L Bernatchez

N Bierne

M Docker

J-C Simon

Marie-Agnès Coutellec

P A Gagnaire

Manu Eric Petit

Samuel Neuenschwander

C Roux

Benjamin Dufour

Yannick Salaville

André Robbe

Gervaise Février

Marie Thérèse Delaroche

Valérie Lopez

Alice Beaudoin

Yannick Bayonna

Anne Treguier

Marco Limnée

Isa Marion

Yann Echasserieau

Aurélie Clarrisse

Pierre-Lou Lucie

Quentin Rougemont

Arnaud Gaigher

Emilien Lasne

Jessica Côte

Maïra Coke

Anne-Laure Besnard

Sophie Launey

Guillaume Evanno
email: guillaume.evanno@rennes.inra.fr

Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys

Keywords: genetic structure, Lampetra, life history strategy, parapatry, reproductive barrier, sympatry

Prendre le temps de réfléchir, sur tout, sur rien, semble devenir un luxe dans ce monde gouverné par les notions de rendement, productivisme, utilité et autre valeur ajouté, pourtant sources de déchirement et non du bonheur de l'homme. Durant 3 années, j'ai eu la chance de réfléchir, de me poser des questions que beaucoup considèrent inutile dans cette société productiviste, mais qui permettent de comprendre le fonctionnement du monde qui nous entoure, et qui aujourd'hui me force à rester humble face à l'étendue de mon ignorance. A l'issue de ces 3 ans de thèse, je continuerais encore plus qu'avant à me poser des questions sur tout, sur rien, et à m'égarer dans des digressions scientifiques qui ne peuvent être que bénéfiques pour la culture scientifique, mais aussi car seule la réduction de mon ignorance contribuera à mon épanouissement personnel. En cette fin de thèse, plus que jamais en accord avec Socrate, « je ne sais qu'une seule chose : c'est que je ne sais rien ».

Pour m'avoir donné l'opportunité d'effectuer cette thèse, de l'orienter selon mes propres réflexions, quitte à faire fausse route, je remercie mes encadrants Guillaume et Sophie. Merci à vous pour votre ouverture d'esprit, vos qualités humaines, votre enthousiasme, et vos idées. Merci de m'avoir laissé l'opportunité de partir à l'étranger de rencontrer des personnes qui m'ont permis aujourd'hui de largement enrichir mes connaissances.

Introduction

he current diversity of life on earth is the result of millions of years of evolution and past speciation events. Understanding how diversity arises is at the heart of evolutionary biology and has both fundamental and applied consequences. Understanding species diversification therefore requires comprehension of the process of speciation [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF]). To do so, one needs at first to define what a species is. Here comes the trouble as the definition of a species is unclear and gathers more than twenty different concepts [START_REF] Mayden | A hierarchy of species concepts: the denouement in the saga of the species problem[END_REF][START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Hausdorf | Progress toward a general species concept[END_REF]. The main difficulty with this number of species concepts is that each one conveys a different definition of what speciation is [START_REF] Hausdorf | Progress toward a general species concept[END_REF].

The most widely adopted definition, the biological species concept [START_REF] Mayr | Systematics and the Origin of Species[END_REF], defines species as "groups of actually or potentially interbreeding individuals reproductively isolated from other such groups". This simple definition allows the study of evolutionary processes leading to the establishment of reproductive isolation (RI) between taxa. It is particularly useful to study the appearance of prezygotic and postzygotic barriers to gene flow. However, hybridization and introgression are commonplace in nature [START_REF] Mallet | Hybridization as an invasion of the genome[END_REF].

Furthermore, there is accumulating evidence for adaptive introgression (i.e. the introgression of advantageous alleles from one genome to the other through hybridization) in plants [START_REF] Rieseberg | Evolution: Replacing Genes and Traits through Hybridization[END_REF][START_REF] Arnold | Adaptation by introgression[END_REF]) and animals [START_REF] Hedrick | Adaptive introgression in animals: examples and comparison to new mutation and standing variation as sources of adaptive variation[END_REF], including humans (Hawks & Cochran 2005;[START_REF] Racimo | Evidence for archaic adaptive introgression in humans[END_REF]. These phenomena should not exist under the strict definition of the biological species concept [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]). The "genic species concept" [START_REF] Wu | The genic view of the process of speciation[END_REF]) on the contrary allows for barrier semipermeability (Harrisson, 1986) so that most of the genome may freely be exchanged when species co-occur in sympatric areas, while key genomic regions remain impermeable to gene flow [START_REF] Wu | The genic view of the process of speciation[END_REF][START_REF] Harrison | Hybridization, Introgression, and the Nature of Species Boundaries[END_REF].

Hybrid zones are particularly well suited to the study of speciation [START_REF] Hewitt | Quaternary phylogeography: the roots of hybrid zones[END_REF]) and the effect of semi-permeability and heterogeneity along the genome [START_REF] Barton | Analysis of Hybrid Zones[END_REF]. In this context, when significant genetic differentiation is observed in spite of gene flow in sympatry, it is particularly important to determine if differentiation was truly initiated without geographic barriers or whether it was initiated in allopatry and followed by secondary contact. Both theory and empirical evidence overwhelmingly favor scenarios of allopatric divergence and admixture [START_REF] Turelli | Theory and speciation[END_REF][START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Harrison | Hybridization, Introgression, and the Nature of Species Boundaries[END_REF]. Studies of speciation rates demonstrated that speciation only reaches completion over very long time scales, typically millions of years [START_REF] Hedges | Tree of Life Reveals Clock-Like Speciation and Diversification[END_REF]. However a growing body of literature (e.g. [START_REF] Rundle | Ecological speciation[END_REF][START_REF] Schluter | Evidence for ecological speciation and its alternative[END_REF][START_REF] Nosil | Ecological Speciation[END_REF]) suggests that speciation can proceed i) quickly (hundreds to thousands of years) ii) in the presence of gene flow and iii) due mainly to the action of natural selection.

Without a solid theoretical framework and explicit tests of hypotheses that do not rely on selection it is complicated to draw conclusions about these new findings [START_REF] Lynch | The frailty of adaptive hypotheses for the origins of organismal complexity[END_REF][START_REF] Nei | The Neutral Theory of Molecular Evolution in the Genomic Era[END_REF][START_REF] Hughes | Evolution of adaptive phenotypic traits without positive Darwinian selection[END_REF][START_REF] Sorrells | Making sense of transcription networks[END_REF]. Fortunately, models of speciation have been developed [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Gavrilets | Models of Speciation: Where Are We Now[END_REF]) and greatly help to understand the conditions necessary for speciation to occur.

T

As noted by [START_REF] Abbott | Hybridization and speciation[END_REF] (Fig 1) speciation is a multi-level process "unfolding through space and time" and the genetic makeup of contemporary species is influenced by alternative cycles of isolation and connection [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF]. Taking these multiple components into account can be difficult, but combinations of experimental and modelling approaches together with population genetics could help understan how speciation proceeds through space and time. Understanding how biodiversity arises also has applied consequences for the future. Indeed, we may have entered into the sixth mass extinction on Earth [START_REF] Barnosky | Has the Earth/'s sixth mass extinction already arrived[END_REF], which is largely man-driven (Vitousek et al. 1997;[START_REF] Palumbi | Humans as the world's greatest evolutionary force[END_REF]. Thus, if man aims at preserving biodiversity -at least for the sake of its own survival-understanding how speciation proceeds may be very helpful. For instance, different species concepts will have different implications for species conservation [START_REF] Frankham | Implications of different species concepts for conserving biodiversity[END_REF]. Similarly, understanding the effect of human perturbations on the genetic structure and functioning of populations can help preserve these populations.

Figure 1: "Speciation is a multi-level process unfolding through space and time" (From Abbott et al. 2013). This figure summarizes the main steps that can lead to reproductive isolation over space and time. Over the course of time populations will undergo different demographic events combined with periods of gene flow and periods of strict geographic isolation. Geographic isolation can lead populations to adapt to divergent environments and start to accumulate barrier loci (e.g. DM incompatibilities (see 1.2.2.1) that can either collapse (barrier breakdown) or couple to enhance reproductive isolation so that at a certain point most of the genome becomes differentiated.

It is within this conceptual framework that I have studied the speciation process in the European river lamprey (Lampetra fluviatilis) and brook lamprey (Lampetra planeri), two species for which relatively little is known in terms of evolutionary biology and divergence history. The major goal was to characterize the level of reproductive isolation and the magnitude of the genetic barriers to gene flow between these closely related species. These inferences were jointly performed with investigations of the historical processes that have generated the observed level of reproductive isolation. Finally I also investigated how natural fragmentation of riverine habitat, enhanced by current human induced perturbations, can impact the level of population genetic diversity and structure and eventually lead to modification of population genetic evolutionary trajectories.

In the following sections of this introduction, I will first present the different mechanisms of speciation, the underlying theoretical genetic models and their limits. In a second part I will explain the importance of the geographical and historical conditions under which speciation arises. The third part presents some new methodological advances in the genomic era and discusses their limits. The last section presents the model species and what makes it a relevant model to study the process of speciation.

Speciation and Reproductive isolation 1.1 Overcoming the homogenizing effect of gene flow

Under the biological species concept (BSC), the most basic question to address is to determine which reproductive barriers arise first in space and time and what are the most important barriers that maintain species isolation [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. The most influential parameter on the probability of establishment of a genetic barrier is simply the effect of the level of gene flow between the diverging populations. Accordingly, recombination breaks up associations between adaptive alleles [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF]) and impedes divergence between nascent species.

The central question is thus to determine the quantity of gene flow that can pass freely between two diverging pools without stopping them from diverging. [START_REF] Mayr | Systematics and the Origin of Species[END_REF]1963) proposed a classification based on a biogeographical scale (the species distribution range) that indirectly takes into account the intensity of gene flow between diverging pools. Allopatry defines the situation when populations are in disjoint geographical areas and the gene flow m is equal to 0. When populations have completely overlapping ranges they occur in sympatry and m = 0.5. When populations have partially overlapping ranges, they are considered parapatric and m lies in between the extreme values of 0 and 0.5. From this classification, it is obvious that speciation will not proceed in the same manner in each situation. In allopatry, when populations are separated by mountains or oceans, no other factor but time is required for populations to eventually become reproductively isolated [START_REF] Turelli | Theory and speciation[END_REF]). They will undergo the action of genetic drift and randomly accumulate mutations. They can also undergo divergent selection that will largely accelerate the process [START_REF] Gavrilets | Perspective: models of speciation: what have we learned in 40 years? Evolution[END_REF]. All these processes drive the accumulation of genetic incompatibilities through time (see section 1.2.2.1). The model of allopatry is theoretically well supported and several examples exist in the literature (see examples in [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]). In sympatry, conditions for divergence to occur appear rather complicated because even with strong selection on many loci recombination will impede divergence [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF]. In parapatry, the conditions for the evolution of reproductive isolation (RI) are highly conditioned by the level of gene flow [START_REF] Gavrilets | Perspective: models of speciation: what have we learned in 40 years? Evolution[END_REF][START_REF] Bank | The Limits to Parapatric Speciation: Dobzhansky-Muller Incompatibilities in a Continent-Island Model[END_REF].

It is important to note that this classification of the geographical modes of speciation in discrete categories has been criticized [START_REF] Butlin | Sympatric, parapatric or allopatric: the most important way to classify speciation?[END_REF][START_REF] Fitzpatrick | What, if anything, is sympatric speciation[END_REF]) because it does not reflect the continuous nature of the speciation process. According to [START_REF] Butlin | Sympatric, parapatric or allopatric: the most important way to classify speciation?[END_REF] long distance migration between allopatric populations can almost always occur even at very low frequency and complete sympatry almost never occurs because the distribution of habitat between coexisting populations is often patchy. As a result, speciation would always occur in parapatry [START_REF] Butlin | Sympatric, parapatric or allopatric: the most important way to classify speciation?[END_REF]). However, it is clear from theory that the geographical context will determine if RI can occur and at which pace. The importance of this context was recognized again recently (Marie [START_REF] Curie | What do we need to know about speciation?[END_REF]). We will see in chapters 2 and 4 that the geographical context is important in determining the level of gene flow in our focal species. However, since species spatial ranges undergo contractions and extensions [START_REF] Hewitt | Genetic consequences of climatic oscillations in the Quaternary[END_REF]; the current distribution of species may have little to do with the initial conditions of divergence. Hence, to understand under which conditions divergence was initiated one has to reconstruct the underlying history (see section 2.1). But first, an important step under the BSC is to quantify the strength of reproductive barriers that can act to maintain differentiation between populations [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]).

The barriers to gene flow

A barrier can be endogenous (independent of the environment) or exogenous (dependent on the environment). Barriers that occur before the formation of the zygote are called pre-zygotic barriers, those that occur after are called post-zygotic barriers (Dobzhansky 1937) (Fig. 2). Most post-zygotic barriers involve genetic mechanisms whereas pre-zygotic barriers involve genetic mechanisms but also ecological factors. [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF] and [START_REF] Rundle | Ecological speciation[END_REF].

Prezygotic barriers

A first type of barrier called habitat isolation arises when species colonize two different ecological niches hence reducing their probability of mating together. This type of isolation involves spatial separation of the taxa but differs from a geographic barrier as it is based on genetic differences between taxa, different habitat choice and survival ability in these habitats [START_REF] Rundle | Ecological speciation[END_REF]. Geographic isolation on the contrary is linked to historical processes such as mountain formation, glaciations or continental drift [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. A classic example of habitat isolation is the coexistence of the Rhagoletis pomonella, phytophagous insect that inhabits apples and hawthorn, and Rhagoletis mendax whose mating and oviposition is restricted to blueberry. Hybrids of the two species have never been found in the wild [START_REF] Feder | Heredity -Abstract of article: Gene frequency clines for host races of Rhagoletis pomonella in the Midwestern United States[END_REF]. It is possible that this mutualism between insects and hosts plants is genetically based and efficiently maintains RI [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF].

Temporal isolation is a type of prezygotic barrier that occurs when two species mate at different time periods, reducing their hybridization probability. An interesting example is the anadromous pink salmon

Oncorchynchus gorbuscha that has a two year life cycle and exists as two genetically isolated populations (odd-year and even-year). Allelic frequencies are generally similar between either distantly spaced odd-year or even-year populations. On the contrary, allelic frequency differences occurred between odd-and evenyear populations occupying the same streams suggesting that these two alternate-year broodlines are reproductively isolated [START_REF] Aspinwall | Genetic Analysis of North American Populations of the Pink Salmon, Oncorhynchus gorbuscha, Possible Evidence for the Neutral Mutation-Random Drift Hypothesis[END_REF]. It is likely that even and odd year broodlines have allopatric origins [START_REF] Aspinwall | Genetic Analysis of North American Populations of the Pink Salmon, Oncorhynchus gorbuscha, Possible Evidence for the Neutral Mutation-Random Drift Hypothesis[END_REF][START_REF] Brykov | Geographical and temporal mitochondrial DNA variability in populations of pink salmon[END_REF]. To better understand the effect of this barrier crosses between broodlines and measurements of hybrid fitness would be required.

Behavioral and mating isolation can also act as prezygotic barriers to gene flow. They include species differences reducing mate attraction and heterospecific mating [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. One classical example is the coexistence of Pieris occidentalis and P. protodice sympatric butterflies. In this system, males of P. occidentalis have much darker forewings than males of P. protodice while female wing patterns do not differ. Field observations have shown that females of P. occidentalis mate with conspecific males but reject nearly all heterospecific males. When male P. protodice wings were experimentally darkened they were more easily accepted by female P. occidentalis [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]). The genetics of behavioral isolation has been studied in the Drosophila system and generally supports the view of a polygenic adaptation [START_REF] Wu | Sexual isolation in Drosophila melanogaster: a possible case of incipient speciation[END_REF]. In addition, behavioral isolation can evolve rapidly due to sexual selection [START_REF] Mj | Sexual Selection, Social Competition, and Speciation[END_REF][START_REF] Wu | Sexual isolation in Drosophila melanogaster: a possible case of incipient speciation[END_REF][START_REF] Ting | Incipient speciation by sexual isolation in Drosophila: Concurrent evolution at multiple loci[END_REF].

Mechanical isolation is defined as the inhibition of fertilization between species due to incompatibilities of their reproductive structures in relation to sexual selection [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Arnqvist | Comparative evidence for the evolution of genitalia by sexual selection[END_REF][START_REF] Arnqvist | Sexual conflict promotes speciation in insects[END_REF]. A classic example involves pollinator isolation due to structural incompatibilities in plants. Similar examples are found in animals as in Drosophila where the evolution of male genitalia in the D. melanogaster group is proportional to divergence time [START_REF] Lachaise | Historical Biogeography of the Drosophila melanogaster Species Subgroup[END_REF]. Other examples of mechanical isolation include size assortative mating as observed in water striders [START_REF] Arnqvist | Assortative mating by size: A meta-analysis of mating patterns in water striders[END_REF] and stickleback [START_REF] Mckinnon | Evidence for ecology's role in speciation[END_REF]. It seems that most genital differences have a polygenic basis [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Eberhard | Evolution of genitalia: theories, evidence, and new directions[END_REF]Masly & Masly 2011) and most of the interspecific variance would be additive [START_REF] Liu | Genetic Analysis of a Morphological Shape Difference in the Male Genitalia of Drosophila Simulans and D. Mauritiana[END_REF]. This view is supported by studies in Drosophila [START_REF] Liu | Genetic Analysis of a Morphological Shape Difference in the Male Genitalia of Drosophila Simulans and D. Mauritiana[END_REF][START_REF] Zeng | Genetic Architecture of a Morphological Shape Difference Between Two Drosophila Species[END_REF]Masly et al. 2011;[START_REF] Mcneil | Multiple Quantitative Trait Loci Influence the Shape of a Male-Specific Genital Structure in Drosophila melanogaster[END_REF][START_REF] Levasseur-Viens | Individual Genetic Contributions to Genital Shape Variation between Drosophila simulans and D. mauritiana, Individual Genetic Contributions to Genital Shape Variation between Drosophila simulans and D. mauritiana[END_REF] and Carabus [START_REF] Sasabe | The genetic basis of interspecific differences in genital morphology of closely related carabid beetles[END_REF][START_REF] Sasabe | QTL for the species-specific male and female genital morphologies in Ohomopterus ground beetles[END_REF].

Post-mating gametic isolation includes all barriers acting between copulation/pollination and fertilization [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]). These barriers are divided in non-competitive forms (without the presence of conspecific or heterospecific male gametes) and competitive forms (with conspecific or heterospecific male gametes). For instance studies in Drosophila have shown that D. sechelia males mated with D. simulans females had a very low quantity of sperm transferred and when D. simulans males were present few hybrids were produced due to noncompetitive and competitive isolating mechanisms [START_REF] Price | Cryptic reproductive isolation in the Drosophila simulans species complex[END_REF]. Other non-competitive barriers include gamete inviability or failure of gametes to produce fertilization (see [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. Competitive isolation involves conspecific sperm precedence (CSP) in animals and conspecific pollen precedence (CPP) in plants. CSP can be defined as the greater fertilization success of conspecific versus heterospecific sperm in conspecific crosses [START_REF] Howard | Conspecific Sperm and Pollen Precedence and Speciation[END_REF]. Several studies have shown that CSP can contribute to gene flow reduction and form an efficient barrier between species including fishes, insects, marine invertebrates and corals [START_REF] Daniel | Conspecific Sperm Precedence is an Effective Barrier to Hybridization Between Closely Related Species[END_REF][START_REF] Howard | The genetics of reproductive isolation: a retrospective and prospective look with comments on ground crickets[END_REF][START_REF] Chang | Conspecific sperm precedence in sister species of drosophila with overlapping ranges[END_REF][START_REF] Geyer | Conspecific sperm precedence in two species of tropical sea urchins[END_REF][START_REF] Ludlow | Gametic isolation in guppies (Poecilia reticulata)[END_REF][START_REF] Fogarty | Asymmetric conspecific sperm precedence in relation to spawning times in the Montastraea annularis species complex (Cnidaria: Scleractinia)[END_REF][START_REF] Manier | Postcopulatory sexual selection generates speciation phenotypes in Drosophila[END_REF][START_REF] Yeates | Cryptic Choice of Conspecific Sperm Controlled by the Impact of Ovarian Fluid on Sperm Swimming Behavior[END_REF] although some other studies did not find such barriers in other taxa [START_REF] Larson | Post-mating prezygotic barriers to gene exchange between hybridizing field crickets[END_REF][START_REF] Williams | Quantifying Reproductive Barriers in a Sympatric Pair of Darter Species[END_REF].

In conclusion, understanding the respective roles of these barriers is critical to explain the maintenance of RI. However, it appears fundamental but challenging to reconstruct the temporal order of appearance of these barriers as currently observed reproductive barriers may not be those involved in the initiation of RI [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. Some barriers may also be a consequence of speciation and not the initial cause generating RI. We will see that most of them may contribute to reproductive isolation in our focal lamprey species pair.

Postzygotic barriers to gene flow

Various postzygotic barriers to gene flow can arise after zygote formation: (i) hybrid lethality, (ii) hybrid sterility and (iii) reduced viability and/or fitness of advanced hybrid generations. Under the BSC, RI is complete when the fitness of every F1 is zero. Selection against hybrids can be endogenous (independent of the environment) when hybrids show reduced fertility or viability. Alternatively it can be exogenous when different phenotypes (or genotypes) are favoured in different environments. The genetic basis of postzygotic isolation has been extensively studied [START_REF] Coyne | The evolutionary genetics of speciation[END_REF], 2004) and is presented below.

Endogenous barriers

Several models exist to explain the evolution of endogenous barriers but few have received empirical support [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. The best supported is the Dobzhansky-Muller (DM) model of hybrid incompatibility. Models of polyploid speciation, hybrid underdominance, and extensions of the DM model have been developed [START_REF] Nei | Roles of Mutation and Selection in Speciation: From Hugo de Vries to the Modern Genomic Era[END_REF] but will not be presented here. A new mutation appears in population 1 and 2 derived from the ancestral population. These populations evolved independently (allopatry) and the mutation can become fixed by drift and/or by the action of natural selection. These fixed alleles may become incompatible between the two genetic backgrounds and are revealed upon secondary contact. Compatibility alleles here are dominant. Note that the ancestral genotype can be A 1 A 1 B 1 B 1 and unchanged in population 1 but changed to A 2 A 2 B 2 B 2 in the population 2. (Taken from [START_REF] Nei | Roles of Mutation and Selection in Speciation: From Hugo de Vries to the Modern Genomic Era[END_REF].

The DM model of hybrid incompatibility (Dobzhansky 1937;Muller 1942) requires the negative epistatic interaction of two or more biallelic loci (A and B) in hybrid individuals (Fig. 3). These loci are initially neutral (or advantageous) in their respective parental background. The DM model does not require crossing of an adaptive valley or display of a deleterious mutation [START_REF] Orr | The population genetics of speciation: the evolution of hybrid incompatibilities[END_REF]). Let's consider an ancestral population containing A and B loci and its genotype A 0 A 0 B 0 B 0 . This population now diverges in two allopatric populations. In population 1, allele A 0 mutates to allele A 1, advantageous or neutral, and this mutant becomes fixed by genetic drift or selection. In population 2 allele B 0 mutates to allele B 2 that also becomes fixed. If the populations hybridize (e.g. upon secondary contact) the two alleles will be combined in the hybrids. It becomes possible then that the combination of A 1 and B 2 reduces the fitness of hybrids as compared to parental populations. In this model, mutations accumulated during population divergence interact negatively through epistatic interactions to produce hybrids of low fitness. Note that the ancestral genotype could have been A 1 A 1 B 1 B 1 maintained in one population and changed to A 2 A 2 B 2 B 2 in population 2.

When taking into account a dominance effect between alleles hybrid breakdown can be observed in F1 [START_REF] Turelli | Dominance, epistasis and the genetics of postzygotic isolation[END_REF]. However, in diploids, hybrid depression/breakdown is generally observed more frequently in advanced hybrid generations (F2s and backcrosses) than in F1 [START_REF] Edmands | Heterosis and Outbreeding Depression in Interpopulation Crosses Spanning a Wide Range of Divergence[END_REF].

Recombination breaks down co-adapted gene complexes in F2s so that hybrids become homozygote for parental alleles at each locus. If the A 1 and B 2 alleles are recessive and localized on autosomes, then hybrid depression will take place only in homozygotes (A 1 A 1 B 2 B 2 ) (F2s). If alleles are carried out by sexual chromosomes, then DMI will be stronger in the heterogametic sex (Haldane's Rule. Haldane 1922).

A central question is the average fixation time of A 1 and B 2 in allopatry for DMI to occur. If a neutral mutation occurs in a population, its fixation is conditioned only on genetic drift so that the waiting time (in generations) until A 0 is replaced by A 1 , will be 1 µ + 2𝑁 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠 with µ the mutation rate and N the effective population size [START_REF] Nei | Roles of Mutation and Selection in Speciation: From Hugo de Vries to the Modern Genomic Era[END_REF]. This time can thus be particularly long under pure drift and when the effective population size is large. If the mutation is advantageous however, the allele will invade the population more rapidly than under the sole effect of drift. When the population size is small, the waiting time until replacement is approximately the same as the time of appearance of the new mutation [START_REF] Li | Persistence of common alleles in two related populations or species[END_REF].

Mathematical models [START_REF] Orr | The population genetics of speciation: the evolution of hybrid incompatibilities[END_REF][START_REF] Turelli | The dominance theory of Haldane's rule[END_REF][START_REF] Orr | The evolution of postzygotic isolation: accumulating dobzhansky-muller incompatibilities[END_REF] suggest that populations will diverge through a series of weak and independent mutations that should accumulate exactly with the square of time. With simple DMI involving two loci, this accumulation is quadratic, an observation called the "snowball effect". Some studies have found support for this hypothesis in Drosophila species [START_REF] Matute | A test of the snowball theory for the rate of evolution of hybrid incompatibilities[END_REF], Solanum [START_REF] Moyle | Complex Epistasis for Dobzhansky-Muller Hybrid Incompatibility in Solanum[END_REF] and more recently in hybrid males of Mus [START_REF] Wang | The Pace of Hybrid Incompatibility Evolution in House Mice[END_REF]. Another mathematical model attempted to extend the Orr's model (1995). It uses gene networks for the evolution of DMI but does not predict a snowball effect [START_REF] Palmer | Dynamics of Hybrid Incompatibility in Gene Networks in a Constant Environment[END_REF].

In addition, models suggested that complex DMI should be common. Complex DMI involve negative epistatic interaction at more than two loci. Similarly to the single-gene versus two-gene speciation case, where the path to speciation requires passing through the unfit genotype in the single-gene case, several paths exist in the two-gene case to obtain two species. The reasoning is the same as with DMI, the more loci are involved, the more paths exist to produce two divergent lineages [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]).

Empirical studies have often shown that multiple DMI contribute to male sterility in Drosopihla (Tao et al. 2003a;[START_REF] Masly | High-resolution genome-wide dissection of the two rules of speciation in Drosophila[END_REF]). However, one must clearly distinguish whether incompatibilities represent many evolutionarily independent two-locus DMI caused by distinct mechanisms or a small number of multilocus DMI with the same mechanistic basis [START_REF] Maheshwari | The Genetics of Hybrid Incompatibilities[END_REF]. Here also, evidence for complex DMI is supported by experiments in Drosophila [START_REF] Davis | The broom of the sorcerer's apprentice: the fine structure of a chromosomal region causing reproductive isolation between two sibling species of Drosophila[END_REF][START_REF] Orr | Complex epistasis and the genetic basis of hybrid sterility in the Drosophila pseudoobscura Bogota-USA hybridization[END_REF]Tao et al. 2003b;[START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. Finally, DMI have been identified in animals and plants diverging in allopatry (reviewed in [START_REF] Lowry | The strength and genetic basis of reproductive isolating barriers in flowering plants[END_REF][START_REF] Presgraves | The molecular evolutionary basis of species formation[END_REF]) but one can ask what is their ability to maintain separate taxa in other geographical contexts?

Model of evolution of postzygotic isolation in parapatry

The simple DM model with two bi-allelic loci has been used by [START_REF] Bank | The Limits to Parapatric Speciation: Dobzhansky-Muller Incompatibilities in a Continent-Island Model[END_REF] in a parapatric case under an island continent model. They showed that the maximum rate of gene flow is limited by exogenous selection. When gene flow occurs and DMI are neutral, they cannot be maintained. On the other hand when selection occurs, DMI can evolve either by selection against immigrants or by selection against hybrids. In the first case, this involves exogenous selection on one locus involved in the DMI. In the second case, this involves endogenous selection due to incompatibility of the two parental genetic backgrounds. The authors also showed that the genetic architecture maximizing gene flow supported by a DMI was not the same in the two cases. In selection against immigrants, tightly linked DMI of any strength are favoured. Selection against hybrids favours the evolution of strong unlinked DMIs. In addition, if selection act against hybrids and the environment is homogenous, the order of mutations is important (mutation order speciation).

An older model was that of Navarro & Barton (2003a) who proposed that chromosomal rearrangements favour the evolution of RI between species, in the presence of gene flow when DM incompatibilities accumulate within these rearrangements. In line with their model, the authors have shown that differentiation between human and chimpanzee was twice as high in genes mapped in rearranged chromosomes compared to collinear chromosomes (Navarro & Barton 2003b). However, their results (not the model) were largely controversial, as one study did not find such differences [START_REF] Zhang | Testing the Chromosomal Speciation Hypothesis for Humans and Chimpanzees[END_REF]) and technical criticism of the approach of Navarro & Barton was made by others (e.g. [START_REF] Lu | Comment on "Chromosomal Speciation and Molecular Divergence-Accelerated Evolution in Rearranged Chromosomes[END_REF].

Finally, one of the conclusions about the debate following these studies was that finding evidence for or against parapatric speciation "remains a fascinating but elusive goal" [START_REF] Lu | Comment on "Chromosomal Speciation and Molecular Divergence-Accelerated Evolution in Rearranged Chromosomes[END_REF]Navarro et al. 2003).

Model of evolution in sympatry

Evolution of RI in sympatry requires individuals to adapt to divergent habitats within the same area and the underlying genetic polymorphisms to be different in each habitat. The evolution of RI through divergent ecological selection also requires the linkage disequilibrium between genes involved in postzygotic isolation and those involved in prezygotic isolation. The main difficulty is that recombination will break up the coupling between these postzygotic and prezygotic isolating factors (genes) at each generation [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF]. To understand how coupling can be maintained, [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF] proposed two models: a one allele and a 2 allele model. In the one allele model, a new allele becomes fixed in all populations and allows individuals to recognize each other and to mate preferentially with the same adapted genotype. In this model, homogamy is controlled by a single locus. In the 2 allele model, two alleles (A 1 and A 2 ) have to be fixed in the populations. These alleles will also favor homogamy so that individuals fixed for A 1 will mate together and those fixed for A 2 will mate together. In this model, divergence is possible only if the 2 alleles are associated differentially to habitat choice, which requires strong linkage disequilibrium. The second model is generally considered as more realistic [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF] but some authors considered the first model more reasonable [START_REF] Kirkpatrick | Speciation by Natural and Sexual Selection: Models and Experiments[END_REF]. Indeed, recombination does not play a homogenizing role in this case, so that RI can evolve more easily. Under the 2 allele model another way to establish associations between prezygotic and postzygotic factors is through pleiotropic interactions between alleles of habitat choice and of local adaptation [START_REF] Rice | Disruptive Selection on Habitat Preference and the Evolution of Reproductive Isolation: A Simulation Study[END_REF][START_REF] Doebeli | A quantitative genetic competition model for sympatric speciation[END_REF]. Such pleiotropic traits were called "magic traits'" [START_REF] Servedio | Magic traits in speciation: "magic" but not rare?[END_REF]. One example of this kind of interaction may be the threespine stickleback [START_REF] Nagel | Body Size, Natural Selection, and Speciation in Sticklebacks[END_REF]. However few other convincing examples exist in nature [START_REF] Servedio | THE ROLE OF REINFORCEMENT IN SPECIATION: Theory and Data[END_REF].

There are many other quantitative models of sympatric speciation. They usually rely on the variance of some quantitative traits that will promote variance in resource use through competition between individuals within a sympatric population [START_REF] Dieckmann | On the origin of species by sympatric speciation[END_REF]. In this model, frequency dependent selection and disruptive selection favor extreme individuals, consuming unexploited resources.

In these conditions, assortative mating may lead to RI between ecologically diverging subpopulations. In the model, if assortative mating depends on a trait unlinked to resource use, genetic drift is necessary to break linkage equilibrium between the assortative trait and the trait for resource use. This model was criticized since during resource use, competition for extreme resources arises (e.g. competition for small and big seeds) leaving more intermediate resources and then favoring intermediate individuals.

Finally although theory predicts that sympatric speciation may occur, model assumptions are numerous and their empirical validity remains contentious [START_REF] Bolnick | Sympatric Speciation: Models and Empirical Evidence[END_REF]. Today, few convincing examples of sympatric speciation exist as the magnitude of RI between putatively incipient species is generally low (e.g. in Rhagoletis [START_REF] Feder | Mayr, Dobzhansky, and Bush and the complexities of sympatric speciation in Rhagoletis[END_REF] or Tinema [START_REF] Soria-Carrasco | Stick Insect Genomes Reveal Natural Selection's Role in Parallel Speciation[END_REF] suggesting that local adaptation, rather than speciation, is at play, or more simply because the null hypothesis of build-up of RI in allopatry has not been tested or could not be rejected. For instance, in Rhagoletis it is likely that the divergence was initiated in allopatry, with one race having diverged in North America and the other in Mexico, the latter having accumulated chromosomal rearrangements that have been maintained upon secondary contact in the United States.

The genes of post-zygotic isolation

The identification of genes involved in RI, their number, effect size, pleiotropic and epistatic effects is a central question in speciation [START_REF] Orr | The genetic theory of adaptation: a brief history[END_REF]. Population genetic theory predicts that many alleles of small effects should lead to adaptation of populations in different environments (Fisher 1930), a view defended by others (Pritchard et al. 2010;[START_REF] Hancock | Adaptations to new environments in humans: the role of subtle allele frequency shifts[END_REF]Pritchard & Di Rienzo 2010;[START_REF] Rockman | The Qtn Program and the Alleles That Matter for Evolution: All That's Gold Does Not Glitter[END_REF]).

Regarding speciation, several studies have suggested that a large number of loci were involved. Studies of hybrid zones for instance suggested that 50 loci explained the maintenance of RI between Bombina bombina and B. variegata while a minimum of 150 loci could be involved in the isolation of races of Podisma pedestris. More recent studies have shown that many genes with moderate effects were involved in hybrid breakdown of Arabidopsis complex [START_REF] Bomblies | Arabidopsis and relatives as models for the study of genetic and genomic incompatibilities[END_REF]). In the lake whitefish complex Coregonus clupeaformis, it seems that a polygenic basis is also involved [START_REF] Rogers | The genetic architecture of ecological speciation and the association with signatures of selection in natural lake whitefish (Coregonus sp. Salmonidae) species pairs[END_REF][START_REF] Bernatchez | On the origin of species: insights from the ecological genomics of lake whitefish[END_REF]Gagnaire et al. 2013a). Finally, reviews demonstrated a substantial variability in the number of genes implied in RI (Allen Orr 2001;[START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]). As stated by [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF] it is probable that many genes contribute to the total post-zygotic isolation. However, the number of genes that initiate RI may be much lower [START_REF] Seehausen | Genomics and the origin of species[END_REF]. Indeed according to [START_REF] Seehausen | Genomics and the origin of species[END_REF] when taking into account standing genetic variation, gene flow or changing environment, a few number of genes of large effects may be sufficient. In that sense a simulation study has showed that a concentrated architecture containing fewer, larger, and more tightly linked divergent alleles favored adaptation under migration-selection-drift balance [START_REF] Yeaman | The genetic architecture of adaptation under migration-selection balance[END_REF][START_REF] Yeaman | Establishment and Maintenance of Adaptive Genetic Divergence Under Migration, Selection, and Drift[END_REF]. Thus, finding QTLs of large effect composed of many tightly linked alleles of smaller effect can be facilitated in these conditions. However, adaptation is not speciation, and it is not clear whether such settings will facilitate divergence.

Exogenous barriers

Exogenous barriers imply that hybrids from differentially adapted populations have a smaller fitness than parental populations in their respective environments. For example the sympatric butterflies Heliconius melpomene and H. cnydo in Central America are closely related and display differences in wing colour. One is black, red and yellow coloured while the other is black and white. When hybrids are formed in the wild they have a lower fitness than the parental species [START_REF] Jiggins | Reproductive isolation caused by colour pattern mimicry[END_REF]. Another example is that of the benthic/limnetic ecotypes of G. aculeatus in British Columbia [START_REF] Hatfield | Ecological Speciation in Sticklebacks: Environment-Dependent Hybrid Fitness[END_REF][START_REF] Rundle | Natural Selection and Parallel Speciation in Sympatric Sticklebacks[END_REF][START_REF] Rundle | A test of ecologically dependent postmating isolation between sympatric sticklebacks[END_REF]). The two groups differ in their habitat use: the limnetic group eats plankton in open water and displays a smaller body with a narrow jaw gape whereas the benthic morph feeds on invertebrates in the littoral zone and has a larger body with a wide jaw gape. F1 crosses obtained in laboratory conditions were fit while they performed poorly in the wild environment.

Coupling of barriers to gene flow

Coupling of endogenous barriers to gene flow is a well-known process in the hybrid zone literature [START_REF] Barton | Multilocus Clines[END_REF][START_REF] Barton | Analysis of Hybrid Zones[END_REF][START_REF] Kruuk | A comparison of multilocus clines maintained by environmental adaptation or by selection against hybrids[END_REF]. It is well established that when selection affects many loci, coupling will occur between these loci and the strength of the barriers to gene flow will be strongest (dependent on selection strength, recombination and the number of loci [START_REF] Barton | Multilocus Clines[END_REF]). One recent paper extended [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF] two allele model which focused only on prezygotic isolation, to any number of incompatibilities and showed how coupling of any kind of barrier, either prezygotic or postzygotic, can emerge to maintain strong RI within a single population [START_REF] Barton | The evolution of strong reproductive isolation[END_REF]. In their model the authors showed that coupling of multiple (single-locus or multi-locus) incompatibilities increase mean fitness in case of positive epistasis. In addition, they showed that single locus incompatibilities involved in assortative mating can be coupled with loci reducing hybrid fitness, hence contributing to the theory of reinforcement [START_REF] Servedio | THE ROLE OF REINFORCEMENT IN SPECIATION: Theory and Data[END_REF].

Another question is the coupling between exogenous barrier and endogenous barrier (Moore & Price, 1993). Theoretical developments in hybrid zones have already shown that endogenous barriers were preponderant and formed "tension zones 1 ", independent of the environment [START_REF] Barton | Analysis of Hybrid Zones[END_REF] and this was also applicable for multi-locus barriers [START_REF] Kruuk | A comparison of multilocus clines maintained by environmental adaptation or by selection against hybrids[END_REF]. It was later shown that these tension zones were often trapped by environmental barriers [START_REF] Barton | Heredity -Abstract of article: The dynamics of hybrid zones[END_REF][START_REF] Barton | Analysis of Hybrid Zones[END_REF], meaning that DMI and signatures of ecological adaptation may be confounded. A recent study has investigated in detail how such coupling can result in genetic-environmental-associations (GEA) especially in heterogeneous environments [START_REF] Bierne | The coupling hypothesis: why genome scans may fail to map local adaptation genes[END_REF].

2 Modelling gene flow across space and time 1 Tension zones: cline maintained by a balance between random dispersal and selection against hybrids [START_REF] Barton | Analysis of Hybrid Zones[END_REF] Understanding which barriers reduce gene flow and promote speciation is crucial to our understanding of the speciation process, especially under the BSC. However, within the last two decades, the development of the coalescent theory, together with new methods to model gene flow have allowed us to further our understanding of how speciation proceeds through time and space, and also across the genome. Understanding the history of speciation across space and time is fundamental to draw general inferences about the geographical settings under which speciation can happen and determine the quantity of gene flow necessary to impede divergence. In addition, a number of studies focus on detecting positive selection at the genome scale. However, disentangling the effects of selection from those of other factors, including historical demographic processes and other non-selective forces that act together to leave their footprints along the genome, remains a challenging task.

The history of speciation

Studying the historical processes that have shaped the current distribution of life on earth lies at the heart of phylogeography (Avise, 2009;[START_REF] Hewitt | Quaternary phylogeography: the roots of hybrid zones[END_REF]). Phylogeography has greatly improved our understanding of the evolutionary processes involved in species diversification through the study of expansion-fragmentation and colonization events on patterns of genetic differentiation [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF][START_REF] Hewitt | Quaternary phylogeography: the roots of hybrid zones[END_REF][START_REF] Taberlet | Comparative phylogeography and postglacial colonization routes in Europe[END_REF]. Understanding the consequences of the climatic oscillations during ice ages on the spatial distribution of genetic diversity on earth was a crucial step [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF][START_REF] Hewitt | Genetic consequences of climatic oscillations in the Quaternary[END_REF]. Throughout historical times, populations have undergone spatial range expansions and contractions during which they were connected or separated so that gene flow has varied. For instance, the Pleistocene glaciations, which lasted approximately two million years and terminated around 10 000 years ago with a period of global warming [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF], were suggested to be one of the most important historical events involved in shaping the large-scale population structure and genetic differentiation of contemporary species [START_REF] Bernatchez | Comparative phylogeography of Nearctic and Palearctic fishes[END_REF][START_REF] Taberlet | Comparative phylogeography and postglacial colonization routes in Europe[END_REF]. During Pleistocene glaciations, ice sheets restricted populations to southern glacial refuges [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF][START_REF] Hewitt | Post-glacial re-colonization of European biota[END_REF]. Following glacial retreats, the recolonization of suitable northern areas frequently involved a few founder individuals, leading to a reduction in genetic diversity at the front of colonization [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF]. Differentiated populations then had large opportunities to interbreed in areas of secondary contact zones (or hybrid zones). These hybrid zones formed "windows on evolution" [START_REF] Hewitt | Quaternary phylogeography: the roots of hybrid zones[END_REF] and have provided great insights into the evolutionary process of speciation [START_REF] Barton | Analysis of Hybrid Zones[END_REF]. Phylogeographic studies have allowed us to trace the movement of species from refugial areas into previously glaciated regions and to identify the main glacial refugia and sutures zones from Europe in Italy, the Balkans, and the Iberian Peninsula, where several species were known to occur [START_REF] Taberlet | Comparative phylogeography and postglacial colonization routes in Europe[END_REF][START_REF] Hewitt | The genetic legacy of the Quaternary ice ages[END_REF][START_REF] Hewitt | Genetic consequences of climatic oscillations in the Quaternary[END_REF]. However, finer investigations of refugial areas depicted a more complex story and some studies have identified the English Channel area as a potential refugium for some species (eg. [START_REF] Finnegan | Use of multiple markers demonstrates a cryptic western refugium and postglacial colonisation routes of Atlantic salmon (Salmo salar L.) in northwest Europe[END_REF]. Overall, reconstructing the history of species divergence implies understanding in which areas and for how long populations and species were connected by gene flow or were separated. Phylogeographic methods do not allow such inference but new methods based on the coalescent theory allow us to to draw more complex and realistic inferences [START_REF] Beaumont | Approximate Bayesian Computation in Population[END_REF][START_REF] Beaumont | Approximate Bayesian Computation in Evolution and Ecology[END_REF][START_REF] Csilléry | Approximate Bayesian Computation (ABC) in practice[END_REF].

In addition, new genomic data gathered across a wide range of species allowed addressing with greater accuracy a basic question in speciation research: what is the average time to speciation (TTS)? A recent study found that the average TTS in plants and animals was approximately 2 million years [START_REF] Hedges | Tree of Life Reveals Clock-Like Speciation and Diversification[END_REF]. Based on these results the authors suggested that the accumulation of genetic incompatibilities proceeded mainly by neutral processes and that adaptive changes were almost decoupled from the speciation process. The authors subsequently suggested that described species separated by only tens of thousands of years are not real species. Similarly, species divergence in hybrid zones was estimated to be at least 1 million years, and was likely underestimated due to hybridization [START_REF] Barton | Adaptation, speciation and hybrid zones[END_REF]). In addition, estimations of the age of adaptive alleles in some species [START_REF] Colosimo | Widespread Parallel Evolution in Sticklebacks by Repeated Fixation of Ectodysplasin Alleles[END_REF][START_REF] Brawand | The genomic substrate for adaptive radiation in African cichlid fish[END_REF] suggested that they predated glacial retreats. These estimates are in contrast with the current literature on ecological speciation [START_REF] Nosil | Ecological Speciation[END_REF] and speciation with gene flow [START_REF] Smadja | A framework for comparing processes of speciation in the presence of gene flow[END_REF] which suggests that natural selection can rapidly promote the evolution of RI. This can be explained by the fact that speciation rates are in fact decoupled from RI [START_REF] Rabosky | Diversity-Dependence, Ecological Speciation, and the Role of Competition in Macroevolution[END_REF][START_REF] Rabosky | Macroevolutionary speciation rates are decoupled from the evolution of intrinsic reproductive isolation in Drosophila and birds[END_REF]. To reconcile these contradictory results, Rosenblum et al. (2012) introduced the notion of ephemeral species for groups of populations which may finally never reach the status of isolated species. Finally, to disentangle the role of gene flow, ecology and historical processes on species formation, more complex scenarios of divergence must be contrasted. These considerations are fundamental for our focal lamprey species pair, for which the level of reproductive isolation and divergence time are not well known.

New methods to infer the history of speciation

The development of coalescent models based on gene genealogy allows a better estimation of population genetic parameters such as effective population size, migration rate and timing of divergence between populations [START_REF] Pinho | Divergence with Gene Flow: Models and Data[END_REF]. However, a first prerequisite before estimating demographic parameters is to obtain a null realistic model of divergence for the studied populations. Fitting phylogenetic tree-based models allows drawing explicit inferences about history. However, these models assume a simple bifurcating tree with no subsequent gene flow, which may be incorrect when populations are connected by gene flow [START_REF] Edwards | Is a New and General Theory of Molecular Systematics Emerging?[END_REF]. Solutions for this issue were proposed by [START_REF] Pickrell | Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data[END_REF] and [START_REF] Gautier | Inferring Population Histories Using Genome-Wide Allele Frequency Data[END_REF]. The first model is an extension of Cavalli-Sforza and Edward approaches that estimates allelic frequencies based on a multivariate Gaussian model [START_REF] Pickrell | Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data[END_REF].

Migration allows for population split and mixture among multiple populations and is represented as edges along a graph instead of a tree. However, divergence estimates based on a Gaussian model may be reliable only for recent divergence times. The method of Gauthier & Vitalis (2013) relies on a diffusion process (forward in time) in contrast to most phylogenetic approaches and also allows handling several populations at a time. However, the method of [START_REF] Pickrell | Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data[END_REF] does not allow explicit comparisons of alternative scenarios and works better for full genome sequences with outgroup data. Overall the two methods do not allow estimating parameters such as effective population size or migration rates.

Coalescent methods such as the one developed by [START_REF] Nielsen | Distinguishing migration from isolation: a Markov chain Monte Carlo approach[END_REF] and [START_REF] Hey | Isolation with Migration Models for More Than Two Populations[END_REF] allow estimating demographic parameters, but assume either continuous gene flow (i.e. the Isolation with Migration model) or no gene flow. In addition, such models use a full likelihood approach, which is computationally intensive. Other recently developed methods used Hidden Markov Model (HMM) and full genome data to reconstruct the history of divergence. However, these methods are often limited to a few genomes or populations (eg. PSMC, [START_REF] Li | Inference of human population history from individual whole-genome sequences[END_REF]. Methods based on admixture fraction (Liang & Nielsen 2014b) or admixture tract length (eg. Harris & Nielsen 2013;Liang & Nielsen 2014a;[START_REF] Sedghifar | The Spatial Mixing of Genomes in Secondary Contact Zones[END_REF] are currently under development in human populations and should be soon applicable to natural populations, as full genome data become available. On the other hand, new methods such as Approximate Bayesian Computation (ABC) bypass the need to compute likelihoods [START_REF] Csilléry | Approximate Bayesian Computation (ABC) in practice[END_REF]) by making use of simulated data under a given historical scenario. The method simply compares a set of observed summary statistics with a set of simulated statistics under different scenarios [START_REF] Beaumont | Approximate Bayesian Computation in Population[END_REF][START_REF] Beaumont | Approximate Bayesian Computation in Evolution and Ecology[END_REF]). These methods have been widely developed in recent years and used to infer the history of species or population divergence [START_REF] Ross-Ibarra | Patterns of Polymorphism and Demographic History in Natural Populations of Arabidopsis lyrata[END_REF], 2009;[START_REF] Duvaux | Isolation and gene flow: inferring the speciation history of European house mice[END_REF][START_REF] Pettengill | Phylogeography of speciation: allopatric divergence and secondary contact between outcrossing and selfing Clarkia[END_REF][START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF]. ABC methods work as follows: first a set of different models are simulated based on prior distributions of a set of parameters corresponding to the tested model. Typically a set of around one million datasets (i) are computed under each model. Then for each simulation a set of summary statistics S(i) are computed and compared to the observed set of summary statistics (So) based on a distance, such as the Euclidian distance. When the distance is below a tolerance threshold (δ), the parameter value is accepted. Parameter values can then be adjusted by local linear regressions, logistic regressions or neural network layers, giving more weight to the simulated summary statistics that are closest to the observed data and allowing a posterior distribution then to be drawn. Ultimately, model selection is performed based on posterior probabilities and Bayes factor.

Other methods use information from the site frequency spectrum (SFS, the number of derived alleles within a population), which constitutes a full summary of the data and implies no information loss [START_REF] Gutenkunst | Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data[END_REF]. When a pair of populations is compared under a given divergence scenario, then the Joint Site Frequency Spectrum (JSFS) is used. In general, data from an outgroup species is needed to polarize the JSFS (i.e. to determine proportions of shared ancestral alleles versus proportions of shared derived alleles). JSFS can be simulated under a given demographic scenario using diffusion2 approximation to the one-locus, two-allele, Fisher-Wright Model [START_REF] Gutenkunst | Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data[END_REF]. The method of [START_REF] Gutenkunst | Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data[END_REF] is primarily designed to work on few populations (one, two or three) with stable allele frequency changes at each generation and assumes independence of the polymorphisms, otherwise, the likelihood becomes a composite likelihood and bootstrap methods must be used to validate the accuracy of estimates [START_REF] Gutenkunst | Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data[END_REF]. The method has been improved, to compare more complex divergence scenarios, taking into account the heterogeneity of migration rates along the genome and by improving the exploration of the likelihood landscape through the use of simulated annealing methods in addition to the Broyden-Fletcher-Goldfarb-Shanno (BGFS) [START_REF] Tine | European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation[END_REF]. These methods, combined with genomic data, allow exploring the heterogeneity of migration rates along the genome in line with the view of semipermeable barriers to gene flow (Harrison, 1989[START_REF] Wu | The genic view of the process of speciation[END_REF][START_REF] Harrison | The Language of Speciation[END_REF][START_REF] Harrison | Hybridization, Introgression, and the Nature of Species Boundaries[END_REF]) (box 1).

Box 1. The Semi Permeable nature of barrier to gene flow

So far we have focused on Mayr's BSC which is a "whole genome concept". The varying degree of RI observed across the genome has led to the emergence of the genic view of speciation (Fig 4). This view is directly linked to the fact that reproductive barriers are generally semipermeable due to variations in recombination rates and selection (Harrison, 1986). As a consequence the migration rate is heterogeneous across the genome, with some regions of strong differentiation harboring barrier loci and linked neutral alleles for which introgression into the genome of the foreign population is delayed in proportion to their linkage. Revealing which barriers are impermeable to gene flow is best performed in hybrid zones where only loci involved in RI will resist the homogenizing effect of gene flow. One important question to address will be whether the hybrid zone arises from primary differentiation (where two populations diverge in the face of gene flow) or from secondary contact between formerly isolated populations. 37% of the 106 hybrids zones in [START_REF] Barton | Analysis of Hybrid Zones[END_REF] are likely secondary contact zones. More recently the term "genomic island of speciation" was proposed [START_REF] Turner | Genomic Islands of Speciation in Anopheles gambiae[END_REF]) and a theory of divergent hitchhiking during speciation in the face of gene flow was developed [START_REF] Feder | The Efficacy of Divergence Hitchhiking in Generating Genomic Islands During Ecological Speciation[END_REF]Feder et al. 2012c;Nosil & Feder 2012). However, the role of islands of differentiation during divergence is currently debated (see section 3.). Barton and Hewitt (1981). The vertical bars represent a chromosome with 3 loci contributing to reproductive barriers (indicated by *). Immediately after contact, linkage disequilibrium along the chromosome will be high, but over time recombination breaks down associations among loci. Barrier genes or genes under divergent selection will remain differentiated (light grey regions), whereas alleles at loci that are neutral (white regions) will be exchanged between species. Many generations of recombination in hybrid zones allow fine-scale mapping of genes contributing to reproductive isolation and estimates of the strength of selection on individual alleles. (B) The idea that the genomes of diverging lineages become less permeable over time, as shown by [START_REF] Wu | The genic view of the process of speciation[END_REF]. Each pair of horizontal bars represents chromosomes of 2 diverging lineages. Very recently diverged species (pair I) may have few genes contributing to reproductive isolation (indicated by *). These regions will remain differentiated (light and dark grey regions), whereas gene exchanges can occur in other parts of the genome (white regions). With increasing genetic divergence (chromosome pairs II and III), more loci contribute to reproductive barriers, thus restricting gene flow on a larger proportion of the genome (from Harrison & Larsson 2014).

Heterogeneous genomic divergence

Recent genome wide studies have documented heterogeneous genomic divergence, corroborating the idea of barrier permeability. Some studies revealed a few regions of large size [START_REF] Turner | Genomic Islands of Speciation in Anopheles gambiae[END_REF][START_REF] Jones | A genome-wide SNP genotyping array reveals patterns of global and repeated species-pair divergence in sticklebacks[END_REF][START_REF] Via | Localizing FST outliers on a QTL map reveals evidence for large genomic regions of reduced gene exchange during speciation-with-gene-flow[END_REF][START_REF] Martin | Genome-wide evidence for speciation with gene flow in Heliconius butterflies[END_REF] whereas others have identified multiple regions of smaller size spread across the genome [START_REF] Michel | Widespread genomic divergence during sympatric speciation[END_REF][START_REF] Renaut | Genomic islands of divergence are not affected by geography of speciation in sunflowers[END_REF][START_REF] Burri | Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers[END_REF]. These observations have led to the development of a verbal theory of "divergence hitchhiking" facilitating divergence with gene-flow [START_REF] Via | The genetic mosaic suggests a new role for hitchhiking in ecological speciation[END_REF][START_REF] Via | Divergence hitchhiking and the spread of genomic isolation during ecological speciationwith-gene-flow[END_REF]. This theory states that divergent selection and non-random mating will reduce recombination in the face of gene flow and generate large islands of differentiation. The four steps of the model are shown in Fig 5 from (Feder et al. 2012a).

Figure 5: The four potential phases of speciation-with-gene-flow involving differences in the relative importance of DS, DH, and GH. Plots depict the general expected relationship of divergence (FST) for a neutral site at varying recombination rates ranging from r = 0 cM (completely linked) to r = 0.5 (unlinked) to a divergently selected locus, as speciation proceeds through the four phases. Taken from Feder et al. (2012).

However, the formalization of the model and simulation studies have shown that conditions for such genomic islands to be generated were restricted [START_REF] Feder | The Efficacy of Divergence Hitchhiking in Generating Genomic Islands During Ecological Speciation[END_REF]Feder et al. 2012c;[START_REF] Flaxman | Theoretical models of the influence of genomic architecture on the dynamics of speciation[END_REF]. In addition the historical demography has rarely been investigated in empirical studies [START_REF] Harrison | Hybridization, Introgression, and the Nature of Species Boundaries[END_REF]) and one cannot exclude that similar patterns would arise from secondary contacts. Most importantly, the role of genomic islands in speciation has been questioned for several reasons [START_REF] Noor | Islands of speciation or mirages in the desert? Examining the role of restricted recombination in maintaining species[END_REF][START_REF] Turner | Genomic islands of speciation or genomic islands and speciation[END_REF]. First, most empirical studies have relied on relative measures of divergence (mainly F ST ) to identify genomic islands. However F ST is known to be dependent upon both divergence between and variations within populations so that inflated F ST may arise because of reduced nucleotide diversity [START_REF] Charlesworth | Measures of divergence between populations and the effect of forces that reduce variability[END_REF][START_REF] Charlesworth | The Relations Between Recombination Rate and Patterns of Molecular Variation and Evolution in Drosophila[END_REF] or other processes such as reduced recombination rates [START_REF] Noor | Islands of speciation or mirages in the desert? Examining the role of restricted recombination in maintaining species[END_REF]. Accordingly, recent, empirical studies have shown that genomic islands often occur in regions of low recombination in stickleback [START_REF] Roesti | Recombination in the threespine stickleback genome-patterns and consequences[END_REF], sunflower [START_REF] Renaut | Genomic islands of divergence are not affected by geography of speciation in sunflowers[END_REF] and Ficedula flycatchers [START_REF] Burri | Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers[END_REF]. Second, in a recent review [START_REF] Cruickshank | Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow[END_REF] proposed the use of estimators of absolute divergence, such as the D XY , jointly with measures of F ST . The D XY [START_REF] Nei | Mathematical model for studying genetic variation in terms of restriction endonucleases[END_REF] measures the average number of pairwise differences between sequences from two populations but excludes all comparisons between sequences within populations (so it is not affected by current levels of within population diversity). D XY =∑ 𝑥 𝑖 𝑦 𝑗 𝑖𝑗 𝑑 𝑖𝑗 with X and Y the two populations and 𝑑 𝑖𝑗 the number of nucleotide differences between the i th haplotype from X and j th haplotype from Y. If genomic islands are indeed caused by barrier permeability to gene flow, then both F ST and D XY should be higher than the neutral background. Alternatively, if D XY in islands is similar to the measure in neutral regions, then the authors suggest that this is not due to variable levels of ongoing gene flow but rather to variations in local genomic architecture and suggest a model of postspeciation selection at linked sites [START_REF] Cruickshank | Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow[END_REF]. Applying their model to a series of well described organisms i.e. Mus [START_REF] Geraldes | Higher differentiation among subspecies of the house mouse (Mus musculus) in genomic regions with low recombination[END_REF], Ficedula [START_REF] Ellegren | The genomic landscape of species divergence in Ficedula flycatchers[END_REF], Anopheles [START_REF] Turner | Genomic Islands of Speciation in Anopheles gambiae[END_REF] Oryctolagus [START_REF] Carneiro | Speciation in the European Rabbit (oryctolagus Cuniculus): Islands of Differentiation on the X Chromosome and Autosomes[END_REF] and Heliconius [START_REF] Nadeau | Genomic islands of divergence in hybridizing Heliconius butterflies identified by large-scale targeted sequencing[END_REF]) they showed that these species were not experiencing gene flow. As illustrated in Fig 6. this prediction was also recently validated in Ficedula flycatchers [START_REF] Burri | Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers[END_REF].

Studying speciation: selection vs endogenous barriers

With the recent advent of Next Generation Sequencing technologies [START_REF] Mardis | The impact of next-generation sequencing technology on genetics[END_REF]) which allow for genome-wide studies in non-model organisms, several new questions can now be addressed in speciation research, notably the respective roles of ecological divergence and endogenous barriers in genomic divergence. In this section, I will first recall some of the footprints of selection left across the genome and how to detect them with genome scans, and then discuss some current debates in speciation genomics.

Genetic hitchhiking, hard and soft sweeps

When a single new adaptive mutation occurs with a selective advantage (s), its frequency quickly rises until fixation. Individuals carrying the mutation will be favored by selection generating a selective sweep, the socalled "hard" 3 sweep. This allele frequency shift leads to a similar shift in allele frequency of loci in close proximity to the selected locus, a process called genetic hitchhiking [START_REF] Smith | The hitch-hiking effect of a favourable gene[END_REF]. Under a strong selective sweep, a local loss in genetic diversity occurs in the neighborhood of the hitchhiker alleles, so that almost only one haplotype remains. The selective sweep size will be enhanced by selection that will increase the frequency of all alleles in the same neighborhood, but it will be eroded by recombination and this erosion will increase as a function of time [START_REF] Kim | Detecting a Local Signature of Genetic Hitchhiking Along a Recombining Chromosome[END_REF]. Hitchhiking is supposed to be efficient regardless of effective population size [START_REF] Gillespie | Genetic Drift in an Infinite Population: The Pseudohitchhiking Model[END_REF][START_REF] Gillespie | Is the population size of a species relevant to its evolution? Evolution[END_REF]. In the classical model of local hitchhiking, genetic differentiation decreases as a function of the distance from the hitchhiker locus (Fig 7a,b) (Charlesworth et al. 1997a). This corresponds to the case where a mutation appears favorable in its deme but unfavorable in another deme. This model implies a strong genetic differentiation between populations at the (selected) hitchhiker locus and other loci in its neighboring environment. In a second model, a mutation can be favorable globally, that is to say in two structured populations, and its classical signature implies two peaks of differentiation on each side of the selected locus (Fig7 c, d). This model of global hitchhiking in structured populations implies that the intensity of the sweep will be smoother than in the first model (details in [START_REF] Bierne | The Distinctive Footprints of Local Hitchhiking in a Varied Environment and Global Hitchhiking in a Subdivided Population[END_REF]. Such patterns may lead to false inferences of genome scan data in search of footprints of ecological selection, as will be discussed below. 3 There is currently a debate on the role of "hard sweeps" (adaptation from a single beneficial mutation) versus "soft sweeps" (adaptation from multiple beneficial mutation due to mutation or migration) [START_REF] Hermisson | Soft sweeps: molecular population genetics of adaptation from standing genetic variation[END_REF]. Some argue that the probability of a soft sweep to occur is very low [START_REF] Jensen | On the unfounded enthusiasm for soft selective sweeps[END_REF] while others have proposed that adaptation from soft sweeps should be easier [START_REF] Messer | Population genomics of rapid adaptation by soft selective sweeps[END_REF]. Empirical evidence suggests that distinguishing a "soft sweep" from a "hard sweep" is difficult (e.g. [START_REF] Schrider | Soft Shoulders Ahead: Spurious Signatures of Soft and Partial Selective Sweeps Result from Linked Hard Sweeps[END_REF]. In addition, most tools were initially designed to identify "hard sweep" patterns. Investigations on this debate are clearly beyond the scope of this thesis, however one should bear in mind that this is another confounding factor for which further investigations are certainly required in the future. To add to complexity, partial sweeps may also occur: either the beneficial mutation is still increasing in frequency and has not reached fixation (the sweep is "in action") or the initially beneficial sweep started to increase in frequency and then lost its selective advantage so began to drift. A potential alternative scenario to the many partial sweeps inferred from common methods (e.g. iHs) is that they could be false positive located in the "shoulders" [START_REF] Schrider | Soft Shoulders Ahead: Spurious Signatures of Soft and Partial Selective Sweeps Result from Linked Hard Sweeps[END_REF] of a true hard sweep that is fixed (P.A. Gagnaire, personal communication).

Genome scans of adaptation

A series of techniques to detect the classical signature of a hard sweep have been developed. They rely on the SFS [START_REF] Tajima | Statistical method for testing the neutral mutation hypothesis by DNA polymorphism[END_REF][START_REF] Kim | Detecting a Local Signature of Genetic Hitchhiking Along a Recombining Chromosome[END_REF], linkage disequilibrium or most commonly on patterns of decay of homozygosity along a haplotype as a function of recombination (e.g. [START_REF] Sabeti | Detecting recent positive selection in the human genome from haplotype structure[END_REF][START_REF] Voight | A Map of Recent Positive Selection in the Human Genome[END_REF][START_REF] Gautier | rehh : An R package to detect footprints of selection in genome-wide SNP data from haplotype structure[END_REF][START_REF] Ferrer-Admetlla | On Detecting Incomplete Soft or Hard Selective Sweeps Using Haplotype Structure[END_REF]. Another class of methods relies on patterns of genomic differentiation (F ST ) across a large number of markers and search for those with a stronger differentiation ("outliers") as compared to neutral expectations. Such "genome scans" may be useful to find genomic regions contributing to RI between populations. This method was first proposed by [START_REF] Lewontin | Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms[END_REF] (LK test) and tests the heterogeneity of F ST between loci. The central assumption is that if a few markers depart from a neutral distribution obtained under an island model, then these loci may be under disruptive selection. The LK test was criticized since it assumed no influence of the structure between populations on the variance of F ST , and did not take into account the variance in mutation rates or in population size [START_REF] Nei | Letters to the editors: Lewontin-Krakauer test for neutral genes[END_REF][START_REF] Robertson | Letters to the editors: Remarks on the Lewontin-Krakauer test[END_REF][START_REF] Meirmans | The trouble with isolation by distance[END_REF][START_REF] De Mita | Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations[END_REF]. The method was later shown to be robust to different population structures and was then implemented under the infinite island model assuming equal sizes and equal migration rates between islands [START_REF] Beaumont | Evaluating Loci for Use in the Genetic Analysis of Population Structure[END_REF]. [START_REF] Vitalis | Interpretation of Variation Across Marker Loci as Evidence of Selection[END_REF] then developed a method assuming an instantaneous split between population pairs of constant size without gene flow. Major improvement were then made by [START_REF] Foll | A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective[END_REF] who implemented a Bayesian approach to solve the among deme allele frequencies correlation problem. More specifically, assuming an island model, subpopulation allele frequencies can be correlated through a common migrant gene pool (common ancestor), from which they will differ through the use of a multinomial Dirichlet distribution. Other methods were then developed to take into account more complex demographic scenarios or the hierarchical structure occurring among populations [START_REF] Bonhomme | Detecting Selection in Population Trees: The Lewontin and Krakauer Test Extended[END_REF][START_REF] Günther | Robust Identification of Local Adaptation from Allele Frequencies[END_REF][START_REF] Vitalis | Detecting and measuring selection from gene frequency data[END_REF]. Another class of methods aiming at detecting selection using correlations of ecological/environmental data with genetic datasets are also increasingly developed [START_REF] Coop | Using environmental correlations to identify loci underlying local adaptation[END_REF][START_REF] Frichot | Testing for associations between loci and environmental gradients using latent factor mixed models[END_REF][START_REF] Duforet-Frebourg | Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data[END_REF].The two last methods are based on principal components or 'latent factors' to correct for population structure.

Genome scans generally display high rates of false positives due to several factors (reviewed in [START_REF] Bierne | The coupling hypothesis: why genome scans may fail to map local adaptation genes[END_REF]. First, populations are often spatially structured, and not correcting for the higher variance in F ST is known to lead to false inferences [START_REF] Robertson | Letters to the editors: Remarks on the Lewontin-Krakauer test[END_REF][START_REF] Bonhomme | Detecting Selection in Population Trees: The Lewontin and Krakauer Test Extended[END_REF]. [START_REF] Bonhomme | Detecting Selection in Population Trees: The Lewontin and Krakauer Test Extended[END_REF] introduced an extension of the LK test that corrects for genealogical relationship in structured populations. Similarly [START_REF] Günther | Robust Identification of Local Adaptation from Allele Frequencies[END_REF] standardized the F ST by the covariance among populations. A recent review has shown that these two methods were the less biased under a pattern of isolation by distance or range expansion, whereas methods such as Bayescan [START_REF] Foll | A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective[END_REF]) and FDIST2 [START_REF] Beaumont | Evaluating Loci for Use in the Genetic Analysis of Population Structure[END_REF] displayed higher rates of false positives [START_REF] Lotterhos | Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests[END_REF]. Indeed, a second potential great concern for the focal species of this thesis is the high rate of false positives that occurs in fractal networks such as hydrographic network. A high rate of outliers is generally observed in these systems, where departures from the one or two dimensional stepping stone model can be very strong [START_REF] Fourcade | Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection[END_REF]. Fragmented populations in these landscapes can show strong departures from demographic equilibrium, with founding events and fixations events (e.g. [START_REF] Perrier | Parallel and nonparallel genome-wide divergence among replicate population pairs of freshwater and anadromous Atlantic salmon[END_REF] potentially leading to false inferences. A third potential bias is that of allele surfing, which is expected during population expansion, when a few founder individuals colonize a new habitat. A neutral mutation arising at the front could quickly spread in frequency, generating a pattern similar to that observed under hitchhiking [START_REF] Klopfstein | The Fate of Mutations Surfing on the Wave of a Range Expansion[END_REF]. Fourth, background selection against deleterious mutations can reduce effective population size in subpopulations hence contribute to increase genetic differentiation (Charlesworth et al. 1997a). Ultimately, the coupling of endogenous barriers (see 1.2.3) [START_REF] Bierne | The coupling hypothesis: why genome scans may fail to map local adaptation genes[END_REF] creates tension zones expected to coincide with exogenous barriers through time and to be trapped at environmental boundaries [START_REF] Barton | Analysis of Hybrid Zones[END_REF] forming Genetic Environmental Association (GEA). These

GEAs may be easily and spuriously detected by genome scans.

Overall, to circumvent these problems, one possible solution is to jointly infer demography and selection [START_REF] Li | Joint analysis of demography and selection in population genetics: where do we stand and where could we go[END_REF][START_REF] Singh | Inferences of demography and selection in an African population of Drosophila melanogaster[END_REF][START_REF] Bank | Thinking too positive? Revisiting current methods of population-genetic selection inference[END_REF]. Inferring a neutral demographic model allows drawing subsequent inferences about the proportion of loci departing from the neutral envelope of that model (cf Chapter 4). In addition, comparisons between replicate pairs of populations should be performed to test the extent of parallelism (cf 3.4) and verify the robustness of those inferences. Ideally, inferences should be performed in hybrid zones where only reproductive barriers will resist the homogenizing effects of gene flow. Hybrid zones best highlight which genomic regions are affected by selection and which are affected by neutral processes. This is very important in species with small effective population sizes or species in fragmented habitats where founder events can play a key role in the spatial distribution of genetic diversity. Finally, several of the restrictions made above may apply to our focal species. In particular, the distribution of brook lampreys along fragmented habitats, together with the possibility of founder events may have profound impacts on genome scans. Thus, studying river and brook lampreys in sympatry in "hybrid zones" may be a better setting to perform adequate genome scans.

Polygenic adaptation

Most common traits in humans (and other model species) are known to have a polygenic basis [START_REF] Hindorff | Potential etiologic and functional implications of genome-wide association loci for human diseases and traits[END_REF][START_REF] Yang | Common SNPs explain a large proportion of the heritability for human height[END_REF]. The response to selection at any locus is usually modest, displaying subtle and coordinated allele frequency changes going in the same direction (Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait land trait in a subdivided population under selection[END_REF], 2012;Kremer & Le Corre 2012). Such a pattern is not restricted to man but is also becoming increasingly recognized in other organisms (eg. Arabidopsis thaliana [START_REF] Atwell | Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines[END_REF])) and Pritchard & Di Rienzo (2010) suggested that this may be the case for many traits. Footprints of polygenic adaptation at the level of individual loci is likely to go undetected without a great amount of annotation of the sites that are the target of selection (Pritchard et al. 2010). In these cases, the traditional sweep framework and genome scan methods relying on F ST are likely to be inadequate (Le Corre & Kremer 2012). These authors showed first that when neutral differentiation is already high, local adaptation does not necessarily further increase differentiation and thus adaptation may be undetected. Second, when increased covariance of allelic effects (under the infinitesimal model) is involved to reach adaptation, then genome scans will be inefficient. This is a possible scenario under high gene flow and recent selection involving a high number of loci. Le Corre & Kremer (2012) proposed to shift to a quantitative framework, combining Q ST -F ST analyses, together with the integration of the covariance of allele frequencies and the covariance of allelic effects.

Another interesting approach was that of [START_REF] Renaut | SNP signatures of selection on standing genetic variation and their association with adaptive phenotypes along gradients of ecological speciation in lake whitefish species pairs (Coregonus spp.)[END_REF]

Insight from studies of parallel adaptation and parallel speciation

A promising way to understand the origin of species and their historical mode of divergence is to study independent replicate pairs of populations. The independent evolution of the same phenotypic trait in independent populations is called parallel evolution and it is suggested that this is "strongly due to the action of natural selection" because genetic drift is unlikely to result in such concerted patterns in different places [START_REF] Schluter | Parallel Speciation by Natural Selection[END_REF][START_REF] Johannesson | Parallel speciation: a key to sympatric divergence[END_REF]. A classical scenario of parallel evolution at the phenotypic level is provided in Figure 8. When a trait that induces RI evolves independently in different populations, [START_REF] Schluter | Parallel Speciation by Natural Selection[END_REF] proposed that it shows a case of parallel speciation. In general, the best examples demonstrated a strong role for size assortative mating. The best studied systems of parallel speciation are Gasterosteus aculeatus [START_REF] Rundle | Natural Selection and Parallel Speciation in Sympatric Sticklebacks[END_REF], Tinema [START_REF] Nosil | Host-plant adaptation drives the parallel evolution of reproductive isolation[END_REF], Litorina (Butlin et al. 2014a) Coregonus clupeaformis [START_REF] Bernatchez | On the origin of species: insights from the ecological genomics of lake whitefish[END_REF] and cichlid fishes complexes [START_REF] Elmer | Parallel evolution of Nicaraguan crater lake cichlid fishes via non-parallel routes[END_REF]). However, parallel divergence at the phenotypic level may have little to do with genetic parallelism [START_REF] Elmer | Adaptation in the age of ecological genomics: insights from parallelism and convergence[END_REF]. The examples of parallel evolution at the molecular level involve parallel adaptation, but not necessarily parallel speciation. These include the repeated evolution of resistance to insecticides within insect species [START_REF] Ffrench-Constant | Cyclodiene insecticide resistance: from molecular to population genetics[END_REF], the resistance of malaria to antimalarial drugs [START_REF] Pearce | Multiple Origins and Regional Dispersal of Resistant dhps in African Plasmodium falciparum Malaria[END_REF]) and the evolution of pigmentation within vertebrate species such as cavefish [START_REF] Gross | A Novel Role for Mc1r in the Parallel Evolution of Depigmentation in Independent Populations of the Cavefish Astyanax mexicanus[END_REF] or mice [START_REF] Hoekstra | A Single Amino Acid Mutation Contributes to Adaptive Beach Mouse Color Pattern[END_REF][START_REF] Steiner | Adaptive Variation in Beach Mice Produced by Two Interacting Pigmentation Genes[END_REF].

Each of these examples involved independent mutations that have led to the same or functionally equivalent adaptive phenotypes [START_REF] Ralph | Parallel Adaptation: One or Many Waves of Advance of an Advantageous Allele[END_REF]). More generally, there are three genetic sources of adaptation to explain parallel phenotypic divergence. Either a mutation occurs de novo (as in the evolution of malaria resistance), or mutations can segregate into the standing variation (as in the evolution of pigmentation in mice [START_REF] Steiner | Adaptive Variation in Beach Mice Produced by Two Interacting Pigmentation Genes[END_REF]). Alternatively, variation can arise through gene flow, either by a connecting population, i.e. the transporter hypothesis Schluter & Conte (2009) [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF] or by interspecific hybridization (e.g. [START_REF] Abbott | Hybridization and speciation[END_REF]. Given the current popularity of the ecological speciation framework, it is important to distinguish the sources of adaptation. For instance, the origin of adaptation in marine-freshwater populations of Gasterosteus aculeatus is generally attributed to recent (<10 000 years old) and independent ecological adaptation of freshwater populations from the marine population due to standing variation, but this is still unclear. Below I present this system and associated literature in more detail, as it is conceptually similar to the lamprey species pair system studied in this thesis.

In stickleback, completely plated marine populations evolved repeatedly towards low-plated freshwater populations, an often cited example of parallel and independent evolution (Colossima et al. 2005). The lateral plate formation is mainly controlled by one major gene with pleiotropic effects:

Ectodysplasin (Eda) [START_REF] Colosimo | Widespread Parallel Evolution in Sticklebacks by Repeated Fixation of Ectodysplasin Alleles[END_REF]. Recently [START_REF] Roesti | The genomic signature of parallel adaptation from shared genetic variation[END_REF] (3) Freshwater alleles arose by recurrent mutations in the marine habitat. These alleles, which may be identical by descent, are then introduced to the novel freshwater habitats. (4) Freshwater alleles were maintained in a refugium and introduced into novel freshwater habitats via the marine habitat. Although both ( 3) and ( 4) involve adaptation from standing variation in the marine habitat, the source of that variation is fundamentally different in the two cases.

From Welsh & Jiggins 2014.

The first scenario corresponds to parallel adaptation from independent de novo mutations, a scenario that is rather unlikely in that case. The second scenario is secondary contact involving segregation of freshwater alleles in multiple refugia. The authors proposed that their data do not fit this scenario because the peak-valley-peak between freshwater populations was unlikely. The third and fourth scenarios are adaptations from shared variation and are the most difficult to discriminate; they involved the transport of freshwater allele at very low frequency in the marine habitat to colonize other freshwater habitats. They differed in that they imply recurrent mutations (third scenario) (i.e. adaptation from standing variation) or recurrent migration (fourth scenario) requiring a population preadapted to freshwater (transporter hypothesis). In this last scenario adaptive alleles can be very old, whereas this is not the case in the third scenario. Finally [START_REF] Roesti | The genomic signature of parallel adaptation from shared genetic variation[END_REF] suggested that their data matched a scenario of divergence with gene flow that would be similar to the fourth scenario [START_REF] Welch | Standing and flowing: the complex origins of adaptive variation[END_REF]). However, since scenario 4 can involve secondary contacts, the question of primary versus secondary differentiation remains the same. Finally, one should bear in mind that many populations remain polymorphic for lateral plate number [START_REF] Mccairns | Landscape genetic analyses reveal cryptic population structure and putative selection gradients in a large-scale estuarine environment[END_REF]2012;Berner et al. 2012). In addition, there are also anadromous populations that may significantly contribute to gene flow between marine and freshwater populations [START_REF] Raeymaekers | A gene with major phenotypic effects as a target for selection vs. homogenizing gene flow[END_REF] Bernatchez 2008). In their study, [START_REF] Raeymaekers | A gene with major phenotypic effects as a target for selection vs. homogenizing gene flow[END_REF] investigated the effect of gene flow and selection on the dynamics of the Eda locus in natural populations that were polymorphic for lateral plate numbers. Their analysis clearly showed a lack of correlation between differentiation at Eda and habitat characteristics. They also performed a meta-analysis and showed that signatures of selection at Eda were rather weak compared to widely cited studies. Their results further suggested that introgression between marine and freshwater populations is possible (corresponding to the transporter hypothesis). Overall, their study casts some doubts onto the role of gene flow under "ecological" selection in maintaining local adaptation of populations. A more recent study of stickleback inhabiting different environments performed one of the first demographic analyses using genome wide data [START_REF] Ferchaud | The impact of selection, gene flow and demographic history on heterogeneous genomic divergence: threespine sticklebacks in divergent environments[END_REF]. The authors showed that most freshwater populations have undergone bottlenecks, a result that may impact genome wide patterns of divergence and seems to have been largely overlooked so far. Finally, it is worth pointing out that sympatric species pairs of benthic-limnetic stickleback probably evolved from a double colonization event from marine stickleback (i.e. lakes were colonized twice) resulting in the speciation of the benthic morph and then the limnetic ecotype following the second colonization event [START_REF] Schluter | Ecological Character Displacement and Speciation in Sticklebacks[END_REF]. This period of isolation may thus explain some of the divergence between ecotypes and is undoubtedly a confounding factor in studies related to the ecological process of speciation.

In the case of Littorina periwinkle ecotypes coexisting in adjacent coastal habitats, alternative models of divergence have been tested recently (Butlin et al. 2014a) and the best supported model was that of parallel divergence in the face of continuous gene flow in different locations. Alternative scenarios are possible because the authors used mainly neutral markers that are expected to converge to the same equilibrium after secondary contact to that observed under primary differentiation (see Chapter 3 and details in Bierne et al. 2013). In addition, excluding long distance gene flow remains difficult. Further investigations yielded puzzling results since levels of outlier sharing was very low at either a large or small geographical scale [START_REF] Westram | Do the same genes underlie parallel phenotypic divergence in different Littorina saxatilis populations?[END_REF][START_REF] Ravinet | Shared and nonshared genomic divergence in parallel ecotypes of Littorina saxatilis at a local scale[END_REF]. These results would suggest the independent evolution of the populations through de novo mutations rather than from standing variation. In the Coregonus whitefish limnetic-benthic pairs, it was hypothesized that divergence was initiated in allopatry and that the two glacial lineages then came into secondary contact in different lakes [START_REF] Bernatchez | Genetic Evidence for Reproductive Isolation and Multiple Origins of Sympatric Trophic Ecotypes of Whitefish (Coregonus)[END_REF][START_REF] Bernatchez | On the origin of species: insights from the ecological genomics of lake whitefish[END_REF]. The divergence between ecotypes probably involved a polygenic basis [START_REF] Bernatchez | On the origin of species: insights from the ecological genomics of lake whitefish[END_REF]Gagnaire et al. 2013a,b). Despite the common history of divergence, only partial parallelism was found with genome scans (Gagnaire et al. 2013a). This result could be explained by the independent erosion of the weakest barriers to gene flow in different lakes. A polygenic basis was also found recently by linkage mapping of traits involved in body shape [START_REF] Laporte | RAD-QTL Mapping Reveals Both Genome-Level Parallelism and Different Genetic Architecture Underlying the Evolution of Body Shape in Lake Whitefish (Coregonus clupeaformis) Species Pairs[END_REF].

5 Lampreys as a model of speciation research

General presentation

Lampreys (Petromyzontiformes) form, together with hagfishes, the cyclostomes that are the only surviving representatives of jawless vertebrates (agnathans) (Hardisty & Potter, 1971). They have diverged from the gnathosthome (jawed vertebrate) lineage more than 500 milions years ago [START_REF] Kuraku | Time Scale for Cyclostome Evolution Inferred with a Phylogenetic Diagnosis of Hagfish and Lamprey cDNA Sequences[END_REF][START_REF] Hedges | Tree of Life Reveals Clock-Like Speciation and Diversification[END_REF] and represent the most basal position in the vertebrate lineage. This position explains why lampreys are often used to understand the origin and evolution of vertebrate genomes and body plans (reviewed in [START_REF] Shimeld | Evolutionary crossroads in developmental biology: cyclostomes (lamprey and hagfish)[END_REF][START_REF] Green | The lamprey: a jawless vertebrate model system for examining origin of the neural crest and other vertebrate traits[END_REF]. However, lampreys are not an easy study system because they are difficult to maintain in experimental settings and larvae have a long larval period (> 4 years) and are very difficult to rear [START_REF] Kuratani | Lamprey as an evo-devo model: lessons from comparative embryology and molecular phylogenetics[END_REF]. These limitations may explain why so little is known about the evolutionary relationships among lamprey species and their underlying mechanisms of divergence.

42 species separated into three families are currently recognized in lampreys. The family Petromyzontidae lives in the Nothern Hemisphere and the Geotriidae and Mordaciidae are found in the Southern Hemisphere. 18 lamprey species display a parasitic (mostly hematophagous) lifestyle. Among them nine are anadromous and the others remain entirely freshwater resident (e.g. the three

Ichthyomyzon species, three Entosphenus species, Tetrapleurodon spadiceus, Eudontomyzon danfordi). The 24 remaining species display a nonparasitic lifestyle, do not feed at the adult stage and are strictly freshwater resident [START_REF] Potter | The Biology of Lampreys[END_REF][START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. Most lampreys (seven out of ten genera) occur as "paired" species [START_REF] Zanandrea | Speciation among Lampreys[END_REF][START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF] with one species displaying a parasitic strategy and the other, closely related, and supposedly derived from the parasitic one, being nonparasitic. These paired species therefore share some similarities with the anadromous/marine and freshwater resident stickleback

Gasterosteus aculeatus, a classical model of speciation research and thus make lampreys a potentially interesting model to study the processes of speciation. In Western Europe, lampreys are represented by the parasitic and anadromous sea lamprey Petromyzon marinus, the parasitic and anadromous river lamprey Lampetra fluviatilis and its paired species, the brook lamprey Lampetra planeri which is non parasitic and freshwater resident (Fig 10 and Fig 11). The two latter have split from the sea lamprey around 10-30 Mya ago [START_REF] Docker | Phylogeny of the lamprey genus Lampetra inferred from mitochondrial cytochrome b and ND3 gene sequences[END_REF][START_REF] Kuraku | Time Scale for Cyclostome Evolution Inferred with a Phylogenetic Diagnosis of Hagfish and Lamprey cDNA Sequences[END_REF]. These three species are widespread in Western Europe, where the brook lamprey can be found all along the river networks whereas the river lamprey is often restricted to downstream areas due to anthropogenic barriers to upstream migration (dams, weirs…). The sea lamprey is also present in North America, especially in the Great Lakes where it has become invasive. Both Lampetra species reproduce in freshwater and can be found in areas of sympatry where they sometimes spawn together [START_REF] Huggins | Communal spawning of brook and river lampreys, Lampetra planeri Bloch and Lampetra fluviatilis L[END_REF][START_REF] Lasne | Communal spawning of brook and river lampreys (Lampetra planeri and L. fluviatilis) is common in the Oir River (France)[END_REF]. The larvae (called ammocoetes) then spend 3 to 7 years buried in river bed sediments (Fig. 11) [START_REF] Maitland | Review of the Ecology of Lampreys in Northern Europe[END_REF]. At this stage, larvae of both taxa are morphologically indistinguishable. It is only after metamorphosis that both species can be morphologically distinguished. They can be best recognized at the adult stage due to an important size difference: L. planeri measures 10-15 cm while L. fluviatilis adults are about 25-30 cm long. This size difference is found in all paired species, since parasitic lampreys feed during the adult stage and thus display a larger body size than the non parasitic taxa which stop feeding after metamorphosis- [START_REF] Potter | The Biology of Lampreys[END_REF][START_REF] Vladykov | Satellite species among the holarctic lampreys (Petromyzonidae)[END_REF][START_REF] Potter | Ecology of Larval and Metamorphosing Lampreys[END_REF]. During the larval period, it is likely that ammocoetes move passively downstream [START_REF] Maitland | Review of the Ecology of Lampreys in Northern Europe[END_REF], especially during important flood events. Studies in Petromyzon marinus have shown that age-0 siblings tended to be found up to 0.9 km from each other 3 months after emergence and age-1 larvae had dispersed more than 1 km downstream (Derosier et al. 207 in Dawson et al. 2015). Rapid downstream disperal is also suggested in pouched lamprey Geotria australis larvae (Kelso and Todd 1993 in [START_REF] Dawson | The ecology of larval and metamorphosing lampreys[END_REF]. Some studies suggest that larval lampreys are also capable of short upstream movement but,

given the relatively poor swimming capacity of larvae, upstream movements are more limited [START_REF] Dawson | The ecology of larval and metamorphosing lampreys[END_REF]. The metamorphosis occurs from September to November when ammocoetes reach the macrophthalmia stage and develop functional eyes and an oral disc which in the parasitic L. fluviatilis has sharpened teeth for feeding while these teeth are blunt in brook lampreys [START_REF] Hardisty | The Life History and Growth of the Brook Lamprey (Lampetra planeri)[END_REF][START_REF] Maitland | Review of the Ecology of Lampreys in Northern Europe[END_REF][START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]). During metamorphosis, brook lampreys begin sexual maturation, with females developing oocytes (Huggins & Thompson 1970, Rougemont, personal observation); they reproduce in the following months and subsequently die. Upon metamorphosis, river lampreys migrate to sea where they become parasitic for one or two years [START_REF] Taverny | Les lamproies en Europe de l'Ouest: écophases, espèces et habitats[END_REF]. The transformation of river lampreys when going from freshwater to seawater is quite similar to the smoltification process in anadromous salmonids (the suite of morphological and physiological changes involved with adaptation to sea-water) [START_REF] Reis-Santos | Ionoregulatory changes during metamorphosis and salinity exposure of juvenile sea lamprey (Petromyzon marinus L.)[END_REF][START_REF] Stefansson | Smoltification[END_REF]. They have different host species including herring, sea trout, twaite and Allis shad, smelt and sprat [START_REF] Taverny | Les lamproies en Europe de l'Ouest: écophases, espèces et habitats[END_REF]. Anadromous lampreys then migrate back into rivers for spawning but they may not reproduce in their natal river: there is probably no 'homing' [START_REF] Maitland | Review of the Ecology of Lampreys in Northern Europe[END_REF][START_REF] Waldman | Sea lamprey Petromyzon marinus: an exception to the rule of homing in anadromous fishes[END_REF]. In Petromyzon marinus, the 'choice' of a river for spawning could be linked to the presence of conspecifics as migrating adults may follow hormones (pheromones) released by ammocoetes in rivers ( [START_REF] Sorensen | Mixture of new sulfated steroids functions as a migratory pheromone in the sea lamprey[END_REF][START_REF] Johnson | A synthesized pheromone induces upstream movement in female sea lamprey and summons them into traps[END_REF]. This pheromone-induced behavior seems to occur also in L.

fluviatilis (Gaudron & Lucas, 2006). This behavior can explain why the 'homing' is supposed to be modest in lampreys [START_REF] Waldman | Sea lamprey Petromyzon marinus: an exception to the rule of homing in anadromous fishes[END_REF], hence potentially resulting in high levels of gene flow among rivers (Spice et al. 2012;[START_REF] Hess | Population genomics of Pacific lamprey: adaptive variation in a highly dispersive species[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF].

5.2

Lamprey paired species: one or two species?

The taxonomic status of paired species is often controversial. Some pairs represent two good species and others different ecotypes from a single species with various degrees of divergence (reviewed in [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. Most paired species are phylogenetically closely related (Fig. 12) and it is usually assumed that nonparasitic species derived from their parasitic counterparts [START_REF] Zanandrea | Speciation among Lampreys[END_REF][START_REF] Vladykov | Satellite species among the holarctic lampreys (Petromyzonidae)[END_REF][START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]).

The taxonomy of lampreys is generally based on morphological criteria such as body proportions, dentition and the number of trunk myomeres [START_REF] Potter | The Biology of Lampreys[END_REF][START_REF] Vladykov | Satellite species among the holarctic lampreys (Petromyzonidae)[END_REF][START_REF] Potter | Ecology of Larval and Metamorphosing Lampreys[END_REF], which have remained contentious so far (Potter et al. 2015. Furthermore, apart from differences in adult body size, the closely related paired species possess few diagnostic morphological differences. Other characters (e.g., the presence of chloride cells in the gills, a key element for osmoregulation in marine water of adult parasitic lamprey) may also fail to distinguish among recently diverged paired species. The nonparasitic Lampetra planeri and L. richardsoni, for example, still develop chloride cells following metamorphosis, despite residency entirely in fresh water [START_REF] Bartels | Changes in the cellular composition of the gill epithelium during the life cycle of a nonparasitic lamprey: functional and evolutionary implications[END_REF][START_REF] Bartels | Variations in the presence of chloride cells in the gills of lampreys (Petromyzontiformes) and their evolutionary implications[END_REF]. The presence of such cells together with the low differentiation of the dentition in L. planeri and L. richardsoni from their respective ancestors L.

fluviatilis and L. ayresii was interpreted as evidence of a very recent divergence [START_REF] Bartels | Changes in the cellular composition of the gill epithelium during the life cycle of a nonparasitic lamprey: functional and evolutionary implications[END_REF][START_REF] Bartels | Variations in the presence of chloride cells in the gills of lampreys (Petromyzontiformes) and their evolutionary implications[END_REF].

However, chloride cells are also present in Lethenteron appendix, which is distinctly allopatric from its parasitic ancestor Lethenteron camtschaticum; this species pair display fixed difference in their mtDNA [START_REF] Docker | Phylogeny of the lamprey genus Lampetra inferred from mitochondrial cytochrome b and ND3 gene sequences[END_REF]) suggesting that divergence is not as recent as with the above two species pairs. To date, the only nonparasitic species in which chloride cells are absent is a very old species [START_REF] Bartels | Variations in the presence of chloride cells in the gills of lampreys (Petromyzontiformes) and their evolutionary implications[END_REF]. Chloride cells seem to take a long time for freshwater resident lamprey to lose so that their retention is not a good evidence of a very recent divergence. These results clearly highlight the high uncertainty associated with the use of morphological criteria to determine the evolutionary relationships between paired species. Using molecular data this difficulty remains and is summarized in Fig 12 showing

that the phylogeny in some species pairs is not well resolved [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. Current barcoding analyses have also not provided enough resolution to discriminate paired species [START_REF] April | Genetic calibration of species diversity among North America's freshwater fishes[END_REF][START_REF] Knebelsberger | Molecular Diversity of Germany's Freshwater Fishes and Lampreys assessed by DNA barcoding[END_REF]. For instance, 13 out of the 27 North American lamprey species could not be distinguished using DNA barcoding. The 13 species were separated into 5 groups, all containing at least one parasitic and one non-parasitic taxon [START_REF] April | Genetic calibration of species diversity among North America's freshwater fishes[END_REF]. This is particularly the case between Lethenteron camtschaticum and L. kessleri where few differences were found based on mtDNA analysis [START_REF] Yamazaki | Taxonomy and molecular phylogeny of Lethenteron lampreys in eastern Eurasia[END_REF][START_REF] Okada | Repetitive sequences in the lamprey mitochondrial DNA control region and speciation of Lethenteron[END_REF]. Similarly, mtDNA, RFLP and microsatellite analyses of the parasitic silver lamprey (Ichthyomyzon unicuspis) and nonparasitic northern brook lamprey (I. fossor) indicated two mtDNA lineages that were shared between the two species as well as strong levels of gene flow in areas of sympatry [START_REF] Docker | Contemporary gene flow between "paired" silver (Ichthyomyzon unicuspis) and northern brook (I. fossor) lampreys: implications for conservation[END_REF]. Some nonparasitic species, however, appear to have diverged from a parasitic ancestor a long time ago. One such example is the nonparasitic Lampetra aepyptera, which has been separated from its (now extinct) ancestor for at least 2 million years [START_REF] Docker | Phylogeny of the lamprey genus Lampetra inferred from mitochondrial cytochrome b and ND3 gene sequences[END_REF][START_REF] Bartels | Variations in the presence of chloride cells in the gills of lampreys (Petromyzontiformes) and their evolutionary implications[END_REF]. Lampetra aepyptera is not paired with any parasitic species and is morphologically and genetically distinct. The divergence between the European river lamprey and brook lamprey may have occurred since the last glacial retreat 10 000 years ago [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Blank | A mitochondrial multigene approach contributing to the systematics of the brook and river lampreys and the phylogenetic position of Eudontomyzon mariae[END_REF]. A first study found a low level of divergence between both species based on allozymes [START_REF] Schreiber | Population genetics of a cyclostome species pair, river lamprey (Lampetra fluviatilis L.) and brook lamprey (Lampetra planeri Bloch)[END_REF]. mtDNA analysis revealed the existence of three distinct lineages: one was restricted to the Iberian Peninsula and the two other lineages were widespread among the studied populations [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF]). In addition, the Iberian Peninsula displayed more genetic diversity than other populations suggesting ancient glacial refugia. More recently, further investigations in the Iberian Peninsula performed by [START_REF] Mateus | MtDNA markers reveal the existence of allopatric evolutionary lineages in the threatened lampreys Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) in the Iberian glacial refugium[END_REF] revealed several differentiated clades of L. planeri but did not allow drawing further inferences about the taxonomic relationships between L. fluviatilis and L. planeri. A recent investigation using microsatellite markers found that contemporary gene flow was ongoing between landlocked L. fluviatilis and L. planeri in areas of sympatry around Loch Lomond in Scotland [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF]. The first genome-wide study on L.

planeri and L. fluviatilis found a strong divergence between both species (F ST = 0.37) based on RADsequencing data from a single putatively sympatric pair located at the southern limit of the L. fluviatilis range [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF]. The authors found 166 fixed markers between species and subsequently identified a list of genes putatively involved in their divergence. They also concluded that they provided "the first genetic evidence for the taxonomic validity of the two European lamprey species L. planeri and L.

fluviatilis". Unfortunately, there are several shortcomings in this study. First, it is based on a single population pair (n = 37 individuals) located at the southern limit of the species' range, which prevents any generalization of results at a wider scale. Second, L. fluviatilis has almost disappeared in this area and has probably a very low population size [START_REF] Mateus | REVIEW Lampreys of the Iberian Peninsula: distribution, population status and conservation[END_REF], which can strongly alter the genetic diversity due to genetic drift. In addition, given the temporal heterogeneity of the sampling and the large size of the Sorraria watershed, it is probable that individuals were sampled in different (potentially disconnected) sections of the river since in such river systems, anadromous lampreys are typically captured by nets in estuarine areas, while resident individuals are more likely to be found in upstream sections of smaller size.

We will see in chapters 2 and 4 that the spatial design of sampling has strong implications on downstream conclusions about levels of gene flow between L. planeri and L. fluviatilis. Overall, all the studies performed so far on the genetic divergence between river and brook lampreys suffered from limitations in the sampling design (no replicate pairs in sympatry) and no study investigated the demographic history of population pairs in this system, which is extremely important to understand the processes underlying the genomic divergence (see section 2 above).

Level of reproductive isolation between L. fluviatilis & L. planeri inferred from experimental approaches

Investigations about the level of pre-and post-zygotic isolation and overall strength of reproductive barriers between lamprey paired species have been limited due to the difficulty (if not impossibility) of rearing larvae for more than a few months. The most important isolating barrier that is widely recognized is the size difference between parasitic and nonparasitic taxa [START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF]. [START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF] suggested complete size assortative mating when size difference was higher than 25% between North American river and brook lampreys, L. ayresii and L. richardsoni. However, size assortative mating is an exogenous prezygotic barrier, which does not necessarily imply that genetic incompatibilities (e.g. DMI)

will accumulate if the two species co-occur in sympatry. In particular, during breeding of European river and brook lampreys, intraspecific communal spawning is frequently observed (Huggins & Thompson, 1971[START_REF] Lasne | Communal spawning of brook and river lampreys (Lampetra planeri and L. fluviatilis) is common in the Oir River (France)[END_REF]). However, this communal spawning can also be interspecific [START_REF] Huggins | Communal spawning of brook and river lampreys, Lampetra planeri Bloch and Lampetra fluviatilis L[END_REF][START_REF] Lasne | Communal spawning of brook and river lampreys (Lampetra planeri and L. fluviatilis) is common in the Oir River (France)[END_REF]. In this situation, sneaker4 males of brook lamprey have been observed (Hume et al. 2013) and this sneaking strategy may generate some gene flow that will break up any association between assortative mating traits and other traits that could putatively play a role in RI. The few experimental measurements made so far have shown that it was possible to artificially cross the two species and to obtain viable hybrid larvae (Hume et al. 2013). In their study however, Hume et al. (2013) did not separate the effect of mortality before or after fertilization of the eggs.

Finally, lamprey paired species share many similarities with other pairs of diadromous species, especially, the marine-freshwater stickleback system. Like the stickleback, lampreys were proposed a few years ago as a model to study the process of "ecological speciation" [START_REF] Salewski | Satellite species in lampreys: a worldwide trend for ecological speciation in sympatry[END_REF]. While my thesis was originally embedded within this framework, which remains contentious and largely lacks theoretical support, I decided to investigate additional scenarios of divergence. An integrative approach combining experiments, population genetics, genomics, linkage mapping and simulations was developed as a starting point that may improve our understanding of how speciation operates in this system.

Possible threats on fragmented populations

L. planeri, in opposition to L. fluviatilis, is widespread in many rivers that are fragmented by anthropogenic barriers to migration (dam, weirs…). Approximately 1 million dams are fragmenting riverine habitats across the world [START_REF] Nilsson | Fragmentation and Flow Regulation of the World's Large River Systems[END_REF]. Habitat fragmentation divides the populations in smaller subsets and may also cause significant losses to the spawning and nursery habitat of both brook and river lampreys (Renaud, 1997). For instance, in the Iberian Peninsula, 80% of P. marinus and L. fluviatilis spawning habitat is now unreachable due to the construction of dams in the lower part of rivers [START_REF] Mateus | REVIEW Lampreys of the Iberian Peninsula: distribution, population status and conservation[END_REF]. Since the brook lamprey is supposed to display a reduced migratory behavior at the adult stage [START_REF] Malmqvist | The spawning migration of the brook lamprey, Lampetra planeri Bloch, in a South Swedish stream[END_REF][START_REF] Mateus | MtDNA markers reveal the existence of allopatric evolutionary lineages in the threatened lampreys Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) in the Iberian glacial refugium[END_REF], populations can be highly isolated in upstream reaches and be subject to founder events and losses of diversity through genetic drift [START_REF] Brook | Contribution of inbreeding to extinction risk in threatened species[END_REF][START_REF] Perrier | Parallel and nonparallel genome-wide divergence among replicate population pairs of freshwater and anadromous Atlantic salmon[END_REF]. Recent investigations by [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF] suggest that anthropogenic factors may play a role in reducing the genetic diversity of L. planeri populations in the United Kingdom, but further analyses are required to quantify the effect of fragmentation on the distribution of diversity within and among populations. Understanding this effect may also be very useful for the conservation of both Lampetra species. Other factors such as pollution, over-exploitation, and habitat degradation (Dudgeon et al. 2006) are among the major threats that man imposed on biodiversity and this includes lampreys. Indeed, their long larval phase makes lampreys particularly vulnerable to pollution events [START_REF] Moyle | Complex Epistasis for Dobzhansky-Muller Hybrid Incompatibility in Solanum[END_REF]. It is even possible that some populations have been extirpated because of strong pollution [START_REF] Mateus | REVIEW Lampreys of the Iberian Peninsula: distribution, population status and conservation[END_REF]. The river lamprey, as most anadromous species (e.g. salmon, shad) has undergone a strong decline due to habitat fragmentation and pollution. Its low migratory ability as compared to other anadromous species may render this species even more vulnerable [START_REF] Lucas | Availability of and access to critical habitats in regulated rivers: effects of low-head barriers on threatened lampreys[END_REF][START_REF] Foulds | Extreme inefficiency of two conventional, technical fishways used by European river lamprey (Lampetra fluviatilis)[END_REF]. In spite of this strong decline for over 40 years (Taverny & Elli 2005) no specific conservation efforts have been planned in Europe or in France. The river lamprey is nevertheless considered vulnerable in France on the IUCN red list, whereas the brook lamprey is listed as 'least concern'.

Goals of the thesis

The major goal of my thesis was to understand the process of divergence at play between the European river lampreys L. planeri and L. fluviatilis. This question was addressed via a multidisciplinary approach including experimental tests of reproductive isolation, investigations of gene flow in natural populations based on microsatellite and genome wide data and simulations to investigate the demographic history of divergence. A second goal related to conservation issues was to characterize the effect of natural or human-induced river fragmentation on the genetic integrity of L. planeri populations. We used a landscape genetics approach to test the effect of distance, barriers to migration as well as admixture between ecotypes on the spatial distribution of genetic diversity among L. planeri populations. To address these two main issues a multi-step approach was developed, which will be presented in the next chapters in the form of scientific papers:

In Chapter 2; the level of RI between L. planeri and L. fluviatilis was quantified both experimentally In Chapter 3, these most relevant populations are used to identify the most likely evolutionary scenario of ecotypic divergence using Approximate Bayesian Computation on microsatellite data. Given the limited power of these markers, the scenarios of secondary contacts versus ongoing migration could not be disentangled, which highlighted the necessity of a genome-wide approach.

In Chapter 4, I used RAD-seq to determine the historical divergence of L. fluviatilis and L. planeri by a diffusion approximation approach and to investigate whether a signature of parallel genomic divergence was found among replicate population pairs. I identified markers putatively involved in the divergence of lamprey ecotypes and validated the hypothesis of partial parallel genomic divergence possibly stemming from a common divergence history and generating genomic islands (heterogeneous differentiation).

In Chapter 5, the goal was to quantify the effect of river fragmentation on the genetic integrity of L.

planeri populations. Furthermore, taking advantage of our sampling of the two ecotypes in sympatry, parapatry and allopatry we investigated the potential benefit of recurrent introgression from L. fluviatilis in maintaining the genetic diversity of L. planeri populations. A moderate effect of barriers to migration was found on level of genetic diversity in L. planeri populations. A greater influence of the distance to the source and admixture with L. fluviatilis was revealed.

I close this manuscript with a discussion synthesizing the main results and proposing new ideas to answer unresolved questions or bypass some model assumptions that were used during my PhD.

Chapter 2

Investigating gene flow and reproductive isolation between European river and brook lampreys Article 1 Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys

Rougemont Q, Gaigher A, Lasne E, Côte J, Coke M, Besnard AL, Launey S, Evanno G

In press in Journal of Evolutionary Biology DOI: 10.1111/jeb.12750

Introduction

Understanding the process of speciation, i.e. the evolution of reproductive isolation, is a central issue in evolutionary biology. Reproductive barriers among populations can be due to genetic incompatibilities that cause intrinsic reproductive isolation and / or divergent selection that produces extrinsic reproductive isolation [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Seehausen | Genomics and the origin of species[END_REF]. In allopatry, the cumulative effects of selection (including sexual selection), genetic drift and mutation can lead to speciation. Alternatively, sympatric speciation has been regarded as less likely since gene flow between nascent species will contribute, via recombination, to continuously break down associations between alleles linked to divergent adaptation [START_REF] Felsenstein | Skepticism Towards Santa Rosalia, or Why are There so Few Kinds of Animals[END_REF][START_REF] Kirkpatrick | Speciation by Natural and Sexual Selection: Models and Experiments[END_REF]. In parapatry, reproductive isolation can be maintained only under restricted values of gene flow [START_REF] Bank | The Limits to Parapatric Speciation: Dobzhansky-Muller Incompatibilities in a Continent-Island Model[END_REF]. The spatial context of speciation could thus greatly influence the evolution of reproductive isolation by constraining or facilitating gene flow [START_REF] Sobel | The Biology of Speciation[END_REF][START_REF] Curie | What do we need to know about speciation?[END_REF]Studies under these different geographical settings (sympatry, allopatry and parapatry) have provided evidence for the role of natural selection in promoting speciation [START_REF] Jiggins | Reproductive isolation caused by colour pattern mimicry[END_REF][START_REF] Nosil | Host-plant adaptation drives the parallel evolution of reproductive isolation[END_REF][START_REF] Mckinnon | Evidence for ecology's role in speciation[END_REF][START_REF] Barluenga | Sympatric speciation in Nicaraguan crater lake cichlid fish[END_REF][START_REF] Langerhans | Ecological Speciation in Gambusia Fishes[END_REF][START_REF] Soria-Carrasco | Stick Insect Genomes Reveal Natural Selection's Role in Parallel Speciation[END_REF] and have shown variable levels of divergence from panmixia to complete reproductive isolation resulting in a divergence continuum (Nosil et al. 2009b;[START_REF] Hendry | Ecological speciation! Or the lack thereof?This Perspective is based on the author[END_REF][START_REF] Praebel | Ecological speciation in postglacial European whitefish: rapid adaptive radiations into the littoral, pelagic, and profundal lake habitats[END_REF]. It remains challenging to discriminate the respective roles of gene flow, mutation and population size relative to the action of natural selection along this continuum (Barrett & Hoekstra 2011a).

The relative importance of these factors is usually assessed by studying replicate pairs of taxa or populations either in sympatry (Nosil et al. 2009a;Gagnaire et al. 2013b;[START_REF] Powell | Genetic Divergence Along the Speciation Continuum: The Transition from Host Race to Species in Rhagoletis (diptera: Tephritidae)[END_REF] or parapatry [START_REF] Berner | Variable progress toward ecological speciation in parapatry: stickleback across eight lake-stream transitions[END_REF][START_REF] Kaeuffer | Parallel and nonparallel aspects of ecological, phenotypic, and genetic divergence across replicate population pairs of lake and stream stickleback[END_REF]Roesti et al. 2012a) but rarely in both situations simultaneously. In most cases, results have been interpreted as evidence of recent and independent parallel divergence due to ongoing (ecological) selection and the role of demographic history has been usually overlooked [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. However, it can be particularly difficult to disentangle the role of different past demographic events that can leave similar signatures in the genetic makeup of present-day populations (e.g. [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF][START_REF] Hewitt | Quaternary phylogeography: the roots of hybrid zones[END_REF]. For instance, it is often challenging to distinguish between primary divergence in sympatry versus a secondary contact following differentiation in allopatry because neutral markers often used for demographic inference may converge to the same equilibrium under both scenarios [START_REF] Endler | Geographic Variation, Speciation, and Clines[END_REF][START_REF] Barton | Analysis of Hybrid Zones[END_REF][START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. In addition, population divergence after primary or secondary contact does not always lead to complete reproductive isolation (sensu Mayr, 1947) and to the formation of a new species [START_REF] Mallet | Hybridization, ecological races and the nature of species: empirical evidence for the ease of speciation[END_REF][START_REF] Hendry | Ecological speciation! Or the lack thereof?This Perspective is based on the author[END_REF][START_REF] Nosil | Ecological Speciation[END_REF][START_REF] Elias | Factors Influencing Progress toward Ecological Speciation[END_REF]. As a result, it is important to combine experimental approaches analyzing reproductive barriers with inferences of gene flow in replicated population pairs to measure the level of reproductive isolation between putative species (e.g. [START_REF] Dey | Global Population Genetic Structure of Caenorhabditis remanei Reveals Incipient Speciation[END_REF][START_REF] Sobel | Strong premating reproductive isolation drives incipient speciation in Mimulus aurantiacus[END_REF]. Here we used such an integrative approach by focusing on both parapatric and sympatric population pairs in an emerging model species that displays two extremely different life history strategies.

Lampreys are ancient jawless vertebrates (agnathans) in which most genera include 'paired' species [START_REF] Zanandrea | Speciation among Lampreys[END_REF] with divergent life histories, which represent putative cases of ecological speciation [START_REF] Salewski | Satellite species in lampreys: a worldwide trend for ecological speciation in sympatry[END_REF]. These paired species reproduce in freshwater but have extremely different feeding strategies at the adult stage: some taxa are parasitic (hematophagous) and migratory (either migrating to sea, i.e. anadromous, or migrating entirely within fresh water) whereas others are nonparasitic and freshwater resident. Larvae from paired parasitic and nonparasitic taxa are morphologically indistinguishable but adults can be distinguished mainly by the larger body size of parasitic taxon (Hardisty & Potter, 1971;[START_REF] Vladykov | Satellite species among the holarctic lampreys (Petromyzonidae)[END_REF][START_REF] Potter | Ecology of Larval and Metamorphosing Lampreys[END_REF]. Paired species are phylogenetically closely related and it is usually assumed that nonparasitic species derived from their parasitic counterparts [START_REF] Zanandrea | Speciation among Lampreys[END_REF][START_REF] Vladykov | Satellite species among the holarctic lampreys (Petromyzonidae)[END_REF][START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF].

In Western Europe, the nonparasitic brook lamprey (Lampetra planeri, Bloch 1784) and the parasitic river lamprey (L. fluviatilis, Linnaeus 1758) display a low to moderate genetic differentiation as measured with allozymes [START_REF] Schreiber | Population genetics of a cyclostome species pair, river lamprey (Lampetra fluviatilis L.) and brook lamprey (Lampetra planeri Bloch)[END_REF], mitochondrial DNA and microsatellite markers [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Blank | A mitochondrial multigene approach contributing to the systematics of the brook and river lampreys and the phylogenetic position of Eudontomyzon mariae[END_REF][START_REF] Mateus | MtDNA markers reveal the existence of allopatric evolutionary lineages in the threatened lampreys Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) in the Iberian glacial refugium[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF]. In addition, the high viability of F1 hybrid larvae (Hume et al., 2013a), communal spawning of both species on the same nest [START_REF] Huggins | Communal spawning of brook and river lampreys, Lampetra planeri Bloch and Lampetra fluviatilis L[END_REF][START_REF] Lasne | Communal spawning of brook and river lampreys (Lampetra planeri and L. fluviatilis) is common in the Oir River (France)[END_REF] and sneaking behavior of males towards spawning females from the other species have been observed (Hume et al., 2013b). These results led to the hypothesis that brook and river lampreys may represent alternative life-histories strategies (or ecotypes) within a single species [START_REF] Beamish | Evidence that Parasitic and Nonparasitic Life History Types are Produced by One Population of Lamprey[END_REF][START_REF] Yamazaki | Taxonomy and molecular phylogeny of Lethenteron lampreys in eastern Eurasia[END_REF][START_REF] April | Genetic calibration of species diversity among North America's freshwater fishes[END_REF][START_REF] Docker | Contemporary gene flow between "paired" silver (Ichthyomyzon unicuspis) and northern brook (I. fossor) lampreys: implications for conservation[END_REF][START_REF] Knebelsberger | Molecular diversity of Germany's freshwater fishes and lampreys assessed by DNA barcoding[END_REF]. Alternatively, it was argued that the divergence between the two species may be very recent [START_REF] Docker | Phylogeny of the lamprey genus Lampetra inferred from mitochondrial cytochrome b and ND3 gene sequences[END_REF][START_REF] Salewski | Satellite species in lampreys: a worldwide trend for ecological speciation in sympatry[END_REF][START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Okada | Repetitive sequences in the lamprey mitochondrial DNA control region and speciation of Lethenteron[END_REF]. However, Mateus et al.

(2013) found a strong genome-wide divergence between L. fluviatilis and L. planeri sampled in a single river and concluded on the taxonomic validity of the two species. In addition, the different size of adults of both species has been hypothesized to induce size assortative mating leading to reproductive isolation [START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF]. Nevertheless, this hypothesis has never been thoroughly investigated by testing whether the sneaking behavior of L. planeri males can lead to the fertilization of L. fluviatilis eggs and the production of viable hybrids (Hume et al. 2013b). The various population genetics studies led so far have also rarely distinguished the situations of sympatry and parapatry and it remains unclear whether the moderate to strong levels of genetic differentiation observed between both species within the same river was due to reproductive isolation in sympatry, isolation by distance or anthropogenic barriers (e.g. dams or weirs) in parapatry [START_REF] Schreiber | Population genetics of a cyclostome species pair, river lamprey (Lampetra fluviatilis L.) and brook lamprey (Lampetra planeri Bloch)[END_REF][START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF].

In this study, we used an integrative approach combining experimental measures of reproductive isolation and estimates of gene flow in replicated population pairs to better understand the evolution of divergence between L. fluviatilis and L. planeri. Hereafter we use the term 'species' as it is the current taxonomic status of these lampreys but we acknowledge that other terms like 'ecotypes' or 'forms' may also be appropriate. First, we measured the reproductive success of L. fluviatilis and L. planeri males under semi-natural conditions where only L. fluviatilis females were present in order to test whether sizeassortative mating induces a strong prezygotic isolation. Second, we performed in vitro fertilizations of L.

fluviatilis eggs with semen from L. fluviatilis and L. planeri males to compare the fertilization and hatching rates of eggs from intra-and inter-specific crosses. Third, we performed a large-scale population genetic analysis including five sympatric and five parapatric population pairs to infer the level of gene flow between species and among populations within species. We hypothesized that if the level of reproductive isolation between L. fluviatilis and L. planeri is high, a strong level of pre-and post-zygotic isolation will be observed in our experiments as well as a low level of gene flow among sympatric populations. Alternatively, if L. fluviatilis and L. planeri were ecotypes of a single species at a very early stage of divergence or lineages subject to a secondary contact after a period of allopatric divergence, we expected a low reproductive isolation combined with high levels of gene flow in natural populations.

Materials and methods

Figure 1:

Map showing the sampling sites in France (numbers match those given in table 1).

*The Loire, Dordogne and Garonne populations are composed of upstream populations of L. planeri sampled in smaller streams located on the watersheds: the Cens, the Jalles de Tiquetorte and the Saucats rivers respectively.

Reproductive isolation: reproductive success under semi-natural conditions

We quantified the reproductive success of L. fluviatilis and L. planeri males under semi-natural conditions where only L. fluviatilis females were present. We aimed at testing whether size assortative mating may prevent any mating between L. planeri males and L. fluviatilis females. Four L. planeri males, two L. fluviatilis males and two L. fluviatilis females were caught by electrofishing in March 2013 on the downstream part of the Oir River (Fig. 1). We used a greater number of L. planeri males to compensate for their samller size. We did not use L. planeri females, because we were especially interested in testing whether smaller males could fertilize females of greater size as a previous study [START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF] suggested that mating was not possible when size differences are greater than 20%. Individuals were kept together in a 300 liter tank with 3-5 cm of fine gravel (0.5 -1.5 cm diameter) in a recirculated water system.

Temperature was set at 12±1°C with a 12:12 photoperiod. After spawning of both females, 129 larvae as well as tissue samples from each adult were collected and genotyped using 13 microsatellite markers [START_REF] Gaigher | Characterization of thirteen microsatellite markers in river and brook lampreys (Lampetra fluviatilis and L. planeri)[END_REF]. Parentage analyses were performed with CERVUS 3.0 [START_REF] Kalinowski | Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment[END_REF]) using the trio logarithm of the odd score, a 95% confidence level and allowing either no or up to two mismatches between putative parents and offspring.

Reproductive isolation: artificial fertilization

We performed in-vitro fertilizations of L. fluviatilis eggs with sperm from both species. Six L.

fluviatilis females were crossed with four males of each species in a full factorial design producing 48 sib groups. Eight males and three females were captured by electrofishing in the Oir River (Fig. 1) whereas three other females were collected in the Loire River by a professional fisherman. We used females from two genetically differentiated populations (Oir and Loire) to discriminate the effects of outbreeding among populations and reproductive isolation between species [START_REF] Waser | The effect of nectar guides on pollinator preference: experimental studies with a montane herb[END_REF], 1994;Schierup & Christiannsen 1996). We used an experimental design similar to that of [START_REF] Rodríguez-Muñoz | Genetic compatibility and hatching success in the sea lamprey (Petromyzon marinus)[END_REF], presented in detail as supporting information. The fertilization success for each sib group was estimated three hours after fertilization based on the presence of a perivitelline space in the eggs [START_REF] Ciereszko | Fertilization in landlocked sea lamprey: storage of gametes, optimal sperm: egg ratio, and methods of assessing fertilization success[END_REF]. We then measured the hatching rate at the individual level in microplates using only successfully fertilized eggs to avoid confounding dead and non-fertilized eggs. The average time to hatch was approximately 288°C-days (240-324°C-days).

Generalized linear mixed models (GLMM) with a binomial error family were used to test the influence of the cross type (within species ♀Lf × ♂Lf versus between species ♀Lf × ♂Lp) and maternal population (Oir versus Loire) on fertilization success and hatching rate. Cross type, population and cross type × population were considered as fixed effects. Sire, dam, sire × dam and microplates (only for hatching rate) were treated as random effects. To test the significance of each factor on the response variable, we compared models including or not the focal variable using likelihood ratio tests (LRT) based on a χ² distribution [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R[END_REF]. Differences among populations were also investigated for each cross type separately to account for significant cross type × population interactions. Statistical analyses were performed with the lme4 package [START_REF] Bates | Fitting Linear Mixed-Effects Models using lme[END_REF] in the R software (R Development Core Team 2011).

Experiments were approved by the Ethics Committee in Animal Experiment of Rennes (file number: R-2012-EG-02).

Sampling for population genetic analyses

A total of 1023 lampreys were sampled in 19 sites spread over 13 rivers in France during the spawning period in 2010, 2011 and 2014 (Table 1, Fig. 1). Individuals were anesthetized with benzocaine, measured to the nearest millimeter and a fin clip was collected on each specimen and preserved in 95%

EtOH. We sampled both species in sympatry simultaneously on the same spawning ground in the Aa, Béthune and Oir Rivers. Two other sites were also considered as sympatric: the Bresle River, where the two species were captured 8 km apart (with no anthropogenic barrier in between) and the Hem River where L.

planeri individuals were sampled above a dam occasionally passable for L. fluviatilis, depending on water level. We also sampled L. planeri in five parapatric upstream sites inaccessible to L. fluviatilis due to dams or weirs: Risle, Odon, Cens, Saucats and Jalle de Tiquetorte Rivers. Such upstream sites inaccessible to L.

fluviatilis were also sampled in the Aa, Hem, Bresle and Oir Rivers (i.e. the sympatric sites) in order to quantify the within-river genetic variability of L. planeri. In addition, two L. planeri and one L. planeri individuals were captured in sympatry in the Odon and Risle River respectively.

Molecular and statistical analyses

Genomic DNA was extracted from fin clips using a modified Chelex protocol [START_REF] Estoup | Rapid one-tube DNA extraction for reliable PCR detection of fish polymorphic markers and transgenes[END_REF].

Genotyping was performed with 13 microsatellite markers specifically developed for L. planeri and L.

fluviatilis [START_REF] Gaigher | Characterization of thirteen microsatellite markers in river and brook lampreys (Lampetra fluviatilis and L. planeri)[END_REF]. Allelic richness (Ar), observed heterozygosity (Ho), expected heterozygosity (He) and inbreeding coefficient (Fis) for each population were calculated using FSTAT 2.9.3 [START_REF] Goudet | FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3)[END_REF].

The number of private alleles (Pa) was estimated with GENALEX 6.5 [START_REF] Peakall | GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research -an update[END_REF]. Exact tests implemented in GENEPOP 4.1.0 [START_REF] Rousset | genepop'007: a complete re-implementation of the genepop software for Windows and Linux[END_REF] were used to test the Hardy-Weinberg Equilibrium (HWE)

and the linkage disequilibrium. The Bonferroni correction was used to adjust the significance level for multiple tests [START_REF] Rice | Analyzing Tables of Statistical Tests[END_REF]; α = 0.05). Potential differences of expected heterozygosity (He) and allelic richness (Ar) between species were investigated using the permutation test implemented in FSTAT (15 000 permutations). Differences of He and Ar between L. fluviatilis and L. planeri from the same river and the same year were further tested with Wilcoxon paired signed-rank tests (using values per locus) in R. F ST among populations within species was estimated by θ [START_REF] Weir | Estimating F-Statistics for the Analysis of Population Structure[END_REF] and pairwise values were tested using 17 000 permutations and the Bonferroni correction in FSTAT. An Analysis of Molecular Variance (AMOVA) was performed with ARLEQUIN 3.5.1.2 (Excoffier & Lischer 2010) to quantify the hierarchical distribution of genetic variability between the two species, among populations within each species and within populations. The significance of variance components was tested with 15 000 permutations.

Genetic structure and gene flow

The genetic structure was analyzed with the Bayesian clustering approach implemented in STRUCTURE 2.3.3 (Pritchard et al., 2000). The number of genetic clusters (k) varied from 1 to 12 and 20 independent replicates per k value were performed. Markov Chain Monte Carlo simulations (MCMC) were based on 250 000 burn-in followed by 500 000 iterations using an admixture model with correlated allele frequencies [START_REF] Falush | Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies[END_REF]. The most likely number of clusters was determined with the estimated log likelihood Ln Pr(X|K) and the ΔK method [START_REF] Evanno | Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study[END_REF] using STRUCTURE HARVESTER [START_REF] Earl | STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method[END_REF]. Plots were drawn using DISTRUCT 1.1 [START_REF] Rosenberg | distruct: a program for the graphical display of population structure[END_REF]) by averaging individual membership values over the 20 runs for the best k. A global analysis using the full dataset of 1023 individuals was performed and then the level of divergence was investigated within population pairs and in each species separately.

Ongoing gene flow between sympatric and parapatric pairs of L. planeri and L. fluviatilis was estimated using BAYESASS 1.3 [START_REF] Wilson | Bayesian Inference of Recent Migration Rates Using Multilocus Genotypes[END_REF]. Delta values for migration rates, inbreeding coefficient and allele frequencies were optimized to obtain acceptance rates between 40 and 60% of the total number of iterations to ensure proper chain mixing. The program was run with a burn in of 2 000 000 followed by 7 000 000 iterations with 5 runs initiated with random seed numbers.

To test for a pattern of isolation by distance (IBD), the correlation between pairwise geographic and genetic distances measured by F ST /(1-F ST ) [START_REF] Rousset | Genetic Differentiation and Estimation of Gene Flow from F-Statistics Under Isolation by Distance[END_REF] was tested using a Mantel test in R with 10 000 permutations. Geographic distances between each sampling site along coastline and within rivers were computed using ArcGIS 9.3.

Results

Reproductive success under semi-natural conditions

The 129 larvae were all successfully assigned with a 95% confidence level and up to two loci mismatches to a pair of parents and 73 out of 129 were assigned with no loci mismatches between parents and offspring (Fig. 2). From these 73 larvae, 59 (81%) were assigned as pure L. fluviatilis and 14 as hybrids (19%). Each L. planeri male produced two to four offspring while the two L. fluviatilis males sired 35 and 24 offspring, respectively. The two L. fluviatilis females produced 47 and 26 offspring, respectively. Size of the 2 L. fluviatilis females was 206 and 211 mm respectively. The size of each male is provided in Figure 2.

Artificial fertilization: fertilization success and hatching rates

Fertilization rates (± s.e.) of eggs from both homospecific (♀Lf × ♂Lf) and heterospecific (♀Lf × ♂Lp)

crosses were extremely high: 95.5% ± 3.2 and 95.8% ± 3.8, respectively. We found a significant interaction between cross type and maternal population on fertilization success (Table S1). However, considering each cross type separately, no population effect was found for both hetero-and homospecific crosses (LRT χ² = 3.28, P = 0.07; LRT χ² = 1.50, P = 0.22). Hatching rates were also extremely high: all eggs from homospecific crosses successfully hatched (100%) and only 5 out of 563 eggs (99.1% ± 1.74) from heterospecific crosses failed to hatch. Given the lack of variance in homospecific crosses, we could not statistically test for an effect of cross type on hatching rate. Considering heterospecific crosses, no population effect was found (LRT χ² = 0, P = 1).

Within-species and population genetic diversity

A total of 1023 individuals were successfully genotyped at 13 loci. The number of alleles per locus varied from 4 to 12, for an average of 5.8 (Table S2). No linkage disequilibrium was observed between pairs of loci after Bonferroni correction. Tests on deviation from HWE showed a significant excess of heterozygotes only for the Lf Dor D 2010 population after correction (Table 1). Average allelic richness (based on 14 samples) and average expected heterozygosity were significantly higher (15 000 permutations, P < 0.01) in L. fluviatilis (3.270 and 0.501 respectively) than in L. planeri (2.871 and 0.444) (Table1). L. planeri sampled in upstream and downstream parts of the Aa, Hem, Bresle and Oir Rivers showed no difference of genetic diversity (two-sided paired Wilcoxon test, P > 0.05, Table S3) except for expected heterozygosity in Aa 2014 and allelic richness in Oir 2014, which were both significantly higher downstream (Table S3). In most rivers, the allelic richness was significantly higher in L. fluviatilis than L.

planeri (two-sided paired Wilcoxon test, P < 0.05 and Tables 1 andS3), except in sympatric populations and in the Risle River. The same trend was observed for expected heterozygosity (Table S3).

Table 1 Genetic

Genetic structure

Pairwise F ST values ranged from 0 to 0.324 (Tables 2, 3 andS4), with an overall F ST of 0.082 (99% IC = 0.065-0.106). The AMOVA revealed that the percentage of variance among populations of the same species (6.25%) was much higher than between species (1.55%) and the largest part of variance (92.20%) was found within populations. F ST among L. fluviatilis populations was significant but much smaller than among L. planeri populations: 0.022 and 0.134, respectively (15 000 permutations, P < 0.001). L. planeri populations sampled upstream and downstream of barriers in the same river (Aa, Hem, Bresle and Oir Rivers) were not significantly differentiated (F ST-Aa = 0.006; F ST-Hem = 0.008; F ST-Bresle = 0.005) except in the Oir River (F ST-Oir-2011 = 0.031, F ST-Oir-2014 = 0.020).

We observed contrasting levels of population differentiation between L. fluviatilis and L. planeri depending on rivers (Table 2 andTable 3). The sympatric population pair in the Oir (2014) was not significantly differentiated (F ST = 0.008) whereas a moderate structuration was observed in the Aa, Hem, Oir (2010 and 2011), Béthune and Bresle ( 2011) Rivers (F ST = 0.080; 0.081; 0.048; 0.032; 0.028 and 0.074, respectively, Table 2). F ST was generally higher in parapatry with population pairs from the Odon, Loire-Cens and Garonne-Saucats Rivers being the most differentiated (Table 3). The parapatric population pair from the Risle River was an exception with a low F ST of 0.028 and 0.036 in 2011 and 2014 respectively (Table 3).

Overall, F ST between L. planeri and L. fluviatilis from the same river system was always smaller than the mean F ST between the L. planeri population from this river and all other L. planeri populations (Fig. 3).

Similarly, the mean F ST between a given L. planeri population and all other L. fluviatilis populations was always smaller than among L. planeri populations (Fig. 3).

A positive correlation between genetic and geographic distances was found in the global data set (r Spearman = 0.27, P = 0.004) but when each species was considered separately the pattern of isolation by distance was stronger in L. fluviatilis (r Spearman = 0.79, P < 0.001) than in L. planeri (r Spearman = 0.40, P = 0.005).

Bayesian clustering analyses

Results from STRUCTURE for each population pair illustrated a continuum of differentiation from apparent panmixia in some rivers to strong differentiation in other cases (Fig. 4). In the Oir, Béthune (sympatry) and Risle (parapatry) Rivers all individuals were assigned to both clusters, hence suggesting that L. planeri and L. fluviatilis formed a single population. However, in the three other sympatric situations (Aa, Hem and Bresle) where the differentiation was higher, two clusters were observed but some individuals were assigned to the cluster of the other species. In the parapatric Loire-Cens, Dordogne-Garonne-Saucats and Dordogne-Garonne-Jalles de Tiquetorte systems both species clustered in two groups and very few individuals were assigned to the cluster of the other species. Interestingly, the few L. planeri individuals (n =

3) captured in sympatry on the Risle and Odon Rivers were mostly assigned to the cluster of L. fluviatilis (Fig. 4 and Fig. S1a,b,c).

Results from STRUCTURE based on the 1023 individuals showed the highest likelihood for k = 8 and 10

(Table S5a). The best ∆k values were observed at k = 3, 6 and 8 (Table S5a). We also investigated k = 2 to illustrate the level of admixture between both species (Fig. S1a). At this clustering level, all individuals had mixed membership proportions between the two species. When considering k = 8 (Fig. S1b), we found two widely admixed clusters for L. fluviatilis: the first one included samples from the Atlantic coast and the second one those from the English Channel area. The six other clusters included L. planeri samples: four clusters corresponded each to one river (the Aa, Hem, Jalles and upstream part of the Oir River), the fifth cluster gathered samples from the Odon and Garonne-Saucats Rivers and the sixth cluster included samples from the Bresle and Loire-Cens Rivers. L. planeri samples from the Oir, Risle and Béthune were strongly admixed with L. fluviatilis populations. Analyzing species separately confirmed the existence of two clusters for L. fluviatilis (Table S5b and Fig. S1d). For L. planeri, the most probable number of cluster was k = 9 and each population formed a distinct cluster except the Béthune and Risle, which clustered together (Table S5c and Fig. S1e).

Estimates of recent migration rates within the different population pairs obtained with BAYESASS revealed an asymmetric pattern when considering the whole dataset with a significant tendency for a higher gene flow from L. planeri to L. fluviatilis (two-sided paired permutations test, T = 78, P = 0.015) (Table S6). Interestingly, this pattern was driven by asymmetric migration occurring mainly in parapatric situations (T = 45, P = 0.006) whereas there was no significant difference in the direction of migration in sympatry (T = 3, P = 0.343).

Discussion

The main aim of our study was to investigate the level of reproductive isolation between two lamprey species by combining experimental measurements of reproductive barriers and analyses of gene flow between sympatric and parapatric population pairs. Our experiments demonstrated that brook lamprey males could reproduce with river lamprey females under semi-natural conditions despite the important size difference between species. Results from artificial fertilizations further supported a low level of reproductive isolation. Population genetic analyses of replicated pairs revealed a continuum of gene flow between species with a pattern of panmixia in some sympatric populations, a moderate differentiation in some other sympatric sites and a strongly reduced gene flow between populations separated by anthropogenic barriers. This gradient of divergence suggests ongoing gene flow in certain sympatric population pairs and some degree of reproductive isolation in other sites. However, secondary contacts after a period of allopatry could also explain this variable degree of genetic differentiation among sympatric sites. In addition, anthropogenic barriers strongly restrict the level of gene flow between species and may thus ultimately promote the evolution of reproductive isolation.

Using artificial fertilizations, we found that L. planeri and L. fluviatilis males have the same capacity to fertilize eggs of L. fluviatilis females. Hatching survival of larvae was also high (nearly 100%) and identical regardless of the male's species. Using a genetically distinct female population (F ST = 0.055), we found that hatching rates were as strong as with crosses using females from the same population, further suggesting a low postzygotic isolation and no outbreeding depression.

Recently, Hume et al. (2013a) also observed viable hybrids using a similar experimental approach but they obtained much lower values of survival potentially because their experimental design did not allow a distinction of unfertilized and dead embryos. Viable hybrid crosses have been obtained between different pairs of lamprey species but they have never been raised up to the adult stage due to the difficulty of rearing juvenile lampreys [START_REF] Weissenberg | Fluss-und Bachneuunauge (Lampetra fluviatilis L. und Lampetra planeri Bloch), ein morphologisch-biologischer Vergleich[END_REF][START_REF] Piavis | Experimental hybridization among five species of lampreys from the Great Lakes[END_REF][START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF]Hume et al., 2013a). As a result, the fitness of hybrids has never been thoroughly assessed and was only limited to the F1 generation, which prevents an accurate assessment of intrinsic postzygotic barriers. Indeed, genetic incompatibilities are generally best revealed in F2s or backcrosses while heterosis is expected in the F1 generation (e.g. [START_REF] Edmands | Heterosis and Outbreeding Depression in Interpopulation Crosses Spanning a Wide Range of Divergence[END_REF][START_REF] Wiley | Postzygotic isolation over multiple generations of hybrid descendents in a natural hybrid zone: how well do single-generation estimates reflect reproductive isolation? Evolution[END_REF].

For instance, [START_REF] Bierne | Assortative fertilization and selection at larval stage in the mussels Mytilus edulis and M. galloprovincialis[END_REF][START_REF] Bierne | Fitness landscapes support the dominance theory of post-zygotic isolation in the mussels Mytilus edulis and M. galloprovincialis[END_REF] found a pattern of heterosis in F1 crosses of Mytillus edulis and M. galloprovincialis while F2s were selected against at the larval stage. As a consequence, further studies are needed to better understand the potential mechanisms of postzygotic isolation in lampreys.

Even when postzygotic isolation is low, premating barriers can contribute to reproductive isolation (e.g. [START_REF] Sobel | Strong premating reproductive isolation drives incipient speciation in Mimulus aurantiacus[END_REF]. Our results based on mating trials under semi-natural conditions showed for the first time that L. planeri males were able to fertilize L. fluviatilis females despite the important size difference between species. This interbreeding produced viable hybrid larvae, which suggested a low level of premating isolation and confirmed the low postzygotic isolation at an early developmental stage observed with in vitro fertilizations. Size-assortative mating has been suggested to promote divergence and partial or complete reproductive isolation in many taxa including seahorses [START_REF] Jones | Sympatric speciation as a consequence of male pregnancy in seahorses[END_REF]), sticklebacks (McKinnon et al. 2004) or water striders [START_REF] Han | Size-assortative mating and sexual size dimorphism are predictable from simple mechanics of mate-grasping behavior[END_REF]. A similar process has been suggested to induce reproductive isolation in lampreys when size differences are greater than 20 % [START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF], a hypothesis that was not confirmed by our results. However, in our experiment the reproductive success of L. planeri males was much lower than that of L. fluviatilis males and their size differences were far greater than 20 % (Fig. 2) hence some degree of size-assortative mating may occur. L. planeri males may also adopt a sneaking strategy, in which they would fertilize some eggs of a L. fluviatilis female despite its tendency to mate with larger conspecific males (Hume et al., 2013b). This tactic is widespread in many fish species and may thus limit the evolution of prezygotic isolation in species pairs of lampreys [START_REF] Gross | Sunfish, salmon, and the evolution of alternatives reproductives strategies and tactics in fishes[END_REF][START_REF] Gage | Effects of Alternative Male Mating Strategies on Characteristics of Sperm Production in the Atlantic Salmon (Salmo salar): Theoretical and Empirical Investigations[END_REF][START_REF] Fleming | Reproductive strategies of Atlantic salmon: ecology and evolution[END_REF]. However, in the absence of L. planeri females in our experiment, L. planeri males may have been somehow "forced" to mate with interspecific females, which may have led to an underestimation of the strength of prezygotic barriers. Further experiments including males and females from both species are thus required to produce quantitative estimates of prezygotic isolation in this system.

The low reproductive isolation measured in our experiments on individuals from a single river (Oir) was mirrored by high levels of gene flow in this sympatric site. However, by studying a total of 10 population pairs we found a gradient of increasing differentiation with some sympatric pairs forming a genetically homogeneous population, some others being significantly differentiated and parapatric pairs displaying a high level of divergence. Such a gradient of divergence across multiple pairs in sympatry suggests variable levels of reproductive isolation within a single species complex, which has been observed in relatively few systems (e.g. Gagnaire et al., 2013b;[START_REF] Powell | Genetic Divergence Along the Speciation Continuum: The Transition from Host Race to Species in Rhagoletis (diptera: Tephritidae)[END_REF] and emphasizes the interest of using lampreys as a model in speciation studies.

Sympatric pairs in the Oir and Béthune Rivers were not (or weakly) genetically differentiated demonstrating that gene flow can be high between resident and migratory lampreys as suggested in earlier studies [START_REF] Schreiber | Population genetics of a cyclostome species pair, river lamprey (Lampetra fluviatilis L.) and brook lamprey (Lampetra planeri Bloch)[END_REF][START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Blank | A mitochondrial multigene approach contributing to the systematics of the brook and river lampreys and the phylogenetic position of Eudontomyzon mariae[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF]. Similarly, a low differentiation has been observed between ecotypes of resident and migratory rainbow trout Onchorynchus mykiss (e.g. [START_REF] Docker | Genetic comparison between sympatric anadromous steelhead and freshwater resident rainbow trout in British Columbia, Canada[END_REF] and is also well documented in brown trout Salmo trutta (e.g. [START_REF] Hindar | Genetic relationships among landlocked, resident, and anadromous Brown Trout, Salmo trutta[END_REF][START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Pettersson | Does dispersal from landlocked trout explain the coexistence of resident and migratory trout females in a small stream[END_REF][START_REF] Charles | Lack of genetic differentiation between anadromous and resident sympatric brown trout ( Salmo trutta) in a Normandy population[END_REF]. This suggests that L. planeri and L. fluviatilis may also represent two ecotypes of a single species. However, we found other sympatric situations (Aa, Hem and Bresle Rivers) where the two species were moderately but significantly differentiated. Accordingly, [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF] found a strong differentiation (F ST = 0.37) in a population pair sampled in the same river system in Portugal.

These pairs may be a step further along the divergence continuum, which suggests that disruptive selection and other isolating factors may act in these systems. For instance, some temporal isolation during the spawning season and patchiness of breeding habitat may contribute to ecological divergence between the two species. Analogously, temporal and spatial differences in spawning were linked to genetic differentiation between ecotypes or sub-populations of various salmonid species [START_REF] Deiner | Population structure and genetic diversity of trout (Oncorhynchus mykiss) above and below natural and man-made barriers in the Russian River, California[END_REF][START_REF] Pearse | Over the falls? Rapid evolution of ecotypic differentiation in steelhead/rainbow trout (Oncorhynchus mykiss)[END_REF]). In addition, the magnitude of size differences between species seems to vary among rivers (Table 1) and this factor may contribute to variations of reproductive isolation as predicted by theory [START_REF] Bolnick | Sympatric Speciation in Threespine Stickleback: Why Not[END_REF]) and observed in other taxa [START_REF] Arnqvist | Assortative mating by size: A meta-analysis of mating patterns in water striders[END_REF][START_REF] Mckinnon | Evidence for ecology's role in speciation[END_REF][START_REF] Martin | Strong assortative mating by diet, color, size, and morphology but limited progress toward sympatric speciation in a classic example: Cameroon crater lake cichlids[END_REF]. Our experiments showed a low premating isolation in lampreys from the Oir River where the size difference and the genetic differentiation are low between species. Similar experiments in other sympatric sites would thus be required to better understand the role of size assortative mating in the evolution of reproductive isolation. Besides, the significant genetic differentiation observed in sympatric situations may also reflect some genetic barriers to gene flow [START_REF] Wu | The genic view of the process of speciation[END_REF]Tuner et al., 2005). However, if they exist, these barriers may not be distributed over large portions of the genome and may not efficiently counteract gene flow since genetic differentiation as well as overall reproductive isolation were low [START_REF] Barton | The barrier to genetic exchange between hybridising populations[END_REF][START_REF] Wu | The genic view of the process of speciation[END_REF].

The continuum of genetic differentiation observed in sympatric sites could arise from two different historical scenarios of divergence: (1) ecologically based speciation with gene flow or [START_REF] Gwee | Characterization of the neurohypophysial hormone gene loci in elephant shark and the Japanese lamprey: origin of the vertebrate neurohypophysial hormone genes[END_REF] differential introgression following a secondary contact after a period of allopatric divergence. If the populations have diverged in allopatry for a period of time too short to allow complete reproductive isolation it is also possible that secondary contacts have occurred at different times in different areas so that in some cases (e.g. Oir River) a single panmictic population is currently found while in other situations the genome swamping between L. planeri and L. fluviatilis is still incomplete. Such scenarios of secondary contacts have been suggested in many taxa including Cameroon crater lake cichlids [START_REF] Martin | Complex histories of repeated gene flow in Cameroon crater lake cichlids cast doubt on one of the clearest examples of sympatric speciation[END_REF], whitefish (Gagnaire et al., 2013b), voles [START_REF] Beysard | Structure and dynamics of hybrid zones at different stages of speciation in the common vole (Microtus arvalis)[END_REF] and may have also played a crucial role in the evolution of reproductive isolation in the apple maggot, which is considered as a classical model of sympatric speciation [START_REF] Feder | Allopatric genetic origins for sympatric host-plant shifts and race formation in Rhagoletis[END_REF]. Nevertheless, cases of ongoing gene flow in sympatry between closely related species have been often interpreted as evidences for ecological speciation whereas the hypothesis of admixture following a secondary contact (or the one of local adaptation) was either not considered or could not be definitively rejected [START_REF] Via | Sympatric speciation in animals: the ugly duckling grows up[END_REF][START_REF] Michel | Widespread genomic divergence during sympatric speciation[END_REF][START_REF] Hohenlohe | Population Genomic Analysis of Model and Nonmodel Organisms Using Sequenced RAD Tags[END_REF][START_REF] Kautt | Genomic signatures of divergent selection and speciation patterns in a "natural experiment", the young parallel radiations of Nicaraguan crater lake cichlid fishes[END_REF]. These different scenarios of divergence are difficult to disentangle but new modelling approaches may help tackle this issue as described in several recent studies [START_REF] Duvaux | Isolation and gene flow: inferring the speciation history of European house mice[END_REF][START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF]Butlin et al., 2014;Tine et al., 2015).

In contrast to sympatric situations, we observed high levels of differentiation in most parapatric sites as expected under the joint effects of isolation by distance and anthropogenic barriers to migration. In these situations migration was reduced and asymmetric from L. planeri (upstream) to L. fluviatilis (Table S6), highlighting the low migratory ability of L. fluviatilis in the presence of obstacles [START_REF] Russon | Gauging weirs impede the upstream migration of adult river lamprey Lampetra fluviatilis[END_REF][START_REF] Foulds | Extreme inefficiency of two conventional, technical fishways used by European river lamprey (Lampetra fluviatilis)[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF]. Combined negative effects of distance and barriers on gene flow have also been observed in several fish species [START_REF] Thrower | A Comparison of Genetic Variation Between an Anadromous Steelhead, Oncorhynchus mykiss, Population and Seven Derived Populations Sequestered in Freshwater for 70 Years[END_REF][START_REF] Raeymaekers | Modeling genetic connectivity in sticklebacks as a guideline for river restoration[END_REF][START_REF] Gomez-Uchida | Interaction of landscape and life history attributes on genetic diversity, neutral divergence and gene flow in a pristine community of salmonids[END_REF]) and more generally in many taxa (see [START_REF] Templeton | Disrupting evolutionary processes: The effect of habitat fragmentation on collared lizards in the Missouri Ozarks[END_REF][START_REF] Fahrig | Effects of Habitat Fragmentation on Biodiversity[END_REF]. Our results thus highlight the importance of untangling the effects of habitat fragmentation inducing restricted gene flow from ecological divergence between habitats when studying speciation. Inferences about the speciation process might be obscured by the effect of barriers to migration and isolation by distance. Sampling should thus be carefully designed to clearly distinguish sympatric and parapatric sites even at a within-river scale. Ultimately, habitat fragmentation could promote the evolution of reproductive isolation and lead to founder-induced speciation but it seems more likely to induce local extinctions [START_REF] Templeton | The theory of speciation via the founder principle[END_REF][START_REF] Templeton | The reality and importance of founder speciation in evolution[END_REF]. Anthropogenic barriers to migration did not have the same impact on patterns of genetic diversity and differentiation of L. planeri and L. fluviatilis populations. We found high levels of genetic structure combined with clustering at the river level in L. planeri suggesting that each resident population tends to evolve as an independent evolutionary unit due to low dispersal ability and isolation by anthropogenic barriers in upstream reaches. Similar results were obtained in earlier studies based on allozyme or mtDNA in northern Europe and in the Iberian Peninsula [START_REF] Schreiber | Population genetics of a cyclostome species pair, river lamprey (Lampetra fluviatilis L.) and brook lamprey (Lampetra planeri Bloch)[END_REF][START_REF] Pereira | Phylogeographical analysis reveals multiple conservation units in brook lampreys Lampetra planeri of Portuguese streams[END_REF] and also recently based on microsatellite data in United Kingdom [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF]. In contrast, populations of the migratory L. fluviatilis were weakly differentiated and structured at the regional level. The genetic diversity (both allelic richness and expected heterozygosity) of L. planeri populations was also lower than the one of L. fluviatilis populations indicating an important role of genetic drift in isolated brook lamprey populations. A similar pattern has been observed in several salmonid species between freshwater resident and anadromous populations [START_REF] Gomez-Uchida | Interaction of landscape and life history attributes on genetic diversity, neutral divergence and gene flow in a pristine community of salmonids[END_REF][START_REF] Perrier | Parallel and nonparallel genome-wide divergence among replicate population pairs of freshwater and anadromous Atlantic salmon[END_REF]. Finally, the fact that isolation by distance was lower in L. planeri than in L. fluviatilis further highlights the impact of anthropogenic barriers on the genetic differentiation among L. planeri populations (see also [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF].

To conclude, our results suggest that L. fluviatilis and L. planeri may form partially reproductively isolated ecotypes. The variable levels of gene flow among sympatric sites show that different pairs of populations are either at different stages of divergence along the speciation continuum or at different levels of fusion following secondary contacts. In the first hypothesis, size assortative mating and selection could act together to maintain phenotypic differences in the face of gene flow. The relative strength of these factors may vary among rivers, resulting in variable progress toward sympatric speciation or even stalled speciation [START_REF] Bolnick | Sympatric Speciation in Threespine Stickleback: Why Not[END_REF]. In the second hypothesis, similar patterns of varying levels of divergence would arise by secondary contacts [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. Combining genome-wide analyses and modeling of complex historical processes may help untangling these different scenarios. Ultimately, experimental approaches testing the long-term fitness of F1s and later generation hybrids will allow deeper investigations of the mechanisms of postzygotic reproductive isolation in this system. Common garden experiments may also allow clarify the relative roles of phenotypic plasticity and genetic factors in the emergence of parasitic and nonparasitic life histories. Lf Gar D 2011 0.037 0.051 0.053 0.043 0.049 0.057 0.037 0.039 0.046 0.005 0.046 0.044 0 Lp Bet D 0.022 0.025 0.028 0.035 0.018 0.041 0.031 0.028 0.023 0.034 0.015 0.027 0.027 0.033 0.066 0. Lp Ris U 2014 0.033 0.036 0.051 0.026 0.035 0.041 0.028 0.036 0.031 0.048 0.028 0.033 0.048 0.042 0.075 0.108 0.047 0.129 0.122 0.111 0. Lp Oir D 0.013 0.019 0.018 0.022 0.013 0.019 0.009 0.019 0.019 0.019 0. Lp Oir U 0.054 0.056 0.046 0.052 0.045 0.046 0.059 0.061 0.073 0.074 0.052 0.045 0.052 0. In prep for Peer J

Introduction

Understanding the spatio-temporal conditions favouring species emergence is a fundamental question in evolutionary biology. One long standing controversy concerns the geographical setting promoting species divergence [START_REF] Butlin | Sympatric, parapatric or allopatric: the most important way to classify speciation?[END_REF][START_REF] Fitzpatrick | What, if anything, is sympatric speciation[END_REF]. Theory predicts that the accumulation of genetic incompatibilities is rather straightforward under allopatric conditions without gene-flow [START_REF] Turelli | Theory and speciation[END_REF][START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF][START_REF] Barton | The evolution of strong reproductive isolation[END_REF]. In contrast, speciation with gene flow theoretically requires (i) strong divergent selection and non-random mating, (ii) high genetic variance and (iii) non-random association of traits under disruptive selection and those involved in assortative mating [START_REF] Dieckmann | On the origin of species by sympatric speciation[END_REF][START_REF] Gavrilets | Perspective: models of speciation: what have we learned in 40 years? Evolution[END_REF][START_REF] Gavrilets | Models of Speciation: Where Are We Now[END_REF][START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]). Importantly, the current geographical distribution of contemporary species may not reflect the initial conditions of divergence as most species may have undergone alternative phases of separation and contact over historical periods [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF][START_REF] Hewitt | Quaternary phylogeography: the roots of hybrid zones[END_REF][START_REF] Bierne | The coupling hypothesis: why genome scans may fail to map local adaptation genes[END_REF]. As a result, reconstructing the history of demographic events that have shaped the genetic architecture of present-day populations is of primary importance to understand how speciation occurred and infer the role of gene flow during divergence. The accuracy of this reconstruction will depend on an adequate statistical method for demographic inferences, but also on the relevance of the sampling scheme.

Simulation-based methods are helpful for inferences although the tested models are always simplification of the real -and usually unknown -demographic history of the populations studied [START_REF] Wakeley | Coalescent Theory: An Introduction[END_REF]). For instance, several studies using full likelihood approaches implemented in the IM and IMa programs [START_REF] Hey | Multilocus Methods for Estimating Population Sizes, Migration Rates and Divergence Time, With Applications to the Divergence of Drosophila pseudoobscura and D. persimilis[END_REF][START_REF] Lynch | The frailty of adaptive hypotheses for the origins of organismal complexity[END_REF][START_REF] Hey | Isolation with Migration Models for More Than Two Populations[END_REF]) have compared Isolation-and-Migration (IM) models against a model of strict isolation (SI) and revealed a widespread effect of gene flow during divergence [START_REF] Pinho | Non-equilibrium estimates of gene flow inferred from nuclear genealogies suggest that Iberian and North African wall lizards (Podarcis spp.) are an assemblage of incipient species[END_REF][START_REF] Niemiller | Recent divergence with gene flow in Tennessee cave salamanders (Plethodontidae: Gyrinophilus) inferred from gene genealogies[END_REF][START_REF] Strasburg | Molecular demographic history of the annual sunflowers Helianthus annuus and H. petiolaris--large effective population sizes and rates of long-term gene flow[END_REF]). However, the method makes a number of simplifying assumptions [START_REF] Strasburg | How Robust Are "Isolation with Migration" Analyses to Violations of the IM Model? A Simulation Study[END_REF], 2011) and does not allow for reconstruction of complex scenarios with several parameters, due to computation burden or intractable likelihood computation. Thus the complexity of demographic events may have been missed [START_REF] Nielsen | Distinguishing migration from isolation: a Markov chain Monte Carlo approach[END_REF][START_REF] Hey | Isolation with Migration Models for More Than Two Populations[END_REF]. For instance, most of these studies failed to distinguish between primary versus secondary differentiation (i.e. allopatric divergence followed by secondary contact), hence no general conclusion about the ubiquity of either mechanism during speciation can be drawn yet. Recent advances in coalescent theory [START_REF] Wakeley | Coalescent Theory: An Introduction[END_REF]) and Bayesian methods [START_REF] Tavare | Inferring Coalescence Times from DNA Sequence Data[END_REF][START_REF] Beaumont | Approximate Bayesian Computation in Population[END_REF][START_REF] Beaumont | Approximate Bayesian Computation in Evolution and Ecology[END_REF]) now allow for explicit tests of alternatives and complex models of divergence. In particular, Approximate Bayesian Computation (ABC) bypasses the need to compute full likelihoods, as this is not possible or is computationally too intensive for complex models with many parameters and large datasets [START_REF] Beaumont | Approximate Bayesian Computation in Population[END_REF]. ABC has been used with success to test alternative models of divergence in various taxa and has provided useful information on the level of interspecific introgression and complexity of demographic history underlying population divergence [START_REF] Fagundes | Statistical evaluation of alternative models of human evolution[END_REF][START_REF] Duvaux | Isolation and gene flow: inferring the speciation history of European house mice[END_REF][START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF][START_REF] Nadachowska-Brzyska | Demographic divergence history of pied flycatcher and collared flycatcher inferred from whole-genome re-sequencing data[END_REF][START_REF] Nater | Reconstructing the demographic history of orang-utans using Approximate Bayesian Computation[END_REF].

Several studies have focussed on single population pairs to infer demography, so that drawing more general conclusions may be complicated. On the other hand, studies of replicated pairs of diverging natural populations have proven very useful to understand the genetic mechanisms of divergence and speciation and have shown that populations can independently evolve similar reproductive barriers in the face of ongoing gene-flow (e.g. [START_REF] Schluter | Character displacement and replicate adaptive radiation[END_REF][START_REF] Nosil | Host-plant adaptation drives the parallel evolution of reproductive isolation[END_REF][START_REF] Colosimo | Widespread Parallel Evolution in Sticklebacks by Repeated Fixation of Ectodysplasin Alleles[END_REF][START_REF] Johannesson | Are we analyzing speciation without prejudice?[END_REF]). Their results were generally interpreted as evidence for parallel adaptation of diverging populations due to the action of recent natural selection.

However, alternative scenarios of divergence including secondary contact after periods of allopatry have rarely been investigated [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF][START_REF] Welch | Standing and flowing: the complex origins of adaptive variation[END_REF]Butlin et al., 2014).

Lampreys are jawless vertebrates (agnathans) thought to have diverged from the gnathostomes lineage (jawed vertebrates) approximately 590 million years ago [START_REF] Hedges | Tree of Life Reveals Clock-Like Speciation and Diversification[END_REF].

At least half of the approximately 40 species of lampreys around the world occur as 'paired' species which often overlap in geographic distribution but which show strong divergence in adult life history strategies. One member of each pair is migratory (migrating to sea or downstream to lakes or large rivers) and becomes parasitic-hematophagous, while the other member (the so-called brook lampreys) are non-migratory (i.e., are entirely freshwater resident, remaining within their natal stream) and non-parasitic (i.e., not feeding at all after metamorphosis) [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. Despite a large number of evolutionary and developmental studies in lampreys [START_REF] Heimberg | microRNAs reveal the interrelationships of hagfish, lampreys, and gnathostomes and the nature of the ancestral vertebrate[END_REF][START_REF] Shimeld | Evolutionary crossroads in developmental biology: cyclostomes (lamprey and hagfish)[END_REF][START_REF] Smith | Sequencing of the sea lamprey (Petromyzon marinus) genome provides insights into vertebrate evolution[END_REF][START_REF] Lagadec | The ancestral role of nodal signalling in breaking L/R symmetry in the vertebrate forebrain[END_REF], there is a high uncertainty about the taxonomic relationships among lamprey paired species (but see [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. For instance, the European river lamprey (Lampetra fluviatilis) and brook lamprey (L. planeri) display marked morphological differences at the adult stage, with adults of the anadromous and parasitic river lamprey on average 2.2 times longer than resident and non-parasitic brook lampreys. While adults of both species have been found on the same spawning ground [START_REF] Lasne | Communal spawning of brook and river lampreys (Lampetra planeri and L. fluviatilis) is common in the Oir River (France)[END_REF], this size difference likely forms the most important prezygotic barrier to gene-flow [START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF], Rougemont et al. 2015). However, the genetic differentiation between these two taxa is usually low when measured either with allozymes [START_REF] Schreiber | Population genetics of a cyclostome species pair, river lamprey (Lampetra fluviatilis L.) and brook lamprey (Lampetra planeri Bloch)[END_REF], mtDNA [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Blank | A mitochondrial multigene approach contributing to the systematics of the brook and river lampreys and the phylogenetic position of Eudontomyzon mariae[END_REF] or microsatellites markers [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF], Rougemont et al. 2015) and these species have also been hypothesized to be different ecotypes of a single species [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. Only one study based on restriction site-associated-DNA sequencing of a single population pair reported a strong differentiation between L. planeri and L. fluviatilis [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF]. Currently, there has been only one large scale phylogeographic study using mtDNA to investigate demographic history among Lampetra [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF]). The authors found a very low level of divergence, that was hypothesized to result from ongoing gene flow or very recent divergence following postglacial dispersion. However, it is known that widespread mtDNA introgression among sympatric taxa can easily obscure their taxonomic relationship [START_REF] Shaw | Conflict between nuclear and mitochondrial DNA phylogenies of a recent species radiation: what mtDNA reveals and conceals about modes of speciation in Hawaiian crickets[END_REF]. More recently [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF] drew similar conclusions of recent divergence following postglacial dispersion. They concluded that founder event fuelled the evolution of diversity and may have promoted speciation. However, phylogeographic approaches do not allow contrasting alternative scenarios of divergence and do not address gene flow following divergence. As a consequence, relatively little is known so far about the history of divergence within lampreys, and most conclusions have been related to recent postglacial divergence [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF] or linked to ecological processes [START_REF] Salewski | Satellite species in lampreys: a worldwide trend for ecological speciation in sympatry[END_REF]. Overall, few studies have used a wide number of pairs of river and brook lamprey connected by gene flow and realistic scenarios of demographic history have never been modelled.

Recently [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF] studied ten pairs of sympatric and parapatric populations of L. Coalescent simulations were performed using the ms software [START_REF] Hudson | Generating samples under a Wright-Fisher neutral model of genetic variation[END_REF]

Parameters Models Prior

θ A = 4N Anc µ SI, IM, AM, SC Uniform [0-3] θ 1 = 4N 1 µ SI, IM, AM, SC, PAN Uniform [0-3] θ 2 = 4N 2 µ SI, IM, AM, SC Uniform [0-(θ 1 )] M 1 =M 2 = 4N 1 m IM, SC Uniform [0-20] M ANC = 4N 1 m AM Uniform [0-20] τ = 4N 1 t SI, IM, AM, SC Uniform [0-25] τ isol = 4N 1 t AM Uniform [0-τ] τ sc = 4N 1 t SC Uniform [0-τ]

Model Selection

ABC approach

We evaluated the posterior probabilities of each demographic model using an ABC framework implemented in the abc package in R [START_REF] Csilléry | abc: an R package for approximate Bayesian computation (ABC)[END_REF]. We compared all models simultaneously by computing posterior probabilities using a feed forward neural network based on a nonlinear conditional heteroscedastic regression in which the model is considered as an additional parameter to be inferred. This procedure allows taking into account correlations of summary statistics and distortion hence reducing the problem of curse of dimensionality [START_REF] Blum | Non-linear regression models for Approximate Bayesian Computation[END_REF]. In the rejection step, we retained the 0.02% simulations closest to the observed summary statistics, which were subsequently weighted by an Epanechnikov kernel that peaks when S obs = S sim .

The regression step was performed using 50 neural networks and 15 hidden layers.

ABC cross-validation

We performed model checking to compute the robustness of the inferred model using pseudo-observed simulated datasets (PODS). We randomly selected 1,000 PODS from one million simulations computed under each simulated model. We used the same ABC selection procedure as above to compute the probability that the best model was indeed the best model given the posterior probability computed from the observed dataset: we kept the 0.02% simulated closest simulations, weighted them with an Epanechnikov kernel in the rejection step and performed the regression with 50 neural networks and 15 hidden layers. We then computed the robustness of each scenario: we computed the type I error rate that corresponds to the risk of excluding the previously inferred scenario when it is the true scenario and the type II error rate that corresponds to the risk of selecting the previously inferred scenario when it is false.

Random Forest model selection and cross-validations

In parallel to our ABC based model selection and cross-validation procedure we explored the ability of a Random-Forest algorithm [START_REF] Breiman | Random Forests[END_REF] to discriminate the different models and to estimate which summary statistics were the most informative. Random Forest (RF) is a machinelearning algorithm whose use has recently been advocated for model choice in ABC inference to circumvent curse of dimensionality problems and those linked to the choice of summary statistics [START_REF] Pudlo | ABC model choice via random forests[END_REF]. This approach is a non-parametric classification algorithm that uses bootstrapped decision trees to perform classification using a set (p) of defined predictor variables (here the summary statistics). Multiple (i.e. hundred to thousand) decision trees are grown and merged together and the ensemble makes the forest [START_REF] Breiman | Random Forests[END_REF]. Simulations that are not used in tree building at each bootstrap (the so called out-of-bag simulations OOB) are then used to compute the OOB error rate, which provides a direct method for cross-validation [START_REF] Breiman | Random Forests[END_REF][START_REF] Cutler | Random forests for classification in ecology[END_REF]). This method allows reducing the dimensionality of the data [START_REF] Cutler | Random forests for classification in ecology[END_REF]) but also estimating the relative importance of variables (here the summary statistics) through rankings.

Variable importance is measured by random permutations of the specified variable in OOB observations and new predictions are then obtained and compared to the original OOB data [START_REF] Cutler | Random forests for classification in ecology[END_REF]. One particularly attracting feature of random-forest is its insensitivity to strong correlations and high noise within data [START_REF] Pudlo | ABC model choice via random forests[END_REF].

We first constructed 6 random forests (one by river) using the randomForestSRC package in R allowing for parallelization and fast computations (Iswharan & Kogalur 2007[START_REF] Frankham | Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow[END_REF]Ishwaran et al. 2008). We grew 1,000 trees on subsets of 50,000 simulated dataset (5%) that were used as a training set. Prior analysis using different numbers of trees and training set sizes indicated that the OOB errors reach stationarity using between 500-1,000 trees (see also Fig 4), so we did not grow a bigger forest that would have required extensive computations. All summary statistics were included to get an estimation of the importance of each variable. This allowed us to estimate the OOB error rate for each comparison, which is similar to a prior error rate in ABC inference [START_REF] Pudlo | ABC model choice via random forests[END_REF]). Ultimately our forest was used as a prediction tool to compute the probability that our observed data belongs to one of the 5 alternatives models.

Parameter Estimation and cross-validation

Parameter estimation was performed for the best models using nonlinear regressions. We first used a logit transformation of the parameters on the 2,000 best replicate simulations providing the smallest Euclidian distance δ [START_REF] Csilléry | abc: an R package for approximate Bayesian computation (ABC)[END_REF]. We then jointly estimated parameters' posterior probability using the neural network procedure implemented in the abc package. We obtained the best model by weighted nonlinear regressions of the parameters on the summary statistics using 50 feed-forward neural networks and 15 hidden layers. We performed posterior predictive checks for cross-validation in an attempt to check the ability of our parameter estimates to generate data summary statistics close to the observed summary statistics. For each best model, we selected 10,000 posterior samples obtained after parameter estimation (from the abc package) and simulated 10,000 new datasets by using again ms and custom R scripts. We then again plotted the distance between our observed original values and our new simulations and computed the p-value for each statistic.

Results

Population diversity and divergence

A total of 6 populations pairs (727 individuals) were analysed using 13 microsatellite markers.

As already observed [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF], the genetic diversity of river lamprey, as measured by the averaged allelic richness was significantly greater than that of brook lamprey (Ar Lf =3.43, Ar Lp = 3.116, 15,000 permutations, P = 0.0010, Table 2). On the contrary there was no significant difference in expected heterozygosity between river lamprey (He Lf = 0.507) and brook lamprey (He Lp =0.46) (15,000 permutations, P =0.208). Global population genetic differentiation between river and brook lamprey was F ST = 0.061 (99%IC = 0.044-0.079) and ranged from 0 to 0.192. Genetic differentiation among river lamprey populations was significantly lower (F ST = 0.002) than among brook lamprey populations (F ST = 0.109) (15,000 permutations, P= 0.003). No river lamprey populations differed significantly from the others whereas all brook lamprey populations were significantly differentiated from one another (Table S1). Pairwise comparison within rivers revealed significant differentiation between river and brook lamprey populations in all cases except for the Bethune River. This differentiation varied between rivers and ranged from 0.028 (Oir and Bethune river) to 0.091 on the Bresle River.

Model comparisons

The classical ABC model-choice and random forest approaches generally yielded similar results as detailed in Table 3. In all population pairs, the model of strict isolation (SI) and of ancient migration followed by a period of strict isolation (AM) were clearly rejected. In two population pairs (Aa and Bresle), the best supported model by both the ABC and RF approaches was the SC model. In the Bethune, the best supported model by both methods was the IM model. In two cases (Hem and Risle), none of the methods was able to accurately discriminate between the two scenarios (SC and IM model). Finally, in the Oir river the two methods gave incongruent results with the model of panmixia (PAN) being the best supported model under the ABC framework, while the RF failed to distinguish between the IM and SC models.

Robustness and misclassifications errors from ABC analysis and random-forest

We checked whether our model comparison analysis was reliable and fitted well to the data by using pseudo observed datasets (PODS) and running the same ABC procedure as for our observed data. We found that robustness of our analysis was high for both the SI and PAN model in all population pairs (Table 3). However, the accuracy of the IM and SC models was always low and highly unreliable based on the classical ABC model choice procedure (Table 3). Since our simulations were mainly the same (the sole difference was the number of individual genotyped at each locus in each dataset) and model checks were similar between population pairs, we therefore present only robustness results

for one population pair (The Aa River, Table 3, Robustness). We secondly tested if a random-forest approach could help confirm the robustness of the rejected models and distinguish between the IM and SC models. The RF results confirmed that the models of strict isolation, ancient migration and panmixia were classified with a high accuracy (Table 4, Figure 3). The overall error rate (28.79%)

hides very different accuracies depending on the models. The OOB error (averaged over the population pairs) was still high between the AM and SI models (24.2%, see also table 4). However both the ABC and RF analyses (Table 3) clearly showed that these two models were never supported by our data so the classification error between these two models was a minor concern. On the contrary average OOB errors in pairwise analyses of IM versus SC model were as high as 45%

demonstrating that it was generally not possible to correctly classify our simulated data in their correct categories (see details in table 4). The estimation of variable importance (example in Figure 3) indicated that the most informative variables were systematically the mean and variance of G ST and of Delta mu 2 , generally followed by the estimators of allelic richness and expected heterozygosity in each population and globally (Figure 4 and Table S2).

Parameters estimation from the best models

We estimated the parameters in each population pair for both the IM and SC models that we failed to accurately distinguish and for the panmictic model in the Oir population pair. Results of parameter estimation are presented in Table 4. Data based on one random forest, each composed of 1,000 trees obtained from a trained set of 50,000 simulated predictor variables (summary statistics). The response variable is the demographic model. Estimation for the 6 remaining rivers yielded similar results and are presented in table S2 and Figure S1 Under the SC model, the (averaged) median effective mutation rate was 1.11 and 2.26 times smaller in river lamprey and brook lamprey respectively than their ancestral populations (Table 4). Similarly, θ Lf was always larger than θ Lp , reflecting the probable lower effective population size of the latter.

Estimates of thetas were on average 2.82 times larger in river lampreys than in brook lampreys under the IM model. Under the SC model estimates of thetas were 2.03 times larger in river lampreys than in brook lampreys. Under both the IM and SC models we also consistently noted an asymmetric migration, with a tendency towards higher migration from river lamprey to brook lamprey: averaged median 4Nem =16.45 and 16.86 under IM and SC respectively versus averaged 4Nem =3.48 and 6.64

under IM and SC respectively (Table 5). However, in one case out of 5 the reverse tendency was observed under the IM model (Bethune River), and in two cases under the SC model (Risle and Hem).

Estimates of divergence time and timing of secondary contact (SC only) yielded variable results and were not always accurately estimated (Table 5). Estimates from the Aa and Hem population pairs were the most accurate under both scenarios. Overall estimates of separation times were highly congruent under the SC model but revealed that populations would have come into secondary contact for a long period with the averaged time of secondary contact representing one third of the time since divergence. Finally, simulations of the Oir population pair were summarized by a single parameter (the effective mutation rate of a panmictic population) that was estimated with high accuracy (median = 0.57 95HPD:0.56-0.59]).

Posterior predictive checks

We performed posterior predictive checks in order to assess the ability of the models to accurately reproduce summary statistics close to our observed statistics based on 10,000 simulated datasets and computed the robustness of our inference. Under both the IM and SC models we consistently found some statistics that differed significantly from our observed data. These were the variance of expected heterozygosity (p<0.05) and the variance of allelic number (p<0.05). In some cases the mean Garza-Williamson index was not accurately reproduced (Table S4). Similarly, in three cases, the variance in F ST did not yield accurate results (p=0) (Table S4). Under the panmictic model (Oir population pairs) we found that the variance in Allelic Richness and the Garza Williamson index

were not accurately reproduced by our data (p=0, Table S4).

Discussion

Our goal was to test whether we could discriminate alternative scenarios of divergence between river and brook lampreys using a set of microsatellite markers and an ABC approach. We were able to reject the model of strict isolation and of ancient migration. In one case, the model of panmixia received the best support, whereas in other population pairs it was not possible to discriminate divergence with ongoing gene flow from a model of allopatric divergence followed by secondary contact.

Difficulty in distinguishing between ongoing migration and secondary contact

In spite of the availability of large amounts of genetic data and computer resources, few studies have explicitly tested alternative models of speciation (e.g. [START_REF] Ross-Ibarra | Historical Divergence and Gene Flow in the Genus Zea[END_REF][START_REF] Duvaux | Isolation and gene flow: inferring the speciation history of European house mice[END_REF][START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF]Butlin et al. 2014). While populations may diverge (and eventually become reproductively isolated) under various demographic scenarios, our results indicate that distinguishing between primary differentiation (divergence with gene-flow) versus allopatric divergence followed by secondary contact remains difficult when using genetic data from a limited number of neutral markers, even with advanced computational tools. Indeed, ABC as well as RF cross-validation clearly showed that the two models were wrongly classified almost half the time. The SC model tended to display a greater proportion of simulations wrongly classified into the IM model, a result that can be explained by the greater complexity of this model that displays one supplementary parameter and is inherently more difficult to infer. On the contrary, even though the OOB error rate was still high in the IM model, it tends to display fewer simulations wrongly classified in the SC model. Given the inherent difficulty of properly classifying the SC model even when it is true, our support for this model in some cases may suggest that it could be the true model under which lampreys have diverged.

Inability to distinguish between a scenario of isolation with migration and secondary contact is in accordance with theoretical expectations from [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. Using a simple modelling approach, these authours showed how genetic environmental associations at neutral markers such as microsatellites can quickly be lost after secondary contacts and then reach migration/drift equilibrium together with a pattern of isolation by distance, which corresponds to the populations in our study (see [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. In particular, they applied their model to the well-studied freshwater/marine stickleback (e.g. [START_REF] Colosimo | Widespread Parallel Evolution in Sticklebacks by Repeated Fixation of Ectodysplasin Alleles[END_REF][START_REF] Hohenlohe | Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags[END_REF][START_REF] Hohenlohe | Population Genomic Analysis of Model and Nonmodel Organisms Using Sequenced RAD Tags[END_REF] which shares several characteristics with our lamprey system, such as the existence of a single nearly panmictic marine population and small and almost independent freshwater populations. The application to the stickleback model showed that introgression proceeded independently between the different streams and was strongly asymmetric from the migratory to the resident populations, which is exactly the pattern we observed here (Table 4) under both the isolation with migration model (migration 4.2 times greater from river lamprey to brook lamprey) and the secondary contact model (migration 2.6 times greater).

The failure to reject panmixia in the Oir River can also be explained in the light of the conclusions of [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. It could be attributable to the low genetic divergence observed (F ST = 0.028) especially

given the small number of markers we used, but this pattern of near panmixia can also be attributed to a stronger introgression in this system than in all other investigated streams. In this particular case the mean size of river lampreys (225 mm, n=134) was much smaller than the size observed in other rivers (mean =303 mm, n = 389). Assuming that size difference is the most important cause of reproductive isolation [START_REF] Beamish | The Importance of Size as an Isolating Mechanism in Lampreys[END_REF]) a smaller size difference may facilitate mating of the two ecotypes and subsequent genome swamping. In both cases, inferences from this system based on neutral markers are necessarily difficult as this pattern may be explained by strong gene flow after an isolation period as well as by a very early stage of ongoing divergence.

Demographic parameter estimations and new insights on lamprey history

We assumed a mutation rate of 2.5e -4 , that is somewhat similar to what is observed in several fishes species [START_REF] Shimoda | Zebrafish Genetic Map with 2000 Microsatellite Markers[END_REF][START_REF] Steinberg | Rates and Patterns of Microsatellite Mutations in Pink Salmon[END_REF][START_REF] Yue | Mutation rate and pattern of microsatellites in common carp (Cyprinus carpio L.)[END_REF] Importantly, confidence intervals were rather large and overlapping between the two ecotypes. It is possible that we underestimated population size of brook lampreys in areas where they are strongly connected with river lampreys. Our analysis also suggested a population size reduction in brook lamprey as compared to its ancestral population (averaged mode = 1498, 95% CI = 1044-2198 under SC, 1922 and 95% CI=1470-2221 under IM). Similarly, since these estimates overlap with those from river lamprey, the evidence for a size reduction remains thin given that the two sister populations appeared connected.

Our estimates of timing of divergence provided similar estimates under both IM and SC. They suggested that the two ecotypes may have separated around 228,000 years ago (95% CI:178,000-307,000 years ago) under the IM model and around 259,000 years ago (95% CI:165,000-367,160 years ago) under the secondary contact model (assuming a generation time of 5 years, Hardisty & Potter, 1971). Such estimates are rather similar to what was observed in Dicentrarchus labrax [START_REF] Tine | European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation[END_REF]) but differ drastically from mtDNA estimates of divergence time available so far in lamprey [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF]. This discrepancy might be explained by the different type of molecular data used in each study, but also by the rough estimates of molecular evolution and microsatellite mutation rate.

Importantly, under the SC model, the secondary contact would have started around 85,000 years ago, representing one third of the total divergence time in lamprey. This result is rather older than the hypothesis of postglacial colonization of river by resident ecotypes as generally assumed in European fishes [START_REF] Bernatchez | Comparative phylogeography of Nearctic and Palearctic fishes[END_REF][START_REF] Aldenhoven | Phylogeography of ninespine sticklebacks (Pungitius pungitius) in North America: glacial refugia and the origins of adaptive traits[END_REF]. Such an ancient secondary contact implies that the genetic signature of historical geographic isolation carried by neutral markers may have been lost. In these conditions, neutral markers can converge to the same state than the one observed under primary differentiation [START_REF] Barton | Analysis of Hybrid Zones[END_REF]Charlesworth et al. 1997;[START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]). The SC model implies the accumulation of some Dobzhansky-Muller DMI in allopatry when the two ecotypes started to diverge.

While both theory [START_REF] Orr | The population genetics of speciation: the evolution of hybrid incompatibilities[END_REF] The ability to distinguish alternatives between SC and IM was low in both cases. In line with Pudlo et al.

(2014) we find that the RF approach possesses a series of advantages over the ABC approach such as 1) fast model choice procedure with simultaneous cross validation through OOB computations, 2) estimation of variable importance (i.e. of the summary statistics), 3) considerable reduction of computational time.

Estimating variable importance can be particularly interesting when a large set of variables are used without prior knowledge about the pertinence of the summary statistics used. Choice of summary statistics is an important process in ABC methodology [START_REF] Csilléry | Approximate Bayesian Computation (ABC) in practice[END_REF] for which relatively few tools are available. RF may provide such an objective tool that may be complementary to conventional ABC model choice and cross validation procedures. Note, however, that the neural network method provided in the abc packages performed very well and provided similar results to the RF model.To the extent of our knowledge, we provided the first study which empirically combines ABC and RF for model choice and crossvalidation.

Conclusion and perspectives

Our study sheds new light on the demographic process that has shaped the current genetic makeup of population pairs of European river and brook lampreys. In particular, we were able to reject a scenario of divergence in strict isolation and a scenario of ancient sympatric divergence. The scenario of panmixia was also supported only once and it is thus unlikely to be a generalizable scenario across the species range. In addition it illustrates the necessity of explicitly exploring alternative models of divergence before concluding on the prevalence of rapid parallel speciation [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. This study also illustrates how combining new modelling approaches can help improve our understanding of the complex process of speciation. However, it was not possible to firmly discriminate the SC or IM models but it is likely that distinguishing between these alternative scenarios is complicated in cases of ancient secondary contacts, especially when investigations are performed with a limited number of neutral markers. Finally combining modelling approach (e.g. SFS, Tine et al. 2014) with a higher number of markers and allowing for heterogeneous migration rate among loci (e.g. [START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF], variation of migration rate in time and variation of effective population size along the genome, may allow fine-tuning of our demographic investigations and provide great insight into the prevalence of secondary contact versus speciation with continuous gene-flow in nature. Data based on one random forest, each composed of 1,000 trees obtained from a trained set of 50,000 simulated predictor variables (summary statistics). The response variable is the demographic model.

Each page corresponds to a different river:

Launey, Guillaume Evanno

In prep for Molecular Ecology, abstract accepted for the special issue "molecular basis of adaptation and speciation"

Inferring the demographic history underlying parallel genomic divergence among pairs of parasitic and non-parasitic lamprey ecotypes

Quentin Rougemont 1,2 , Pierre-Alexandre Gagnaire

Introduction

Understanding the mechanisms responsible for the build-up and maintenance of species divergence is of primary importance in evolutionary biology. In particular, it is now common to observe highly heterogeneous differentiation patterns across the genome. However it is not always straightforward to determine whether these patterns are due to semipermeable barriers to gene flow [START_REF] Wu | The genic view of the process of speciation[END_REF][START_REF] Harrison | The Language of Speciation[END_REF]Larson 2014, Harrison 1986) The study of replicated pairs of populations (e.g. [START_REF] Schluter | Character displacement and replicate adaptive radiation[END_REF][START_REF] Nosil | Host-plant adaptation drives the parallel evolution of reproductive isolation[END_REF]Nosil et al. , 2009a;;[START_REF] Berner | Variable progress toward ecological speciation in parapatry: stickleback across eight lake-stream transitions[END_REF][START_REF] Kaeuffer | Parallel and nonparallel aspects of ecological, phenotypic, and genetic divergence across replicate population pairs of lake and stream stickleback[END_REF]Gagnaire et al. 2013b) inhabiting different environments offer great opportunities to investigate all these processes for which variable outcomes are expected [START_REF] Welch | Standing and flowing: the complex origins of adaptive variation[END_REF][START_REF] Lindtke | The genetic architecture of hybrid incompatibilities and their effect on barriers to introgression in secondary contact[END_REF]. It is often hypothesized that these populations offer independent replicates of a repeated evolutionary response to similar ecological constraints, and thus provide ideal systems to study ecological speciation (e.g. Feder et al. 2012). Parallel phenotypic and genetic divergence among population pairs is generally interpreted as the outcome of convergent parallel evolution due to the repeated action of natural selection driving speciation [START_REF] Schluter | Parallel Speciation by Natural Selection[END_REF][START_REF] Johannesson | Parallel speciation: a key to sympatric divergence[END_REF].

However, different scenarios can lead to such patterns of parallel genomic divergence [START_REF] Johannesson | Repeated evolution of reproductive isolation in a marine snail: unveiling mechanisms of speciation[END_REF][START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. For instance, allopatric divergence followed by secondary contacts in multiple refugia can mimic or contribute to this pattern of parallel divergence but is difficult to distinguish from primary differentiation [START_REF] Endler | Geographic Variation, Speciation, and Clines[END_REF][START_REF] Nj Endler | Problems in Distinguishing Historical from Ecological Factors in Biogeography[END_REF][START_REF] Barton | Analysis of Hybrid Zones[END_REF][START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. Therefore, understanding the origin of adaptive variation becomes a key issue in studies of parallel evolution, since there is a need to determine if divergence has been fuelled by new mutations, standing variation which arose by mutation or gene flow in the ancestral population, or recent secondary contact [START_REF] Welch | Standing and flowing: the complex origins of adaptive variation[END_REF]. Reconstructing the demographic history of species divergence may help in identifying the evolutionary scenarios that can generate observed divergence patterns, and therefore has the potential to reveal how much parallel divergence patterns reflect parallel evolutionary histories. Moreover, understanding the demographic history of divergence may be a necessary requisite to the implementation of selection detection tests, and may thus contribute to make better sense of genome scans [START_REF] Nielsen | Recent and ongoing selection in the human genome[END_REF][START_REF] Nielsen | Darwinian and demographic forces affecting human protein coding genes[END_REF][START_REF] Li | Joint analysis of demography and selection in population genetics: where do we stand and where could we go[END_REF].

New methods, relying on full Bayesian likelihood approaches [START_REF] Li | Inference of human population history from individual whole-genome sequences[END_REF][START_REF] Mailund | A New Isolation with Migration Model along Complete Genomes Infers Very Different Divergence Processes among Closely Related Great Ape Species[END_REF], approximating the likelihood through simulations [START_REF] Tavare | Inferring Coalescence Times from DNA Sequence Data[END_REF][START_REF] Beaumont | Approximate Bayesian Computation in Population[END_REF][START_REF] Beaumont | Approximate Bayesian Computation in Evolution and Ecology[END_REF] or computing composite likelihood from the site frequency spectrum [START_REF] Williamson | Simultaneous inference of selection and population growth from patterns of variation in the human genome[END_REF][START_REF] Gutenkunst | Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data[END_REF]) have greatly helped testing increasingly complex hypotheses about demographic history. However several challenges remain. One particularly challenging task in reconstructing the history of species divergence is to integrate temporal variations in gene flow intensity as well as the possibility for heterogeneous amounts of gene flow across the genome. Only a few studies have taken this heterogeneity into account (e.g. [START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF][START_REF] Sousa | Identifying Loci Under Selection Against Gene Flow in Isolation-with-Migration Models[END_REF][START_REF] Tine | European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation[END_REF] to address the effect of genetic barriers reducing the effective migration rate at linked neutral loci [START_REF] Barton | The barrier to genetic exchange between hybridising populations[END_REF][START_REF] Feder | The Efficacy of Divergence Hitchhiking in Generating Genomic Islands During Ecological Speciation[END_REF]). Here, our goal was to address whether parallel phenotypic differentiation between pairs of lamprey ecotypes was accompanied by parallel genetic differentiation, and if these patterns have likely resulted from independent or shared divergence histories.

To address this issue, we used several population pairs of parasitic river lamprey (Lampetra fluviatilis, Linnaeus 1758) and non-parasitic brook lamprey (Lampetra planeri, Bloch 1784), which exhibit varying level of geographical connectivity (i.e. from complete sympatry to completely disjunct distributions). Lampreys are jawless vertebrates (agnathans) generally occurring as "paired" species (or ecotypes) with drastically divergent life-history strategies at the adult stage, with parasitic anadromous and nonparasitic freshwater resident forms. Relatively little is known about the speciation level among parasitic and freshwater resident ecotypes [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. Most genetic studies have found little to moderate levels of genetic differentiation between ecotypes [START_REF] Schreiber | Population genetics of a cyclostome species pair, river lamprey (Lampetra fluviatilis L.) and brook lamprey (Lampetra planeri Bloch)[END_REF][START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Blank | A mitochondrial multigene approach contributing to the systematics of the brook and river lampreys and the phylogenetic position of Eudontomyzon mariae[END_REF][START_REF] Pereira | Phylogeographical analysis reveals multiple conservation units in brook lampreys Lampetra planeri of Portuguese streams[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF], but they usually did not distinguish sympatric and parapatric sites and they did not use enough markers to address the extent of divergence across the genome. [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF] used RAD-Sequencing and found a strong genome-wide differentiation between ecotypes from a single population pair from the southern limit of the distributional range and provided a list of candidate genes putatively involved in adaptation to migratory versus resident life-styles, which could be implicated in reproductive isolation between ecotypes. However, this study provided limited insights into the historical, demographic and selective aspects underlying genetic divergence as it was on a single population pair. More recently, [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF] studied ten population pairs located in the northern part of the distributional range and varying in their level of geographic connectivity. They showed that within-river opportunities for gene flow have a strong influence on the average level of differentiation. The parapatric pairs displayed stronger genetic differentiation than sympatric pairs, which were less divergent than the southern sympatric pair described in [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF]. They thus hypothesize that incomplete reproductive isolation and gene flow have allowed stronger genetic introgression in northern compared to southern sympatric pairs, which would have important consequences for choosing the most relevant population pairs to disentangle the effects of selection from genetic drift during divergence. In the populations connected by gene flow, only genetic regions involved in divergence are expected to resist its homogenizing effect, and thus these populations would be the best candidates for the study of reproductive isolation (RI).

In this study, we used a model-based approach to reconstruct the divergence history of L.

fluviatilis and L. planeri using RAD sequencing data. We took advantage of the original distribution of resident and migratory lamprey in both sympatry and parapatry in nine replicated pairs to first document levels of gene-flow between ecotypes and among population pairs. We then used these population pairs, in order to i) compare alternative models of demographic divergence history, ii)

estimate the proportion of the genome experiencing reduced gene flow, iii) identify genomic markers showing particularly high differentiation between ecotypes, and iv) evaluate the extent of parallelism among replicate pairs of ecotypes.

Methods

Sampling

We focussed on 9 populations pairs of lampreys sampled in France including 4 sympatric and Lampetra planeri (Lp) from the Odon being captured far upstream in sites unlikely to be colonized by Lampetra fluviatilis (Lf), whereas Lp on the Risle might still be connected to Lf through passive drift during flood events. Similarly, pairs from the Loire-Cens, Dordogne-Jalles, and Garonne-Saucats were strongly disconnected, with river lampreys collected in estuarine areas before spawning and brook lampreys collected in isolated upstream reaches not accessible to river lampreys. Details of sampling methods are provided in [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. A total of 338 individuals were included in the present analysis (Table 1). Three sea lamprey (Petromyzon marinus) individuals were also included as outgroup to polarize SNPs.

Library preparation and Sequencing

Genomic DNA was extracted from each individual using individual extraction kits (NucleoSpin Tissue, Macherey Nagel) following manufacturer's recommended protocols. DNA quality was checked using a spectrophotometer (Nanodrop 2000, Thermo Scientific) and quantity was assessed

Genotyping and Bioinformatics

Raw-reads were first demultiplexed using GBSX [START_REF] Herten | GBSX: a toolkit for experimental design and demultiplexing genotyping by sequencing experiments[END_REF], filtered for overall quality, checked for the presence of a barcode using Cutadapt [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF]) and trimmed to 85-bp.

We then took advantage of our paired-end sequencing to remove PCR duplicates using Stacks'

program clone_filter [START_REF] Catchen | Stacks: an analysis tool set for population genomics[END_REF]. Our data contained an average of 38.5% of PCR duplicates that were removed. We subsequently used the Stacks pipeline to identify RAD loci from forwards reads using all individuals from all populations. We used ustacks with a minimum stack depth of 4 (m = 4) and a maximum of four mismatches (M = 4). These parameters were determined using replicated individuals included at random in each sequencing platform. This allowed us to minimize the presence of paralogs in the dataset while maximizing the genetic diversity obtained. Due to the large divergence with P. marinus (see below) and an incompletely assembled genome for this species [START_REF] Smith | Sequencing of the sea lamprey (Petromyzon marinus) genome provides insights into vertebrate evolution[END_REF] we performed a de novo alignment using cstacks. Sequences with less than 3 nucleotide differences were considered as homologs, resulting in a catalog containing 28,2610 RAD tags. Each individual was finally genotyped after matching its RAD data against the catalog, and the genotypes were exported in vcftools format [START_REF] Danecek | The variant call format and VCFtools[END_REF] for further filtering. Different datasets were created: for population genetics analyses we constructed nine pairwise datasets corresponding to each river and composed of the two ecotypes of brook and river lampreys; for demographic analyses we constructed another series of nine datasets with different filtering criteria (see below) and we ultimately created a global dataset to investigate patterns of global population structure. We applied a series of filtering steps as we were particularly concerned about recovering biologically meaningful SNPs. To do so the dataset was first split into nine datasets corresponding to each river. We further split each of them into two datasets corresponding to each ecotype. We kept markers genotyped in at least 80 % of the individuals in each population, with a minimum sequencing depth of 10 and a maximum of 100 (to avoid the inclusion of highly repetitive tags that could reflect paralogy). We then excluded loci deviating from Hardy Weinberg equilibrium assumptions by keeping only those loci with a p-value greater than 0.05. In order to limit the presence of weakly polymorphic loci being uninformative (Roesti et al. 2012b), we kept loci with a minor allele frequency (MAF) greater than 0.1 in at least one of the populations separately or with a MAF greater than 0.05 in each global dataset (containing the two ecotypes) for all analyses except for demographic inferences. This filtering step allowed us to recover polymorphism occurring at low frequency in some river lampreys but monomorphic in brook lampreys and vice versa in order to obtain an equivalent representation of low frequency polymorphisms present in each population. In a final filtering step, we excluded loci with an observed heterozygosity greater than 0.5 in both freshwater and migratory lamprey in each river [START_REF] Hohenlohe | Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout[END_REF]. The nine 'pairwise' datasets obtained were then merged into a global dataset in which only markers genotyped in at least 70 % of the individuals across all populations were kept to reduce the proportions of missing data. All filters were performed using VCFtools and R scripts embedded in custom bash scripts.

Population genetic differentiation, diversity and individual clustering

The level of genetic differentiation between ecotypes from a given river and over all populations was computed using F ST [START_REF] Weir | Estimating F-Statistics for the Analysis of Population Structure[END_REF] for each locus in VCFtools. Negative values were set to zero to compute mean genome-wide F ST . Significance of F ST values and 95% confidence intervals were computed in R using bootstrap methods. We further investigated the proportions of shared polymorphisms between these pairs. To investigate the level of population clustering we first computed a matrix of Nei's genetic distance Da, [START_REF] Nei | Accuracy of estimated phylogenetic trees from molecular data. II. Gene frequency data[END_REF]) across all populations to create a UPGMA dendrogram describing broad patterns of genetic structure. Secondly we used Admixture [START_REF] Alexander | Fast model-based estimation of ancestry in unrelated individuals[END_REF] to perform individual clustering and to estimate individual admixture proportions. Given the strong geographical component of genetic differentiation in L. planeri (see results), we were not able to distinguish the two morphs based on this full dataset. However, we were concerned about distinguishing putatively recent hybrids from pure individuals to avoid biases in our demographic inferences. We therefore used the 10% most highly differentiated SNPs that were shared between the four least differentiated pairs (i.e. global F ST < 0.10), based on the distribution of between-pairs F ST, to improve our discrimination power between the two ecotypes and identify potential hybrids. This resulted in a set of 40 SNPs that were also shared between the remaining population pairs (see results). We then tested the ability of these markers to assign individuals to their respective ecotypes and to identify putative hybrid individuals (i.e. possible F1 and backcrosses) using Structure2. 3.3 (Pritchard et al. 2000) and NewHybrids [START_REF] Anderson | A Model-Based Method for Identifying Species Hybrids Using Multilocus Genetic Data[END_REF]. Details of Structure and NewHybrids settings are presented in supplementary materials. The robustness of inference from NewHybrids was evaluated using simulated data with HybridLab [START_REF] Nielsen | hybridlab (version 1.0): a program for generating simulated hybrids from population samples[END_REF]. Briefly, individuals with a q-value greater than 0.9 of being either river lamprey or brook lamprey were used to generate individual genotypes belonging to 4 different hybrid classes (F1; F2 and first generation backckrosses), which were then reclassified in NewHybrids (see supplementary Materials and Results). Since the subset of 40 SNPs offered a high power to identify hybrid genotypes (> 99%), we excluded all individuals identified as hybrids from the real dataset containing the nine populations pairs, and performed admixture analysis using the 40 markers and populations to better discriminate the ecotypes.

Demographic history of divergence

We used a modified version of the diffusion approximation method implemented in δaδi [START_REF] Gutenkunst | Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data[END_REF] to analyse the joint site frequency spectrum (JSFS) of river and brook lampreys in each population pair. Different models of demographic divergence were compared for each pair and the model providing the best fit to the observed JSFS was determined using the Akaike Information Criterion (AIC). The main changes to the original δaδi program that were implemented in the modified version by [START_REF] Tine | European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation[END_REF] include (i) the addition of two Simulated Annealing optimization phases prior to the BFGS to better explore the likelihood landscape and improve global convergence, (ii) the introduction of a parameter that accounts for the proportion of mis-oriented SNPs in the JSFS, and (iii) most importantly, the incorporation of varying migration rates across the genome to account for semi-permeability. This is done by defining two categories of loci occurring in proportions P and 1-P across the genome, the first one containing neutral loci that are exchanged between populations with a gross migration rate (m12 and m21), and the second category comprising selected and hitchhiker loci that experience a reduced effective migration rate (me12 and me21).

Seven alternatives models of speciation were fitted to the observed JSFS and compared (Fig τ sc is the number of generations since the two morphs have entered into a secondary contact after a period of isolation. M 12 and M 21 represent the effective migration rates expressed in 2.N ref m units per generation with m the proportion of population made of migrants from the other populations. In addition to these four models, three additional models incorporating heterogeneity in divergence along the genome were tested: isolation with heterogeneous migration (IM2m), ancient migration with heterogeneous effective migration along the genome (AM2m) and secondary contact with heterogeneous migration (SC2m)

The observed JSFS was built using a SNP dataset with no minor allele frequency threshold filter. We randomly sampled a single SNP per RAD tag to avoid as much as possible including linked SNPs in the spectrum, which produced JSFS composed of 5000 to 10000 SNPs depending on the analysed pair. We excluded the population from the Dordogne River as it contained only 7 individuals suitable for this analysis (one individual had a too low genotyping rate). A total of 25 independent runs were performed for each model to check for convergence. The run providing the lowest AIC for each model was kept for comparisons among models and parameter estimations. Demographic parameters were all scaled by theta in the ancestral population and were not converted into biological estimates as relevant estimates of mutation rate are not available in lampreys. In addition, estimating the total number of nucleotides that effectively contribute to the JSFS construction remains complicated when no reference genome is available. However, some parameter ratios which do not depend on theta or on the mutation rate can be used for interpretation, even though their absolute values must be interpreted very carefully. We used the folded JSFS for model choice because using the sea lampey as the outgroup resulted in a too low number of polymorphisms available. Nevertheless, we validated diffusion accuracy for estimates of migration rate and proportion of neutral loci using the unfolded JSFS. In these cases, the previously estimated time of separation, time of secondary contact or time of migration stop and effective population size were fixed based on estimates from the folded JSFS.

Detecting selection and measuring parallel differentiation

Our demographic investigations confirmed that some populations exchanged genes while others experienced almost no gene flow (see results). Consequently populations experiencing gene flow (identified in δaδi) were more appropriate to investigate the basis of reproductive isolation than populations with low gene flow as only regions involved in reproductive isolation should resist the homogenising effect of gene flow. In addition, this complex history challenges the use of classical model-based outlier detection tests which may result in high rates of false positives in such cases [START_REF] Lotterhos | Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests[END_REF][START_REF] Frankham | Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow[END_REF]. To circumvent these problems, we took advantage of our previous analyses where model parameters were inferred from the best model identified, in order to simulate datasets using neutral gene flow estimates and subsequently compute the neutral envelop of F ST under this model. Markers with F ST values greater than this envelop were considered as putatively under direct or indirect selection. We used ms [START_REF] Hudson | Generating samples under a Wright-Fisher neutral model of genetic variation[END_REF]) and msstatsFst (Eckert & al. 2010) embedded within custom scripts to perform such analyses and compute Weir et Cockerham's F ST . We generated 200 000 SNPs with the same number of individuals as those observed in our real dataset in each of our simulated datasets.

In order to test for parallelism in divergence we first counted the number of shared outliers (determined from our neutral model) between connected pairs (i.e. 10 comparisons) and performed randomizations tests to verify that this number was not due to chance alone. We subsequently constructed coplots of F ST /F ST between pairs of rivers to better visualize the extent of outlier sharing and parallelism. We then fitted linear models and compared their slope to investigate patterns and directionality of introgression. A slope of 1 indicates a similar level of differentiation while a slope significantly different from 1 indicates asymmetric introgression into the compared pairs. To validate our hypothesis that the most disconnected populations were less suited to investigate patterns of parallelism, we also performed coplots of F ST /F ST between pairs of disconnected rivers based on outliers identified from our neutral model. We then compared the distribution of correlation coefficients between connected pairs and disconnected populations pairs using simple boxplots. To further gain insight into allele frequency shifts we determined the frequency of the derived allele (using the P. marinus as the outgroup) when this information was available. We used a stringent approach in which the ancestral state of the allele was inferred only when fixed in P. marinus. We then tested for asymmetries in allelic frequency between the two morphs using t-tests. All analyses were performed using custom R scripts. Overall Lf displayed 2.7 times more unique polymorphic sites than did Lp. As for heterozygosity, this pattern hides a more complex situation in which sympatric populations of migratory lampreys contained 1.8 times more private polymorphic loci than resident lamprey whereas parapatric migratory populations contained 3.2 times more polymorphic loci than isolated resident populations. The subset of 40 highly differentiated markers revealed a F ST of 0.7 between Lp and Lf. Structure analysis indicated that this marker set was able to accurately discriminate the two morphs (mean qvalue from Structure was 0.97 and 0.95, in river and brook lamprey respectively). Structure analysis also identified a total of 22 individuals (5 females, 9 males, 7 with undetermined sexes, Table S3)

Results

Genome-wide diversity

with mixed q-values resembling F1 and one individual of possible backcross origin. NewHybrids confirmed all Structure results and identified 20 F1 hybrids, 1 F2 and 1 backcross. The majority of individuals (64%) displayed a river lamprey phenotype and the 36% remaining a brook lamprey phenotype (Table S3). Simulated parents, F1 and backcrosses with Lp individuals using HybridLab were always correctly assigned into their respective categories at the q-value threshold greater than 0.9, yielding a detection power of 100% for these three hybrid classes (table S4). One individual backcrossed with Lf and 7 F2 were correctly classified as of hybrid origin but with a q-value threshold below 0.9, hence yielding a power of 99.6% and 97.6% respectively.

Pattern of genome-wide differentiation

After exclusion of hybrids, the global genome-wide differentiation between the two morphs was 0.128 (95%CI =0,124-0,131, P <0.00001) but hides very different levels of genetic differentiation across pairs as shown table 1. Most parapatric rivers were more genetically differentiated than sympatric pairs (Table 1). In sympatry, genome wide F ST between the two morphs ranged from 0.045 in the Oir River to 0.135 in the Bresle River. F ST in parapatry ranged from 0.06 in the Risle River to 0.20 in the Odon River. Global F ST between river lamprey populations was 0.01 (95%CI=0.005-0.015, P <0.0001, ranging from F ST = 0 to 0.024). In contrast, brook lamprey populations displayed a stronger genetic structure (F ST = 0.167, 95%CI=0.162-0.173, P <0.0001) ranging from F ST = 0.062 to 0.262. The genome-wide variance of F ST increased with geographical isolation and displayed increasing values of the right tail of an L-shaped F ST frequency distribution (Fig 4). Accordingly, the number of differentially fixed loci increased with genome-wide divergence between ecotypes from 0 in the Oir and Aa rivers to more than 20 on the Bresle, Dordogne and Odon. 21 fixed loci were found on the Bethune, despite low genome-wide divergence. Similarly the 90% F ST quantile increased with increasing patterns of genome wide divergence and of increasing geographical isolation in all cases but the Bresle, which was moderately differentiated despite the close geographical proximity of river and brook lamprey samples.

Demographic history

Demographic inference results are provided detailed in Table 2, For each best model, m12, m21, me12, me21 and P were estimated using the unfolded JSFS.

Blast analysis

Blasts analyses (Altschul et al. 1997) of a set of 34 independent sequences (see supplementary methods) of parallel outliers resulted in the identification of 2 SNPs and their sequences (85pb) with significant hits (e-value < 10e-7, sequence identity greater than 93%) (Table S4). None matched to the sea lamprey contigs but we found significant matches to the Arctic lamprey Lethentheron camtshaticum draft genome (see Table S4). Among them, one gene known to play a key role in osmoregulation was found. This gene was also found to play a key role in the expression of GnRH, a key developmental hormone. In the second sequence, we also found key genes involved in the immune system, an axial patterning gene, a pineal gland specific opsin gene and a sodium channel gene that was also known to play a role in osmoregulation. Two studies [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF]Yamazaki and Nagai 2013) found similar results with their sequence matching to the same genes.

Discussion

Disentangling the relative influence of demographic and selective processes at the genomic level is a challenging issue in speciation studies. In particular, most studies that tested whether parallel phenotypic divergence is mirrored by genetic parallelism did not attempt to infer historical divergence scenarios to explicitly account for the confounding effects of demography in the detection of selection [START_REF] Hohenlohe | Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags[END_REF][START_REF] Kaeuffer | Parallel and nonparallel aspects of ecological, phenotypic, and genetic divergence across replicate population pairs of lake and stream stickleback[END_REF]Gagnaire et al. 2013;[START_REF] Roda | Genomic evidence for the parallel evolution of coastal forms in the Senecio lautus complex[END_REF]Butlin et al. 2014;[START_REF] Westram | Do the same genes underlie parallel phenotypic divergence in different Littorina saxatilis populations?[END_REF][START_REF] Ravinet | Shared and nonshared genomic divergence in parallel ecotypes of Littorina saxatilis at a local scale[END_REF]. Here, we compared contrasted demographic divergence models to determine the most likely evolutionary scenario underlying genome-wide patterns of divergence among nine pairs of L. fluviatilis and L. planeri displaying variable levels of geographic connectivity. Gene flow was strong in four connected pairs whereas the most geographically isolated populations were highly genetically differentiated and showed reduced contemporary gene flow. The most connected populations consistently revealed a signal of historical divergence followed by a recent secondary contact between L. fluviatilis and L. planeri. In addition, there was a higher degree of parallelism in the differentiation level of shared outliers in connected populations than in disconnected ones. Altogether, these results suggest that correlated genomic differentiation patterns among replicate ecotype pairs results from a common history of divergence and gene flow rather than independent gene reuse due to the repeated action of natural selection [START_REF] Via | Localizing FST outliers on a QTL map reveals evidence for large genomic regions of reduced gene exchange during speciation-with-gene-flow[END_REF]Roesti et al. 2015).

Level of connectivity as a determinant of hybridization

Before analysing genomic differentiation patterns and demography, a prerequisite was to confirm that RAD sequencing analysis of population genetic diversity and structure corroborates recent findings based on neutral markers [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF], and then to determine the most pair) a relatively high proportion of hybrids was observed. Genome-wide differentiation levels between L. fluviatilis and L. planeri thus appeared to be mostly determined by the degree of geographic connectivity, which directly influence the opportunity for gene exchange through hybridization. Among the 4 highly connected pairs, the Aa and Oir rivers displayed the largest proportions of F1 hybrids (Aa: 16%: Oir 13%). Evidence for frequent hybridization in these hotspots may be exacerbated by sampling effects, especially if individuals were collected in places where hybrids tend to occur at higher densities (Vines et al. 2015) as it can be the case during the simultaneous sampling of individual in the same nest. The comparatively small proportions of later generation hybrids that were detected (one backcross and one F2) may suggest some form of hybrid breakdown (i.e. selection against hybrids or reduced fertility), an hypothesis that would require a more extensive sampling in these hybrid zones to be validated. These results confirm the interest of these populations for the study of speciation in lampreys [START_REF] Barton | Analysis of Hybrid Zones[END_REF][START_REF] Abbott | Hybridization and speciation[END_REF][START_REF] Harrison | Hybridization, Introgression, and the Nature of Species Boundaries[END_REF], and raise the necessity of investigating the effect of gene flow during the long term divergence history of lamprey ecotypes.

A globally shared history of divergence

Our demographic inferences provided new insights into the recent history of divergence, revealing a relatively longer period of divergence in allopatry compared to the subsequent recent episode of secondary contact. In accordance with previous studies [START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF][START_REF] Tine | European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation[END_REF], we found that integrating the heterogeneity of migration rate strongly improves models' prediction accuracy, thus supporting the importance of taking this source of variation into account for inferring the history of gene flow during speciation. Our analysis revealed two well supported scenarios (ΔAIC > 10) related to the degree of connectivity between L. fluviatilis and L. planeri populations. Connected populations pairs generally held a signal of secondary contact with heterogeneous introgression rates (SC2m), whereas disconnected populations pairs held a signal of recent divergence preceded by heterogeneous migration (AM2m). These contrasted divergence models may however not necessarily reflect radically different divergence histories. Indeed, the signal of a past secondary contact may have been lost or obscured by recent drift in parapatric populations. In any case, our results do not support the hypothesis of divergence with ongoing migration (IM/IM2m), except in the case of the Bresle River where it could not be excluded. However in this river, all models including heterogeneous migration rates displayed nearly similar supports (ΔAIC < 10 for AM2m and SC2m compared to IM2m). A possible explanation is that a large difference in effective population sizes during the initial phase of the secondary contact has facilitated gene swamping from the largest population into the small introgressed population.

In the connected populations where the secondary contact scenario was unambiguously detected, only regions involved in divergence are expected to resist the homogenizing effect of gene flow. This makes these population pairs good models to study the evolution of reproductive isolation.

These results, together with the finding of hybrids confirm that RI remains partial as suggested earlier [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. Thus, the genetic architecture of divergence between lamprey ecotypes seems mostly consistent with the existence of barrier loci that locally reduce the effective migration rate along the genome (Barton & Bengtson 1986;[START_REF] Feder | The Efficacy of Divergence Hitchhiking in Generating Genomic Islands During Ecological Speciation[END_REF]. Parameters estimates in connected populations further suggest that the period of allopatry has been (on average) 8 times longer than the duration of secondary contact, which matches well the idea of a differential erosion of past genetic differentiation outside the direct vicinity of barrier loci. This seems also consistent with the overall low level of reproductive isolation measured experimentally by [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. This interpretation is also compatible with the lack of divergence observed with mitochondrial markers [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Blank | A mitochondrial multigene approach contributing to the systematics of the brook and river lampreys and the phylogenetic position of Eudontomyzon mariae[END_REF]) and the moderate level of divergence observed at neutral loci [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF][START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. We also repeatedly found higher effective population size in L. fluviatilis compared to L. planeri, which may contribute to increased genome swamping in L. planeri. In these conditions, the signal of adaptation and reproductive isolation held by divergent loci can quickly be lost as may be the case in the least differentiated populations (eg. Oir) (Yeaman 2015).

Recent gene flow and the origin of genetic parallelism

Estimates from our genome scan model revealed a high proportion (between 2% to 14%) of loci that were departing from neutrality in each ecotype pair. The level of parallelism was significantly larger in connected pairs compared to disconnected pairs (Fig. 6 And Fig. 7), which is the exact pattern expected after differential introgression [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]). Thus, our results support that recent gene flow has played a key role in generating genetic parallelism, because effective migration is strongly reduced in some regions of the genome. These genomic regions experiencing restricted introgression, which are believed to harbour speciation genes, are best revealed in connected population pairs where the confounding effect of drift is less important than in disconnected populations. We found 28 loci shared across the 5 connected pairs. Such level of parallelism was greater than expected by chance, and in most cases stronger than that observed in several other systems (Gagnaire et al. 2013;[START_REF] Perrier | Parallel and nonparallel genome-wide divergence among replicate population pairs of freshwater and anadromous Atlantic salmon[END_REF][START_REF] Westram | Do the same genes underlie parallel phenotypic divergence in different Littorina saxatilis populations?[END_REF][START_REF] Ravinet | Shared and nonshared genomic divergence in parallel ecotypes of Littorina saxatilis at a local scale[END_REF]. In addition, our demographic inferences suggested that about 6 to 12% of the sampled genome was not evolving neutrally in connected populations against 11 to 17% in disconnected ones, in which our modelling approach may underestimate the neutral variance in differentiation values.

Different hypotheses can explain the partial genetic parallelism observed (black and maroon points in Fig. 6). First, such "private" outliers may belong to the peak-valley-peak signature of divergence between different freshwater derived populations left by a selective sweep or a recent spatial recolonization by the brook ecotype [START_REF] Bierne | The Distinctive Footprints of Local Hitchhiking in a Varied Environment and Global Hitchhiking in a Subdivided Population[END_REF]Kim & Maruki 2011;[START_REF] Roesti | The genomic signature of parallel adaptation from shared genetic variation[END_REF]). Second, coupling of endogenous barriers (e.g. Dobzhansky-Muller Incompatibilities) and exogenous barriers may occur [START_REF] Barton | The evolution of strong reproductive isolation[END_REF], resulting in complex architectures. Depending on the timing and duration of secondary contact, these architectures can be differentially eroded by gene flow, hence also contributing to partial parallelism among different pairs. Finally the very high number of outliers in disconnected populations can be simply explained by the major role of drift that impacts genomewide differentiation among populations inhabiting fragmented networks such as rivers [START_REF] Fourcade | Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection[END_REF]. In these conditions, genome scans are probably not suited to detect the genomic regions that were influenced by selection. On the other hand, these populations were extremely useful to highlight the instrumental role of gene flow in generating partial parallelism in the connected population pairs.

We also aimed at investigating the origin of genetic variation that contributes to parallel divergence patterns at the molecular level. The modelling approach indicated that parallelism was not generated by independent de novo mutations arising during divergence with gene flow. Other possible scenario may involve (i) secondary contact from multiple freshwater refugia, (ii) parallel gene reuse from standing variation present in the parasitic population, or (iii) secondary contact between parasitic and non-parasitic populations having diverged in different refugia, with a subsequent spread (spatial re-assortment) of alleles involved in non-parasitism in the neighbouring rivers (i.e. the transporter hypothesis, [START_REF] Schluter | Genetics and ecological speciation[END_REF][START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF][START_REF] Welch | Standing and flowing: the complex origins of adaptive variation[END_REF]. The scenario (ii) would imply divergence with gene flow, a model that was not supported in our demographic inferences. Without further support for the multiple refugia hypothesis, the hypothesis of a spatial re-assortment of ancestral variation by migration between rivers appears more parsimonious. This raises the question of whether L. planeri alleles are segregating at low frequency in L. fluviatilis populations at linkage equilibrium, or instead spread among rivers through hybrid genotypes. In any case, the repeated colonization of new rivers by a few individuals with brook-adapted alleles is expected to have driven rare mutations to high frequencies. This allele surfing effect (Travis et al. 2007;Excoffier & Ray 2008;Lehe et al. 2012) has been detected here since we found an excess of derived mutations reaching higher frequencies in L. planeri than L. fluviatilis populations for the most highly differentiated loci (Fig. 8). This last line of evidence supports a scenario involving a spread of founder genotypes from river to river during the post-glacial recolonization of rivers by the L. planeri ecotype.

The 28 strongly differentiated loci, repeatedly found across multiple locationsnine population pairs could be considered as good candidates implied in the divergence between L. planeri and L.

fluviatilis. Accordingly, some of these outliers were located in genomic regions containing key genes of the GnRh2 family involved in maturation and growth, another gene involved in fast skeletal development and two genes involved in immunity. Interestingly, these genes were also identified as outliers between L. planeri and L. fluviatilis by [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF] in a population pair from Portugal, which further suggests a shared history of divergence and adaptation at a larger spatial scale.

Finally a last question is whether the heterogeneous patterns of differentiation found here between L. planeri and L. fluviatilis were exclusively compatible with the hypothesis of a semipermeable barrier to gene flow [START_REF] Wu | The genic view of the process of speciation[END_REF]). An alternative hypothesis is post-speciation selection at linked sites that locally increased FST measures by reducing within population diversity [START_REF] Cruickshank | Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow[END_REF]. Our demographic inferences were consolidated by the occurrence of hybrids which support a role for gene flow in eroding most of the differentiation across the genome. This interpretation is not incompatible with recombination rate variations across the genome favouring the accumulation of differentiation in regions of low recombination [START_REF] Noor | Islands of speciation or mirages in the desert? Examining the role of restricted recombination in maintaining species[END_REF][START_REF] Turner | Genomic islands of speciation or genomic islands and speciation[END_REF][START_REF] Tine | European sea bass genome and its variation provide insights into adaptation to euryhalinity and speciation[END_REF]). However, given the high level of genetic differentiation observed in some regions, which contrast to the low levels of differentiation elsewhere, it appears unlikely that linked selection in low recombining regions would produce such heterogeneity alone. The two processes may act jointly to shape genomic divergence in lampreys, and would be better evaluated using a genetic map.

Conclusion and perspectives

Our results clearly support that parallel patterns of genetic divergence between L. planeri and L. fluviatilis can be caused by a common history of divergence initiated in allopatry, and then followed by secondary gene flow eroding past divergence at variable rates across the genome. The level of geographic connectivity between population pairs was a strong determinant of observed divergence patterns, with direct impacts on both demographic inference and genome scans. In particular, stronger drift in populations of small effective size could obscure signals of divergence and result in smaller level of parallelism. In these situations, gene swamping and further ecological divergence can also act to respectively erode divergent regions or create new regions of divergence that will not bear any signal of parallel divergence. Overall, our data support the idea that the speciation process is best studied in population pairs experiencing high levels of gene flow. L. fluviatilis and L. planeri were thus best described as partially reproductively isolated ecotypes. In addition, the use of replicated pairs enabled us to identify candidate regions under the direct or indirect effect of selection. Further investigations of these genomic regions will have to be performed in the future to determine their role in the evolution of life history divergence between L. planeri and L. fluviatilis. Finally, it would be particularly interesting to accurately quantify the relative contribution and interactions between recombination rate variations, selection at linked site [START_REF] Charlesworth | The Relations Between Recombination Rate and Patterns of Molecular Variation and Evolution in Drosophila[END_REF] and differential introgression (Harrison 1986) on the heterogeneous patterns of differentiation. Full genome data combined with demographic modelling at the European scale and integrating local variations in effective population size could help address these issues.

Table S3: NewHybrids performance on simulated data

Simulations are based on 100 individuals of each category except on the Risle river, where 50 individuals were generated, due to a small initial sample size. Simulations are performed on each river separately. Results are averaged over the three rivers. Corresponding author: quentinrougemont@orange.fr

Inferred status

Introduction

Human activities strongly modify natural ecosystems (Vitousek et al. 1997b), and have strong impacts on evolutionary trajectories of wild species [START_REF] Palumbi | Humans as the world's greatest evolutionary force[END_REF]. In particular, habitat fragmentation is a major threat on wild species (Vitousek et al. 1997b;[START_REF] Fahrig | Effects of Habitat Fragmentation on Biodiversity[END_REF]. Habitat fragmentation can decrease dispersal rates, which reduces gene flow among subpopulations and ultimately decreases effective population size and genetic diversity [START_REF] Frankham | Inbreeding and Extinction: Island Populations[END_REF][START_REF] Frankham | Genetics and extinction[END_REF][START_REF] Couvet | Deleterious Effects of Restricted Gene Flow in Fragmented Populations[END_REF][START_REF] Dibattista | Patterns of genetic variation in anthropogenically impacted populations[END_REF][START_REF] Blanchet | Species-specific responses to landscape fragmentation: implications for management strategies[END_REF][START_REF] Whiteley | Fragmentation and patch size shape genetic structure of brook trout populations[END_REF]. Small populations are expected to experience stronger effects of genetic drift, potentially leading to higher levels of inbreeding (Frankham, 1995a(Frankham, ,b, 1998) ) which can lead to local extinctions [START_REF] Saccheri | Inbreeding and extinction in a butterfly metapopulation[END_REF][START_REF] Spielman | Most species are not driven to extinction before genetic factors impact them[END_REF]. Habitat fragmentation has influenced the genetic composition of hundreds of species of birds [START_REF] Alonso | Moult strategies of Cory's Shearwaters Calonectris diomedea borealis: the influence of colony location, sex and individual breeding status[END_REF], fishes [START_REF] Hänfling | Concordant Genetic Estimators of Migration Reveal Anthropogenically Enhanced Source-Sink Population Structure in the River Sculpin, Cottus gobio[END_REF][START_REF] Raeymaekers | Modeling genetic connectivity in sticklebacks as a guideline for river restoration[END_REF][START_REF] Blanchet | Species-specific responses to landscape fragmentation: implications for management strategies[END_REF][START_REF] Torterotot | Influence of Forest Road Culverts and Waterfalls on the Fine-Scale Distribution of Brook Trout Genetic Diversity in a Boreal Watershed[END_REF]) and plants [START_REF] Young | The population genetic consequences of habitat fragmentation for plants[END_REF]. A key challenge is thus to accurately understand how human induced habitat fragmentation alters gene flow to better predict the future viability of populations and help design management strategies.

Freshwater ecosystems have been particularly affected by fragmentation worldwide [START_REF] Dynesius | Fragmentation and Flow Regulation of River Systems in the Northern Third of the World[END_REF][START_REF] Nilsson | Fragmentation and Flow Regulation of the World's Large River Systems[END_REF] due to the construction of dams, weirs, and to artificial modifications of river trajectories. Such fragmentation alters the possibility of gene flow between populations of aquatic organisms, so that upstream isolated populations are particularly exposed to genetic drift and its consequences on genetic diversity and ultimately inbreeding. This is particularly problematic in river systems that are naturally shaped as dendritic networks, which poses several challenges to traditional models used in population genetics such as Wright's (1951) infinite island model or the linear stepping stone model [START_REF] Kimura | The Stepping Stone Model of Population Structure and the Decrease of Genetic Correlation with Distance[END_REF]. Indeed, migration is often expected to occur following water currents, generating pattern of asymmetric gene flow [START_REF] Hänfling | Concordant Genetic Estimators of Migration Reveal Anthropogenically Enhanced Source-Sink Population Structure in the River Sculpin, Cottus gobio[END_REF][START_REF] Pollux | Gene flow and genetic structure of the aquatic macrophyte Sparganium emersum in a linear unidirectional river[END_REF]) and structuring populations along linear networks. As a result, populations are structured following a source-sink model [START_REF] Kawecki | Evolutionary consequences of asymmetric dispersal rates[END_REF][START_REF] Hänfling | Concordant Genetic Estimators of Migration Reveal Anthropogenically Enhanced Source-Sink Population Structure in the River Sculpin, Cottus gobio[END_REF] in which the genetic diversity of upstream source populations will be smaller than the downstream sink populations. Thus, upstream populations are naturally expected to deviate from the migration drift equilibrium. A recent study has investigated three possible processes responsible for this observed pattern of downstream increase in genetic diversity across They showed that each of these processes can theoretically influence patterns of downstream increase in genetic diversity. In such conditions, human mediated alterations of habitat connectivity in rivers may obscure these patterns or exacerbate it. To date, most studies focused on delineating the effect of barriers on migration in species targeted by fisheries such as salmonids that display an important migratory ability [START_REF] Morita | Effects of Habitat Fragmentation by Damming on the Persistence of Stream-Dwelling Charr Populations[END_REF][START_REF] Yamamoto | Genetic Differentiation of White-Spotted Charr (Salvelinus leucomaenis) Populations After Habitat Fragmentation: Spatial-Temporal Changes in Gene Frequencies[END_REF]) and may thus not be the more relevant to study the effect of fragmentation. Few empirical studies have focused on species with a low dispersal ability (e.g. [START_REF] Hänfling | Concordant Genetic Estimators of Migration Reveal Anthropogenically Enhanced Source-Sink Population Structure in the River Sculpin, Cottus gobio[END_REF][START_REF] Raeymaekers | Modeling genetic connectivity in sticklebacks as a guideline for river restoration[END_REF][START_REF] Blanchet | Species-specific responses to landscape fragmentation: implications for management strategies[END_REF] whereas effects of fragmentation are expected to impact more strongly the genetic diversity of these species.

In addition, species can display various life history strategies or ecotypes that may differ in their dispersal capacity and thus be differentially affected by changes in habitat connectivity. For instance, in certain fish species some individuals are freshwater-resident while others are anadromous (i.e. reproduce in freshwater and juveniles migrate to sea for growth, [START_REF] Jonsson | Partial migration: niche shift versus sexual maturation in fishes[END_REF][START_REF] Dodson | The evolutionary ecology of alternative migratory tactics in salmonid fishes[END_REF].

Anadromous individuals can either display a homing behavior as they return back to their native river to spawn, or disperse into neighboring rivers, creating large opportunities for gene flow. Consequently, anadromous populations generally display lower levels of population genetic structure than resident populations [START_REF] Hohenlohe | Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags[END_REF]Spice et al. 2012;[START_REF] Hess | Population genomics of Pacific lamprey: adaptive variation in a highly dispersive species[END_REF][START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF], Quéméré et al. 2015). Resident populations are also expected to be more strongly impacted by barriers to migration, which can isolate populations located in upstream reaches (e.g. [START_REF] Perrier | Parallel and nonparallel genome-wide divergence among replicate population pairs of freshwater and anadromous Atlantic salmon[END_REF].

The European brook lamprey Lampetra planeri is a widespread freshwater resident species with a putatively low dispersal ability linked to its small size (15-22 cm) and its particular life cycle, as the adults stay only a few months in the river before they reproduce and subsequently die [START_REF] Taverny | Les lamproies en Europe de l'Ouest: écophases, espèces et habitats[END_REF]. It is closely related to the river lamprey Lampetra fluviatilis that is parasitic and anadromous at the adult stage.

The two ecotypes share many similarities as they spend 3 to 6 years burrowed in the substrate of river beds.

L. fluviatilis however is often located in lower parts of the river due to their low upstream migratory ability, particularly when confronted with barriers [START_REF] Lucas | Availability of and access to critical habitats in regulated rivers: effects of low-head barriers on threatened lampreys[END_REF][START_REF] Russon | Gauging weirs impede the upstream migration of adult river lamprey Lampetra fluviatilis[END_REF][START_REF] Kemp | The influence of discharge and temperature on the ability of upstream migrant adult river lamprey (Lampetra fluviatilis) to pass experimental overshot and undershot weirs[END_REF][START_REF] Foulds | Extreme inefficiency of two conventional, technical fishways used by European river lamprey (Lampetra fluviatilis)[END_REF]). However, due to dispersal abilities through the marine environment and apparent moderate homing behavior, populations from nearby watersheds remain connected and display a low genetic differentiation [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF][START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. In contrast, L. planeri has a highly reduced migratory behavior: it does not move outside its watershed and only migrates short distances within the river for breeding purposes [START_REF] Malmqvist | The spawning migration of the brook lamprey, Lampetra planeri Bloch, in a South Swedish stream[END_REF]. Thus, the most isolated brook lamprey populations located in the upper reaches of rivers can be strongly genetically differentiated from other populations either downstream or in other rivers [START_REF] Pereira | Phylogeographical analysis reveals multiple conservation units in brook lampreys Lampetra planeri of Portuguese streams[END_REF][START_REF] Mateus | MtDNA markers reveal the existence of allopatric evolutionary lineages in the threatened lampreys Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) in the Iberian glacial refugium[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF][START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. These isolated populations often display a low genetic diversity (Rougemont et al. 2015a) as revealed for instance by low levels of expected heterozygosity, an indicator of effective population size [START_REF] Nei | Effective population size, genetic diversity, and coalescence time in subdivided populations[END_REF]. Brook lamprey populations living in sympatry with river lampreys have been found to display a higher level of genetic diversity than populations located in upstream reaches where river lampreys are absent [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. As a result, brook lamprey populations may benefit from the connection with L. fluviatilis that may act as a 'reservoir' of genetic diversity. To test this hypothesis one should compare the level of genetic diversity between rivers with only brook lampreys and those where both species coexist.

The main aims of this study were to understand: i) the role of river fragmentation on population genetic diversity and structure of L. planeri, ii) the spatial distribution of genetic diversity among L. planeri populations at a large scale by comparing populations from the UK, Ireland and France and iii) the possible role of L. fluviatilis in increasing genetic diversity in sympatric L. planeri populations via introgression. In order to assess the effect of downstream increase in genetic diversity and the potential role of migration barriers in this effect we performed extensive sampling of L. planeri upstream and downstream of barriers to migration in 29 rivers from three geographical regions: Brittany, Normandy and the Upper Rhône in France. Moreover, two watersheds were sampled more extensively in order to better test for a pattern of downstream increase in genetic diversity and fragmentation effects. To test the prediction that L. planeri populations found in sympatry with river lampreys will display greater levels of genetic diversity than populations where river lampreys are absent, we sampled populations of L. planeri in sympatry or parapatry with its sister species L. fluviatilis in Normandy and populations in Brittany where river lampreys are absent.

Materials and Methods

Sampling design

A total of 2472 lamprey individuals were captured at 81 sites spread over 29 rivers. All individuals were captured in 2013 and 2014. 228 L. fluviatilis were sampled in 8 rivers: 7 rivers from Normandy and one from the Loire, to better analyse the distribution of genetic diversity between L. fluviatilis and L. planeri at a global scale. We targeted L. planeri located upstream and downstream of a putative barrier to dispersal and, if possible, close to the barrier (less than 1 km upstream or downstream) to limit the effect of isolation by distance. In this study a site thus corresponds to a sampled point located either downstream or upstream of a barrier to migration. We considered all kind of barriers of moderate size (mean height = 1.07 m, min height = 0.25 m and max height = 5 m, table S1) that may limit the active dispersal of lampreys. In some cases, we were unable to capture lampreys immediately downstream or upstream of dams and some sampled points were separated by more than one obstacle. 2302 L. planeri lampreys were collected from 73 sites in four French major regions (Mediterranean area (Rhone), Normandy, Brittany and Upper Rhine), as well as 3 sites in the United Kingdom and Ireland to better understand the large scale distribution of genetic diversity. The 17 sites (n= 536 individuals) from Northern France and Normandy all occurred in sympatry or parapatry with L. fluviatilis. Conversely, the 32 sample sites from Brittany (n= 969 individuals) were completely allopatric. Indeed, despite being located along the coast of Atlantic, L. fluviatilis is currently not present in the rivers in this area. In addition, two sites (one upstream and one downstream of a migratory barrier) were sampled from a tributary of the Loire (the Cens River). In Brittany, for 2 rivers we failed to capture L. planeri both upstream and downstream of a barrier, so only 30 sites were suitable to study the effect of fragmentation in this area. Ultimately we sampled 18 sites (n= 575 individuals) from the Rhone area and Upper Rhine to better capture the geographic distribution of genetic diversity. We focussed only on populations distributed in Northern France and along the Atlantic to study the effect of river fragmentation (see below and results). A total of 49 sites (30 from Britany, 2 from the Loire, 17 from Northern France, n=1440 individuals) were suitable for such analysis.

A fin was clipped on each specimen and preserved in 95% EtOH. Physical variables used as explanatory variables of genetic parameters included the number of obstacles, their cumulative height, the geographic distances between both sample points and the distance of the sampling point from the river source. Data about obstacle height were gathered from the French "Referentiel des Obstacles à l'Ecoulement".

Geographic distances were computed using QGis 2.10.1. In addition, we performed two linear transects on two independent rivers (the Arz and Scorff) with a total of 8 and 7 sample sites respectively, to investigate the respective effects of obstacles and isolation by distance.

DNA extraction for microsatellite and genomic analysis

Microsatellite DNA extraction and genotyping

Genotyping was performed with 13 microsatellite markers specifically developed for L. planeri and L. fluviatilis after DNA extraction using a Chelex protocol modified from [START_REF] Estoup | Rapid one-tube DNA extraction for reliable PCR detection of fish polymorphic markers and transgenes[END_REF] and strictly following protocols of [START_REF] Gaigher | Characterization of thirteen microsatellite markers in river and brook lampreys (Lampetra fluviatilis and L. planeri)[END_REF] and [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF].

Statistical analysis of microsatellite data

Genetic diversity within populations

We tested deviations from Hardy-Weinberg equilibrium using GENEPOP 4.1.0 [START_REF] Rousset | genepop'007: a complete re-implementation of the genepop software for Windows and Linux[END_REF] exact tests with Bonferroni corrections [START_REF] Rice | Analyzing Tables of Statistical Tests[END_REF], α = 0.05) and computed the inbreeding coefficient (Fis) for each population using FSTAT 2.9.3 [START_REF] Goudet | FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3)[END_REF]. Genetic diversity indices were computed and included the number of alleles (An), Allelic richness (Ar), observed heterozygosity (Hobs) and expected heterozygosity (Hnb). We computed Loiselle relatedness coefficients [START_REF] Loiselle | Spatial Genetic Structure of a Tropical Understory Shrub, Psychotria officinalis (Rubiaceae)[END_REF] among individuals within each population using the software Genodive [START_REF] Meirmans | genotype and genodive: two programs for the analysis of genetic diversity of asexual organisms[END_REF].

Genetic structure among populations

We computed [START_REF] Weir | Estimating F-Statistics for the Analysis of Population Structure[END_REF] estimator of F ST between all pairs of populations and used permutation tests with Bonferroni corrections to test for significance in FSTAT. We tested for global pairwise differences in F ST between upstream and downstream sites and among the three major regions using permutations tested in FSTAT (10,000 TO 15,000 test in each cases) as well as pairwise t-test adjusted for multiple testing using FDR corrections in R. However, populations are expected to deviate from migration drift equilibrium and to show a downstream increase in genetic diversity resulting in biased F ST that may reflect this gradient effect rather than true differences. As a result, we also used indices of genetic differentiation that are independent from variations in genetic diversity among populations: the Jost D [START_REF] Jost | GST and its relatives do not measure differentiation[END_REF]) and Hedrick G'st [START_REF] Hedrick | A Standardized Genetic Differentiation Measure[END_REF]. We illustrated the distribution of pairwise F ST and Jost D values in R using the heatmap.2 function implemented in the gplots package, which computes a heatmap together with a hierarchical clustering tree.

The Bayesian clustering program STRUCTURE 2.3.3(Pritchard et al. 2000) was used to evaluate the number of clusters (k) from 1 to 49 in the complete dataset. 10 independent replicates per k value were performed. Markov Chain Monte Carlo simulations (MCMC) used 200 000 burn-in and 200 000 iterations under the admixture model with correlated allele frequencies [START_REF] Falush | Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies[END_REF]. We used log likelihood Ln Pr(X|K) and the ΔK method [START_REF] Evanno | Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study[END_REF] to determine the number of clusters in STRUCTURE HARVESTER [START_REF] Earl | STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method[END_REF]. Plots were drawn using DISTRUCT 1.1 [START_REF] Rosenberg | distruct: a program for the graphical display of population structure[END_REF]). Due to the high differentiation of the populations of the upper Rhone we tested for population substructure by splitting the dataset in three sub-dataset: i) upper Rhône separately (575 L. planeri individuals), ii) Brittany and iii) Normandy (L. fluviatilis and L. planeri) Ireland and the UK together (1727 L. planeri individuals).

Landscape genetic analyses

Different approaches were used to test the effect of fragmentation in each site. In a first approach, based on linear mixed models (LMM hereafter) we used only sites that were truly independent (i.e. a sampling site was never used in more than one comparison), leading to a total of 25 independent upstream/downstream pairs of sites. The Hnb, Ar and relatedness differential between upstream and downstream sites were used as estimator of difference in genetic diversity within each river. We used the linearized genetic distance F ST /(1-F ST ) [START_REF] Rousset | Genetic Differentiation and Estimation of Gene Flow from F-Statistics Under Isolation by Distance[END_REF]) between both sites in each river. The number of obstacles, cumulative height, and geographic distance between sites were treated as fixed effects while the river was treated as a random effect. We also integrated the distance to the source as a co-variable in our models. Models were tested using likelihood ratio tests and AIC as implemented in the MASS (Venables et Ripley 2002), lme4 and nlme packages in the R software [START_REF] Core | European Environment Agency (EEA)[END_REF]. Due to the very low genetic diversity of the Upper Rhone (see results) and an expected lack of power (e.g. the downstream populations of Upper Rhone displayed less genetic diversity than the upstream populations of Atlantic and northern France), it was not included in the analysis.

In addition to this analysis, we tested the prediction of an increase of neutral genetic diversity downstream [START_REF] Raeymaekers | Modeling genetic connectivity in sticklebacks as a guideline for river restoration[END_REF][START_REF] Blanchet | Species-specific responses to landscape fragmentation: implications for management strategies[END_REF][START_REF] Paz-Vinas | The demographic history of populations experiencing asymmetric gene flow: combining simulated and empirical data[END_REF]. We used point estimates of Ar, Hnb and Relatedness in linear mixed models with a Gaussian error family. In these cases we included all upstream and downstream sample sites from all rivers (ranging from 1 site per river to up to 8 sites). The distance to the source was used as predictor variables (fixed effect) while the river was considered as a random effect. In this case again we did not include the Upper Rhone.

In addition, we tested for a pattern of IBD, and tested to which extent it was affected by the presence of obstacles on the Crano and Arz River (Brittany region) where more sites had been sampled ( 7and 8 sites respectively). We used Mantel and partial Mantel tests in R. We constructed matrices of linearized F ST (G' ST and D), Ar and Hnb differentials and matrices of pairwise geographical distance, number of obstacles and cumulated height for each river.

Results

Genetic diversity within populations

Fis was only statistically significant (Table S1) in four populations: the downstream sites on the Leguer River (Brittany), the downstream sites on the Oignin, Calonne and Neyrieux Rivers (Upper Rhône).

Levels of allelic richness (Ar) based on a minimal sample size of 11 varied from 1.180 (Reyssouze River, Upper Rhone) to 3.791 (Bethune River, Normandy) and from 1.20 to 3.85 for the mean number of alleles per locus (Table 1, Table S1, Table S2). Levels of Hnb averaged over all loci per population also varied substantially, ranging from 0.011 (Reyssouze River) to 0.563 (Aa River, Normandy). Population of the Upper Rhine displayed similar level of diversity to those of Britany (Table 1). On average populations of L. fluviatilis were significantly more diversified than populations of L. planeri in terms of allelic richness (p < 0.0042, 15 000 permutations) and expected heterozygosity (p < 0.0057, 15 000 permutations) (See also Figure 4).

However there was important regional difference in patterns of genetic diversity. In Normandy, L. fluviatilis populations were not different from downstream L. planeri populations in terms of expected heterozygosity and relatedness (fdr corrected t.test > 0.05) except for allelic richness (p = 0.049) (see table S3 and Figure 4). However, the genetic diversity of L. fluviatilis populations was systematically higher than the one of upstream L. planeri populations of Normandy (i.e. parapatric) and from the neighboring L.

planeri populations of Brittany (all p<0.05, see tableS3 and Figure 4). Levels of genetic diversity of the Frome (UK) and Shannon (Ireland) populations (Table 1) were similar to those observed in Normandy.

Comparisons among geographical areas revealed a lower genetic diversity of L. planeri populations from the Upper Rhône compared to Brittany and Normandy (Figure 4, Table S3).

Genetic differentiation among populations

Global F ST over the full dataset was 0.377 (95%IC = 0.334-0.418). When excluding L. fluviatilis, global F ST was 0.394 (95%IC=0.353-0.437) (Table 1). Figure 2 illustrates 2 main groups of populations: the Upper Rhône population vs all other populations. Populations of L. fluviatilis were weakly differentiated (F ST = 0.003). Populations of L. planeri were significantly differentiated from populations of L. fluviatilis (p < 0.0003, 6000 permutations). L. planeri populations were generally highly differentiated from one another.

The highest F ST was observed between the Reyssouze River and the Moulin du Rocher River (Brittany) with a value of 0.90. The lowest F ST was 0 between several sites (Figure 2 and table S4). Average pairwise F ST between upstream and downstream sites within a river was 0.025, with a minimal value of zero and a

Structure analysis indicated an optimal clustering solution for K = 22 (Table S5) based on the mean Ln (K) while the ΔK produced multiple peaks and reach a maximum at K =36. Investigating different levels of population structure in between these values indicated that clustering values in between 20 and 30 were more realistic, while greater values were less biologically interpretable (Figure 2). In this case, Structure analysis indicated two clustering solution, one for K = 4 and one for K = 6. The ΔK produced multiple peaks of small values with the highest peak occurring at K = 6. The clustering solution for K = 4 and K =6 allow to separate the Ange-Oignin river (from the same watershed) from the 5 remaining cluster. All remaining rivers displayed mixed membership contribution to these 5 groups (Figure S1). Plots with increasing values of K (up to six) resulted in increasing admixture values between these populations. For Brittany Lp populations, likelihood values reached a plateau for K values in between 10 and 16 whereas the ΔK produced multiple small peaks, with the highest peak obtained for K =21. In general, results based on mean likelihood values appeared more biologically realistic with K = 10 corresponding to a grouping of all rivers independently, while increasing values of K resulted in increased admixture values within rivers. For Normandy L. fluviatilis and L. planeri populations, two optimal clustering solutions were found for K = 6 and 8 (table S5). Figure 2 clearly demonstrated that L. fluviatilis were admixed whereas L. planeri form distinct clusters on the Aa, Hem, Bresle, Bethune and Odon River but were more admixed on the Oir and Risle River. In addition, the downstream population of the Bethune River also displayed mixed membership probabilities. S2). On the Arz, the source population formed a distinct cluster while downstream populations were admixed (Figure S2). is the occurrence of bottlenecks following usptream river colonization after glacial retreats [START_REF] Hewitt | Some genetic consequences of ice ages, and their role in divergence and speciation[END_REF][START_REF] Taberlet | Comparative phylogeography and postglacial colonization routes in Europe[END_REF]. It remains unclear so far whether freshwater populations have recovered from ancestral bottlenecks and disentangling the two hypotheses will require further data.

Landscape genetics

As previously suggested, independent drift of resident populations can explain our inability to detect any global signal. A pattern of independent drift of freshwater populations was expected as demonstrated in a simulation study of river colonization of freshwater and marine Gasterosteus aculeatus populations [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. Note also that the strongest differentiation between upstream and downstream sites occurred on the Crano river (F ST = 0.095) between two sites located in the headwater of the river, but not separated by any physical barriers. Finer investigations on the Crano and the Arz revealed a significant effect of distance on differentiation in the Crano River suggesting migration-drift equilibrium in this river. On the Arz River on the contrary, the effect of distance was only revealed when controlled for the effect of obstacle number. Finally, our results do not allow drawing any general conclusion on the impact of obstacle height and number on the extent of genetic differentiation. Such impacts may be best revealed by focusing on a single catchment and with bigger obstacles to migration (eg [START_REF] Raeymaekers | Modeling genetic connectivity in sticklebacks as a guideline for river restoration[END_REF][START_REF] Blanchet | Species-specific responses to landscape fragmentation: implications for management strategies[END_REF][START_REF] Gouskov | Fish population genetic structure shaped by hydroelectric power plants in the upper Rhine catchment[END_REF]. In addition, we investigated the impact of obstacles of small to moderate size and it is possible that these obstacles do not influence the downstream passive drift of lamprey larvae, which may be sufficient to homogenize populations and obscure patterns of differentiation [START_REF] Faubet | Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates[END_REF].

River lamprey as a source of genetic diversity for resident lampreys

Understanding the evolutionary relationship between parasitic and nonparasitic lamprey is a long standing debate [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF][START_REF] Frankham | Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow[END_REF]. Recent evidence [START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF]Rougemont et al. 2015a) have shown that gene flow is ongoing between L. fluviatilis and L. planeri, lowering their level of genetic differentiation at a genome-wide scale. Notably, Rougemont et al (2015b) suggested the occurrence of asymmetric introgression from anadromous to freshwater populations following secondary contact. Such introgression from a large marine population toward freshwater populations is also known to occur in Gasterosteus aculeatus [START_REF] Hohenlohe | Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags[END_REF][START_REF] Hohenlohe | Population Genomic Analysis of Model and Nonmodel Organisms Using Sequenced RAD Tags[END_REF]. Here, genetic analyses of populations of L. planeri and L. fluviatilis in sympatry (on the same nest), in parapatry (where the two species co-occur in the same watershed but are geographically separated by impassable dams) and in allopatry (in coastal rivers where L. fluviatilis is absent) revealed that allopatric L. planeri lamprey displayed a lower genetic diversity than sympatric and parapatric populations (Figure 4). Additionally, Bayesian clustering analysis revealed higher levels of admixture in sympatry than in allopatry and parapatry (Figure 3), confirming findings of contemporary introgression [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF][START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. In contrast, the Bayesian clustering analysis also confirmed that each L. planeri population formed an independent genetic cluster. In addition, we found a significant pattern of IBD in the connected pairs complementary hypotheses can be made to explain such a result. First, there is evidence for at least three major evolutionary lineages existing in L. planeri [START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF]. It is thus possible that colonization of the Mediterranean area (Upper Rhone region) following postglacial colonization (the usual pattern in European fish species, [START_REF] Bernatchez | Comparative phylogeography of Nearctic and Palearctic fishes[END_REF]) was due to a different lineage than the one having colonized the Atlantic and Channel areas. In these conditions it is possible that our microsatellite marker set (originally developed using L. planeri and L. fluviatilis samples from the Atlantic and channel area) is not the most appropriate to perform accurate population genetic inference of Rhône samples. The low performance of Structure analysis confirms this lack of power.

Second, L. fluviatilis no longer colonises this area and was already reported to be declining during the last century (Bernard 1909;[START_REF] Gensoul | Monographie des poisons d'eau douce de Saone et Loire[END_REF]). Consequently, it is possible that the history of divergence between Mediterranean and Atlantic populations was initiated a long time ago and that gene flow between neighbouring rivers of the Mediterranean area was further reduced during the last century.

Conservation implications

Fragmentation of rivers may impact lamprey populations, especially the most upstream populations that do not receive migrants from downstream sites. Whether the most isolated populations from headwaters suffer a mutation load and greater extinctions risk would require further investigations. It is now known from theory and empirical data that small isolated populations suffer greater inbreeding and extinction risks [START_REF] Lynch | The Genetic Interpretation of Inbreeding Depression and Outbreeding Depression[END_REF][START_REF] Higgins | Metapopulation extinction caused by mutation accumulation[END_REF][START_REF] Spielman | Most species are not driven to extinction before genetic factors impact them[END_REF][START_REF] Frankham | Genetics and extinction[END_REF][START_REF] Frankham | Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow[END_REF]. On the other hand it is not clear if maintaining a possibility for upstream migration by removing obstacles may help prevent the loss of genetic diversity in the source populations of L. planeri through the beneficial effects of gene flow [START_REF] Frankham | Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow[END_REF]. The source populations on the Crano and St Sauveur, as well as the observed genetic differentiation on the Tamoute River despite the absence of migratory barrier illustrate this problem well.

Importantly, our study revealed positive impacts of the presence of L. fluviatilis in maintaining genetic diversity in sympatric populations of L. planeri. However, in Europe, the L.

fluviatilis abundance has strongly declined in some areas (Maitland et al. 2015) and it is now considered as Vulnerable in France in the IUCN red list (UICN France et al. 2010). In addition the low upstream migratory ability of the anadromous ecotype often restricts its distribution to downstream areas where L. planeri are often less abundant. In terms of conservation priority it appears fundamental to first ensure that L. fluviatilis will have access to upstream reaches of rivers. This will benefit both the river and L. planeri in sympatric and parapatric areas. In these areas a joint regional management of the two ecotypes could be envisioned, whereas in allopatric areas, a management at the river scale may be more parsimonious.

Conclusion

We have shown here that impacts of barriers to migration were modest on the extent of genetic differentiation, but we provided evidence that headwater populations of L. planeri displayed reduced genetic diversity, high levels of relatedness and were the most genetically differentiated.

Given the strong asymmetric downstream gene flow (probably due to passive drift of larvae) it is not clear whether restoring the possibility for upstream migration could have beneficial impact through facilitating gene flow from downstream populations. In addition, we have shown that populations of L. planeri in sympatric areas displayed higher levels of genetic diversity probably due to introgression from L. fluviatilis. Potential strong gene flow or even genome swamping from anadromous population to resident populations is fundamental in maintaining genetic diversity of L. planeri. In addition it may play a key role through adaptive introgression and also through the movement of freshwater adaptive alleles between adjacent rivers (i.e. the transporter hypothesis [START_REF] Schluter | Genetics and ecological speciation[END_REF], a topic that will definitely require further investigations. Further investigations about the effective population size of populations along river networks based on genome wide data and tests of mutation load in isolated populations may provide additional cues for conservation management purposes. -How (did) divergence proceed between the European L. planeri and L. fluviatilis?

This question was addressed via a multidisciplinary approach including experimental tests of reproductive isolation, investigations of gene flow in natural populations based on microsatellite and genome wide data and simulations to investigate the demographic history of divergence.

Results have demonstrated that L. fluviatilis and L. planeri display very a low level of RI when crossed experimentally and were able to produce viable F1 hybrids in semi-natural conditions. This low level of RI was mirrored by strong levels of gene flow measured either by microsatellite data or with a genome-wide RAD sequencing approach. Approximate Bayesian computation and diffusion approximations suggested that contemporary levels of introgression between L. fluviatilis and L.

planeri were more likely to have emerged following a secondary contact, as divergence was likely to have been initiated in allopatry rather than in the face of continuous gene flow. The level of geographic connectivity has a strong influence on the level of gene flow: populations highly connected in areas of sympatry (or hybrid zones) generally displayed high levels of gene flow whereas populations disconnected, especially those occurring in different branches of the river network, were highly differentiated suggesting that no gene flow was currently ongoing;

-What are the effects of natural or human-induced river fragmentation on the genetic integrity of L. planeri populations?

I used a landscape genetics approach to test the effect of distance, barriers to migration as well as admixture between ecotypes on the spatial distribution of genetic diversity among L. planeri populations.

Extensive sampling of L. planeri in fragmented areas suggested limited evidence for a role of human induced fragmentation (at least when obstacles are small) but demonstrated a higher influence of asymmetric gene flow on the extent of genetic differentiation and diversity. It seems that the most isolated population displayed high level of relatedness and low levels of genetic diversity and this decreased exponentially when moving away from the headwaters.

Below I review some of these findings, discuss some limitations of the methods used and propose new avenues of research. Specifically, to complement and synthesize the discussions that were developed in each chapter, I will discuss four issues: i) The challenges in measuring hybrid fitness and investigating the strength of pre-and post-zygotic barriers ii) the importance of the current and historical geographic settings of populations divergence (i.e. importance of biogeography), iii) the complexity of histories of divergence and iv) the interest of better understanding how isolated L. planeri populations evolved.

The challenges in measuring hybrid fitness and investigating the strength of pre and postzygotic barrier

A first major step when investigating levels of RI is to identify barriers to gene flow, and if possible to quantify their strength and order of appearance [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF]. Relatively little was known on the level of RI in L. planeri and L. fluviatilis. Hume et al. (2013) recently demonstrated that viable F1s could be artificially produced, but they did not control for fertilization rates in their estimates of hatching rates, potentially resulting in bias in measures of post-zygotic reproductive isolation. Here, I separated the two effects and demonstrated that both fertilization success and hatching rates were close to 100% (Rougemont et al. 2015, Chapter 2). These results suggested no intrinsic hybrid inviability at an early developmental stage only. Breakup of co-adapted gene complexes generally occurs in F2s [START_REF] Edmands | Heterosis and Outbreeding Depression in Interpopulation Crosses Spanning a Wide Range of Divergence[END_REF] and heterosis is more generally expected and frequently observed in F1s. In addition extrinsic postzygotic isolation (ecological or hybrid inferiority, see figure 1 in chapter 1) may occur and could not be measured. Getting further insights into the level of reproductive isolation would require rearing the F1 hybrids up to maturity, and I participated in the ongoing development of new rearing methods. A major improvement was performed in early life-stages feeding techniques, which led to survival rates close to 100% after about 3 months.

Unfortunately, developing a rearing method up to maturity requires many trials and thus a lot of technical and human resources, which may not be easily available.

To complement this approach, I tested whether L. planeri and L. fluviatilis were effectively able to mate in semi-natural conditions. This was a necessary step since in the artificial crosses ova and sperm were mated artificially, and other premating barriers may exist in the wild (for instance, behavioral barriers). This experiment demonstrated that L. planeri males were indeed able to mate with L. fluviatilis and produce viable offspring at an early developmental stage (Rougemont et al. 2015, Chapter 2). Of course, this does not exclude that in the wild other isolating barriers can occur, such as temporal isolation or local habitat isolation. In addition, the least genetically differentiated population pair from the Oir River (pairwise F ST range: 0.008 to 0.032) was used in this experiment. In this river, L. fluviatilis are significantly smaller (224 mm, p<0.001) than the average size of L. fluviatilis across the studied range (mean = 288 mm). It would thus be interesting to investigate whether L.

planeri males are able to mate with females of bigger size. In addition, using males and females of the two species together may result in more matings of males with their conspecific females rather than with females of the other species, resulting in more realistic estimates of intra-and interspecific reproductive success.

Despite the current difficulty in measuring long term levels of reproductive isolation, lampreys are a great model to test other reproductive barriers. For instance, as most species with external fertilization, lampreys allow for experimental measures of levels of conspecific sperm precedence (CSP), a form of postmating prezygotic barrier [START_REF] Coyne | An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland*[END_REF], see also Chapter 1). During my PhD I began to explore levels of sperm competition between L. fluviatilis and L. planeri. The preliminary question was whether it was possible to accurately measure some parameters of sperm quality, such as motility, sperm concentration and the level of viability. First results (not shown here)

revealed that sperm of good quality could be obtained even from adults that had been kept for several days in a hatchery tank, and had already been used for experimental crosses. Differences (that could not be statistically tested due to small sample size) were observed between sperm of L. [START_REF] Beamish | Evidence that Parasitic and Nonparasitic Life History Types are Produced by One Population of Lamprey[END_REF], see also review in [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. Instead, I hypothesized that some genetic barriers may act to reduce effective gene flow in parts of the genome, but these barriers may act over a small proportion of the genome so that they could not be identified based on a set of 13 microsatellite markers. A first question that arose was whether this ongoing gene flow in spite of significant differentiation was a sign of primary or secondary intergradation (Harrison, 2011). Interestingly, the

The RAD sequencing approach allowed validation of the high levels of gene flow but also to discrimination between the two species and putative hybrids with nearly 100% accuracy (Chapter 4).

However, most hybrids were identified as F1s, and very few as F2s or backcrosses, which questioned the survival of hybrids beyond the F1 stage. The current weakness of such hybrid detection is that it remains purely statistical. In addition, only a small number of individuals were initially sampled in each pair (approximately 20 per river and ecotype). As a consequence no conclusion can be drawn on whether the absence of F2 and backcross hybrids reflects strong counter-selection due to some genetic incompatibility or is simply a consequence of our small sample size. A simulation approach (e.g. using Nemo [START_REF] Guillaume | Nemo: an evolutionary and population genetics programming framework[END_REF])) could be developed but would require larger sample sizes of parental populations in hybrid zones. For instance, simulating diverging populations and incorporating various proportions of DMI across the genome could help estimate the expected proportion of hybrids as a function of the number of endogenous barriers.

Our results of genetic structure within and between population pairs of lampreys based on RADseq data were in line with the microsatellite based estimates, but very different from those of [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF], as they did not reveal strong divergence between both ecotypes. Indeed, [START_REF] Mateus | Strong genome-wide divergence between sympatric European river and brook lampreys[END_REF] found a strong genome wide divergence in a southern sympatric pair using a RAD-seq approach. Leaving aside the problems pointed out in chapter 1, their results raised the possibility that southern populations of L. fluviatilis and L. planeri may have an older divergence time than northern populations. This hypothesis is supported by the fact that 1) several lineages of L.

planeri coexist in the Iberian peninsula [START_REF] Mateus | MtDNA markers reveal the existence of allopatric evolutionary lineages in the threatened lampreys Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) in the Iberian glacial refugium[END_REF][START_REF] Mateus | REVIEW Lampreys of the Iberian Peninsula: distribution, population status and conservation[END_REF]Perreira et al. 2010;[START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF]) and 2) the Iberian peninsula could have been an ancient refugium during the last glacial period, as for other species of fishes and vertebrates [START_REF] Taberlet | Comparative phylogeography and postglacial colonization routes in Europe[END_REF][START_REF] Hewitt | Post-glacial re-colonization of European biota[END_REF].

While recent improvements in genome wide analyses have greatly improved the knowledge of the process of speciation, it seems to me that the old but important debate surrounding the geographical context of speciation has been put aside in favor of other lines of research. In particular, the argument that the geographical context of speciation was unimportant [START_REF] Butlin | Sympatric, parapatric or allopatric: the most important way to classify speciation?[END_REF], the simplifying hypothesis of the speciation with gene flow literature [START_REF] Smadja | A framework for comparing processes of speciation in the presence of gene flow[END_REF] or the tendency to focus mainly on rapid adaptation in the face of gene flow may lead to misleading conclusions. Recent investigations on the architecture of genomic divergence in sunflower have demonstrated that genomic islands of speciation were not affected by geography but that the functional architecture of the genome was more important, as expected from population genetic theory (Charlesworth et al. 1997a;[START_REF] Charlesworth | Measures of divergence between populations and the effect of forces that reduce variability[END_REF][START_REF] Charlesworth | The Effects of Deleterious Mutations on Evolution at Linked Sites[END_REF][START_REF] Noor | Islands of speciation or mirages in the desert? Examining the role of restricted recombination in maintaining species[END_REF]. However, based on results of this thesis, I suggest that the geography of speciation and spatial arrangement of populations do matter, especially in species with small Ne and in species arranged along linear networks [START_REF] Fourcade | Is local selection so widespread in river organisms? Fractal geometry of river networks leads to high bias in outlier detection[END_REF]. Indeed, in these conditions demographic disequilibria can generate false patterns of high genome wide differentiation and eventually generate genomic islands of differentiation that have little to do with the process of speciation. On the other hand, studying species undergoing the homogenizing effect of gene flow allows highlighting key processes of speciation and revealing genomic regions truly involved in speciation. The combined use of parapatric and sympatric population pairs of lampreys was instrumental to better understand the process of speciation throughout this thesis (Chapter 2 and 4). As a consequence, accurately taking into account variation in recombination rates, selection at linked sites due to positive selection and background selection (BGS) will probably be the cornerstone of future studies aiming at better understanding how speciation proceeds throughout the genome. Full genome sequencing in hybrid zones (e.g. Aa and Oir river) combined with new modelling approaches (eg. Harris & Nielsen 2013b;[START_REF] Sedghifar | The Spatial Mixing of Genomes in Secondary Contact Zones[END_REF] should help further understand speciation in lampreys.

The complexity of histories of divergence a. Secondary contact, ancient migration or ongoing ecological differentiation?

Investigating the history of divergence in Lp and Lf suggested that the two ecotypes probably display some genetic barriers maintaining their genetic integrity in the face of strong gene flow. The scenarios tested in an ABC framework and using diffusion approximation now strongly suggest that the species have initially diverged in allopatry. First, using a small set of loci I demonstrated, in line with old theory [START_REF] Endler | Geographic Variation, Speciation, and Clines[END_REF][START_REF] Nj Endler | Problems in Distinguishing Historical from Ecological Factors in Biogeography[END_REF][START_REF] Harrison | The Language of Speciation[END_REF]) and recent investigations [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF], that distinguishing between primary versus secondary differentiation with a small number of putatively neutral loci was difficult (Chapter 3). In light of these findings it would be interesting to review studies based on the IM/IMa2 approaches [START_REF] Hey | Multilocus Methods for Estimating Population Sizes, Migration Rates and Divergence Time, With Applications to the Divergence of Drosophila pseudoobscura and D. persimilis[END_REF][START_REF] Hey | Isolation with Migration Models for More Than Two Populations[END_REF]) which often have found evidence for divergence in the face of ongoing gene flow and to test whether primary versus secondary differentiation can be distinguished.

To circumvent this problem, moving toward a genomic approach and incorporating heterogeneity of migration rates along the genome clearly improved the results. One key finding highlighting the importance of studying sympatric populations is that of a secondary contact scenario in hybrid zones (after having removed putative hybrid individuals). In this situation, it is more likely that only genomic regions truly involved in reproductive isolation were revealed by the eroding effect of gene flow. Being able to use Petromyzon marinus genome data to polarize SNPs also yielded interesting results with regards to estimation of demographic parameters. In particular, the proportion of the genome under neutrality was estimated to be around 90%. This result, combined with other evidence accumulated so far suggest that barrier loci may not form strong genomic islands acting over large regions but may only act on small parts of the genome. Unfortunately in the absence of a full genome sequence we cannot locate these regions. Another important result was that of asymmetric introgression from the anadromous (large) populations to the small resident heterogeneity of effective population size Ne along the genome to better take into account selection at linked sites (see also Chapter 4). This work is ongoing but could not be completed in the thesis' timeframe; I will only present here the methodology and preliminary results (Annex 1). To do so I have used the same ABC approach as the one developed by [START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF][START_REF] Roux | Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone[END_REF] incorporating heterogeneous m through a beta distribution subsequently rescaled. In the new development of C.

Roux (unpublished), heterogeneity of Ne is drawn from a distribution 1 -α where the α distribution is the proportion of "neutral" loci sharing the same Ne. The 1 -α loci are then rescaled by a beta distribution, to incorporate heterogeneity. I skip the methodological implementation which is the same as in [START_REF] Roux | Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species[END_REF] but with modified scripts and rescaled priors to fit biological parameters of lamprey populations. Ongoing ABC analyses (not shown here) performed on sympatric populations suggest that taking this parameter into account is very important in model inferences. In addition, simple model comparisons of the traditional scenarios have allowed me to validate previous δaδi inferences when comparing the 3 traditional models of AM, IM and SC with heterogeneous migration. For instance on the Aa, posterior probability of AM is 0, P(IM) is 0.160 and P(SC) is 0.84. Similar results were observed in the Bethune population. Interestingly, in the Oir, the method does not seem to be able to distinguish between IM and SC, the posterior probability being 50/50. It is possible that we are reaching the limit of the ABC and coalescent methods in this case of very low population differentiation. This is expected under a mode with a short period of divergence followed by a longer period of secondary contact where barriers to gene flow may couple or scatter depending on several conditions (see [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. Future work based on haplotypes to estimate introgression from tract length should help validating our hypothesis of secondary introgression and may help unravel the complexity of the history of lamprey divergence (Harris & Nielsen 2013b).

These estimations of Ne for each locus are important because although barrier semi-permeability and gene flow are certainly instrumental in revealing islands of divergence (or at least barrier loci formed by DMI), the role of selection at linked sites in regions of low recombination cannot be neglected as a force shaping patterns of differentiation. Finally, given the suggestion that individuals carry massive amounts of deleterious mutations [START_REF] Charlesworth | The Effects of Deleterious Mutations on Evolution at Linked Sites[END_REF], the effect of background selection can be expected to impact a wide range of organisms and should no longer be neglected [START_REF] Ewing | The consequences of not accounting for background selection in demographic inference[END_REF]. In lampreys, disentangling the relative contributions of selection at linked sites and barrier semi-permeability due to speciation will require further genomic resources such as full genome sequences and further estimations of parameters that measure absolute divergence (e.g. D xy ) rather than F ST alone (but see Chapter 1). Given the current lack of such resources, we are currently developing a linkage map that could help understand the origin of barrier loci.

Finally, based on the joint analysis of sympatric and parapatric populations, I conclude that the demographic history of L. fluviatilis and L. planeri may well be more complicated than a single event of allopatry and introgression. Given the known alternation of contractions and expansions of species ranges [START_REF] Hewitt | The genetic legacy of the Quaternary ice ages[END_REF] it is possible that populations have undergone several periods of contact and isolation. The intriguing pattern of species distribution that we observed in Brittany (Figure 1 of Chapter 5) may be related to historical processes as this region was proposed as a refugial area for Atlantic salmon [START_REF] Finnegan | Use of multiple markers demonstrates a cryptic western refugium and postglacial colonisation routes of Atlantic salmon (Salmo salar L.) in northwest Europe[END_REF]. The Channel was a massive fluvial river flowing in Northern

France until recently (20 000 years ago). It was a major drainage system in Europe flowing during glacial retreats according to a cycle occurring every 100 000 yr during the last million years. [START_REF] Lericolais | The Quaternary Channel River: seismic stratigraphy of its palaeo-valleys and deeps[END_REF][START_REF] Toucane | Reconstruction des transferts sédimentaires en provenance du système glaciaire de mer d'irlande et du paleo-fleuve manche au cours des derniers cycles climatiques[END_REF]. During these periods it is possible that populations have had large opportunities to alternatively exchange genes and be separated by glacial sheets, ultimately largely influencing their current genetic makeup.

Ultimately, I hope to develop a more integrative ABC approach that would allow putting all pairs of population pairs from all rivers together. Such an approach might be the best solution to infer a global scenario that would make sense at the scale of the species distribution range. This may help better estimate demographic parameters that are shared by all populations. Such an approach will require some developments that were beyond the scope of the thesis.

b. Historical divergence and reproductive barriers: endogenous or exogenous?

Under the secondary contact model, endogenous barriers to gene flow such as DMIs presented in Chapter 4 are expected to accumulate across the genome, as opposed to the ecological speciation scenario proposed in lampreys [START_REF] Salewski | Satellite species in lampreys: a worldwide trend for ecological speciation in sympatry[END_REF] in which exogenous barriers should accumulate as a result of divergent ecological selection [START_REF] Rundle | Ecological speciation[END_REF]. As discussed in Chapter 1, the distinction between the two scenarios is fundamental for our understanding of the speciation process. In addition, genome scan results are often interpreted as evidence for the ongoing action of exogenous selection. Unfortunately, there was insufficient time during my thesis to further validate the hypothesis that the observed barriers are endogenous. To address this issue, I started the development of a RAD derived linkage map. Its ongoing construction (see Annex 2) will allow (hopefully) location of the most differentiated markers and to test whether outlier loci systematically deviate from expected Mendelian segregation ratios. If this is the case, then it is most likely that segregation distortion patterns are caused by endogenous selection against hybrids. Hundreds of distorted markers have already been identified. If some of them might be technical noise, it is however possible that others will be linked to RI. To push investigations a step further a modelling approach should be performed to simulate linkage maps containing variable number of DMIs (T.

Leroy Personnal Communication). The simulation approach developed by T. Leroy demonstrated that the linkage map integrity can be strongly affected by the history of diverging populations. In particular, the distortion of map length should be proportional to time since divergence and accumulation of genetic incompatibilities. which can be caused by passive drift of ammocoetes. This passive downstream drift may well generate sufficient gene flow to circumvent the establishment of strong genetic differences in populations located downstream of barriers. However, the barriers to migration that were investigated here are small (0.20-2 meters high, representative of the most widespread type of barrier across rivers in France). The effects may be stronger if taller barriers were investigated. The results are suggestive of source-sink dynamics with the most upstream populations acting as "source" populations. The low diversity of these upstream populations (that never receive migrants), their high level of differentiation (particularly noticeable in the Arz, Crano and Bethune Rivers where multiple sites were available) and this even when no obstacle was present, represent cumulative evidence suggesting that such a source-sink dynamic is the most parsimonious hypothesis. The results suggest also a more important role of isolation by distance rather than physical barriers in shaping patterns of genetic diversity and structure. However this study also suffered from several biases that may contribute to the lack of significant differences. First, populations living in independent coastal drainages evolved more or less independently, as found especially in Brittany. In these conditions, each population may have drifted independently and this signal may be much stronger in shaping population differences than the effect of obstacles tested on populations sampled in each river. Thus I argue that more meaningful results could be obtained by focusing on a single (or two) watershed and by performing extensive sampling of a single river and its tributaries.

Most studies that found significant influences of barriers to migration focused on multiple sample sites within a single catchment (eg. Raeyemaekers et al. 2008;[START_REF] Torterotot | Influence of Forest Road Culverts and Waterfalls on the Fine-Scale Distribution of Brook Trout Genetic Diversity in a Boreal Watershed[END_REF][START_REF] Gouskov | Fish population genetic structure shaped by hydroelectric power plants in the upper Rhine catchment[END_REF]. In this case, the main difficulty is to find statistically valid methods that correct for nonindependency among sites, as the number of obstacles and several variables will be correlated with geographic distance, and accurately quantifying their respective effects in shaping population structure and diversity may be challenging. Finally, a solution could be to rely upon simulation tools (e.g. ABC) to accurately quantify gene flow and size of populations located upstream and downstream of barriers to migration. An approach focusing on a single catchment could be performed in the near future in the well characterized Sélune River. Indeed several obstacles exist on this watershed and the major dams (35 meters high) will be removed soon. Monitoring the evolution of population genetic structure in this catchment before and after dam removal may provide very useful information to improve future actions aiming at restoring river connectivity. Parameter estimates for RAD sequencing made using de best demographic scenario (Heterogeneous N and M). N1 (N2) = downstream (upstream) ratios of effective population size, Nanc = ratios of effective population size T = ratio of Tsplit/4Nref, M1 = migration from pop2 to 1 M2 = migration from pop 1 to 2.

1: Estimations performed with 200 posterior samples ²: Estimations performed with 500 posterior samples From a management point of view, one may be interested in estimating migration M. The striking result here is that when retaining the same number of posterior samples for parameter estimation, confidence intervals are very wide in microsatellite data while they were often narrow using RAD data. The second result is that migration downstream was really high and often reaches the maximum values used in prior parameters (ie. M = 30). While this urged me to used wider priors, this also demonstrates that migration is asymmetric and strong from upstream sites to downstream sites. Note also that upstream migration was strong but slightly reduced as CIs never reached the maximum value of 30. The Tamoute, where the two populations are not separated by any obstacle was a notable exception to our results. Migration was reduced as compared to the three other cases and strongly asymmetric. However, this can be explained by isolation by distance as the two sites were separated by 3.5 km whereas sites were much closer in the three other rivers.

General conclusion

This thesis investigates in details the process of speciation in European lampreys and provides an overview of the level of population genetic structure both between and within species. It brings some key results with regards to the level of speciation, by clearly suggesting that Lampetra fluviatilis and L. planeri are ecotypes of a single species, as revealed by the very low level of reproductive isolation, but these ecotypes display partial reproductive isolation, as suggested by genetic analyses.

Both genetic and genomic analyses revealed the importance of the geographical context in understanding the speciation process. Besides, demographic inferences revealed the importance of historical processes during species formation. The identification of local hybrid zones was fundamental to better understand the process of speciation. Some key findings emerged with regards to current questions from the speciation literature.

First, it appears that European lamprey paired species, which were initially proposed as a model of "ecological speciation" [START_REF] Salewski | Satellite species in lampreys: a worldwide trend for ecological speciation in sympatry[END_REF], have in fact diverged following a period of allopatry. A key prediction of allopatric isolation is the accumulation of Dobzhansky-Muller incompatibilities (DMI) that will form reproductive barriers to gene flow. However, inferences revealed high contemporary gene flow. One future question will thus be to understand how DMI interact upon secondary contact.

The variable levels of genetic differentiation observed in some sympatric rivers, suggest an erosion of DMI in certain populations (Oir River) but not in others (Aa and Bresle River). A hypothesis for this contrasted situation is that coupling of some incompatibilities occurs in some rivers, but not in others where barriers may break down [START_REF] Barton | The evolution of strong reproductive isolation[END_REF]. This coupling can be affected by various factors such as migration rate or drift of resident populations [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF]. This may contribute to explain the variable levels of genetic parallelism observed across the studied populations. Importantly, inferences of parallelism upon secondary contact cast some doubts about the claimed role of natural selection driving parallel evolution of phenotypes in the face of gene flow (ex: Butlin et al. 2014).

A second major result is the importance of heterogeneity of gene flow across the genome. Taking this factor into account has not only allowed us to refine demographic inferences, but also provided key insights into the debate about the role of genomic islands of divergence. Indeed, genomic islands were proposed to emerge as byproducts of ecological selection during speciation with gene flow.

Here, inferring a secondary contact suggests that heterogeneous divergence was initiated by the accumulation of DMI that subsequently formed local barriers to gene flow and resulted in increased genetic differentiation in parts of the genome, while the remainder is currently homogenized by gene flow. Obviously, this does not mean that ecological selection or past local adaptation have not played a role or do not currently operate, but these factors are unlikely to have been the initiators of divergence. More recently, [START_REF] Cruickshank | Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow[END_REF], in line with population genetics theory, proposed that genomics islands originate as a result of background selection after divergence in regions of low recombination (i.e. postspeciation selection at linked sites). In Lampetra, demographic inferences suggest a role for genomic islands during divergence and not after speciation. As mentioned above however, If DMI accumulate in allopatry then it is possible that following episodes of strong gene flow some of them are removed, hence generating genomic heterogeneity in differentiation. Besides, exploratory analyses of background selection in isolated populations, suggest that this process does play a key role in shaping genetic architecture within populations of lampreys. As a consequence, heterogeneous differentiation may emerge due to selection at linked sites within populations. I hope that we will be able to perform further investigations on this topic, both within and between lamprey ecotypes. Combining demographic inferences and analyses of full genome data could help unravel the role of this process in the evolution of populations. Another question that will be hopefully addressed is the localization and extent of the genomic islands of divergence. Using linkage mapping and demographic inference should allow us to address this question.

A third key finding related to the strong level of gene flow was that the genome of resident L.

planeri may be swamped by neutral alleles of L. fluviatilis. This swamping seems to contribute to the maintenance of genetic diversity of L. planeri that otherwise evolve by drift and display small effective population sizes. L. fluviatilis not only form a "reservoir "of genetic diversity for brook lampreys but may also play a key role in promoting the transport of adaptive alleles between neighboring freshwater populations. From a conservation point of view, maintaining connectivity and access to spawning habitat of L. fluviatilis may be fundamental for the maintenance of the adaptive potential of the two ecotypes.

strictly freshwater resident species that displays a low migratory and dispersal ability [START_REF] Malmqvist | The spawning migration of the brook lamprey, Lampetra planeri Bloch, in a South Swedish stream[END_REF]and its populations can be isolated in upstream headwaters of rivers resulting in strong genetic differentiation [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. L. planeri is closely related to the anadromous and parasitic river lamprey L. fluviatilis whose populations are weakly differentiated and display higher levels of genetic diversity [START_REF] Docker | A review of the evolution of nonparasitism in lampreys and an update of the paired species concept[END_REF]. We have previously demonstrated that isolated L. planeri populations show a very reduced genetic diversity as measured by expected heterozygosity, a proxy of effective size (Ne) [START_REF] Nei | Effective population size, genetic diversity, and coalescence time in subdivided populations[END_REF] and allelic richness [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF]. In contrast, populations of the same species occurring in sympatry with L. fluviatilis displayed higher levels of diversity and higher Ne. L. planeri populations living in rivers where L. fluviatilis is absent are most prone to demographic instability (i.e. bottlenecks) and may undergo strong effects of genetic drift even in non-fragmented habitats (Rougemont et al. in prep). Altogether this makes L. planeri an interesting model to investigate the effect of random drift load in natural populations. In contrast, genetically more diverse L. fluviatilis populations should not display a high drift load.

The aim of the present study was to compare the strength of heterosis between isolated populations of L. planeri and L. fluviatilis populations. We also sampled a L. planeri population living in sympatry with L. fluviatilis as it may display a lower heterosis effect than isolated L. planeri populations.

We performed artificial crosses within and between-populations and within and betweenspecies to estimate heterosis and outbreeding depression, based on relative performances of offspring resulting from these crosses. Crosses among the most isolated and genetically differentiated populations allowed us to evaluate the importance of the load in these populations.

Theoretically, offspring from between-population crosses should display higher performance than individuals from within-population crosses.

Methods

Sampling and crosses:

5 L. planeri populations were sampled in isolated rivers (Montafilan, Rance, Rocher, Cens and Odon) and two populations of L. fluviatilis were sampled in the Loire and Oir river. We also collected individuals in the sympatric and weakly differentiated Oir L. planeri population. 3 to 10 individuals from each sex were sampled with electrofishing in each population. They were then kept in the laboratory and crossed with in vitro fertilization following the partially factorial design presented below (Figure 1). All individuals did not mature at the same time so we aimed at producing at least three intra-population and three inter-population crosses with progenitors from each population leading to a total of 102 crosses. The fertilized eggs were reared in 24 well plates at constant 12°C (±1°C) [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF] and we measured the survival and length of larvae at hatching. Multiple comparison tests showed that the survival of offspring from crosses between populations of L. planeri was significantly higher than the survival of offspring from crosses between females of L. fluviatilis and males of L. planeri (p=0.048; figure 2). All other pairwise comparisons were not significant (Figure 2). Clearly, offspring from L. fluviatilis females displayed a higher variance in survival (Figure 2).

We also found that cross type had a significant effect on the mean size of offspring (table 1).

Multiple comparison tests indicated first that the mean size of offspring from L. planeri females x L. fluviatilis males were significantly larger than offspring from other crosses among and within populations of L. planeri and from L. fluviatilis females x L. planeri males. However these crosses were not different from within and between-population crosses of L. fluviatilis (figure 3). Second, offspring from crosses using L. planeri males and females were significantly smaller than crosses between populations of L. fluviatilis. We also found a significant correlation between female size and offspring size when considering only crosses from L. planeri (r=0.46; p=0.016). However there was no evident relationship between other cross types and parental size. Overall, analyses did not show any significant differences in survival rate or larval size between populations of L. planeri and between populations of L. fluviatilis indicating no clear effect of heterosis or outbreeding depression among populations of the same species. Conversely, our analyses indicated differences in interspecific hybrid performances and at least one of the species used in crosses. Offspring survival rates from L. fluviatilis female x L. planeri male crosses were significantly lower than those from crosses using males and females of L. planeri from different populations but they were not different from crosses using L. fluviatilis (either between or withinpopulation crosses).

Larval size of hybrid offspring from L. planeri female x L. fluviatilis male crosses was significantly higher than size of intra-specific crosses revealing a tendency for heterosis for this trait.

Finally, we found significant genetic effects within populations as reflected by significant dam and sire effects. However such effects may be primarily driven by maternal effects as revealed by the positive relationship between female size and offspring larval length in L. planeri. Using half-sib crossing designs may allow disentangling these (environmental) maternal from purely additive genetic effects. planeri is non-parasitic and strictly freshwater resident. Yet their level of reproductive isolation and history of divergence remain poorly understood. A multidisciplinary approach including experiments, population genomics analyses and historical reconstruction was undertaken to address these issues. Experimental crosses revealed a very low level of reproductive isolation, partially mirrored by variable levels of gene flow in wild populations. Genetic analyses revealed that the two taxa were best described as partially reproductively isolated ecotypes suggesting that endogenous genetic barriers partially reduced effective migration between ecotypes. Genome wide analyses showed the importance of the current and ancient geographical context of speciation. In particular, parapatric L. planeri populations diverged mostly through drift and displayed a reduced genetic diversity. Demographic inferences suggested that divergence have likely emerged in allopatry and then secondary contacts resulted in partial parallelism between replicate population pairs. A strong heterogeneity of divergence across the genome was revealed by sympatric populations suggesting that genomic islands of differentiation were not linked to ongoing ecological divergence. Further investigations showed that the genetic diversity of L. planeri populations was weakly affected by human-induced river fragmentation. Resident populations displayed a higher diversity when gene flow was possible with L. fluviatilis populations in downstream sections of rivers. Overall this thesis showed that parasitic and non-parasitic lamprey ecotypes represent a promising model for studying speciation and notably the genomic architecture of divergence.

Résumé étendu en

Keywords: Speciation, Gene flow, parallelism, modelling, biogeography, evolutionary history, Lampetra

Figure 2 :

 2 Figure 2: Classification of the main mechanisms of reproductive isolation according to their environmental dependency and their stage of occurrence during the life cycle. Taken from Ravigné et al. 2007 and modified according to Coyne & Orr (2004) and Nosil et al. (2005).

Figure 3 :

 3 Figure 3: Model of Dobzhansky-Muller incompatibility for the evolution of postzygotic reproductive isolation in diploid (A) and haploid organisms (B). A new mutation appears in population 1 and 2 derived from the ancestral population. These populations evolved independently (allopatry) and the mutation can become fixed by drift and/or by the action of natural selection. These fixed alleles may become incompatible between the two genetic backgrounds and are revealed upon secondary contact. Compatibility alleles here are dominant. Note that the ancestral genotype can be A 1 A 1 B 1 B 1 and unchanged in population 1 but changed to A 2 A 2 B 2 B 2 in the population 2. (Taken from Nei & Nozawa, 2011).

Figure 4 :

 4 Figure 4: Illustrations of the semipermeable nature of species boundaries. (A) Gene flow following secondary contact as depicted byBarton and Hewitt (1981). The vertical bars represent a chromosome with 3 loci contributing to reproductive barriers (indicated by *). Immediately after contact, linkage disequilibrium along the chromosome will be high, but over time recombination breaks down associations among loci. Barrier genes or genes under divergent selection will remain differentiated (light grey regions), whereas alleles at loci that are neutral (white regions) will be exchanged between species. Many generations of recombination in hybrid zones allow fine-scale mapping of genes contributing to reproductive isolation and estimates of the strength of selection on individual alleles. (B) The idea that the genomes of diverging lineages become less permeable over time, as shown by[START_REF] Wu | The genic view of the process of speciation[END_REF]. Each pair of horizontal bars represents chromosomes of 2 diverging lineages. Very recently diverged species (pair I) may have few genes contributing to reproductive isolation (indicated by *). These regions will remain differentiated (light and dark grey

Figure 6 :

 6 Figure 6: Example of population genomic parameters along a chromosome from different flycatcher species (Burri et al. 2015). Blue: collared; green: pied; orange: Atlas; red: semicollared. Color codes for dxy: green, collared-pied; light blue, collared-Atlas; blue, collared-semicollared; orange, pied-Atlas; red, pied-semicollared; black, Atlassemicollared. Differentiation islands clearly occurred in regions of low recombination. Dxy is reduced in differentiation islands showing the reduction of Ne via Hill-Robertson Interference.

Figure 7 :

 7 Figure 7: The distinct signatures of local hitchhiking in a heterogeneous environment and global hitchhiking in a structured population. Distance (x-axis) is represented as the ratio between the recombination rate and the selection coefficient (r/s). (A) Local hitchhiking: if an advantageous mutation appears in habitat 1 (circles), it rises in frequency (u*) sweeping its genomic background (20 000 generations simulated). Circles depict frequency in habitat 2. (B) Local hitchhiking producing the peak of genetic differentiation at the hitchhiker allele between the two habitats (filled diamonds) after 20,000 generations. Differentiation decreases as a function of the distance from the site. (C) Global hitchhiking: frequency of an initially rare neutral allele that hitchhiked with an unconditionally favorable mutation, in the population in which the favorable mutation originated and in a population reached by the favorable mutation by migration. (D) Global hitchhiking: genetic differentiation between the two populations. Two domes of differentiation are observed on each side of the advantageous mutations together with the signature of a selective sweep of various intensity. The intensity of the sweep decreases as a function of LD with the selected locus. Taken from Bierne (2010).

Figure 8 :

 8 Figure 8: Example scenarios for parallel evolution and parallel speciation. Shaded areas are geographical ranges; shading type indicates environment type. Arrows indicate establishment of new derived populations from a phenotypically uniform ancestral species/population. (A) Colonization of replicate new environments leads to repeated evolution of small body size (and of mate preferences for small size). (B) Colonization of two environments causes repeated divergence in body size (and of mate preferences for body size) between daughter populations inhabiting different environments. (Extracted from Schluter & Nagel, 1995).

  performed simulations to investigate the origin of adaptation in marine-freshwater populations of Gasterosteus aculeatus. They simulated the replicated colonization of a freshwater habitat (where the derived population occurs) by a source population adapted to the marine habitat and analyzed the pattern observed at three strong candidate loci for local adaptation. Their simulations showed a peak of F ST between source and derived populations due to a barrier to gene flow. When comparing freshwater populations, they obtained the peak-valley-peak signature (cf section 3.1.1) which is somewhat similar to the pattern described above when a selective sweep arises in a structured population[START_REF] Bierne | The Distinctive Footprints of Local Hitchhiking in a Varied Environment and Global Hitchhiking in a Subdivided Population[END_REF]. The authors hypothesized that the freshwater populations adapted in parallel to freshwater, using alleles introgressed from the marine population. In a review[START_REF] Welch | Standing and flowing: the complex origins of adaptive variation[END_REF] proposed four scenarios (Fig9) to explain the results from[START_REF] Roesti | The genomic signature of parallel adaptation from shared genetic variation[END_REF] and all of these were already more or less presented in[START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF].

Figure 9 :

 9 Figure 9: Some of the possible scenarios underlying adaptation to the freshwater environment. (1) Recurrent mutations occur independently in different freshwater populations. (2) Freshwater alleles were retained in multiple refugia, which then came into secondary contact in the marine population. Over time admixture may lead to considerable homogenization of genomes between the two habitats apart from regions involved in local adaptation.(3) Freshwater alleles arose by recurrent mutations in the marine habitat. These alleles, which may be identical by descent, are then introduced to the novel freshwater habitats. (4) Freshwater alleles were maintained in a refugium and introduced into novel freshwater habitats via the marine habitat. Although both (3) and (4) involve adaptation from standing variation in the marine habitat, the source of that variation is fundamentally different in the two cases. From Welsh & Jiggins 2014.

Figure 10 :

 10 Figure 10: European distribution of the genus Lampetra. Blue outline represents areas in which the species is now extirpated. Extracted from Kottelat, 2007.

Figure 11 :

 11 Figure 11: Life cycle of the parasitic anadromous L. fluviatilis (left); of the nonparasitic resident L. planeri (right).

Figure 12

 12 Figure 12 Phylogenetic relationships between parastic and non parasitic species of the three lamprey families. Dervied from cytochrome b sequences data using Bayesian analyses. Asterisks designate parasitic species. Data derive from Lang et al. (2009) with additional data for Mordacia mordax from New South Wales, Australia (NSW). VIC: Victoria, WA: Western Australia. Bayesian posterior probabilities are given when values are higher than 0.95. Taken from Potter et al. 2015.

  with controlled crosses and in the wild with measures of gene flow based on microsatellite data. I also characterized the extent of genetic structure among and within L. planeri and L. fluviatilis populations in French coastal rivers. Given the moderate levels of reproductive isolation and variable levels of gene flow, I suggested that L. fluviatilis and L. planeri may form partially reproductively isolated ecotypes. I identified replicated population pairs connected by high levels of gene flow as the most relevant populations to study the historical process of speciation.

Figure 2 :

 2 Figure 2: Reproductive successes of four L. planeri and two L. fluviatilis males under semi-natural conditions after spawning with two L. fluviatilis females. White bars represent the percentage of offspring assigned to a male at the 95% confidence level when no mismatch was allowed in assignment tests (73 individuals assigned). Grey bars represent the percentage of offspring assigned to a male at the 95% confidence level when up to 2 mismatches were allowed (129 individuals assigned). Numbers on top of each bar depict the absolute number of offspring assigned.

  diversity estimates of L. fluviatilis (Lf, n = 523) and L. planeri (Lp, n = 500) populations based on 13 microsatellite loci for each site and year. Site numbers refer to Figure 1, N = Number of individuals, Ar = Allelic richness (based on resampling of 14 individuals), An = Number of alleles (averaged by loci), Ho = Observed heterozygosity, He = Expected heterozygosity, Fis = Inbreeding coefficient (*significant deviation from the Hardy-Weinberg Equilibrium) and (S), U, and D refer to sympatric, upstream and downstream sites, respectively. Rivers (abbreviation) : Béthune (Bet) -Bresle (Bre) -Risle (Ris) -Odon (Odon) -Oir (Oir) -Loire (Loi) -Cens (Cens) -Dordogne (Dor) -Garonne (Gar) -Jalle de Tiquetorte (Jal) -Saucats (Sau

Figure 3 :

 3 Figure 3: Comparison of F ST values between sympatric (S) and parapatric (P) pairs of river and brook lampreys (dark-grey bars with their 95% confidence intervals), brook lampreys from a given river and all other brook lamprey populations surveyed (grey bars ± s.e.) and brook lampreys from a given river and all other river lamprey populations (white bars ± s.e.).

Figure 4 :

 4 Figure 4: Bayesian analysis performed with STRUCTURE for each sympatric (S) or parapatric (P) pair of river (Lf) and brook (Lp) lamprey populations.

Figure S1 :

 S1 Figure S1: Bayesian individual clustering results with STRUCTURE: k = 2, 8 and 10 for the two species analysed together (a, b and c, respectively) and k = 2 and 9 for L. fluviatilis (d) and L. planeri (e) respectively.

Figure 1 :

 1 Figure 1: Map of sampling sites across the channel area.

  assuming an infinitesites model of mutation, in which most parameters are scaled by the effective population size of an arbitrarily chosen reference population (N ref ) with impact on conclusions drawn by the ABC analysis. Each model was also characterized by a scale effective population size θ: θ A / θ Ref ,θ lf / θ Ref , θ lp /θ Ref where θ Ref =4N Ref μ, μ represents the mutation rate per generation. Patterns of genetic diversitysuggested that river lamprey display a greater Ne than populations of brook lamprey(Rougemont et al. 2015). Thus, θ lf was sampled on the interval 0-3 and θ lp in the interval 0-max(θ lf) . θ Ref was set to 1 (i.e. we assumed N Ref = 1,000 and μ=2.5e-4 ). The panmictic model was only characterized by the unique effective mutation rate θ which was also modelled on the interval 0-3. All models (except PAN) also incorporated the scaled time of divergence, τ split /4N Ref , where τ split is the time measured in number of generations and drawn from a uniform distribution in the interval 0-25. The two parameters τ iso (AM model) and τ sc (SC model) were computed from uniform distributions defined on the interval 0-τ split . Since the genetics and ecology of lampreys is poorly known, we chose large uninformative prior distributions for all parameters to include commonly used parameters from the literature[START_REF] Pinho | Divergence with Gene Flow: Models and Data[END_REF] after exploring different combinations of priors following[START_REF] Cornuet | Inference on population history and model checking using DNA sequence and microsatellite data with the software DIYABC (v1.0)[END_REF] (Table1). Binary simulated data from ms were converted into microsatellite data using a stepwise mutation model (SMM). Probability of changes of the repeat number in each mutation event was modelled by a geometrical parameter α distributed following a uniform prior distribution sampled on the interval 0 -0.5. All computations were run in R and took into account differences in sample size for each of the thirteen loci. Summary statistics were computed from the transformed microsatellite data. One million simulations composed of the thirteen microsatellite loci were computed under each demographic model. All R code used for ABC computation is available from the authors.

Figure 2 :

 2 Figure 2: Different scenarios of divergence between L. planeri and L. fluviatilis. (A ) five models with different parameters are tested and compared. Two null models: Stict Isolation (SI) and Panmixia (PAN). Three models of migration: isolation with constant migration (IM), ancient migration (AM) and secondary contact (SC). The following parameters are shared by all models: τ div : number of generation since divergence time. θ A , θ Lf , θ Lp : effective population size of the ancestral population, of L. fluviatilis and L. planeri respectively. τ isol is the number of generations since the two ecotypes have stopped exchanging genes. τ sc is the number of generations since the two ecotypes have entered into a secondary contact after a period of isolation. M 12 and M 21 represent the migration rates expressed in 4.Nm units per generation with m the proportion of population made of migrants from the other populations

  The accuracy of posterior parameter estimation varied greatly among populations pairs, with the Aa, Hem and Risle presenting accurate parameter estimation under both the IM and SC model. On the contrary posterior parameter estimates on the Bethune population pairs were almost flat and not different from the prior. In general thetas estimated from the IM model were more accurate than under the SC model. Under both models we generally observed a reduction of thetas in both river and brook lampreys as compared to their ancestral population. Under the IM model the respective median effective mutation rate (θ Lf and θ Lp ) of the river lamprey and brook lamprey were on average 1.67 and 4.71 times smaller than the ancestral population.

Figure 3 :

 3 Figure 3: Curves of out-of-bag errors rates and Estimation of variable importance for the Aa river.Data based on one random forest, each composed of 1,000 trees obtained from a trained set of 50,000 simulated predictor variables (summary statistics). The response variable is the demographic model. Estimation for the 6 remaining rivers yielded similar results and are presented in tableS2and FigureS1

Figure S1 :

 S1 Figure S1: Curves of out-of-bag errors rates and Estimation of variable importance for each population pairs.

  Figure S2: Distribution of summary statistics obtained from the posterior predictive check.

  or to postspeciation selection at linked sites[START_REF] Turner | Genomic islands of speciation or genomic islands and speciation[END_REF][START_REF] Cruickshank | Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow[END_REF]. It is even less easy to understand which sequences of historical and selective events underlie these heterogeneous patterns of differentiation. For instance, many studies have used genome scans to investigate patterns of heterogeneous differentiation and identify so-called genomic islands of differentiation[START_REF] Seehausen | Genomics and the origin of species[END_REF]. However, accurately disentangling the relative influence of gene flow, historical processes and recombination rate variations(Barrett & Hoekstra 2011b;[START_REF] Nachman | Recombination rate variation and speciation: theoretical predictions and empirical results from rabbits and mice[END_REF][START_REF] Roesti | Recombination in the threespine stickleback genome-patterns and consequences[END_REF][START_REF] Ellegren | Genome sequencing and population genomics in non-model organisms[END_REF]) on the genomic landscape of differentiation has remained highly challenging so far.

5

  parapatric pairs varying in their level of neutral genetic differentiation and geographic connectivity (Rougemont et al. 2015) (Fig 1). Populations were collected in sympatry in the Aa, Oir and Bethune and very close to each other in Bresle, where sampling sites were located only 8 km apart. Pairs from the Risle and Odon River were collected on the same river but were separated by dams, with

  using a fluorimeter (Qubit 2.0) and standardized to 22 ng.µL -1 . DNA was then used to construct a total of 13 libraries, each composed of 48 randomly distributed individuals including other lamprey populations (not presented in this study) and following the protocol from Baird et al. (2008) using the restriction enzyme Sbf1. Samples were individually barcoded and paired-end sequenced on 8 lanes of a Illumina Hiseq 2500 (125-bp paired-end reads) and 5 lanes of a Illumina Hiseq 2000 (100-bp pairedend reads) at Montpellier Genomix and Genotoul sequencing platforms, respectively.

Figure 1 :

 1 Figure 1: Map of sampling site across the Atlantic and channel area. River names match those given in Table 1. Number of shared (red) and private polymorphisms (orange = Lf, yellow = Lp)

2 )

 2 , including the model of Strict Isolation (SI), three different models incorporating homogeneous migration along the genome (IM, AM and SC), and three models incorporating heterogeneous migration along the genome (IM2m, AM2m, SC2m). Gene flow was supposed to be either ongoing during the whole divergence history (Isolation-with-Migration models IM and IM2m), only present at the beginning of divergence (Ancient Migration models AM and AM2m), or starting after a period of complete isolation (Secondary Contact models SC and SC2m).

Figure 2 :

 2 Figure 2: Representation of the 7 demographic scenarios compared (A) Seven models with different parameters are tested and compared. Strict Isolation (SI), isolation with constant migration (IM), ancient migration (AM) and secondary contact (SC). The following parameters are shared by all models: τ div : number of generation since divergence time. θ A , θ Lf , θ Lp : effective population size of the ancestral population, of L. fluviatilis and L. planeri respectively (in units of 4N ref µ). τ isol is the number of generations since the two ecotypes have stopped exchanging genes (in units of 2 N ref generations). τ sc is the number of generations since the two morphs have entered into a secondary contact after a

Figure 4 :

 4 Figure 4: Boxplot of observed Heterozygosity in each pair of river and brook lamprey and violin plot of F ST between pairs. Populations are sorted by increasing order of differentiation. Significance of difference in heterozygosity between river and brook lampreys (as measured by t-tests) are depicted by *** when significant with a p-value < 0.0001 or NS when non-significant. Blue = Connected population pairs, Green = Disconnected population pairs.

Table 2 :

 2 Fig 5 and Fig S3. The Strict Isolation model (SI) was weakly supported as it never captured asymmetries in the observed JSFSs and failed to recover the most differentiated SNPs. In comparison, the three models with homogeneous gene flow (AM, IM, SC) provided better fit to the data with a good prediction of asymmetric gene flow. However, in every case, incorporating heterogeneous gene-flow (AM2m, IM2m, SC2m) largely improved the fit (Table2). In connected populations, under the AM (and AM2m) model, the timing of ancient migration stop (τ am ) reached a value of zero meaning that this model converged to the simple IM (and IM2m) model. Overall we repeatedly found that the secondary contact model incorporating heterogeneous migration rates (SC2m) provided the best fit to the observed JSFS in the four population for which F ST was <0.10. In the sympatric population with a F ST >0.10 (Bresle) the most likely scenario was IM2m but both the SC2m and AM2m produced very similar AIC. The 3 parapatric populations converged unambiguously to the AM2m. In each case the effective population size was larger in the migratory species than in the resident species as indicated by the ratio of (Ne Lf)/(Ne Lp) in which effective population size under the SC2m model was on average 8 times greater in Lf and 19 times greater in Lf under the AM2m model. Using unfolded JSFS allowed estimating gene flow parameters (m, me, P) with great accuracy. We observed asymmetries in gene flow with the ratio of m21/m12 indicating a stronger migration from river lampreys to brook lampreys in all rivers except the Aa under the SC2m. When taking into account effective population size, ratios of Nem indicated similar levels of migration in the Aa river and greater level of migration from river lampreys to brook lampreys in the remaining cases. Under the AM2m model, the inverse trend was observed with higher levels of migration from brook lampreys to river lampreys except in the Odon River where migration was not correctly estimated in brook lampreys. Ratio of τ sc /τ split indicated variable secondary contact times ranging from 7% of the total divergence time to 22% of the total divergence time. The proportion of loci freely exchanged ranged from 0.55 (Aa) to 0.68 (Risle) indicating that from 32 to 45% of loci displayed a reduced effective migration rate between the two morphs. In disconnected populations ratios of τ am /τ split suggest a very recent stop of geneflow. Comparison of 7 different models obtained from δaδi between each pair using the folded JSFS and estimation of their demographic parameters.The best models are in bold. An additional line (in italic) provides the results of parameter estimation using unfolded JSFS for each best model. The time of split τs, contact τsc, migration stop τam and effective population size (Nu1, Nu2) were fixed based on values inferred from the folded JSFS. Lines highlighted in grey represent models for which ΔAIC is < 10. AIC = Akaike Information Criterion, MLE = Maximum Likelihood, Theta = 4 Nrefμ, effective mutation rate of the reference population, which here corresponds to the ancestral population. Nu Lf and Nu Lp: effective population size of L. fluviatilis and L. planeri. m12 and m21: migration from L. planeri to L. fluviatilis and from L. fluviatilis to L. planeri, respectively. me12 and me21: effective migration rate estimated in the most differentiated regions of the genome from L. planeri to L. fluviatilis and from L. fluviatilis to L. planeri, respectively. τs = Time of split of the ancestral population in the two daughter species. τsc and τam: Time of initiation of the secondary contact (SC and SC2m) and time of period with ancestral migration. P = proportion of the genome freely exchanged (1-P provides the proportion of the genome non-neutrally exchanged).

Figure 5 :

 5 Figure 5: Results of the diffusion approximation models for eight pairs of populations. The observed data and the best fitting models are displayed. Plots of all other models are provided in supplementary results together with their residuals. The Dordogne river was excluded at this stage due to the low number of individuals available (n=7). Blue = Connected population pairs, Green = Disconnected population pairs.

Figure 6 (

 6 Figure 6 (above): Coplots of parallelism in genome-wide divergence based on the neutral model estimated from δaδi for connected and disconnected population pairs. FST values of putatively neutral markers are displayed with grey open circles. Black and maroon circles represent markers outside the neutral model not shared between population pairs and putatively under selection. Blue (connected population pairs) and green (disconnected population pairs) circles denote shared markers falling outside the neutral envelope (regression lines are provided for illustration).

Figure 7 :

 7 Figure 7: Comparison of the distribution of the coefficient of correlation (r²) obtained from the linear regression of shared outlier F ST in connected population (left) and disconnected populations (right).Figure 8: Derived Allele Frequency (DAF) distribution in Lf and Lp determined with the P.

Figure 8 :

 8 Figure 7: Comparison of the distribution of the coefficient of correlation (r²) obtained from the linear regression of shared outlier F ST in connected population (left) and disconnected populations (right).Figure 8: Derived Allele Frequency (DAF) distribution in Lf and Lp determined with the P. marinus outgroup. Only markers previously detected as outliers base on our neutral model were kept for calculation.

Figure S1 :Figure S3 :

 S1S3 Figure S1: Distribtuions of allelic frequency

  taxa (Paz-Vinas et al. 2015): i) downstream biased dispersal generating downstream gene flow (Paz-Vinas et al. 2013), ii) increase in downstream habitat availability (e.g. Raeymaekers et al. 2008) and iii) upstream founding events with loss of genetic diversity (e.g. following postglacial colonization Cyr & Angers 2012).

Figure 4 :

 4 Figure 4: Comparisons of the mean allelic richness (upper boxplots), expected heterozygosity (middle) and levels of relatedness. Left parts depict L. fluviatilis. The three other pair of boxplot depicts difference in each region and compares upstream and downstream site.

  of L. planeri (i.e. populations of downstream areas in Normandy) whereas populations from Brittany were not at migration-drift equilibrium. This result further suggests that the current genetic makeup of L. planeri populations in Normandy is influenced by ongoing gene flow with L. fluviatilis. In the absence of inter-basin gene flow mediated by L. fluviatilis, populations of Brittany evolved independently from each other and are not globally at migration drift equilibrium (which does not imply that populations within rivers deviate from this equilibrium). Populations from the Upper Rhone area, together with the few populations from the Rhine, Ireland and United Kingdom were also very informative with regards to the historical divergence of the two forms. First, populations from Ireland, the United Kingdom and the Rhine displayed moderate levels of genetic differentiation, suggesting that these populations have undergone gene exchange with L. fluviatilis and share a more recent history with populations of L. planeri from Normandy, than did populations of L. planeri from Brittany. In contrast, all populations from the Upper Rhone area displayed a highly reduced genetic diversity and were strongly differentiated (e.g. Fig 2) from all other populations. Different

Figure

  Figure S2: Sampling along the Arz (8 sites) and Crano river (7 sites) and levels of admixture (a) Arz River

2 .

 2 fluviatilis and L. planeri. In particular, sperm of L. fluviatilis from the Oir river displayed lower quality (lower motility, viability and concentration) than sperm of L. planeri from the same river. These results will open the way to address exciting questions about CSP. For instance we could test whether sperm from L. planeri sneaker males are more competitive than sperm from allopatric populations of L. planeri and L. fluviatilis males. The importance of the geographical context in maintaining gene flow between lamprey ecotypes It was necessary to validate whether the experimental measurement of low RI was reflected by contemporary gene flow in wild populations. A set of ten pairs of L. fluviatilis and L. planeri populations were thus sampled in different rivers and genotyped with microsatellite markers. Five pairs were putatively connected by gene flow as individuals were sampled simultaneously on the same nests in the Aa, Oir and Bethune Rivers, and at close proximity on the Hem and Bresle. Fiveother populations were parapatric: the Odon and Risle on the same river and the Garonne-Saucats, Dordogne-Jalles and Loire-Cens watersheds. The results indicated that gene flow could be strong between both ecotypes in sympatry (Oir and Bethune F ST varied from 0.0076 to 0.0323) but the extent of gene flow was lower in the Aa River (F ST = 0.08). However the inter-population differentiation was weaker in sympatric population pairs than in parapatric populations pairs (except the Risle where we hypothesized asymmetric downstream gene flow) for which reductions in gene flow was stronger (F ST ranged between 0.10 and 0.20). Based on these results, the genetic differentiation was sometimes too strong in sympatry to validate the hypothesis of a single population displaying phenotypic plasticity for the life history strategy as suggested earlier (e.g.

Fig. 1 :

 1 Fig. 1: Matrix of crosses performed between six L. planeri populations and two L. fluviatilis populations (females are identified by letters and males by numbers). Within and among pouplation crosses were dependent on the timing of maturation of individuals, which varied both within and

Figure 2 :

 2 Figure 2: Box plot of hatching rates by cross type (BL: brook lampret, RL: river lamprey), letters (a, b and c) indicate significant difference between crosses.

Figure 3 :

 3 Figure 3: Box plot of larval size by cross type, letters (a, b and c) indicate significant differences between crosses.

Français

  Evolution de la divergence entre la lamproie fluviatile (Lampetra fluviatilis) et la lamproie de Planer (L. planeri) inférée par des approches expérimentales et de génomique des populations Q. Rougemont Comprendre les processus à l'origine de la formation des espèces est un enjeu fondamental de la biologie évolutive. Selon le concept biologique de l'espèce, une espèce correspond à un groupe d'individus se reproduisant entre eux et isolé reproductivement d'autres groupes. Selon cette définition l'isolement reproducteur est une propriété du génome et les hybrides ne devraient pas exister dans la nature. Hors, l'hybridation adaptative et le flux de gènes interspécifique sont communément observés. Le concept génique de l'espèce en revanche permet de prendre en compte la nature semi-perméable de la barrière aux flux de gènes si bien qu'une grande partie du génome peut être librement échangée entre espèces alors que d'autres régions restent imperméables au flux de gènes. Cette thèse étudie le processus de spéciation entre la lamproie fluviatile (Lampetra fluviatilis) et la lamproie de planer (L. planeri) en mettant en lumière la notion de barrière semiperméable aux flux de gènes. Ces deux espèces présentent des stratégies d'histoire de vie extrêmement différentes : L. fluviatilis est parasite et anadrome alors que L. planeri n'est pas parasite et reste strictement dulcicole. Toutefois, leur degré d'isolement reproducteur et leur histoire de divergence demeurent méconnus. Le but majeur est de comprendre le processus de divergence historique ou en cours entre L. fluviatilis et L. planeri. Cette question a été étudiée par une approche multidisciplinaire combinant des mesures expérimentales de l'isolement reproducteur et des inférences du flux de gènes en populations naturelles basées sur des marqueurs 'neutres' microsatellite et des marqueurs RAD. Des approches par simulations ont aussi été implémentées afin de reconstruire le processus historique de divergence sous-jacent. Un second objectif, de nature plus appliqué en lien avec les thématiques de biologie de la conservation visait à déterminer l'impact des barrières naturelles et de la fragmentation anthropique sur l'intégrité génétique des populations de L. planeri. Une approche de génétique du paysage testant les effets de la distance, des barrières à la migration ainsi que de l'admixture avec l'écotype de L. fluviatilis sur la distribution spatiale de la diversité génétique des L. planeri a été développée. Le degré d'isolement reproducteur entre L. planeri et L. fluviatilis a été mesuré expérimentalement ainsi que dans les populations naturelles (Chapitre 2). Nous avons tout d'abord mesuré l'isolement reproducteur post-zygotique par fécondation in vitro entre mâles et femelles des deux espèces et avons mis en évidence un isolement reproducteur très faible avec des taux de fécondation et de survie proche de 100 % des croisements interspécifiques. Afin de vérifier la capacité des mâles de L. planeri à s'accoupler avec des femelles de L. fluviatilis et à produire des descendants viables, des expériences de croisements en condition semi-naturelles ont été effectuées. Les analyses de parenté montrent que les mâles sont capable de s'accoupler avec les femelles L. fluviatilis même s'il est possible qu'un isolement par la taille existe. Les analyses effectuées en populations naturelles sont basées sur un total de 10 paires de populations dont 5 sympatriques et 5 parapatriques. Les résultats montrent des degrés de flux de gènes variables en sympatrie avec une paires très faiblement différenciée (FST = 0.008) et d'autres paires plus fortement différenciées (FST = 0.08). A partir de ces résultats, nous proposons l'existence d'un gradient de divergence avec certaines populations correspondant à deux écotypes d'une seule espèce et d'autres populations correspondant à des écotypes à isolement reproducteur partiel. Ces résultats suggèrent que les barrières génétiques endogènes peuvent exister mais ne réduisent la migration efficace que sur des faibles proportions du génome. L'analyse des populations parapatriques montre une forte différentiation des populations de L. planeri due à l'effet combiné de l'isolement géographique et des barrières anthropiques à la migration. Alors que chaque population de L. planeri de chaque cours d'eau forme un cluster indépendant, les populations de L. fluviatilis forment deux clusters proches de la panmixie, de manière similaires aux observations chez les écotypes d'épinoche marine et résidente Gasterosteus aculeatus. A partir de ces résultats, nous formulons deux hypothèses d'histoires démographiques qui ont pu générer ces patterns : soit la sélection écologique en cours génère une différenciation génétique en présence de flux de gènes, soit les populations ont initialement divergé en allopatrie, accumulé un certains nombre d'incompatibilités génétiques et échangent à nouveau des gènes suite à un ou plusieurs contact(s) secondaire(s). Nos résultats suggèrent par ailleurs que le contexte géographique joue un rôle majeur dans la divergence des populations, surtout pour les populations de petite taille efficace pouvant facilement être affecté par la dérive génétique. Dans le chapitre 3, nous utilisons les populations connectées par flux de gènes précédemment identifiées pour tester différents scénario d'histoire démographiques. Nous testons 5 scénarios à partir d'une approche d'Approximate Bayesian computation (ABC). Le premier scénario correspond à une séparation d'une population ancestrale en deux, suivie d'un isolement strict sans flux de gènes par la suite (SI). Le second scénario est celui d'une spéciation sympatrique avec décroissance du flux de gènes progressive puis arrêt total d'échanges de gènes (AM, ancient migration). Le troisième scénario correspond à une divergence de la population ancestrale en deux populations qui échangent continuellement des gènes (IM, isolement avec migration). Le quatrième scénario est celui d'une divergence allopatrique pour une durée variable suivie par des flux de gènes lors d'un contact secondaire entres les 2 populations ayant plus ou moins divergées (SC). Le cinquième scénario finalement correspond au maintien d'une seule population à l'équilibre (scénario de panmixie). Les résultats suggèrent que dans la plupart des cas il est difficile de distinguer de manière robuste le scénario d'isolement avec migration de celui impliquant des contacts secondaires que ce soit avec une approche classique d'ABC ou avec une approche basée sur l'utilisation des forêts aléatoires. Dans le cas de l'Oir, où la population est la moins différenciée génétiquement, le scénario de panmixie est le plus probable. Il s'agit aussi de la population ou L. fluvatilis est significativement plus petite que les autres populations de L. fluviatilis étudiées, ce qui pourrait faciliter le flux de gènes contemporain entre les deux formes. Notre capacité à distinguer entre IM et SC peut s'expliquer simplement par le fait que dans un signal de divergence allopatrique suivi d'un contact secondaire les marqueurs de types 'neutres' telles que les microsatellites convergent vers un signal d'équilibre migration dérive après un certain temps de contact. Si la durée de divergence allopatrique a été trop courte relativement à la période de flux de gènes, le signal de divergence historique est simplement perdu. Dans le chapitre 4 nous utilisons une approche génomique afin d'apporter davantage de résolution dans le but de mieux comprendre l'histoire de la divergence chez Lampetra. Par ailleurs nous exploitons nos différents répliquas de paires d'espèces pour tester l'étendue du parallélisme génétique dans le contexte des barrières semi-perméables au flux de gènes. Des analyses de séquençage RAD ont été effectuées sur 9 paires de populations sympatriques et pararatriques puis des scénarios de divergences ont été testés à l'aide d'approximations de diffusion du spectre joint des fréquences alléliques. Les analyses génomiques confirment les patterns globaux de divergence observés à l'aide des marqueurs microsatellite, tout en apportant une résolution beaucoup plus fine et de nombreux éléments supplémentaires dans la compréhension des processus de divergence. En premier lieu, les résultats permettent pour la premières fois de clairement discriminer les écotypes migrateurs et résident et d'identifier de manière statistique des hybrides F1, F2 et backcrosses avec une grande robustesse. D'autre part, les analyses d'approximation de diffusion suggèrent deux scénarios de divergences différents : les populations connectées par le flux de gènes ont généralement divergé en allopatrie suivie de contacts secondaires résultant en un parallélisme génomique partiel entre répliquas de paires de populations. Une hétérogénéité de la divergence génomique a démontré que les îlots génomiques de différenciation ne résultaient pas de l'action récente de la divergence écologique mais avaient plus probablement été générés suite à l'accumulation d'incompatibilités génétiques en allopatrie et étaient bien révélées par l'action du flux de gènes récent dans les populations où les deux écotypes étaient bien connectés. Au contraire, un degré de parallélisme moins important a été observé dans les populations parapatriques et un scénario de divergence sympatrique suivi d'un arrêt récent de la migration a été révélé dans ces populations. Il est possible que le signal de divergence obtenu dans ce cas soit aussi biaisé par l'histoire récente de dérive dans les populations de petite taille efficace. Par ailleurs, l'utilisation de nos modèle démographiques pour générer des prédictions neutres ont permis d'identifier un plus grand nombre de marqueurs hautement différenciés génétiquement dans les populations parapatriques que dans les populations sympatriques et représentant des 'outliers' potentiels. Toutefois, une plus grande similarité dans les régions divergentes est détectée chez les populations sympatriques, mettant encore une fois en évidence le rôle fondamental du flux de gènes pour révéler l'histoire commune et le parallélisme partiel à l'échelle moléculaire. Bien que le rôle potentiel de la sélection sur des sites liés, en particulier dans les régions de faible recombinaison ne puisse être rejeté, ces résultats suggèrent fortement que l'hétérogénéité de la différenciation et du parallélisme génétique ont été générés par des réductions locales de flux de gènes dans les îlots génomiques. Dans le chapitre 5 nous avons testé l'influence de la fragmentation anthropique des cours d'eau sur la diversité génétique des populations de L. planeri à travers un échantillonnage à large échelle de 81 populations situées en amont et aval d'obstacles et dans des contextes géographiques bien différents. Plus précisément, 2472 individus ont été collectés dans 29 cours d'eau en Normandie et Nord de la France en sympatrie et parapatrie avec L. fluviatilis, en Bretagne et dans le Haut Rhône en allopatrie avec L. fluviatilis. Les individus ont été génotypés à l'aide de 13 marqueurs microsatellites. Les résultats ont démontré un effet négatif du nombre d'obstacles et de leur hauteur uniquement sur la richesse allélique des populations de L. planeri. Par ailleurs, une augmentation significative et forte de diversité génétique vers l'aval est observée suggérant un rôle majeur de l'asymétrie du flux de gène, corroboré par des analyses génomiques sur 4 paires de populations. Les populations de L. planeri possèdent une diversité génétique plus grande lorsque le flux de gènes avec L. fluviatilis dans les parties aval des cours d'eau est possible, ce qui n'est pas le cas des populations allopatriques. L'hypothèse explicative la plus probable est une introgression des populations de L. planeri par les L. fluviatilis de taille efficace plus grande. Ces résultats ont une implication importante en ce qui concerne la biologie de la conservation en démontrant i) un impact modéré des barrières à la migration sur la diversité génétique locale des populations de L. planeri et ii) que les deux ecotypes devraient idéalement être gérés conjointement lorsqu'ils coexistent en sympatrie. Pour conclure, nous avons notamment mis en évidence l'importance de la nature semi -perméable des barrières au flux génique, engendrant une forte hétérogénéité de la différenciation à l'échelle génomique. Nos analyses montrent que les îlots génomiques partagés entre toutes les paires ont été révélé grâce à l'effet érosif du flux de gènes suite à des contacts secondaires et n'ont pas émergé suite à des événements indépendants de spéciation sympatrique dûs à la sélection écologique. Nos résultats n'excluent pas un rôle majeur de la sélection d'arrière-plan dans les régions de faibles recombinaison et des analyses en cours suggèrent même que ce processus est important entre populations d'un même écotype. Nous avons par ailleurs démontré la difficulté à discriminer avec précision l'histoire évolutive de la divergence entre les deux écotypes de Lampetra que ce soit avec un nombre faible de marqueurs ou à une échelle génomique. Il est probable que la véritable histoire démographique soit beaucoup plus compliquée que ce que les dernières méthodes statistiques permettent de tester actuellement. Par ailleurs, une cartographie génétique en cours de développement permettra de mieux connaître la distribution des zones du génome les plus différenciées. Globalement cette thèse a démontré que les paires d'écotypes parasites et non-parasites de lamproies représentent un excellent modèle d'étude de la spéciation et notamment de l'architecture génomique de la divergence. Evolution de la divergence entre la lamproie fluviatile (Lampetra fluviatilis) et la lamproie de planer (L. planeri) inférée par des approches expérimentales et de génomique des populations Quentin Rougemont -UMR 985 ESE INRA -AGROCAMPUS OUEST -Université de Rennes 1 Directeur de thèse : Guillaume Evanno -Chargé de Recherche INRA -AGROCAMPUS OUEST Co-encadrement : Sophie Launey -Chargé de Recherche INRA -AGROCAMPUS OUEST Résumé : Cette thèse étudie le processus de spéciation entre la lamproie fluviatile (Lampetra fluviatilis) et la lamproie de Planer (L. planeri). Les deux espèces présentent des stratégies d'histoire de vie extrêmement différentes : L. fluviatilis est parasite et anadrome alors que L. planeri n'est pas parasite et reste strictement dulcicole. Toutefois, leur degré d'isolement reproducteur et leur histoire de divergence demeurent méconnus.Ces questions ont été abordées par des approches expérimentales, de génomique de populations et de simulations démographiques. Des croisements expérimentaux ont révélé un faible isolement reproducteur, confirmé par des degrés variables de flux géniques dans les populations naturelles. Les analyses génétiques ont montré que les deux taxons représentaient probablement des écotypes avec un isolement reproducteur partiel suggérant que les barrières reproductives endogènes ne réduisaient que partiellement la migration efficace entre écotypes. L'importance du contexte géographique actuel et passé dans l'étude de la spéciation a aussi été mise en évidence par des analyses à l'échelle du génome. Ainsi, les populations isolées de L. planeri évoluent principalement sous l'effet de la dérive génétique et ont une diversité réduite. Les inférences démographiques ont suggéré que la divergence a été initiée en allopatrie puis suivie de contacts secondaires résultant en un parallélisme génomique partiel entre réplicas de paires de populations. Une hétérogénéité de la divergence génomique a démontré que les ilots génomiques de différenciation ne résultaient pas de l'action récente de la divergence écologique. En outre, nos résultats suggèrent un impact faible de la fragmentation anthropique des cours d'eau sur la diversité génétique des populations de L. planeri. Les populations résidentes possèdent une diversité génétique plus grande lorsque le flux de gènes avec L. fluviatilis dans les parties aval des cours d'eau est possible. Globalement cette thèse a démontré que les paires d'écotypes parasites et non-parasites de lamproies représentent un excellent modèle d'étude de la spéciation et notamment de l'architecture génomique de la divergence. Mots clés : Spéciation, flux de gènes, parallélisme, modélisation, biogéographie, histoire évolutive, Lampetra Evolution of divergence between the river lamprey (Lampetra fluviatilis) and the brook lamprey (L. planeri) inferred by experimental approaches and population genomics Summary: This thesis investigates the process of speciation between the European lampreys Lampetra fluviatilis and L. planeri. The two species have drastically different life history strategies: L. fluviatilis is parasitic and anadromous while L.

Table 2 :

 2 Pairwise F ST values for sympatric populations (non-significant values are grey coloured and negative values were set to zero).

	Populations	Lf Aa 2014	Lf Hem 2014	Lf Bre 2011	Lf Bet 2014	Lf Oir 2010	Lf Oir 2011	Lf Oir 2014	Lp Aa 2014	Lp Hem 2014	Lp Bre 2011	Lp Bet 2014	Lp Oir 2010	Lp Oir 2011
	Lf Hem D 2014	0
	Lf Bre D 2011	0	0
	Lf Bet D 2014	0.002 0.001 0.003
	Lf Oir D 2010	0.013 0.023 0.021 0.024
	Lf Oir D 2011	0.001 0.001 0.002 0.017 0.018
	Lf Oir D 2014	0	0	0	0	0.018	0
	Lp Aa D 2014	0.080 0.083 0.092 0.076 0.074 0.102 0.083
	Lp Hem D 2014 0.083 0.081 0.082 0.094 0.087 0.074 0.062 0.112
	Lp Bre D 2011	0.081 0.092 0.074 0.111 0.089 0.077 0.087 0.185 0.187
	Lp Bet D 2014	0.022 0.025 0.018 0.028 0.034 0.015 0.027 0.110 0.093 0.080
	Lp Oir D 2010	0.080 0.087 0.080 0.086 0.048 0.073 0.071 0.091	0.12	0.115 0.072
	Lp Oir D 2011	0.030 0.041 0.025 0.027 0.033 0.032 0.029 0.078 0.122 0.090 0.034 0.032
	Lp Oir D 2014	0.013 0.019 0.013 0.018 0.019 0.013 0.008 0.082 0.075 0.072 0.031 0.035 0.019

Table 3 :

 3 Pairwise F ST values for parapatric populations (non-significant values are grey coloured and negative values were set to zero). 1

	Populations Lf Aa 2014	Lf Hem 2014	Lf Bre 2011	Lf Ris 2011	Lf Ris 2014	Lf Odo 2011	Lf Oir 2011	Lf Oir 2014	Lf Loi 2010	Lf Loi 2011	Lf Dor 2010	Lf Dor 2011	Lf Gar 2011	Lp Aa 2014	Lp Hem 2014	Lp Bre 2011	Lp Ris 2011	Lp Ris 2014	Lp Odo 2011	Lp Oir 2011	Lp Oir 2014	Lp Cen 2011	Lp Jal 2011
	Lf Hem D

Table S1 :

 S1 Results of a Generalized linear mixed model (GLMM) testing the effect of cross type (homoversus heterospecific) and maternal population (Loire versus Oir) on fertilization success of L.

	fluviatilis eggs.
			d.f.	χ 2	p-value	Estimates (± s.e.)
		Cross type	1	0.16	ns	-0.11 ± 0.12
	Fertilization success	Pop.	1	2.45	ns	0.51 ± 0.45
		Cross type × Pop.	1	4.31	0.04	0.52 ± 0.24

Table S2 :

 S2 Genetic diversity estimates for each locus in each population.

		Ar Ar Ar	2.736 1.986 1.936 3.723 2 2.998 2 3 2.871 2 2 2.874 4.608 7.48 5	2.737 2 2	5.41 3.933 3.545 3.958 2.878 3.999 3.737 4 3.933	2 2 2	3.737 2.997 3.737 4.931 2.933 3 3.965 2 3.377
		Ho Ho Ho	LP-03 LP-06 LP-43 LP-09 LP-18 LP-22 LP-27 LP-28 LP-30 LP-37 LP-39 LP-45 0.316 0.158 0.105 0.632 0.737 0.368 0.684 0.737 0.789 0.211 0.632 0.737 0.737 LP-46 0.786 0.4 0.6 0.643 0.667 0.267 0.733 0.733 0.6 0.071 0.533 0.4 0.733 0.371 0.543 0.343 0.4 0.629 0.457 0.382 0.571 0.543 0.4 0.743 0.429 0.457
	Lf Oir D 2014 Lp Bre U 2011	He Na He Na He	0.279 0.149 0.102 0.607 0.781 0.482 0.713 0.734 0.652 0.275 0.674 0.595 0.675 2 2 7 4 3 4 4 2 3 3 3 3 5 0.505 0.548 0.476 0.599 0.793 0.238 0.657 0.707 0.617 0.071 0.607 0.555 0.679 3 1 2 2 6 2 2 6 3 2 3 3 3 0.321 0.5 0.388 0.34 0.637 0.504 0.478 0.647 0.56 0.438 0.676 0.466 0.487
	Lf Aa D 2014	Na	3	2	7	3	2	4	6	3	4	3	4	4	5
		Fis Ar Fis Ar Fis	-0.131 -0.059 -0.029 -0.041 0.056 0.236 0.041 -0.004 -0.211 0.234 0.063 -0.238 -0.091 1.995 1.861 4.654 2.783 1.997 3.352 3.837 1.946 2.838 2.68 2.981 2.413 3.443 -0.554 0.27 -0.26 -0.073 0.159 -0.12 -0.116 -0.037 0.027 0 0.122 0.279 -0.081 2.699 1 2 1.882 5.134 1.988 1.944 5.326 3 1.975 2.5 2.754 2.755 -0.157 -0.086 0.117 -0.175 0.013 0.093 0.2 0.116 0.031 0.086 -0.099 0.081 0.061
		Ar	2.357 1.859 4.849 2.565 1.697 3.762 4.206 2.143 3.097 2.917 3.423 2.817 3.511
		Ho Ho	0.4333 0.2333 0.1154 0.8214 0.7667 0.3667 0.5333 0.8276 0.5862 0.214 0 0.429 0.107 0.679 0.214 0.143 0.786 0.714 0.083 0.357 0.2 0.5862 0.6333 0.6552 0.25 0.464
		Ho	0.4412 0.1765 0.1471 0.6176 0.7576 0.3824 0.7059 0.7059 0.5882 0.1765 0.5588 0.6176 0.6765
	Lf Ris D Lp Aa D 2014 Lp Oir D	Na He Na He Na	3 0.413 0.2096 0.1802 0.5844 0.7469 0.4898 0.6164 0.7241 0.5693 0.2825 0.6358 0.4842 0.6316 2 3 4 8 3 4 5 3 2 4 3 3 2 2 5 3 2 3 5 2 3 4 4 3 4 0.2 0 0.381 0.103 0.627 0.194 0.135 0.747 0.663 0.158 0.391 0.335 0.545 2 3 2 3 7 3 4 4 4 2 5 2 3
		He	0.4091 0.2107 0.1383 0.6119 0.7846 0.3863 0.6787 0.7392 0.6234 0.2998 0.5931 0.5571 0.6457
		Ar Fis Ar Fis Ar	2.359 -0.05 -0.115 -0.027 0.255 0.364 0.137 -0.146 0.296 -0.416 -0.03 0.079 -0.315 -0.038 1.99 2.282 3.349 6.71 2.892 3.992 4.331 2.999 1.932 3.349 2.58 2.997 1.986 1.986 3.535 2.415 1.999 2.925 4.167 1.241 2.861 3.806 3.507 2.98 2.921 -0.073 NA -0.125 -0.038 -0.082 -0.102 -0.059 -0.051 -0.077 0.471 0.086 0.253 0.148 2 2.969 2 2.867 5.714 2.907 3.474 3.995 2.875 2 4.683 1.998 2.997
		Fis	-0.08 0.165 0.035	0.01 -0.065 -0.041 0.046 0.415 -0.009 0.057 0.059 -0.111 -0.048
		Ho Ho Ho	0.436 0.4667 0.4667 0.5333 0.2 0.125 0.625 0.8 0.758 0.552 0.545 0.303 0.688 0.469 0.536 0.636 0.375 0.382 0.667 0.303 0.625 0.7 0.35 0.65 0.725 0.65 0.15 0.575 0.45 0.6 0.6 0.4 0.7333 0.7333 0.8667 0.0345 0.6333 0.5333 0.5172
	Lf Loi D 2010 Lp Bre D 2011 Lp Sau U 2011	He Na He Na He Na	0.449 0.222 0.164 0.638 0.772 0.445 0.694 0.696 0.621 2 3 2 3 5 3 5 4 4 0.3638 0.3638 0.4723 0.5949 0.5718 0.5356 0.6198 0.7616 0.7124 0.0345 0.5847 0.6571 0.5862 0.14 0.592 0.506 0.562 2 4 3 3 3 2 2 2 6 1 2 5 3 2 3 3 4 0.502 0.504 0.484 0.272 0.675 0.526 0.539 0.736 0.537 0.381 0.614 0.26 0.529 3 1 1 3 3 3 4 2 3 2 2 3 3
		Fis Ar Fis Ar Fis Ar	0.03 2 -0.289 -0.289 -0.05 0.256 -0.132 -0.187 0.038 0.098 0.238 0.02 0.094 0.213 0.063 -0.042 -0.046 -0.068 0.028 2.431 2 2.951 4.313 2.818 4.409 3.95 3.937 1.703 3.822 2.829 0.11 0 -0.353 -0.221 -0.085 0.191 0.119 -0.068 3 2.664 1.667 2 1.667 5.594 1 1.894 4.982 3 2 2.998 2.789 3.666 -0.509 -0.094 -0.127 -0.115 -0.018 0.109 0.006 0.135 0.301 -0.005 -0.085 -0.164 -0.182 2.995 1 1 2.964 3 2.982 3.467 2 2.72 1.855 2 2.992 2.998
		Ho Ho Ho	0.375 0.281 0.438 0.563 0.633 0.286 0.048 0.19 0.048 0.762 0.433 0 0 0.367 0.433 0.567 0.833 0.367 0.2 0.563 0.813 0.625 0.065 0.531 0.344 0.656 0 0.095 0.81 0.81 0.2 0.381 0.19 0.714 0.6 0.1 0.533 0.433 0.6
	Lf Ris D Lp Aa U 2014 Lp Oir D	Na He Na He Na He	3 0.308 0.249 2 2 3 0.256 0.048 0.319 0.048 9 3 0.38 0.486 0.644 0.244 3 4 6 3 4 3 0.7 0 2 2 2 3 6 3 0.473 0 0 0.345 0.667 0.567 0.683 0.345 5 3 0.59 0.691 0.658 0.063 0.573 0.477 0.625 3 3 4 3 4 4 2 3 4 4 3 4 0.093 0.751 0.665 0.332 0.487 0.181 0.631 4 4 3 2 4 2 3 0.53 0.097 0.487 0.434 0.575
		Ar Fis Ar Fis Ar Fis	2.197 1.752 5.722 2.346 2.148 3.566 4.153 2.007 2.806 2.849 3.155 2.806 3.147 -0.216 -0.13 -0.151 -0.156 0.017 0.181 0.046 -0.176 0.051 -0.017 0.072 0.279 -0.05 1.858 2.323 4.216 2.179 2.353 2.811 3.95 1.452 2.92 3.797 3.354 2.849 3.091 -0.116 0 0.403 0 -0.088 NA -0.026 -0.078 -0.216 0.397 0.218 -0.053 -0.132 2 2 2 3 5.47 2.824 3.823 4 2.824 2 3.973 2 2.824 0.084 NA NA -0.062 0.35 0.001 -0.221 -0.063 -0.131 -0.036 -0.094 0.001 -0.043
		Ho Ho Ho	0.4 0.2368 0.4103 0.4872 0.5897 0.5946 0.4872 0.4615 0.8108 0.7368 0.0769 0.5263 0.4615 0.5128 0.1143 0.1429 0.7429 0.8 0.3429 0.6571 0.8 0.6286 0.1429 0.6176 0.6 0.5429 0.353 0.412 0.353 0.706 0.588 0.588 0.529 0.765 0.471 0.294 0.529 0.471 0.647
	Lf Loi D 2011 Lp Ris U	He Na He Na He	0.446 0.159 0.3118 0.5909 0.8033 0.4008 0.6658 0.7068 0.5996 0.2083 0.6264 0.576 0.5959 2 3 2 3 5 3 5 4 4 2 5 3 3 0.2116 0.4462 0.4466 0.5931 0.7079 0.4952 0.5861 0.7579 0.7172 0.0749 0.5582 0.5991 0.5941 4 3 3 3 5 2 4 4 3 2 3 3 5 0.298 0.489 0.298 0.546 0.68 0.439 0.513 0.761 0.509 0.257 0.577 0.368 0.504
		Fis Ar Fis Ar Fis	0.104 0.284 0.004 0.146 0.545 0.013 -0.134 0.317 -0.262 -0.049 0.014 -0.042 2 2.987 2 3 4.636 2.925 4.63 3.974 3.684 1.688 4.463 2.907 -0.213 -0.017 0.529 -0.088 0.06 0.115 -0.137 NA -0.226 -0.03 0.113 0.204 0.003 0.09 3 3.875 2.863 2.999 2.875 4.862 2 3.874 3.999 3 1.999 3 3 4.851 -0.185 0.158 -0.185 -0.293 0.135 -0.339 -0.032 -0.005 0.076 -0.143 0.083 -0.28 -0.285
		Ho Ho	0.406 0.469 0.375 0.563 0.594 0.313 0.563 0.781 0.563 0.063 0.375 0.531 0.688 0.625 0.188 0.375 0.625 0.813 0.375 0.5 0.813 0.813 0.188 0.625 0.563 0.563
	Lf Odo D 2011 Lp Hem D 2014 Lp Oir D	Na He Na He Na	2 0.449 0.524 0.381 0.598 0.676 0.304 3 2 4 8 4 2 3 6 3 2 4 0.498 0.179 0.377 0.481 0.621 0.313 0.423 0.702 0.671 0.175 0.656 0.671 0.571 5 4 3 3 4 3 3 0.54 0.717 0.627 0.061 0.486 0.558 0.67 4 1 3 4 4 4 5 2 2 6 3 2 4 5 2 3 3 4 2 3
		Ar Fis Ar Fis Ar	2 0.094 0.106 0.016 0.059 0.122 -0.028 -0.042 -0.089 0.102 -0.016 0.228 0.048 -0.026 2.424 1.997 3.434 6.453 2.875 4.266 3.997 2.998 2.125 3.685 2.437 2.998 1.999 2.12 3.376 2.179 1.904 3.232 3.86 1 2.993 2.342 3.028 2.892 3.738 -0.255 -0.047 0.006 -0.299 -0.309 -0.2 -0.182 -0.157 -0.211 -0.071 0.048 0.161 0.015 1.999 1.96 4.535 2.304 1.853 3.222 4.151 1.956 2.667 2.884 3.614 1.999 2.979
		Ho Ho Ho	0.484 0.188 0.219 0.625 0.625 0.438 0.5641 0.3077 0.2308 0.8205 0.2308 0.5641 0.5641 0.8205 0.4103 0.75 0.688 0.719 0.094 0.531 0.625 0.563 0 0.5385 0.3846 0.6667 0.5909 0.2609 0.2174 0.6087 0.7 0.5217 0.3636 0.8182 0.6667 0.2381 0.6818 0.5 0.7727
	Lf Dor D Lp Ris U	He Na He Na He	0.403 0.226 0.246 0.632 0.742 0.395 0.684 0.717 2 3 2 3 5 3 5 4 0.4662 0.3027 0.2454 0.671 0.4862 0.5191 0.6364 0.7229 0.3986 0.61 4 7 3 6 3 3 4 4 2 3 0.4598 0.2937 0.1981 0.4841 0.659 0.5034 0.519 0.7336 0.5889 0.2846 0.5613 0.4598 0.6543 0.092 0.543 0.501 0.532 2 5 2 3 0 0.6061 0.4822 0.6687 4 3 3 4
	Lf Bre D 2010	Na	2	2	2	4	5	3	6	5	4	2	3	4	3
		Fis Ar Fis Ar Fis	-0.2 2 -0.121 0.082 0.162 0.016 -0.092 0.215 -0.071 -0.027 0.006 -0.028 0.058 0.232 0.138 0.171 0.111 0.011 0.158 -0.109 -0.096 0.041 -0.179 -0.022 0.022 -0.249 -0.058 2.998 2 2.903 4.826 2.661 4.531 3.9 3.348 1.359 4.354 2 3 3.769 2.795 3.386 2.12 1.83 3.047 3.636 1.598 2.659 3.281 2.878 2.934 2.446 -0.294 0.114 -0.064 -0.037 -0.1 0.304 -0.118 0.167 -0.265 -0.136 -0.221 -0.09 -0.186
		Ar	2	1.933 1.998	4	4.933	3	5.8	4.933 3.933 1.933	3	3.931	3
		Ho Ho	0.757 0.538 0.769 0.368 0.692 0.211 0.622 0.846 0.711 0.026 0.333 0.436 0.658 0.4419 0.3953 0.1628 0.5116 0.4884 0.1628 0.5814 0.5952 0.6667 0.1163 0.5814 0.6279 0.3256
		Ho	0.333 0.067 0.133 0.357 0.733 0.533	0.6	0.933 0.667 0.067 0.467	0.6	0.6
	Lf Oir D Lp Hem U 2014 Lp Oir U	Na He Na He Na	3 0.503 0.531 0.487 0.356 0.736 0.196 0.663 0.711 0.581 0.026 0.417 0.398 0.674 3 2 3 9 3 4 4 3 3 5 3 4 2 3 5 2 3 4 4 1 3 3 3 2 3 0.5193 0.4462 0.1529 0.49 0.5425 0.3253 0.5352 0.6899 0.6687 0.1108 0.5289 0.6293 0.4134 2 2 6 2 2 3 4 2 3 3 4 3 3
		He	0.286 0.067 0.129 0.544 0.679 0.536	0.69	0.776 0.652 0.067 0.519 0.605 0.514
		Ar Fis Ar Fis Ar	2.842 2.894 -0.504 -0.014 -0.581 -0.035 0.06 -0.074 0.062 -0.191 -0.223 2 3 6.767 2.657 3.988 3.998 2.944 2.393 4.627 2.658 0 0.201 -0.095 0.024 3.42 1.906 2.45 3.327 2 2.279 3.523 3.922 1 2.981 2.239 2.864 1.998 2.996 0.151 0.115 0.101 0.503 -0.065 -0.088 0.139 -0.05 -0.045 0.003 -0.101 0.002 0.214 1.888 1.996 4.093 2 1.8 2.519 3.783 1.977 2.38 2.8 3.736 2.209 2.65
		Fis	-0.167	0	-0.037 0.343 -0.081 0.004 0.131 -0.202 -0.022	0	0.101 0.008 -0.167
		Ho Ho Ho	0.613 0.281 0.545 0.697 0.824 0.294 0.706 0.727 0.485 0.235 0.618 0.588 0.667 0.2692 0.3077 0.3333 0.6538 0.5 0.6923 0.8077 0.8077 0.4231 0 0.5769 0.3846 0.8 0.2581 0.3226 0.1935 0.3548 0.7 0.6452 0.5161 0.6452 0.6774 0.2903 0.7419 0.3548 0.3548
	Lf Dor D Lp Odo U 2011	He Na He Na He	0.53 2 0.2376 0.3989 0.3927 0.6365 0.4879 0.5068 0.6584 0.7443 0.3477 0.303 0.469 0.585 0.793 0.343 0.652 0.732 0.571 0.213 0.596 0.533 0.623 3 2 3 6 3 4 4 3 2 3 3 3 0 0.6041 0.4344 0.6784 3 2 2 2 4 2 1 3 3 1 3 2 3 0.2285 0.4188 0.1777 0.3866 0.665 0.5034 0.4098 0.6531 0.5732 0.3369 0.6838 0.3802 0.5336
	Lf Bre D 2011	Na	4	2	2	3	7	3	5	4	3	2	3	4	5
		Fis Ar Fis Ar Fis	-0.156 0.073 -0.164 -0.191 -0.039 0.143 -0.083 0.007 0.152 -0.102 -0.036 -0.103 -0.071 2 3 2 2.933 5.933 2.998 4 3.998 2.998 2 2.931 2.998 3 -0.136 0.232 -0.025 -0.376 0.154 -0.232 -0.087 NA -0.028 -0.222 0.046 0.117 -0.184 2.815 1.898 1.424 1.97 3.888 2 1 3 2.999 1 2.424 1.944 2.984 -0.132 0.233 -0.054 -0.288 -0.091 -0.265 0.012 0.14 0.083 -0.185 -0.087 0.068 0.339
		Ar	2.91	1.982 1.971 2.999 5.556 2.951 4.331 3.973 2.982 1.971	3	3.745 3.904
		Ho Ho	0.867 0.533 0.533 0.333 0.455 0.121 0.03 0.182 0.606 0.455 0.8 0.2	0.533 0.867 0 0.606 0.848 0.6	0.2 0	0.2 0.364 0.152 0.636 0.733 0.667
		Ho	0.463	0.22	0.195 0.585 0.625 0.415 0.692 0.625 0.537 0.195 0.634 0.585	0.61
	Lf Oir D Lp Bet D 2014 Lp Cen U 2011	Na He Na He Na	4 0.495 4 0.372 0.116 2 0.55 2 3 1	2 0.495 0.343 0.824 0.305 0.607 0.676 0.569 0.186 4 7 3 4 5 3 2 7 2 2 3 5 2 3 3 0.03 0.168 0.634 0.501 0 0.623 0.638 0 3 2 5 1 2 3 3 1	4 0.19 3 0.511 0.142 0.535 3 4 0.564 0.681 4 4 2 2 4
		He	0.388 0.198 0.178 0.605 0.716 0.401 0.661 0.668	0.6	0.178 0.626 0.527 0.609
		Ar Fis Ar Fis Ar	2.93 -0.75 2.823 1.993 5.171 1.661 1.944 2.356 4.286 1.944 2.895 2.964 2.942 2.795 3.791 1.99 1.984 3.349 5.443 2.725 3.915 4.348 2.932 2 3.592 2.892 3.344 0.03 -0.077 0.028 0.029 0.344 0.122 -0.282 -0.054 -0.077 -0.05 -0.3 0.021 -0.221 -0.049 0 -0.085 0.045 0.093 NA 0.027 -0.329 NA 0.288 -0.067 -0.189 2.416 1 2.807 1.898 3.869 1 1.974 2.951 2.984 1 2 2 3.322
		Fis	-0.193 -0.111 -0.096 0.032 0.127 -0.035 -0.047 0.064 0.105 -0.096 -0.013 -0.112 -0.002
		Ho Ho Ho	0.475 0.5882 0.3529 0.2941 0.7059 0.5625 0.1176 0.3529 0.8235 0.5882 0.1765 0.4706 0.4706 0.7647 0.15 0.225 0.725 0.575 0.325 0.513 0.825 0.6 0.4 0.667 0.5 0.625 0.273 0 0.242 0.061 0.3 0 0.188 0.406 0.387 0 0.406 0.515 0.576
	Lp Oir U	He He Na He	0.456 0.222 0.202 0.611 0.684 0.303 0.571 0.735 0.571 0.379 0.677 0.546 0.589 0.5276 0.3708 0.2585 0.6132 0.754 0.1141 0.3084 0.713 0.5847 0.2585 0.5348 0.3939 0.6988 3 2 2 3 6 2 4 4 3 2 4 2 4 0.242 0 0.294 0.116 0.301 0 0.172 0.525 0.403 0 0.364 0.46 0.504

Table S3 :

 S3 Results of permutations tests comparing Allelic richness (Ar) and expected heterozygosity (He) between Lf and Lp in each river system. Significant values are in bold-italic and all tests were performed using paired Wilcoxon tests in R.

	Bre D 2011 Bre D 2011	66 0.168
	Bre D 2011 Bre U 2011	76 0.033
	Bet D 2014 Bet D 2014	46 1.000
	Lf Oir D 2011 Oir D 2011 Lf	V P-value 59 0.376
	Ar Oir D 2010 Oir D 2010	55 0.542
	Aa D 2014 Oir D 2011 Oir U 2011 Aa D 2014	63 0.244 53 0.636
	Aa D 2014 Oir D 2014 Oir D 2014 Aa U 2014	61 0.305 39 0.685
	Hem D 2014 Hem D 2014 68 0.127 Oir D 2014 Oir U 2014 55.5 0.507
	Hem D 2014 Hem U 2014 69 0.108 Ris D 2011 Ris U 2011 44 0.947
	Bre D 2011 Bre D 2011 Ris D 2014 Ris U 2014	67 0.142 64 0.216
	Bre D 2011 Bre U 2011 Odo D 2011 Odo U 2011 81 0.011 73 0.057
	Bet D 2014 Bet D 2014 Loi D 2011 Cen U 2011 91 0.000 51 0.727
	Oir D 2011 Oir D 2011 Dor D 2011 Jal U 2011	58 0.147 52 0.685
	Oir D 2010 Oir D 2010 Gar D 2011 Jal U 2011	66 0.038 67 0.149
	Oir D 2011 Oir U 2011 Gar D 2011 Sau U 2011	65 0.046 78 0.021
	Oir D 2014 Oir D 2014	55 0.542
	Oir D 2014 Oir U 2014 Lp U Lp D	71 0.080 V P-value
	Ris D 2011 Ris U 2011 Ar	41 0.787
	Ris D 2014 Ris U 2014 Aa D 2014 Aa U 2014 38 0.636 60 0.340
	Odo D 2011 Odo U 2011 84 0.005 Hem D 2014 Hem U 2014 48 0.505
	Loi D 2011 Cen U 2011 82 0.008 Bre D 2011 Bre U 2011 38 0.689
	Dor D 2011 Jal U 2011 Oir D 2011 Oir U 2011 18 0.636 66 0.037
	Gar D 2011 Jal U 2011 Oir D 2014 Oir U 2014 75 0.040 50 0.025
	Gar D 2011 Sau U 2011 He	75 0.043
	Aa D 2014	He Aa U 2014 73 0.057
	Aa D 2014 Hem D 2014 Hem U 2014 19 0.415 Aa D 2014 30 0.294
	Aa D 2014 Bre D 2011 Bre U 2011 52 0.685 Aa U 2014 52 0.685
	Hem D 2014 Hem U 2014 44 0.946 Oir D 2011 Oir U 2011 35 0.497
	Hem D 2014 Hem D 2014 42 0.839 Oir D 2014 Oir U 2014 51 0.367

Table S4 :

 S4 Pairwise F ST among all populations (non-significant values are grey coloured).

	Populations	Lf Aa D 2014	Lf Hem D 2014	Lf Bet D 2014	Lf Bre D 2010	Lf Bre D 2011	Lf Ris D 2010	Lf Ris D 2011	Lf Ris D 2014	Lf Odo D 2011	Lf Oir D 2010	Lf Oir D 2011	Lf Oir D 2014	Lf Loi D 2010	Lf Loi D 2011	Lf Dor D 2010	Lf Dor D 2011	Lf Gar D 2011	Lp Aa D 2014	Lp Aa U 2014	Lp Hem D 2014	Lp Hem U 2014	Lp Bet D 2014	Lp Bre D 2011	Lp Bre U 2011	Lp Ris U 2011	Lp Ris U 2014	Lp Odo U 2011	Lp Oir U 2011	Lp Oir D 2010	Lp Oir D 2011	Lp Oir D 2014	Lp Oir U 2014	Lp Cen U 2011	Lp Jal U 2011
	Lf Hem D 2014	-0.005
	Lf Bet D 2014 0.002 0.001
	Lf Bre D 2010	0	0.005	0.006
	Lf Bre D 2011 -0.002 -0.002 0.003 -0.001
	Lf Ris D 2010	-0.007 -0.004 -0.003 0.006	0.004
	Lf Ris D 2011	-0.004 -0.001 0.002	0.002 -0.001 0.001
	Lf Ris D 2014	-0.006 -0.008 -0.001 0.008	0.002 -0.005 -0.004
	Lf Odo D 2011	-0.002 -0.002 0.008	0.009	0.006	0.004 -0.003 -0.003
	Lf Oir D 2010	0.013	0.023	0.024	0.024	0.021	0.031	0.013	0.013	0.018
	Lf Oir D 2011	0.001	0.001	0.017	0.012	0.002	0.006	0.006	0.004	0.009	0.018
	Lf Oir D 2014 -0.006 -0.003	0	0.004	0	-0.006 -0.004 -0.006 0.001	0.018 -0.001
	Lf Loi D 2010	0.024	0.034	0.032	0.018	0.015	0.032	0.028	0.031	0.033	0.021	0.025	0.026
	Lf Loi D 2011	0.026	0.043	0.041	0.026	0.026	0.041	0.027	0.031	0.035	0.01	0.032	0.03	0.008
	Lf Dor D 2010 0.059 0.068 0.078	0.06	0.056	0.089	0.055	0.058	0.063	0.022	0.065	0.061	0.033	0.014
	Lf Dor D 2011 0.086 0.102 0.116 0.102 0.101	0.12	0.092	0.086	0.091	0.029	0.094	0.091	0.072	0.032	0.011

Table S5 :

 S5 Results of STRUCTURE analysis for each dataset: (a) full dataset, (b) L. fluviatilis only and (c) L. planeri only. Best k and ∆k values are in bold.

		(b)
		k Replicate Mean LnP(K) Stdev LnP(K)	Ln'(K)		|Ln''(K)|		Delta K
		(a) 1			-12861.2267	0.0458				NA		NA		NA
		k Replicate Mean LnP(K) Stdev LnP(K) 2 -12732.6067 2.5647	Ln'(K) 128.620000 314.826667 122.752647 |Ln''(K)| Delta K
		1 3				-25615.3133 -12918.8133	0.0352 54.0977			NA -186.206667 476.553333 8.809126 NA NA
		2 4				-25385.4267 -13581.5733 210.5316 -662.760000 298.886667 1.419676 4.0411 229.886667 34.620000 8.566928
		3 5				-25120.9200 -13945.4467 128.4933 -363.873333 95.186667 0.740791 3.7126 264.506667 82.513333 22.225421
		4 6				-24938.9267 -14404.5067 332.8450 -459.060000 317.206667 0.953016 28.7455 181.993333 63.606667 2.212749
		5 7				-24693.3267 -14546.3600 242.4818 -141.853333 35.733333 0.147365 9.1600 245.600000 18.573333 2.027647
		6 8				-24466.3000 -14723.9467 243.8772 -177.586667 42.626667 0.174787 5.3195 227.026667 186.376667 35.036462
		7 9				-24425.6500 -14944.1600 396.4927 -220.213333 124.566667 0.314171 11.3011 40.650000 216.333333 19.142722
																	071 0.107	0.15	0.096	0.094	0.083	0.125	0.087	0.069	0.094	0.102	0.079	0.081	0.164	0.013	0.052	0.014	0.02
	2011 Lp Cen U	0.161 10 8	0.182	0.234	0.145 -24168.6667 0.159 0.204 -15039.8067 260.0662 0.187 0.183 8.0090	0.19	0.166 256.983333 301.263333 37.615679 0.163 0.173 0.136 0.155 0.173 0.203 0.203 -95.646667 NA NA	0.219	0.243	0.276	0.246	0.175	0.156	0.164	0.173	0.161	0.273	0.222	0.208	0.152	0.207	0.204
		9				-24212.9467	54.6664			-44.280000 110.811282 2.027045
	Lp Jal U 2011	0.053	0.073	0.087	0.073	0.059	0.077	0.061	0.071	0.066	0.07	0.065	0.066	0.064	0.07	0.086	0.12	0.102	0.143	0.142	0.136	0.136	0.058	0.117	0.108	0.103	0.094	0.275	0.149	0.091	0.079	0.073	0.113	0.22
	Lp Sau U 2011	0.124 10 (c)	0.113	0.143	0.122 -24146.4154 0.126 0.121	0.117 56.8238 0.12	0.137	0.143 66.531282 250.635897 4.410754 0.138 0.135 0.167 0.157 0.192 0.228 0.189	0.185	0.186	0.215	0.196	0.215	0.24	0.275	0.162	0.149	0.194	0.181	0.211	0.181	0.139	0.156	0.324	0.21
		11 k Replicate Mean LnP(K) Stdev LnP(K) -24330.5200 431.2098 -184.104615 72.157949 0.167338 Ln'(K) |Ln''(K)| Delta K
		12 1			-24442.4667 389.5193 -111.946667 459.993333 1.180926 -12478.7667 0.0488 NA NA NA
		13 2			-25014.4067 302.4341 -571.940000 428.646667 1.417323 -12041.1267 0.6486 437.640000 187.593333 289.233338
		14 3			-25157.7000 386.5390 -143.293333 -11791.0800 7.0161 250.046667 37.193333 5.301122 NA NA
		4				-11503.8400	4.4946			287.240000 68.473333 15.234686
		5				-11285.0733	2.7886			218.766667 158.206667 56.733035
		6				-11224.5133	31.5086			60.560000 12.853333 0.407931
		7				-11151.1000	55.0251			73.413333 66.313333 1.205148
		8				-11011.3733	61.4216			139.726667 7.501212		0.122127
		9				-10864.1455	2.5426			147.227879 309.122424 121.578115
		10			-11026.0400	61.6090			-161.894545 196.727879 3.193169
		11			-10991.2067	40.4605			34.833333 223.246667 5.517642
		12			-11179.6200	54.9826			-188.413333	NA		NA

Table S6 : Estimates of ongoing migration rates (m) obtained with BayesAss (Wilson and Ranalla 2003) from

 S6 river lampreys to brook lampreys (m in Lp from Lf) and from brook lampreys to river lampreys (m in Lf from Lp). Parapatric populations are indicated (P) and other estimates correspond to sympatric populations. River abbreviations match those given in table 1.

		Chapter 3:
	Rivers	m in Lp from Lf (95% CI)	m in Lf from Lp (95% CI)
	Aa 2014	0.076 (0 -0.17)	0.025 (0 -0.070)
	Aa 2014 (P)	0.016 (0 -0.046)	0.020 (0 -0.054)
	Hem 2014	0.028 (0 -0.07)	0.060 (0 -0.13)
	Hem 2014 (P)	0.023 (0 -0.066)	0.076 (0.033 -0.011)
	Bre 2011	0.022 (0 -0.060)	0.040 (0 -0.111)
	Bre 2011 (P)	0.015 (0 -0.038)	0.070 (0 -0.18)
	Bet 2014	0.136 (0 -0.37)	0.245 (0.07-0.42)
	Ris 2011 (P)	0.281 (0.206 -0.355)	0.014 (0 -0.042)
	Ris 2014 (P)	0.025 (0 -0.088)	0.163 (0 -0.306)
	Odo 2011 (P)	0.011 (0 -0.033)	0.051 (0 -0.12)
	Oir 2010	0.138 (0.064 -0.211)	0.050 (0 -0.121)
	Oir 2011	0.290 (0.19 -0.41)	0.075 (0 -0.19)
	Oir 2011 (P)	0.023 (0 -0.061)	0.077 (0.038 -0.116)
	Oir 2014	0.290 (0.25 -0.33)	0.030 (0 -0.06)
	Oir 2014 (P)	0.045 (0 -0.11)	0.260 (0.18 -0.41)
	Cen -Loi 2011 (P)	0.013 (0 -0.037)	0.051 (0 -0.11)
	Jal -Dor 2011 (P)	0.021 (0 -0.589)	0.066 (0 -0.171)
	Sau -Gar 2011 (P)	0.011 (0 -0.033)	0.059 (0 -0.13)

Investigating divergence history of European river and brook lamprey Article 2: Reconstructing the demographic history of divergence between European river and brook lampreys using Approximate Bayesian Computations Quentin Rougemont, Camille Roux, Samuel Neuenschwander , Jérôme Goudet , Sophie Launey 2 , Guillaume Evanno

Table 1 :

 1 Prior for all models. θ A, θ1 , θ2 = effective mutation rate for t he ancestral, river lamprey and brook lamprey populations respectively. M 1, M 2, M Anc = Effective migration rate for the ancestral, river lamprey and brook lamprey populations respectively. τ = divergence time, τ isol, τ sc divergence time under the ancient migration model and time of secondary contact respectively.

SI: strict isolation, IM: isolation with migration, AM: ancient migration, PAN: Panmixia SC: secondary contact model.

Table 2 : Estimates of populations genetic parameters for each pairs of river and brook lamprey populations

 2 . N = number of individuals used for ABC analysis Ar= Allelic richness, He= expected heterozygosity, GW= Garza-Williamson Index. *For the ABC inference, individuals of river lamprey from the AA and Hem (F ST =0) river were pooled together to obtain a sample size similar to the one of brook lampreys. ᵟ Brook lamprey samples from the AA and Hem rivers are composed of upstream and downstream samples from[START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF] study.

	Pop N Lf	N Lp F ST	Ar Lf	Ar Lp	He Lf	He Lp GW Lf GW Lp Delta µ 2
	OIR	104	74	0.028 4.45	3.61	0.52	0.508 0.525 0.622 0.204
	BET	14	14	0.028 3.51	3.36	0.516	0.471 0.452 0.464 0.507
	RIS	75	75	0.033 3.84	3.92	0.503	0.472 0.497 0.421 0.842
	HEM 30*	65 δ	0.077 4.21	3.53	0.504	0.477 0.406 0.487 1.633
	AA	34*	69ᵟ	0.084 4.21	3.76	0.514	0.522 0.406 0.505 0.915
	BRE 93	80	0.091 4.14	4.91	0.49	0.49	0.466 0.263 34.37

Table 3 : ABC classification (posterior probability), Random-Forest (RF) prediction and robustness

 3 (ABC only) of each model of speciation in each river.

	MODEL

Table 4 : Random Forests out-of-bag confusion matrix and classification error

 4

			Predicted Model (Averaged over each river)	Averaged OOB error rate
	Observed AM	I	IM	PAN	SC
	AM	78.0%	16.7%	2.4%	0.0%	2.9%	21.99%
	I	25.2%	73.6%	0.6%	0.0%	0.6%	26.38%
	IM	1.6%	0.2%	57.2%	0.8%	40.1%	42.76%
	PAN	0.0%	0.0%	0.2%	99.7%	0.1%	0.30%
	SC	2.1%	0.3%	43.6%	0.6%	53.3%	47.12%

. Data based on 6 random forests, each composed of 1,000 trees based on a trained set of 50,000 simulated predictor variables (summary statistics). The response variable is the demographic model. Proportions of correctly classified demographic models are in bold. The two grey italic values represent models with high error rates. Simulation between rivers differed only by the number of individual loci simulated and produced very similar values that were subsequently averaged over each demographic model.

Table 5 : Estimates of demographic parameters under the model of ongoing migration (IM) and secondary contact (SC) in each river.

 5 Ne = effective population size. Lf = lampetra fluviatilis, Lp = Lampetra planeri.

	Ne Lf	Ne Lp	Ne Anc	migration Lf to Lp (=4N12 m12)	migration Lp to Lf = 4 N1 m21		Split time	Time of Secondary Contact
	RIVER model median 95HPD	median	95HPD	median	95HPD	median	95HPD	median	95HPD	median	95HPD	median	95HPD

  and empirical evidence[START_REF] Matute | A test of the snowball theory for the rate of evolution of hybrid incompatibilities[END_REF] predict that DMI should accumulate faster than linearly in time, our results suggest a limited amount of isolation. In this case the amount of time was certainly not enough to allow for sufficient DMI to occur and to develop strong barriers to gene flow. This would likely explain the low differentiation observed for mtDNA[START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Blank | A mitochondrial multigene approach contributing to the systematics of the brook and river lampreys and the phylogenetic position of Eudontomyzon mariae[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF] and is fully compatible with the observation of viable F1 (Hume et al.

	2013; Rougemont et al. 2015).
	Our results also suggest that the RF model provides a valuable complement to the standard ABC

model comparison

(Robert et al. 2011;[START_REF] Pudlo | ABC model choice via random forests[END_REF][START_REF] Pudlo | ABC model choice via random forests[END_REF]

. The two methods provided similar outcomes in terms of model choice and subsequent cross-validation except in one case (Oir River).

Table S1 :

 S1 Pairwise F ST values. Non-significant values are in italics and grey-colored. LF Aa LF Hem LF Beth LF Bre LF Ris LF Oir LP Aa LP Hem LP Bet LP Bre LP Ris

	LF Hem 0.000
	LF Beth 0.002 0.001
	LF Bre 0.000 0.000	0.003
	LF Ris 0.000 0.000	0.001 0.003
	LF Oir 0.000 0.003	0.011 0.006 0.003
	LP Aa 0.084 0.090	0.079 0.090 0.088 0.090
	LP Hem 0.079 0.077	0.088 0.083 0.071 0.066 0.102
	LP Bet 0.022 0.025	0.028 0.027 0.032 0.020 0.115 0.090
	LP Bre 0.099 0.116	0.135 0.091 0.102 0.087 0.192 0.189 0.088
	LP Ris 0.033 0.035	0.050 0.034 0.033 0.030 0.121 0.103 0.055 0.097
	LP Oir 0.040 0.048	0.044 0.044 0.046 0.028 0.075 0.087 0.040 0.087 0.063

Table S4 : Estimation of variable importance from random-forest analyses.

 S4 Estimation is performed pairwise for computational tractability. For each pairwise comparison, the three most discriminatory variables are in bold.

				MODEL
	Summary Stat	IvsAM IvsIM IvsSC IvsPAN AmvsIM	Scvs AM	AmvsPAN SCvsIM ImvsPAN PANvsIM ScvsPAN Average
	mean He pop1*	76.64 31.58 30.98 18.37 35.76 38.31 19.66 24.23 37.28	37.28 38.94 32.62
	var He pop1 55.06 26.87 25.24 12.90 24.56 27.62 10.53 19.00 27.76	27.76 27.45 23.35
	mean He pop2*	45.32 27.99 29.70 19.03 38.88 40.61 21.76 18.55 46.75	46.75 48.50 37.16
	var He pop2 37.46 21.60 22.84 13.88 33.64 35.13 13.10 17.17 29.06	29.06 28.48 25.33
	mean He total	58.42 48.69 48.79 17.88 67.61 66.55 24.14 25.90 42.59	42.59 45.80 41.26
	var He total 42.27 35.04 32.25 16.30 32.66 36.05 12.12 20.94 28.28	28.28 28.18 25.64
	mean Ar pop1	90.18 27.56 28.04 13.33 35.17 36.22 13.05 25.68 30.79	30.79 26.85 27.23
	var Ar pop1 49.78 23.27 22.09 10.40 36.39 38.03 14.59 19.10 25.36	25.36 25.48 24.66
	mean Ar pop2	57.54 25.95 25.37 13.16 34.15 31.60 15.00 28.02 22.31	22.31 22.68 23.65
	var Ar pop2 46.81 25.68 22.56 10.06 36.83 35.89 8.23	23.40 16.03	16.03 18.05 19.61
	mean Ar total	56.41 38.12 44.20 14.32 43.63 52.08 16.48 23.71 27.01	27.01 24.98 28.54
	var Ar total 61.87 21.91 22.81 11.07 31.53 35.19 11.97 19.85 23.42	23.42 25.35 23.20
	mean R pop1	47.75 30.25 30.34 8.61 37.93 39.47 10.77 21.22 21.85	21.85 20.81 22.66
	var R pop1 57.89 41.89 42.03 7.08 43.97 47.00 9.64	27.19 18.67	18.67 18.13 23.22
	mean R pop2	59.87 27.06 32.17 11.53 26.88 32.14 12.87 27.23 26.38	26.38 23.35 24.72
	var R pop2 69.44 36.76 46.21 9.39 39.87 46.10 9.37	23.71 21.94	21.94 18.44 23.58
	mean R total	44.62 27.08 28.98 11.01 32.01 35.34 11.42 17.09 22.67	22.67 22.07 21.88
	var R total 46.63 21.70 21.47 7.86 30.13 29.35 9.54	21.86 19.34	19.34 16.84 19.38
	var GW pop1	94.41 39.08 43.83 11.70 37.76 37.70 11.88 22.05 28.71	28.71 28.28 26.22
	mean GW pop1	50.64 25.46 20.65 9.07 37.06 38.73 6.98	12.90 13.26	13.26 16.00 16.85
	var GW pop2	64.23 31.60 37.27 12.00 31.62 37.62 12.35 25.52 26.56	26.56 25.91 25.75
	mean GW pop2	43.42 26.60 21.40 7.83 40.62 38.83 7.77	8.55 15.02	15.02 14.02 16.54
	var GW total	59.44 31.61 31.51 9.88 35.88 38.19 10.89 20.88 26.82	26.82 27.07 25.11
	mean GW total	39.53 24.50 22.06 6.14 38.91 34.47 5.46	18.77 12.46	12.46 13.00 16.10
	mean GST 65.74 62.09 66.67 34.69 80.61 85.93 57.28 34.11 84.59	84.59 84.41 71.82
	var GST 37.25 37.52 38.70 27.29 51.50 50.54 40.93 30.75 104.78 104.78 107.81 73.27
	mean deltamu 2 71.55 74

.59 72.38 52.09 76.51 76.05 61.36 33.14 103.26 103.26 98.59 79.28

	var deltamu 2 60.48 43.46 44.62 41.85 47.22 48.70 45.17 23.59 57.10	57.10 60.18 48.64

*Pop 1 = Lampetra fluviatilis population *Pop 2 = Lampetra planeri population

Table S5 : Robustness of summary statistics computation under the two best models (IM and SC).

 S5 Estimation based on 10 00 dataset drawn from the posterior distribution of the parameters. Summary statistics that deviate significantly from the observed distribution are in bold.

	mean_R_pop1 0.9785 0.9916 1 0.9916	1	0.999	1	1 0.9508 0.9999
	var_R_pop1	0.8185 0.9522 0.989 0.8252 0.9995 0.916 0.9818 1 0.7275 0.9991
	mean_R_pop2 0.974 0.9997 0.164 0.9702 0.9999	1	1	0.713 0.8687 0.9832
	var_R_pop2	0.9956 0.9867 0.006 0.6688 0.962	1 0.9954 0.249 0.5827 0.9346
	mean_R_total 0.9874	1	IM 0.441 0.9884	1	1	1	SC 0.848 0.9425 0.9988	PAN
	Statistics var_R_total	AA 0.8553 0.9729 0.108 0.8635 0.9782 0.934 0.9892 0.635 0.787 0.967 BET BRE HEM RIS AA BET BRE HEM RIS OIR
	mean_He_pop1 0.9915 0.9912 0.997 0.9888 var_He_pop1 4.00E-004 2.00E-004 0.002 4.00E-004 mean_GW_pop1 0.4724 0.0458 0.075 0.5732 var_GW_pop1 0.783 0.6298 0.954 0.8314 0.6711 0.83 0.6003 0.921 0.8376 0.7077 1 1 1 1 0.9485 0.9994 0 0 4.00E-0.513 0.0192 0.009 0.6205 0.003 004 1.00E-004 0 0.0089 0
	mean_He_pop2 0.8836 0.9962 0.962 0.8489 mean_GW_pop2 0.0285 0.0242 0.99 0.0197 0.2775 0.002 0.0055 0.901 0.0849 0.2945 1 0.948 1 1 0.8033 0.9859
	var_He_pop2 0.0471 var_GW_pop2 0.9567 0.5846 0.182 1.00E-004 0.01 0.0156 0 mean_GW_total 0.4408 0.0326 1 0.7969 0.0809 0.456 0.0119 0.975 0.8305 0.0972 6.00E-0.033 0 0.462 0.967 0.5361 0.216 0.012 0.4693 0 0.0451 0.0316 004
	mean_He_total 0.9539 0.9984 0.999 0.9752 var_GW_total 0.7364 0.6681 0.907 0.9359 0.2853 0.794 0.6458 0.852 0.9416 0.2726 1 0.997 1 1 0.9111 0.9995
	var_He_total 0.0011 mean_GST 0.2031 0.3586 0.546 0.5067 0.7748 0.191 0.2718 0.015 0.3924 0.6114 0 0.002 1.00E-004 0 0.01 0 0 0.011 0
	mean_A_pop1 0.9909 0.9912 0.999 0.9889 var_GST 0.0845 0.0245 0.76 0.2878 0.2154 0.046 0.0045 0.091 0.3036 0.2239 1 1 1 1 0.9491 0.9994
	var_A_pop1 mean_deltamu2 0.9328 0.9917 0.117 0.8329 0.979 0.976 0.9969 0.181 0.7456 0.9464 4.00E-004 5.00E-004 0.001 4.00E-004 0 0 1.00E-004 0 0.0097 0 var_deltamu2 0.9387 0.9883 0.17 0.6931 0.9705 0.979 0.9944 0.243 0.6096 0.9378
	mean_A_pop2 0.887 0.9962 0.967 0.8542	1	0.962	1	1 0.8079 0.9864
	var_A_pop2	0.0384	1.00E-004	0.01 0.0132	7.00E-004	0.031	0	0 0.0405 0.0342
	mean_A_total 0.9549 0.9984 0.999 0.9765	1	0.997	1	1 0.9128 0.9995
	var_A_total	0.0011	0	0.002	0	0	0.01	0	0 0.0103 0
	mean_Ar_pop1 0.9928 0.9906 1 0.9978	1	1	1	1 0.9776 0.9999
	var_Ar_pop1 0.2832 0.3585 0.435 0.2961 0.4841 0.598 0.4826 0.732 0.2514 0.6358
	mean_Ar_pop2 0.9075 0.9962 0.934 0.9931	1	0.999	1	1 0.8948 0.99
	var_Ar_pop2 0.6802 0.4688 0.045 0.3868 0.5767 0.882 0.4959 0.352 0.4541 0.6036
	mean_Ar_total 0.9909 0.9999 0.98 0.9982	1	1	1	0.999 0.9849 0.9991
	var_Ar_total	0.3833 0.4709 0.109 0.4236 0.6422 0.629 0.569 0.376 0.3807 0.6889
	mean_V_pop1 0.9926 0.9906 1 0.9978	1	1	1	1 0.9767 0.9999
	var_V_pop1	0.2701 0.3257 0.439 0.2796 0.4769 0.586 0.4457 0.736 0.2421 0.6298
	mean_V_pop2 0.9057 0.996 0.928 0.9924	1	0.999	1	1 0.8929 0.9895
	var_V_pop2	0.6692 0.4395 0.044 0.3845 0.5762 0.874 0.458 0.341 0.4504 0.6036
	mean_V_total 0.9909 0.9999 0.979 0.9981	1	1	1	0.999 0.9845 0.9987
	var_V_total	0.3747 0.4431 0.109 0.4195 0.638 0.621 0.5385 0.377 0.3764 0.6837

  3,4* , Charles Perrier 5* , Clémence Genthon 6 , Anne-

	Laure Besnard 1,2 , Sophie Launey 1,2 , Guillaume Evanno 1,2
	Author for Correspondence: quentinrougemont@orange.fr
	1 INRA, UMR 985 Ecologie et Santé des Ecosystèmes, 35042 Rennes, France

2 Agrocampus Ouest, UMR ESE, 65 rue de Saint-Brieuc, 35042 Rennes, France 3 Institut des Sciences de l'Evolution (UMR 5554), CNRS-UM2-IRD, Place Eugène Bataillon, F-34095 Montpellier, France 4 Station Méditerranéenne de l'Environnement Littoral, Université Montpellier 2, 2 Rue des Chantiers, F-34200 Sète, France 5 CEFE-CNRS, Centre D'Ecologie Fonctionnelle et Evolutive, Route de Mende, 34090 Montpellier, France 6 Plateforme génomique INRA GenoToul Chemin de Borderouge -Auzeville, 31320 Castanet-Tolosan France * Both authors contributed equally to this work.

Table 1 : Pattern of genome-wide differentiation across the nine population pairs.

 1 Situation indicates the location of the 2 populations in the watershed (S=sympatry, P=parapatry) see materials and methods for more information. Number of lampetra fluviatilis and Number of Lampetra planeri (N Lf / N Lp) are provided as well as the estimates of genome-wide differentiation from microsatellite markers. Expected Heterozygosity (Exp Het) in each populations are indicated as well as genome-wide F ST , maximum F ST value, number of F ST reaching the maximum value of 1, the 90 th quantile and the number of outliers found using either de 97% quantile method or the neutral model.

	Outliers

Het Lf Obs Het Lp Fst SNPs Fst max Fst = 1 90%Fst quantile

  The neutral expectations for the DOR population pairs cannot be computed. For comparisons purposes, we used a quantile of Fst that provided similar number of outlier (i.e. quantile of Fst greater than 0.86, providing 2428 "outliers"

													Neutral
													model
	OIR	S	28 / 25	0.030	16557	7	0.294	0.296	0.042	0.823	0	0.117	324
	BET	S	15 /13	0.028	14199	0	0.297	0.293	0.053	1	21	0.168	643
	RIS	P	22 / 16	0.040	16672	6	0.294	0.296	0.065	1	2	0.166	958
	AA	S	24 /27	0.080	15405	9	0.292	0.280	0.076	0.821	0	0.169	883
	BRE	S	21 /17	0.076	15511	0	0.300	0.257	0.143	1	24	0.315	2150
	DOR	P	21 / 8	0.190	17330	0	0.293	0.237	0.150	1	23	0.312	NA*
	GAR	P	21 / 15	0.087	16926	0	0.293	0.261	0.157	1	24	0.354	2483
	LOI	P	21 /19	0.150	16407	1	0.300	0.254	0.153	1	5	0.327	2535
	ODO	P	21 / 22	0.190	15908	0	0.305	0.247	0.207	1	29	0.404	3317

*

Table S4 : Blasts and annotation results Gene family E-value %Identity Known Function Ref Accession Number Sequence ID

 S4

	Myosin Heavy Chain		3e-20 100	Contraction		1 AB720829.1
	GnRH2, Vasotocin, Protein		3e-18 98	Maturation and		2 FJ195978.1
	True pedigree Pure LF tyrosin phosphatase (Ptpra) Pure LP	F1 F2 Backcross Lf growth Backcross Lp	Admixed hybrid	Type I error	Accuracy Power
	Pure LF Precursor	250							0		-	1	1
	Pure LP	250							0		-	1	1
	F1 Hox Genes (Hox-delta 3,	249 0 3e-5 to 95 to 98 Development 0 0		1 3 KF318006 -	1	0.998
	F2 delta2, eta 4, epsilon 9, epsilon	0 243 1e-17	1	0		6	-KF318008	1	0.976
	Backcross LF BackcrossLP 10, gamma 9, Hox-zeta-1, Evx),	0 0	0 0	249 0	0 250	0	1	--KF318008	0.992 0.996	0.996 1
	zinc finger protein.								KF318011
	Mannose-binding lectin-		7e-14 96	Immunity			0.022 5 AB078894.1	0.998	0.992
	associated serine protease
	Pineal gland specific opsin		3e-12 93%	Photoreception		6 AB116381.1
	gene
	IVariable Lymphocyte Receptor	3e-6	97%	Immunity			7 AB275449.1
	(IVLR)
	Hedgehog			7e-7	95%	????			4 FP929026.1
										FP929027.1

  1,2 , Victoria Dolo 1,2 , Adrien Oger 1,2 , Anne-Laure Besnard 1,2 , Marie-Agnès Coutellec 1,2 , Sophie Launey 1,2 , Charles Perrier 3 ,GuillaumeEvanno 1,2

	1 INRA, UMR 985 Ecologie et Santé des Ecosystèmes, 35042 Rennes, France
	2 Agrocampus Ouest, UMR ESE, 65 rue de Saint-Brieuc, 35042 Rennes, France

Table 1 : Summary statistics of genetic diversity of L. planeri and L. fluviatilis populations for each geographic

 1 analyses of populations structure in the Crano-St Sauveur (n=7 sites) and Arz River (n=8 sites) revealed different patterns of admixture. On the Crano-St Sauveur, two distinct upstream tributaries formed distinct clusters, whereas downstream populations displayed increased admixture values (Figure

	area. N = Number of individuals, NbA = Number of Alleles (averaged of all loci), Ar = Allelic
	Richness, He = unbiased Expected Heterozygosity, Ho = Observed Heterozygosity
		N	NbA	Ar	He	Ho	F ST [95%IC]
	Global	2268	2.73	2.43	0.354	0.344	0.377 [0.334 -0.418]
	L. fluviatilis (Normandy)	209	3.84	3.39	0.505	0.491	0.003 [0 -0.007]
	L. planeri (all regions)	2059	2.65	2.37	0.344	0.334	0.396 [0.353 -0.437]
	Normandy. L. planeri	574	3.15	2.88	0.435	0.440	0.139 [0.113 -0.170]
	Britanny & Normandy L.	1474	2.94	2.62	0.416	0.411	0.241 [0.207 -0.284]
	planeri
	Britanny L. planeri	910	2.88	2.58	0.406	0.396	0.279 [0.235 -0.334]
	Upper Rhone L. planeri	575	1.76	1.52	0.111	0.089	0.249 [0.047 -0.317]
	UK L. planeri	83	3.5	2.96	0.476	0.463	NA
	Ireland L. planeri	48	3.31	2.79	0.458	0.453	NA
	Upper Rhine L. planeri	36	3.00	2.58	0.355	0.323	NA

Table 2 : Effect of landscape fragmentation on genetic diversity and differentiation between pairs of sites.

 2 Mixed Linear Model result based on populations of Northern and Western France (25 pairs of sites)

					Ar Differential		He differential
	model	effect tested	AIC	df estimate Chi 2	P	AIC df estimate Chi 2	P
	1		-15.75 11	0.159			-96.06 11 0.044
	2	obstacle height	-14.31 5	-0.006 11.44 0.0433 -96.61 6	-0.008 9.452 0.092
	3 number of obstacles -12.30 6	-0.072 13.45 0.0195 -96.31 6	-0.034 9.752 0.083
	4	distance	-22.85 6	0.004	2.90 0.7152 -97.55 6	-0.001 8.513 0.130
		distance to the
	5	source	-14.01 6	-0.006 11.73 0.0386 -96.42 6 -0.0004 9.646 0.086
				Relatedness Differential		Fst/(1-Fst)
	model	effect tested	AIC df estimate Chi 2	P	AIC	df estimate Chi 2	P
	1		-66.228 11 -0.044			-88.35 11 0.044
	2	obstacle height	-71.347 6 0.009 4.8807 0.4306 -95.21 6 -0.014 3.144 0.6778
	3	number of obstacles -71.362 6 0.0228 4.8655 0.4325 -94.61 6 -0.007 3.738 0.5877
	4	distance	-72.598 6 0.0004 3.6298 0.6038 -96.63 6 -0.00002 1.718 0.8867
		distance to the
	5	source	-72.046 6 0.0015 4.1812 0.5236 -72.05 6 -0.0008 26.3 7.79E-005

Table S1 : Characteristics of each obstacle

 S1

	ROE27349	Aa	Normandy	591909 2634711	Paepterie	2.5
	ROE27345	Aa	Normandy	591909 2634711	Seuil du Cours leulieux 2	0.6
	Code_ROE ROE27344 ROE25520 ROE27343 NA ROE27339 ROE39631 ROE27341 ROE32742 ROE27364 ROE32752 ROE27365 ROE32765 ROE27367 ROE327936 ROE27376 ROE32891 ROE48991 ROE232898 ROE48995 ROE32907 ROE48997 ROE41251 ROE12583 ROE46812 ROE41498	River Aa Montafilan Aa Rance Aa Saint Emilion Aa St Sauveur Aa St Sauveur Aa St Sauveur Aa St Sauveur Aa St Sauveur Ange-Oignin St Sauveur Ange-Oignin Ange-Oignin St Sauveur/Crano Veyle St Sauveur/Crano Veyle Veyle	Area Normandy Britanny Normandy Britanny Normandy Britanny Normandy Britanny Normandy Britanny Normandy Britanny Normandy Britanny Normandy Britanny Upper rhone 895980 6562533 X_L2E Y_L2E 591077 2634659 265308 2397805 590682 2634481 245314 2374669 590375 2634608 166120 240980 590297 2634567 171360 233878 588619 2634767 172165 2334535 588603 2634932 173067 2336925 587951 2635053 173783 2337323 587295 2635039 174115 2337490 Britanny 174260 2337575 Upper rhone 896223 6.6E+07 Upper rhone 896877 6566296 Britanny 176120 2337455 Upper rhone 820719 2129572 Britanny 176620 2337155 Upper rhone 820724 2129689 Upper rhone 820672 2129784	Obstacle Name Moulin Pidoux Camboeuf A26 Chaos Quemelin Moulin de Confosse 1 Etang Beffou Moulin de Confosse 2 Moulin de Tronchateau Vannage de Ferssinghen Moulin de Mélien Moulin Colbert Moulin du Moustoir Seuil Pourdrerie Moulin de Restraudan Ancien Moulin Roland Moulin de Kerviden Station Jeaugeage Diren Moulin de Kerviden Bassins Autoroute Radier de Pont du Mollard Bel Air 03 Prise d'eau pont en daul Prise d'eau kerhault Bel Air 02 Bel Air 02	height 1 0.8 1 2.5 0.8 1.6 2.8 1.8 3.5 0.2 1.3 0.25 0.6 NA 0 0.8 0.25 2 1.2 0.3 0 0.32 0 0.28 0.19	remarks removed removed
	ROE32910 ROE41207	St Sauveur Veyle	Britanny Upper rhone 820741 2129884 176935 2339390	Moulin de Becherel Moulin Longchamp	3 0.28
	ROE32911 ROE46810	St Sauveur Veyle	Britanny Upper rhone 820393 2130170 177450 2340353	Moulin de Malachappe Moulin Maillet01	0.2 0.73
	ROE32924 ROE41490	Kerousseau Veyle	Britanny Upper rhone 820440 2130644 169665 2328173	Moulin de Kerousseau Moulin Maillet02	1.85 0.73
	ROE40867 ROE41496	l'Arz Veyle	Britanny Upper rhone 820475 2132075 223686 2319252	Moulin de Luhan Gourion Voie SNCF	0.85 0.35
	NA ROE46808	l'Arz Veyle	Britanny Upper rhone 820201 2132181 229200 2318057	Moulin de Kerfily Colon	0.6 0.56	removed
	ROE15566 ROE46807	l'Arz Veyle	Britanny Upper rhone 819910 2132932 233819 2316207	Moulin du Helfau La fretaz	0.4 1.57
	ROE15866 ROE46805 ROE11672 ROE41505 ROE11666 ROE41508	l'Arz Veyle l'Arz Veyle l'Arz Veyle	Britanny Upper rhone 819707 2133858 236914 2314780 Britanny 239735 2313380 Upper rhone NA NA Britanny 241203 2312630 Upper rhone NA NA	Moulin du Pont de Molac La Vermée La Viergé Moulin de Larré Moulin de l'échange Granges Blanches 03	0 0.44 0.6 0 0 0	removed removed removed removed
	ROE67738 ROE41510 ROE41512	l'Arz Veyle Veyle	Britanny Upper rhone 819305 2135380 242436 2312782 Upper rhone NA NA	Seuil de Jeaugeage du Pont du Favre Granges Blanches 01 Granges Blanches 02	0.1 0.25 0	removed
	ROE11661 ROE46804	l'Arz Veyle	Britanny Upper rhone 819149 2136908 244499 2312319	Moulin du Bois Bréhan Le Chatelard Moulin Neuf	0.6 0.4
	ROE11652 ROE46803	l'Arz Veyle	Britanny Upper rhone 819019 2137098 245574 2312462	Moulin de Bragou Moulin Neuf	1.5 1.6
	ROE11641 ROE46802	l'Arz Veyle	Britanny Upper rhone	248223 2311557 NA NA	Moulin de Quenelet Les combes	0.8 0	removed
	ROE11637 ROE41530	l'Arz Veyle	Britanny Upper rhone	248765 2311882 NA NA	Clapet de la ville Boury Confluence gravier	0 0	removed removed
	ROE11632 ROE46801	l'Arz Veyle	Britanny Upper rhone	249782 2311738 NA NA	Moulin d'Arz Barrage Chamambard	0.65 0	removed
	ROE62238 ROE46800	l'Arz Veyle	Britanny Upper rhone 817437 2139890 250405 2311378 Ancien moulin de quiquéma Barrage Chamambard	0 1	removed
	ROE11626 ROE46799	l'Arz Veyle	Britanny Upper rhone 816964 2140464 Barrage Moumin de Loyasse 231416 2311280 Moulin de l'éthier	0.3 0.95
	ROE11619 ROE54149	l'Arz Solnan	Britanny Upper rhone 830594 2153393 253659 2310760	Moulin du Quiban Prairie de Presle	1.1 0.5
	ROE11479 ROE54150	l'Arz Solnan	Britanny Upper rhone 830789 2153540 255312 2311194	Gué de l'épine Les cailloux	0.85 0.5
	NA	Moulin du Rocher	Britanny	266169 2293175	NA Prise d'eau du Moulin des	3
	ROE41477 ROE564305	Kergroix Solnan	Britanny Upper rhone 829248 2152253 195130 2324501	Moulin Castellin Ponts	0.4 1
	ROE13091 ROE564306 ROE8503 ROE564304	Oir Solnan Oir Solnan	Normandy Upper rhone 829334 2152223 338931 2410294 Normandy 333847 2409329 Upper rhone NA NA	Mongothier Moulin des Ponts Cerisel Barrage du Moulin des Ponts	1 1.3 2 0	removed
	ROE12912 ROE27801	Oir Calonne	Normandy Upper rhone 790063 2125213 333298 2409315	Moulins des geins Moulin Crozet	1.5 1.3
	ROE69925 ROE27807 ROE18864 ROE42374	Cens Calonne Illet Furans	Atlantic Upper rhone 790151 2125265 296962 2259848 Britanny 3111647 2369451 Upper rhone NA NA	Moulin Crozet Moulin de Piguel Passage à Gué d'Arbigneu	1.9 1.25 0 0	removed removed
	ROE22554 ROE42376 ROE 22520 ROE42379	Illet Furans Odon Furans	Britanny Upper rhone 858555 2087896 311023 2369195 Normandy 382038 2451484 Upper rhone NA NA	Ancien Moulin du Pont Moulin de la Hurlais NA Barrage de la pie	0 0.6 1.97 0	removed removed
	ROE27745 ROE42381	Risle Furans	Normandy Upper rhone 859609 2095141 472498 2483963	Ouvrage le foll Passage à Gué voie ferré	2.36 0.4
	ROE27745 ROE42387	Risle Furans	Normandy Upper rhone 859106 2095496 472498 2483963	Seuil des échauds barrage Roissey	0.7 1.1
	ROE233 ROE42390	Risle Furans	Normandy Upper rhone 858250 2095721 471746 2484213	Ouvrage les 3 Moulins Cheminet	0.75 1
	ROE44020 ROE48987	Bresle Furans	Normandy Upper rhone 857858 2096360 557144 2528609 Microcentrale de la chaussée Pris d'eau pisciculture	2.1 2.1
	ROE15296	Bethune	Normandy	543078 2520496	Moulin à huile	2.69
	ROE15264	Bethune	Normandy	591416 6956015	Moulin de Saint Saire	1.5
					Minoterie de Recques sur
	ROE15278	Hem	Normandy	582197 2648778	Hem	3.5
	ROE15259	Hem	Normandy	582867 2650269	Moulin Bleu	0.7
	ROE35628	Aa	Normandy	596555 2636417	Moulin Snick	0.8
	ROE34472	Aa	Normandy	596214 2636130	Moulin Marin	0.4
	ROE27362	Aa	Normandy	595480 2635993	Moulin Fer Blanc	0.25
	ROE27360	Aa	Normandy	595098 2635917	Moulin de Westhore	0.2
	ROE27357	Aa	Normandy	594336 2635310	Moulin de Wins	2
	ROE35613	Aa	Normandy	593728 2634920	Ancien Moulin	0.2
	ROE27354	Aa	Normandy	593316 2635084	Seuil de Gondardene	NA
	ROE27353	Aa	Normandy	592675 2635011	Le choquet	0.1

Table S2 : synthesis of population genetic diversity indices

 S2

	FromeD	UK	Frome	no	79	48	3.69	3.02	0.490	0.505	-0.031
	FromeU	UK	Frome	no	80	35	3.31	2.92	0.463	0.422	0.091
	Pop Sha	Area Ireland	river Shannon	sympatry no	id 81	N 48	NbA 3.31	Ar 2.79	He 0.458	Ho 0.453	Fis 0.011	Kinship
	Lf Hem	Normandy	Hem	yes	1	30	3.77	3.29	0.504	0.497	0.013	0.072
	Lf Arq	Normandy	Aa	yes	2	34	3.85	3.26	0.514	0.504	0.019	0.059
	Lf bre	Normandy	bresle	yes	3	30	3.69	3.24	0.497	0.476	0.044	0.057
	Lf Beth	Normandy	bethune	yes	4	17	3.54	3.28	0.514	0.476	0.078	0.086
	Lf Ris	Normandy	risle	yes	5	35	3.77	3.22	0.515	0.503	0.024	0.066
	Lf Odo	Normandy	odon	no	6	30	3.77	3.11	0.491	0.470	0.042	0.082
	Lf Oir	Normandy	oir	yes	7	30	3.46	3.00	0.498	0.514	-0.034	0.073
	Lf Loire	Normandy	loire	no	8	19	3.08	2.90	0.458	0.465	-0.015	0.084
	HemRq	Normandy	Hem	yes	9	39	3.46	2.86	0.477	0.469	0.017	0.164
	HemRqU	Normandy	Hem	no	10	26	2.92	2.71	0.471	0.504	-0.071	0.172
	LpAAD	Normandy	Aa	yes	11	30	3.23	2.94	0.528	0.563	-0.068	0.148
	LpAAU Temp	Normandy Britanny	Aa Temple	no no	12 57	39 34	3.46 2.62	3.05 2.27	0.522 0.330	0.492 0.320	0.059 0.030	0.168 0.387
	LpBreAud CensAm	Normandy Atlantic	bresle Cens-Loire	no no	13 58	40 27	2.92 2.31	2.48 2.01	0.335 0.239	0.335 0.224	0.000 0.060	0.295 0.363
	LpBreAum CensAv	Normandy Atlantic	bresle Cens-Loire	no no	14 59	40 24	2.54 2.23	2.36 1.99	0.335 0.261	0.321 0.224	0.044 0.144	0.276 0.305
	LpBreD CensAdu	Normandy Atlantic	bresle Cens-Loire	yes no	15 58b	31 25	2.77 2.54	2.47 2.25	0.321 0.297	0.351 0.323	-0.095 -0.087	0.273 0.687
	LpBethD BetBea OigU	Normandy Upper Normandy Rhone Upper	bethune bethune Oignin	yes no no	16 17 60	17 23 30	3.38 2.62 1.23	3.15 2.45 1.18	0.472 0.440 0.055	0.482 0.428 0.050	-0.023 0.026 0.088	0.116 0.614 0.266
	BetBeth OigD	Normandy Rhone	bethune Oignin	no no	18 61	25 30	2.77 1.69	2.55 1.54	0.453 0.110	0.452 0.045	0.001 0.598	0.201 0.918
	BetBou LpOdoDAu Reys	Normandy Upper Normandy Rhone Upper	bethune odon Reysouze	no no no	19 20 62	30 30 51	3.15 3.00 1.38	2.84 2.59 1.17	0.462 0.386 0.019	0.446 0.403 0.011	0.034 -0.046 0.447	0.158 0.735 0.221
	LpOdoUAu Ser	Normandy Rhone	odon Séran	no no	21 63	30 47	2.23 1.77	2.12 1.41	0.313 0.110	0.352 0.101	-0.124 0.084	0.338 0.610
	LpRisD SolAusU	Normandy Upper Rhone	risle Solnan	no no	22 64	28 28	3.08 1.77	2.84 1.59	0.460 0.127	0.435 0.110	0.055 0.140	0.152 0.696
	LpRisU	Normandy Upper	risle	no	23	43	3.77	3.05	0.466	0.435	0.066	0.145
	LpOirD SolD LpOirU SolM	Normandy Rhone Normandy Upper Rhone	Oir Solnan Oir Solnan	yes no no no	24 65 25 66	34 29 31 26	3.31 1.69 3.00 1.85	2.96 1.44 2.77 1.70	0.503 0.091 0.458 0.171	0.538 0.076 0.466 0.139	-0.071 0.167 -0.018 0.188	0.533 0.089 0.159 0.722
	TamConf	Britanny Upper	Tamoute	no	26	25	3.46	3.14	0.502	0.465	0.076	0.082
	TamMi SolU TamPo VeyBPMU	Britanny Rhone Britanny Upper Rhone	Tamoute Solnan Tamoute Veyle	no no no no	27 67 28 68	26 44 24 36	3.31 1.85 3.31 1.69	3.05 1.66 3.07 1.50	0.486 0.137 0.444 0.101	0.445 0.121 0.417 0.090	0.087 0.122 0.064 0.112	0.728 0.145 0.185 0.791
	MonD	Britanny Upper	Montafillan	no	29	20	2.77	2.69	0.453	0.492	-0.089	0.128
	MonU VeyChamD RanAd AinFoug	Britanny Rhone Britanny Upper Rhone	Montafillan Veyle Rance Ain	no no no no	30 69 31b 70	29 35 13 13	3.00 1.54 2.23 1.38	2.74 1.36 2.21 1.36	0.479 0.081 0.339 0.102	0.459 0.073 0.362 0.110	0.043 0.105 -0.074 -0.074	0.724 0.134 0.348 0.661
	RanD	Britanny Upper	Rance	no	31	17	2.08	2.04	0.326	0.327	-0.003	0.378
	RanU AngeSRD	Britanny Rhone Upper	Rance Oignin	no no	32 71	32 30	2.15 1.62	1.95 1.38	0.300 0.074	0.324 0.068	-0.082 0.081	0.761 0.398
	LegD CalD	Britanny Rhone	Leguer Calonne	no no	33 72	30 30	2.46 1.69	2.07 1.52	0.239 0.111	0.178 0.057	0.259 0.495	0.960 0.765
	LegU ChStSv CalU	Britanny Upper Britanny Rhone Upper	Leguer Scorff Calonne	no no no	34 38 73	29 32 30	1.92 2.62 1.62	1.77 2.43 1.39	0.245 0.438 0.085	0.224 0.438 0.062	0.087 0.002 0.274	0.916 0.707 0.387
	Scor FurU	Britanny Rhone	Scorff Furans	no no	41 74	30 32	3.15 1.54	2.65 1.47	0.419 0.134	0.354 0.111	0.157 0.170	0.340 0.714
	Crano2013 CranoU FurCondU	Britanny Upper Britanny Rhone Upper	Scorff Scorff Furans	no no no	37 36 75	35 25 33	2.62 2.23 2.08	2.34 2.13 1.74	0.421 0.367 0.142	0.444 0.408 0.114	-0.055 -0.112 0.198	0.455 0.692 0.470
	StSvD FurD	Britanny Rhone	Scorff Furans	no no	40 76	30 30	2.62 1.85	2.37 1.60	0.418 0.139	0.406 0.123	0.029 0.113	0.434 0.539
	StSvKer StSvU Ney	Britanny Upper Britanny Rhone Upper	Scorff Scorff Ain	no no no	39 35 77	33 24 21	2.54 2.31 2.08	2.38 2.20 1.89	0.438 0.369 0.209	0.469 0.359 0.137	-0.071 0.028 0.349	0.420 0.450
	ArzLuhU Alsace	Britanny Rhine	Arz Fischbaechel	no no	42 78	32 36	2.85 3.00	2.57 2.58	0.441 0.355	0.459 0.323	-0.041 0.090	0.199

Table S3 : Population genetic diversity for each locus

 S3

	See electronic files

Table S4 : Results of pairwise t.test for difference in Allelic Richness, Expected Heterozygosity and relatedness

 S4

		(FDR adjusted) a= L. fluviatilis, b= L. planeri downstream (Normandy), c = L. planeri
	upstream (Normandy), d= L. planeri downstream (Britanny), e = L. planeri upstream (Britanny), f = L.
	planeri downstream (Ain), g = L. planeri upstream (Ain),
				Allelic Richness
	pop	a	b	c	d	e	f
	b	0.51433	-	-	-	-	-
	c	0.10327	0.3435	-	-	-	-
	d	0.00138	0.01077	0.10327	-	-	-
	e	0.00013	0.00127	0.0179	0.45148	-	-
	f	2.70E-013 1.70E-012 2.10E-011 4.40E-009 7.00E-008	-
	g	6.

30E-014 2.90E-013 3.20E-012 8.00E-010 1.50E-008 0.9626

	Whole	34	10	-43356.4	375.714	-101.66	2201.63	5.859855
	Whole	35	10	-45659.69	6538.803	-2303.29	4299.5	0.657536
	Whole	36	10	-43663.48	264.3743	1996.21	2102.46	7.952588
	Whole Area	37 K	10 Reps	-43769.73 Mean LnP(K)	230.296 Stdev LnP(K)	-106.25 Ln'(K)	179.31 |Ln''(K)|	0.778607 Delta K
	Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Whole Normandy Normandy	38 2 39 3 40 4 41 5 42 6 43 7 44 8 45 9 46 10 47 11 48 12 49 13 14	10 10 10 10 10 10 10 10 10 10 10 10	-44055.29 -17334.46 -46894.85 -17008.7 -44671.19 -16701.1 -44677.35 -16358.38 -44811.49 -16116.89 -45992.31 -16061.97 -45279.72 -15953.74 -45748.91 -16066.62 -45597.03 -16263.04 -45754.6 -16371.63 -46356.28 -16403.24 -46472.77 -16522.22 -16568.34	508.8362 2.4985 8688.7813 12.6167 283.4375 22.3663 472.3086 3.6651 575.4831 6.4309 3100.018 11.0253 638.35 7.6072 867.523 40.3092 465.016 111.419 618.2294 268.2642 973.2859 311.0902 685.6923 318.5147 121.3671	-285.56 NA -2839.56 325.76 2223.66 307.6 -6.16 342.72 -134.14 241.49 -1180.82 54.92 712.59 108.23 -469.19 -112.88 151.88 -196.42 -157.57 -108.59 -601.68 -31.61 -116.49 -118.98 -46.12	2554 NA 5063.22 18.16 2229.82 35.12 127.98 101.23 1046.68 186.57 1893.41 53.31 1181.78 221.11 621.07 83.54 309.45 87.83 444.11 76.98 485.19 87.37 NA 72.86 188.31	NA 5.019297 1.439367 0.582731 1.57022 7.867062 27.620054 0.270967 29.011463 1.818785 4.83523 0.610774 29.065925 1.851304 2.072479 0.715912 0.788286 0.665461 0.286956 0.718358 0.280851 0.498507 0.228749 NA 1.551574
	Normandy	15			-16802.77	132.2892	-234.43	140.52	1.062218
	Area Normandy AIN Normandy AIN Normandy AIN Normandy AIN Normandy	K 16 2 17 3 18 4 19 5 20		Reps	Mean LnP(K) -16896.68 -3342.17 -17170.1 -3266.06 -17413.67 -3054.47 -17632.34 -3631.83 -17825.91	Stdev LnP(K) 147.077 2.6289 261.6498 95.4 238.9002 168.2079 221.9244 1534.5069 306.2531	Ln'(K) -93.91 NA -273.42 76.11 -243.57 211.59 -218.67 -577.36 -193.57	|Ln''(K)| 179.51 NA 29.85 135.48 24.9 788.95 25.1 1240.15 NA	1.220517 Delta K 0.114084 NA 0.104228 1.420125 0.113102 4.690328 0.808175 NA
	AIN	6			-2969.04	33.3695	662.79	617.8	18.513901
	AIN	7			-2924.05	29.1361	44.99	227.36	7.803391
	AIN	8			-3106.42	16.6814	-182.37	222.5	13.338172
	AIN	9			-3066.29	24.6844	40.13	63.37	2.567205
	AIN	10			-3089.53	33.0915	-23.24	24.65	0.744904
	AIN	11			-3088.12	41.8663	1.41	11.31	0.270146
	AIN	12			-3075.4	36.4818	12.72	32.88	0.901271
	AIN	13			-3095.56	28.0957	-20.16	56.33	2.004932
	AIN	14			-3172.05	45.0292	-76.49	41.44	0.920292
	AIN	15			-3289.98	76.1441	-117.93	18.49	0.242829
	AIN	16			-3389.42	53.7612	-99.44	73.9	1.374597
	AIN	17			-3414.96	47.1226	-25.54	89.39	1.896965
	AIN	18			-3529.89	99.2124	-114.93	94.12	0.948672
	AIN	19			-3550.7	92.5576	-20.81	37.55	0.405693
	AIN	20			-3533.96	125.9373	16.74	NA	NA
	Area	K		Reps	Mean LnP(K)	Stdev LnP(K)	Ln'(K)	|Ln''(K)|	Delta K
	Britanny	2		9	-20967.8444	125.6852	NA	NA	NA
	Britanny	3			-19620.05	270.4727	1347.794444	394.7	1.459297
	Britanny	4		9	-18666.9556	85.435	953.094444	10.05	0.117633
	Britanny	5		9	-17703.8111	61.387	963.144444	581.755556	9.476856
	Britanny	6		9	-17322.4222	151.4653	381.388889	20.4	0.134684
	Britanny	7		9	-16961.4333	129.331	360.988889	138.333333	1.069607
	Britanny Britanny	8 9		9 9	-16738.7778 -16527.9	265.8443 Expected Heterozygosity 222.655556 127.6357 210.877778	11.777778 134.566667	0.044303 1.054303
	pop Britanny Britanny	10 11		a 9	b -16451.5889 -16433.83	c 60.1105 13.8073	d 76.311111 17.758889	e 58.552222 58.241111	0.974076 f 4.21813
	Britanny	12			-16357.83	65.4586	76	112.7	1.7217
	b Britanny Britanny	13 14		0.04888	--16394.53 -16338.59	-28.2714 100.7041	--36.7 55.94	-92.64 71.47	3.276807 -0.709703
	Britanny	15			-16211.18	35.2316	127.41	247.15	7.015008
	c Britanny Britanny	16 17		0.00123	0.18619 -16330.92 -16344.25	-156.8005 241.2342	--119.74 -13.33	-106.41 26.33	0.678633 -0.109147
	d Britanny Britanny	18 19	0.000036	0.02123 -16331.25 -16327.44	0.29686 172.9993 42.5705	-3.81 13	-9.19 135	0.053122 -3.17121
	Britanny	20			-16458.63	183.778	-131.19	152.53	0.829969
	e Britanny Britanny	2.70E-008 0.000058 21 -16437.29 22 -18732.12	0.00331 109.7405 6906.2805	0.04355 21.34 -2294.83	-2316.17 4422.72	21.10588 -0.640391
	Britanny	23			-16604.23	139.2101	2127.89	2226.98	15.997263
	f Britanny	3.20E-013 3.30E-010 1.70E-008 3.70E-007 3.60E-004 24 -16703.32 133.2807 -99.09 98.12	-0.736191
	Britanny	25			-16704.29	117.8404	-0.97	245.97	2.087314
	g Britanny Britanny	26 7.50E-015 6.70E-012 3.30E-010 8.30E-009 1.60E-005 0.48228 -16951.23 219.6445 -246.94 160.14 0.729087 27 -17038.03 206.4291 -86.8 30.42 0.147363
	Britanny	28			-17094.41	148.107	-56.38	145.74	0.984019
	Britanny	29			-17296.53	143.4651	-202.12	NA	NA

4. The genetic consequences of spatial isolation in L. planeri populations a. A low effect of human induced fragmentation

  While many studies have reported negative effects of human induced fragmentation on species diversity and differentiation (e. g.Hanfling et Weetman 2006; Rayemakers et al. 2008; Alp et al. 2012;[START_REF] Torterotot | Influence of Forest Road Culverts and Waterfalls on the Fine-Scale Distribution of Brook Trout Genetic Diversity in a Boreal Watershed[END_REF], investigations in lampreys did not reveal such strong negative effects. Instead, I found a downstream increase in genetic diversity(DIGD, Paz-Vinaz et al. 2015)

Table 3 :

 3 Estimates of migration rate and effective population size from ABC approaches

	River	Markers	N1 [95%CI]	N2[95%CI]	Nanc[95%CI]	T[95%CI]	M1[95%CI]	M2[95%CI]
	ARZ	SNPs	1.24 [1.08 -1.59]	0.03 [0.03 -0.04]	9.80 [9.74 -9.89]	0.05 [0.045 -0.054]	30 [30 -30]	21.23 [18.92 -23.65]
	ARZ	microsat 1	0.95 [0.40 -1.90]	0.54 [0.38 -0.79]	1.86 [1.70 -2.05]	7.07 [5.45 -9.82]	16.40 [13.67 -18.10]	8.40 [4.50 -12.60]
	ARZ	microsat²	1.11 [0.22 -2.69]	0.51 [0.31 -0.89]	0.85 [0.11 -2.06]	14.18 [4.14 -22.20]	14.77 [6.90 -19.46]	9.92 [1.64 -17.95]
	RAN	SNPs	4.11 [2.69 -6.38]	4.86 [3.63 -6.68]	8.51 [7.62 -9.33]	0.094 [0.072 -0.124]	29.12 [26.54 -29.99]	26 [18.27 -29.58]
	RAN	microsat	1.15 [0.51 -2.42]	0.47 [0.14 -1.47]	2.15 [1.55 -2.62]	5.46 [3.65 -7.62]	9.85 [10.88 -20.36]	5.46 [3.65 -7.61]
	RAN	microsat	0.89 [0.16 -2.58]	0.52 [0.16 -1.55]	1.76 [0.37 -2.83]	15.70 [5.20 -22.75]	12.30 [4.16 -18.40]	8.62 [1.36 -17.10]
	TAM	SNPs	4.65 [4.12 -5.23]	5.06 [3.39 -6.78]	7.20 [6.00 -8.16]	18 [17.26 -18.70]	9.88 [7.11 -12.69]	0.89 [0.45 -1.65]
	TAM	microsat	0.88 [0.53 -1.31]	1.28 [1.2 -1.37]	2.07 [1.77 -2.33]	11.82 [11.15 -12.47]	7.01 [4.96 -8.69]	10.86 [8.84 -12.88]
	TAM	microsat	0095 [0.40 -1.76]	1.18 [0.43 -2.30]	1.47 [0.47 -2.43]	12.96 [5.16 -19.97]	10.33 [3.87 -16.29]	12.68 [5.22 -18.72]
	SCO	SNPs	0.23 [0.101 -0.548] 0.119 [0.08 -0.183] 9.95 [9.88 -9.99]	0.318 [0.229 -0.4507]	27.88 [24 -29.75]	22 [16.17 -26.57]
	SCO	microsat	1.07 [0.68 -1.66] 0.713 [0.337 -1.47] 1.96 [1.56 -2.26]	13.15 [9.72 -16.32]	18.10 [14.77 -19.54]	10.53 [4.58 -16.10]
	SCO	microsat	1.18 [0.32 -2.55]	0.60 [0.19 -1.81]	1.78 [0.55 -2.72]	12.55 [2.13-22.79]	17.15 [7.88 -19.77]	8.79 [0.69 -18.77]

Table 1 :

 1 Results of GLMMs and LMM testing the effect of cross type on survival and length of the larvae. Tests are performed including L. planeri and L. fluviatilis. AIC value, degree of freedom (df), Chi2 and P-values are provided. Significance: * P < 0.05; ** P < 0.01; *** P < 0.001)

					Survival			length
	model effect tested AIC	df Chi 2	P	AIC	df Chi 2	P
	1		433.263 9			22572.19 13
	2	cross type 439.455 4 16.191 0.0063 **	22597	8 12.294	0.0309 *
	3	dam id	566.22 8 134.96 2.10e-16 *** 22761.37 12 191.97 2.10e-16 ***
	4	sire id	441.835 8 10.572 0.0011 ** 22616.85 12 45.722 2.10e-16 ***
	5	pop dam	--	--	--	--	22572.14 12 1.2961	0.255
	6	pop sire	--	--	--	--	22570.69 12 0.2891	0.591
	7	date	434.349 8 3.0857	0.079	22570.41 12 0.1822	0.669
	8	bloc					22697.9 12 127.47 2.10e-16 ***

Table 2 :

 2 Results of GLMMs and LMM testing the effect of cross type on survival and length of the larvae using L. planeri crosses only.

					Survival			length
		effect
	model	tested	AIC	df Chi 2	P	AIC	df Chi 2	P
	1		260.01 5			14615 13
	2	cross type 267.58 4 9.5757 0.001972 ** 14619 8 6.2115	0.01269
	3	dam id	281.96 4		9.9e-7 ***	14744 12 131.61	2.2e-16 ***
	4	sire id	262.81 4	4.8	0.02846 *	14642 12 28.82	7.943e-08 ***
	5	pop dam	--	--	--	--	14616 12 2.973	0.08467
	6	pop sire	--	--	--	--	14613 12 0.764	0.382
	7	date	258.01 4	0	0.9999	14613 12 0.0377	0.8461
	8	bloc					14684 12 70.963	2.2e-16 ***

Diffusion process: A continuous-time stochastic process that tracks a quantity that changes continuously in time and whose future depends only on the present state.

Male of the non migratory species (or early maturing male in other species than lampreys) that used a sneaking strategy to fertilize eggs of females during spawning with a (larger) conspecific male.

(a) Connected Pairs

(b)Disconnected pairs

Scientific Activities

Acknowledgments

Acknowledgements We thank F. Marchand, J. Tremblay, A. Oger, V. Dolo, Y. Salaville, R. Lemasquerier, V. Lauronce, C. Taverny, B. Rigault C. Rigaud, Y. Perraud, C. Perrier, G. Sanson, J.-L. Fagard and P. Domalain (ONEMA) who helped us collect the samples. We thank L. Benestan and J.-S. Moore for valuable comments on the manuscript as well as D. R. Matute and two anonymous referees. This study was funded by the European Regional Development Fund (Transnational program Interreg IV, Atlantic Aquatic Resource Conservation Project). Data deposited at Dryad: doi: 10.5061/dryad.5qv85 Acknowledgments We thank A. Oger who helped us collecting samples and extracting DNA. We thank F. Marchand, J. Tremblay, V. Dolo,Y. Salaville, R. Lemasquerier, V. Lauronce, C. Taverny, B. Rigault C. Rigaud, Y. Perraud, C. Perrier, G. Sanson, J.-L. Fagard and P. Domalain who helped us collect the samples. We are grateful to the Genotoul bioinformatics platform Toulouse Midi-Pyrenees for providing computing and storage resources. This study was funded by the European Regional Development Fund (Transnational program Interreg IV, Atlantic Aquatic Resource Conservation Project) and by the Office National de l'Eau et des Milieux Aquatiques (ONEMA).

no no no no no no no no no no no Nul allele no no no no no no no no no no no no no no no no no no no no no no no Nul allele no no no no no no no no no no no no no

Appendix 3: Testing outbreeding and heterosis in isolated L. planeri populations

Scientific Activities

Supporting information

Detailed protocol for artificial fertilizations

Each progenitor was anesthetized with benzocaïne and gametes were then collected by manual stripping. Eggs from each female were distributed into eight Petri dishes. We added 20 to 30 µl of milt in each Petri dish, which were then half-filled with dechlorated water. The fertilization success for each sib group was estimated three hours after fertilization based on the presence of a perivitelline space in the eggs [START_REF] Ciereszko | Fertilization in landlocked sea lamprey: storage of gametes, optimal sperm: egg ratio, and methods of assessing fertilization success[END_REF].

A total of 24 fertilized eggs from each sib group were then individually distributed in 24 wells plates filled with 2ml of chemically standardized water reconstituted according to OECD guidelines (OECD 1992). Eggs were then incubated in a climate chamber at a constant temperature of 12°C +/-1°C to measure hatching rate.

OECD. 1992. OECD guideline for the testing of chemicals 203 (fish acute toxicity test). Annex 2. (2011 and 2014), and Risle rivers (2011 and 2014). A set of 13 microsatellites was used to genotype a total of 727 individuals following the protocol described in [START_REF] Gaigher | Characterization of thirteen microsatellite markers in river and brook lampreys (Lampetra fluviatilis and L. planeri)[END_REF].

Summary statistics

Given the lack of genetic differentiation between samples collected in different years in the same river, they were merged together. Similarly, we pooled brook lamprey individuals sampled in upstream and downstream areas on the Aa and Hem river as they displayed no significant genetic differentiation (Table S1). To obtain samples of river lampreys of similar size we also pooled individuals from the Aa and Hem river together as our previous genetic analysis indicated a single panmictic population of river lamprey across the English Channel area [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF].

Summary statistics were then computed for each pooled sample. For summary statistics used for comparison between simulated and observed datasets, we computed the average and standard deviation values of: the number of alleles (A), Allelic richness (Ar), observed and expected heterozygosity (Ho and He), allele size in base pairs, the Garza-Williamson index (GW, [START_REF] Garza | Detection of reduction in population size using data from microsatellite loci[END_REF], G ST , δμ 2 [START_REF] Goldstein | Genetic absolute dating based on microsatellites and the origin of modern humans[END_REF], [START_REF] Weir | Estimating F-Statistics for the Analysis of Population Structure[END_REF] estimation of F ST . All statistics were computed using R scripts [START_REF] Core | European Environment Agency (EEA)[END_REF] available upon request to the authors.

Testing alternatives demographic scenario

ABC coalescent simulations

For each population pair we used an Approximate Bayesian Computation [START_REF] Beaumont | Approximate Bayesian Computation in Population[END_REF][START_REF] Csilléry | Approximate Bayesian Computation (ABC) in practice[END_REF])) framework to statistically compare five alternative models of demographic history (Fig. 2): [START_REF] Ikeda | Lampreys have a single gene cluster for the fast skeletal myosin heavy chain gene family[END_REF] the two studied populations derive from a single panmictic gene pool (PAN); [START_REF] Gwee | Characterization of the neurohypophysial hormone gene loci in elephant shark and the Japanese lamprey: origin of the vertebrate neurohypophysial hormone genes[END_REF] the strict isolation model (SI) between sister populations; [START_REF] Mehta | Evidence for at least six Hox clusters in the Japanese lamprey (Lethenteron japonicum)[END_REF] However, increased similarity in the most divergent genomic regions was found among connected ecotypes pairs, indicating that gene flow was instrumental in generating parallelism at the molecular level. These results suggest that heterogeneous genomic differentiation and parallelism among replicate ecotype-pairs have partly emerged through local gene flow reduction in genomic islands.

Based on their genetic characteristics, the Risle and Bresle River were two "outliers" with respect to the geographical context. The number of unique polymorphisms in migratory and resident lamprey was indeed similar on the Risle River (949 vs 901) whereas it was strongly asymmetric between the sympatric populations from the Bresle river (2758 vs 889 polymorphic only in migratory and resident populations respectively). In agreement with these results, histograms of minor allelic frequencies showed that most isolated brook lamprey population displayed an L-shaped distribution whereas allelic frequencies were more evenly distributed in connected Lp and in Lf populations (Fig

S1)

.

Population structure and introgression

Patterns of population structure inferred from an Admixture analysis, and neighbor-joining

Inference of outliers and extent of parallel differentiation

Gene flow was always strong in connected populations, as opposed to completely disconnected populations, making the former good candidates to study the genetic architecture of reproductive isolation. In line with this result, we consistently observed a larger proportion of outlier SNPs in parapatry (Table 1). Five populations were used for these inferences (the Oir, Bet, Ris, Aa and Bre) (Table 1). Outliers SNPs represented from 1.96 to 5.75 % of the total dataset in each connected river resulting in an average of 4.55 % of SNPs shared between population pairs. The Bresle was again an "outlier" with a high number of SNPs departing from neutrality in this population (13.9%).

Conversely, outlier SNPs represented from 14.7 to 20.9% of the dataset in disconnected pairs (Table 1).

We investigated the proportion of outlier loci that were shared across the five connected population pairs. In the ten F ST /F ST pairwise comparisons, the majority of putatively 'neutral' loci were shared across the pairs (mean=10,667). Simulations under the neutral model yielded a total of 28 outliers (24 independent) shared across the ten comparisons and an average of 100 SNPs (46 to 172) shared between pairs. In all cases these loci displayed high correlation in F ST values (r range= 0.8-0.89, p-value <0.0001, see Fig 6). This amount of sharing was higher than expected by chance alone

(1,000 permutations, all p-values < 0.0001). Inference from our neutral models in disconnected populations revealed a much higher number of putative outliers (2150 to 3317, Table 1, Fig 6). In addition, these populations displayed lower values of the correlation coefficients obtained from regression analysis than the connected populations ( Fig 7) demonstrating that the level of parallelism was obscured by other processes than selection in parapatry. Using data from P. marinus allowed us to identify the derived allele (DAF) in some outliers for the connected pairs. The DAF was significantly greater in brook lampreys than in river lampreys (Fig 8,.

appropriate populations for understanding the history of ecotypic divergence. First, we validated the high levels of gene flow among L. fluviatilis populations [START_REF] Rougemont | Low reproductive isolation and highly variable levels of gene flow reveal limited progress toward speciation between European river and brook lampreys[END_REF][START_REF] Bracken | Contrasting population genetic structure among freshwater-resident and anadromous lampreys: the role of demographic history, differential dispersal and anthropogenic barriers to movement[END_REF] suggesting no homing behaviour, as observed in other migratory lamprey systems (Spice et al. 2012;[START_REF] Hess | Population genomics of Pacific lamprey: adaptive variation in a highly dispersive species[END_REF]. Second, the admixture analysis (Fig. 3b) confirmed that L. planeri populations could be broadly classified into two categories: populations located in downstream areas and highly connected by gene flow to L. fluviatilis, and isolated upstream populations that currently do not exchange genes with L. fluviatilis. Ecotype pairs consisting of well-connected populations of L.

fluviatilis and L. planeri consistently displayed low levels of genetic differentiation (genome-wide FST <0.10). In these pairs, populations of L. planeri also showed levels of heterozygosity similar to the migratory ecotype (table 1, Fig 2 ) and clustered together with L. fluviatilis in the population tree (Fig. 3a). Contrastingly, the most disconnected pairs displayed higher levels of genetic differentiation (genome-wide FST > 0.10). Populations of L. planeri had a lower heterozygosity, shared less polymorphism with river lamprey and formed a separate cluster on the population tree (Fig. 3a).

Interestingly, two L. planeri populations (the Bresle and Risle) were outliers with respect to the geographic context (geographical connection or not), as they displayed lower polymorphism and high differentiation in sympatry (Bresle), or higher polymorphism and low differentiation in parapatry (Risle) compared to other populations in comparable geographical contexts. This suggests that the spatial distribution of these populations has changed through time, or, in the case of Risle, gene flow may occur despite the barriers to migration (e.g. through downstream migration of juvenile Lp or occasional upstream migration of adult Lf). The current geographical distribution of these populations might thus not reflect the demographic history that has shaped their genetic structure [START_REF] Bierne | The geography of introgression in a patchy environment and the thorn in the side of ecological speciation[END_REF].

Using a subset of 40 highly differentiated SNPs (FST = 0.7) shared among connected population pairs (identified as such prior to demographic analyses) allowed us to circumvent geographical effects and to distinguish the two ecotypes across all nine population pairs. The low genetic differentiation generally observed between L. fluviatilis and L. planeri has been interpreted either as a result of phenotypic plasticity [START_REF] Beamish | Evidence that Parasitic and Nonparasitic Life History Types are Produced by One Population of Lamprey[END_REF][START_REF] Yamazaki | Taxonomy and molecular phylogeny of Lethenteron lampreys in eastern Eurasia[END_REF][START_REF] April | Genetic calibration of species diversity among North America's freshwater fishes[END_REF][START_REF] Docker | Contemporary gene flow between "paired" silver (Ichthyomyzon unicuspis) and northern brook (I. fossor) lampreys: implications for conservation[END_REF] or as evidence of a very recent divergence [START_REF] Docker | Phylogeny of the lamprey genus Lampetra inferred from mitochondrial cytochrome b and ND3 gene sequences[END_REF][START_REF] Espanhol | Evolutionary history of lamprey paired species Lampetra fluviatilis (L.) and Lampetra planeri (Bloch) as inferred from mitochondrial DNA variation[END_REF][START_REF] Okada | Repetitive sequences in the lamprey mitochondrial DNA control region and speciation of Lethenteron[END_REF]). However, these interpretations mostly relied on mitochondrial variation patterns (the most widely used genetic marker in lamprey studies so far), which can be obscured by introgression [START_REF] Shaw | Conflict between nuclear and mitochondrial DNA phylogenies of a recent species radiation: what mtDNA reveals and conceals about modes of speciation in Hawaiian crickets[END_REF]. Our results clearly refute the hypothesis of phenotypic plasticity within a single gene pool, and demonstrate that shared genetic differences exist among replicate pairs of brook-river lamprey ecotypes. The subset of highly discriminating markers also allowed us to document geographical hybridization patterns. In general, sympatric populations displayed higher proportions of hybrids compared to parapatric pairs. However, the Bresle River (i.e. the "outlier" sympatric pair) did not contain any hybrid whereas in the Risle River (i.e. the "outlier" parapatric

Supporting Information Supplementary Methods

Structure analysis

To discriminate the two ecotypes using the 40 informative SNPs and identify admixed individuals we first used the program STRUCTURE 2.3.3 [START_REF] Pritchard | Inference of population structure using multilocus genotype data[END_REF]. We performed five independent replicates at a fixed k value of 2 under the admixture model with correlated allele frequencies [START_REF] Falush | Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies[END_REF]. Markov Chain Monte Carlo simulations (MCMC) used 100 000 burn-in followed by 100 000 iterations. We also computed 95% confidence interval to help determining admixture values. At this stage individuals displaying admixture q-value <0.90 and for with 95% did not reach the values of 1 were considered as of putative hybrid origin. We also excluded 8 individuals for which too many SNPs were not genotyped.

New Hybrid analysis

The software program New Hybrids [START_REF] Anderson | A Model-Based Method for Identifying Species Hybrids Using Multilocus Genetic Data[END_REF] was used to confirm Structure result and gain insights onto the putative hybrid category of admixed individuals. More specifically, we modelled the probability that individuals belong to one of the following categories: Pure river lamprey, pure brook lamprey, F1, F2, first generation backcrosses. We ran the software using a burnin period of 500 000 MCMC followed by 1 million sweeps using Jeffreys-like priors for the distribution of allelic frequency and mixing proportions.

Hybrid lab

As noted by Gelman et al. 1995 andemphasized in Nielsen et al. 2005, the drawback of the two former Bayesian approach is that is that validity of the prior needs to be assessed, which requires a simulating approach. We consequently used Hybridlab [START_REF] Nielsen | hybridlab (version 1.0): a program for generating simulated hybrids from population samples[END_REF] to generate simulated data and assess the power, accuracy and type I error of our New Hybrids to correctly assign individuals to their true categories. Power and accuracy were define following [START_REF] Vähä | Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci[END_REF]. (i.e. power=(number of correctly identified individuals to the category)/(true number of simulated individuals of that category) and accuracy=(number of correctly identified individuals in a category)/(total number of individuals assigned to this category) , the type error = (sum of falsely assigned hybrids)/ ( total number of pure individuals simulated).

We simulated individuals belonging to the same category as those defined in New Hybrids (Pure Lf, Pure Lp, F1, F2, Backrosses with Lf and Backcross with Lp) using individuals with a q-value >0.90 of being pure Lf or Lp. For this purpose, we used only populations in which hybrids were found, that is the Aa, Oir and Risle. Given the very high power of our approach (see below) we did not perform simulations on the Loire River were only one hybrid was found. Given the modest sample size population we generated only one hundred individual of each category in each river.

Blast and annotation

We used the 29 sequences potentially under selection between the two ecotypes to perform BLAST (Altschul et al. 1990) analysis on the NCBI public database using an e-value threshold of 1 x 10 -10 .

Among the 40 SNPs shared across the nine population pairs, 34 were located in independent reads and two of these reads yielded significant hits as provided in table S4. S1) maximal value of 0.095 on the Crano between two sites located near the river source and without obstacles (Table S4). Populations from the Upper Rhine, Frome and Shannon Rivers were moderately differentiated from L. fluviatilis (Table S4). The Frome Downstream in particular displayed modest differentiation from L. Tests of isolation by distance indicated a significant relationship between distance and linearized genetic differentiation in the upper Rhone area (r spearman = 0.469, p=2e -4 ). The pattern of isolation by distance was less pronounced in Brittany, but still significant (r spearman = 0.188, p = 0.016). In contrast, isolation by distance was not significant in Normandy (r= 0.145, p = 0.143). This absence of correlation was largely driven by the lack of genetic differentiation between the upstream/downstream populations of the Oir River as compared to the remaining L. planeri Normandy populations. When this population was removed, the pattern of IBD was the strongest in Normandy populations (r spearman = 0.55, p=1e -4 ). To gain further insights into the evolutionary relationships among watersheds from coastal areas either connected with river lampreys (i.e. Normandy) or disconnected (i.e. Brittany), we tested the pattern of IBD by keeping one site by river. In this case IBD remained significant in Normandy (r spearman = 0.43, p=0.042), while there was no more significant relationship in Brittany (r spearman = -0.0208, p=0.53).

Supplementary Results

Table S1: Summary of SNPs number at each filtering steps in each populations

Mixed linear models performed on 25 pairs of sites from Normandy and Brittany revealed a significant influence of the number of obstacles and cumulative height on allelic richness differentials (Table 2). The geographic distance between sites did not influence patterns of genetic diversity or differentiation but the distance to the source of the upstream site had a strong influence on allelic richness and genetic differentiation (F ST ) (Table 2).

Investigating patterns of downstream increase in genetic diversity revealed a strong influence of the Mantel tests and partial Mantel tests on the two rivers where a sufficient number of sites were available (Arz and Crano) indicated different influences of distance and obstacle-related variables. On the Arz River, all variables significantly influenced allelic richness (Table 3) whereas it was influenced solely by distance on the Crano. The extent of pairwise differentiation was also influenced by distance in the Crano River whereas this pattern was only revealed in the Arz when the influence of the number of obstacles was controlled for (table 3).

We also investigated the intensity of gene flow between upstream and downstream sites using BayesAss [START_REF] Wilson | Bayesian Inference of Recent Migration Rates Using Multilocus Genotypes[END_REF] but failed to obtain reliable results as confidence intervals were too large (data not shown)

Discussion

The goal of this study was threefold: testing the effect of river fragmentation on patterns of population genetic diversity and structure, investigating the spatial distribution of genetic diversity in L. planeri populations at a large scale, and exploring the potential influence of the presence of L.

fluviatilis on the level of genetic diversity of L. planeri. We used L. planeri as a model to test the effect of fragmentation as this species displays a reduced migratory behaviour and, in particular, a limited ability to move upstream (Malmqvist, 1983). Our large dataset composed of sympatric, parapatric and allopatric populations of L. planeri, located in downstream and isolated areas of different watersheds revealed a key role of L. fluviatilis in maintaining genetic diversity of L. planeri populations in the lower part of rivers where they co-occur. Strikingly different levels of genetic diversity and population genetic structure were observed in different regions. We found limited evidence for the effect of fragmentation on genetic diversity and differentiation of populations and models indicated that the distance to the source was a more pertinent variable to explain patterns of genetic diversity and relatedness of individuals within populations.

Impacts of river fragmentation on distribution of genetic diversity and differentiation

While several studies have reported strong impact of barriers to migration on either genetic diversity and/or structure [START_REF] Hänfling | Concordant Genetic Estimators of Migration Reveal Anthropogenically Enhanced Source-Sink Population Structure in the River Sculpin, Cottus gobio[END_REF][START_REF] Leclerc | The landscape genetics of yellow perch (Perca flavescens) in a large fluvial ecosystem[END_REF][START_REF] Raeymaekers | Modeling genetic connectivity in sticklebacks as a guideline for river restoration[END_REF][START_REF] Blanchet | Species-specific responses to landscape fragmentation: implications for management strategies[END_REF][START_REF] Faulks | Islands of water in a sea of dry land: hydrological regime predicts genetic diversity and dispersal in a widespread fish from Australia's arid zone, the golden perch (Macquaria ambigua)[END_REF][START_REF] Torterotot | Influence of Forest Road Culverts and Waterfalls on the Fine-Scale Distribution of Brook Trout Genetic Diversity in a Boreal Watershed[END_REF][START_REF] Gouskov | Fish population genetic structure shaped by hydroelectric power plants in the upper Rhine catchment[END_REF], here evidence for "negative" impacts was less obvious.

Investigating the effect of fragmentation on levels of population genetic diversity and structure revealed that population genetic diversity and relatedness was mostly affected by its distance to the source, as upstream populations showed lower levels of allelic richness and heterozygosity and higher levels of relatedness (Figure 4 and Table 5). This so called downstream increase in genetic diversity is expected in riverine habitat (Morrissey & (see Figure 1 in chapter 4). Overall, our populations displayed similar levels of genetic and genome wide divergence as other fish models of speciation (Table 1). The low levels of expected heterozygosity and allelic richness observed in upstream isolated populations (see also section 5 below) were particularly suggestive of a departure from demographic equilibrium in these populations. Such a pattern can be explained by two non-exclusive processes: random genetic drift in finite populations and non-recovery of historical bottlenecks following colonization of the river with few founder individuals. These results pointed to the necessity of testing histories of divergence.

In parallel to investigations of historical scenarios with microsatellite data (see below) a RADsequencing genotyping on a subset of nine pairs of population was undertaken. The goals were (i) to confirm the patterns of genetic structure given by microsatellite markers and especially (ii) to obtain a better understanding of the process of speciation at play between lamprey ecotypes, and (iii) to find genomic regions of high differentiation.

populations. This pattern is expected from theory [START_REF] Barton | The barrier to genetic exchange between hybridising populations[END_REF]in Bierne et al. 2013) and may lead to genome swamping of resident populations, as might be the case in the Oir River.

On the other hand, the inference of a scenario of ancient migration with a relatively high fit to the data in parapatric population pairs was unexpected but nevertheless interesting. I hypothesized that this inference is in fact wrong because of the inadequacy of the JSFS in populations with strong deviations from demographic equilibrium. This disequilibrium may exist in isolated L. planeri populations, and could generate spurious patterns that are not accurately captured by the δaδi method used here. Indeed a major hypothesis in δaδi is a large Ne, which may not apply to isolated L. planeri populations. In addition, it is still not clear to me how to integrate demographic perturbations (e.g. post divergence bottlenecks) in models of speciation and it is not clear how recent bottlenecks may affect the JSFS. A recent study has suggested some bias in joint inferences of selection and demography from the JSFS [START_REF] Mathew | Evaluating the ability of the pairwise joint site frequency spectrum to co-estimate selection and demography[END_REF]. Further development and theoretical modeling are thus certainly required.

Incorporating variations of Ne across the genome is also important as reflects genomic variation in selective processes, the so called selection at linked site process [START_REF] Cutter | Genomic signatures of selection at linked sites: unifying the disparity among species[END_REF], due either to positively selected mutations (selective sweep) [START_REF] Cutter | Genomic signatures of selection at linked sites: unifying the disparity among species[END_REF] or deleterious mutations and their subsequent elimination a process called background selection (BGS) [START_REF] Charlesworth | The effect of deleterious mutations on neutral molecular variation[END_REF][START_REF] Charlesworth | The Effects of Deleterious Mutations on Evolution at Linked Sites[END_REF]. Such selective effects result in reduced variability, reduced efficacy of selection and materialize as a reduction of effective population size at the site (a process called Hill-Robertson effect) [START_REF] Hill | The effect of linkage on limits to artificial selection[END_REF]. This process occurs particularly in genomic regions of low recombination [START_REF] Cutter | Genomic signatures of selection at linked sites: unifying the disparity among species[END_REF]. In these regions the effects are larger, drift is increased, and ultimately genetic differentiation as measured by F ST is increased [START_REF] Cruickshank | Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow[END_REF]. This is very important to take into account in the speciation research and investigation of genomic islands. Indeed, the first observation of genomic islands [START_REF] Turner | Genomic Islands of Speciation in Anopheles gambiae[END_REF], was interpreted as "speciation islands" protected from gene flow by selection. It was then suggested that during speciation with gene flow (under primary differentiation) genomic islands could be generated by selective sweeps and hitchhiking of neutral variants [START_REF] Via | Divergence hitchhiking and the spread of genomic isolation during ecological speciationwith-gene-flow[END_REF] while the remainder of the genome will be freely exchanged. Upon secondary contact, differentiation islands are revealed by the eroding effect of gene flow in regions unrelated to speciation. This was one of the concluding results from Chapter 4. This can also be related to the concept of barrier semi-permeability described in Chapter 1. Now in the light of the theory presented above it appears necessary to test whether the two processes can jointly act to model patterns of genome wide differentiation.

To do so, and bypass some of the assumptions of δaδi (large Ne and independence of the SNPs) that may not be fulfilled in the populations studied, I propose to analyze the RAD-sequencing data in an ABC framework incorporating both heterogeneity of migration rates along the genome but also

b. River lamprey as a "reservoir" of genetic diversity

Investigating spatial patterns of genetic diversity and differentiation at a larger scale revealed interesting results. First, populations of L. planeri located in streams where L. fluviatilis were present displayed higher genetic diversity levels than completely allopatric population such as those located in Brittany where the L. fluviatilis is rarely reported. This raises two questions: why L. fluviatilis does not currently migrate to spawn in Brittanny as this species is present in nearby rivers? Is this best explained by historical processes or ecological factors? I currently feel that it is very difficult to give a firm reply because of the complex patterns of genetic diversity observed in Brittany. The second important result was linked to the very high differentiation observed in the Upper Rhône. This may reflect either some kind of ascertainment bias linked to the development of our microsatellite markers or the fact that populations from this area represent different lineages. Moving toward a genomic approach in these populations, and more generally, performing a detailed genomic study at the European scale should help unravel the evolutionary history of divergence between L. fluviatilis and L. planeri.

c. Mutation accumulations in isolated populations: do lampreys suffer from a high drift load?

The loss of genetic diversity due to drift in finite populations can lead to increased genetic risks [START_REF] Lynch | Mutational Meltdowns in Sexual Populations[END_REF][START_REF] Frankham | Inbreeding and Extinction: Island Populations[END_REF][START_REF] Frankham | Genetics and extinction[END_REF]. Indeed, effects of drift increase inversely as a function of ½ Ne. In particular, drift decreases the efficiency of positive selection and increases the accumulation of deleterious mutations when s < ½ Ne. As a consequence, accumulation of weakly deleterious mutations is expected in small populations, such as isolated populations of L. planeri. Additionally, drift leads to increased variance in allele frequencies and increased homozygosity, and since most deleterious alleles are recessive or partially recessive [START_REF] Charlesworth | The effect of deleterious mutations on neutral molecular variation[END_REF]), this will lead to a reduction in average fitness. Weakly deleterious mutations and increased homozygosity together result in a 'drift load'. This load can have profound evolutionary consequences on the adaptive potential of small populations. These small populations should show a low inbreeding depression (reduced fitness of inbred individuals as compared to a randomly mating population) according to population genetics theory: in small populations, partial purging efficiently removes recessive deleterious (especially lethal) mutations exposed in a homozygous state [START_REF] Glémin | How Are Deleterious Mutations Purged? Drift Versus Nonrandom Mating[END_REF]. Mildly deleterious mutations can also be fixed by drift and those fixed mutations do not contribute to fitness differences between inbred populations and randomly mating ones [START_REF] Lynch | Mutational Meltdowns in Sexual Populations[END_REF][START_REF] Glémin | How Are Deleterious Mutations Purged? Drift Versus Nonrandom Mating[END_REF]. As a consequence, crosses between genetically differentiated isolated populations can lead to heterosis (hybrid vigor) of offspring relative to progeny from random mating within each parental population. The outcrossing of individuals will result in increased heterozygosity and subsequent masking of recessive or partially recessive deleterious alleles. As a consequence, offspring will be heterozygotes at each of these fixed sites and are expected to display higher fitness than parental populations. In theory, the smaller the populations the higher the heterosis should be [START_REF] Glémin | Patterns of inbreeding depression and architecture of the load in subdivided populations[END_REF]. On the other hand, breakup of co-adapted gene complexes and epistatic interactions between distant populations may lead to outbreeding depression and smaller fitness [START_REF] Lynch | The Genetic Interpretation of Inbreeding Depression and Outbreeding Depression[END_REF][START_REF] Tallmon | The alluring simplicity and complex reality of genetic rescue[END_REF]; see also Chapter 1). In light of this theory, and given the observations of reduced genetic diversity in small isolated lamprey populations, I tested the existence of a drift load by performing crosses among distantly related populations of L. planeri and between L. fluviatilis for which no load was expected. The preliminary results of this experiment are no evidence for heterosis and are presented in Annex 3.

d.

The importance of asymmetric gene flow in isolated populations: moving toward a genomic approach

In Chapter 5 I began to address issues related to the conservation of L. fluviatilis and L. planeri in fragmented rivers and across a variety of geographical contexts.

Using ABC on the set of 13 microsatellite markers and on RAD data I attempted to estimate the asymmetry of migration between populations located upstream and downstream of barriers. A set of 4 population pairs was randomly chosen and genetic characteristics of these populations are presented in Table 2. 3 below.

Appendices Appendix 1: Exploring the effect of selection at linked genomic sites

Here I present some additional investigations related to selection at linked sites. They are not related to the current debates about speciation islands and deserts of recombination [START_REF] Cruickshank | Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow[END_REF]. Instead, I had the idea to study the effect of background selection within isolated L. planeri populations displaying interesting conditions of small size and potential accumulations of several mildly deleterious mutations

The initial idea was to evaluate the utility of combining ABC and RAD data to measure the asymmetry of gene flow. To quantify gene flow and ratios of effective population size, the same ABC approach as previously developed in Chapter 3 was used for microsatellite data. I attempted to estimate asymmetric gene flow using the traditional island models. For RAD data, I took benefit of scripts of C. roux to compare alternative island models. More specifically I compared the four following scenarios:

(1) homogenous effective population size Ne and homogeneous migration m

(2) Heterogeneous m and homogeneous Ne;

(3) Heterogeneous Ne and homogenous m;

(4) Heterogeneous m and heterogeneous Ne.

My reasoning was that isolated populations of brook lamprey may undergo the effect of BGS, which may translate into low Ne in regions of low recombination whereas few traces of positive selection were expected. In this condition, migration across the genome may be mostly homogeneous while Ne can (eventually) be heterogenous. Here fascinating results emerged with regards to model comparison from these RAD data. The most likely model was largely that of heterogeneous migration and heterogeneous Ne (Table 1). [START_REF] Charlesworth | The Effects of Deleterious Mutations on Evolution at Linked Sites[END_REF].

In this scenario, lamprey populations of small size (small Ne) could, under the effects of genetic drift, accumulate deleterious mutations. In principle, background selection needs to be strong to efficiently eliminate these mutations (i.e. populations will have a significant load if s < ½ Ne) (Whitlock et al. 2000, Whitlock et Burger 2004). BGS will have a key role in reducing genetic diversity at linked loci [START_REF] Charlesworth | The effect of deleterious mutations on neutral molecular variation[END_REF][START_REF] Charlesworth | The Effects of Deleterious Mutations on Evolution at Linked Sites[END_REF]. This reduction in nucleotide diversity (π/π 0 ) at a neutral locus due to BGS is a function of the number of deleterious mutations (µd), the selection (s) against heterozygotes for deleterious alleles, and recombination rates (c). These effects can be summed across linked sites that mutate into deleterious alleles according to Nordborg et al. We crossed (in-vitro fertilization) a female river lamprey from the Loire River with a male brook lamprey from the Oir River to produce F1 offspring. They were reared until they reached a size sufficient for extraction of DNA suitable in quantity and quality for subsequent RAD sequencing. A total of 90 offspring was sequenced, and parents were sequenced twice. RAD sequencing and genotyping were performed as described in Chapter 4. A total of 10 700 markers were available and 78 larvae were suitable for construction of the linkage map. We developed a python script to determine which markers were suitable for linkage mapping, to calculate distortion segregation errors and to provide suitable data for subsequent mapping. Markers suitable were of 4 types and coded as follows: homozygous in the female and heterozygous in the male (segregating 1:1); homozygous in the male and heterozygous in the female (segregating 1:1); homozygous in both parents (segregating 1:1:1:1), heterozygous in both parents (segregating 1:2:1). CarthaGene [START_REF] Givry S De | Carh ta Gene: multipopulation integrated genetic and radiation hybrid mapping[END_REF] was used for linkage mapping using outbred data of a type F2 intercross with unknown phase. Different tests were performed to optimize the map. In a final run CarthaGene was used with a LOD score of 4 and a distance threshold of 0.2. We constructed a female map, a male map and a consensus map. We performed Chi² test to detect significant deviations from Mendelian inheritance at the 5% level followed by standard Bonferonni correction for multiple testing.

Preliminary results: From the 5185 markers that could be mapped unambiguously, 95 linkage groups were obtained when removing groups with less than six markers. The average number of SNPs per group was 66 (median = 60 min = 6 max=208).

Removing groups with less than 4 markers resulted in 110 linkage groups and removing groups containing less than three markers results in 129 groups. This linkage map will allow positioning of outliers and study of the patterns of LD to determine the origin of barriers to reproductive isolation.

Appendix 3: No evidence for heterosis in small and isolated brook lamprey populations

Species extinction can be influenced by stochastic demographic processes generating fluctuations in population size. Such fluctuations are currently largely enhanced by human perturbations of natural ecosystems (Vitousek et al. 1997;[START_REF] Palumbi | Humans as the world's greatest evolutionary force[END_REF]. Habitat fragmentation is one of the most important threats, reducing gene flow, decreasing genetic diversity and hence effective population size [START_REF] Lynch | Mutational Meltdowns in Sexual Populations[END_REF][START_REF] Frankham | Inbreeding and Extinction: Island Populations[END_REF][START_REF] Frankham | Genetics and extinction[END_REF]. In these conditions, small populations will undergo stronger effects of genetic drift than populations of large effective size as the strength of drift increases inversely with the size of the populations as a function of ½ Ne.

Ultimately, drift can lead to higher levels of inbreeding (Frankham, 1995a,b;1998) and results in local extinctions [START_REF] Saccheri | Inbreeding and extinction in a butterfly metapopulation[END_REF][START_REF] Spielman | Most species are not driven to extinction before genetic factors impact them[END_REF].

Genetic drift also decreases the efficiency of positive selection and leads to the accumulation of deleterious mutations when s < ½ Ne. As a consequence, the accumulation of weakly deleterious mutations is expected to be stronger in small populations. Secondly, drift increases the variance in allele frequencies and increases homozygosity, and since most deleterious alleles are recessive or partially recessive, this will lead to a reduction in average population fitness due to a 'drift load'. This load can have profound evolutionary consequences on the adaptive potential of small populations. In these populations, partial purging can efficiently remove recessive deleterious mutations (especially lethal ones) exposed in a homozygous state (e.g. [START_REF] Glémin | How Are Deleterious Mutations Purged? Drift Versus Nonrandom Mating[END_REF]). This should result in a low inbreeding depression that is the reduced fitness of inbred individuals as compared to a randomly mating population (Charlesworth & Charlesworth 1999). However, mildly deleterious mutations could be fixed by drift as they do not contribute to fitness differences between inbred and outbred individuals [START_REF] Lynch | Mutational Meltdowns in Sexual Populations[END_REF][START_REF] Glémin | How Are Deleterious Mutations Purged? Drift Versus Nonrandom Mating[END_REF]. Such mutations can contribute to fitness differences between individuals originating from within and among population crosses.

In particular, crosses between genetically differentiated isolated populations can lead to heterosis (hybrid vigor) of offspring relative to progeny from random mating within each parental population. The outcrossing of individuals will result in increased heterozygosity and subsequent masking of recessive or partially recessive deleterious alleles. Offspring are then expected to display higher fitness than parental populations. In theory, the smaller the population, the higher the heterosis should be [START_REF] Glémin | Patterns of inbreeding depression and architecture of the load in subdivided populations[END_REF]. On the other hand, breakup of co-adapted gene complexes and epistatic interactions between distant populations may lead to outbreeding depression and smaller fitness in inter-population crosses [START_REF] Lynch | The Genetic Interpretation of Inbreeding Depression and Outbreeding Depression[END_REF][START_REF] Tallmon | The alluring simplicity and complex reality of genetic rescue[END_REF]). Importantly, outbreeding depression is expected to occur mainly in F2s and subsequent generations whereas heterosis is already observed in F1 hybrids.

The brook lamprey (Lampetra planeri) is a jawless vertebrate that may allow testing hypotheses about the evolution of a drift load in small and isolated populations. It is a non parasitic, among populations. In black: All crosses. In blue: crosses used for hatching and length analysis. In orange: crosses used only for length measurement. In pink: crosses used only for hatching rates.

Data analysis

The data were analyzed using Generalized Linear mixed effect Models (GLMM) and Linear Mixed effect Models (LMM). We tested the influence of the cross type on larval hatching rate (binomial error family) and length (Gaussian error family. The cross type was a factor with six modalities: within L. planeri populations, among L. planeri populations, within L. fluviatilis populations, among L. fluviatilis population, hybrid from a L. fluviatilis female and hybrid from a L. planeri female. Sire and dam effects were treated as random effects nested within their respective population of origin. The date of cross and the shelf where the eggs were located during incubation were also treated as random effects. GLMMs were performed using either all data (Table 1) or only crosses among L. planeri (Table 2)

Results and discussion

The average hatching success over all crosses was 89.5% (±16%). The average hatching success in crosses between L. planeri was 93.9% (±12%) while it was 83.1% (±12%) in L. fluviatilis crosses.

The mean size of offspring over all crosses was 6.81 mm (±0.51 mm). Larvae from crosses between L. planeri had a mean size of 6.78 mm (±0.51 mm) and those from L. fluviatilis crosses had a mean size of 6.91 mm (±0.50 mm). Results of GLMMs and LMMs are given in tables 1 and 2 and reveal significant effects of cross type as well as the paternal and maternal identity of offspring.