 models.Coe cients of variation (CV) for sea ice concentration in % (A) and thickness in meters (B), and o shore surface wind stress in Pa (C) and speed in m.s -1 (D). In (A), sea ice extent is additionally de ned as the ratio of sea ice surface with a concentration below 15%. CV are computed per model, or per periods.

  §-125 Sample collection and DNA extraction. Material was collected from six locations of East Antarctica, between 2004 and 2012: Cape Washington in the Ross sea area (« WSH »), Pointe Géologie (« DDU ») in Adélie Land, Mertz East (« MZE ») and Mertz West (« MZW ») in George V Land, Atka bay (« NEU ») in Dronning Maud Land area, and Halley Bay (« HAL ») in the Weddell Sea area. On the DDU colony, blood samples were collected from 23 chicks prior to edging. I n W SH, M ZE, M ZW, N EU a nd H AL, m uscle s amples w ere c ollected f rom frozen chick carcasses collected around the colony (WSH: N=4, MZE: N=25, MZW: N=14, NEU:

eFigure 35 |

 35 Figure 35 | Consensus SNP calling. Venn diagram of SNP calls by three di erent algorithms: GATK, ANGSD with Samtools model, and Stacks.

π

  MZW =0.0023; π NEU =0.0023, π HAL =0.0023; π WSH =0.0026), going along with the expectation for long-lived high-investment species (Romiguier et al. 2014), and despite very di erent colony census sizes. Finally, Tajima's D averaged across the whole genome does not deviate from neutral expectations (D DDU = -0.87; D MZE = -0.66; D MZW = -0.578; D NEU = -0.64; D HAL = -0.68; D WSH = -0.28).

Figure 36 |

 36 Figure 36 | e i mprint o f g eography o n t he Emperor synnome. ( A) Principal c omponent analysis (percentage of explained variance along the axes) -the colours on the plot refer to the colours used on the colony locations. (B) NeighbourNet built from Fst values according to Reich's estimator (table: upper triangle, point estimates, lower triangle, standard deviation). (C) ree examples of pairwise derived-allele frequency spectra, between two nearby South Paci c colonies (MZE and MZW), two distant colonies (HAL and MZE), and two South Atlantic colonies (HAL and NEU).

eFigure 37 |

 37 Figure 37 | Pairwise genetic distance matrix. Upper triangle: identity-by-descent inference (IBD). Lower triangle: identity-by-state pairwise (IBS).

Figure 38 |

 38 Figure 38 | Inference of population splits. Population tree reconstruction based on the TreeMix model. (A) Including polymorphism data from the King penguin as an outgroup. (B) Without the King penguin and xing the root as the same position as inferred in A.

1 2Figure 39 |

 139 Figure 39 | Haplotype network for mitochondrial sequences. Each dot represents a mutation. e areas of the discs is proportional to the number of copies found in the sample. Coloured sectors indicate the geographical

  25). In the present chapter, we use a direct, observational method, based on ground surveys, aerial and satellite photography, and a long-term monitoring program (see §24 p. 79) to shed light upon the possible mechanisms underlying migration in Aptenodytes penguins.

  §-139 Animal ethics were not an issue as we counted the birds from aerial pictures. No manipulation or experimentation was con-ducted on live birds and care was taken to avoid any disturbance. e F rench Polar I nstitute ( IPEV) a nd t he Terres A ustrales e t A ntarctiques F ranc aises (TAAF) are the authorities that issued the permit to visit the colonies.Empirical evidence of heterogeneous dispersal - §140During the 2012-2013 austral summer, the French Polar Institute's resupply ship MSS L'Astrolabe visited the Antarctic coastline near to the Mertz Glacier. L'Astrolabe's classi cation is ice class 1A super, the highest class of vessels that can operate in di cult polar conditions without the assistance of an ice breaker. On November 1 st and 2 nd , using a Eurocopter Squirrel B-3 single-engine helicopter, aerial surveys were conducted along the northern edge of the Mertz Glacier in an attempt to locate the new breeding sites of the emperor penguin colonies. Aerial photographs, used for recording penguin location and for counting the chicks and adults, were taken obliquely in clear weather from an altitude of 300 meters, with a 35-mm Canon 40D digital camera tted with a 200 mm f/4 lens. §-140 Aerial photographs were stitched together to form photo-mosaics in PTGui(c) software, and both chicks and adults were counted separately using the count tool in Photoshop CS5©.

Figure 40

 40 Figure 40 | Western Emperor penguin colony. A. Quickbird satellite view of the eastern emperor penguin colony (67°199S, 145°529E) showing adults and chicks present on November 1st, 2012. B. Ground view of the same (blue arrows at matching locations). Photograph by Robin Cristofari.

Figure 41 |

 41 Figure 41 | Eastern Emperor penguin colony. A. Location of the eastern colony (67°199S, 145°529E) on the sea-ice at the time of our visit and previous locations of the colony on top of icebergs, ca. 10 m high, covered with guano (arrow). B & C. Layers of droppings (arrows) covered by several layers of snow indicate abundant snow-covered episodes during the breeding season. Photographs by Robin Cristofari.

  are not missed due to heavy snowfall, deep shadows or topographic features such as ice cli s. If possible, these observations should be backed up with aerial or ground counts as the limited special resolution of satellite imagery results in large inherent variances when calculating breeding Empirical evidence of heterogeneous dispersal - §143 populations. Innovative cameras combined with biologging would also be very useful to complete the satellite survey. For instance, since the satellite survey based on 2009 imagery, four more emperor penguin colonies have been discovered: two on the West Ice Shelf detected by aerial survey[START_REF] Wienecke | Emperor penguins at the West Ice Shelf[END_REF], and two others on the West Ice Shelf and near the Jason Peninsula identi-

Figure 42 |

 42 Figure 42 | Satellite images of the Mertz glacier. Circle area is proportional to colony size. Continent is on top, open sea at the bottom. A. Estimated location of the Mertz emperor penguin colony on November 13th, 2009 (red circle) on the eastern ank of the Glacier tongue. A large crack in the Mertz Glacier is visible (red arrow). e green circle corresponds to Pointe Géologie colony. B. Location of the two new colonies of emperor penguins on December 3rd, 2012 (blue circles), 2 years after the 2010 calving of the Glacier tongue. e berg (overall length of 80 km and a width of 40 km) broke o the Mertz Glacier after being rammed by another iceberg. e green circle corresponds to Pointe Géologie colony. Images downloaded from the USGS website (https://lta.cr.usgs.gov).

  to Aptneodytes penguins, P. garnotii enjoys a mostly stable oceanographic landscape. It breeds on a handful of o shore islands of central Northern Chile and Southern Peru (Hays 1989; Simeone et al. 2003), that are part of the Humboldt Current System. §-147 e Humboldt Current System. e Humboldt Current System (HCS) is a Northward extension of the Paci c subantarctic watermasses. Like the Benguela Current in the Atlantic basin, it is one of the world's major Eastern Bounday Currents: North of the West Wind Drift zone (see §7 p. 35), the Southern Paci c anticyclonic gyre drives nearly constant equatorwards winds along the coast of South America. ese winds result in a strong northward drift of cold subantarctic surface waters along the coast, and in o shore Ekman transport: near the shore, the drifting surface waters draw the upwelling of tropical nutrient-righ subsurface waters, which result in one of the most highly productive oceanic systems on the planet (see iel et al. 2007 for details). Upwelling activity is regulated by the seasonal displacement of the Paci c anticyclonic gyre, and is constant only in Northern Central Chile and in Peru. It is also strongly in uenced by the topology of the South American continental shelf, where local cape and island systems promote upwelling activitye.g. in the Coquimbo Coastal System[START_REF] Montecino | Bio-physical interactions off western South-America[END_REF]. us, although the El Niño Southern Oscillation (see §8 p. 40), just like in the Southern Ocean, has a deep impact on local productivity, it does not in uence the location of upwelling hotspots, that remain at the same distance from breeding islands.is relative stability of the HCS topological and oceanographic features sets a very di erent expectation for the population structure of seabirds 1 .

  Figueroa et al. 2011), hunting (Hays 1989), bycatch (Hays 1989; García-Godos & Goya 2006), human-introduced predators (Araya Modinger & Du y 1987; Simeone et al. 2003) and habitat competitors (Fernández Zamora 2011), food competition with sheries (Hays 1989; Jahncke & Goya 1998), and the increased e ects of ongoing climate change on the productivity of the Humboldt Current System (HCS, see iel et al. 2007). is led to the decline of their nesting habitats, from thirteen colonies reported in the past to seven nesting sites at present (Araya Modinger & Du y 1987; Vilina 1992; Figueroa & Stucchi 2008; Figueroa et al. 2011). Its breeding range has then been reduced and fragmented into two main areas, divided by a 1,300 km gap -one on the central Peruvian coastline, around 8°S and 14°S (Isla Corcovado, Isla San

  §-153 Sequencing data. Genome-wide short-locus RAD-sequencing and mitochondrial data (see Methods) are in good agreement in Pelecanoides garnotii. e I llumina s equencing l ane yielded 182,319,948 paired-end reads with a mean phred-scaled quality score of 37, of which 144,048,478 were retained after barcode and cut-site sequence ltering. e n al dat abase included 65,582 distinct RAD loci, of which 37,170 were polymorphic, with an average of 1.3 SNPs per locus. After PCR-duplicate removal, SNP calling from the higher-quality reads in Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §154 GATK yielded 43,175 SNPs, of which 32,964 were genotyped in at least 75% of individuals, with a mean depth of 3.2X, and a median depth of 3.1X. Nucleotide diversity computed from genome-wide SNP data was very similar across islands, with π CHR = 0.00224 and π AZR = 0.00226 (and pooled diversity π ALL = 0.00233), at a level in accordance with the expectation for long-lived seabirds (Romiguier et al. 2014), and Tajima's D supported neutral evolution at the selected sites (D CHR = -0.364 D AZR = -0.361). mtDNA-based indices support this view: nucleotide diversity was of the same order of magnitude for both islands, although slightly higher in Choros (π CHR = 0.00232 and π AZR = 0.00161, and pooled diversity π ALL = 0.00218). Both Tajima's D and Fu & Li's F indices supported neutral evolution of the polymorphic sites (D CHR = -0.463 and D AZR = -0.649, P > 0.10; F CHR = -1.26 and F AZR = -1.34, P > 0.10). e sequenced cytochrome-b fragment contained 13 polymorphic sites, only 6 of which were polymorphic at both locations. §-154 Population structure. All analyses support an outstandingly high level of genetic divergence at such a short geographical distance. Fixation index is high for genome-wide SNP data (Fst = 0.049 ±0.0082, averaged over 500-SNPs windows), and higher from mtDNA data (Fst=0.158).

Figure 44 | 5 [ 1 .

 4451 Figure 44 | Consistent genetic structure within the Chilean range. (A) Principal component analysis performed on genome-wide genotype likelihood data; (B) NeighbourNet built from nuclear SNP data; (C) Individual components of admixture as inferred from genome-wide genotype likelihood data (median values from 500 bootstrap replicates); (D) Fitch-distance haplotype graph for the mitochondrial cytochrome-b gene (disc area pro-portional to the number of haplotype copies). In all panels, "AZ" stands for Isla Pan de Azúcar and "CH" for Isla Choros. AZ1 and AZ2, and CH1 and CH2 respectively stand for two colonies of each island.

  tion of ~ 19,000 breeders in Isla Choros (Martinez Palma 2014) and ~ 220 in Isla Pan de Azúcar (Mattern et al. 2002), but more in tune with the historical populations (e.g. 100,000 breeders on the major breeding location of Isla Chañaral in 1938, and similarly large colonies elsewhere, Araya Modinger & Du y 1987): a dire testimony to the rapidity and extent of the population collapse in the past decades (Roman & Palumbi 2003).

  g.Milot et Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §159 al. 2008), the higher investment needed for burrow construction[START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF], together with the scarcity of available habitat, may reinforce that behaviour in the diving petrel. §-159 Implications for conservation. e observed level of population isolation has important implications for the conservation of the species. Whereas the panmictic character of the HCS population of Peruvian pelican or Peruvian booby has been suggested to increase their robustness in the face of environmental change[START_REF] Jeyasingham | Specialization to cold water upwellings may facilitate gene flow in seabirds: new evidence from the Peruvian pelican Pelecanus thagus (Pelecaniformes: Pelecanidae)[END_REF], see also e E mperor s ynnome, p. 211), the high fragmentation of the P. garnotii population, on the other hand, is expected to add to their already great vulnerability to land habitat destruction and marine environment degradation. Indeed, high philopatry and low dispersal imply that the loss of a combined safe breeding island and productive foraging area can not be mitigated by mass dispersal, but will rather lead to a local extinction, and the irremediable loss of the associated genetic diversity. An associated prediction is that current populations are demographically independent[START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. erefore, the observed local trajectories are unlikely to be an artifact of labile migratory uxes, but rather represent the true local demographic trend: thus, the local census increases that are observed (in particular in Isla Choros, Martinez Palma 2014) may be interpreted as the positive outcome of successful conservation measures, such as the creation of the Humboldt Penguin National Reserve (that includes Isla Choros and the surrounding marine area) -an encouragement to persevere in such endeavours.Our results make it especially clear that any conservation strategy in the Humboldt Current System should focus on each island (and the surrounding marine areas, Luna-Jorquera et al. 2012) as a signi cant u nit. W ithin i slands, c olonies d o n ot s tand o ut a s g enetically s eparate e ntity, although philopatric behaviour may still apply at that scale for stretches of a few generations. But the distinctness of each island's gene pool, as well as their inferred demographic independence, makes the conservation of each single one of the four remaining Chilean breeding islands an ab-Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §161

  §-160 Sample collection and DNA extraction. Access to the protected areas and sampling were performed under permit N°38/2012, delivered by the CONAF authority (Atacama region). Sampling was performed between april and may, outside the reproductive peak season (Fernández com. pers.) to minimise disturbance. §-161 Sampling and DNA extraction. Four colonies of Pelecanoides garnotii were sampled throughout its Chilean breeding range: two in Isla Choros (Fig. 43 -Ch1 and Ch2: 32 and 11 individuals respectively), and two in Isla Pan de Azúcar (Fig. 43 -Az1 and Az2: 27 and 11 individuals respectively). Adults were captured using a mist net placed at the entrance of the colony at dusk. Blood was sampled from the retia in the interdigital membrane of the foot, using a he-Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §163 parinized microcapillary, dried, and frozen. DNA extraction was performed using a standard spin-column protocol (Qiagen DNeasy® Blood & Tissue kit). §-162 Mitochondrial marker sequencing. A fragment of the mitochondrial cytochrome-b gene was

(

  both within and between individuals). e corresponding paired-end contigs were assembled using Velvet[START_REF] Zavalaga | Velvet: algorithms for de novo short read assembly using de Bruijn graphs[END_REF] and Stacks, setting a minimum contig length of 200bp in order to lter out multiple non-overlapping short paired-end reads. (ii) Resulting loci were concatenated, together with their respective paired-end contigs, to form the sca olds of a reduced reference genome, or RADome. At that point, loci comprising multiple paired-end contigs (a potential sign of collapsed paralogous loci) were removed. e raw paired-end fastq les were then mapped onto this RADome using Bowtie2[START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF]). e r esulting alignments were processed using Samtools 0.1.19[START_REF] Li | Fast and accurate short read alignment with Burrows-Wheeler transform[END_REF], PicardTools 1.113 (http://picard.sorceforge.net), and custom R and shell scripts in order to discard orphaned reads and lowquality pairs. ese cleaned alignments were further ltered for PCR and optical duplicates using Picard MarkDuplicates. (iii) SNP and genotype calling was restricted to the rst-in-par reads. We then used GATK's HaplotypeCaller pipeline[START_REF] Depristo | The annual breeding cycle of unbanded king penguins Aptenodytes patagonicus on Possession Island (Crozet)[END_REF] to simultaneously call SNPs

  gence between the King and the Emperor penguin: inter-individual intraspeci c variance may be almost of the same order of magnitude as interspeci c variance, and shared polymorphism is important compared to xed di erences. Although the question of speciation and possibly incomplete lineage sorting between A. forsteri and A. patagonicus will require further investigation, based amongst other things on whole-genome resequencing data 2 , the present study support the view of Gavryushkina et al. 2015, who place the divergence of the two Aptenodytes lineages in the relatively recent past (1.52 million years ago, but possibly as recently as 730,000 years ago), at a slightly more recent period than the divergence of the still-hybridising Spheniscus magellanicus and S. humboldti. e question of the extent of prezygotic isolation between the two Aptenodytes 1.

  sise the importance of climate change in shaping the future of penguin. §-172 Seabird response to environmental change. Despite the overwhelming evidence for a deep impact of climate change on biological communities (and that our work clearly supports), reducing the current threats to the sole e ects of climate change is misleading. In a recent study, Trivelpiece et al. 2011 demonstrated how direct anthropogenic e ects, such as the hunt of cetaceans to near-extinction, or the present-day overexploitation of krill stocks (e.g. for farmed-sh food), had a considerable e ect on Chinstrap penguin Pygoscelis antarctica breeding success and recruitment, that outweighed the impacts of climate change. Generally, over shing has been identi ed as the major cause of decline in seabird populations, far before the change in climate (Cury et al. 2011).

35 )

 35 , and a major input into the lower latitudes (in particular through the eastern boundary currents such as the Humboldt Current, see §147 p. 276) -and changes in its circulation patterns have strong consequences on the world's ocean circulation and productivity patterns. us, the e ects of climate change that we document here in three seabird species are by no means limited to their particular ecosystems, but must rather be interpreted as a marker of large-scale changes in the global state of the Oceans. Indeed, the extreme adaptations of polar species often motivate the misleading impression that they reveal equally extreme, and perhaps marginal e ect of climate change. Yet although we can, from a phylogenetic standpoint, infer that Aptenodytes penguins' adaptations are extreme compared to their avian ancestors, there is nothing extreme in the species themselves, as seen from within their own habitats: they are, in that sense, strongly representative of the world's ecosystems at large. If the e ects of climate change are more visible in polar regions than anywhere else, it is Annexes - §176 Although ca half of the global king penguin population is resident in the Crozet basin, and this top predator represents one of the largest components of the sub-Antarctic marine ecosystem as measured by biomass and energy ux (Charrassin & Bost 2001), the long-term response of this species to the warming period following the LGM remains entirely unknown. Here, we use a genomic-based demographic reconstruction to test whether the king penguin population from the Crozet archipelago ('La Baie du Marin' colony, Possession Island) was strongly a ected by the colder conditions characterising the LGM. We tested the applicability of genome-wide data produced by restriction site-associated DNA (RAD) sequencing (Baird et al. 2008; Davey et al. 2011) within a multi-locus coalescent-based framework (Extended Bayesian Skyline Plot, EBSP, see Heled & Drummond 2008) that aims to average the stochasticity of mutation and drift across tens of genomic loci and to overcome the idiosyncrasy of the results obtained from one or a few molecular markers.We rst d emonstrated t hat g enomic regions w ith d i erent l evels o f p olymorphism consistently show a similar pattern of diversity, and that a consistent demographic signature is recognizable across the entire genome, thus providing evidence that a reduced subset of anonymous genomic loci can be safely employed to estimate past population trends. Historic demography was then inferred by employing a random selection of RAD loci sequenced in multiple individuals and time calibrated according to the mitochondrial substitution rate as estimated in the Ade ĺie penguin Control Region (CR) (in substitutions.site -1 .Myr -1 : median = 0.55, 95% interval = 0.29-0.88, see[START_REF] Ritz | Phylogeography of the southern skua complex-rapid colonization of the southern hemisphere during a glacial period and reticulate evolution[END_REF]. Our data show that the king penguin population from Crozet was strongly reduced on this potentially ice-covered sub-Antarctic archipelago before the end of the LGM, or king penguins may have been totally absent, but they (re)colonized the island as soon as the ecological conditions required by this species were met during the following Holocene warming.

Figure 46 |

 46 Figure 46 | Demographic inference based on the AFS. (a) Minor AFS analysis performed on 1-SNP class loci: observed (blue) and simulated (red) spectra and deviation (green) are given. (b) Mismatch distribution density analysis performed on 2-to 6-SNP class loci: mean values (red) and number of loci in each class (in brackets).

  - §177 less, our EBSP demographic reconstructions were highly consistent with each other, with those produced by the di usion approximation of the AFS (Fig.46 p. 334) and with standard singlelocus mtDNA analyses(supplementary material, Fig. 50 p. 346). e AFS analysis, based on approximation to the neutral Wright-Fisher di usion of an allele(Gutenkunst et al. 2009), has

Figure 48 |

 48 Figure 48 | Past demographic trend of the king penguin colony of 'La Baie du Marin' on Posses- sion Island, Crozet archipelago: median value (red) and 95% con dence interval (pale red). Trend of temperature anomalies recorded in the EPICA Dome C ice core (black

Fig. 50 p

 50 Fig. 50 p. 346). Nevertheless, analysing a fairly large number of (presumably) unlinked loci should be considered as best practice as it allows to us average the stochasticity of mutation and drift over many independent genealogies, thus avoiding the risk of a biased demographic inference owing to the idiosyncratic history of a single marker. In addition, when a large number of

  - §180 the settlement of a viable king penguin population on the archipelago before the post-LGM warming. As soon as breeding grounds became available, the king penguin could extensively settle on Crozet and exploit the food resource. However, with growing SST temperature owing to the global warming, the AFP can soon be too far from Crozet, thus making the area unsuitable again. Extinction, beginning locally and potentially mitigated by migration events (depending on their ability to follow spatial and temporal changes in food/breeding resource availability, as seen in the Gentoo penguin Pygoscelis papua, seeDucklow et al. 2007), is a probable outcome of the ongoing climate warming.Material and methods §-179Sampling, DNA extraction and quality assessment. Sampling was conducted during the 2010 breeding season in the king penguin colony of 'La Baie du Marin' on Possession Island (46°250 S, 51°8450 E), Crozet Archipelago. Blood (approx. 100 ml) was collected from the brachial vein of chicks hatched in the long-term monitored area 'ANTAVIA', transferred to a lter paper (Whatman 113), dried and later frozen at -20°C. Individuals were randomly selected along a 120

  7.4 package[START_REF] Drummond | Historical variations in food consumption by breeding seabirds of the Humboldt and Benguela upwelling regions[END_REF]. Analyses were performed on the Bioportal facility (now LifePortal)running on the ABEL cluster, University of Oslo. A GTR+G+I substitution model was set for the mitochondrial sequence. A relaxed uncorrelated log-normal clock prior was set for the substitution rate to take into account uctuations of the molecular clock along di erent branches of the phylogeny; a log-normal priors with mean in the real space of 0.55substitution/site/Myr respectively were set[START_REF] Ritz | Phylogeography of the southern skua complex-rapid colonization of the southern hemisphere during a glacial period and reticulate evolution[END_REF]. eBayesian Skyline plot was set as coalescent tree prior model. Convergence among three runs, with a MCMC length of 30 million generations for each parameter setting was checked. E ective sample size was checked inTracer 1.5 (Rambaut & Drummond 2005) and plots of population size change through time were drawn. §-186 RAD sequencing and genome-wide demographic inference. Eight king penguin individuals were pooled and genotyped by RAD sequencing[START_REF] Ahmed | Isolation and characterization of macaroni penguin (Eudyptes chrysolophus) microsatellite loci and their utility in other penguin species (Spheniscidae, AVES)[END_REF] in one library sequenced on an ILLUMINA HiSeq2000, yielding ca. 65 million 100-bp reads. All raw sequence reads are available on Gen-Bank at the Sequence Read Archive (Run Num.:SRR942341).

Add 75 μL

 75 AmpureXP beads to each, mix for 10min. Rinse twice in fresh 80% EtOH according to the standard protocol. Dry to near-cracking point. Resuspend each bead pellet in 50 μL EB bu er. Add binding solution in increasing volume, from 15 μL (0.3X) to 70 μL (1.4X). Mix for 10min. Repeat the rinsing and drying steps. Elute in 50 μL EB bu er. Run on a BioAnalyser DNA1000 chip. Appropriate ratio should cut the ladder around 200bp without a ecting the molarity of the 250bp peak. DNA extraction and puri cation: W e w ill n eed h igh-quality, p ure g enomic D NA -Qiagen DNeasy do ne f or a nimal b lood o r t issue s amples, b ut o ther m ethods p erform j ust a s well. Check size range on a 1% agarose gel: there should be a clear, concentrated band above 10 kb. If in doubt, perform additional bead cleaning with stringent size selection to gkeep only high molecular weight DNA. Setting up the library: Starting setup : small pools seem better for diversity. Say 12 Gb genome per pool (e.g. 8 penguins of 16 tuna shes). Take e nough f or 2 s onication r uns i f p ossible, i.e. each pool should sum up to 6 μg DNA (8 samples with 750 ng each for ex).STAGE 1 : Barcoding steps ble in the ligation with no P1. Pay attention to the apparition of P1 dimers around 130bp : when they start increasing noticeably, P1 has reached saturation. Just-saturated P1 is ne. NOTE: with the Vienna adapters, use 5μL of 0.1μM adapters per sample.

  the aliquots and purify into a nal 30 μL EB bu er.

  non-interprétée de la vie des animaux demeure l'individu. Dès lors, l'on comprend que toute étude empirique qui d'appuie sur l'échantillonnage de groupes ou de populations ne fait, en réalité, qu'assembler une théorie d'individus uniques : leur attribution à tel ou tel groupe n'est qu'une décision postérieure de l'observateur (ce qui pose entre autres de réellees questions pour la conception d'expériences -voir entre autres Fine scale structure, p.135).Et pourtant, l'individu se dissout soit verticalement, soit transversalement, dans toute la théorie de la génétique des populations. La notion essentielle de fréquence allélique (voir §45), par exemple, n'existe que par l'analyse transversale (c'est à dire, à un point donné du temps) d'un grand nombre de chromosomes, à une position génomique donnée, sans plus retenir aucune information quant aux associations particulières des allèles au sein des individus : ont peut ainsi la concevoir comme l'abolition des frontières synchroniques entre individus -une idée qui trouve d'ailleurs sa consécration mathématique dans l'approximation de la dérive génétique par les équations de di usion (voir §39). La théorie du coalescent (voir §40), quant à elle, se fonde sur l'analyse verticale (c'est à dire transgénérationnelle) des séquences : dès lors, l'avènement et la disparition de l'individu (ou la génération) devient un accident, et une simple mesure du temps. Ainsi la notion même de coalescence réclame l'abolition des frontières diachroniques entre individus : un concept que formalise la représentation en temps continu du coalescent, qui écarte totalement les notions d'individu ou de génération (voir §45).Cette représentation paradoxale du singulier dans le cadre de la génétique des populations forme un contraste frappant avec la forme que prend d'ordinaire l'individu tant dans le cadre de la systématique (où la singularité est réduite à la dispersion autour de l'holotype dans l'espace morphologique), que dans celui de l'écologie (où la diversité est soit remplacée par des paramètres démographiques homogènes tels que la densité -voir[START_REF] Bolnick | Why intraspecific trait variation matters in community ecology[END_REF] -ou au contraire devient le point focal de l'étude des choix d'histoirede vie -voir Le Bohec 2008;[START_REF] Weimerskirch | Seabirds-individuals in colonies[END_REF]. L'un des dé s de la théorie synthétique de l'évolution reste donc la réconciliation de ces di érents paradigmes, en dépit de la divergence de leurs fondations. En particulier, la di érence entre les concepts de taille de population ou taux de migration dans le paradigme écologique etAnnexes - §1 (traduction française)Annexes - §2 (traduction française) celui de la génétique des populations (voir §41 et Fig.1) rend ardue l'intégration de sources interdisciplinaire (voir e E mperor s ynnome p .2 11). N on m oins a rdue e st l a r éconciliation des di érentes é chelles d e t emps d ans l esquelles c es p aradigmes s 'inscrivent: p ar b ein d es aspects, lagénétique des populations occupe une position intermédiaire entre la systématique (qui opère à l'échelle « géologique » des spéciations) et l'écologie (qui s'occupe de la vie discrète des individus).Si la synthèse de ces di érents modes dépasse le cadre de ce travail, les questions qui nous occupent ici, parce qu'elles sont au croisement de l'écologie et de la génétique des populations, prennent tout leur sens à travers cette inévitable ambivalence. §-2 Population, dème, colonie. Tout comme la distribution des positions individuelles dans les espaces morphologiques, comportementaux ou génétiques dé nit la forme de la diversité spatiale et temporelle d'une espèce, l'étendue du recouvrement entre la zone de mouvement d'un individu, et la répartition total de l'espèce dé nit un spectre de structures spatiales allant de formes homogènes et panmictiques à des ensembles hétérogènes et fragmentés. L'architecture particulière d'une espèce intègre des processus qui se déploient à des échelles très diverses : et sa description change selon le cadre théorique dans lequel l'observateur se place (Esler et al. 2006). La dé nition d'une population -un concept pourtant central en génétique des populations -change radicalement selon qu'on se positionne dans le paradigme écologique ou génétique (Waples & Gaggiotti 2006). Dans le paradigme écologique, les critères retenus sont d'ordinaire la géographie (par exemple la co-occurrence d'individus au même endroit, voir Krebs 2013), les interactions entre individus (comme le partage de ressources en nourriture, voir Hu aker & Gutierrez 1 984), o u, de façon plus importante, l'indépendance démographique des groupes étudiés (c'est à dire, si la migration entre ces groupes su t à les ramener à une unique trajectoire démographique commune, ou à in uencer le risque d'extinction d'un de ces groupes -voir McElhany et al. 2000). Wapples and Gaggiotti (2006) résument de la façon suivante la dé nition écologique : « un groupe d'individus de la même espèce qui se trouvent au même point de l'espace et du temps, et ont l'occasion d'interagir les uns avec les autres » -une dé nition qui se base sur l'étude de la distribution, la démo-il se distingue aussi d'un système panmictique, car l'extrême discontinuité dans la distribution des individus et leur forte philpatrie contredisent l'hypothèse de la reproduction aléatoire à l'échelle d'une génération. Quoique de nombreuses recherches soient encore nécessaires a n d'en comprendre els mécanismes sous-jacents, il est possible que des processus démographiques hétérogènes soient impliqués : par exemple, une dispersion pulsatile, qui impliquerait des événements d'exode local catastrophiques durant certaines générations, équilibrés par des générations où la philopatrie totale serait de règle (voir Empirical evidence of heterogeneous dispersal pour un possible exemple de ce phénomène). A n de décrire cette structure paradoxale, dans cette étude, nous proposons le terme synnome, que nous dérivons du grec σύννομος, une « pâture commune »qui a en particulier été utilisé pour décrire les vols d'oiseaux 1 , et de façon plus géénérale pour désiger les réunions, les rassemblements, et même la parentèle (vois §123). Le concept central de pâture commune rend compte avec précision de l'importance de la prospection alimentaire centripète 2 dans la structure coloniale de l'espèce, tout en la distinguant d'un système de métapopulations fragmentées. Ces di érentes signi cations, prises ensemble, capturent la particularité du système pbservé chez les grands manchots, que nous dé nirons ici comme « un ensemble d'individus unique et homogène, distribué de façon extrêment hérétogène et discontinue dans son aire de répartition. Les concentrations locales, ou colonies, sont très cohérentes à l'échelle de quelques générations, et à cette échelle la philopatrie est la norme. Pourtant, la migration entre ces zones est assez forte pour maintenir un parfaite homogénéité dans les ressources génétiques de l'espèce, de sorte qu'à une échelle micro-évolutive, la seule unité pertinente est l'espèce entière ». §-4 Dispersion et migration. Comme nous l'avons mentionné plus haut, une population peut être dé nie dans deux paradigmes divergents -l'un écologique, l'autre génétique (voir §2). Dans ces deux paradigmes, une population est un ensemble délimité -géographiquement ou génétiquement, elle est dé nie par son aspect autarcique. Toute forme de mouvement entre groupes oeuvre à l'encontre de cette autarcie. Démographiquement, le mouvement couple les trajectoires des 1.Voir en particulier Les Oiseaux d'Aristophane, v. 1755 sqq.2.A défaut d'une meilleure traduction pour l'expression central place foraging.Annexes - §4 (traduction française) populations, et contrecarre l'évolution de l'indépendance démographique. Génétiquement, le mouvement cause le transfer d'allèles entre dèmes, et travaille à homogénéiser la variation génétique et les fréquences alléliques. Cependant, la distinction entre les paradigmes écologiques et génétiques s'appplique aussi aux mouvements entre groupes -là encore, cette distinction a motivé le choix de termes di érents. Une di culté particulière, et qu'il s'agit de clari er d'emblée, est le choix convergent du mot « migration » pour désigner deux phénomènes très di érents. Le sens le plus courant du mot « migration », et ce tout particulièrement dans le contexte de la biologie des oiseaux, est le mouvement pendulaire saisonnier, souvent de colonies entières, entre des terrains d'hiver et des terrains de reproduction estivale. Les espèces migratrices, en ce sens, ont dévelopé des adaptations particulières (voir par ex. Berthold 1991) qui leur permettent de synchroniser leurs trajets, a n de suivre tout au long de l'année les environnements les plus favorables. Il est à noter que le comportement de migration pose un dé pour la dé nition des populations selon le paradigme écologique, puisque chaque groupe peut changer ses frontières démographiques et géographiques entre ses aires d'hiver et d'été. Cependant, la migration entendue en ce sens ne sera pas examinée dans notre étude, tout simplement parce qu'elle n'est pas observable de façon classique dans notre système (quoique les voyages de prospection ailmentaires des manchots, entre leurs périodes de reproduction, puisse être comparés à des migrations). Un mouvement plus simple, et unidirectionnel, est la dispersion des individus qui quittent leur groupe d'origine. La dispersion fut originellement décrite par Howard (1960) comme « le mouvement que fait un animal de son point de naissance au point où il se reproduit ». Du point de vue de chaque individu, c'est donc « la distance maximale à laquelle il transmet ses caractéristiques génétiques, plutôt que la distance maximale qu'il lui arrivera de parcourir au cours de sa vie depuis son lieu de naissance ». La dispersion peut être dé nie tant dans le paradigme écologique que dans le paradigme génétique. Ecologiquement, la dispersion est la force qui pousse au couplage démographique des populations -un excès de juvéniles à un endroit peut par exemple compenser un Annexes - §4 (traduction française) §-6 Philopatrie et dispersion dans les systèmes océaniques. En ce qui concerne la disersion, la dynamique des structures océaniques di ère radicalement des structures terrestres (Steele 1985). Bien qu'aux plus bas niveaux du système trophique, la dispersion passive sur de longues distances soit également prévalente dans les écosystèmes marins et terrestres (les courants atmosphériques y jouent en e et le même rôle que les courants marins pour la dispersion des graines et propagulesvoir Cain et al. 2000; Nathan & Muller-Landau 2000; Cowie & Holland 2006; Nathan 2006; Nathan et al. 2008; Nikula et al. 2013), les di érences d'organisation spatiale deviennent évidentes pour les organismes de plus grande taille, en particulier les vertébrés (Carr et al. 2003). Les écosystèmes terrestres montrent de façon typique un plus fort degré de fragmentation, mois de dispersion et de ux génique, et des systèmes globalement plus fermés (Waser & Jones 1983; Turchin 1998; Carr et al. 2003) : l'interaction entre les gradients latitudinaux et altitudinaux a pour résultat une distribution complexe des habitats, en contraste avec les habitats pélagiques volontiers zonaux (Burrows et al. 2014). Par ailleurs, les océans peuvent être considérés comme un paysage uide dans lequel la dispersion (active ou passive) prend place à grande échelle (Queiroz 2005; Cowie & Holland 2006; Nikula et al. 2013). La fragmentation des habitats, quoique documentée (Acosta 1999; González-Wevar et al. 2010) s'y restreint d'ordinaire aux zones côtières ou aux espèces benthiques aux niches très spécialisées (Rex et al. 1993; Whitlatch et al. 1998; Poulin et al. 2014). La dispersion passive de longue distance (mue par le courant) a été observée chez des organismes allant du plancton (par exemple les stades vagiles des laminaires, voir Fraser et al. 2009; Nikula et al. 2010) aux vertébrés supérieurs, en particulier dans leurs premiers stades de développement (comme c'est le cas chez les tortues marines, Gaspar et al. 2012). La dispersion active des vertébrés est courante à des échelles transocéaniques (Bowen & Sini 1999; Le Boeuf et al. 2000). Dans les systèmes océaniques caractérisés par de forts courants (comme le Courant Circumpolaire Antarctique ou ACC, voir §7 -ou le courant de Humboldt, voir §147), la dispersion active comme passive sont des caractères généraux des écosystèmes. Dans l'ACC, une forte dispersion a été mise en évidence pour des espèces allant des algues et petits invertébrés (Nikula et al. 2010) Annexes - §6 (traduction française) aux invertébrés benthiques (Arango et al. 2011) et aux poissons (Matschiner et al. 2009; Damerau et al. 2012). Cependant, la diversité génétique le long des distributions circumpolaires demeure variable entre taxons, et semble dépendre largement du potentiel de dispersion et des traits d'histoire de vie (Rogers 2007). Dans de nombreux cas, des discontinuités telles que la Péninsule Antarctique (qui s'étend au Nord jusqu'à presque 62°S) rompt ces systèmes de dispersion homogène, et apparaît comme une province biogéographique distincte, en terme de climate (Mulvaney et al. 2012) comme de biogéographie (Terauds et al. 2012). La pouvoir de dispersion active qu'ont les vertébrés a néanmoins une conséquence paradoxale : leur plus grande capacité de mouvement leur permet de ne pas disperser avec le courant. Tandis que les espèce sessiles résistent à la dispersion par la pure force de leur inertie, les nageurs actifs et les oiseaux font preuve de philopatrie ( §5 -voir aussi Frederiksen & Petersen 1999; Steiner 2005; Bicknell et al. 2012; Fernández-Chacón et al. 2013), qui s'oppose à la grande uidité des environnements océaniques par un mouvement actif. La philopatrie permet donc la perpétuation de dèmes relativement stables (par exemple chez l'otarie, Bonin et al. 2013), de groupes faliliaux (comme chez l'orque, Hoelzel 1998), ou de colonies (comme chez la plupart des oiseaux marins, Friesen et al. 2007). De fait, dans la plupart des cas, le comportement philopatrique est assez fort pour mener à la di érentiation génétique entre des population qui se recouvrent pourtant largement dans leurs aires d'alimentation (Dearborn et al. 2003; Friesen et al. 2007; Smith et al. 2007). De telles structures ont été observées chez les mammifères marins (cétacés ou pinnipèdes, voir Hoelzel 1998; Baker et al. 2008; Bonin et al. 2013), chez les tortues marines (par ex. Molfetti et al. 2013), et chez la plupart des oiseaux de mer (Friesen et al. 2007; Bicknell et al. 2012). Les déplacements libres et rapides permis par les systèmes océaniques ont donc la conséquence inattendue de permettre de forts taux de retour au lieux de naissance pour la plupart des vertébrés, plutôt que de favoriser le brassage génétique. L'Antarctique et l'Océan Austral Annexes - §6 (traduction française) tions globales. Cependant, la réconciliation des di érentes sources disponibles demeure problématique, tant les signaux peuvent être confondus entre les environnements glaciaires, marins et terrestres (voir Armand & Leventer 2010; McGlone et al. 2010). Malgré des variations locales et des conditions divergentes dans les di érents bassins océaniques, il est possible d'identi er quatre périodes majeures dans l'histoire récente de l'Océan Australpériodes qui semblent cohérentes à l'échelle globale. (i) les conditions du Quaternaire (du Pléistocène moyen à 22 Ka), (ii) le Dernier Maximum Glaciaire (LGM -21-18 Ka), (iii) la déglaciation de la n du Pléistocène et l'Optimum Climatique de l'Holocène (17-9 Ka), et (iv) les conditions hypsithermales et néoglaciales de la n de l'Holocène (8-0 Ka) -voir §101 pour plus de détails sur ces périodes. §-10 Biogéographie de l'Océan Austral. L'Océan Austral actuel a une biogéographie relativement simple, faite de systèmes généralement zonaux, mais qui abritent pourtant une biodiversité étonnante (Smetacek & Nicol 2005; Chown et al. 2015). Quatre régions distinctes se distinguent : (i) la zone subantarctique, qui dépasse l'Océan Austral au sens strict, mais lui appartient d'un point de vue biogéographique, et est limitée au sud par l'APF, et au nord par la STC, (ii) la zone de l'océan antarctique, au sud de la zone subantarctique, et limitée au sud par la limite sud de l'ACC, (iii) la zone côtière antarctique qui inclut le contrecourant antarctique et le plateau continental, aisni que la zone des glaces côtières, et (iv) la péninsule antarctique (Convey et al. 2012; Terauds et al. 2012). Dans cette étude, nous nous concentrerons surtout sur les trois premières zones concentriques. La région Subantarctique se caractérise par des températures plus modérées, d'importantes précipitations, aucune glace de mer, et de forts vents d'Ouest. Les îles sont couvertes de tourbières et de végétation rase, ou un habitat périglaciaire. Les îles subantarctiques sont rares et espacées (du Paci que vers l'Atlantique : Macquarie, Heard-et-McDonald, Kerguelen, Crozet, Marion-et-Prince-Edward, Bouvet, Sandwichs du Sud, Géorgie du Sud, Terre de Feu et Malouines), et concentrent oiseaux et pinnipèdes. L'Océan antarctique, au sud de l'APF, se caractérise par des eaux de surface beaucoup plus froides, et une importante couverture de glace hivernale -les îles y sont rares et couvertes de glace (Peter I, Scott, Balleny et les Orcades du Sud). La Annexes - §10 (traduction française) maine a été la cause déterminante du réchau ement observé depuis le milieu du XXème siècle » (Stocker et al. 2014). Des e orts intenses et coordonnés sont en oeuvre à présent pour comprendre les mécanismes sous-jacents, et les issues possibles des changemens actuels (voir §50 pour une présentation des e orts de l'IPCC) -malheureusement, il n'en va pas de même des e orts politiques. Le résultat de cet e ort scienti que est une connaissance croissante de l'articulation entre trois di érents domaines : le changement climatique, le changement environnemental, et la météorologie. Quoique profondément connectés, ces trois paradigmes ne devraient pas être confondus. Le Changement climatique est seul l'objet de la dé nition de l'IPCC, et se limite aux e ets visibles dans la circulation océan-atmosphère -c'est à dire aux changements de long terme de la température, des précipitations, du régime des vents, etc. Ces phénomènes peuvent être précisément décrits et saisis avec les méthodes de la physiques, comme les modèles de circulation générale océan-atmosphère(AO-GCM, voir §50), dont les limites résident surtout dans notre compréhension de la nature des processus physiques à l'oeuvre, ainsi que dans les hypothèses socio-économiques qui gouvernent nos projections des futures concentrations de GHG. Quoique les AO-GCMs soient de plus en plus e caces pour prédire l'environnement physique de la Terre au XXIème siècle, la réponse des communautés biologiques, ou « changement environnemental », est un sujet plus complexe (voir §13 sqq. pour un aperçu de ces di cultés), à la fois par manque d'un exemple passé (quoique le réchau ement de l'Holocène soit souvent utilisé comme exemple, son rythme est en e et beaucoup plus lent -voir Petit et al. 2008; Blois et al. 2013; Moritz & Agudo 2013). Les divers mécanismes par lesquels ces changements a ectent les écosystèmes les rendent particulièrement di ciles à prévoir. Ces e ets vont du dérèglement des processus dévelopementaux des organismes de bas niveau trophique par le rachau ement de l'eau (Gregg et al. 2003; Behrenfeld et al. 2006) ou son acidi cation (Harvey et al. 2013), qui se répercutent aux niveaux trophiques supérieurs (McMahon & Burton 2005), jusqu'au changement des aires de distributions et des réductions de biodiversité qui en résultent (par exemple chez les copépodes arctiques, voir Beaugrand et al. 2002). Les écosystèmes marins, tout comme les écosystèmes terrestres, sont déjà Annexes - §11 (traduction française)

  Par ailleurs, de nombreuses études ont étudié les e ets du changement climatique en prenant pour modèle la variabilité cyclique du climat, qui est particulièrement prévalente dans les systèmes océaniques (voir §8). Ces études ont souvent exploité le for contraste entre les phases positives et négatives de l'ENSO (par exemple, LeBohec et al. 2008), du SAM[START_REF] Weimerskirch | Changes in wind pattern alter albatross distribution and lifehistory traits[END_REF]) ou de l'Oscillation d'Atlantique Nord (NAO,[START_REF] Frederiksen | Scale dependent climate signals drive breeding phenology of three seabird species[END_REF]). C'est souvent une approximation raisonnable, et, de façon importante, souvent la seule possible -cependant, ces oscillations météorologiques ne devraient pas être complètement assimilées au changement climatique. La météorologie, qui est surtout en jeu dans ce cas, est seulement l'interface entre le changement climatique et les espèces. Les phases chaues de l'ENSO reproduisent peut-être précisément les conditions d'une planète réchau ée dans l'Océan Austral, mais pas les mécanismes de téléconnexion plus vastes, par exemple avec l'hémisphère Nord : et ce malgré le fait que le couplage Arctique-Antarctique soit sans conteste un aspect central du changement climatique au sens strict[START_REF] Barbante | One-to-one coupling of glacial climate variability in Greenland and Antarctica[END_REF]. Si l'approximation météorologique est souvent une nécessité méthodologique, il convient de garder à l'esprit la di érence essentielle entre changement météorologique et changement climatique. §-12 Particularités du changement climatique dans les régions polaires. Les e ets du changement climatique sont particulièrement visibles dans les régions polaires, un fait dû à leurs caractéristiques climatologiques, mais aussi à la structure de leurs communautés biologiques. D'un point de vue purement climatologique (ou « abiotique », voir §49), les régions polaires apparaissent di érentes du reste de la planète. Les singularités de la circulation Océan-Atmosphère dans l'Arctique -et tout particulièrement l'abrupt changement d'albedo selon l'étendue de la glace -donne lieu à un phénomène d'ampli cation arctique (Serreze & Francis 2006; Pithan & Mauritsen 2014). Annexes - §12 (traduction française) mantier et al. 2008; Ho mann & Sgrò 2011) et (iii) les changements de distribution pour suivre les isohabitats[START_REF] Walther | Ecological responses to recent climate change[END_REF][START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF][START_REF] Vanderwal | Focus on poleward shifts in species' distribution underestimates the fingerprint of climate change[END_REF]. §-14 Micro-évolution et plasticité phénotypique. La micro-évolution et la plasticité phénotypique partagent une structure commune : a n de s'adapter à son environnement local, une espèce change ses caractéristiques physiologiques, phénologies, ou généralement comportementales (Ho mann & Sgrò 2011). Les mécanismes impliqués, par contre, sont très di érents. Une réponse micro-évolutive nécessite un certain nombre de générations, par sélection des traits héritables conférant un meilleur avantage (et déterminés soit génétiquement, soit épigénétiquement) : c'est donc un processus de population. La plasticité phénotypique, par contre, est un phénomène individuel. Soit par des modi cations développementales, soit par des processus purement comportementaux, chaque individu optimise sa valeur sélective dans les conditions présentes -mais son nouvel état n'est pas héritable, si nous laissons de côté la relativement marginale (dans l'état actuel des connaissances) transmission culturelle des comportements (Whitehead & Rendell 2014). Des réponses évolutives au changement lcimatique anthropogénique ont déjà été mises en évidence chez plusieurs espèces à temps de génération court, comme les moustiques (Gienapp et al. 2008; Ho mann & Sgrò 2011). Cependant, aucune étude conduite sur des vertébrés n'a encore pu mettre au jour de processus évolutif clair (soir Møller et al. 2010 pour un exposé complet chez les oiseaux). Le décalage entre le rythme rapide du changement, les temps de génération plus longs des vertébrés, et leurs stratégies reproductices généralement de type K, rend la tâche ardue pour la sélection naturelle.La plasticité, d'un autre côté, joue un rôle important dans la réponse de court terme de nombreuses espèces[START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF][START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF]. Elle a ecte en priorité la phénologie, a n de conserver une synchronisation avec les conditions météorologiques, et en particulier le pic de nutriments, comme cela a été démontré chez la mésange charbonnière Parus major[START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF][START_REF] Husby | Contrasting patterns of phenotypic plasticity in reproductive traits in two great tit (Parus Annexes - §186 major) populations[END_REF] ou le Gobemouche à collier (Ficedula hypoleuca, Annexes - §14 (traduction française) A des échelles de temps plus courtes, au contraire, le conservatisme de niche est la force principale qui pousse les individus à suivre leur habitat plutôt qu'à s'adapter à de nouvelles conditions. En ce sens, les espèces suivent leur habitat -un mouvement par lequel une population entière se déplace avec ses conditions environnementales. Le suivi d'habitat, observé depuis une perspective humaine, résulte dans le déplacement des distributions, une modi cation globale de la distribution géographique de l'espèce en réponse aux tendances de son habitat. Le réchau ement climatique, par exemple, a pour résultat typique (quoique schématique) un déplacement des habitats et des espèces vers les pôles, ou les sommets (Jump et al. 2009; Fraser et al. 2012). Dans un système idéal, on peut s'attendre à ce que les espèces suivent leur habitat de façon linéaire (Sche er et al. 2001). Cependant, le caractère anisotropique des biozones implique que les habitats ne se transforment pas de façon homogène (Di enbaugh & Field 2013), et que les changements sont vraisemblablement doubelemtn hétérogènes -entre écosystèmes, et entre espèces à l'intérieur des écosystèmes. Une étude récente sur les oiseaux australiens (VanDerWal et al. 2013), par exemple, a montré que durant les 60 dernières années, les déplacements de distributions ont suivi des trajectoires majoritairement idiosyncratiques à l'échelle du continent, et qui ne re ètent que partiellement les déplacements d'habitats. Un tel exemple montre l'importance des interactions biotiques dans la détermination de la distribution des espèces : plusieurs relations interspéci ques peuvent être a ectées simultanément par le changement climatique (par exemple le comportement alimentaire -voir Charmantier et al. 2008, ou la compétition -voir Stenseth et al. 2015), avec pour résultat des réponses non linéaires et parfois paradoxales (comme c'est le cas pour la Bernache nonnette Branta leucopsis, voir Bauer et al. 2008; Eichhorn et al. 2009). Ainsi, la compréhension des distributions présentes et futures est presque impossible si l'on se limite à l'étude d'une seule espèce (par exemple dans une approche de type « envelope climatique », voir Pearson & Dawson 2003), et doit au contraire être étendue pour intégrer les interactions biotiques les plus importantes (un point développé au §49). L'hétérogénéité et l'idiosyncrasie des déplacements de distributions est exacerbé à chaque fois que les discontinuités de l'environnement provoquent des convergences d'habitat (Burrows et al. 2014) Annexes - §15 (traduction française) à l'évolution le temps de se réaliser. Mais elle pourrait, de ce fait, être totalement impuissante à saisir le mouvement relativement rapide des changements actuels. Pour ces raisons, ce qui se passe réellement dans une situation telle que les changements climatiques anthropogéniques actuels peut être conçu comme une disruption du rythme et de la manière selon laquelle ces di érentes échelles sont articulées, et une probabilité croissante de décalage entre les di érents niveaux de variation spatiale et temporelle. La durée d'une génération, par exemple, peut de façon simpliste être considérée comme le laps de temps durant lequel les conditions environnementales sont intégrées par un individu qui est soumis à la sélection naturelle : cette durée est su sante pour faire la moyenne des saisons, voire des modes climatiquesmais assez courte pour qu'un grand nombre de générations s'écoulent a n de laisser place à l'évolution au long des changements géologiques. Un changement au rythme plus serré a de fortes chances de mettre à mal cette synchronisation. De même, l'organisation dans l'espace des individus et des espèces re ète l'étendue au sein de laquelle les conditions les rendent les mieux adaptés (voir §49), et la taille et la mobilité des individus est adaptée à cet espace : là encore, la convergence ou la divergence des habitats brise cet équilibre. Ainsi, par bien des aspects, il semble que la clef de la compréhension des changements climatiques contemporains soit la façon dont les di érents paradigmes chronologiques et spatiaux s'articulent pour les écosystèmes, les espèces et les individus. §-17 Comprendre les échelles do'rganisation des oiseaux de mer. Le but principal de notre travail sera de comprendre l'organisation des di érentes échelles spatiales et temporelles dans la démographie et la dynamique des espèces d'oiseaux de mer -un groupe aux premières lignes du changement climatique (voir §18) -en se basant sur le contraste de trois espèces : le manchot Royal (Aptenodytes patagonicus), le manchot Empereur (Aptenodytes forsteri) et la pu nure de Garnot (Pelecanoides garnotii). Si notre étude se place principalement dans une perspective évolutioniste, en s'appuyant sur les outils de la génétique des populations, nous explorerons aussi une large gamme d'approches complémentaires -comme le suivi par marquage individuel, ou la modélisation d'habitats. Pour chacune des deux espèces du genre Aptenodytes, nous présentons deux études Annexes - §17 (traduction française)

.162 Index of tablesTable 1 :

 1 Microsatellite analysis: multiplexing parameters and summary statistics. 144 ....................

161 .......................................................................................................

Table 2 :

 2 Pairwise cluster comparison of inbreeding and relatedness distributions 153 .....................

Table 3 :

 3 Ensemble members used in habitat predictions. 201 .........................................................

Table 4 :

 4 Correlation of observed and modelled SST in the Southern Ocean. 203 ...........................

Table 5 :

 5 Ensemble members used in sea ice modelling. 227 ............................................................

Table 6 :

 6 Pairwise Fst, according to Reich's estimator. 244 ...............................................................

Table 7 :

 7 Migration rates and sizes as estimated from the joint allele frequency spectrum. 252 .........

Table 8 :

 8 2012 census of the Mertz emperor penguin colonies. 268 .................................................

  Myr, see[START_REF] Haywood | Middle Miocene to Pliocene history of Antarctica and the Southern Ocean[END_REF] Knorr & Lohmann 2014). is idea is con rmed by the DNA-inferred radiation date of several marine taxa, for which Antarctic and Subantarctic clades have been isolated from each other around theMMCT (Poulin et al. 2014), which is also coincident with the radiation of crown penguins (see §21 p. 72).

	Introduction - §9
	~16-11.5

[START_REF] Ashok | Individual and combined influences of ENSO and the Indian Ocean dipole on the Indian summer monsoon[END_REF]

is thought to have settled around the early Miocene

[START_REF] Pfuhl | Evidence for late Oligocene establishment of the Antarctic Circumpolar Current[END_REF]

, but the present-day water mass boundaries appear to have been established later, perhaps around the Middle Miocene Climatic Transition (MMCT,

  , reconstructing oceanographic and atmospheric processes at these relatively recent periods relies on to di erent approaches. First, direct or indirect evidence may be obtained from cores, whether of marine sediment throughout the Southern Ocean, of ice on the Antarctic ice sheet, or of peat on the subantarctic islands. Alternatively, General Circulation Model (GCM) developed for the historical period may perform rather well in that recent past, since the major features of Ocean-

	Atmosphere circulation are assumed stable over that period: the Coupled Model Intercomparison
	Project phase 5 (CMIP5, see §50 p. 130) panel thus includes Pleistocene Last Glacial Maximum
	and mid-Holocene experiments (see Fig. 5 p. 45 and §50 p. 130 for details on these experi-

45 Myr ago for Prince Edward (see a complete review in Quilty 2007). us, both in terms of geography and of oceanography, we may consider that the Southern Ocean did not take its present-day shape before the Pleistocene. Although better known, Pleistocene (~2.6 Myr to 11.7 Kyr BP) and Holocene (~11.7 Kyr BP to the present) history in the Southern Ocean is still subject to considerable uncertainties. General-lyments). Core evidence provides chemical, physical and biostratigraphic markers that may be used as proxies for a variety of variables Gersonde et al. 2005; Armand & Leventer 2010; Hodgson et al. 2014, either directly at the location of the core (which is how marine sediment and terrestrial peat cores are usually interpreted, see Gersonde et al. 2005; Martínez Garcia et al. 2009; Armand & Leventer 2010), or as a mixture of local and oceanic source area signal (see Wol et al. 2003; Wol et al. 2006). Foraminiferan and diatom assemblages in marine sediment cores o er an accurate index of the state of the surface water masses, that can be used to locate the position of the Southern Ocean fronts through changes in surface temperature (e.g. the complex frontal structure around the Campbell plateau, in Neil et al. 2004, or the APF in the Indian and Atlantic oceans,

  §10of the Polar Front, has much colder surface waters and extensive winter sea ice cover, islands are rare and ice-covered (from the Paci c t o t he A tlantic: P eter I , S cott, B alleny, a nd t he South Orkneys). Vegetation is scarce and mostly limited to lichens, liverworts and mosses[START_REF] Convey | Antarctic Ecosystems: An extreme environment in a changing world[END_REF]). e Antarctic coastal zone (Tynan 1998), nally, is a shelf environment, characterised by almost perennial pack-ice, and a strong in uence of the Antarctic Ice Shelf. Vegetation is almost absent, and displays extreme adaptations, and breeding vertebrate fauna is limited to a handful of species: Adélie and emperor penguins, snow, cape, antarctic and giant petrels, Wilson's storm-petrels, southern fulmars and southern polar skuas, as well as a few pinniped species. Of these, only the Emperor penguin breeds during the Southern winter.

Islands are dominated by peat and low vegetation, or periglacial habitat. Subantarctic islands are few and far inbetween (from the Paci c to the Atlantic: Macquarie, Heard-and-McDonald, Kerguelen, Crozet, Marion-and-Prince Edward, Bouvet, the South Sandwich, South Georgia, Tierra del Fuego and the Falklands), and concentrate both avian and pinniped fauna. e Antarctic Ocean zone, South Introduction -

responses to anthropogenic climate change in the Southern Ocean

  .§-11 Climate is changing. Palaeoclimate reconstructions teach us that climate change is, by itself, a natural and unavoidable phenomenon at the millennial scale (see §9 p. 42), and may be linked to external forcings (e.g. changes in solar activity or in Milankovitch orbital cycles, with positive feedbacks such as the « snowball earth » loop, see e.g.[START_REF] Hyde | Contribution à l'éthologie et à l'écologie du manchot empereur (Aptenodytes forsteri Gray) à la colonie de Pointe Géologie[END_REF][START_REF] Berger | Milankovitch and climate: understanding the response to astronomical forcing[END_REF]), or to internal biogenic modi cations of atmosphere composition (e.g. Frolking & Roulet 2007). However, the pace of these changes is slow. Even the LGM did not alter signi cantly the composition or

	Introduction - §11
	Species'

tion of latitude and altitude may blur surface wind currents to a considerable extent and result in very heterogeneous patterns

[START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF]

, the zonal structure of the Southern Ocean places biogeographic boundaries in regular accordance with annular, circumpolar circulation patterns. As a result, the extent and location of these biozones are directly in uenced by global circulation patterns (see §18 p. 65). structure communities in Europe on the long term (see e.g.

[START_REF] Yeakel | The impact of climate change on the structure of Pleistocene food webs across the mammoth steppe[END_REF]

. Current climate change, on the other hand, sees modi cations of the same magnitude as the Holocene transition happening at the decadal -and not millennial -scale. Climate change in general has been unambiguously and rather intuitively de ned b y t he I ntergovernmental P anel o n C limate Change (IPCC) as a « change in the state of the climate that can be identi ed (e.g. by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings such as modulations of the solar cycles, volcanic eruptions and persistent anthropogenic changes in the composition of the atmosphere or in land use »

(Solomon et al. 2007)

. During the last decades of the 20th century, accumulated evidence increasingly pointed towards unprecedented rapid changes in the climate of the earth, starting shortly after the 19th century Industrial Revolution. e combination of deforestation and large-scale use of fossil fuels led to higher production, and lower xation, of greenhouse gases (GHG), in particular carbon dioxide and methane, to the point that « atmospheric concentrations of carbon dioxide, methane and nitrous oxide

[...]

are unprecedented in at least the last 800,000 years »

[START_REF] Pachauri | Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]

. Increased GHG results in an increase in greenhouse e ect, or e ective radiative forcing on global climate. us, a ll a vailable evidence make it « extremely likely that human in uence has been the dominant cause of the observed warming since the mid-20th century »

[START_REF] Stocker | Climate change 2013: The physical science basis[END_REF]

.

  ). ese s tudies h ave o ften e xploited t he h igh c ontrast b etween p ositive a nd negative phases of the El Niño Southern Oscillation (ENSO, e.g. Le Bohec et al. 2008), the Southern Annular Mode (SAM, e.g. Weimerskirch et al. 2012), or the North Atlantic Oscillation (NAO, e.g.

  , the polar ecosystems have little margin for further displacement, and the high latitudes are expected to become « climate sink areas »[START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF] where habitats contract and ultimately disappear, rather than shift (see §15 p. 57). Further complexity arises from the fact that human activity has heavily in uenced the Southern Ocean from the bottom of the food chain (i.e. through anthropogenic climate change and reduction in primary production) as well as from the top (i.e. through the massacre of top-level predators such as whales, seals and penguins -seeAinley et al.

2007), which results in an intricate combination of e ects

[START_REF] Smetacek | -le changement climatique est-il spécial en lui même dans les régions polaires, au delà de son rythme accéléré ; ou ses e ets sur les communautés biologiques sont-ils surtout le résultat de la relative intégrité de ces communautés encore peu anthropisées ? §-13 Réponse des espèces au changement climatique[END_REF]

-see also a spectacular and concrete example of this intricate system in

[START_REF] Travis | Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica[END_REF]

.

  gent to answer. §-13 Species' responses to climate change. e i mportance o f t he i mpact o f c urrent global changes on the world's biological communities is now recognised beyond question by the scienti c c ommunity ( see § 11 p . 5 0 a nd omas et al . 20 04; Wa rren et al . 20 13; Pa ci ci et al. 2015), although the detail of its impact is complex and debated (Pereira et al. 2010; He & Hubbell 2011; Paci ci e t a l. 2 015). e di erent le vels of im pact (r anging from ph ysiology to community level, see §11 p. 50), the complexity of biotic interactions[START_REF] Davis | Making mistakes when predicting shifts in species range in response to global warming[END_REF] Nor-

berg et al. 2012;[START_REF] Norberg | La plupart des études, cependant, s'accordent sur l'idée que trois mécanismes fondamentaux interagissent dans la détermination de la réponse d'une espèce au changement : (i) la microévolution adaptative par sélection naturelle[END_REF]

, and the idiosyncratic character of each species' sensitivity and exposure to climate change

[START_REF] Moritz | The future of species under climate change: resilience or decline[END_REF][START_REF] Comte | Species traits and phylogenetic conservatism of climate-induced range shifts in stream fishes[END_REF][START_REF] Díaz | Separating sensitivity from exposure in assessing extinction risk from climate change[END_REF]

) contribute to apparently heterogeneous responses at the ecosystem level

(Parmesan & Yohe

  tion, is a longer-time process. It has been evidenced for several short-lived organisms (Van Heerwaarden & Ho mann 2007; Ho mann & Willi 2008; Ho mann & Sgrò 2011; Krehenwinkel et al. 2015), but the extreme velocity of current changes (Burrows et al. 2011; Mahlstein et al.

2013;

Poloczanska et al. 2013

) is a particular challenge for species that have long generation times

[START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF]

and low genetic diversity

[START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF][START_REF] Norberg | La plupart des études, cependant, s'accordent sur l'idée que trois mécanismes fondamentaux interagissent dans la détermination de la réponse d'une espèce au changement : (i) la microévolution adaptative par sélection naturelle[END_REF]

  feeding behaviour, see Charmantier et al. 2008, or and interspeci c competition, see Stenseth et al. 2015), resulting in nonlinear, and sometimes downright paradoxical habitat tracking (e.g. in the Barnacle goose Branta leucopsis, see Bauer et al. 2008; Eichhorn et al. 2009). us, u nderstanding c urrent a nd future species' distributions may not be properly achieved based solely on single-species (e.g. « climate envelope », see Pearson & Dawson 2003) models, but need to be extended to include essential bi-

otic interactions (a point further developed in §49 p. 127). e heterogeneity and idiosyncratic character of range shifts is exacerbated whenever discontinuities prompt habitat convergences

[START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF]

or « subduction », for instance when possible climatic corridors, where range shift could theoretically happen linearly, are interrupted either by natural, or anthropogenic barriers. Habitat fragmentation, which is often a result of human in uence ( although i t m ay b e n atural, i n p articular i n i nsular e nvironments, s ee e Ki ng synnome, p. 163 and Unexpected philopatry in an insular seabird, the Peruvian diving-petrel, p. 275) thus interacts with climate change and has rather unpredictable consequences. is is especially true around "climate sink areas" where local conditions disappear instead of being displaced

[START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF]

. Moreover, individual species are in reality not expected to respond linearly to habitat shift: insu cient dispersal possibilities may lead to a lagged range shift

[START_REF] Bertrand | Changes in plant community composition lag behind climate warming in lowland forests[END_REF][START_REF] Scheffer | Dispersal will limit ability of mammals to track climate change in the Western Hemisphere[END_REF]

, increased competition around sink areas may prevent range extension

[START_REF] Norberg | Eco-evolutionary responses of biodiversity to climate change[END_REF]

, and compensatory metabolic processes may mask the e ects of climate change until a tipping-point is reached

[START_REF] Doak | Demographic compensation and tipping points in climate-induced range shifts[END_REF]

. ese delayed e ects can lead us to underestimate the depth of current changes, while placing species in a state of climatic debt, ultimately giving way to sudden ecosystem collapses

(Sche er et al. 2001;[START_REF] Bruno | [END_REF][START_REF] Barnosky | Approaching a state shift in Earth's biosphere[END_REF]

. us, the polar regions, which undergo accelerated environmental change (Serreze & Francis

  RAD library preparation involves several sensitive steps that may contribute to decreasing the overall protocol e ciency (see §29 p. 85), and thus lead to increased presence of PCR duplicates in the raw sequencing output (see §35 p. 93). In our data, the presence of duplicates was rst n oticeable b ecause o f t he h eterogeneous regression between heterozygosity and depth of sequencing between libraries, and generally the strong correlation between individual inbreeding estimators and library identity. Removing PCR duplicates e ciently solved this problem, however, the resulting e ective coverage was consequently decreased. As a result, the number of sites that could be reliably called by conventional (e.g. maximum-likelihood) SNP and genotype calling algorithms (see §33 p. 90) was much reduced. us, in order to

1.

A character string summarising the structure of the alignment -number of consecutive matching bases, gaps, etc. (see https://samtools.github.io/hts-specs/SAMv1.pdf ).

Concepts and methods - §35

Concepts and methods

- §36 §-36 e duplicate bias in our data. keep a SNP-based analysis framework, we must balance two antagonistic parameters, (i) the minimum depth of coverage to retain a genotype and (ii) the number of loci necessary for the analysis. While low coverage is reducing the certainty of genotypes (see §35 p. 93), and thus potentially introducing noise in the data, increasing the minimum genotype depth e ectively amounts to increasing the amount of missing data, and thus decreasing the number of exploitable loci (see §33 p. 90). We tested this tradeo on a dataset containing RADseq data from two well-separated populations of Peruvian Diving-petrels Pelecanoides garnotii (see Unexpected philopatry in an insular seabird, the Peruvian diving-petrel, p. 275), by running the Structure clustering algorithm (Pritchard et al. 2000) on ltered datasets with a minimum depth ranging from 20X to 40X, and a minimum proportion of genotyped individuals per locus ranging from 40% to 90%. While structure is perfectly recovered for balanced datasets (e.g. 30X and 60% representation), it decays with more stringent ltering (e.g. 90% representation) due to the low number of remaining loci.

  Concepts and methods - §46 between populations. us, the general structure of the population history is set beforehand, and only the value of the parameters may be optimised over. Although it is possible to compare di erent historical models (normally through AIC model choice), it may happen that only models that are fully incompatible with the true underlying history have been tested: thus, strong external evidence is required to formulate relevant historical hypotheses before testing them. §-46 e Stairway plot approach. e Stairway plot (STY) model has been recently proposed byLiu & Fu 2015 as a model-exible a lternative t o t he g eneral A FS-based m ethods ( see§ 45 p. 117). By considering only a 1-dimensional AFS, marginalised for a single population, the STY

excludes all information relative to historical migration rates, and focuses on population size change events, which allows for a considerable simpli cation o f t he p arameter s pace. us, instead of requiring the speci cation of a number of demographic events, epochs, or growth rates, it is able to use a piecewise-constant population size prior, with a exible number of population size changes informed by the data (through a likelihood ratio test, as models with decreasing numbers of demographic epochs are fully nested within each other) -a characteristic shared with the EBSP approach (see §44 p. 112, although EBSP includes the number of population size changes as a parameter to be co-estimated together with substitution models, gene trees and population sizes during the MCMC sampling, rather than a likelihood ratio test).

  §48the population's average MRCA, thus loosing nearly all the informative content of the distribution of TMRCAs along the genome. us, t he M SMC s eems m ore s uited f or c ases w here the phasing can be established with high con dence -which is nearly never the case in non-model species (see §37 p. 97). §-48 Rate of evolution. Accurate reconstruction of past and present population size changes requires an accurate estimate of the substitution rate -a notoriously di cult parameter to assess[START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF][START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. A robust estimate of the substitution rate of the mitochondrial hyper-variable control region (HVR) or the Adélie penguin, based on mitochondrial heteroplasmy transmission through lineages, has been proposed byMillar et al. (Millar et al.

	). However, this requires precise phasing of each
	haplotype, which is a considerable challenge in non-model species (see §37 p. 97): indeed, recon-
	structing the local mismatch distribution is impossible without an accurate representation of the
	local haplotypes. If phasing is not accurate, the precise local distribution of pairwise di erences

will be averaged to its mean value, and the estimated local MRCA will consistently be closer to

Concepts and methods -

  Concepts and methods - §48segregating sites per locus (λ=1.47 , chi-squared test of goodness-of-t p-value= 0.232). us, we extracted μ as e (s.λ+i) ~ 1.02e-2 substitutions per site per Myr, or 1.08e-7 substitutions per site per generation. is r ate i s c a. t wice s lower t han t he o ne r eported b y Trucchi e t a l. ( 2.6e-7 subst.site -1 .generation -1 , see[START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF], but much faster than the one reported by Li et al. (8.11e-9 subst.site -1 .generation -1 , see Li et al. 2014). While the former was not used in Trucchi et al.'s analysis, but rather derived from it, Li et al.'s result, on the other hand, relies on two exterior and uncertain assumptions: 1) the divergence time between the Emperor and the Adélie penguin is set to ~23 Myr, which may be a large overestimate (based on a state-of-the-art total evidence bayesian analysis, Gavryushkina et al. 2015 proposes ~9 Myr instead), and 2) the generation time is taken to be 5 years in both species; however it has been shown to be 16 years in the Em-

	Concepts and methods - §49
	ly determined by biotic factors may come under increasingly strong abiotic constraints as a result
	of climate change.

peror penguin

[START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF]

, and 6.46 years in the Adélie penguin

[START_REF] Ritz | Phylogeography of the southern skua complex-rapid colonization of the southern hemisphere during a glacial period and reticulate evolution[END_REF]

)thus 11 years would be a closer (although inaccurate because assuming a single rate) estimate of a common generation time. Applying these corrected estimates to Li et al.'s ndings would give a rate of ~4.55e-8, which is more than ve times faster than proposed, and ca. half our estimate.

And still, this calculation does not take into account the possible rate heterogeneity between lineages, and most importantly the changes in generation time between the Aptenodytes/Pygoscelis common ancestor and the extant species, which may explain the remaining di erence.

Generally, the rate of evolution of penguins has been a rather challenging subject, with a wide discrepancy between the paleontological and molecular evidence (see §1 p. 65). While fossil data has been recognised to support a very recent radiation of penguins (about 10 Myr BP, see

[START_REF] Slack | Early penguin fossils, plus mitochondrial genomes, calibrate avian evolution[END_REF]

, molecular data has been interpreted as implying a much more ancient origin (~45 Myr for

[START_REF] Baker | Multiple gene evidence for expansion of extant penguins out of Antarctica due to global cooling[END_REF]

. is molecular-derived radiation has successively been brought to a closer agreement with the fossil evidence by

Subramanian et al. (~20 Myr, Subramanian et al. 2013)

and

Gavryushkina et al. (~12.5 Myr, Gavryushkina et al. 2015)

. e rate that we propose here is in accordance both with the hypothesis of a very fast diversi cation of the spheniscids, and with the ndings of

Trucchi et al.

;

  [START_REF] Chevin | The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes[END_REF] and is very seldom done in climate change biology. Modelling a habitat is a slightly easier task, as the problem may be restricted with a reasonable level of approximation to non-functional aspects (e.g. static distribution of abiotic parameters, and a few key biotic parameters such as prey abundance). Yet even within the approximation of habitat modelling, a fundamental distinction arises between fully correlative, and what we could call causal models. e overwhelming majority of studies of species' response to climate change use the « climate enve-

lope » or « climate space » approach

(Box 1981;[START_REF] Berry | Modelling potential impacts of climate change on the bioclimatic envelope of species in Britain and Ireland[END_REF]

: through a multivariate correlative approach, the observed distribution of a species is correlated to a set of (usually abiotic only) environmental parameters, and this set is used to project the species' future distribution (e.g.

[START_REF] Viblanc | Stress hor-mones in relation to breeding status and territory location in colonial king penguin: a role for social density?[END_REF] Durner et al. 2009)

or demography (e.g.

[START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF][START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF]

based on numerical climate models predictions. One obvious limitation of this ap-

  163, as we are convinced it allows for much more robust forecasts of population trajectories. §-50 Climate projections in the Coupled Model Intercomparison Project. §50 cells of varying dimensions, and the state of each cell is computed for each time step as a function of the other cells in the system. e Coupled Model Intercomparison Project (CMIP) proposes an international framework to assess and understand the di erences between the representation of climate in di erent models, by de ning standard experiments that can be compared between

	systems. e c urrent f ramework, t he C MIP p hase 5 ( CMIP5, d etailed in Taylor e t a l. 2012),
	builds upon the previous CMIP3 ensemble to achieve more consistent and accurate model pre-
	dictions. In particular, CMIP5 models have a generally higher resolution, and a more accurate
	representation of bathymetry, than the previous generation of models, which leads to a much im-
	proved simulation of the Southern Ocean (Meijers 2014). While most CMIP5 models perform
	Our current understanding
	of the Earth's climate system is summarised in Earth System models, that mainly implement
	General Circulation Models (GCM) -i.e. numerical models which reproduce energy transfer be-

tween atmospheric, oceanic, land and ice masses. ese models usually involve a discretisation in Concepts and methodsgenerally well, sea ice representation in the Antarctic Ocean remains a weakness, as most models predict a slight decrease in sea ice extent in East Antarctica in the early 21st century, while the observed trend is a slight increase

[START_REF] Zhang | Comparative genomics reveals insights into avian genome evolution and adaptation[END_REF][START_REF] Turner | Ecological importance of the southern boundary of the Antarctic Circumpolar Current[END_REF][START_REF] Xu | Simulation of sea ice in FGOALS-g2: Climatology and late 20th century changes[END_REF] Wang et al.

  Concepts and methods - §52[START_REF] Stocker | Climate change 2013: The physical science basis[END_REF]), but their future concentration is largely determined by political and socioeconomical trajectories. us, four di erent forcing scenarios or Representative Common Pathways (RCP) have been established, each corresponding to a di erent atmospheric concentration of GHG in 2100[START_REF] Meinshausen | The RCP greenhouse gas concentrations and their extensions from 1765 to 2300[END_REF]. Under the RCP-2.6 scenario, e cient political action leads to a peak in radiative forcing at ~ 3 W/m 2 before 2100, followed by a decline. Under the RCP-4.5 and RCP-6 scenarios, radiative forcing stabilises before 2100, either at 4.5 W/m 2 or at 6 W/m 2 . Under the RPC-8.5, no political action is taken and radiative forcing follows the current trend, reaching 8.5 W/m 2 by 2100. ese four scenarios lead to widely di erent predictions as to global temperature increase[START_REF] Meinshausen | The RCP greenhouse gas concentrations and their extensions from 1765 to 2300[END_REF]) -yet the direction taken by political deci-Concepts and methods - §52 three have been included in the CMIP5 panel: the21 kyr, 6 kyr, and past1000 (Braconnot et al. 2011). While model parametrisation follows the same rules as piControl, historical and rcp runs, initial conditions, speci cation of external forcings, and boundary conditions are di erent, and are determined as part of the experiments de nition. ese rely either on direct computation (e.g. the state of solar radiative forcing at each point of time conditional on Milankovitch cycles, etc.), or on evidence from palaeontology and sedimentology (see §9 p. 42 for more details). In the Southern Ocean, one particularly important parameter needs to be addressed, in the con guration of the Antarctic Ice Sheet. e continental ice sheets themselves (either in Antarctica or in Greenland) are currently not explicitly modelled in the PMIP3/CMIP5 panels(Kusahara et al. §54 tend not only to the colony, but also to the sub-area within the colony where the individual was born (e.g. in Black guillemots Cepphus grylle, see[START_REF] Frederiksen | Philopatry and dispersal within a Black guillemot colony[END_REF]. In turn, strong ne s cale p hilopatry m ay l ead t o v ery h eterogeneous g enetic l andscapes, a nd i n p articular to strong spatial autocorrelation of genotypes at short distances (e.g. as has been observed in Superb Fairy-wrens Malurus cyaneus, see[START_REF] Double | Dispersal, philopatry, and infidelity: dissecting local genetic structure in superb fairy-wrens (Malurus cyaneus)[END_REF]. A direct consequence of this is that uninformed local sampling would be likely to oversample autocorrelated genetic groups (i.e. loosely related individuals), and therefore to overestimate the amount of genetic drift between demes, while also violating the genetic independence assumption of most models. Indeed, very local patterns of genetic variation do not occur on the same level as between-deme genetic structure. §55 sider the link between local habitat heterogeneity, and the shape of genetic variation at the individual level. §-55 Aims of this chapter. In the King penguin, ne s cale l ocal p hilopatry h as b een anecdotally documented[START_REF] Nesterova | Do penguins dare to walk at night? Visual cues influence king penguin colony arrivals and departures[END_REF]), yet the importance of this behaviour in shaping the species genetic landscape is unknown. In this study, we use a dense local sampling of king penguins chicks to assess the importance of very-ne-scale spatial patterns in the structure of the species' colonial units. Our aim is twofold: rst, assessing the strength of local genetic heterogeneity at the colony level will allow us to set an upper limit on the strength of philopatric behaviour in the King penguin from a dense local sample, and thus to set a prior expectation for the study of

	Fine-scale colony structure - §53 Fine-scale colony structure -
	Chapter 3: Fine-scale colony structure
	is chapter has been published as: Cristofari R, Trucchi E, Whittington JD, Vigetta S, Gachot-
	could be used as a benchmark.	us, t he P alaeoclimate M odelling I ntercomparison Project
	(PMIP, currently in phase 3, see Braconnot et al. 2012) has been developed as a counterpart to
	the CMIP experiments. PMIP3 experiments focus on past climatic periods with abundant avail-plies contrasts between groups of individuals (see e.g. Kau man et al. 2003) -but heterogeneity
	able data, in particular the LGM (« 21 kyr experiment), the Early Holocene (« 8.2 kyr experi-between singular breeding places and individual tness a nd b reeding s uccess w ithin a colonial
	ment »), the mid-Holocene period (« 6 kyr experiment »), and the last millennium (« past1000 ex-framework is a rather new concept in the study of coloniality (see Cornet 2014). Here, we con-

Ainley et al. 2010a; Raybaud et al. 2013

), e.g. satellite-derived measures of surface temperature or sea-ice cover, which are directly comparable to model outputs. §-51 e Representative Concentration Pathways. While the uncertainty arising from climate representation within models can be partly addressed through the ensemble approach, another important source of uncertainty is the nature of future forcings. Atmospheric greenhouse gas (GHG) concentration, in particular, is a major driver of e ective radiative forcing

[START_REF] Pachauri | Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]

sions is di cult to forecast. us a common practise when assessing species response to climate change is to repeat predictions under several RCPs, in order to assess the range of possible responses (see e.g.

[START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF] Raybaud et al. 2013;[START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF][START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF][START_REF] Beaugrand | Future vulnerability of marine biodiversity compared with contemporary and past changes[END_REF]

. However, variability between models under a single forcing scenario remains high, so that ensemble predictions may overlap between di erent RCPs (Goberville et al.

2015)

. us, CMIP5 RCP experiments should not be understood as a positive take on future climate: rather, « the goal of working with scenarios is not to predict the future, but to better understand uncertainties in order to reach decisions that are robust under a wide range of possible futures » (Moss et al. 2010). §-52 Palaeoclimate experiments. In order to assess the current GCMs' ability to represent di erent climatic conditions, it has been proposed that comparatively well-known ancient climate periods periment »). Further back, Interglacial and mid-Pliocene experiments are also included. Of these, 2015), and need to be speci ed as boundary conditions, i.e. xed states at the limit of the dynamic cells of the models. Reconstruction of the Antarctic Ice Sheet in the PMIP3 experiments relies on external evidence, in particular the CLIMAP multiproxy reconstructions (CLIMAP 1981). Overall, PMIP3 experiments support the current generation of AO-GCMs, as reconstructed palaeoclimate reconstructions closely match the available direct evidence (Braconnot et al. 2011; Braconnot et al. 2012; Harrison et al. 2015). In this work, we use PMIP3/CMIP5 palaeoclimate experiments as an explanatory framework for our coalescent-based palaeodemographic reconstructions for the King and the Emperor penguin ( e King synnome, p. 163 and e Emperor synnome, p. 211). Neveu H, Stenseth NC, Le Maho Y, Le Bohec C (2015) Spatial heterogeneity as a genetic mixing mechanism in highly philopatric colonial seabirds. PloS one 10: e0117981. Context §-53 Local structure and random sampling in population genetics. Population genetic studies rely on the molecular analysis of individuals sampled from one or several demes or populations (see §2 p.

[START_REF] Anderson | Wind-driven upwelling in the Southern Ocean and the deglacial rise in atmospheric CO2[END_REF]

. With very few exceptions, these individuals represent a small fraction of the focal populations: therefore, when comparing di erent ensembles through reduced subsets of individuals, an underlying assumption is that the population-level sampling process is unbiased, and that fundamental parameters (such as allele frequencies, see §45 p. 117) observed in the sampled individuals are reliable estimators of the same parameters are the population scale. While this hypothesis is normally veri ed when true random sampling is possible, it is often only assumed in less easily accessible wild species. us, an important rst step before engaging in large-scale population genetic studies is to establish whether variation in local sampling schemes is likely to in uence results on a broader geographic scale. Previous studies of colonial seabirds have suggested that natal philopatry could ex-Fine-scale colony structure - Broad-scale genetic structure between demes normally involves long-term reduction of gene ow and random drift in allele frequencies: population-scale genetic sampling, rather than direct kinship, plays the main part in the process. us, although strong local genetic heterogeneity (e.g. kinship structure) at the local level is likely to promote larger-scale genetic structure (e.g. through reduction of dispersal between demes), this is not necessarily the case, as local kinship structures may be regularly dissolved into the broader deme gene pool. §-54 Impact of local habitat on populations. While anthropogenic climate change is a global phenomenon (see §11 p. 50), its realisation is heterogeneous, and its variability happens at all scalesfrom continental to local ones ( §15 p. 57). Understanding the relationship of species with their environment, whether in a context of stability or of change, therefore requires the integration of very broad-scale patterns (that will be examined later, in e King synnome, p. 163), but also of ne-scale, extremely local parameters. Heterogeneity between seabird colonies and breeding locations is a rather straightforward phenomenon that can be intuitively hypothesised, and that im-world-wide gene ow. Second, it will allow informed sampling for larger-scale genetic studies in the following chapters of this work (see e K ing s ynnome, p . 1 63 o r e Em peror sy nnome, p. 211).

Table 1 | Microsatellite analysis: multiplexing parameters and summary statistics.

 1

		Ech030	Ech036	Ech051	Ech071	AM13	B3-2	Emm4	RM3
	PCR	Multi_A	Multi_A	Multi_B	Multi_B	Single	Multi_C	Single	Multi_C
	Genotyping	Seq_A	Seq_A	Seq_B	Seq_B	Seq_C	Seq_C	Seq_A	Seq_C
	Label	HEX	HEX	FAM	FAM	HEX	HEX	FAM	FAM
	Pattern	(CTAT)n	(GT)n	(GT)n	(CTCAT)n	(GT)n	(GT)n	(CT)n	(CA)n
	Allele count	9		8		8		13		6		9		9		4
	Gene	0.773	0.750	0.544	0.866	0.702	0.642	0.658	0.541
	diversity
	Allelic	8.988	7.994	7.988	12.994	6.000	8.994	9.000	4.000
	richness
	Allele	Freq.	Allele	Freq.	Allele	Freq.	Allele	Freq.	Allele	Freq.	Allele	Freq.	Allele	Freq.	Allele	Freq.
	267	0.024	190	0.024	188	0.012	215	0.009	109	0.006	285	0.009	233	0.003	202	0.012
	271	0.045	194	0.179	192	0.006	220	0.027	111	0.066	288	0.033	235	0.012	207	0.518
	275	0.232	196	0.003	194	0.580	225	0.051	115	0.326	290	0.009	237	0.407	208	0.438
	279	0.330	198	0.015	196	0.345	230	0.057	117	0.371	292	0.238	239	0.410	210	0.033
	283	0.223	200	0.244	198	0.039	235	0.152	119	0.228	294	0.536	241	0.087	-	-
	287	0.113	202	0.378	200	0.012	240	0.185	121	0.003	296	0.113	243	0.033	-	-
	291	0.027	204	0.125	204	0.003	245	0.176	-	-	298	0.039	245	0.024	-	-
	295	0.003	206	0.033	206	0.003	250	0.176	-	-	300	0.021	247	0.015	-	-
	299	0.003	-	-	-	-	255	0.092	-	-	302	0.003	249	0.009	-	-
	-

  ). F-statistics were assessed in Fstat together with microsatellite summary statistics (allele range, count and frequencies, and linkage disequilibrium). Global intrinsic di erentiation in the sample, regardless of any spatial grouping, was tested in Structure[START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF], with 200,000 burn-in and 1,000,000 steps, under Admixture model, with K values ranging from 1 to 6 (allele frequencies correlated). Log-probability of the data was calculated in Structure Harvester (Earl 2012). §-63 Spatial autocorrelation analyses. In a population with limited, yet homogeneous, internal dispersal and gene ow, genetic drift is expected to generate a pattern of isolation by distance. Related genotypes will tend to become clustered in spatial patches, and correlation between genotypes will decay with distance (Epperson 2005). In this case, correlation between genetic and spatial

Table 2 | Pairwise cluster comparison of inbreeding and relatedness distributions reveal genetic heterogeneity in the sampling area.

 2 Upper triangle: Ritland's individual inbreeding coe cient. Lower triangle:Ritland's pairwise relatedness coe cient. e empirical coe cient is shown in bold, followed by the 95% con dence interval. Signi cantly divergent comparisons are shown in bold type with asterisks. for tick load from one year to the previous one, starting in 2004: c 2004 =0.400i2004 =0.006 p 2004 ≈0; c 2005 =0.465 i 2005 =0.034 p 2005 ≈0; c 2006 =0.146 i 2006 =0.058 p 2006 <0.05; c 2007 =0.406 i 2007 =0.021 p 2007 ≈0; c 2008 =0.577 i 2008 =0.068 p 2008 <0.001; c 2009 =0.424 i 2009 =0.062 p 2009 <0.01; c 2010 =0.192 i 2010 =0.037 p 2010 <0.01; c 2011 =0.743 i 2011 =0.063 p 2011 ≈0. For site occupancy timing from each year to the previous one, c 2007 =0.526 i 2007 =0.279 p 2007 ≈0; c 2008 =0.712 i 2008 =0.112 p 2008 ≈0; c 2009 =0.462 i 2009 =0.331 p 2009 ≈0; c 2010 =0.664 i 2010 =0.226 p 2010 ≈0; c 2012 =0.159 i 2012 =0.062 p 2012 <0.01; c 2013 =0.010 i 2013 =0.250 NS. Too much missing data in 2011 led us to discard this year, and to Fine-scale colony structure - §68 correlate 2012 values with 2010). Chick survival was found to be correlated between 2010 and 2011 (slope 1.548, intercept 0.168, p-value 0.0427), sampling did not overlap su enciently between years 2011 and 2012 for comparison. All three variables showed a markedly structured,

		Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5	Cluster 6
			-1.1002e-1	-1.5355e-1	-3.9188e-2	-7.0628e-2	-6.3993e-2
	Cluster 1		8.4695e-2 C1<C2*	1.0299e-1 C1<C3*	4.0492e-2 C1=C4	5.2465e-2 C1<C5*	5.8778e-2 C1<C6*
			-8.5559e-2	-1.2204e-1	-4.3607e-2	-5.1926e-2	-5.8408e-2
		1.0118e-2		-4.3531e-2	7.0834e-2	3.9394e-2	4.6029e-2
	Cluster 2	2.0834e-2 C1=C2		1.5727e-1 C2=C3	7.9085e-2 C2=C4	8.1326e-2 C2=C5	1.0041e-1 C2=C6
		-2.3307e-2		-1.6080e-1	-7.1760e-2	-7.3237e-2	-9.9416e-2
		-4.0898e-3	-1.4208e-2		1.1436e-1	8.2925e-2	8.9560e-2
	Cluster 3	2.1038e-2 C1=C3	3.2650e-2 C2=C3		1.1821e-1 C3=C4	1.1181e-1 C3=C5	1.4201e-1 C3=C6
		-2.3405e-2	-3.4437e-2		-8.6573e-2	-8.5461e-2	-1.2877e-1
		4.5949e-3	-5.5231e-3	8.6848e-3		-3.1439e-2	-2.4804e-2
	Cluster 4	1.1684e-2 C1=C4	1.9016e-2 C2=C4	-1.7182e-2 C3=C4		4.7974e-2 C4=C5	4.7573e-2 C4=C6
		-1.1589e-2	-1.6720e-2	1.7959e-2		-4.4700e-2	-5.3248e-2
		8.1903e-3	-1.9277e-3	1.2280e-2	3.5953e-3		6.6350e-3
	Cluster 5	1.4813e-2 C1=C5	2.4833e-2 C2=C5	2.7328e-2 C3=C5	1.2583e-2 C4=C5		5.8991e-2 C5=C6
		-1.3828e-2	-2.0848e-2	-2.2622e-2	-1.1928e-2		-6.3224e-2
		1.8131e-2	8.0130e-3	2.2221e-2	1.3536e-2	9.9407e-3
	Cluster 6	1.6586e-2 C1<C6*	2.6127e-2 C2=C6	2.3997e-2 C3=C6	1.3248e-2 C4<C6*	1.8263e-2 C5=C6
		-1.7630e-2	-2.4755e-2	-2.3723e-2	-1.3465e-2	-2.1122e-2

Fine-scale colony structure - §68 Our analyses revealed strong overlap between this clustered pattern and spatial distribution of ecological site-quality indicators. Tick infestation and site occupancy timing showed strong yearto-year correlation across all 8 sampled years (slope c t , intercept i t and signi cance l evel o f the correlation p t

stone range shifts in response to climate change in the Southern Ocean

	e King synnome - §76
	Chapter 4: e King synnome

Le Vaillant, O. Prudhomme and M. Ripoche for the sample collection. is study was supported by the Institut Polaire Français Paul-Emile Victor (Programme 137), the Centre Na-tional de la Recherche Scienti que (Programme Zone Atelier de Recherches sur l'Environnement Antarctique et Subantarctique), the Fondation de France / Fondation Ars Cuttoli (to JDW), and Marie Curie Intra European Fellowships (FP7-PEOPLE-IEF-2008, European Commission; project No 235962 to CLB, and FP7-PEOPLE-IEF-2010, European Commission; project No 252252 to ET). is c hapter i s b ased o n C ristofari R , L iu X , B onadonna F , C herel Y , L e Maho Y , P istorius P, Raybaud V, Stenseth NC, Le Bohec C & Trucchi E, Stepping-. (in prep.)

  [START_REF] Mackintosh | Seabirds as indicators of aquatic ecosystem conditions: A case for gathering multiple proxies of seabird health[END_REF]) may outpace the potential rate of microevolutionary responses, especially for species that have long generation times[START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF] and low genetic diversity[START_REF] Ho Mann & Sgrò | la plasticité phénotypique et en particulier les changements de phénologie (Char-Annexes - §13[END_REF][START_REF] Norberg | Eco-evolutionary responses of biodiversity to climate change[END_REF]. us, range shift is emerging as a major feature of species response to fast environmental change[START_REF] Wiens | Niche conservatism as an emerging principle in ecology and conservation biology[END_REF]. However, the fragmentation of ecosystems, mostly due to a growing anthropogenic pressure, implies that newly available habitats may be beyond reach for most of the species. Moreover, the uncoupling of climate change between geographical areas (Burrows et al. 2014) imposes additional constraints on species that rely on di erent territories (e.g. migratory species, or long-distance central-place for-§81 cially sensitive to the duration of its central-place foraging trips during the early chick rearing period[START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF], foraging distance at the peak of summer is a critical driver of breeding success(Le Bohec et al. 2008;[START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. e most extensively studied colony, belonging to the most important breeding area for the species (the Crozet archipelago,[START_REF] Bost | King Penguin. In: Penguins, Natural history and conservation[END_REF], appears to have bene ted f rom H olocene w arming( Trucchi e t al.

	predicted response to climate warming for cold-adapted species, the highly fragmented nature of
	the King penguin's habitat precludes continuous population displacement. Because the species
	breeds exclusively on year-round ice-free areas on islands scattered throughout the Southern
	2008; Ho mann & Sgrò 2011), (ii) adaptive microevolution (Bradshaw & Holzapfel 2006; Ho -Ocean (Bost et al. 2013), it may only disperse in a stepping-stone manner amongst the few avail-
	mann & Sgrò 2011), and (iii) range shift (Chen et al. 2011; Beaugrand et al. 2015). Although able islands. e s pecies' f oraging g rounds, o n t he o ther h and, m ove t ogether w ith t he myc-

Using the global King penguin (Aptenodytes patagonicus) population as a model for top-predator climate-induced range shift in a fragmented landscape, and integrating a large set of ecological and genomic data with general circulation models, we show that the panmictic King penguin population responded strongly to climate change throughout the late Quaternary, and predict that up to 70% of the present-day population will be threatened before the end of the century if no immediate step is taken. By showing how habitat fragmentation can exacerbate the e ect of global warming towards biodiversity collapse, we stress the urgency of tangible actions to limit global temperature increase.

Results

§-79 Expected responses to climate change. While the impact of anthropogenic global changes on biological communities is beyond question ( omas et al. 2004; Paci ci et al. 2015), the nature and extent of species responses are generally still imperfectly understood (Pereira et al. 2010; He & Hubbell 2011; Paci ci et al. 2015). At the species level, three fundamental mechanisms interact in determining the response to climate change: (i) phenotypic plasticity (Charmantier et al. important in the short-term, plasticity has a high tness cost (Van Buskirk & Steiner 2009) and may rapidly reach its limits (Auld et al. 2009). On the other hand, the fast rate of current climate e King synnome - §80 change (

agers)

. is is especially true in the Alpine and Polar Regions

[START_REF] Williams | Projected distributions of novel and disappearing climates by 2100 AD[END_REF]

), which may act as "climate sink areas" where local suitable ecological conditions disappear instead of being displaced

[START_REF]Quoique l'expression principale de la réponse plastique se concentre sur la phénologie ( §14), c'est à dire sur le cadre temporel du cycle reproducteur des espèces ou des populations, les déplacements de distributions représentent un changement de son cadre spatial. Par opposition à la micro-évolution et à la plasticité, ce déplacemnt implique uniquement un changement géographique, a priori sans contraintes physiologiques ou autrescependant, les cas de déplacements[END_REF]

. In addition, habitat fragmentation may exacerbate the e ect of climate change for higher trophic level taxa: in particular, the complex response of top predators, which integrate changes occurring at all levels of the trophic network

[START_REF] Zarnetske | Biotic multipliers of climate change[END_REF]

) and largely determine ecosystem resilience

[START_REF] Heithaus | donc de comprendre comment la distribution de chaque espèce s'organise, à des échelles allant de la diversité intra-coloniale, à l'île[END_REF]

, may be the key to understanding the depth of current changes, and forecasting ecosystem collapses

(Sche er et al. 2001;[START_REF] Barnosky | Approaching a state shift in Earth's biosphere[END_REF]

. §-80 Habitat constraints for the King penguin. e highly constrained niche of the long-lived subantarctic King penguin (Aptenodytes patagonicus) makes this species a useful model of top-predator response to environmental change

(Le Bohec et al. 2008)

. While a poleward range shift is the tophid sh stock that strives around the Antarctic Polar Front (APF)

[START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF] Bost et al. 2015)

. Since the King penguin's breeding system, that spans over 12 months, makes it espe-e King synnome -

  Summary statistics were calculated in Arlequin(Exco er et al. 2005)

	e King synnome - §88
	formed using the GATK HaplotypeCaller pipeline (DePristo et al. 2011), with standard parame-
	ters, except for population heterozygosity which was set to 0.01. We retained only SNPs
	genotyped in at least 75% individuals, or 90% for AMOVA and PCA analyses. (iv) Allele-fre-and with custom R scripts either from ltered SNP calls, or from short RAD haplotypes. Pairwise
	quency likelihood and allele frequency spectra. ANGSD 0.9008 (Korneliussen et al. 2014) was used xation index (Fst), calculated using Reich's estimator (Reich et al. 2009), is close to zero (mean
	to compute per-site probability of being variable, and raw genotype likelihoods, using the Sam-pairwise Fst 0.0132 ± 0.00567). Nucleotide diversity π and Tajima's D were computed for full
	tools mpileup/bcftools algorithm, and the complete sample allele frequency information as a pri-RAD haplotypes. In order to avoid possible biases due to low coverage, we randomly sampled
	chemistry, libraries 1-3), or HiSeq2500 (V4 chemistry, libraries 4-6), at the Norwegian Sequenc-or. Per-site allele-frequency likelihood distribution was used to produce a maximum-likelihood one haplotype for each individual, and performed calculations on this haploid subset. Tajima's D
	ing Centre, University of Oslo, spiked with 20% PhiX control library in order to reduce low-di-estimate of the derived allele frequency spectrum, either unidimensional at the population or is slightly negative, and homogeneous across locations (D all : -1.094 ± 0.672, D HEA : -0.329 ±
	0.925, D KER : -0.518 ± 0.899, D CRO : -0.546 ± 0.890, D MAR : -0.404 ± 0.00307, D GEO : -0.448
	± 0.925, D FLK : -0.312 ± 0.953), and nucleotide diversity is low (π ALL : 0.00209 ± 0.00258, π HEA :
	0.00201 ± 0.00326, π KER : 0.00215 ± 0.00304, π CRO : 0.00218 ± 0.00307, π MAR : 0.00200 ±
	0.00306, π 2014) using a ltered SNP
	low 30. e resulting BAM les were then ltered for PCR and optical duplicates by comparing quence for crown-Aptenodytes was reconstructed using PAML (Yang 2007) and Lazarus (project-dataset (minimum depth of coverage of 4x, and minimum 80% individuals genotyped at each lo-
	mapping position and CIGAR string, using Picard MarkDuplicates. is process also allowed to lazarus.googlecode.com/), using PhyML tree topology as a prior. Downstream analysis was re-cus, leaving 4,784 polymorphic loci for analysis), again with 100 replicates and K ranging from 1
	lter out most sequencing errors, since MarkDuplicates only retains the read with the highest av-stricted to the sites that could be reliably polarized. All sites that were identi ed as belonging to to 10. Both approaches unambiguously supported a K=1 model.
	erage Phred score in each duplicate cluster. (iii) SNP calling and genotyping. A draft SNP-calling coding regions (Zhang et al. 2011b), or to sex chromosomes (Zhou et al. 2014), were excluded
	was done in Stacks v1.28 for the general assessment of the dataset, using the "rxStacks" correction from the analysis.

versity bias. §-87 Sequence alignment and genotyping. Data processing was performed using the following work ow: ( i) S equence d emultiplexing. R ead q uality a ssessment w as m ade i n F astQC (http:/ /www.bioinformatics.babraham.ac.uk/projects/fastqc/). Samples were de-multiplexed according to in-line barcodes using Stacks v1.28

[START_REF] Catchen | Stacks: building and genotyping loci de novo from short-read sequences[END_REF][START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF]

, low-quality reads were discarded, and sequences trimmed to 95 bp. (ii) Read mapping and ltering. Demultiplexed fastq les were mapped to the published contigs of the Emperor penguin genome

(Zhang et al. 2011b

) using Bowtie2 2.2.35, with standard settings, allowing only end-to-end mapping.

Resulting SAM les w ere ltered us ing Sa mtools 0. 1.196, Pi cardTools 1. 113 (picard.sorceforge.net), and custom R and shell scripts (github.com/rcristofari/RAD-Scripts.git) in order to discard unpaired reads and full read pairs where at least one mate has a mapping quality score bealgorithm, with a maximum of 5 mismatches allowed between alleles at a single locus (both within and between individuals). For SNP-based analysis, joint SNP and genotype calling was per-species level, or pairwise joint spectrum between pairs of populations. §-88 Ancestral state reconstruction. In order to polarize allele-frequency spectra, we reconstructed the most likely ancestral base for all positions in the RADome. We selected 12 high-quality King penguin samples covering the whole species' range, and 12 Emperor penguin samples processed according to the same protocol (see e Emperor synnome, p. 211). We used BEDtools

[START_REF] Quinlan | BED-Tools: a flexible suite of utilities for comparing genomic features[END_REF]

) and GATK's FastaAlternateReferenceMaker to update the published Emperor penguin genome and establish a reference RADome for both the King penguin, and the Emperor penguin, using only high-quality polymorphisms (phred-scale genotype quality ≥ 80). We aligned this RADome to the Adélie penguin genome (Pygoscelis adeliae, Zhang et al. 2011a) using Bowtie2, and extracted the corresponding regions. For each RAD locus, a maximum-likelihood unrooted tree was built in PhyML

[START_REF] Grémillet | Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms[END_REF]

, and maximum-likelihood ancestral se-e King synnome - §90 S-1: Analysis of genetic data. §-89 Summary statistics. GEO : 0.00199 ± 0.00295, π FLK : 0.00182 ± 0.00294), in keeping with the prediction of Romiguier et al. 2014 for long-lived species. §-90 Clustering analysis. Clustering was performed in ngsAdmix

[START_REF] Simmonds | Relationships between the interannual variability of Antarctic sea ice and the Southern Oscillation[END_REF]

, based on genotype likelihoods calculated in ANGSD with a SAMtools model, and allowing for a maximum of 50% missing data in order to process a site, and keeping only positions inferred as variable with a high likelihood (p-value threshold 1e-6). We performed 100 bootstrap replicates, with K values ranging from 1 to 10. Best-tting K was chosen using Evanno's δK method. An independent clustering was performed in FastStructure (Raj et al.

  §96rate and adult survival rate for the previous generation, de ned a s λ t = ( N t+1 / N t ).e(1/Tt), where Nt+1 and Nt are the population sizes at generations t+1 and t, and Tt is the generation time in years at generation t, and St is a linear function of λt, using empirically derived parameters. is correction was applied recursively from the oldest generation in the reconstruction assuming λ = 1, and towards the present. In order to calibrate other analyses, the mean generation time over the whole reconstruction T = 10.6 years was retained. §-96 Demographic reconstructions: the Extended Bayesian Skyline Plot (EBSP) method. Accurate reconstruction of past and present population size changes requires a robust estimate of the substi-

	e King synnome - §96
	We followed the protocol presented in §44 p. 112 and applied in §136 p. 252, a development of
	the protocol of Trucchi et al. 2014, downsampling the data to haploid individuals, and using in-
	dependent 50 loci with 50 haplotypes each, with 3 to 6 polymorphic sites, in addition to 50 ran-
	domly selected HVR haplotypes. We speci ed one independent site model for each locus class (3,
	4, 5 or 6 SNPs, and HVR). For each class, speci ed a HVR model, allowing for invariant sites for
	that period (intercept: -0.2454, slope: 1.0936, R 2 =0.6): therefore, we extended the empirical rela-the HVR, but not for the short nuclear loci, and for gamma-distributed rate heterogeneity discre-
	tionship between both parameters to our reconstruction. For each generation, the generation tised in 4 classes. Transition-transversion ratio kappa was linked across nuclear models. All chains

lived species, generation time is not a xed parameter, but rather a function of the demographic trend. An estimator has been de ned by

[START_REF] Saether | Finite metapopulation models with density dependent migration and stochastic local dynamics[END_REF]

as α + (S / (λ -S) ), where α is the age at rst breeding for females, S is the yearly adult survival rate, and λ is the yearly growth rate of the population, de ned as λ=1 for a stable population. Using long-term monitoring data, we extracted both yearly growth rate, and adult survival, from a pool of 400 adults of known age (Le Bohec pers. com.), for the 1999-2010 period. S and λ were found to be strongly correlated over time in years was therefore de ned as Tt+1 = α + (St / (λt -St) ), where λt and St are the growth e King synnometution rate. We performed a joint analysis of mitochondrial HVR and RAD data, in a multilocus EBSP framework, using the robustly established substitution rate for the Adélie penguin HVR (in substitutions per site per Myr: median = 0.55, 95% CI = 0.

29-0.88 Millar et al. 2008

) as a calibration. Since the generation time di ers widely between the Adélie penguin

(6.46 years Millar et al. 2008

) and the King penguin

(10.48 years, see §95 p. 186)

, and since we are considering the rate of substitution as determining the frequency of coalescence events, as opposed to the rate of mutation (a purely physiological parameter -see Gibb & Hills 2013), we converted that rate to re ect t he d i erence i n g eneration t ime, t o 0 .34 s ubstitutions.site -1 .Myr -1 ( 95% C I = 0.18-0.54).

  §96Myr, which may be a large overestimate(Gavryushkina et al. 2015, based on a state-of-the-art total evidence Bayesian analysis, proposes ~9 Myr instead), and 2) the generation time is taken to be 5 years in both species; however it has been shown to be 16 years in the Emperor penguin (Je-

	nouvrier et al. 2014), and 6.46 years in the Adélie penguin (Millar et al. 2008) -thus 11 years
	would be a closer (although inaccurate because assuming a single, constant rate) estimate of a
	common generation time. Applying these corrected estimates to Li et al.'s ndings would give a
	rate of ~4.55e-8, which is more than ve times faster than proposed, and ca. half our estimate -
	although this calculation does not take into account the possible rate heterogeneity between lin-
	generation.
	is r ate i s c a. t wice s lower t han t he o ne r eported by Trucchi e t a l. ( 2.6e-7 subst.site -1 .genera-
	tion -1 , Trucchi et al. 2014), but much faster than the one reported by Li et al. (8.11e-9 sub-
	st.site -1 .generation -1 , Li et al. 2014). While the former was not used in Trucchi et al.'s analysis, but
	rather derived from it, Li et al.'s result, on the other hand, relies on two exterior and uncertain as-

Fitted model: intercept i = -5.02, slope s = 0.292, R 2 =0.997). A Poisson model of parameter λ equal to the mean observed number of segregating sites was a good t f or t he e mpirical d istribution o f n umber of segregating sites per locus (λ=1.47 , chi-squared test of goodness-of-t p-value= 0.232). us, we extracted μ as e (s.λ+i) ~ 1.02e-2 substitutions per site per Myr, or 1.08e-7 substitutions per site per sumptions: 1) the divergence time between the Emperor and the Adélie penguin is set to ~23 e King synnomeeages, and most importantly the changes in generation time between the Aptenodytes/Pygoscelis common ancestor and the extant species, which may explain the remaining di erence. Generally, the rate of evolution of penguins has been a rather challenging subject, with a wide discrepancy between the paleontological and molecular evidence. While fossil data has been recognised to support a very recent radiation of penguins (about 10 Myr BP, see

[START_REF] Slack | Early penguin fossils, plus mitochondrial genomes, calibrate avian evolution[END_REF][START_REF] Clarke | Paleogene equatorial penguins challenge the proposed relationship between biogeography, diversity, and Cenozoic climate change[END_REF]

  colony detections/deployments: CDC: 11/5, PMC: 44/10, JPN: 9/5, MAE:1/1). Out of the 9,832 individuals marked as chicks, an average of 2.3 birds per 12-hour antenna deployment were detected on other colonies of the same island.

	One anecdotic recapture, in 2014, of a tagged individual born on Crozet in 2009 (and therefore
	reaching age of rst b reeding a t t he t ime o f recapture) a lso h appened o n t he R atmano beach
	colony, on Kerguelen archipelago. Although a single event has hardly any statistical value, the
	Ratmano colony counts ~140,000 breeding pairs

  We used the latest generation of AOGCMs from the IPCC Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012), which represent a signi cant improvement over CMIP3 in the Southern Ocean

	, and temperature
	drops slightly after ~3 Kyr BP, although with no change in the glacial landscape. Neoglacial con-
	ditions arise after 5 Kyr in East Antarctica, and 3 Kyr in West Antarctica: open water conditions
	are still prevalent throughout the Southern Ocean, although with possible winter sea ice episodes
	at 53°S at some periods (~1-2 Kyr BP).

S-4: Atmosphere-Ocean General Circulation Models (AOGCMs) §-102 AOGCMs choice and multi-model ensemble approach.

Table 3 | Ensemble members used in habitat predictions. Model

 3

	Model	Institution	LGM	mid-Holocene 21st century
	BCC-CSM1	Beijing Climate Center (China)	N/A	r1i1p1	r1i1p1
	CanESM1	Canadian Centre for Climate Modelling and Analysis (Canada)	N/A	N/A	r1i1p1
	CCSM4	National Center for Atmospheric Research (USA)	r1i1p1	r1i1p1	r1i1p1
	CESM1-CAM5	National Center for Atmospheric Research (USA)	N/A	N/A	r1i1p1

outputs were downloaded from the IPCC archive (http://www.ipcc-data.org/sim/gcm_monthly/AR5/Reference-Archive.html). Only one ensemble member was used for each model (r1i1p1 whenever available). Not all models outputs for both 21st century and paleoclimate experiments, thus di erent ensembles were used for LGM, mid-Holocene and 21st century reconstructions.

e King synnome - §103 §-103 Sea Surface Temperature (SST). For palaeoclimate as well as 21 st century projections, we followed a protocol similar to that of

Péron et al. 2012. e 5 °C Sea Surface Temperature (SST)

Table 4 | Correlation of observed and modelled SST in the Southern Ocean.

 4

	Slope, intercept and

  Sequence Read Archive. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in TableS1 ofthis paper) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. §107 beit in very similar conditions, made in Netherlands by Visser et al. 1998. Similarly, the recent study of Stenseth et al. 2015 found important di erences in the response to climate change of di erent neighbouring Belgian and Dutch Blue and Great tit (Pares caereuleus and P. major) populations. From a functional standpoint, the observation of Weimerskirch 2013 of fully segregated foraging grounds in two nearby booby colonies suggests that di erent alimentary strategies may lead to di erent exposure to environmental change. us, before extrapolating the results of localobservations to species-level predictions, it is necessary to assess the nature and extent of the coupling between local and global processes. §-107 Linking generation-scale and coalescent-scale demography. Amongst the hypotheses that allow the extensions of local demographic models to global trends is the assumption of demographic in-§107 of instantaneous dispersal rate, at least a bracket for averaged migration ability in the species, that can help us de ne reasonable assumptions for developing more accurate demographic models.

	e King synnome - §105 e Emperor synnome - §106
	Chapter 5: e Emperor synnome
	served local sea ice conditions -some elements even make this a very likely possibility, see §24 p.
	79), and (ii) part of the observed population processes may be better explained by migration than
	nouvrier et al. 2014) or to a handful of populations in optimal cases (e.g. Dunn et al. 2011). Al-by mortality or growth -that is, without assuming a direct link between local colony size and
	though oftentimes highly informative, these studies all run the risk of misestimating local idio-global population size. Measuring the importance of dispersal in eld conditions is a considerable
	syncrasies. For example, the characteristic plastic response of Great tits to food peak advance, challenge (see §19 p. 68), and can only be done with massive recapture e ort (see e.g. Barlow et

Cristofari R, Bertorelle G, Ancel A, Benazzo A, Le Maho Y, Ponganis PJ, Stenseth NC, Trathan PT, Whittington JD, Zanetti E, Zitterbart DP, Le Bohec C & Trucchi E, Full circumpolar migration ensures evolutionary unity in the Emperor penguin. (Nature Communicationsin prep) Context §-106 Can a species be studied from a single colony? Most studies investigating the e ects of climate change on population dynamics have relied either on « anonymous » count data (i.e. unmarked individuals treated as a group -see for example VanDerWal et al. 2013 or Langham et al. 2015), or capture-mark-recapture, individual-based data (e.g. Le Bohec

et al. 2008[START_REF] Jenouvrier | Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models[END_REF], or Durner et al. 2009

). e l atter a pproach i s c onsiderably m ore p recise, a s i t i s a ble to identify some misleading compensatory e ects (such as local decline masked by density-dependent immigration) -however, eldwork c onstraints t ypically r estrict t his a pproach t o a single study colony or population (as is the case in

[START_REF] Charmantier | Summer diet of king penguins (Aptenodytes patagonicus) at the Falkland Islands, southern Atlantic Ocean[END_REF]

; Le Bohec et al. 2008; Jeobserved in Great Britain by Charmantier et al. 2008, is in strong contrast with observations, al-e Emperor synnomedependence, that Jenouvrier et al. 2014 proposes, in the case of the Emperor penguin, as the idea that « inter-colony dispersal is extremely unlikely

[...]

. us, neither dispersal nor demographic sourcesink dynamics will change our main conclusion about global population declines » -census and capture-mark-recapture data is thus considered self-contained, and the authors « do not consider movement among populations; it is unlikely to be demographically important in this species ». Although hardly an avoidable assumption in that case, since almost no demographic data is available for other colonies, this postulate presents a risk: it does not acknowledge the possibility that (i) the population trend in the Pointe Géologie Emperor penguin colony may be particular to that colony for stochastic, or for idiosyncratic reasons (that is, not directly because of the obal. 2013). Population genetics approaches, on the other hand, allow us to access the long-term migration parameter for a set of populations (see §41 p. 103), and to set, if not a point estimate e Emperor synnome -

  [START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]). e colony breeding on the tongue of the Mertz glacier, for instance, disappeared after the 2010 calving of that glacier[START_REF] Ancel | Emperors in hiding: when icebreakers and satellites complement each other in Antarctic exploration[END_REF], and the following changes in local ice movements also had catastrophic consequences on the nearby Pointe Géolo-

	gie colony, where the number of edged chicks dropped from ~2,500 in 2010 to ~100 in 2014
	(personnal observation). All of these events have in common an identi ed proximal cause, usually

, leaving adult and juvenile survival and breeding output as the sole factors driving population dynamics. is assumption, however, is not based on direct evidence, but is rather motivated by technical di culties in discriminating emigration from mortality of tagged individuals

[START_REF] Hunt | The impact of avian predator-scavengers on King Penguin Aptenodytes patagonicus chicks at Marion Island[END_REF] Ehrlén & Morris 2015)

. §-109 Instability of Emperor penguin colonies. e E mperor p enguin, t he o nly winter-breeding top-predator species of Antarctica (Prévost 1961), stands at the forefront of the impacts of climate warming

(Ainley et al. 2010a)

. Emperor penguins breed nearly exclusively on sea ice: this unstable habitat makes the species immediately sensitive to local environmental changes. One of the northernmost colonies, located on the Antarctic Peninsula, vanished during the last decades, as sea ice retreated in that area

[START_REF] Tomczak | First recorded loss of an emperor penguin colony in the recent period of Antarctic regional warming: implications for other colonies[END_REF]

. Other colonies underwent a dramatic drop in breeding success and population size shortly after a modi cation i n l ocal s ea i ce topology (Kooyman & linked to modi cations i n t he l ocal s ea-ice l andscape t hat e ither f orced a dults t o m ake longer trips over the sea ice to reach open waters for foraging, with subsequent breeding failure resulting e Emperor synnome - §110

  growth[START_REF] Knorr | Seabirds as monitors of upper-ocean thermal structure. King penguins at the Antarctic polar front, east of Kerguelen sector[END_REF]. Recent satellite and ground surveys have also shown that whole emperor penguin colonies are able to relocate with or without an identi ed cause[START_REF] Ancel | Emperors in hiding: when icebreakers and satellites complement each other in Antarctic exploration[END_REF][START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF]. Finally, biologging experiments have emphasized the outstanding distances regularly traveled by adult and juvenile emperor penguins( iebot et al. 2013). Here, we demonstrate that dispersal is a fundamental component of demography in this long-lived species, and propose that it does play a central role in its adaptive response at the continental

scale. As such, migration amongst colonies needs to be incorporated into demographic models in order to achieve accurate projections. §-110 Study design. We produced genome-wide RAD-sequencing data for 110 individuals from 6

  , are very low (see details in Supplementary methods §129 p.

	242 and Table 6), and only about 0.5% of total variance is explained by colony structure (as per
	AMOVA, see §129 p. 242). A neighbour-net based on pairwise Hamming distances shows con-
	siderable admixture between areas: individuals are roughly sorted according to geographical loca-
	tion, but inter-individual variability is largely dominant (Fig. 31B). Inference of population split
	topology based on allele frequency variation amongst populations ( §132 p. 246) also supports

this view: neutral genetic di erentiation from the ancestral population increases eastward from e Emperor synnome - §112

e

  Emperor synnome - §115 aged over many generations, not of instantaneous dispersal rate (see S-0 p. 236): neither population sizes, nor migration rates, should be interpreted as referring literally to the immediate state of extant colonies but rather to the average coalescence-based population size and migration rate over time. Dispersal rate itself may also be very heterogeneous at any given point of time, with individual colonies showing positive or negative dispersal balance according to the current local habitat conditions and modi cations. §-114 Consequences of high dispersal. Dispersal thus appears to be a central aspect of the Emperor penguin's coloniality, and recent observations suggest that this view may be extended to other seabird species(Friesen et al. 2007). For example, we have repeatedly recaptured King penguins tagged at one colony in the Crozet archipelago at di erent colonies in the same archipelago, and as colonies in the Iles Kerguelen, 1,500 km away (unpublished data). As pointed out by Mayr

  §117and shell scripts ( §129 p. 242). For haplotype-based analyses, each individual was randomly downsampled to a haploid genome by discarding one haplotype at each locus, in order to avoid low-coverage biases. Hamming distances and identity-by-descent estimation were calculated in PLINK(Purcell et al. 2007, see §130 p. 244). For population size and migration rate estimates, §117 population was not a ected by the late Pleistocene and Holocene climatic events (which is also con rmed by our reconstructions in e King synnome, p. 163). A corollary of the Emperor's high dispersal ability, is the possibility that colonies may relocate when local habitat conditions become unsuitable, provided better conditions can be found anywhere in the vicinity (see Empirical evidence of heterogeneousdispersal, p. 259, and LaRue et al. 2015).ese ndings rede ne the way the Emperor's habitat may be modelled. In a previous attempt that we discussed earlier in this chapter, Jenouvrier et al. 2014 used the observed complex correlation between sea ice conditions and Emperor penguin survival and breeding success on the

	we used Migrate-n (Beerli & Palczewski 2010), using downsampled haplotypes ( §134 p. 247),
	and fastsimcoal2 (Exco er & Foll 2011; Exco er et al. 2013), using empirical joint-frequency
	spectra calculated in ANGSD ( §135 p. 250). BEAST2 (Bouckaert et al. 2014) was used to co-es-
	timate e ective population size and population size changes through time ( §136 p. 252). See on-
	line supplementary material for more detailed information.
	though the conclusions of this study are very consistent with the demographic trends inferred
	from genetic data, further work may still be required, outside the framework of the coarser
	CMIP5 models, in order to understand the ner local dynamics of sea ice formation and coastal
	Genotype likelihoods and site-allele-frequency likelihood distributions were calculated in polynya activity.
	ANGSD (Korneliussen et al. 2014) under a samtools/mpileup model ( §127 p. 238 and §128 p. §-117 How to model the Emperor's habitat? Our survey of the Emperor penguin's genetic architec-
	241). Only autosomal, high-quality loci were retained. Summary statistics were calculated in ture brings to light two important traits: (i) the whole species is organised as a single synnome,

with minor modi cations (see Supplementary methods §126 p. 237 for details). Sequences were aligned on the Emperor penguin's reference genome

(Zhang et al. 2011b

) and cleaned using standard tools.

Genotyping was performed using GATK's HaplotypeCaller

[START_REF] Depristo | The annual breeding cycle of unbanded king penguins Aptenodytes patagonicus on Possession Island (Crozet)[END_REF]

) pipeline, Stacks

[START_REF] Catchen | Stacks: building and genotyping loci de novo from short-read sequences[END_REF][START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF] ANGSD (Korneliussen et al. 2014)

. ngsTools

[START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]

, adegenet

[START_REF] Jombart | adegenet: a R package for the multivariate analysis of genetic markers[END_REF]

, and custom R e Emperor synnome - Modelling the changes in Emperor penguin habitat. §-116 Aim of this study. e following part was not included in the original article « Full circumpolar migration ensures evolutionary unity in the Emperor penguin ». e strong contrast between the marked demographic response of the King penguin on the one hand (see e King synnome, p. 163) and the lack of detectable response of the Emperor penguin on the other, however, raises the question of the direct environmental determinants of demographic trends in the second species. Here, we propose a preliminary model to explore the relationship of the Emperor penguin to its direct abiotic environment, de ned both by sea ice, and by atmospheric variables. Aland migration between colonies is a central demographic process, and (ii) the Emperor penguin e Emperor synnome - Pointe Géologie colony to de ne a p opulation d ynamic m odel, t hat i s e xtended t o t he whole continent using present-day colony locations as the focal points for assessing local environmental conditions. We showed that a rst shortcoming in this approach is the assumption that dispersal was not interfering with local dynamics in response to icescape changes (or to other forcings such as human disturbance) -which may result in a biased estimate (and hence model) of survival and breeding output.

But a second, and equally important di culty lies in the nature of the Emperor penguin's habitat. Indeed, the Emperor needs both dense and rm ice for establishing its breeding colonies, and open-water polynyas (see §18 p. 65) for foraging during the breeding season. Since only coastal (usually latent heat) polynya are a requisite, the total sea ice extent around the continent is of comparatively little importance, as long as o shore wind stress maintains open-water conditions

Table 5 | Ensemble members used in sea ice modelling.

 5 Model outputs were downloaded from the IPCC archive (http://www.ipcc-data.org/sim/gcm_monthly/AR5/Reference-Archive.html). Only one ensemble member was used for each model (r1i1p1 whenever available).

		e Emperor synnome
	Model	Institution	LGM	mid-Holocene 21st century
	CCSM4	National Center for Atmospheric Research (USA)	r1i1p1	r1i1p1	r1i1p1
		Centre National de Recherches Météorologiques,
	CNRM-CM5	Centre Européen de Recherche et de Formation Avancée en Calcul Scienti que	r1i1p1	r1i1p1	r1i1p1
		(France)
	GISS-E2-R	NASA/GISS (Goddard Institute for Space Studies) (USA)	r1i1p1	r1i1p1	r1i1p1
	IPSL-CM5A-MR	Institut Pierre Simon Laplace (France)	r1i1p1	r1i1p1	r1i1p1
	MPI-ESM-P	Max Planck Institute for Meteorology (Germany)	r1i1p1	r1i1p1	r1i1p1
	MRI-CGCM3	Meteorological Research Institute (Japan)	r1i1p1	r1i1p1	r1i1p1

- §119 Sea Ice Concentration. We focused primarily on Sea Ice Concentration (SIC), as proposed by Jenouvrier et al. 2014. Considering that Emperor penguins are primarily a ected by the local ice conditions around their coastal breeding site, and not by the total sea ice extent (Ainley et al.

Table 6 | Pairwise Fst, according to Reich's estimator.

 6 Upper triangle: mean value, lower triangle: standard deviation. DDU: "Dumont d'Urville", HAL: "Halley Bay", MZE: "Mertz East", MZW: "Mertz West", NEU: "Neumayer", WSH: "Cape Washington".

			e Emperor synnome - §130
		DDU	HAL	MZE	MZW	NEU	WSH
	DDU		0,0214	0,0154	0,0189	0,0177	0,0240
	HAL	0,0117		0,0138	0,0177	0,0120	0,0137
	MZE	0,0107	0,0090		0,0080	0,0112	0,0153
	MZW	0,0148	0,0115	0,0097		0,0152	0,0202
	NEU	0,0120	0,0090	0,0084	0,0116		0,0166
	WSH	0,0321	0,0274	0,0278	0,0302	0,0291
	§-130 Identity-by-state (IBS) and identity-by-descent (IBD). Pairwise indicators of IBS and IBD

Table 7 | Migration rates and sizes as estimated from the joint allele frequency spectrum.

 7

		MEAN	L95	U95
	Population sizes and directional gene ow
	N_HAL	4040	3990	4080
	N_NEU	3060	2920	3200
	N_DDU	1120	1100	1150
	N_MZE	7640	6150	9130
	mHALNEU	0.0166	0.0153	0.0179
	mNEUHAL	0.00323	0.0023	0.00416
	mNEUDDU	0.0391	0.0376	0.0407
	mDDUNEU	0.016	0.014	0.018
	mDDUMZE	0.0416	0.0379	0.0452
	mMZEDDU	0.000868	0.000372	0.00136
	mMZEHAL	0.00397	0.00327	0.00466
	mHALMZE	0.000413	0.000283	0.000544
	Immigration rate (ratio of receiving population)
	HAL	0.0072	0.00557	0.00882
	NEU	0.0326	0.0293	0.0359
	DDU	0.039968	0.037972	0.04206
	MZE	0.042013	0.038183	0.045744

Immigration size per receiving population (in e ective breeders)

	HAL	29	22	36
	NEU	132	117	146
	DDU	161	152	172
	MZE	170	152	187

Table 8 | 2012 census of the Mertz emperor penguin colonies.

 8

		Lat. (S)	Lon. (E)	Chicks	Adults	Chicks-to-adults ratio	Pairs (from chicks)*	Pairs (from adults)
	Eastern	67°19'	145°52'	1,750	1,520	1.15	~2,300	1,900-2,900
	Western	67°14'	145°30'	3,980	2,300	1.73	~5,100	2,900-4,400

  ; Ellis 2005; McCauley et al. 2012): but that double bond makes them highly susceptible to changes on both sides of the shoreline. eir sensitivity to changes in marine ecosystems is now a well-documented subject: it involves either long-term, indirect impact of human activities through climate change (Le Bohec et al. 2008; Sydeman et al. 2012), or direct impact through sheries w hich c ompete w ith s eabirds f or f ood r esource ( Tasker e t al.

  , and that the high investment required for burrow nest construction is likely to promote a strong philopatric behaviour[START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF]. It has thus been proposed that diving petrels occupy a coastal niche and are thought to be year-round residents[START_REF] Mulvaney | Oceanic birds of South America: a study of species of the related coasts and seas, including the American quadrant of Antarctica, based upon the Brewster[END_REF][START_REF] Figueroa | Registros actuales e históricos del Potoyunco peruano Pelecanoides Garnotii en el Perú[END_REF], which would further reduce its dispersal opportunities. §-151 Panmixia in the Humboldt Current System. While most studies conducted in seabirds have emphasized the importance of genetic or phylogeographic structure in species dynamics (see Friesen et al. 2007 for a review), the Humboldt Current System stands out as an exception. Genetic surveys conducted in its major seabird species have concluded in a lack of genetic structure at the continental scale (e.g. in the Peruvian booby (Sula variegata, see Taylor et al. 2010) or the Peruvian pelican (Pelecanus thagus, see Jeyasingham et al. 2013), or in very high gene ow between colonies in the ightless H umboldt p enguin ( Spheniscus h umboldti, s ee S chlosser e t al.

  5 (Exco er et al. 2013) (in a locus-by-locus rationale for SNP data, and haplotypic for mtDNA, and 15,000 permutations to assess signi cance levels). A neighbour-net was built in SplitsTree[START_REF] Huson | Application of phylogenetic networks in evolutionary studies[END_REF]) either directly from the mtDNA sequences, or from a matrix of pairwise Hamming distances, as calculated in PLINK v1.9[START_REF] Purcell | Warming of global abyssal and deep South-Annexes - §186 ern Ocean waters between the 1990s and 2000s: contributions to global heat and sea level rise budgets[END_REF]) from SNP data. Principal

component analysis was performed on genome-wide data either using called genotypes in adegenet, or raw genotype likelihoods in ngsTools

[START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF]

. In order to understand the contribution of individual loci to the overall structure, we performed a discriminant analysis of principal components

[START_REF] Jombart | Discriminant analysis of principal components: a new method for the analysis of genetically structured populations[END_REF]

, as implemented in adegenet. Admixture between populations was estimated using two di erent approaches. First, we used a classical model-based clustering approach relying on the hypothesis of Hardy-Weinberg equilibrium (HWE)

[START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF]

, as implemented with an empirical Bayes algorithm either in fastStructure

(Raj et al. 2014)

for called genotypes, or in ngsAdmix

[START_REF] Simmonds | Relationships between the interannual variability of Antarctic sea ice and the Southern Oscillation[END_REF]

for raw genotype likelihoods, retaining only sites that did not violate HWE, with a number components ranging from 1 to 4, and 100 bootstrap replicates. Model complexity was chosen using Evanno's ΔK method (Evanno

  ), population size changes during the past, and either gene ow remains constant (model 5) or gene ow also changes (model 6). We generated 50 nonparametric spectrum bootstrap replicates, and performed 50 parallel runs for each model and each bootstrap replicate, keeping only the one with the highest composite log-likelihood. Each run necessitated a maximum of 80 ECM cycles, simulating 100,000 spectra at each step. Models were calibrated using the general background genomic mutation rate for waterbirds, 1.6e -3 substitutions.site -1 .Myr -1 , as established from a wide panel of genomic data byZhang et al.

	Discussion - §166
	Chapter 8: Discussion

[START_REF] Zhang | Comparative genomics reveals insights into avian genome evolution and adaptation[END_REF]

, or μ = 9.6 e -9 substitutions.site -1 .generation -1 considering a generation time of 6 years, as suggested by

[START_REF] Welch | Mitochondrial and nuclear DNA sequences reveal recent divergence in morphologically indistinguishable petrels[END_REF]

. Migrate-n models, however, were based on loci with 4 to 6 polymorphic sites: these can not be calibrated directly using the general background substitution rate. Considering that the RADome is assumed to evolve neutrally, the number of polymorphic sites per locus is expected to follow a Poisson distribution, and the background substitution rate μ applies for the expected value λ of that distribution. erefore, after verifying the goodness-of-t o f t hat t he Poisson m odel, w e e xtracted t he e xpected s ubstitution r ates f or loci with S=4, S=5 or S=6 SNPs, as (S/(λ/μ)), and used their weighted average to calibrate our reconstructions.

Population structure in colonial seabirds

§-166 Aptenodytes penguins are synnome species. e most consistent and unexpected aspect of our results is the similar, and original structure of the worldwide Emperor and King penguin populations. Both species have circumpolar, zonal distributions, with large and well de ned colonies, that are either homogeneously distributed along the coast of Antarctica, for the Emperor penguin, or clustered together on the subantarctic archipelagos, for the King penguin. As exposed in e King synnome, p. 163 and e Emperor synnome, p. 211, within these circumpolar distributions, each species is made up of one single, range-wide panmictic population, with all evidence

  Moreover, the empirical case of the Emperor penguin (see Empirical evidence of heterogeneous dispersal, p. 259) provides us with additional indications on how heterogeneity in dispersal probability can be organised in time: the likely splitting of the Mertz colony following changes in the local icescape, and the repercussions on the neighbouring colony of Pointe Géologie, lift the veil on the events of catastrophic dispersal that may punctuate the history of penguin colonies, and maintain genetic mixing even in the case of temporarily reduced gene ow. §-167 Genetic landscapes in the Southern Ocean. As exposed in introduction (see §19 p. 68), most seabird species exhibit a certain degree of phylogeographic structuring, and species with a worldwide distribution are all the more likely to have marked demes -at least one per ocean basin(Friesen et al. 2007). In the Southern Hemisphere, and especially at higher latitudes where interbasin circulation is high (see §6 p.33 and §7 p. 35) and biogeographic zones often have a strong Discussion - §167 zonal component (see §10 p. 48), most species retain a colonial circumpolar distribution similar to that of the Aptenodytes penguins. Yet a marked genetic structure normally evolves, and may result in zonally separated populations that sometimes reach the subspecies level -as in the Giant petrel Macronectes giganteus, in which a Northern and a Southern form have been identi ed (as giganteus and halli, see Techow et al. 2010), or most commonly location-speci c populations, as in also the case in the Giant petrel (Techow et al. 2010), the Snow petrel Pagodroma nivea (Barbraud & Jouventin 1998), or the lesser Sheathbill Chionis minor[START_REF] Viblanc | Stress hor-mones in relation to breeding status and territory location in colonial king penguin: a role for social density?[END_REF]). More remarkable is the evolution of fully separate species along the circumpolar range. is is for example the case of the Wandering Albatross Diomedea sp. species complex, in which at least three di erent clades can be distinguished, each with a full species status -D. exulans in Crozet, Kerguelen, Mar-Discussion - §167exsul in South Georgia and the Southern Indian and Paci c archipelagos, P. u. dacunhae in Tristan da Cunha and Gough, P. u. berard in the Falklands, and P. u. coppingeri in Southern Chile, seeOnley & Sco eld 2013). In the present study on the Peruvian diving-petrel P. garnotii (see Unexpected philopatry in an insular seabird, the Peruvian diving-petrel, p. 275), we demonstrate how this oceanic-scale structure in diving-petrels is likely the consequence of very strong local philopatry: even at the scale of a few hundred kilometers, populations may evolve near-complete isolation. In Pelecanoides petrels, genetic structure therefore appears to organise itself in a complex hierarchical system, with very closely related and partly sympatric species subdivided in subspecies (as in P. urinatrix ssp.), and locally in strongly di erentiated demes.Penguins are no exception here, and phylogeographic structure has been identi ed i n several species across oceanic boundaries, often reaching subspecies or even speciation levels. e Little Fuego and Patagonia, and S. humboldti in the northern Humboldt Current are identi ed as fully separate species with traces of ongoing gene ow between the latter two species at their contact point[START_REF] Simeone | Heterospecific Pairing and Hybridization between Wild Humboldt and Magellanic Penguins in Southern Chile[END_REF]. Similarly, the Eudyptes species complex may be considered as mostly structured by allopatry between breeding islands, as species have very well de ned, and usually non-overlapping breeding range, with E. pachyrhynchus along the Fiorland coast of New Zealand, E. chrysolophus in the Southern where otherwise insigni cant signal is presented as having a tendency towards signi cance1 . In seabirds, we may mention the study of[START_REF] Bouzat | Regional genetic structure in the Magellanic Penguin (Spheniscus magellanicus) suggests metapopulation dynamics[END_REF], in which colonies of Magellanic penguin (S. magellanicus) amongst which 99% of variation was explained by inter-individual di erences, with Fst levels below 0.01, are presented as a « metapopulation system »; or the study of[START_REF] Freer | Limited genetic differentiation among chinstrap penguin (Pygoscelis antarctica) colonies in the Scotia Arc and Western Antarctic Peninsula[END_REF], in which non-signi cant Fst values are presented as « consistent with female bias dispersal ». In these cases, the lack of a strong conceptual and hypothesis-testing framework often results in misleading interpretations. e probability of observing similar values because of random sampling variation is rarely examined, and a selection of statistically signi cant indicators over statistically nonsigni cant ones is common. All of these choices often result in overly constrated population structures -often easier to publish, but at the cost of loosing the more nuanced aspects of the interspeci c variations in demographic organisations.

	Tierra del Fuego, and P. garnotii along the Humboldt Current (Shirihai & Kirwan 2008; Onley
	& Sco eld 2 013), a lso o ers a c lear e xample o f l ocal i solation b oth t hrough d istance, and
	through foraging niche specialisation (Navarro et al. 2013). e Common diving petrel P. urina-
	trix is itself separated in six subspecies that follow geographical areas (P. u. urinatrix in Australia

ion & Prince Edward, and South Georgia archipelagos and Macquarie Island, D. dabbena on Tristan da Cunha, and D. antipodensis on Antipodes, Adams and Campbell islands

[START_REF] Burg | Nonequilibrium Conditions Explain Spatial Variability in Genetic Structuring of Little Penguin (Eudyptula minor)[END_REF]

): a structure that is made more surprising by the fact that Wandering albatross easily circumnavigate the Southern Ocean during one foraging season

[START_REF] Labra | Ajustes fisiologicos para la nidificacion en cuevas: el caso de Pelecanoides garnotii, un enfoque evolutivo. Magister. 161[END_REF]

). e Southern Skua Catharacta sp. species complex o ers a similar picture, with both zonal and regional di erentiation between species (C. maccormicki in Antarctica, C. antarctica lonnbergi in the subantarctic belt, C. a. antarctica in the Falklands and the Patagonian shelf area, C. a. hamiltoni in Gough and Tristan da Cunha, and C. chilensis in Tierra del Fuego and Patagonia), despite ocean-wide seasonal migration

[START_REF] Weimerskirch | Population-specific wintering distribution of adult south polar skua over three oceans[END_REF]

, and ongoing gene ow at the contact zones of species

[START_REF] Ritz | Phylogeography of the southern skua complex-rapid colonization of the southern hemisphere during a glacial period and reticulate evolution[END_REF]

). e D iving-petrel P elecanoides s p. s pecies c omplex, w ith P. urinatrix and P. georgicus breeding in sympatry throughout the subantarctic belt, P. magellani in and Northern New Zealand, P. u. chathamensis in the southern New Zealand archipelagos, P. u. penguin Eudyptula minor, for example, exhibits strong isolation between populations, and has been divided six subspecies (Kinsky & Falla 1976), at least three of which are genetically monophyletic (Peucker et al. 2009), and exhibit local isolation-by-distance patterns (Burridge et al. 2015). e Rockhopper penguin Eudyptes chrysocome has similarly been separated in a Northern and a Southern group by De Dinechin et al. 2008, and the same applies for the Gentoo penguin Pygoscelis papua (De Dinechin et al. 2012). Finally, two groups have been proposed in the Adélie penguin, in the Ross Sea and in the rest of East Antarctica (Roeder et al. 2001). In the Spheniscus sp. group, the geographically isolated S. demersus in southern Africa, S. magellanicus in Tierra del E. robustus on the Snares islands, E. sclateri on Bounty and Antipodes islands, the extinct E. chathamensis on Chatham island, E. schlegeli on Macquarie island, In §137 p. 253, we discussed such a case, in a previous analysis of population structure in the Emperor penguin, using mtDNA, by Younger et al. 2015, where a combination of biased sampling and not fully reproductible analysis led to the conclusion that Emperor penguins were separated in two di erent clades. Similarly, a pioneer study by Viot et al. 1993, using protein gel electrophoresis and a short fragment of the cytochrome-b gene 2 , concluded in a strong isolation between Crozet and Kerguelen king penguins. us, prior ndings using « classical » (sensu §28 p. 84) markers set the expectation of signi cant population structure in both species, on the tem-1.

  Discussion - §170 ods only to essentially panmictic species such as the Aptenodytes penguins, and (ii) when dealing with structured populations such as can be found in Pelecanoides species, to either avoid such approaches altogether, or to restrict the analysis to a single population and to stay aware of the considerable uncertainty that propensity to genetic isolation imposes on the results. §-170 Pleistocene history of the Aptenodytes penguins. e various methods we applied in this work to reconstruct the demographic history of the King and Emperor penguin (in e King synnome, p. 163 and e Emperor synnome, p. 211) showed a satisfactory degree of consistency, (although all did not perform as accurately for the more complex history of the King penguin, see §98 p.192): we can thus obtain a general picture of the recent past of the two species with relative con dence.According to our reconstruction, the Emperor penguin population did not vary signi cantly during the past 100,000 years -a trait found consistently by three independent methods, and based on data either from the whole Antarctic continent, or from six individual colonies. e more ac-Bayesian Skyline Plot), are likely to have biased this reconstruction: thus, they do not contradict our ndings c onvincingly.is st able e ective po pulation si ze al so im plies that metapopulation structure was never prevalent in the species (see §169 p. 305, and let aside a very unlikely hypothesis of balanced compensation between census size and gene ow) -not even during the Last Glacial Maximum, when extensive winter sea ice cover and Antarctic ice sheet expansion could have been thought to have disrupted the Emperor's breeding cycle. Interestingly, the persistence of the Emperor penguin population throughout the period seems to imply that the conditions on the major part of the Antarctic shoreline were not signi cantly o ut o f the species' optimumi.e. somewhere between the harsh conditions now found at the high latitudes of the Ross Sea, and the milder ones that prevail at the lower latitudes of Wilkes Land in Eastern Antarctica. Importantly, the stability of the Emperor penguin population means that coastal K ing p enguin o ers a v ery d i erent p icture ( detailed i n e Ki ng sy nnome, p. 16 3). Allowance made for the possibly complex interplay of population decline and range fragmentation (see above, §169 p. 305), the King penguin di ers from the Emperor by its drastic demographic response to the Last Glacial Maximum, and likely also to the previous Llanquihue glacial maximum -beyond which coalescence signal is lost in our sample, and possibly in the whole extant gene pool of the species. A previous study by[START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF] had proposed a strong postglacial expansion of the species based on RAD data for 8 individuals, with strong support based per-banded, disturbed birds, in the robustness of the correlative envelope approach, and in the accuracy of the CMIP sea ice projections) amount to a considerable lack of knowledge, that leaves room for about any hypothesis.e King penguin's response seems, at rst glance, to be more robust. e negative correlation between sea-surface temperature and adult survival and breeding success is well established(Le Bohec et al. 2008), and its causal mechanism is equally well understood: the warmer the sea surface temperature, the further South the Antarctic Polar Front and associated foraging areas, and the longer the foraging trips for the Northern King penguin colonies (see[START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF], Bost et al. 2015, and e King synnome, p. 163) -a mechanism observed directly using GPS loggers placed on penguins. However, a possibly important aw in this model is the fact that until now, the main driver of sea surface temperature variation is not directly anthropogenic climate change1 , but the natural oscillations of the Southern Hemisphere Ocean-Atmosphere circulation, such as the Southern Annular Mode, the El Niño Southern Oscillation or the Southern Indian Ocean Dipole (see §8 p. 40 for a summary of these phenomena). e changes in temperature associated with these oscillations can reasonably be taken as proxies for global change (although this is, in itself, subject to some uncertainties, see §11 p. 50). But approximating the response of seabirds to global long-term change by their response short-term oscillations is another matter.Indeed, oscillating phenomena such as the ENSO force the alternance of « warm » and « cold » periods over 3 to 6 years cycles: while breeding success is very low during the warm years, it may be partly compensated during the cold years. Considering that the most exposed populations (on Crozet archipelago, see e King synnome, p. 163) are still stable demographically[START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF] Saraux et al. 2011a), it is possible that the cyclic character of the changes makes even plastic adaptation un-necessary for the time being: put simply, « waiting for better times » can still be Discussion - §172 a bene cial strategy for a bird like the King penguin. Whether this strategy will still hold under long-term irreversible changes, is another question. Several alternative strategies will still be possible: altogether not trying to breed during the warmer phases of ENSO (as has been reported in the Galapagos islands, see Du y & Siegfried 1987) and building up reserves for increased breeding success during the more favorable years, developing nearshore foraging strategies, etc.Whether King penguins will react di erently to linear changes than to cyclic changes is of course speculative, but it does add to the uncertainty of predicted demographic trajectories. If the actual timing and extent of climate change e ects is far from certain, the reality of these e ects is, at any rate, beyond doubt. Indeed, Pleistocene changes in King penguin population are qualitatively consistent with the expectations set by oscillative climate change e King synnome, p. 163: in our case, long term data validates hypotheses set by very short-term data, both converging to empha-

	Discussion - §170 gle-locus Discussion - §171
	curate EBSP and Stairway Plot approaches (see §44 p. 112 and §46 p. 121) also suggest a popu-
	lation growth before 100,000 years, and back to 1,000,000 years according to the EBSP -howev-
	er, these are deep times by the yard of penguin evolution, and at these scales demographic and
	speciation processes may start to interfere. is c on dently st able po pulation du ring th e later
	Pleistocene contrasts with the ndings of Younger et al. 2015: however, as we suggested in §137
	p. 253, the steep population growth inferred from mtDNA data by Younger and colleagues
	rather seems to stem from inadequate analysis. Indeed, the use of unexplained diploid sites in
	haploid mitochondrial DNA (likely sequencing errors caused by secondary structures in the Em-
	peror penguin control region) is expected to arti cially increase recent population size by overes-
	timating the importance of low frequency variants. Other improper choices, such as treating mi-
	tochondrial cytochrome-b gene and mitochondrial HVR as unlinked, independent markers in an
	EBSP framework (instead of concatenating them and remaining within the frame of a simple sin-

- §168 polynya activity was comparable to what we observe nowadays (see §18 p. 65) despite very di erent global sea ice conditions (see §9 p. 42) -a hypothesis con rmed by our preliminary analysis of ice conditions in the coastal Antarctic area (see §116 p. 223 to §122 p. 235). It also con rms the hypothesis of Wol et al. 2006 who, based on iron ux reconstructed from the EPICA ice core

[START_REF] Augustin | Eight glacial cycles from an Antarctic ice core[END_REF]

, suggested that marine productivity did not change signi cantly during the last Pleistocene glacial cycles: indeed, the robustness of the Emperor penguin population implies that food resource was never a limiting factor for the species, even during the coldest times of the last era. e 1.

While anthropogenic climate change clearly has an in uence on Southern Ocean sea surface temperature, as we showed for example in e King synnome, p. 163, it mostly a ect the mean temperature, and its underlying trend. But interanual variability is still central in local processes, and is of the same, or of a larger order than man-made trends.

  Gagnaire et al. 2013b), and on more resolutely functional aspects of genome evolution, such as quantitative trait locus analysis and association mapping[START_REF] Hohenlohe | Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags[END_REF] Gagnaire et al. 2013a), genetic maps[START_REF] Amores | Genome evolution and meiotic maps by massively parallel DNA sequencing: spotted gar, an Annexes - §186 outgroup for the teleost genome duplication[END_REF][START_REF] Amores | A RAD-tag genetic map for the platyfish (Xiphophorus maculatus) reveals mechanisms of karyotype evolution among teleost fish[END_REF]), quantitative genomics (McGuigan et al. 2010). In a preliminary study,[START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF] proposed an extension of the 1.We propose this neologism to describe the extension in time of the deme idea, on the model of the chronospecies concept.RADseq analysis paradigm to take advantage of its short-haplotype structure in a coalescent framework ( §29 p. 85 and §31 p. 87), using a subset of highly variable loci for EBSP reconstruction of past population size changes. In this work, we developed this idea further, and applied it to population size and migration reconstruction, concurrently with SNP-based analyses. Based on our results (see e.g. the demographic reconstructions in e King synnome, p. 163), RAD-sequencing o ers clear advantages over whole-genome resequencing: data can be gathered for a much larger number of individuals for a similar sequencing e ort, and the accuracy of demographic reconstructions is increased using this wide dataset. However, we did encounter a number of methodological limitations. Besides the random biases that do occasionally appear during library preparation (see §35 p. 93), the main caveat of RAD-Aptenodytes species, further understanding may be gained from the functional and adaptive aspects of their genome. In particular, understanding whether speciation was the result of allopatric isolation and neutral drift (see §168 p. 304), or rather of a strong and sudden local selection pressure, may give us insights into the forces that shaped the Southern Ocean biodiversity during the Pliocene and the Pleistocene. For these processes, whole-genome resequencing is likely to o er a framework for the more precise interpretation of RAD data, e.g. by allowing us to locate RAD markers in relation to genome landmarks. Further investigation methods, such as bisul te-conversion ( Deng e t al. further, by identifying very short-term local plastic responses though modulation of gene expression levels and patterns, for example during development. Yet the plethora of new techniques in the eld of genome analysis should not divert us from the real originality of the Aptenodytes model. e wealth of life-history data made accessible by the advanced monitoring program on Possession Island (see §26 p. 81 and Gendner et al. 2005; Le Bohec 2008) o ers a unique framework for studying the interconnection of ecological and genomic processes in the wild, and further research will highly bene t by exploring this interface on a large number of individuals. In particular, the study of QTL associations between behavioural traits (such as breeding output, foraging e ciency, or partner and site delity) and genomic or epigenomic features may shed new lights on the evolution of life histories. In this respect too, our work lays a strong foundation for further investigation, since the identi cation of neutral popula-

	Discussion - §175 Discussion - §175

, phylogeography

(Emerson et al. 2010)

, palaeodemography

[START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]

, and generally population genetics

[START_REF] Davey | Genome-wide genetic marker discovery and genotyping using next-generation sequencing[END_REF]

-but also on speciation analysis (seq is its discontinuous character ( §31 p. 87): the unsequenced gaps between markers make it di cult to identify continuous structural processes in the genome. us, while focusing on neutral processes, we left aside functional phenomena -in particular the signature of local selection (that may be detected through clues like selective sweeps -see

[START_REF] Nielsen | Genotype and SNP calling from next-generation sequencing data[END_REF]

, or long runs of homozygosity -see

[START_REF] Kirin | Genomic runs of homozygosity record population history and consanguinity[END_REF]

, or properly genomic processes such as the evolution of recombination hotspots

[START_REF] Smagulova | Global phylogeography of the band-rumped storm-petrel (Oceanodroma castro; Procellariiformes: Hydrobatidae)[END_REF]

or replication origins

[START_REF] Lucas | High throughput mapping of origins of replication in human cells[END_REF]

). However, now that a strong neutral framework has been established for both 2009), possibly in a RADseq framework

(Trucchi et al. in prep.)

can o er insights into epigenetic phenomena (e.g. methylated cytosine residues). RNAseq (e.g.

[START_REF] Mortazavi | The next generation of scenar-ios for climate change research and assessment[END_REF]

may allow us to go tion structure is a pre-requisite to the study of adaptation (Price et al. 2006) -in our particular

  - §176 ter, and decade-scaled variability is dominated by yearly environmental e ects, that are evened out at longer scales. Yet, this raises the question of the methodological link that can be established between local demographic monitoring surveys and evolutionary processes. As we mentioned in our introduction (see in particular §16 p. 60), most studies focusing on response to climate change, in particular in vertebrates, are necessarily restricted to the observation of a limited number of individuals and generations: this individual-based monitoring is by nature focused on individual responses to changes. But as we see, making a transition from individual to collective responses, even in such a basic parameter as growth rate, is not trivial. Or, in Tolstoy 's words: « a human lives consciously for himself, but is an unconscious instrument in the accomplishment of the his-, for penguins, which are not organised in « nations » or populations, only the consensus reaction of the whole species should really be considered meaningful: local trajectories may lead (from a local perspective) to « local extinctions », or (from a global perspective) to « range shifts », but the species' eventual evolutionary (and adaptive) response can only be determined globally.

toric, universal, aims of humanity. A deed done is irrevocable: and its result, coinciding in time with the actions of millions of other men, assumes a historical signi cance

[...]

. As long as we will only write the history of singled-out characters -be they Cesar, Alexander, Luther or Voltaire -and not the history of every man without exception who took part in the event, we will not be able to explain the movements of mankind without conceiving a force constraining men to strive towards a common end » (Tolstoy 1869). Put simply, individual responses matter less than the synchronised, convergent response of great numbers of individuals: population phenomena can be represented as global, species-scaled trends, yet the essence of these phenomena lies in the joint individual trajectories that underly the common response. us

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.......................................................................................

e Emperor synnome - §120

e Emperor synnome - §121

Acknowledgements

If ever a PhD was the fruit of an enthusiastic and supportive team, it is the work I present here. It was only made possible thanks to the unfailing dedication and patience of Dr Céline Le Bohec and Dr Emiliano Trucchi, who assumed the doubly essential roles of supervisors and friends. I sincerely thank you both, with the joyful certitude that this work is just the beginning of a long series..! I warmly thank Pr Yvon Le Maho for giving me the chance to enter the insular world of polar biology, and for his continued guidance throughout this endeavour. His support made it possible not only to design this work, but also to realise it with the help of the Ecophy team: André Ancel, who co-directed this work, Julien Courtecuisse and Nicolas Chatelain, who invented every possible penguin-tracking contraption used here and there in this work, Aymeric Houstin, Matthieu Boureau, Gildas Lemonnier and Anne-Catherine Klein on the eld. And, of course, the whole DEPE team in Strasbourg for their support and welcome every time I visited. I spent the best days of this work on the research stations of Dumont D'Urville, in Adélie Land, and Alfred Faure, in Crozet Archipelago: I sincerely thank the French Polar Institute for this opportunity, as well as for their essential technical support that made this work possible, and Marie Pellé, Quentin Schull, Gaël Cardinal, Caroline Bost and Paco Decina for making these days the happiest ones. I whole-heartedly thank Pr Nils C. Stenseth and the CEES team, who welcomed me at the University of Oslo, where almost all this work was done. Nanna Winger Steen and Emelita Rivera Nerli had the patience and kindness to help through the long and messy lab-work. e Norwe-Acknowledgements Acknowledgements is study was undertaken within the framework of the Programme 137 of the Institut Polaire Français Paul-Emile Victor (IPEV), with additional support from the French National Research Agency (ANR) "PICASO" grant (ANR-2010-BLAN-1728-01), from Marie Curie Intra European Fellowships (FP7-PEOPLE-IEF-2008, European Commission; project no. 235962 to CLB and FP7-PEOPLE-IEF-2010, European Commission; project no. 252252 to ET), from the Centre Scienti que d e M onaco t hrough b udget a llocated t o t he L aboratoire I nternational Associé 647 'BioSensib' (CSM/CNRS-University of Strasbourg), and from the British Antarctic Survey Ecosystems Programme, NERC (PT). Logistic and eld c osts o f r esearch w ere s upported by IPEV (CLB), the British Antarctic Survey Ecosystems Programme, NERC (PT), AWI (DPZ), US NSF grant number NSF 0229638 (PP). is w ork w as p erformed o n t he A bel Cluster, owned by the University of Oslo and the Norwegian metacenter for High Performance Computing (NOTUR), and operated by the Department for Research Computing at USIT, the University of Oslo. We are very grateful to Morten Skage, Ave Tooming-Klunderud, Marianne Selander-Hansen, and the Norwegian Sequencing Center for their very valuable help in the laboratory, as well as Lex Nederbragt and Michael Matschiner for their assistance with the Abel cluster, Matteo Fumagalli and or nn Korneliussen for their precious advice regarding ngsTools and ANGSD, and Alexis Drummond for his insights on BEAST2 parametrization. We thank Matthieu Boureau, and the British Antarctic Survey sta at Halley Bay, for the sample collection. Last but not least, we thank Romain Loriol for his help with Greek lexicology. Acknowledgements We thank the crew of the MSS L'Astrolabe (resupply ship of the P&O Company, chartered by the Institut Polaire Franc ais Paul-Emile Victor (IPEV) and the Terres Australes et Antarctiques Franc aises) for its logistic support, the helicopter pilot Sebastien Vignoud (Groupe SAF-HELI-CAP) for making the visit possible, Patrice Godon, Gae lle Sellin, Alain Pierre and Serge Drapeau (IPEV) for their assistance in the eld, Stan Zamora (P&O) for providing the satellite images, and Alain Hubert (International Polar Foundation) for providing the data relative to Princess Ragnhild coast. We are grateful to the USGS (https://lta.cr.usgs.gov) for providing satellite images for free. is study was supported by the IPEV (Programme 137) and the Centre National de la Recherche Scienti que. Empirical evidence of heterogeneous dispersal - §145 Acknowledgements We thank Diego Miranda-Urbina and Nicole Licuime Castillo for their invaluable help on the eld, as well as Rasme Hereme at the CEAZA, Morten Skage, Marianne Selander Hansen and Ave Tooming-Klunderud of the Norwegian Sequencing Center for their assistance in the the laboratory. is work was conducted with the support of the French National Research Agency (ANR) "PICASO" grant (ANR-2010-BLAN-1728-01), from Marie Curie Intra European Fellowships (FP7-PEOPLE-IEF-2008, European Commission; project no. 235962 to CLB and FP7-PEOPLE-IEF-2010, European Commission; project no. 252252 to ET), from the Centre Scienti que de Monaco through budget allocated to the Laboratoire International Associé 647 'BioSensib' (CSM/CNRS-University of Strasbourg). is work was performed on the Abel Cluster, owned by the University of Oslo and the Norwegian metacenter for High Performance Computing (NOTUR), and operated by the Department for Research Computing at USIT, the University of Oslo.

Unexpected philopatry in an insular seabird, the Peruvian diving-petrel - §165

 [START_REF] Fahrbach | qui ont des caractéristiques océanographiques particulières -par exemple la résurgence d'eaux chaudes profondes en mer de Weddell, qui y entretient une polynye à chaleur sensible (c'est à dire une polynye causée par la température de l'eau[END_REF]). Both currents form the two main gyres of the Southern Ocean, in the Ross and Weddell seas (Gouretski 1999;[START_REF] Meredith | On the sources of Weddell Gyre Antarctic bottom water[END_REF], that have distinct oceanographic features -for example the warmer deep water upwelling and resulting stable sensible heat polynya in the Weddell sea (i.e. a polynya caused by water heat rather than wind stress, see omas & Dieckmann 2008). Lighter arrows in the Antarctic Circumpolar Current represent Ekman transport (Based on [START_REF] Sarmiento | High-latitude controls of thermocline nutrients and low latitude biological productivity[END_REF].

Ocean General Circulation Models, to reconstruct the prevailing oceanographic and climatic conditions. However, reconciling these diverse sources is still problematic, with mixed signals between the glacial, oceanic, and terrestrial environments [START_REF] Garcia | Les assemblages de diatomées et de foraminifères dans le sédiment marin o rent un indice précis de l'état des masses d'eau de surface, et peuvent être utilisés pour identi er la position de l'APF[END_REF][START_REF] Mccauley | Divergent trends in land and ocean temperature in the Southern Ocean over the past 18,000 years[END_REF].

2011).

idence such as alkenones or Mg/Ca ratio (as a proxy for sea surface temperature, see [START_REF] Kucera | Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO)[END_REF][START_REF] Mackintosh | Seabirds as indicators of aquatic ecosystem conditions: A case for gathering multiple proxies of seabird health[END_REF].

Introduction - §9

Concepts and methods - §38

In our case, however, the low number of sequenced individuals (3 per species) prevented the use of any computational method. erefore, we reconstructed the local haplotypes using the graphbased algorithm implemented in HapCompass [START_REF] Aguiar | HapCompass: a fast cycle basis algorithm for accurate haplotype assembly of sequence data[END_REF], and used the resulting partially-phased data whenever phase information was necessary. However, the resulting phasing proved insu cient, and results visibly lacked robustness, presenting artefact patterns similar to those encountered in unphased data: we therefore did not retain it for nal analyses (see e King synnome, p. 163 for details). §-38 A point on data management. Handling of high-throughput sequencing data presents important scale challenges, both in terms of computational power and of data storage and management. e methods used to address these challenges vary according to the speci c analysis stages, although some general guidelines can be retained. When processing sequencing data, four main « generations » of data les can be identi ed, of increasing speci city and decreasing redundancy:

(i) raw data formats, (ii) alignment formats, (iii) variant formats, and (iv) analysis input formats.

(i) Raw data is mostly stored as (compressed) fasta or fastq format -a sequencer-output format listing sequence base calls (as performed, in our case, by Illumina's CASAVA software), physical read coordinates on the ow cell, and base quality encoded as Phred33 quality scores. Redundancy is high, as several reads, or dozens of reads, may cover the same position in the target genome.

(ii) Alignment les are output by read mapping algorithms, usually formatted as SAM (Sequence Alignment Map), or as its binary equivalent BAM (Binary Alignment map). After alignment, the complete information from read sequence and quality scores is retained, but mapping coordinates are added, relative to a reference genome: at that stage, redundancy is still high, as each individual read is represented by a single entry (and not collapsed by genomic position). SAM/ BAM les are typically handled using the exible Samtools package (Li et al. 2009), or the Broad Institute's Picard Tools package (http://picard.sorceforge.net). BAM format is the preferred data storage format in this work, as it retains all the necessary information for alternative SNP calls.

Concepts and methods - §41 earlier (see §2 p. [START_REF] Anderson | Wind-driven upwelling in the Southern Ocean and the deglacial rise in atmospheric CO2[END_REF], the fundamental concept of population is de ned v ery d i erently i n the ecological, and the evolutionary frameworks.

Revolving essentially around the notion of individual, the ecological paradigm naturally considers the population size to be the number of individuals alive at the same time in the group under scrutiny. Here, we call census size this direct observation of the population size. While the census size of a full species is a rather understandable concept (« how many king penguins are there at this moment on Earth? » does have a true answer), the census size of a population is a rather more abstract idea as soon as life history strategies become elaborate (see [START_REF] Lecomte | Tug of war between continental gene flow and rearing site philopatry in a migratory bird: the sex-biased dispersal paradigm reconsidered[END_REF] for an interesting practical example): in these seabirds, adults will only breed up to two years out of three, and will probably not come ashore at all during the third year -so that the maximum number of adults in reproductive age on the colony will only represent two-thirds of the total number of breeders who « belong » in that colony. Juveniles that are born on a colony regularly spend several traveling years at sea, and may or may not choose to disperse after that time: should they be counted as part of the colony in advance, or not until they e ectively settled? What about juveniles in r-strategy species, where the number of juvenile is very high, but mortality tremendous? us, although the concept itself appears fairly straightforward, its application is much more subjective in wild populations -all the more so if we lack precise knowledge about the recruitment and the proportion of non-breeders that may be temporarily away. e evolutionary paradigm, on the other hand, is based on the notion of gene pool. us, is only counted as part of the genetic population an individual that actively contributes to the gene pool: population size is there de ned as the number of successfully breeding individuals. To distinguish it from the census size, we call this measure the e ective size of the population. Here again, this denition is simple in the ideal case of a Wright-Fisher population, with non-overlapping generations and replacement of each parent by one o spring (see §39 p. 99). But it becomes more dicult to estimate whenever life history diverges from this abstract statee.g. when a breeder may have several o springs, or when breeding success is heterogeneous within the population. E ec-Concepts and methods - §44 §-44 e E xtended B ayesian Skyline P lot a pproach. e Ex tended Ba yesian Sk yline Pl ot (EBSP) method was proposed by Heled and [START_REF] He | Habitat saturation, benefits of philopatry, relatedness, and the extent of cooperative breeding in a cichlid[END_REF], as a Bayesian multilocus extension of the Skyline Plot (SP). In its orignal form [START_REF] Pybus | An integrated framework for the inference of viral population history from reconstructed genealogies[END_REF], the Skyline Plot is a maximum-likelihood non-parametric method that exploits the relationship between population size and coalescence probability (see §41 p. 103) to reconstruct e ective population size at di erent time points. e density of coalescence events is therefore indicative of the population size (a higher rate of coalescence generally means a smaller population). However, the classical Skyline Plot has severe limitations: for example the fact that the gene tree is assumed errorfree, and estimated independently, or the fact that, as a single-locus approach, it may be severely a ected by the random sampling processes -a true random coalescent may diverge vastly from the neutral expectation by mere chance [START_REF] Ho | Sample Planning Optimization Tool for conservation and population Genetics (SPOTG): a software for choosing the appropriate number of markers and samples[END_REF]). e rst limitation was overcome by the replacement of the maximum-likelihood framework by a Bayesian approach (as Bayesian Skyline Plot or BSP, [START_REF] Double | Dispersal, philopatry, and infidelity: dissecting local genetic structure in superb fairy-wrens (Malurus cyaneus)[END_REF], in which gene tree and demographic history are co-estimated, and averaged over a very large Markov-chain Monte-Carlo (MCMC) sampling.

However, the locus-by-locus stochasticity limitation of the Skyline Plot remains with the BSP.

Another limitation of the BSP is that it is not fully model-exible: the number of demographic groups needs to be xed a priori. e EBSP, as implemented in BEAST2 [START_REF] Bouckaert | BEAST 2: a software platform for Bayesian evolutionary analysis[END_REF]), overcomes all these major limitations. As a natively multilocus approach, EBSP relies on the Bayesian co-estimation of gene trees and demographic histories from a set of unlinked loci, thus considering large pannel of stochastic representations of the underlying « true » coalescent history of the population. EBSP is also a truely model-exible approach, in the sense that the number of periods in the demographic function is informed solely by the genetic data. In its current form, its main limitation is that, as it relies on the full likelihood of the DNA sequences, it is computationally intensive, and hardly scalable to full-genome-scale sequence data.

In this work, we exploit the speci c features of RAD sequencing to deploy EBSP demographic reconstructions (see Supplementary sections of e King synnome, p. 163, and of e Emperor

Concepts and methods - §49

Habitat and climate analysis. niche. e set of external conditions required by a species to thrive de ne its habitat. Generally, these conditions include both abiotic, and biotic factorsi.e. factors that are only dependant on (a) physical and chemical characteristics, or (b) interactions with other living organisms. Abiotic habitat determinants usually include temperature and precipitation mean and variability, a range of elevations, sometimes wind or currents regime, but also the length and variability of the photoperiod or the intensity of insolation. ese parameters (although some, such as temperature and precipitation, are themselves in a certain measure in uenced by biotic communities), may be accurately estimated, and predicted on medium time scales, and are the focus of general circulation models (see §50 p. 130). eir i mpact o n b iological s ystem i s multiple. ey p rimarily i nteract w ith s pecies' p hysiology: e ach o rganism h as a n o ptimum s et o f abiotic conditions in which its metabolic processes are least costly, and in which it thrives. But they also impact species indirectly, through direct e ects on other organisms which themselves interact with the focal organism -in other words, through the mediation of biotic interactions. For example, prey or food resource availability, predation pressure, vegetal cover for shelter, parasites and pathogens, etc. play a central role in de ning t he s uitable h abitat f or a s pecies. e respective parts of abiotic and biotic factors in the de nition of a habitat are debated: their contribution is certainly species-dependent, but may also vary across the di erent margins of a species distribution -for example, it has been proposed that the polar boundaries of habitats are more often dened by abiotic limits (e.g. physiologic e ects of the coldest winter temperatures), while the equatorial boundaries are rather de ned b y b iotic c onstraints ( such a s a n i ncreasing d ensity of competitors and parasites equatorwards -themselves prevented from a polewards spread by their own abiotic limits). Biotic and abiotic factors may also interact across phases (e.g. overwintering and summer grounds, foraging and breeding areas, etc.), or evolve through time -a habitat main- "BDM", N=15, Crique de la Chaloupe "CDC", N=16, Petite Manchotière "PMC", N=15, Jardin Japonais "JPN", N=16, and Mare aux Elephants "MAE", N=16 -all samples were edged juveniles), and all four colonies from Marion Island (S46°54′ E37°44′ -Good Hope Bay "GHB", e King synnome - §98 to retrieve -in particular the low population size during the Llanquihue glacial episode. Due to the low number of SNPs in the loci we include in EBSP analysis, however, less resolution is expected for ancient time periods, so neither the observed discrepancy between simulated and empirical runs, not the loss of precision compared to the simulated scenario, is surprising.

(iii) PSMC' reconstruction, on the other hand, exhibits a more unexpected behaviour when applied to our data. When assuming equal substitution and recombination rates, none of the bottlenecks is retrieved, but one single bottleneck is inferred instead around 40 Kyr BP, while a large population size peak (absent from our simulation) is inferred in the early holocene (Fig. 28C).

Decreasing the recombination rate down to 1/16th of the substitution rate allows us to recover both bottlenecks, yet the artefactual additional population depression remains around 40 Kyr BP, as well as a sharp artefactual population peak after the most ancient bottleneck (Fig. 28D). None of the reconstructions performed on simulated data matches the true demography in a satisfactory way: however, the very recent events on which we focus may be at the limit of the PSMC' method [START_REF] Li | Inference of human population history from individual whole-genome sequences[END_REF]. It is noteworthy, however, that the empirical PSMC' inference follows the expected general demographic trend as given by both the Stairway plot analysis and the EBSP analysis, smoothing out both bottlenecks in one single population increase from the early Pleistocene to the late Holocene. functions of the covariance of allele frequencies between populations. In the hypothesis that our estimate of the frequency spectrum may be biased is some way, e.g. by moderate sample size, we also performed a coalescent-based structure analysis using Migrate-n [START_REF] Beerli | Unified framework to evaluate panmixia and migration direction among multiple sampling locations[END_REF]. As opposed to frequency-spectrum based approaches, coalescent-based analysis relies on phased polymorphisms to infer population parameters. After verifying that the number of polymorphisms in each RAD locus followed a Poisson distribution of λ equal to the mean number of polymorphisms per locus, we selected 50 random loci comprising between 5 and 6 polymorphisms as an unbiased representation of the neutrally evolving part of the genome [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]. In order to correct for potential over-representation of sequences in case a heterozygous individual was mis-called as homozygous, we randomly sampled one allele only for each individual. For all colonies except WSH, we randomly picked a set of haplotypes in the population (16 in MZE,DDU,HAL,and NEU,14 in MZW,and 4 in WSH). We ran a cold chain and 3 heated chain of 50,000,000 generations, recording every 500 generations, with a 5,000,000-generation burn-in. We used a static heating scheme, raising the cold chain to a power of 1.5, 3 and 1e6, and proposing chain swapping every 100 steps. We used a uniform prior for population sizes (Θ), bounded between 0 and 0.1 (with a δ of 0.01), and for the migration rates (M), bounded at 4,000 with a δ of 400. Proper mixing under these conditions was ensured using the highest parametrisation model (model 2). We compared 5 models of increasing complexity:

1) a panmictic model, in which all colonies were gathered in one population, estimating only a general Θ, 2) a full-matrix model, in which asymmetric migrations were allowed between all pairs of colonies, 3) a step-wise model, in which asymmetric migrations were allowed, but only between neighbouring colonies, in a closed circle, 4) a rst meta-population model, in which the Ross Sea (WSH), Adélie and George V Lands (MZE, MZW and DDU), and the South Atlantic Context §-138 How do coalescent-scale migration translate at the ecological scale? In the previous chapter ( e Emperor synnome, p. 211), we showed how continent-wide genomic data allows us to reconstruct large-scale migration patterns in the Emperor penguin. Inferred gene ow is outstandingly high, with colonies receiving between ~1% and ~5% of their e ective population size as migrants every generation (see §113 p. 220). However, these estimates are averaged over the period spanned by the coalescent history, i.e. up to 4.Ne generations (see §41 p. 103): their distribution in time may be homogeneous, with a low and constant trickle of migration, or on the other hand highly heterogeneous, with periods of high or complete dispersal alternating with periods of « stasis » and philopatric isolation. Similarly, dispersal may be homogeneous, or heterogeneous in space: the determinants of the strength of dispersal may be global (e.g. planetary climate change and modi cations of the glacial or oceanographic landscape), and a ect every colony equally, or Empirical evidence of heterogeneous dispersal - §138 changes, had indeed only identi ed o ne c olony. ese tw o co lonies (w ith a to tal of ca . 7,400 breeding pairs) are located near the Mertz Glacier in an area that underwent tremendous habitat change after the glacier tongue broke o in February 2010. Our ndings therefore suggest that a satellite survey, although o ering a major advance since it allows a global imaging of emperor penguin colonies, may miss certain colony locations when challenged by certain features of polar ecosystems, such as snow cover, evolving ice topology, and rapidly changing habitat. Moreover our survey shows that this large seabird has considerable potential for rapid adaptation to sudden habitat loss, as the colony detected in 2009 may have moved and settled on new breeding grounds. Overall, the ability of emperor penguin colonies to relocate following habitat modi cation underlines the continued need for a mix of remote sensing and eld surveys (aerial photography and ground counts), especially in the less-frequented parts of Antarctica, to gain reliable knowledge about the population demography and dynamics of this agship s pecies o f the Antarctic ecosystem.

Empirical evidence of heterogeneous dispersal - §138 and 77°S [START_REF] Wilson | Distribution and abundance of Antarctic and sub-Antarctic penguins: a synthesis of current knowledge[END_REF][START_REF] Wiuf | Recombination as a point process along sequences[END_REF][START_REF] Lea | Discovery of the first Emperor penguin Aptenodytes forsteri colony in Marie Byrd Land, Antarctica[END_REF][START_REF] Ancel | The different breeding strategies of penguins: a review[END_REF]. However, our present knowledge of the biology of the emperor penguin falls far behind the picturesque notoriety of this iconic species. e b reeding b ehaviour a nd p henology o f t he s pecies h ave b een described from only a few sites, e.g. from the Dion Islands [START_REF] Tomczak | First recorded loss of an emperor penguin colony in the recent period of Antarctic regional warming: implications for other colonies[END_REF] and Pointe Géologie archipelago [START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF], usually settled in the vicinity of overwintering research stations.

Indeed, most colonies are just too remote from permanently occupied research stations, and because of the unique breeding cycle of this species (in which egg laying, hatching, and early chick rearing all occur during the austral winter, see Prévost 1961), ground visits to most breeding colonies are virtually impossible for most of the breeding season [START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF]). Yet, because the French station in Adélie Land, Dumont d'Urville, has been located next to the Pointe Géologie colony, this is the only colony which has been visited almost every day for more than 45 years [START_REF] Ponchon | Divergence between Antarctic and South American marine invertebrates: What molecular biology tells us about Scotia Arc geodynamics and the intensification of the Antarctic Circumpolar Current[END_REF][START_REF] Isenmann | Ecoéthologie du Manchot empereur (Aptenodytes forsteri) et comparaison avec le Manchot Adélie (Pygoscelis adeliae) et le Manchot royal (Aptenodytes paragonica)[END_REF]Isenmann 1971;Jouventin 1971a;Jouventin 1972a;[START_REF] Jouventin | Mortality parameters in emperor penguins Aptenodytes forsteri[END_REF][START_REF] Langmead | The Emperor Penguin: A Strategy to Live and Breed in the Cold: Morphology, physiology, ecology, and behavior distinguish the polar emperor penguin from other penguin species, particularly from its close relative, the king penguin[END_REF][START_REF] Ancel | Emperor penguin mates: keeping together in the crowd[END_REF]). However, it has been shown that demographic studies extrapolated from the colony-level can be strongly misleading as they tend to give too much weight to local stochastic events [START_REF] Lynch | Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula[END_REF]). e r ecently d eveloped s atellite-based r emote-sensing m ethods ( Barber-Meyer e t a l. 2007; [START_REF] Fretwell | Holocene radiative forcing impact of northern peatland carbon accumulation and methane emissions[END_REF][START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]) are a major breakthrough. By detecting faecal deposits on the sea ice in satellite images, it is possible to locate emperor penguin colonies. However, present-day satellite technology is constrained in Polar Regions as the darkness of the polar winter and the frequent cloud cover during winter and early spring limit window of opportunity for satellite-surveys. Further, wind-blown snow may obscure the faecal signal. erefore, t o expand our knowledge of this species beyond the boundaries of the few available local observations, it is critical to corroborate and calibrate these remote-sensing methods through ground visits with detailed and precise observations of breeding colonies.

Empirical evidence of heterogeneous dispersal - §145 arduous matter of trial-and-error, since inter-annual variations make it di cult for the birds to evaluate rapidly the consistency of ice conditions at a particular location. erefore, after the loss of their original habitat, some birds are probably breeding in a sub-optimal location, which may explain the di erence in breeding success between neighbouring and possibly related colonies. §-145 Status of the Emperor Penguins Census. Emperor penguins breed almost around the entire coastline of the Antarctic Continent [START_REF] Melick | A recently discovered breeding colony of emperor penguins (Aptenodytes forsteri) on the Budd Coast, Wilkes Land, East Antarctica[END_REF][START_REF] Coria | The adaptive capacities of seabirds to face environmental variability: the role of heterogeneity within populations[END_REF][START_REF] Wiuf | Recombination as a point process along sequences[END_REF]Todd et al. 2004;[START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]. eir known breeding distribution extends from Snow Hill Island (64°31'S, 57°26'W) to Gould Bay (77°43'S, 47°41'E) in the Antarctic Peninsula, and Cape Crozier on Ross Sea (77°28'S, 169°19'E). e Snow Hill Island and Gould Bay [START_REF] Wiuf | Recombination as a point process along sequences[END_REF] colonies are the most northerly and the most southerly known emperor penguin colonies, respectively. Colony sizes vary from more than 20,000 pairs (Coulman Island, Cape Washington, Halley Bay) to just a few hundred pairs (Umbeashi Rock, Amundsen Bay, Fold Island, Cape Crozier). e two largest known colonies are in the Ross Sea: Cape Washington with ca. 24,000 pairs and Coulman Island with ca. 28,000 pairs (Kooyman 1993). e closest colonies to the Mertz Glacier are at Pointe Géologie (66°40'S, 140°01'E) and Davis Bay (69°21'S, 158°29'E). While ca. 2,500 chicks of emperor penguins were raised at the Pointe Ge ólogie colony during the 2012 breeding season, the two new colonies together numbered ca. 5,700 chicks. Since a pair of emperor penguins may only successfully raise one chick per year, the population of breeding emperor penguins in this area of Antarctica can therefore be estimated, based on these chick counts, to more than ca. 16,400 breeding adults, about a fourth more than previously estimated [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]. is c ount r epresents a m inimum e stimate f or the breeding population, given uncertainty about the mortality rate of chicks prior to our visit.

Because of the persistence of the sea ice, few ships are able to reach the Antarctic coasts before post-breeding adults disperse from their colonies between December and early January. Except for colonies close enough to research stations, during the breeding season (from March-April to Empirical evidence of heterogeneous dispersal - §145 December-January, depending on the latitude), access to colonies is di cult or impossible due to adverse weather and/or extensive pack ice (Kooyman 1993). Consequently, many emperor penguin colonies have never been counted and some have presumably not even yet been discovered.

Further-more, in addition to the Mertz colonies, another colony, on the Princess Ragnhild coast and for which location was reported in 2009 by satellite images [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF], was rst visited in early January 2013 by Belgian scientists, who surveyed ca. 20,000 adults, compared to 6,870 adults detected from satellite survey [START_REF] Fretwell | An emperor penguin population estimate: the first global, synoptic survey of a species from space[END_REF]). e c urrent g lobal emperor penguin population can therefore be estimated to be, at least, ca. 260,000 pairs from 52 breeding colonies (Fretwell et al. 2012 and present study), or ca. 25% more than estimated only 15 years ago [START_REF] Woehler | The status and trends of Antarctic and sub-Antarctic seabirds[END_REF].

Over the past ve years satellite surveys have proved a very e ective method for nding new emperor penguin colonies. However, as our visit to the western colony demonstrated, the nitrogensignature of snow-covered droppings around colonies that underwent numerous snowfalls, and the shade of an ice front indicate that at certain times of year, under challenging environmental conditions it is virtually impossible to detect some colonies from satellite images. Importantly, several colonies have not been observed since they were rst reported. is problem, of missing colony locations by satellite survey may be solved by taking multiple satellite images within a breeding season and especially towards the end of the breeding season when the guano is more apparent. Multi-temporal and multi-year satellite surveys, backed up with con rmation and further ground truthing from aerial or ground based counts are essential if we wish to fully understand the population demography and dynamics of emperor penguins. As a consequence, we can still hope to discover more emperor penguin colonies in the future if further investigations are conducted along the Antarctic coast. Context §-146 e Peruvian diving-petrel as a reference species. In the previous chapters, we introduced the particular case of the synnomes of the King and Emperor penguins -a highly original population organisation, in which the whole species, despite its fragmented colonial distribution, remains fully panmictic. is structure may be seen as an extreme case in the broad spectrum of seabird genetic and spatial organisations (see e.g. a review in Friesen et al. 2007): in the previous chapters (see e King synnome, p. 163 and e Emperor synnome, p. 211), we argue that this particular form of transient philopatry is a signi cant behavioural trait that allows Aptenodytes penguins to cope with the unstable polar environment, driven either by the extent of coastal upwelling and polynya activity, or by the latitudinal variations in the Polar Front location. In order to test that prediction, we review here the contrasting case of the Peruvian diving petrel Pelecanoides garnotii.

Unexpected philopatry in an insular

A procellariimorphe like the penguins [START_REF] Zhang | Comparative genomics reveals insights into avian genome evolution and adaptation[END_REF], this small auk-like procellariform bird Chilean range. First, population sizes and migration rates were co-estimated based on multilocus haplotype data in a Bayesian framework, as implemented in Migrate-n [START_REF] Beerli | Unified framework to evaluate panmixia and migration direction among multiple sampling locations[END_REF]. We selected three sets of 50 random nuclear loci with 4 to 6 polymorphisms, as an unbiased representation of the neutrally evolving part of the genome [START_REF] Trucchi | King penguin demography since the last glaciation inferred from genome-wide data[END_REF]. In order to correct for potential allele dropout, we randomly sampled one haplotype only for each individual, at each locus. We ran a cold chain and 3 heated chain under a static heating scheme, raising the cold chain to a power of 1.5, 3 and 1e6, and proposing chain swapping every 100 steps.

Chains were run for 50,000,000 generations, recording every 500 generations, with a 5,000,000generation burn-in, specifying uniform priors both for population sizes (Θ, bounded between 0 and 0.1 with a δ of 0.01), and for the migration rates (M, bounded at 4,000 with a δ of 400).

Proper mixing under these conditions was ensured using the highest parametrisation model, and checking convergence between the three independent random datasets. Models were ordered by log Bayes factors, as de ned by Kass and Raftery (Kass & Raftery 1995), u sing thermodynamic integration approximation of marginal likelihoods. Four models were tested: (1) the two islands are fully isolated populations, (2) the two islands are independent population exchanging migrants with symmetric gene ow, (3) the two islands are independent population exchanging migrants with directional gene ow, a nd ( 4) t he t wo i slands b elong t o a s ingle, panmictic population.

Co-estimation of population size, gene ow, and population history was performed through explicit model testing in fastsimcoal2 (Exco er et al. 2013), through Akaike Information Criterion (AIC) comparison of the optimised composite likelihood of the two-dimensional folded allele frequency spectrum under six di erent models. We rst tested two simple models: (1) a full isolation model with two stable, independent populations, and (2) a classical isolation-with-migration model, with population sizes, migration rates and divergence time as free parameters. We then tested four more complex scenarios [START_REF] Abram | Evolution of the Southern Annular Mode during the past millennium[END_REF][START_REF] Ackerley | Quantifying the effects of perturbing the physics of an interactive sulfur scheme using an ensemble of GCMs on the climateprediction.net platform[END_REF][START_REF] Ma | Cette idée semble con rmée par la date de radiation évolutive de plusieurs taxa, inférée génétiquement, et dont les clades Antarctiques et Subantarctiques semblent avoir été isolés depuis la MMCT[END_REF][START_REF] Adams | Seasonal variation in the diet of the king penguin (Aptenodytes patagonicus) at sub Antarctic Marion Island[END_REF], in which the two population diverge after the last glacial maximum, ca. 7,000 generations ago (a conservative biogeographic estimate for most Discussion - §166 makes a case for the importance of taking the full evidence into account when describing a species' genetic architecture. Restricting the analysis to the sole observations that support some kind of genetic structure could allow us to suggest that an isolation by distance process is at work in both species: however, extensive model-based testing clearly shows that this is not the case.

In the Emperor penguin, the slightly more gradual geographical pattern does retain information as to the intensity and directionality of gene ow b etween c olonies: b oth h aplotype-based and spectrum-based approaches converge on similar estimates. In the King penguin, however, the even higher extent of panmixia obliterates any quantitative information as to the intensity of gene ow: model-based approaches hardly converge, but propose very high and seemingly random values for all migration parameters -an expected behaviour, since full mixing can inform a lower bound, but not an upper bound on gene ow parameters. e slight di erence in intensity of genetic mixing between the two species may be interpreted in several ways. First, it may be an artifact of our sampling design: our geographical representation of the King penguin being far more complete than that of the Emperor penguin, it is possible that our view of the latter is overly contrasted by the arti cial break in the sampling distribution (between the Adélie Land and the Dronning Maud Land groups), and that sampling intermediate colonies would bring the ensemble closer to a mixed whole. Indeed, the four sampled Ross Sea individuals hint to that direction, as they do not seem to t in even a slight isolation-by-distance pattern (see Fig. 36A). Alternatively, the contrasting demographic histories of the two species may have played a role in this di erence. Whether the Pleistocene bottleneck in the King penguin (see e King synnome, p. 163) « reset » a state of full panmixia 20,000 years ago that light drift could not yet disrupt, or rather (a more likely hypothesis in our opinion) the 20 th century history of massacres and the subsequent recovery (see §22 p. 75) involved a very recent increase in gene ow for the recolonisation of the most severely a ected archipelagos, it seems certain that the King penguin had a more tumultuous history than the Emperor -which could justify even slightly higher genetic mixing. However, we can not insist enough on the fact that, for both species, any kind of struc- [START_REF] Williams | Projected distributions of novel and disappearing climates by 2100 AD[END_REF][START_REF] Borboroglu | Penguins: natural history and conservation[END_REF].

Discussion

Considering this general picture, it appears that Southern Ocean seabirds (penguins included) have a marked geographic structure of genetic diversity, where the intensity of gene ow will determine the level of structure. Higher gene ow keeps groups on a purely intraspeci c phylogeographic level (as in Eudyptula minor), while a decreasing level of admixture gradually leads to subspecies systems (as in the Eudyptes chrysocome ssp. or the Pelecanoides urinatrix ssp. complexes) and full species systems (as in the Diomedea sp. or the Spheniscus sp. complexes). As suggested in our introduction (see §6 p. 33), the balance between philopatry, dispersal, and at larger scale incipient local adaptations, appears to vary along a gradient of increasingly strongly structured biogeographies. e continuous nature of this gradient, however, does raise the question of the signicance of observed structures. While full speciation may be rather easy to observe when morphological, behavioural, and genetic cues converge, the lower reaches of phylogeographic structure are more unclear [START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF][START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF], especially as genetic and ecological separation entertain a rather blurred relationship in the lower di erentiation areas (Esler et al. 2006;[START_REF] Wang | What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity[END_REF]. In those lower ranges, it may sometimes appear that very subjective factors -such as publication pressure -in uence t he i nterpretation o f observed structures. Publication bias is a recognised confounding factor in several domains, including genetics [START_REF] Mulvaney | Oceanic birds of South America: a study of species of the related coasts and seas, including the American quadrant of Antarctica, based upon the Brewster[END_REF][START_REF] Pan | Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature[END_REF], and usually takes the form of a bias towards reporting signi cant r esults o ver n onsigni cant on es -or, mo re of ten, of re porting ba rely signi cant results as indicative of a strong process. In the particular eld of phylogeography and ecological population genetics, it also often leads to the overinterpretation of barely signi cant results, Discussion - §169 species if ever their ranges were to overlap, in particular, is currently unresolved, and will be of particular interest for understanding the future of those species if ever climate change and subsequent range contraction brought them into close contact again. e synnome structure of the two Aptenodytes species also raises questions as to the mechanisms involved in their recent speciation. Indeed, reduced gene ow and local adaptation under allopatry is often invoked in the case of sister species currently occurring in allopatry, as is the case of the King and Emperor penguins. However, the synnome structure that appears to be plesiomorphic in large penguins makes allopatric speciation a rather counter-intuitive event. A strong disruption of this structure may have been necessary at the time of speciation: either through colonisation of hew habitats on the Antarctic continent, or through a sudden interruption of a previously much borader range (for example through changes in the structure of the Southern Ocean water masses, although no such event has been documented yet in the stratigraphic record). is i s a n i mportant c aveat f or t he i nference o f p opulation h istory, s ince p opulation s ize and metapopulation dynamics are by no means independent parameters. Migration, for one, is often Discussion - §169 a density-dependent parameter: a common prediction is that migration increases with density, i.e. usually with population size [START_REF] Saether | Finite metapopulation models with density dependent migration and stochastic local dynamics[END_REF][START_REF] Tajima | Speciation and phylogeography of giant petrels Macronectes. Molecu-Annexes - §186 lar phylogenetics and evolution[END_REF] mellifera), this is only possible when population structure has a clear-cut phylogeographic component, that is to say, when populations have diverged in a tree-like manner, and remained isolated ever since, and that divergence happened much before the demographic events of interest, so that isolation and demographic processes do not interfere in shaping genetic diversity. A more realistic case is that the degree of isolation between populations is uctuating together with population size and species density during the history of the population, and that the present-day population structure, if ever it exists, does not fully re ect the past population structure -as groups that have come in secondary contact may have totally re-shu ed in the meantime. us, suspicion of past

Seabirds on the evolutionary scale. §-169

population structure may be an important hindrance to successful inference of past demography solely from contemporary genetic data uninformed by, for example, paleontological or ancient DNA data.

e Aptenodytes breeding system and genetic organisation is particularly favorable to past population size inference. e present-day synnome structure appears to be rooted in behavioural characteristics of the species (such as high dispersal ability) rather than being solely the result of recent population history (see §167 p. 299): so that there is no compelling grounds to suspect that population fragmentation was ever an important trait in the past. However, we should be cautious about the interpretation of strong population reduction events, such as the one observed during the Last Glacial Maximum in the King penguin (see e King synnome, p. 163). What we really reconstruct, in this study, is a reduction in e ective population size, likely brought about by range contraction and refugial fragmentation of the species. is explicitly involves a form of population subdivision, and likely an associated reduction -even minor -in gene ow between refugial areas. us, translating the observed reduction in e ective population size, as a reduction in census population size is not as straightforward as it would seem, even considering that the life history of the species remained fully unchanged during the period. On the contrary, the observed restructuration of genetic diversity during the Pleistocene is the joint consequence of all the forces we mentioned earlier. Yet, our observed baseline (the present-day state of the species) allows us to go further. With an e ective population size of ~20,000 to 25,000 breeders, density is currently such that gene ow precludes any population structure. Situation is likely to have been similar

prior to the LGM bottleneck, when inferred e ective population size was strong. us, the state change during the LGM gives us at least one crucial qualitative information: disruption of the King penguin's habitat was strong enough to provoque a combination of population fragmentation and decline.

Discussion - §169

In contrast with the Aptenodytes synnomes, the very strong population structure that we observe in a strongly philopatric seabird such as the Peruvian diving-petrel (see Unexpected philopatry in an insular seabird, the Peruvian diving-petrel, p. 275) makes any attempt at inferring past demography rather risky. Indeed, present-day isolation patterns are a convincing testimony to the behavioural capacity of the species to evolve strongly isolated structures: but it does not imply that these structures have remained the same during the whole history of the species. us, even if we restricted ourselves to one single present-day colony, that did not bear any signs of internal fragmentation, we would not be able to ascertain whether that population was, at any time in the past, split into several units and later recast into its present shape. It is possible that further methodological developments will alleviate that important limitation: it has been argued that the full ancestral recombination graph (see §42 p. 107) should contain enough information to reconstruct not only past population size, but also past population structure and admixture patterns.

An attempt has been made in that direction by Schi els & Durbin 2014, although still within the Sequential Markovian Coalescent framework -that is to say, still without taking the topology of recombination events into account, which e ectively limits reconstructions to tree-like divergence patterns. We suggest that integrating the local recombination topology may be more informative as to past admixture events, since the probability of two given haplotypes merging through recombination at any point of time is conditional on their probability of co-occurring in a given deme. However, this would imply stepping out of the SMC framework, and would prob- King penguin, and that further increase in temperature beyond these conditions is equally likely to bring about population decline. Indeed, the replacement of the Emperor penguin by the King penguin as the dominant Aptenodytes biomass during the early Holocene implies that the latter is already the « warm adapted » species of the clade -and its glory days may well be behind it. In order to retrieve evidence about more ancient times, two lines of action are possible. First, we may try to enlarge the sample size, in the hope that the increased diversity may retain information as to more ancient ancestors. However, past a modest sample size (as low as ~16 individuals), the full coalescent should already be reasonably well described, and increasing sample size is not expected to help much [START_REF] Felsenstein | Accuracy of coalescent likelihood estimates: do we need more sites, more sequences, or more loci[END_REF]Hoban et al. 2013a;Hoban et al. 2013b). Second, we may directly try to shift the time limit by integrating ancient samples in our reconstructions. In a « pure » coalescent framework, like the EBSP (see §44 p. 112), this may take the form of serial sampling (i.e. individuals from di erent points of time can be integrated in a single time-tree) in order to use the full evidence jointly. In a derived framework like the Stairway plot approach (see §46 p. 121), however, serial sampling is not directly possible, since the model is tted to a timeat object, the allele frequency spectrum (see §45 p. 117). Extending this method to serial sampling would require (i) that several ancient, but mutually contemporaneous samples are available, in order to establish a « palaeospectrum ». is is a rather drastic restriction imposed on the experimental design, and would only be possible in rather speci c cases (e.g. the ice-core sampling of penguin moult feathers and remains, as performed by Emslie et al. 2007 for isotopic niche studies), and (ii) to have some knowledge about the relationship between the sampled palaeo-population, and the modern population (see e.g. [START_REF] Hellenthal | King penguin population on Macquarie Island recovers ancient DNA diversity after heavy exploitation in historic times[END_REF]. If these two conditions can be met, further implementation could involve (i) a simple model in which independent reconstructions would be performed for modern and ancient samples, using the current algorithms, (ii)

Seabird response to

preferably, an integrated reconstruction, in which population size at each point of time would be chosen in order to maximise the likelihood of SNP classes in both spectra simultaneously (thus assuming that the modern population is the direct descendant of the ancestral one), or (iii) more Discussion - §176 also, as noted by [START_REF] Smetacek | -le changement climatique est-il spécial en lui même dans les régions polaires, au delà de son rythme accéléré ; ou ses e ets sur les communautés biologiques sont-ils surtout le résultat de la relative intégrité de ces communautés encore peu anthropisées ? §-13 Réponse des espèces au changement climatique[END_REF], because direct human impacts occurred later in the high latitudes than in the tropical and temperate belts, and that less climate change is to some extent visible there in a more « raw » form. Yet it seems that we are now rapidly catching up on these delayed impacts -to mention only the steep rise in marine overexploitation in the Southern Ocean [START_REF] Myers | Rapid worldwide depletion of predatory fish communities[END_REF][START_REF] Ainley | Ecological repercussions of historical fish extraction from the Southern Ocean[END_REF], and the increasing concentration of contaminants at high latitudes [START_REF] Nash | Persistent organic pollutants in Antarctica: current and future research priorities[END_REF].

us, regarding the fate of penguins and other seabirds in the face of man-made climate change, our study may have brought us as many questions as answers. Yet there may be some optimism in cases such as the one of the Peruvian diving petrel, in which resolute conservation measures appear to be having actual e ect. Yet in all cases, the disproportionate e ect of direct human impact (through population decimation in the King penguin, habitat destruction in the Diving-petrel, and human disturbance on the Emperor penguin) competes with indirect impact through anthropogenic climate change, so that it may be vain to separate both questions. Indeed, in the present state of things, climate change, ecosystem overexploitation and habitat destruction are but di erent faces of one general stance, that doubtlessly needs to be renewed.

is is however a fully di erent topic, that we will hopefully have the opportunity to explore further on. In the meantime, I wish to nish this work by thanking the reader for his patience and perseverance, and by expressing once again all my gratitude to Dr Céline Le Bohec and Dr Emiliano Trucchi for their guidance, and to Dr Beaugrand and Dr Davey for accepting the heavy task of reviewing it.

Discussion - §176

Annexes - §176

Annexes

King penguin demography since the last glaciation inferred genome-wide data e following work was performed during the earliest stages on this PhD, with a limited participation on my side -but it served as an important methodological basis throughout this thesis.

We therefore include it here, mostly for reference: While the biological impacts of recent Quaternary glaciation events on the largely terrestrial Northern Hemisphere have been relatively well documented [START_REF] Bellard | Impacts of climate change on the future of biodiversity[END_REF], the oceanic Southern Hemisphere, particularly at higher latitudes, remains poorly described [START_REF] Fraser | Poleward bound: biological impacts of Southern Hemisphere glaciation[END_REF] owing to its relative inaccessibility. Recent hypotheses suggested that winter sea ice during the Last Glacial Maximum (LGM) could have extended as far north as South Georgia, Crozet,

Kerguelen and Macquarie Islands, while Falkland, Gough and New Zealand Islands may have acted as ice-free refugia (Fig. 45 p. 330, and Fraser et al. 2009). As southern ocean conditions uctuated between glacial and interglacial periods, local species had to contend with changes in both habitat and resource availability. In particular, the dis-tribution of most seabirds is primarily constrained by the spatial location of suitable breeding sites on land and foraging areas in the ocean [START_REF] Hunt | The impact of avian predator-scavengers on King Penguin Aptenodytes patagonicus chicks at Marion Island[END_REF]. For a population to thrive, productive foraging areas must exist within reach of a suitable breeding site. Climate oscillations a ect both these key ecological constraints. On one side, these uctuations cause glaciers to expand and retreat, thus covering or exposing potential breeding sites, on the other side, they a ect the location and intensity of oceanic currents and fronts that in turn determine marine primary production. According to ( atje et al. 2008) the large-scale Southern Ocean productivity may have been nearly shut down during the LGM, at least at the latitude where it is currently present. Highly dispersive species negatively a ected by glacial conditions could have moved northwards to more suitable ice-free breeding habitats during the LGM, while low-dispersive ones may have been strongly reduced in population size or may even have gone extinct [START_REF] Baroni | Abandoned penguin rookeries as Holocene paleoclimatic indicators in Antarctica[END_REF]. erefore, post-glacial warming and the subsequent retreat of glaciers and sea ice, probably favoured the re-colonization of Antarctic and sub-Antarctic territories by dispersive species dependent on ice-free breeding sites (Fraser et is is caused by the longer distance the individuals have to swim to get to their main foraging ground (the Antarctic Polar Front during the summer season) whose latitudinal location is in uenced by SST (the higher the SST, the higher the latitude , see [START_REF] Péron | Risk assessment for the Fildes Peninsula and Ardley Island and the development of management plans for designation as Antarctic Specially Protected or Managed Areas[END_REF]). However, when SST was lower, and food resources probably closer, the Crozet Islands were probably not suitable for the king penguin to complete their breeding cycle as this species needs year-round ice-free grounds for reproduction. Annexes - §176

Annexes

Results

ILLUMINA sequencing of a paired-end RAD library from eight king penguins yielded ca 65 million, 100 bp reads. Front reads only were used in our analyses and are available on GenBank at the Sequence Read Archive (Run Num.: SRR942341). After quality ltering, trimming of the last 5 bp, and barcode sorting, 101,115 anonymous loci (each as a 95 bp nucleotide sequence) with 50 average coverage were aligned in an unreferenced catalogue. GC content of this dataset was estimated as 50.45%. According to our quality criteria, the catalogue was further ltered to 66,172 loci (of which 31 452 were polymorphic) matching all eight individuals without missing data. is dataset was then used in downstream statistical analyses.

First, we tested whether genomic regions with di erent degrees of polymorphism carried consistent information about the demographic history of the king penguin population from Crozet. In order to calibrate our reconstruction, the mitochondrial CR dataset was included in the EBSP analysis together with 50 randomly selected nuclear loci (4-to 6-SNP classes), using the substitu-

tion rate (0.55 substitutions.site -1 .Myr -1 ) published for the Adélie penguin [START_REF] Ritz | Phylogeography of the southern skua complex-rapid colonization of the southern hemisphere during a glacial period and reticulate evolution[END_REF].

When analysed on its own, the mitochondrial locus supported the demographic expansion of the Our EBSP reconstruction based on a random set of 50 RAD loci and one mitochondrial fragment indicated that the king penguin population of 'La Baie du Marin' had a Ne of ca 2,000 individuals during the LGM, and it steeply increased at the onset of the Holocene, around 15,000 years ago. is demographic growth closely matched the trend of temperature anomalies recorded in the Epica Dome C ice core [START_REF] Jouzel | Orbital and millennial Antarctic climate variability over the past 800,000 years[END_REF], which shows a rapid increase between 18,000 and 10,000 years ago (Fig. 48 p. 339). en, t he c olony r eached a n e stimated present population size of ca 170,000 individuals. Using the published mtDNA rate as a reference, the average substitution rate of loci in our dataset was estimated at 2.2.10 -8 substitutions.site -1 .Myr -1

(2.6.10 -7 substitutions.site -1 .Myr -1 ). To obtain this estimate, we used the substitution rates estimated in BEAST for loci with di erent numbers of SNPs (mean values of posterior probabilities in substitutions.site -1 .Myr -1 : 0 SNP = 0.007; 1 SNP = 0.028; 2 SNPs = 0.050; 3 SNPs = 0.071; 4 SNPs = 0.092; 5 SNPs = 0.114; 6 SNPs = 0.135). Note that the number of SNPs at each locus is, in the rst place, a result of the stochasticity of the mutational and coalescent process. Hence,

digestion enzyme like SbfI, whose restriction site is rich in GC, a selection for GC-rich regions occurs. Indeed, the GC content in our dataset was 50.45% that corresponds to the highest estimates for chicken microchromosomes [START_REF] Hellenthal | King penguin population on Macquarie Island recovers ancient DNA diversity after heavy exploitation in historic times[END_REF]). Hence, our dataset is probably centred on GC-rich regions (e.g. microchromosomes), which are characterized by increased levels of gene density, recombination rate, number of CpG sites, methylation and mutation rate [START_REF] Axelsson | Comparison of the chicken and turkey genomes reveals a higher rate of nucleotide divergence on microchromosomes than macrochromosomes[END_REF]. Excess of hypermutable CpG nucleotides in GC-rich sequences has been identi ed as one possible explanation of increased mutation rates [START_REF] Zhao | L'ampleur et l'organisation de cette diversité re ètent certains caractères essentiels de l'espèce : la diversité génétique elle-même a été expliquée par plusieurs paramètres, tels que la complexité de l'organisme[END_REF] so that GCrich microchromosomes can show a 1.2-1.3 faster substitution rate than the average of the genome [START_REF] Waters | Driven by the West Wind Drift? A synthesis of southern temperate marine biogeography, with new directions for dispersalism[END_REF]. is e ect probably contributes, at least in part, at explaining the high mutation rate estimated for our dataset. Even if further investigation is clearly necessary, our interpretation sounds like a cautionary tale about the bias in the selection of genomic regions when using sequence-based restriction enzymes. §-178 King penguin population history during the Last Glacial Maximum. Our genomic data from the Crozet king penguin colony contain a consistent signal of demographic expansion at all levels of variability, with very few outliers in the mismatch analysis. Our demographic reconstruction strongly supports a sudden population expansion following the LGM and starting ca 15,000 years ago. erefore, our results show that even a cold-adapted species, such as the king penguin, was limited by colder glacial conditions, and bene ted from post-glacial warming, which o ered suitable breeding sites and foraging conditions in the Crozet region.

Like several other top-level predators in the Antarctic and Sub-Antarctic, king penguins depend on marine ecosystems for food and on terrestrial habitats for reproduction. is species needs a combination of two ecological factors that are both in uenced b y t emperature: ( i) year-round ice-free breeding grounds, and (ii) access to major feeding grounds (e.g. the Antarctic Polar Front) within swimming distance and compatible with birds' breeding duties (exchanging the egg with their fasting partner, feeding their chick). e king penguin has the longest breeding cycle

of all penguin species, spanning 14 -16 months [START_REF] Barrat | Quelques aspects de la biologie et de l'écologie du manchot royal Aptenodytes patagonicus des îles Crozet[END_REF]. is implies that the reproductive success depends on both summer and winter conditions. Yet, LGM climate strongly in uenced both marine productivity and the availability of suitable breeding grounds from which feeding areas could be exploited. Lower global temperature favoured a northward expansion of both winter and summer sea ice (Gersonde et al. 2005;[START_REF] Fraser | Kelp genes reveal effects of subantarctic sea ice during the Last Glacial Maximum[END_REF][START_REF] Fraser | Poleward bound: biological impacts of Southern Hemisphere glaciation[END_REF]. In turn, ice-sheet and sea ice extensions profoundly a ected the location (shifted northward) and strength of oceanic fronts and circumpolar currents encircling Antarctica [START_REF] Charles | Biogenic opal in Southern Ocean sediments over the last 450,000 years: Implications for surface water chemistry and circulation[END_REF]Gersonde et al. 2005;[START_REF] Fraser | Kelp genes reveal effects of subantarctic sea ice during the Last Glacial Maximum[END_REF], which determine primary productivity in the Southern

Ocean ( atje et al. 2008). Some polar species tracked favourable ecological niches northwards [START_REF] Budd | Diatom distribution and paleoceanographic reconstruction in the Southern Ocean Present and Last Glacial Maximum[END_REF][START_REF] Janko | Did glacial advances during the Pleistocene influence differently the demographic histories of benthic and pelagic Antarctic shelf fishes? Inferences from intraspecific mitochondrial and nuclear DNA sequence diversity[END_REF][START_REF] Fraser | Poleward bound: biological impacts of Southern Hemisphere glaciation[END_REF], and suitable refugia for land-breeding species have been proposed on the Falklands, Gough Island and Southern New Zealand. Genetic evidence showed recent (re)colonizations of the sub-Antarctic islands by several taxa that drifted with the strong eastward ow of the Antarctic Circumpolar Current [START_REF] Waters | Driven by the West Wind Drift? A synthesis of southern temperate marine biogeography, with new directions for dispersalism[END_REF][START_REF] Fraser | Kelp genes reveal effects of subantarctic sea ice during the Last Glacial Maximum[END_REF]). However, current reconstructions of LGM conditions lack the spatial resolution to unequivocally detect the number and geographical distribution of all glacial refugia [START_REF] Convey | Exploring biological constraints on the glacial history of Antarctica[END_REF]. In particular, it is not clear whether ice-free patches existed in the Crozet archipelago during the LGM [START_REF] Hunt | The impact of avian predator-scavengers on King Penguin Aptenodytes patagonicus chicks at Marion Island[END_REF].

EPICA Dome C ice core [START_REF] Jouzel | Orbital and millennial Antarctic climate variability over the past 800,000 years[END_REF]) data show an increase of ca 9°C between 18,000 and 10,000 years ago. In our demographic reconstruction, this interval coincides with a sustained phase of growth for the Crozet king penguin population (Fig. 48 p. 339). is growth trajectory is consistent with a threshold model, where a new colony is established as soon as all of the critical ecological requirements are met. We propose that the availability of breeding grounds within feeding-trip distance from foraging areas played the most critical role in the population expansion of Crozet king penguins after the LGM. Even if a major food resource (linked to the presence of the AFP) may have already been available within a king penguin home-range distance from the Crozet islands during the LGM, winter sea ice and possibly land glaciers, did not allow All animals in this study were handled only once in order to mark them with a plastic tag (Floytagw), to blood-sample them, and to conduct morphological measurements. All procedures employed during this eldwork were approved by the Ethical Committee of the French Polar Institute (Institut Paul Emile Victor 2 IPEV) and conducted in accordance with its guidelines, also complying with French laws including those relating to conservation and welfare. Authorisations to enter the breeding site (permits n82009-57 issued on the 26th of August 2009) and handle birds (permits n82009-59 issued on the 29th of August 2009) were delivered rst by the French "Ministe`re de l'Ame ńagement du Territoire et de l'Environnement" and then by the Terres Australes et Antarctiques Franc aises (TAAF).

Handled animals were removed from the colony in order to minimise the disturbance to neighbouring birds and taken to a few meters away for manipulation. ey were hooded to reduce their stress and manipulations lasted between 5 and 10 minutes. All blood-sampling (quantities adjusted according to the age of the chick) and tagging material was sterilized (either sealed, or through chemical sterilisation). Moreover, Vétédine soap and alcoholic antiseptic solutions were used to disinfect the skin before bleeding and tagging. Flesh wounds did not seem infected thereafter (personal observations on a subset of recaptured birds). [START_REF] Rozas | Distribution of individual inbreeding coefficients, relatedness and influence of stocking on native anadromous brown trout Annexes - §186 (Salmo trutta) population structure[END_REF]. is dataset was used to infer the king penguin past demogra-

μl by DNA capture on magnetic beads (beads solution:DNA = 0.8:1), thus further reducing the carry-over of non-ligated P1 adapters; (v)capture on magnetic beads using the same beads:DNA ratio (0.8:1) was then employed in all following puri cation s teps ( after b lunt-end repairing, poly-A tailing, P2 adapter ligation and library enrichment by PCR); (vi) PCR ampli cation was performed in 8 x 12.5 μl aliquots pooled after the ampli cation in order to reduce ampli cation bias on few loci due to random drift; (vii) the library was then quanti ed by a uorimetric-based method (Qubit, Invitrogen) and molarity was checked on an Agilent Bioanalyzer chip (Invitrogen). A nal volume of 20 μl with a DNA concentration of 45 ng/μl was submitted for sequencing on anILLUMINA HiSeq2000 sequencer at the Norwegian Sequencing Centre, University of Oslo.

Raw reads were then processed using the scripts included in the Stacks package [START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF] running on our server facility on the ABEL cluster, University of Oslo. Raw reads were quality ltered and grouped according to individual barcodes. en individual loci were retrieved and SNPs were called by a maximum-likelihood function that excluded likely sequencing errors.

Several runs with di erent settings of read trimming parameter, quality thresholds,mismatches allowed when building the individual and the population catalogs, were performed to check for consistency of the results. e parameters setting used to build the nal catalog included: -t 95

and the default values for the quality checking when using"process_radtags.pl"; -m 10, -n 7, -M Minor allele frequency spectrum was calculated by functions available in the R package "adegenet" [START_REF] Jombart | adegenet: a R package for the multivariate analysis of genetic markers[END_REF]) using loci included in the 1-SNP class. is information was then passed to the python-based software ∂a∂i [START_REF] Grémillet | Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms[END_REF]) that, using a di usion approximation to the allele frequency spectrum, allows demographic inference from genetic data testing alternative demographic scenarios in a maximum-likelihood framework. A sudden growth in population size was tested against the null hypothesis of constant population size using the"two_epoch" and the "snc" functions, respectively. Several runs of likelihood optimisation were performed changing the extent of the search by the "fold" parameter in the"dadi.Misc.per-turb_params" function. Optimised log-likelihood and eta v alues w ere r ecorded. I n o rder to calculate e ective population size from eta values produced by ∂a∂I,a total sequence length of 1,943,510 bp (95 bp X 20,458 loci used in this analysis) was used.

Functions included in the R package "ape" [START_REF] Paradis | Polar Front around the Kerguelen Islands: An up to date determination and associated circulation of surface/subsurface waters[END_REF]) and the R standard boxplot function [13] were used to calculate the joint mismatch distribution in the pairwise di erences (from

here onwards referred to as mismatch distribution density). Calculations were performed and plotted in each 2 to 6-SNP classes separately.

Di erent random combinations of 50-100 loci in 2 to 6-SNP classes were compared when inferring the past demography of the king penguin population using the coalescent-theory based multi-locus analysis implemented in BEAST 1.7.4. Linkage disequilibrium was tested in all subsets using Genepop [START_REF] Raymond | GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism[END_REF] [START_REF] Lambert | Rates of evolution in ancient DNA from Adélie penguins[END_REF] for the substitution rate of the mitochondrial Control Region. Further analyses con rmed this high gure but it was downscaled to 0.55 sub/s/ Myr [START_REF] Ritz | Phylogeography of the southern skua complex-rapid colonization of the southern hemisphere during a glacial period and reticulate evolution[END_REF]. We used the more conservative 0.55 sub/s/Myr for our calibrated demography. A generation time of 11.49 years (Le Bohec in prep) was used to convert the population size estimates on the EBSP(given by default in e ective population size * generation time).

We then plotted the population trend for the last 35,000 years together with the trend of temperature anomalies as inferred by the analysis of the EPICA Dome C ice core [START_REF] Jouzel | Orbital and millennial Antarctic climate variability over the past 800,000 years[END_REF].

Concerning the calibration of the mean genome-wide substitution rate: rst, the mean of the median values in each SNP-ratio class included in the EBSP analysis was calculated (4 to 6-SNP classes); then, a linear regression was used to infer the substitution rate of those SNP classes excluded from theEBSP analysis (0 to 3-SNP classes); nally, we calculated the mean genomic substitution rate weighting each SNP class accordingly with the frequency (number of loci) of each class.

Annexes - §186

RAD-seq bench protocol optimisation.

In order to maximise sequencing yield and e ciency, we conducted thorough protocol optimisation. We used Lambda phage DNA as a control (50, -Overall e ciency: ligation was rst p erformed u nder r ecommended c onditions ( NEB concentrations, temperatures and times). Re-ligation of digested DNA (condition (c)) appeared sub-optimal, as only a fraction of high-molecular-weight DNA was re-formed.

-Time and temperature: tests were then repeated with three di erent conditions: (a) 25°C for 45 min (recommended), (b) at 16°C for 16 h (NEB test conditions) and (c) cycling between 10°C and 30°C (2°C increments every 30'') for 16 h [START_REF] Lund | Increased cloning efficiency by temperature-cycle ligation[END_REF]. Under all test mixes, ligation

Annexes - §186 e ciency clearly increased from time conditions (a) to (c). We retained conditions (c) as optimal for our updated protocol.

-P1 adapters dimers: in order to test the e ciency of P1 adapter ligation, we did a series of dilu- -P1 adapter molarity: in order to test if current P1 molarity is simply limiting the reaction, we performed ligation under « ideal » (cycled) conditions, with 0.1 pmol, 0.5 pmol [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]), 1.0 pmol, 5 pmol, 10.0 pmol, 20 pmol, 50 pmol, and 100 pmol of P1 adapters per reaction. In order to assess the e ciency of the P1 ligation process, we measured the molarity of high-molecular-weight chimeric lambda phage re-constructs. Indeed, we consider the reaction e cient when all available genomic DNA sticky-ends are ligated to a P1 adapter, which requires these to be in large excess: under those conditions, genomic DNA religation should be minimal.

Fragment length and molarity were assessed on a TapeStation (Agilent (c)) genomic DNA chip.

With 0.1 pmol P1, chimeric religation is very important, but seems to decrease with increasing P1 molarity. Saturation is reached at 5 pmol: chimeric DNA has disappeared, and P1 adapter dimer molarity starts to increase. is n ew m olarity i s 1 0 t imes h igher t han r ecommended by [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF], and leads to enhanced library e ciency. However, when testing our stock P1 adapters on a BioAnalyzer DNA chip (Agilent (c)), it was found to be 10 times less concentrated than expected from the manufacturer's speci cations. M oreover, w hen u sing a f resh s tock of adapters, 0.5 pmol proved su cient to obtain the same result. e most likely hypothesis here is Annexes - §186 thus a manufacturer's failure -a possibility that we had not taken into account at rst, but which may be worth testing on all new adapter stocks.

(iii) P2 ligation. e same set of conditions tested for P1 ligation was assessed for P2 ligation, but with no noticeable e ect on library e ciency. P2 ligation was therefore considered already optimal.

(iv) Puri cation steps. e original protocol includes a series of puri cation steps, realised with a spin-column kit (Qiagen Minelute (c)). ese cause massive DNA loss along the preparation. We tested an alternative protocol, in which magnetic beads (Agencourt AMPure XP (c)) are added to the library after size selection, and kept together with the library until the nal puri cation step.

Solid-state reversible immobilisation (SPRI) beads are inert, paramagnetic carboxyl-coated particles. Reversible precipitation of DNA on the beads happens under high-salt, low-DNA-solubility conditions, which are created by adding a high-salt polyethylene-glycol (PEG-8000) solution to the library. Under low-tonicity conditions, DNA is dissolved in the surrounding bu er, and beads do not interfere with enzymatic reactions. In order to implement this, we prepared a binding solution with 10 g PEG-8000, 7.3 g NaCl, and 50 mL H 2 O. We calibrated its DNA precipitation e ciency by purifying 50bp DNA ladder with a gradient of binding solution to ladder ratio ranging from 0.3X to 1.4X. Puri ed product was assessed on a BioAnalyzer DNA 1000 chip (Agilent (c)). For our solution, a ratio of 1 (equal volumes of binding solution and target DNA solution) was found to provide the appropriate size selection for RADseqapplications, by binding all DNA fragments longer than ~200bp.

Using this updated puri cation m ethod, D NA w as r e-immobilised o n t he m agnetic b eads 3 times (after end repair, A-tailing, and P2-ligation), and beads are removed at the nal step. DNA loss was found to be greatly reduced, and library preparation e ciency (i.e. molarity of properly prepared fragments compared to initial DNA input) was approximately doubled. An additional bene t is the reduction in handling time, and in costs, given the high price of SPRI beads.

(v) Size selection. e original protocol [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]) included a manual DNA excision from an agarose gel. is method was found to lack precision and repeatability, especially due to the inaccurate migration of several DNA ladders. It was replaced by an automated size selection system (BluePippin (c)), with higher DNA recovery and more precise size range as a result.

(vi) New protocol. All these optimisations have been gathered in a revised bench protocol Updated RAD-sequencing protocol, p. 357.

Annexes - §186

-500 mM NaCl -100 mM Tris-Cl -adjust pH to 7.5-8.0 with H2SO4 (or NaOH).

Annexes - §186

Updated RAD-sequencing protocol is is a modi ed bench version of the original protocol by Paul D. Etter [START_REF] Baird | Rapid SNP discovery and genetic mapping using sequenced RAD markers[END_REF]. We found it to improve the overall yield and e ciency of the process, as well as speeding it up. As it is, the library preparation proper can be performed in two (long) days, not including library validation. A typical work ow would be ( 1) DNA extraction optimization -that may take a while.

(2) On the rst run, some steps need parameter optimization. is may take some time too. ( 3)

Sample selection, library planning, DNA quanti cation -one day. ( 4) Library preparation Stage 1 -one day. ( 5) Library preparation Stages 2 and 3 may be combined in one day. e only limit to this is that the whole beads-in Stage 3 should not be stopped overnight, but rather performed in one stretch.

Required reagents and stock solutions

Adapter alignment bu er: Prepare a 10X stock that will be kept at +4°C.

Binding solution:

Prepare a 1X stock that will be kept at +4°C. An aliquot will be brought to room temperature for library preparation. Polyethylene-glycol with high NaCl allows the re-pre- -Mix for 10 min.

-Clean (see above) and re-suspend in 43 μL EB bu er.

A-tailing: is step adds a single 3' A-overhang on virtually every blunt end. is makes them compatible with the T-overhang on the P2 adapter. e reaction is performed directly in the previous tube, with the beads in, but o the magnet. To each library, add :

-5 μL 10X NEB2 bu er -1 μL dATP (10 mM) -3 μL Klenow exo--37°C for 30min with light mixing (300rpm)

-Cool down to RT.

-Add k*52 μL binding solution.

-Mix for 10 min.

-Clean (see above) and re-suspend in 45 μL EB bu er.

P2 ligation: is step adds the second adapter to every single A-tailed fragment available. For low plexity runs, non-barcoded adapters (P2-Ø) are ne. For higher plexities with combinatorial barcoding, use di erent P2 adapters (one per library pool). e reaction is performed directly in the previous tube, with the beads in, but o the magnet. To each library :

-5.2 μL 10X NEB T4 ligase bu er -1 μL P2 adapters (10 μM), mix thoroughly -0.5 μL T4 ligase (2,000,000 U/mL) -Incubate 45min at room temperature.

-Add k*52 μL binding solution.

-Mix for 10 min.

-Clean (see above)and elute in 52 μL EB bu er.

Annexes - §186

-Transfer to a new LoBind tube.

Mark this tube as « Template ». is is the raw library, it will need double-strand adapter conversion and enrichment before sending out for sequencing.

Library validation

Ampli cation test: A rst non-quantitative PCR is done in order to check for correct library preparation. A successful library should amplify without problem (although the strength will vary from one library to another). For each library pool, prepare a reaction with :

-12.5 μL NEB Phusion polymerase premix -7.5 μL H 2 0 -1 μL Solexa primer mix (10 μM) -4 μL library template.

-Solexa PCR conditions : 98°C 30" ; [98°C 10" | 65°C 30" | 72°C 30"]X18 ; 72°C 5' -Add 5 μL 6X orange loading dye, -Load the whole product on a gel with Low Range DNA ladder.

Annexes - §186

A successful library should yield a bright, well-de ned band in the 400-700bp range. If amplication is poor or absent, something probably went wrong during the prep. Good luck with that.

If ampli cation is really good, you may want to test fewer cycles in order to reduce duplication biases to a minimum.

Qubit test: Use 2 μL of each library to measure accurately the dsDNA concentration on the Qubit. A good library with standard DNA loss should be anywhere between 6 and 10 ng.μL -1 . A lower concentration may indicate either a sub-optimal sonication (hence excessive DNA loss during size selection) or a generally ine cient puri cation steps.

Library enrichment: If everything looks good, perform a large-volume PCR with : Mais aucune répétition n'est parfaite [START_REF] Kierkegaard | Gjentagelsen. Et Forsøg i den experimenterende Psychologi af Constantin Constantius[END_REF]) : la variation, ou diversité, joue un rôle essentiel tant dans le temps que dans l'espace [START_REF] Mayr | The growth of biological thought: diversity, evolution, and inheritance[END_REF]. La variabilité phénotypique intraspéci que entre dans le champs de la biologie sous le nom de « di érences individuelles » dans l'oeuvre de Darwin, célèbre pour y avoir trouvé « le matériau sur lequel la sélection naturelle agit et s'accumule » (Darwin 1859): les fondements génétiques de cette diversité sont désormais au centre de la théorie synthétique de l'évolution [START_REF] Mayr | The evolutionary synthesis: perspectives on the unification of biology[END_REF][START_REF] Schlosser | Evidence for gene flow differs from observed dispersal patterns in the Humboldt penguin, Spheniscus humboldti[END_REF]. Cette variation ne sert aucun but prédé ni : elle peut pourtant être a posteriori organisée le long d'un spectre, qui s'étend de la diversité adaptative à la diversité neutre [START_REF] Kimura | Solution of a process of random genetic drift with a continuous model[END_REF][START_REF] Wagner | Neutralism and selectionism: a network-based reconciliation[END_REF]. A l'échelle moléculaire, la variation adaptative se dé nit comme l'apparition de mutations aux e ets phénotypiques avantageux, qui sont positivement sélectionnés dans la population [START_REF] Mayr | Populations, species, and evolution: an abridgment of animal species and evolution[END_REF], tandis que la variation neutre est faite de mutations ne semblant o r aucune prise à la sélection naturelle [START_REF] Kimura | Solution of a process of random genetic drift with a continuous model[END_REF]. Nous ne prendrons pas position quant à l'importance relative de la variation neutre et adaptative dans l'évolution moléculaire (un sujet au coeur de continuels débats, Au-delà de ses conséquences immédiates pour l'étude des Manchots en tant que sentinelles de l'Océan Austral, cette étude montre l'intérêt d'une plus forte intégration de la génomique des populations dans les études démographiques et comportementales.

Mots-clefs :

génétique des populations, RAD-sequencing, océan austral, sphéniscidés, changement climatique.

Résumé en anglais

e Southern Ocean plays a central role in the regulation of the Earth's climate and ecosystems, and accounts for more than 20% of the world's marine productivity.

e complexity of its trohpic networks and its sheer inaccessibility make the use of bioindicator species more necessary there than anywhere else. Several penguin species (such as the King and the Emperor penguin) are therefore the focus of long-term monitoring programs.

In this study, we use the information from population genomics (« RAD-sequencing » data covering the genome of hundreds of individuals from the two species' full distribution) and from IPCC-CMIP5 numerical climate models to calibrate in the long time the more precise demographic analyses realised in the framework of eld surveys, and understand penguin responses to cliamte change.

Beyond its implications for the study of penguins as sentinels of the Southern Ocean, our work demonstrates the interest of a stronger integration of population genomics in demographic and behavioural investigation.

Keywords: population genetics, RAD-sequencing, Southern Ocean, spheniscids, climate change.