Stephen Jay Gould

H Lalagüe

K Csilléry

S Oddou-Muratorio

J Safrana

S De Quattro

B Fady

S C González- Martínez

G G Vendramin
email: giovanni.vendramin@igv.cnr.it

Several intermediate efforts, all basically erroneous but each supplying an important link in a developing

Keywords: drought, budburst phenology, recombination rate, genomic diversity, single nucleotide polymorphism (SNP), insertion deletion mutation

story, preceded the successful conclusion.

An adventure of four years ends up now. It has been a long climb, and like the past explorations in unknown lands, it is first of all a teamwork. The work described in this thesis could not have been accomplished without the support and guidance that I received from many people. It is now the time to give my sincere acknowledgments.

First of all I would like to thank my supervisors Sylvie and Beppe, for accepting me as PhD student.

Thank you for your patience, for encouraging and pushing me. Thank you also for your skill at guiding me step by step while giving me the necessary liberty. It is a difficult task but from my point of view you perfectly deal with; these four years with you have been very fruitful! I gratefully acknowledge the funding received towards my PhD from the Scuola Sant'Anna and for the efficiency of its secretary's office. I am also grateful to the funding received through the Evoltree network that facilitated travels between Italy and France and with the European partners.

Sincere acknowledgments go to PhD committee, Professor Martin Lascoux, Professor Stéphanie Manel, Dr Renaud Vitalis and Professor Enrico Mario Pe' for your willingness to read and evaluate my thesis, and the fruitful discussion during the PhD defense.

I first started the PhD in Italy where I met very nice and generous people always ready to help. Despite the initial barrier language, I learned a lot from you. Thank you Sara, Francesca and Federico for teaching me the basis of the lab work. In addition to the 'lab team' I thank Enrico, Andrea, Nicola, Valeria, Carolina, Johanna and Veronika for their good mood and nice company. The lunch time and parties with all of you were always very pleasant, with a special mention to Andrea for providing me (through shady means) discount tickets, you know what I mean! Thank you Gabriele for taking the time to introduced me to the peculiar programming world; I now start to stand on my own two feet.

When I moved to the INRA of Avignon I discovered a team of passionate and skilled researchers, the door of your office was always open for me; I am very grateful to all of you, especially François Lefevre. I particularly thank Bruno Fady for his numerous advices (whatever the subject), and his incredible talent for getting money. Money is not only the sinews of war but also the sinews of any research program.

Thank you very much Katalin for your help and support during the end of the PhD, your energy and knowledge helped me during this difficult period. I owe you a lot.

I have to thank Anne and Matthieu for their warm welcome and help in the laboratory of Saint Maurice.

A particular thank you to Marie-Claude that manage to make administrative stuff comprehensible and easy.

I am very grateful to Marie-Jo and Cindy, my officemates in France. Thank you for your kindness and good mood, it has been a real pleasure to share the office with you. Thank you Cindy for your help with R each time I was nervously banging my head on the table... Shukran ya Marie-Jo ly mizajik al-ra2iq wa ly durusik al-lughata al-3arabiya rughma Anaa lastu taliban jayyidan. I thank also the other PhD students and post-doc, Julie, Marion, Aurore, Guillaume, Annabelle, Cyrille and Tonya for all the discussions job and not-job related and the memorable parties.

I also thank the sporty colleagues always ready for running, swimming, biking or climbing, particularly Florence, Damien, Eric, François and Guillaume. By providing these relaxing moments, you played a crucial role to the achievement of the PhD.

During my PhD project I have collaborated with many people I would like to mention here: Pauline Garnier-Géré for her fundamental help and support for aligning and checking the numerous DNA sequences. You spent a lot of time for me, I am very grateful. I would like to extend a special thank you to Stefania and Jonathan for their help during their internship. Your work was very thankless and difficult but you did not give up and finally succeed, chapeau bas! Santiago Gonzales Martinez, Delphine Grivet and Katharina Budde for their cordial welcome during my stay in Madrid and more generally for their help during those four years. Many other, through numerous discussions played an important role: Yao Chen Lin, Concetta Burgarella, Anna Roschanski, Yoshiaki Tsuda and Jean Baptiste Lamy.

My hearty thanks go to my family and friends. Thank you for accepting somehow the long periods of silence and my rare and short appearances. I would also like to say a heartfelt thank you to my Mum and Dad for always believing in me and encouraging me to follow my dreams.

It is hardly possible to name everyone who helped me and who influenced my work and life during my PhD. I would like to express my apology to those, whom I could not mention here.

Abstract

A major challenge in population genetics is to understand the local adaptation process in natural population and so to disentangle the various evolution forces contributing to local adaptation. The experimental studies on local adaption generally resort to altitudinal gradients that are characterized by strong environmental changes across short spatial scales. Under such condition, the genetic differentiation of the functional trait (measured by the Qst) as well as the genes coding for trait (measured by Fstq) are expected to be mainly driven by selection and gene flow. Genetic drift and mutation are expected to have minor effect. Theoretic studies showed a decoupling between Qst and Fst under strong gene flow and / or recent selection. In this study, I tested this hypothesis by combining experimental and modelling genomic approach in natural population of Fagus sylvatica separated by ~3 kilometres and under contrasted environments.

Sampling was conducted in south-eastern France, a region known to have been recently colonised by F.sylvatica. Four naturally-originated populations were sampled at both high and low elevations along two altitudinal gradients. Populations along the altitudinal gradients are expected to be subjected to contrasting climatic conditions. Fifty eight candidate genes were chosen from a databank of 35,000

ESTs according to their putative functional roles in response to drought, cold stress and leaf phenology and sequenced for 96 individuals from four populations that revealed 581 SNPs. Classical tests of departure of site frequency spectra from expectation and outlier detection tests that accounted for the complex demographic history of the populations were used. In contrast with the mono-locus tests, an approach for detecting selection at the multi-locus scale have been tested.

The results from experimental approaches were highly contrasted according the method highlighting the limits of those method for population loosely differentiated and spatially close. The modelling approach confirmed the results from the experimental data but revealed that up to 95% of the SNPs detected as outliers were false positive. The multi-locus approach revealed that the markers coding for the trait are differentially correlated compared to the neutral SNPs. But this approach failed to detect accurately the markers coding for the trait if no a priori knowledge is known about them. The modelling approach revealed that genetic changes may occur across very few generation. But while this genetic adaptation is measurable at the trait level, the available method for detecting genetic adaptation at the molecular level appeared to be greatly inaccurate. However, the multi-locus approach provided much more promise for understanding the genetic basis of local adaptation from standing genetic variation of forest trees in response to climate change.

Résumé

Un enjeu majeur de la génétique évolutive est de comprendre comment l'adaptation locale se développe en environnement spatialement hétérogène. Le long de gradients altitudinaux présentant une variation rapide de l'environnement, l'adaptation locale devrait conduire à une différentiation génétique des caractères adaptatifs (mesurée par Q ST ) et des gènes déterminant ces caractères (QTL, mesurée par F STq ) plus forte que celle de marqueurs neutres (mesurée par F ST ), les valeurs des ces indices dépendant de façon prédominante de la sélection (Q ST et F STq ) et des flux de gènes (tous les indices). Cet attendu justifie l'utilisation des approche d'outlier pour identifier les loci sous sélection comme ceux présentant un F ST plus fort qu'attendu sous un modèle neutre. Néanmoins des études théoriques montrent un découplage entre Q ST et F STq et une convergence de F STq et de F STq lorsque que les flux de gènes sont forts et/ou que la sélection est récente. Dans cette thèse, j'ai testé ces attendus en combinant une approche de génomique expérimentale et par simulations dans des populations naturelles de hêtre commun (Fagus sylvatica).

Pour l'approche expérimentale, j'ai échantillonné 4 populations sur deux gradients altitudinaux sur le Mont Ventoux (avec une population à haute altitude et une à basse altitude sur chaque gradient).

Cinquante huit gènes potentiellement impliqué dans la réponse aux stress abiotiques et dans le débourrement ont été séquencés sur un total de quatre-vingt seize individus, révélant 581 SNPs (Single Nucleotide Polymorphisms). Le nombre de SNPs outlier détecté varie fortement selon la méthode utilisée(entre 0 et 28). Par ailleurs, j'ai utilisé un modèle mécaniste individu-centré pour simuler les patrons de diversité phénotypique et génétique attendus le long du gradient pour la phénologie du débourrement végétatif, un caractère généralement adaptatif dans la réponse aux variations de température. Les résultats montrent que 5 générations suffisent pour obtenir une différentiation importante du caractère (Q ST ), et confirment le découplage entre Q ST et F STq (avec Q ST > F STq ). Les tests d'outlier conduits sur les simulations révèlent que plus de 95% des SNPs outlier sont des faux positifs, confirmant les limites des approches d'outliers pour détecter les loci sous sélection dans des populations spatialement proche et génétiquement faiblement différentiée. En revanche, une approche originale multi-locus révéle une forte corrélation inter-populations inter-gènes des QTLs, confirmant les attendus théoriques. Cette approche multi-locus de la sélection ne permet pas à elle seule de détecter les loci sous sélection mais peut être utile en complément des approches d'outliers.

En conclusion, les travaux de cette thèse mettent en évidence la rapidité des changements génétique qui interviennent en moins de 5 générations pendant la modification du climat, et la difficulté de détecter les gènes codant pour des traits complexes. [START_REF] Arnold | Morphology, performance and fitness[END_REF] framework revisited in a plant ecology perspective. Morpho-physio-phenological (M-P-P) traits (from 1 to k) modulate one or all three performance traits (vegetative biomass, reproductive output and plant survival) which determine plant performance and, in fine, its individual fitness. M-P-P traits may be inter-related (dashed double-arrows). For clarity, interrelations among performance traits and feedbacks between performance and M-P-P traits are not represented [START_REF] Violle | Let the concept of trait be functional![END_REF].

SPEED OF ADAPTATION OF FOREST TREES TO CLIMATE CHANGE

Cycles of glacial and interglacial periods occurred on the earth's surface for at least the past 2.7 million years, with glacial periods being characterized by a dry and cold climate. The previous period lasted 100 ka and ended 10 ka ago. The study of the pollen that was captured in sedimentary layers (palynology) highlights how plant species survived the previous glacial era and how they recolonize the land once it was free of ice in Africa, Australia, Europe and North America [START_REF] Adams | Preliminary vegetation maps of the world since the last glacial maximum: An aid to archaeological understanding[END_REF].

These palynological surveys showed that hundreds of square kilometers that are now covered by forest in the Northern Hemisphere were covered by a large ice sheet. The biomes shifted across large distances, resulting in drastic modifications in ecosystem composition and connections, with many populations becoming isolated because of land and sea barriers. The primary forest tree genera are 15 to 60 million years old [START_REF] Collinson | Vegetational and floristic changes around the Eocene/Oligocene boundary in western and Central Europe[END_REF][START_REF] Denk | The evolutionary history of Fagus in western Eurasia: Evidence from genes, morphology and the fossil record[END_REF]. Palynological surveys indicate that combining pollen data with charcoal and genetic data can be used to estimate the speed of latitudinal recolonization to approximately 100 m annually [START_REF] Aitken | Adaptation, migration or extirpation: climate change outcomes for tree populations[END_REF]. A body of studies has indicated that past climate changes were quite slow.

In contrast, the principal characteristic of the current climate change is its velocity [START_REF] Loarie | The velocity of climate change[END_REF].

According the IPCC (2007 report, Figure 1), "the intensity (variation / unit of time) observed and expected has never been reached since the last post-glacial warming". For instance, near-surface temperature is expected to shift at mean rates of 80-430 m yr -1 during the 21 st century for tropical, Mediterranean, temperate and boreal forests [START_REF] Loarie | The velocity of climate change[END_REF], leading to shifts of trees bioclimatic envelopes from 300 to 800 km within one century. Besides, CC predictions (IPPC, [START_REF] Alcamo | Future long-term changes in global water resources driven by socio-economic and climatic changes[END_REF] suggest not only notable changes in near-surface temperature, but also in precipitation and in the frequency and intensity of heat waves, forest fires and pest outbreaks. The effects of the high velocity of climate change (CC) are reinforced by the small size of protected areas in Mediterranean and in some temperate forests, [START_REF] Loarie | The velocity of climate change[END_REF]) stressing the need to account for other components of GC (particularly land-use) when forecasting forest responses.

Forests are keystone species of terrestrial natural ecosystems. Forests are not a mere combination of certain woody species; rather, each tree is the habitat for numerous plants, animal and fungus species. This is particularly true for natural forests, which are generally highly heterogeneous and offer a broad range of ecological niches. Forests host as many as 80% of the world species and are a great biodiversity reservoir.

One challenge is to understand whether forest trees can respond to the current rapid climate change. An ongoing debate that divides scientists can be approximately classified in three categories. First, certain optimistic scientists argue that trees populations may migrate to follow their respective climatic niche (e.g., [START_REF] Pauli | Signals of range expansions and contractions of vascular plants in the high Alps: observations (1994-2004) at the GLORIA*master site Schrankogel, Tyrol, Austria[END_REF][START_REF] Parmesan | A globally coherent fingerprint of climate change impacts across natural systems[END_REF][START_REF] Penuelas | A global change-induced biome shift in the Montseny mountains (NE Spain)[END_REF][START_REF] Walther | Weakening of climatic constraints with global warming and its consequences for evergreen broad-leaved species[END_REF].

Another category of optimistic scientists are convinced that trees populations have high levels of adaptive genetic diversity, which will confer them with a great adaptive capacity in face of climate change (e.g., Hamrick 2004, Petit and[START_REF] Petit | Some evolutionary consequences of being a tree[END_REF]. For yet others, climate change is occurring to rapidly to allow for tree populations to survive either through adaption or migration (e.g., Huges 2000, [START_REF] Davis | Range shifts and adaptive responses to Quaternary climate change[END_REF][START_REF] Thomas | Extinction risk from climate change[END_REF]).

OVERVIEW OF THE CONCEPT OF LOCAL ADAPTATION

Adaptation was first defined as the process by which a novel character (or trait) is maintained by natural selection [START_REF] Gould | Spandrels of San-marco and the Panglossian Paradigm -A Critique of the Adaptationist Program[END_REF]. When individuals exhibiting the new discrete character survive and reproduce better than individuals that lack the new character, the new character increases in frequency in the population in next generation. This ability of an individual organism to transmit its genes more or less efficiently to the next generation defines the fitness. The etymology of adaptation suggests a change "ad" toward a higher aptitude "aptus". This notion of highest aptitude wrongly leads to the notion of evolutionary progress, with individuals/species becoming more complex. Another abuse described by the term adaptationism is the one consisting in incautiously considering a given trait as "functional" or "adaptive" (see Box 1 and Erreur : source de la référence non trouvée) is. An example is provided by the debate regarding leaf retention during winter by certain individuals of common oak (Quercus robur) and beech (Fagus sylvativa). [START_REF] Otto | Why Do Beech and Oak Trees Retain Leaves Until Spring[END_REF] proposed that the retention of dry leaves during winter is an adaptive trait in the sense that it avoids the nutrients being flushed away during winter and allows for the litter to be enriched immediately prior to spring. However, considering this trait in the light of the phylogeny of Fagaceae family, [START_REF] Wanntorp | Historical Constraints In Adaptation Theory -Traits and Non-traits[END_REF] noted that leaf retention is an ancestral character. Of over 1000 Fagaceae species, the majority are evergreen. Moreover, in oaks, a spectrum of leaf shedding habits can be observed, from long persistent to completely deciduous; these different habits are correlated with climate. Evergreen species are primarily located in tropical and Mediterranean regions, semideciduous species are found in intermediate habitats, and deciduous species reach north of Europe. As a consequence, specialization would not be leaf retention but, a contrario, deciduousness.

Since Darwin, adaptation in response to natural selection has been acknowledged to be a keystone concept in evolutionary biology, explaining how individuals suit their environment. Adaptation shapes the tree of life, but it is essentially occurring at local scale. As the forces of natural selection often vary through space, populations of the same species that are distributed within a heterogeneous environment are expected to evolve traits that increase individual fitness in a given habitat. This pattern and the process leading to it is local adaptation (Kawecki and Ebert 2004). In natural populations, it is generally difficult to assess the action of selection and to verify the adaptive advantages of a trait, which requires demonstrating that the trait under consideration is variable, heritable, and correlated to fitness [START_REF] Endler | The newer synthesis? Some conceptual problems in evolutionary biology[END_REF].

Considering two populations A and B in distinct environmental conditions in which the optimum value Z OPT of the trait (the value conferring the highest fitness) differs among both populations (with the two respective values Z OPT-A and Z OPT-B ), we expect divergent selection among habitat A and B to shift the distribution of the trait such that the distribution is ideally centered on the optimum value in each habitat (Kawecki and Ebert 2004). The establishment and maintenance of local adaptation is a balance between conflicting evolutionary forces [START_REF] Yeaman | Establishment and maintenance of adaptive genetic divergence under migration, selection, and drift[END_REF]. Kawecki and Ebert (2004) stated the conditions in which local adaptation may occur: first, gene flow must be sufficiently high as to prevent isolation by distance but not too high to impede specialization; second, selection must be strong against maladaptive genotypes and moderate against intermediate genotypes; third, the temporal variation of selection forces must be very low; fourth, adaptive plasticity must not be favored.

MAIN APPROACHES BY WHICH TO DEMONSTRATE LOCAL ADAPTION

The primary approaches by which patterns of local adaptation in populations can be demonstrated are based on the analysis of either the phenotypic traits or molecular markers. Each approach has its advantage and disadvantages (Hansen et al. 2012). With respect to the traits-based approach, the primary difficulty is separating the portion of the variation that is under genetic control from that which results from environmental variation. A classical approach by which to assess local adaptation is to compare, through reciprocal transplant experiments, the relative fitness of the local vs. foreign genotypes within each habitat (Figure 3). Fitness must be ideally evaluated through lifetime experiments by measuring the contribution of each genotype to the successive generation.

Alternatively, proxies of the genotype contribution to the next generation (fitness) may be used, such as vegetative biomass, reproductive output, and plant survival. The population growth rate proxy works quite well for small and short-lived organisms, such as bacteria [START_REF] Belotte | An experimental test of local adaptation in soil bacteria[END_REF]. In long-lived species, such as trees, the most common approach consists of investigating a performance trait, such as survival, fecundity, or growth, and considering it as representative of fitness (Rehfedlt et al. 2001Savolainen et al 2007). The principal drawback of this approach is the limited knowledge of the relationship between the investigated performance trait and fitness. Moreover, directional selection may not act uniformly on the trait through the different stages of the individual's lifetime. The resulting fitness depends of different traits each being under differential selection directions and strengths at different life stages. Moreover, reciprocal transplant experiments are occasionally not possible for legal or ethical reasons and frequently require unwieldy protocols that are hardly compatible with usual scientific project scales, particularly for long life cycle species.

23

Figure 3. Hypothetical patterns of deme × habitat interaction for fitness. Squares: the average of demes originating from habitat 1; circles: the average of demes originating from habitat 2. The patterns in panels (a) and (b) satisfy the 'local vs. foreign' criterion. The patterns in panels (a) and (c) satisfy the 'home vs. away' criterion. The average difference between the 'sympatric' and 'allopatric' deme-habitat combinations is the same in all graphs Kawecki and Ebert (2004).

With respect to marker-based approaches to investigate local adaptation , they most often rely on indirect approaches, i.e., on the patterns of genetic differentiation or in the distribution of allelic variants that result from selection. In the case of divergent selection considered here, local adaptation should result in genetic differentiation of the trait that affects fitness between populations A and B. Note that other type of selection are possible: for instance in the case of balancing selection, the trait is selected towards the same optimal value in the different environments (i.e., Z OPT-A = Z OPT-B ).

Under divergent selection, genomic regions that contain loci coding for the trait (QTLs, Quantitative Trait Loci) tend to diverge more rapidly than expected under neutrality. This locus-specific process lead to an increase in the frequency of the favorable allele and, to a lesser extent, an increase in the frequency of neutral alleles that are linked with the favorable allele [START_REF] Smith | The hitch-hiking effect of a favourable gene[END_REF].

This process, also referred to as "hitch-hiking selection", leads to a drastic reduction in polymorphisms in large genomic regions and to genetic differentiation between the two populations. Subsequently, divergent selection continuously contributes to allelic fixation and to a decrease in nucleotide diversity within each population. If there is no selection, the average time for one locus to become fixed through genetic drift is 4Ne, where Ne is the effective population size. However, selection may also contribute to maintain allelic diversity via overdominance (the heterozygote individual has higher fitness), heterogeneous selection (the genotypes favored are regularly different) or frequency-dependent selection (the rarest genotypes have high fitness). A selection process that maintains alleles at intermediate frequencies more than would be expected under neutrality is referred to as balanced selection. However, balanced selection may be confounded with an incomplete form of divergent selection. This can occur in two ways. The first case is when one allele is under divergent selection, and the second is continuously brought into the population via migration. In the second case, hitch-hiking is interrupted when the allele is at intermediary frequency [START_REF] Hedrick | Balancing selection[END_REF].

Approaches to searching for signs of selection among populations

Following the original hypothesis of [START_REF] Cavalli-Sforza | Population structure and human evolution[END_REF], [START_REF] Lewontin | Distribution of Gene Frequency as a Test of the Theory of the Selective Neutrality of Polymorphisms[END_REF] proposed to compare levels of genetic diversity and differentiation between populations. This proposal was made given that loci under directional selection should exhibit larger differences between populations than neutrally evolving loci. Similarly, loci under balancing selection should exhibit less divergence than do neutrally evolving loci. Outlier approaches generally use population differentiation as measured by Wrigtht's FST. Loci with an unusually high or low FST values are generally considered to potentially be under the effect of divergent selection and are referred to as "outliers". Outliers may be either directly or indirectly under selection through linkage. The majority of the recent outliers approaches based on FST scans aimed at to accounting for the high expected variance in FST due to the correlation of allelic frequencies among demes that result from common demographic history, likely to make the original approach of Lewontin and Krakauer (1974) unreliable. Among these developments, the frequentist approach of Beaumont and Nichols (1996) relies on coalescent simulation of the expected neutral distribution of FST assuming an infinite island model of population structure ; this analysis is conditioned on heterozygosity. This approach was shown reported to be remarkably robust to a wide range of alternative models of population structure (e.g., colonizsation, stepping stone).

To avoid the bias that is induced by the demographic and selective history specific to each sampled population, [START_REF] Vitalis | Interpretation of variation across marker loci as evidence of selection[END_REF] proposed to focus on pairwise population comparison rather the whole meta-population given that the basic assumptions regarding population structure and history cannot generally be verified. Therefore, they defined new population-specific parameters of population divergence and constructed sample statistics that are estimators of these parameters. One advantage of this method would be the detection of a signature of selection that was specific to one population in cases when the signature would be too weak to be detected when the entire population is considered.

However, this method is not appropriated for all type of loci, in particular for SNPs 1 [START_REF] Eveno | Contrasting patterns of selection at Pinus pinaster Ait. drought stress candidate genes as revealed by genetic differentiation analyses[END_REF]).

Equal migration rates between populations is a strong assumption of the approach that was developed by Beaumont and Nichols (1996). To minimize this constraint, Foll and Gaggiotti (2008), following the model proposed by (Beaumont and Balding 2004), developed a model based on the decomposition of the logit value of FST into i) a locus-specific effect that is shared by all populations and ii) a population-specific effect that is shared by all of the loci. For each loci, posterior probabilities are calculated for the model, with selection being included in or excluded from the model.

1 Single Nucleotide Polymorphisms 3.2. Approaches for searching for the signal of selection within a population

The core of the neutrality test is to compare a descriptive statistic that is computed from the data to the same statistic as computed based on the neutral model. These approaches all are differentially sensitive to different parameters, notably to the time since the selection event occurred, the population structure and the demographic history. An overview of these tests is presented in the Table 1. In the early 2000s, the neutral theory began to be widely used as a "null model" for so-called null hypothesis testing. The core assumption of the standard neutral model is a constant population size, a hypothesis that is likely to be violated in many natural populations. Nevertheless, the first genomics studies that investigated patterns of genetic diversity or differentiation for candidate genes frequently interpreted deviations to the neutral model as evidence of natural selection [START_REF] Wright | Molecular population genetics and the search for adaptive evolution in plants[END_REF].

Population expansion may induce a loss of genetic diversity and sub-population differentiation [START_REF] Wegmann | Molecular diversity after a range expansion in heterogeneous environments[END_REF][START_REF] Slatkin | Serial Founder Effects During Range Expansion: A Spatial Analog of Genetic Drift[END_REF]. Moreover, certain alleles may reach high frequencies due to of successive founder events [START_REF] Edmonds | Mutations arising in the wave front of an expanding population[END_REF]) and genetic surfing [START_REF] Excoffier | Surfing during population expansions promotes genetic revolutions and structuration[END_REF] that mimic the characteristics of a favorable allele under selection. Consequently, a cline in allele frequently that matches a geographic variation, although frequently believed to be a result of selection, may in fact be due to stochastic processes during range expansion [START_REF] Slatkin | Serial Founder Effects During Range Expansion: A Spatial Analog of Genetic Drift[END_REF] Population bottlenecks lead to a random genetic sampling and have a greater effect on allelic richness than nucleotide diversity (Nei et al. 1975;[START_REF] Dlugosch | Founding events in species invasions: genetic variation, adaptive evolution, and the role of multiple introductions[END_REF]. The consequence of sampling is both immediate and occurs throughout the first successive generations, when the likelihood of losing an allele by genetic drift is higher. [START_REF] Santos | From nature to the laboratory: the impact of founder effects on adaptation[END_REF] demonstrated experimentally to what degree both the standing variability following the bottleneck and the stochastic changes during the first generations are important parameters in determining the evolutionary potential in Drosophila subobscura.

Populations with the same genetic background that independently experienced a bottleneck and subsequent genetic drift developed contrasting adaptation responses to the novel environment.

Through diverse strategies, several recent studies attempted to integrate the confounding effects of demography to provide more realistic null models [START_REF] Nielsen | Molecular signatures of natural selection[END_REF][START_REF] Thornton | Controlling the false-positive rate in multilocus genome scans for selection[END_REF][START_REF] Siol | The population genomics of plant adaptation[END_REF]. The basic assumption behind this approach is that demography affects the entire genome, whereas selection acts on specific loci. Therefore, one may generate a distribution of the statistics of interest that is congruent with the observed data and use it as a null model (Figure 1). Early coalescent methods (backward through time) allowed for such approaches (Hudson 2002). Computer capabilities now allow for the simulation of entire populations forward in time (Hernandez 2008). Moreover, likelihood-based approaches can be performed using Markov chain Monte Carlo methods (Beaumont and Rannala 2004;[START_REF] Csillery | Approximate Bayesian Computation (ABC) in practice[END_REF].

LATITUDINAL VERSUS ALTITUDINAL GRADIENT AS MODELS FOR INVESTIGATING LOCAL ADAPTATION

The establishment and maintenance of genetic polymorphism along continuously varying environments have been well studied theoretically using models of clinal variation [START_REF] Slatkin | Gene flow and selection in a cline[END_REF][START_REF] Slatkin | Genetic Drift In A Cline[END_REF][START_REF] Barton | Clines in polygenic traits[END_REF][START_REF] Vasemägi | The adaptive hypothesis of clinal variation revisited: single-locus clines as a result of spatially restricted gene flow[END_REF]). In natural conditions, continuous patterns of differentiation are often observed along environmental gradients. These patterns were first observed in soil that was contaminated by heavy metal [START_REF] Antonovics | Heavy Metal Tolerance in Plants[END_REF]. Such clines were then reported for phenological traits [START_REF] Howe | From genotype to phenotype: unraveling the complexities of cold adaptation in forest trees[END_REF]Savolainen et al. 2007) along latitudinal gradients. For certain trees, populations from northern latitudes flush earlier than do southern populations, such as Pinus sylvestris in Scandinavia [START_REF] Giertych | Provenance, progeny and individual selection in factorial experiments with Norway spruce ( Picea abies)[END_REF] and Picea sitchensis in North America [START_REF] Mimura | Adaptive gradients and isolation-by-distance with postglacial migration in Picea sitchensis[END_REF] and Fagus sylvatica in Europe (von Wuehlish, 1995;[START_REF] Gomory | Trade-off between height growth and spring flushing in common beech (Fagus sylvatica L.)[END_REF]. In contrast,

Quercus petraea [START_REF] Liepe | Growth-chamber trial on frost hardiness and field trial on flushing of sessile oak (Quercus petraea Liebl)[END_REF]Deans & Harvey, 1995;[START_REF] Ducousso | Latitudinal and altitudinal variation of bud burst in western populations of sessile oak (Quercus petraea (Matt) Liebl)[END_REF] and Betulus pubescens [START_REF] Worrell | Variation among seed sources of silver birch in Scotland[END_REF], southern populations flush earlier than do northern populations.

Despite the pronounced advantages of population comparisons over wide scales, altitudinal gradients are ideal situations in which to examine local adaptation with a minimum of confounding biogeographic, historic and genetic isolation influences. Along the altitudinal gradient, climatic and edaphic properties change rapidly, whereas the populations from different habitats are genetically well connected. Evidence for local adaptation has been reported for several plant species, e.g., in Silene ciliata [START_REF] Gimenez-Benavides | Local adaptation enhances seedling recruitment along an altitudinal gradient in a high mountain Mediterranean plant[END_REF]), Poa hiemata [START_REF] Byars | Local adaptation and cogradient selection in the alpine plant, Poa hiemata, along a narrow altitudinal gradient[END_REF] and Festuca eskia (Gonzalo-Turpin and Hazard 2009), as well as among the Fagaceae family, in Fagus grandifolia [START_REF] Ledig | Adaptation of Sugar Maple Populations Along Altitudinal Gradients -Photosynthesis, Respiration, and Specific Leaf Weight[END_REF]. Similar evidence has also been observed for Quercus species, for which populations from higher altitudes flushed later than those from lower altitudes do (Vitasse et al. 2009a).

Moreover, an altitudinal gradient affords the trade of space for time, this is a classical hypothesis in ecology. The space-for-time hypothesis considers that the spatial shift in climatic conditions along a gradient can be equivalent to the temporal shift that each local population will face under changing climate conditions. The major phenotypic traits or genes of major effects that are determined to contribute to the spatial genetic variation among populations may then be used as candidate traits/markers for monitoring over time (Jump et al. 2006;[START_REF] Jump | The altitude-for-latitude disparity in the range retractions of woody species[END_REF]).

OBJECTIVES OF THE THESIS

The general objective of this PhD thesis was to assess the adaptive genetic response of tree populations to spatial climatic variation at local scale. In a first part of this PhD thesis, I used an experimental approach in F. sylvatica populations on Mont Ventoux for which evidence of local adaptation along an altitudinal gradient were reported [START_REF] Gauzëre | Effet de la dispersion du pollen à longue distance sur les capacités d'adaptation de populations de hêtre commun sur un gradient altitudinal[END_REF]. The objective of this experimental study was to identify signatures at the molecular level of recent selection in populations that are connected by high gene flow. The second objective of this PhD thesis was to evaluate the power of the methods that were used to detect recent selection at local spatial scale. For that purpose, I used a simulation approach relying on individual-based and mechanistic model.

The specific aims of each chapter are as follows:

Chapter 1: To describe the principal characteristics of the species (Fagus sylvatica), the site (the Mont Ventoux), and the particular history of Fagus sylvatica on Mont Ventoux.

Chapter 2: To characterize patterns of nucleotide diversity and linkage disequilibrium in Fagus sylvatica. This work is on the point to be submitted to the journal Tree, Genes and Genomes. 1. FAGUS SYLVATICA

Phylogenetic origin

Fagaceae is a small genus of Fagales (family Fagaceae) that consists of two morphologically and genetically distinct subgenera (Fagus and Engleriana). The genus contains 10 species, 1 American (Fagus grandifolia Ehrh) and 9 Eurasian. Members of the subgenus Engleriana descended from a common ancestor and form a well-supported clade2 , whereas relationships between members of the subgenus Fagus are more complex (Figure 1.1). Although the root cannot be determined with confidence, the genus can be considered to be monophyletic (sensu Haeckel, 1866), and a common origin of all of the members is assumed [START_REF] Denk | Patterns of molecular and morphological differentiation in Fagus (Fagaceae): Phylogenetic implications[END_REF]). [START_REF] Nilsson | Ecological and Evolutionary Interactions Between Reproduction of Beech Fagussilvatica and Seed Eating Animals[END_REF].

An origin of

Beech grows on well drained, moderately deep to deep soils that range from sub-alkaline to acidic. It does not grow on rocky or dry sites or in soils with stagnant water or those that are regularly flooded.

On favorable sites, beech out-competes other tree species due to its efficient use of light. Once beech becomes dominant species, low light levels in the understory allow for beech seedlings to survive better than other species. Beech is then considered to be a late succession species that out-competes pioneer species. Whereas beech is predominantly found in the humid lowland of northern Europe, beech stands are found in particular locations in the Mediterranean region. Precipitation is not an effective estimator of beech distribution. In southern climates, where beech occurs at high altitudes, fog appears to compensate for the lack of precipitation. On mountain slopes, beech distribution coincides with areas that are frequently foggy (Teissier du Cros 1981). Drought is a limiting factor for low-altitude population distributions; more precisely, drought duration is more limiting than drought intensity.

Beech wood is much-appreciated worldwide, and many beech forests are regularly harvested for timber production. Apart of its economic importance, European beech has also a major role in soil preservation and water cycles, making this species a target of ecologically oriented conservation programs.

1 Also called mast seeding or mass seeding, it is the intermittent production of large seed crops by a population of plants (Kelly 1994).

Bud burst phenology

Phenology has well-demonstrated links to climate from the level of genetics to landscapes [START_REF] Pau | Predicting phenology by integrating ecology, evolution and climate science[END_REF]. Phenology has been demonstrated to be a major determinant of plant species distribution [START_REF] Chuine | Phenology is a major determinant of tree species range[END_REF] and to be related to individual fitness [START_REF] Rathcke | Phenological Patterns of Terrestrial Plants[END_REF], making this a relevant trait that is related to adaptation. A mismatch between the life cycle and seasonal climate fluctuations may result in frost injuries to leaves or flowers, drought injuries if severe drought occurs during the growing season, and failure in the production of mature fruits and seeds if autumnal frosts occur too early. In this work, I focused on leaf phenology and in particular the bud burst given that this is a key factor in determining the duration of the growing season for deciduous species (Vitasse et al. 2009). Bud burst is regulated by temperature during winter, i.e., the chilling temperatures that break winter dormancy. The date of flushing results from a trade-off between increasing the vegetation length and minimizing the risk of damage by either late spring or early autumn frosts [START_REF] Kramer | The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: an overview[END_REF].

The bud-burst can be described with five stages (Figure 1.4). The scores, from 1 to 5, are summed at each date of measurement to calculate a phenology score sum (PSS) for each tree. The higher the PSS at a given date of measurement, the earlier and more rapid is the process of leaf unfolding for the examined tree.

41 1.5. The genome of F. sylvatica

As for numerous non-model species, little is known of the genomes characteristics of F. sylvatica. The number of chromosomes is 2n=24 [START_REF] Ohri | Giemsa C-banding In Fagus-sylvatica L, Betula-pendula-roth and Populus-tremula L[END_REF]. According to [START_REF] Gallois | Evaluation of the nuclear DNA content and GC percent in four varieties of Fagus sylvatica L[END_REF], the 2C nuclear DNA content is 1.11 pg (± 0.02) or 1070 Mbp. This analysis revealed that F. sylvatica genome size is at the lower range of known 2C genome sizes of higher plants. The GC percent is 40% (± 0.2), which is a typical value for higher plants [START_REF] Gallois | Evaluation of the nuclear DNA content and GC percent in four varieties of Fagus sylvatica L[END_REF]). The chloroplastic DNA length is the shortest of the Fagaceae, with 161,226 bp [START_REF] Kremer | Genomics of Fagaceae[END_REF].

Several studies of geographical variation in Fagus sylvatica have been performed using universal cpDNA markers. Variation is extraordinary low north of the Alps, as only one haplotype is common [START_REF] Demesure | Chloroplast DNA phylogeography of the common beech (Fagus sylvatica L) in Europe[END_REF], Magri et al. 2006). The cpDNA variation is much higher in the Mediterranean areas [START_REF] Vettori | Geographic distribution of chloroplast variation in Italian populations of beech (Fagus sylvatica L.)[END_REF], where a high proportion of the genetic variation is distributed among populations. Highly divergent haplotypes were also observed in the Balkan region, especially in Greece, (Magri et al. 2006[START_REF] Hatziskakis | High chloroplast haplotype diversity in Greek populations of beech (Fagus sylvatica L.)[END_REF]) and in Asia Minor (Turkey) [START_REF] Gailing | Nuclear markers (AFLPs) and chloroplast Microsatellites differ between Fagus sylvatica and F-orientalis[END_REF], where the major proportion of the genetic variation was detected within populations in specific regions. Magri et al (2006) used a very large set of complementary paleobotanical and genetic data to elucidate the genetic consequences of long-term survival in refuge areas and postglacial spread. Their analysis was performed on 450 and 600 beech populations for cpDNA markers (RFLPs and SSRs) and nuclear markers (isozymes) respectively, covering the entire F. sylvatica range in Europe. The cpDNA analysis verified the extraordinary low genetic variation north of the Alps, whereas region-specific patterns occur throughout the Mediterranean range [START_REF] Hatziskakis | High chloroplast haplotype diversity in Greek populations of beech (Fagus sylvatica L.)[END_REF], Magri et al. 2006[START_REF] Vettori | Geographic distribution of chloroplast variation in Italian populations of beech (Fagus sylvatica L.)[END_REF]).

AFLP markers were used to examine differences between Fagus sylvatica and F. orientalis [START_REF] Gailing | Nuclear markers (AFLPs) and chloroplast Microsatellites differ between Fagus sylvatica and F-orientalis[END_REF]) to investigate the spatial genetic structure of European beech and the SGS variations that are observed with different genetic markers (Jump et al. 2012). Jump and Peñuelas (2007) revealed an extensive spatial genetic structure in beech populations using AFLP markers that were not observed with microsatellite markers. A combination of different marker types, including AFLP and microsatellites, was implemented to construct a genetic linkage map for European beech [START_REF] Scalfi | A RAPD, AFLP and SSR linkage map, and QTL analysis in european beech (Fagus sylvatica L.)[END_REF]. Jump et al. (2006) also used AFLP markers to examine the adaptation of European beech to temperature changes in the context of the hypothesized climate change in Europe.

The distribution of chloroplast microsatellites revealed the presence of a single haplotype that is widespread in Europe north of the Alps, a typical Italian haplotype and certain highly divergent haplotypes in the southern part of the Balkan Peninsula and Greece [START_REF] Hatziskakis | High chloroplast haplotype diversity in Greek populations of beech (Fagus sylvatica L.)[END_REF], Magri et al. 2006[START_REF] Papageorgiou | Genetic variation of beech (Fagus sylvatica L.) in Rodopi (NE Greece)[END_REF]. As expected given their biparental inheritance, isozymes display a less pronounced level of genetic structure but a more complex pattern than cpDNA throughout the different bio-regions. The combination of isozymes and cpDNA allows for the detection of groups of populations that were undetected when only chloroplast markers were used on a European scale and for the description of the possible migration processes of this species in Europe (Magri et al. 2006).

MONT VENTOUX

Mont Ventoux geologically belongs to the Alps, although it is often considered to be separated because of the lack of mountains of similar height nearby. During the late Cretaceous, the Pyrenean orogenic process originated an overturned anticline 2 toward the north. This explains why the north slope is steep on average (25° ± 9), whereas the south is characterized by a gentle slope (12° ± 8). For the previous Ma during the Quaternary, the succession of glaciations gave to the mountain its current features through the action of frost weathering and the formations of numerous scree deposits. The mountain is 1909 m high, 24 km long on the west-east axis and 15 km long on the north-south axis. The soil is primarily calcareous (Du Merle and Guende 1978) and poor in organic subtract, which is common in the Mediterranean environment but in this specific case is also due to excessive land use during the previous centuries. The top of the mountain is covered with bare limestone without vegetation or trees.

The climate characteristics are the same as in the Southern Alps, being typically Mediterranean at the base; this gives way to a temperate climate and then continental with highland climate influences at lower altitudes. The precipitation regimes vary across the seasons, with severe drought during the summer. On the north slope, the temperature decreases with the altitude, with a rate of approximately 0.6°C per 100 m (Figure 1.5a), while precipitation increases (Figure 1.5b). The number of spring frost increases with the altitude (Figure 1.5a). Such events may damage buds and young leaves that began their spring growth and then affect tree growth during the successive months.

1 Deformation of the Earth's lithosphere due to the engagement of tectonic plates 2 A fold that is upwardly convex, with its oldest beds at its core

THE HISTORY OF FAGUS SYLVATICA ON MONT VENTOUX

Before human presence, the tree vegetation of the Mont Ventoux was most likely a combination of dominating beech and silver fir with scattered broadleaves in open areas, specifically, mountain ash (Sorbus aucuparia), white beam (Sorbus aria), and Italian maple (Acer opalus). Progressively, between the 13th and 20th century, humans harvested the wood, and the forest was replaced by grazing, except in steep hard-to-reach spots. Deforestation culminated in the second half of the 19th century. A survey in 1838 estimated that that was approximately 9500 ha of beech in the Mont Ventoux, whereas the following survey in 1876 reported only 2276 ha (Lander et al. 2011). This resulted in severe floods along the valleys around the mountain. In 1861, a program of reforestation was begun (Jean 2008).

First, mountain pine (Pinus uncinata), Norway spruce (Picea abies) and larch (Larix decidua) were used. One century later, in the resulting stands, timber was harvested. Meanwhile, a small number of beech and silver fir remnants from the original stands began to colonize artificial stands and reconquer their environment. Beech is now distributed between altitudes of 750 m to 1695 m.

In addition to historical data (see (approximately 1000 years), and the East and West populations diverging at t1. The analysis also identified a recent expansion from a smaller ancestral population that occurred 3 generations ago but did not reveal a past bottleneck. Microsatellites have a high mutation rate, ranging from 10 -2 to 10 -6 nucleotides per locus per generation [START_REF] Sia | Analysis of microsatellite mutations in the mitochondrial DNA of Saccharomyces cerevisiae[END_REF], potentially explaining the fact that only recent events are detectable at short time scales. The bottleneck may also have not been sufficiently strong or have lasted sufficiently long to alter the genetic diversity of microsatellite markers. Moreover, the genetic signal of a bottleneck may be detectable after an intermediate number of generations have past [START_REF] Estoup | Reconstructing routes of invasion using genetic data: why, how and so what?[END_REF].

CONTEXT

The first chapter of this PhD thesis addresses the selection of putative candidate genes that are involved in the physiological response to climate in the non-model species F. sylvatica. At the beginning of my PhD, no such candidate genes had been described for this species. The only genomic resources available in F. sylvatica were three ESTs banks that were developed by INRA Pierroton in the framework of the EVOLTREE network (http://www.evoltree.eu/index.php/elab-start/elab-est-finder).

The first goal was to detect SNPs from the ESTs banks and to design primers for genotyping. However, there was a high risk of detecting false SNPs if the ESTs were poorly aligned, as in the case when conserved parts of different genes were aligned or low-complexity regions were aligned1 . Moreover, the ESTs banks were developed from 3 genotypes, which is a very low number for capturing the nucleotide diversity. Indeed, in silico detection of SNPs from EST banks in non-model species was demonstrated not be the optimum approach in experiments involving samples from narrow genetic backgrounds [START_REF] Eckert | High-throughput genotyping and mapping of single nucleotide polymorphisms in loblolly pine (Pinus taeda L.)[END_REF][START_REF] Lepoittevin | In vitro vs in silico detected SNPs for the development of a genotyping array: what can we learn from a non-model species?[END_REF]. Moreover, the presence of introns is known to cause a high percentage of in silico SNP genotyping failures [START_REF] Wang | Quality assessment parameters for EST-derived SNPs from catfish[END_REF]).

Instead, it was decided to first select putative candidate genes that are involved in the physiological response to climate in the EST banks, to design primers for sequencing these genes in F.sylvatica, and to sequence these genes in a panel of 96 individuals for SNPs discovery. With this second approach, no prior knowledge was required regarding the diversity within the sequences. In addition this approach allowed for all of the polymorphisms to be captured, including insertion-deletions (INDELs) and lowfrequency mutations. The primary drawback of this approach was its reliance on an a priori hypothesis regarding the biological function of the selected candidate genes; this assumption necessarily resulted to the risk of arbitrariness.

Targeted candidate genes were defined as genes that are potentially involved in physiological processes that depend on climatic variables, such as temperature and air humidity. Two groups of candidate genes were selected, one involved in the date of budburst (hereafter, phenology genes) and a second involved in response to abiotic stress (hereafter, stress genes). For the phenology genes, the present study benefited from previous development of candidate genes for phenology in Quercus petraea (Derory et al. 2006[START_REF] Derory | Contrasting relations between diversity of candidate genes and variation of bud burst in natural and segregating populations of European oaks[END_REF]. The selection of candidate genes for the response to abiotic stress was more complex, relying on i) candidate genes that were available from Quercus petraea and robur (A. Kremer and P.

Garnier-Géré, personal communication), ii) sequences of three proteins that are potentially involved in cavitation resistance in F. sylvatica (S. Herbette, personal communication), and iii) published studies of genes expression profiles. Such studies typically measures the levels of proteins that are expressed in different environmental conditions or between two different stages of development of the plant. To identify the genes of the metabolic pathways that is involved in the response to a given stimuli. For example, to reveal genes that are involved in the response to drought, the expression profile from a plant grown under water-stress would be compared to a plant grown without water-stress. The genes observed to be activated or more highly expressed in drought conditions provide candidate genes for response to drought. The primary limitation of expression studies is that they generally report only the protein sequence, and not the DNA sequence, of the gene coding for the protein. Moreover, with such an approach, the candidate genes selected for further population genomic approach are only the genes that directly encode proteins that are involved in specific responses; the underlying assumption is that any change of the primary structure of the protein may be subjected to selection, the new allele being either advantageous or deleterious. Lastly, expressions studies are generally performed in model species (e.g., Arabidopsis taliana, Populus trichocarpa), and their results may not be transposable among species. To address this issue, I searched literature profile expression studies for cold, heat and drought stress to compile a list of potential list of genes/proteins, prioritizing the genes/proteins that were detected many times. Unfortunately, there is a high variability between the different experiments that were used in these studies; among the 750 proteins that were described in 55 studies, very few proteins are detected more than once (Figure 2.1).

Box 2 : Assembly and Alignment

Assembly:

The aligning and merging fragments of DNA sequences to reconstruct the original sequence, which is also referred to as consensus sequence.

Alignment:

The process by which several DNA, RNA or protein sequences are arranged against each other for comparison purposes and to identify regions of similarity. The alignment method greatly varies across algorithms and programs but generally relies first on identity, i.e., small stretches of sequences that are identical and or similar, which allows for sequence comparisons and the identification of base mismatch.

ABSTRACT

Fagus sylvatica is one of the most economically and ecologically important deciduous tree in Europe that is potentially threatened by climate change. Little is knows of its genomic diversity and therefore of the adaptive potential of European beech. We discovered and analyzed 573 SNPs from 56 candidate fragments of genes that are involved in abiotic-stress response and budburst phenology using a panel of 96 individuals from southeastern France. The mean nucleotide diversity (θ π χ 10 -3 = 2.2) was low, similar to a previous study of European beech, but was extremely variable for the different genes (from 0.02 to 10). The diversity also exhibited a positively skewed distribution, potentially indicating the footprint of selection. Genes with insertion/deletion mutations exhibited significantly higher diversity. The decay of linkage disequilibrium (LD) at the long gene fragments (>800 bp) was slow (half distance of 154 bp), consistent with the low average population-scaled recombination rate (ρ χ 10 -3 = 5.4) but slightly more rapid than for Quercus and most Populus species.

Two gene fragments that code for catalase exhibited no decay in LD and consistently very few effective recombination events. Our results contribute to the understanding of the demographic and selective history of a European beech population.

INTRODUCTION

European beech (Fagus sylvatica L., Fagaceae) is an economically and ecologically important latesuccessional tree species that dominates temperate forests in Europe. Regional climate models for central Europe predict hot and dry summers and an increase in extreme precipitation events (European Environmental Agency 2004), which is likely to impose strong selection pressure on F. sylvatica populations. Indeed, species distribution models (e.g., [START_REF] Meier | Cooccurrence patterns of trees along macro-climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L[END_REF]) and analysis of historical data [START_REF] Penuelas | A global change-induced biome shift in the Montseny mountains (NE Spain)[END_REF] suggest that F. sylvatica may disappear from many areas in Europe.

Different strategies (e.g., genetic association, population genetics and expressional studies) can be adopted to identify candidate genes that are potentially involved in adaptation to climate in non-model organisms [START_REF] Neale | Forest tree genomics: growing resources and applications[END_REF]Savolainen and Pyhäjärvi, 2007;[START_REF] Neale | Population, quantitative and comparative genomics of adaptation in forest trees[END_REF][START_REF] Achaz | Frequency Spectrum Neutrality Tests: One for All and All for One[END_REF]. The molecular basis of local adaptation to climate has previously been examined for several tree species using candidate genes, including certain Fagaceae species (e.g., Derory et al. 2006[START_REF] Derory | Contrasting relations between diversity of candidate genes and variation of bud burst in natural and segregating populations of European oaks[END_REF].

Although forest tree genome projects are scarce, transcriptomic data can now be readily produced in non-model species, which has facilitated the identification of candidate genes in many forest trees. In most cases, these studies targeted productive or technological traits (e.g., in Pinus radiata, [START_REF] Dillon | Allelic Variation in Cell Wall Candidate Genes Affecting Solid Wood Properties in Natural Populations and Land Races of Pinus radiata[END_REF]in Picea glauca, Beaulieu et al. 2011;in Eucalyptus urophylla, Mandrou et al. 2012); however, the number of studies devoted to adaptive traits has been increasing in recent years (e.g., in five neotropical species, [START_REF] Chevolot | Isolation of primers for candidate genes for mechano-sensing in five Neotropical tree species[END_REF]in Mediterranean pines, Grivet et al. 2011). With respect to F.

sylvatica, a recent study by [START_REF] Seifert | DNA sequence variation and development of SNP markers in beech (Fagus sylvatica L.)[END_REF] identified 10 candidate genes that are potentially involved in drought stress and bud phenology.

Candidate gene approaches for trees, however, are limited by a priori knowledge of the physiological, biochemical or functional aspects of possible candidates and may generate a biased set of SNPs. New genome-wide association mapping approaches hold great promise for trees species (e.g., [START_REF] Parchman | Genome-wide association genetics of an adaptive trait in lodgepole pine[END_REF]. Nevertheless, the detailed knowledge of the linkage disequilibrium (LD) in the population is essential. This is because LD determines the feasibility and cost of genotype-phenotype association studies. If LD decays rapidly, many markers are required. Consequently, the mapping precision is high.

In contrast, if LD decays slowly, a small number of markers can capture information regarding phenotype-genotype associations, but the mapping precision is lower. However, apart from these strictly statistical considerations, LD is influenced by many important biological processes, such as recombination, mutation, selection and demography. A great number of SNPs is required in long-lived, outcrossing trees in which recombination over many generations has broken up the LD that initially existed between adjacent markers. Consistently, several studies have reported LD that decays rapidly with distance between segregating sites e.g., in conifers (e.g., [START_REF] Heuertz | Multilocus patterns of nucleotide diversity, linkage disequilibrium and demographic history of Norway spruce [Picea abies (L.) Karst][END_REF] (Ingvarsson 2005(Ingvarsson , 2007)). This was not reported in [START_REF] Ismail | Comparative Nucleotide Diversity Across North American and European Populus Species[END_REF], a difference that is possibly because the analyzed gene sequences were too short in early poplar studies.

Here, we identify and characterize a set of 56 candidate genes that are potentially involved in a more general response to abiotic stress (e.g., drought or frosts) or phenology (e.g., budburst phenology). Our aims are i) to provide a ready-to-use and relatively large set of candidate genes that are related to adaptive traits in F. sylvatica and ii) to produce new gene-based information (e.g., the level of LD3 ) to make knowledge-based choices (e.g., between genome-wide or candidate-gene-based approaches) for further genetic studies in this species. program (http://repeatmasker.org) and the UniVec database to mask the possible contamination of vector/primer sequences. Ambiguous sequences and masked regions at the extremes of each sequence were removed using the SeqClean script (http://compbio.dfci.harvard.edu/tgi/software). The sequences that were shorter than 100 bp were discarded. The annotation was performed for each consensus sequence against GenBank's non-redundant (nr) database using the BLASTx algorithm, with a cut-off E-value < 10 -10 , a high-scoring segment pair length cut-off of 33 and a number of BLAST hits of 20.

Candidate gene selection

Two groups of candidate genes were selected, one involved in the date of budburst (hereafter, phenology genes) and a second involved in the abiotic stress response (hereafter, stress genes). The phenology genes were selected using a BLASTn between Quercus petraea sequences (Derory et al. 2006[START_REF] Derory | Contrasting relations between diversity of candidate genes and variation of bud burst in natural and segregating populations of European oaks[END_REF] and F. sylvatica consensus sequences using NCBI software (ftp.ncbi.nih.gov/blast), with a cut-off E-value < 10 -30 . Stress genes were selected from different sources. We first compiled a nonexhaustive list of proteins/genes that are involved in plant response to abiotic stress response from the literature (2009 ISI Web of Knowledge using the following keywords: "candidate gene", "drought", "heat", "cold", "frost" "protein" and "gene expression"). The protein names of published sequences matched the F. sylvatica EST annotations in 55 publications. Second, we used sequences of three proteins that were potentially involved in cavitation resistance in F. sylvatica (S. Herbette, personal communication). Third, 997 annotated amplicons sequenced from Quercus petraea and Quercus robur (A. Kremer and P. Garnier-Géré, personal communication) were added to our list. We used BLASTn between sequences in our final list and F. sylvatica consensus sequences using a cut-off E-value < 10 -30 .

Primer design and gene amplification

A new high quality alignment from the F. sylvatica EST libraries (Megablast, with cut-off E-value < 10 - 30 ) was performed between selected consensus sequences and the raw ESTs. The 2838 ESTs vectortrimming, assembly and alignment were performed using CodonCode Aligner, with parameters that were adapted for each EST assembly. Primer3 v4.0 [START_REF] Rozen | Primer3 on the WWW for general users and for biologist programmers[END_REF] was used to design PCR primers, keeping all of the parameters at default values, except for product size range (min: 450; max: 1300 bp), primer size (min: 18°C; opt: 20°C; max: 23°C), the Tm (min: 53°C; opt: 55°C; max: 57°C), maximum Tm difference (1.0°C) and minimum percentage in GC (30%). The gene fragments were amplified from diploid genomic DNA following three PCR procedures (Appendices 1 and 2).

Candidate gene sequencing

The PCR products were sequenced by the chain termination method using an ABI 3730 DNA analyzer (Life Technology). The sequences were base-called and assembled using the Phred, Phrap and Polyphred programs that are included in the SeqQual pipeline (available upon request from P. Géré-Garnier, INRA, Bordeaux, France). The bases were called only when the Phred scores were over 20 (i.e., corresponding to an error probability of 1/100). All of the alignments and chromatograms at the polymorphic sites were visually verified, and heterozygous indels were recoded as SNPs (see online

Appendix 3).

4.6. Data analysis

Phase reconstruction

We used the algorithm of [START_REF] Stephens | Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation[END_REF] implemented in PHASE 2.1 software to simultaneously infer the phase and to impute the missing genotypes for each candidate gene fragment.

We used the general model for varying recombination rate from [START_REF] Li | Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data[END_REF] (model MR0).

A separate PHASE run was performed for each gene fragment. Each run had a burn-in of 10 3 iterations followed by 10 4 iterations. The haplotypes were determined at a confidence probability of 95%. A total of 56 genes that had a minimum of two bi-nucleotidic segregating sites were analyzed using PHASE.

All of the indels for which the heterozygous state was missing and therefore not recoded as SNPs were excluded from the analysis. The initial data set contained 24.8% missing data, whereas in the imputed data had a missing genotype rate of only 12% (excluding both uncertain genotypes and phases). Only the haplotypes with a minimum frequency of 1% (i.e., at least 1 individual carrying the haplotype) were used in subsequent analysis. Haplotype inference (and the estimation of decay of linkage disequilibrium, LD) depend on the length of the sequence and the level of polymorphism. Although all of the fragments may be useful for detecting selection, here, we only present statistics for the gene fragments with lengths > 800 bp and 10 or more segregating sites (i.e., more than 45 pairwise comparisons between SNPs). Of the 56 genes, 16 met these conditions. For complete information, all of the population genetic statistics for all of the sequences are given in Appendix 4.

Nucleotide diversity, LD patterns and recombination

To describe the level and distribution of polymorphism within and among genes, the number of segregating sites (S), nucleotide diversity (θπ) and Tajima's D (Tajima, 1989) were computed using Arlequin software v.3.5.1.3 (Excoffier and Lischer 2010). The haplotype diversity (H) was based on haplotype frequencies, as estimated using PHASE [START_REF] Nei | Molecular Evolutionary Genetics[END_REF]. H is analogous to the heterozygosity at a single locus and attains its maximum (1) when the observed haplotypes in the sample occur at equal frequencies. A global F ST was computed to estimate genetic differentiation between the four population samples using Arlequin. The correlation between allele frequencies among loci (linkage disequilibrium, LD) was measured using the r 2 value, and the significance of LD was based on Fisher's exact test. H and r 2 were estimated using a function described by the authors of R 2.15.2 (R Development Core

Team, 2012). Estimates of the population-scaled recombination rate (ρ) and pairwise-estimates of the recombination rate were obtained using PHASE (see above). The LD decay with physical distance in base pairs (bp) was assessed between polymorphic sites within each gene using non-linear regression analysis of r 2 values, using the formulas of Hill and [START_REF] Weir | Statistical-analysis of Dna-sequences[END_REF] for the expected LD decay for low mutation rates. We fitted this equation using the nls function in R. As we did not identify any significant population structure (see Results), LD statistics were calculated for all of the individuals from the four sampling sites grouped together.

It is well established that LD patterns can be considerably different depending on the characteristics of the SNPs [START_REF] Pritchard | Linkage disequilibrium in humans: Models and data[END_REF]. Thus, we took advantage of our relatively large data set (both in terms of the number of gene fragments and the number of individuals sequenced) to explore the effect of the sequence length, the number of individuals and the minor allele frequency (MAF), without the aim of giving general guidelines (for which more exhaustive simulations would be necessary). We compared the decay of LD using four MAF levels (0.001, 0.01, 0.05, and 0.1). Clearly, the higher the MAF is, the more SNPs must be excluded. We also tested the effect of sample size by resampling 15 individuals 100 times at the two extreme MAF levels (0.001 and 0.1). For these simulations, we used both measures of LD (r² and D') given that they are expected to have rather different behavior regarding SNP characteristics [START_REF] Pritchard | Linkage disequilibrium in humans: Models and data[END_REF]. Moreover, given that the different MAF criteria leads to the elimination of certain SNPs, to maintain the largest sample sizes possible, we used SNPs from all of the 56 gene sequences.

RESULTS

Gene selection, amplification and sequencing

The different steps used to select the candidate genes are summarized in Figure 3. The 53 Q. petraea phenology genes that were described by Derory et al. (2006[START_REF] Derory | Contrasting relations between diversity of candidate genes and variation of bud burst in natural and segregating populations of European oaks[END_REF] were also successfully identified in the F. sylvatica EST database. Alternatively, 99 stress genes were identified in the EST database.

Among these, 51 were common to 997 Q. petraea/robur amplicons and the literature surveyed; 31 were selected from the literature only; 14 were transferred from Q. petraea/robur based on their annotation and 3 were genes that are potentially involved in cavitation (S Herbette, INRA, France, personal communication).

Among these 152 selected genes, 130 could be amplified, and 91 were successfully sequenced for an initial set of four individuals. Once these amplicons were sequenced for the full sample of 96 individuals, 33 low-quality alignments (principally due to the presence of numerous indels that produced low-quality sequences in heterozygous individuals) were identified and removed from further analysis. After careful visual validation of both the alignments and the polymorphic sites, a final set of 58 genes (20 for phenology, and 38 for stress) was considered to have been successfully sequenced.

The primer sequences for the amplification of the selected genes and the details of the PCR amplification conditions are reported in Appendices 1 and 2. For the subsequent analyses, two genes were excluded given that they contained no or only one SNP, resulting in a total of 56 genes with a minimum of two segregating sites.

63 bp. The length of the sequences varied from 410 to 1503 bp, with 29 sequences that were longer than 800 bp. Insertion/deletion mutations (indels) and microsatellites were also observed: 22 genes had indels, generally one, with a maximum four. Indels with a missing heterozygous state were not recoded for the analyses and were not considered in the diversity measures.

Nucleotide diversity (θ π χ 10 -3 ) was low, with a median of 1.86 and ranging from 0.02 to 10 (Appendix 4). The number of haplotypes ranged from 2 to 20 (median: 6). The gene fragments with indels had significantly higher nucleotide diversity (Wilcoxon rank sum test, p-value = 0.008). Haplotype diversity was relatively high (median: 0.69, ranging from 0.13 to 0.97). Haplotype diversity was positively correlated with diversity, and the two genes with the highest haplotype diversity had one of the two highest nucleotide diversities (genes 23-1 and 155-2).

Genetic differentiation among the four sampling sites was small (F ST = 0.021, ranging from 0.01 to 0.04), indicating no significant population structure.

Linkage disequilibrium (LD)

The correlation between alleles at different loci (LD) within the genes was an average of 0.27 based on 4409 pairwise comparisons. After the Fisher's exact test, ≈53% of the comparisons remained significant, and mean LD for these pairs was 0.39. We compared the decay of LD from all of the gene sequences that were longer than 800 bp and that had at least 45 pairwise comparisons (10 SNPs). We observed that LD decayed to half of its starting value within an average of 154 bp (Figure 2.3, Table 4).

However, we identified two gene sequences for which LD did not decay (genes 91-2 and 98-1). The population scaled recombination rate was highly variable among genes. The median ρ was 3.53, and its distribution was heavily skewed toward higher values. Three genes had a population-scaled recombination rate that was higher than 10 (maximum: 20.6, gene 23-1), and two genes had rates that were near to zero.

We observed that the mean LD (r²) was higher when rarer SNPs were eliminated (i.e., when MAF was higher) (Figure 2.3). Changing the MAF from 0.001 to 0.1 can increase the half distance (i.e., the distance in bp within which LD decays to the half of its value at two completely linked loci, r² -half =0.23) from less than 200 bp (192 bp with 92 diploids, and 150 bp with 15 diploids) to approximately 900 bp (868 bp with 92 diploids, and 923 bp with 15 diploids). We determined that the number of individuals was not nearly as important in the decay of LD as the MAF. In contrast, D' had the opposite different behavior: the smaller the MAF was, the higher D' was. D' was also strongly affected by the sample size (Figure 2.3). Generally, we considered that D' was much less informative for our candidate gene SNPs than r².

DISCUSSION

Forest tree species generally have high SNP frequencies (Neale andIngvarsson, 2008, Savolainen andPyhajarvi, 2007). In agreement with these earlier studies, we observed a high rate of sequence et al., 2010;[START_REF] Mosca | Contrasting patterns of nucleotide diversity for four conifers of Alpine European forests[END_REF]. Although nucleotide diversity varied greatly among the F. sylvatica gene sequences (from 0.0002 to 0.011), its maximum value was ten times lower than that observed in 800 candidate genes in four conifers [START_REF] Mosca | Contrasting patterns of nucleotide diversity for four conifers of Alpine European forests[END_REF].

We observed higher diversity in gene fragments with insertion/deletion length variations (indels) than in fragments that did not contain indels. High divergence near indels has been observed across eukaryotes and bacteria [START_REF] Tian | Single-nucleotide mutation rate increases close to insertions/deletions in eukaryotes[END_REF]. Two hypotheses for this have been suggested related to either the mutagenic properties of indels [START_REF] Tian | Single-nucleotide mutation rate increases close to insertions/deletions in eukaryotes[END_REF] or to the fact that indels are more common in regions that are less affected by background selection or hitchhiking [START_REF] Olson | Nucleotide diversity and linkage disequilibrium in balsam poplar (Populus balsamifera)[END_REF]. Thus, by excluding certain regions with length variation, we may have underestimated the overall level of nucleotide diversity in F. sylvatica [START_REF] Seifert | DNA sequence variation and development of SNP markers in beech (Fagus sylvatica L.)[END_REF].

Jaramillo-Correa et al. (2010) reported that population scale recombination rates were relatively low in various conifers, whereas in Populus tremula, ρ was at least 10 orders of magnitude higher. Using our 16 genes sequences, we estimated that the mean population-scaled recombination rate was low (5.4) in

69

F. sylvatica, which is of the same order of magnitude as for conifers. Although data for more species would be required to reach a solid conclusion, our results indicate no difference with respect to recombination rates between conifers and broadleaves.

Estimating the non-random association between alleles at two loci (LD) has been a topic of longstanding interest in evolutionary genetics given that many biological processes can participate in the shaping the LD patterns, including mutations, recombinations, selections, drifts and demography. For example, LD patterns may reflect variations in the recombination rates and the presence of recombination hot spots (e.g., [START_REF] Myers | A fine-scale map of recombination rates and hotspots across the human genome[END_REF], a recent selective sweep (e.g., [START_REF] Kim | Linkage disequilibrium as a signature of selective sweeps[END_REF], or recent population admixture (e.g., [START_REF] Falush | Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies[END_REF]. Furthermore, LD is not only a sensitive measure of demography and selection but also sensitive to purely statistical properties of a data set, such as the sample size and the minor allele frequency at SNP loci (e.g., [START_REF] Pe'er | Biases and reconciliation in estimates of linkage disequilibrium in the human genome[END_REF]).

Thus, comparing LD estimates across the literature of tree candidate genes for the purposes of drawing biologically valid conclusions is difficult because of different study designs (e.g., the size of the individual tree panel), the differences in the pre-analysis data treatment criteria (e.g., the choice of the minor allele frequency (MAF) and the sequence length), and different sampling schemes (e.g., whether population structure is present). A further problem in comparing the decay of LD across different studies (and thus, across species) is that not always the same quantities are reported (e.g., decay to x in y distance, half distance, mean r², and the minimum number of recombination events). In candidate gene studies of trees, the panel of individuals is generally small (approximately 15-20 individuals). Although sample size primarily affects D', which is rarely used for trees, small samples are often associated with the choice of a large MAF (e.g., 10%), which could lead to inflated estimates of r². This effect may have been the case e.g., in [START_REF] Olson | Nucleotide diversity and linkage disequilibrium in balsam poplar (Populus balsamifera)[END_REF], who reported extremely high LD and no decay for Populus balsamifera using a MAF of 10% from a panel of 15 individuals. In contrast, [START_REF] Ismail | Comparative Nucleotide Diversity Across North American and European Populus Species[END_REF] reported a considerable decay for Populus balsamifera using a MAF of 0.05. The effect of the number of individuals is less pronounced with r²; fewer individuals lead to the underestimation of LD. We suggest that the differences in LD patterns that have been observed for different tree species could, at least in part, be due to the above-mentioned statistical issues and thus could easily be reconciled using data standardization to give a more complete picture of the LD patterns of tree species.

For the LD patterns in our F. sylvatica population, we use a subset of 16 sequences having at least 800 bp, 10 genes and a MAF of 0.001. We observed that LD decays relatively slowly in comparison to other trees but rather rapidly in comparison to model plants, such as Arabidopsis, where the decay of LD takes thousands of base pairs (e.g., [START_REF] Wright | Testing for effects of recombination rate on nucleotide diversity in natural populations of Arabidopsis lyrata[END_REF]. We also observed great inter-gene variation in LD decay, which is consistent with other studies (e.g., [START_REF] Ismail | Comparative Nucleotide Diversity Across North American and European Populus Species[END_REF]. Notably, the two catalase coding genes that were sequenced in beech appear to have very low recombination rates (see Table 2).

Candidate gene approaches have previously been intensively used in several tree species to understand the molecular basis of local adaptation to climate [START_REF] Neale | Forest tree genomics: growing resources and applications[END_REF]). In many cases, candidate gene approaches have been preferred to whole genome scans given that forest trees have large genome sizes and exhibit a rapid LD decay, making whole genome scan approach appear inefficient. In our F. sylvatica population, as LD decays relatively slowly and the population structure appears to be low, association mapping may be a feasible alternative to candidate gene approaches to understand the molecular basis of local adaptation to environmental changes.

supported by a grant by the Italian MIUR project "Bioversitalia" (RBAP10A2T4). HL, SOM and BF were supported by INRA-EFPA project "Innovant 2010".

APPENDICES

Appendix 1. Details of the sequences annotation, the primers used and the specific PCR protocols used. The details about PCR reaction mixture and amplification thermal profile are provided in Appendix 2.

CONTEXT

In this chapter, I focus on the variability and differentiation of 58 F. sylvatica phenology-and stressrelated genes (described in Chapter 3) along two altitudinal gradients on Mont Ventoux. I applied classical outlier methods to detect SNPs that are under selection using these two gradients as replicates.

As the use of these approaches and their interpretation requires accounting for population history, I here briefly describe the impact of F. sylvatica history on neutral diversity and differentiation on Mont Ventoux.

The levels of neutral diversity and differentiation of F. sylvatica populations were first investigated by Lander et al. (2011). A set of 13 microsatellites were amplified in 1932 individuals, covering 51 plots of the mountain. I used a portion of this dataset to select 116 individuals that were genotyped at the 13 selected SSRs (29 per population). Candidate genes were sequenced for 96 of these 116 individuals.

Compared to SNPs, SSRs have a higher mutation rate, and the estimator F ST has been demonstrated to not be fully adapted for SSRs (Hudson et al. 1992). [START_REF] Slatkin | A Measure of Population Subdivision Based On Microsatellite Allele Frequencies[END_REF] proposed an estimator (R ST ) of genetic differentiation that takes into account certain features of microsatellite evolution. For SSR markers, comparing F ST and R ST values provides insight into the principal causes of population differentiation (i.e., drift vs. mutation) given that these statistics have equal expectations when differentiation is caused solely by drift, whereas R ST is expected to be larger than F ST for the SMM [START_REF] Hardy | Microsatellite allele sizes: A simple test to assess their significance on genetic differentiation[END_REF]. The SMM contribution to genetic differentiation was assessed through permutations of allele sizes among alleles within a single locus using the SPAGeDI program, version 1.3 (Hardy and Vekemans 2002). The randomization procedure produced 16000 simulated genotypes data sets, where genotypes were defined in terms of allelic states and were unmodified. The allele sizes were randomly reassigned among allelic states; for each member of the simulated data set, the pR ST value was computed [START_REF] Hardy | Microsatellite allele sizes: A simple test to assess their significance on genetic differentiation[END_REF]. Under the null hypothesis (i.e., stepwise mutations do not contribute significantly to genetic differentiation), the observed R ST was expected to fall within the 95% confidence level of the pR ST distribution. Globally, R ST was not significantly different from pR ST (Figure 3.1), indicating that allele size was not informative for population differentiation. This result may have been observed because the mutation process did not follow the SMM or because the mutation rate μ was very small relative to the effect of drift and migration (m). The R ST was significantly superior to the pR ST and superior to the F ST for only one marker (sfc_1143). Hence, R ST did not provide more After inferring K-values based on the second-order rate of change of the likelihood [START_REF] Evanno | Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study[END_REF] using Structure, which is a model-based clustering method [START_REF] Pritchard | Inference of population structure using multilocus genotype data[END_REF], I did not detect any genetic cluster (data not shnown). Similarly, no difference between the populations in terms of allelic richness were observed. The overall differentiation (REF) was computed for the 12 nuclear microsatellites based on R ST [START_REF] Slatkin | A Measure of Population Subdivision Based On Microsatellite Allele Frequencies[END_REF] and F ST (Weir and Cockerham 1984). The results highlighted an overall low genetic differentiation (F ST =0.251 and R ST =0.246; Table 2) between the four populations.

In conclusion, the set of 12 markers genotyped among 29 individuals for each of the four populations is apparently neutral and without detectable genetic structure at this scale. Except for one marker (which was therefore removed from further analysis), the mutation model has no effect, suggesting an island model in which mutation is negligible relative to migration-drift. This chapter highlighted the absence of significant differences with respect to nucleotide diversity at the SNPs between the four populations. The microsatellites with higher mutation rates did not exhibit signs of clustering, suggesting the presence of high gene flow between the four populations. Given that the populations share the same demographic history since the last glacial event and that they are closely related, we can apply outlier methods that are expected to be free of bias due to genetic structure.

Moreover, the SSRs would be interesting markers for one of the null models.

INTRODUCTION

Understanding how organisms adapt to changing environments is a fundamental part of modern evolutionary ecology. Because of global change, there is a broad societal interest in predicting whether species can adapt to changing environmental conditions over a few generations [START_REF] Grant | Unpredictable Evolution in a 30-Year Study of Darwin[END_REF]Grant 2002, Hairstone et al. 2005). Local adaptation patterns arise when natural populations distributed across heterogeneous environments evolve different phenotypic trait values that confer a fitness advantage in the local environment (Kawecki and Ebert 2004). Among other factors, species response to global change will be affected by both existing patterns of local adaptation across species range (Benito- [START_REF] Garzon | Intra-specific variability and plasticity influence potential tree species distributions under climate change[END_REF]) and micro-evolutionary responses to new environmental conditions [START_REF] Kuparinen | Increased mortality can promote evolutionary adaptation of forest trees to climate change[END_REF].

Therefore, experimental studies addressing how local adaptation patterns arise at short temporal and spatial scales are needed for producing realistic ecological predictions and implementing operational management guidelines for species response to global change (Hansen et al. 2012).

Altitudinal gradients are valuable natural experiments to address these issues [START_REF] Körner | The use of 'altitude' in ecological research[END_REF] In such systems, patterns of genetic diversity and differentiation both at adaptive traits and their underlying genes are expected to be driven mainly by the balance between selection and gene flow, while the effect of mutation and genetic drift are expected to be minimal. In particular, simulation studies have shown that under strong divergent selection and high gene flow, significant genetic differentiation in adaptive traits (typically measured by Q ST statistics; Spitze et al. 1993) is expected, while patterns of differentiation at underlying genes (typically measured by Wright's F ST ) may vary strongly across genes (Latta 1998, Le Corre andKremer 2003). As the number of genes controlling the trait increases, the average F ST at genes is expected to move towards the neutral F ST across the genome (Le Corre and Kremer 2012).

Besides quantitative genetic approaches, population genomic approaches are increasingly used with the specific goals of identifying genes underlying ecologically important traits and describing the fitness consequences of naturally occurring variation at these loci [START_REF] Stinchcombe | Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits[END_REF]. Among the population genomic methods commonly used, genome scans or outlier methods are based on the assumption that past demographic events and neutral processes affect the whole genome whereas natural selection operates locally at specific loci [START_REF] Luikart | The power and promise of population genomics: from genotyping to genome typing[END_REF]. These loci may in theory be identified by higher (or lower) genetic differentiation among populations than expected under a neutral model of population evolution [START_REF] Storz | Using genome scans of DNA polymorphism to infer adaptive population divergence[END_REF]. Outlier approaches have been widely applied to detect selection in natural populations, although these are generally separated by tens to hundreds of kilometres. In studies showing directional selection, F ST at outlier loci was generally found to be two-to twenty-fold higher than the mean F ST at (supposedly) neutral loci (e.g. three-fold in Rana tempororia - [START_REF] Bonin | Explorative Genome Scan to Detect Candidate Loci for Adaptation Along a Gradient of Altitude in the Common Frog (Rana temporaria)[END_REF]17-fold (Beaumont andNichols 1996, Excoffier et al. 2009) and Bayesian outlier tests (e.g. Foll and Gagiotti 2008) the former seems to be more sensitive to high F ST values and prone to type I errors than the latter (Pérez-Figueroa et al. 2010, Narum andHess 2011). Consistently, when applied on the same data, fewer outliers are detected with the Bayesian approach than with the frequentist approach (e.g. [START_REF] Coyer | Genomic scans detect signatures of selection along a salinity gradient in populations of the intertidal seaweed Fucus serratus on a 12 km scale[END_REF], Prunier et al. 2011).

The expected benefits and caveats of outlier approaches are well known [START_REF] Storz | Using genome scans of DNA polymorphism to infer adaptive population divergence[END_REF]. One of their main drawbacks stems from the large expected variability in F ST among selected loci (Latta 1998, Le Corre andKremer 2003). In particular when local adaptation is reached via an increased covariance of allelic effects rather than via allele frequency changes, most (but not all) genes controlling adaptive traits may be undetected, which is more likely under strong gene flow when the number of loci is high and selection is strong and recent. To overcome these drawbacks, Le [START_REF] Corre | The genetic differentiation at quantitative trait loci under local adaptation[END_REF] suggested that studies should account explicitly for the covariance of allelic frequencies among populations, and possibly for the covariance of allelic effects (Ma et al. 2010). Another drawback of outlier approaches arises when complex demographic histories and population structures are not accounted for by the model used to derive expected neutral F ST values, which may lead to a high rate of false positives or false negatives [START_REF] Excoffier | Detecting loci under selection in a hierarchically structured population[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]. Accounting for those has been shown to improve the outcome of the frequentist methods (see Eckert et al. 2010 for one example in forest trees).

Finally, other factors such as environment-independent pre-or post-zygotic genetic compatibilities rather than local adaptation can also be responsible for increased differentiation [START_REF] Bierne | The coupling hypothesis: why genome scans may fail to map local adaptation genes[END_REF].

Notwithstanding their well-known drawbacks, outlier approaches have one main advantage: the rapid investigation of potential molecular targets of local adaptation among a large number of loci in nonmodel species. These loci can then be tested for their putative associations with trait genetic differentiation to get a broader picture of their role in local adaptation patterns. They can also be further studied using gene expression approaches (e.g. Chen et al. 2012).

Outlier methods require a large number of polymorphic markers ideally distributed across the entire genome and for which functional annotations are available, which can be a limitation for non-model species. Easily available without detailed genomic knowledge, AFLP have been previously and are still widely used in genome scan studies, despite subsequent difficulty in estimating the genomic position and functional role of loci potentially under selection. By contrast, single nucleotide polymorphisms (SNPs) are the most widespread type of sequence variation in genomes and can be readily annotated, allowing sequencing efforts to be targeted on genes with known biological functions. Despite their low allelic diversity per locus, large arrays of SNPs can now be easily genotyped even in non-model species (e.g. [START_REF] Andolfatto | Multiplexed shotgun genotyping for rapid and efficient genetic mapping[END_REF] In this study, we investigate whether patterns of polymorphism and differentiation at SNPs from a large number of candidate genes along two Fagus sylvatica altitudinal gradients in Mont Ventoux (southeastern France) support the hypothesis of adaptive differentiation at short spatial scales. Beech populations were previously found to be genetically differentiated along altitudinal gradients for various traits related to climate response, including phenology of budburst (Vitasse 2009, Tessier du Cros and[START_REF] Teissier Du Cros | Variability in beech : budding, height growth and tree form[END_REF], leaf mass per area (LMA), nitrogen content and leaf size [START_REF] Bresson | To what extent is altitudinal variation of functional traits driven by genetic adaptation in European oak and beech?[END_REF]. The populations included in this study originate from a single Holocene gene pool (Magri et al. 2006) and recent re-colonisation after recurrent land-use changes since the Middle-Ages (Lander et al. 2011). Despite their recent origin, nursery-stage common garden experiments have shown a significant genetic differentiation for budburst date between high and low elevation populations which are located less than 1 km apart (Q ST = 5%, J. Gauzëre, pers comm). In this system, genetic differentiation at adaptive traits and at their underlying genes is expected to be due both to random genetic drift (enhanced by foundation events following colonization) and to the deployment in space of polymorphisms already segregating in the ancestral populations, i.e. through soft sweeps [START_REF] Hermisson | Soft sweeps: molecular population genetics of adaptation from standing genetic variation[END_REF].

Dissecting the effects of neutral forces from those of selective forces on patterns of diversity and differentiation at candidate genes among populations at short spatial scales is challenging. Nevertheless, at least two studies along short-scale environmental gradients of Fagus sylvatica have demonstrated that outlier loci can be detected at these scales (Jump et al. 2006, Pluess andWeber 2012). To provide more comprehensive evidence, we combine here outlier-based methods with detailed analyses of the covariance of allelic frequencies among populations for outlier loci, as well as of the contribution of these outlier SNPs to variation at adaptive traits measured in situ. Thirty-eight genes potentially involved in response to temperature and drought stress and 20 genes involved in budburst phenology were selected and re-sequenced in 96 individuals from four populations along two altitudinal gradients. We analysed SNP differentiation patterns at these candidate genes to address the following questions: (i) Can we detect outliers using F ST -based coalescent methods in neighbouring populations from different elevations?

(ii) Do outlier patterns vary among different altitudinal gradients? (iii) Can we detect significant correlations between SNP allelic variation and phenotypic trait variation within the gradients?

MATERIALS AND METHODS

Study species and site

The European beech (Fagus sylvatica L., Fagaceae) is a diploid (2n = 24), monoecious, anemophilous, and mainly outcrossing tree species. Individuals typically begin to reproduce at 40 to 50 years. The study was carried out in Mont Ventoux, a 1912 m high mountain located in south-eastern France. At the onset of the Holocene 11000 years before present (BP), Fagus sylvatica began to recolonize Europe from the refugia in southern Europe. Long-term paleo-records near Mont Ventoux show Fagus sylvatica presence starting 8000 years BP (Magri et al. 2006). From the 12 th century onwards, forests were massively logged and beech stands were severely reduced up until the mid-19 th century. Because of a new forestry law adopted in 1861 (i.e. around three beech generations ago), which banned forest over-logging and restored suitable habitats, beech forest quickly expanded in size and density (Lander et al. 2011) 2.2). Two populations (NH and NL) were sampled on the northern gradient at 1340 and 995 m, respectively, while two populations (SH and SL) were sampled on the southern gradient at 1517 and 895 m, respectively. Compared to low elevation populations, highelevation populations experience a lower average temperature (average difference = 3.5°C) and a higher humidity (average difference = 18%). Leaf samples were collected in 2009 and stored at INRA Avignon (France) at -20°C.

Candidate gene selection, sequencing and SNP validation

We selected a total of 58 amplicons (i.e. gene fragments), among which 20 were putatively involved in bud phenology (hereafter called "phenology genes") and 38 in temperature and stress response (hereafter called "stress genes"). The detailed methodology used to select and sequence these genes is described in Lalagüe et al. (submitted). Briefly, a non-exhaustive list of expressional and functional candidate genes involved in responses to cold, heat, drought stress and differentially expressed during bud development was compiled from the literature and thanks to personal communication (F. Alberto For the outlier analyses, we extracted all SNPs from the 58 genes. To minimise bias due to missing data, SNPs with more than 30% of missing genotypes were excluded from the analyses. Singletons and tri-allelic loci were also excluded, as well as those SNPs in almost complete linkage (r 2 > 0.9). This was done independently for both gradients because allelic variants and the proportion of missing data were different between gradients.

Gen etic diversity, differentiation and linkage disequilibrium at candidate genes

Nucleotide diversity (θ π , [START_REF] Nei | Mathematical model for studying genetic variation in terms of restriction endonucleases[END_REF], observed and expected heterozygosity (Ho and He) and F ST (Weir and Cockerham 1984) at SNP level were estimated using Arlequin v.3.5.1.3 (Excoffier and Lischer 2010). Pairwise linkage disequilibrium (LD) as estimated by the r 2 statistic [START_REF] Weir | Genetic Data Analysis II[END_REF] was computed with the LD function implemented in the package genetics 1.3.6 [START_REF] Warnes | [END_REF] of the R software (R Development Core Team 2010). Analyses were conducted for each population and for each gradient separately. Graphical outputs were obtained with the R package Ldheatmap [START_REF] Shin | LDheatmap: An R Function for Graphical Display of Pairwise Linkage Disequilibria Between Single Nucleotide Polymorphisms[END_REF].

The covariance of allelic effects is 4α i α j D ij where α i and α j are the additive allelic effects at loci i and j, respectively, and D ij is the LD between the two loci (Latta 1998). In our case, allelic effects cannot be estimated, but we can estimate D b(ij) , the between-population component of LD between loci i and j, as:

D b(ij) =Σ k n k /n×(p ik p jk -p i p j ),
where k indexes population, n k is the number of haplotypes sampled in population k , p ik is the allele frequency at loci i in population k and p ik is the frequency in the total sample. We used the estimator Zg of D b(ij) provided by Storz and Kelly (2008, equation 3). Note that this estimator is squared to remove the sign of the LD (which is arbitrary). Also, as it is standardized by the variance of allelic frequencies, this estimator is in fact a correlation coefficient (and not a covariance) meaning that loci with a high F ST value should not systematically have a high Zg value. To avoid inflated covariances between SNPs because of intragenic LD, we only performed pairwise comparisons between SNPs from different genes.

SSR data

From the data by Lander et al. (2011), we extracted a subset of 116 individuals (29 individuals for each of the four populations) genotyped at 13 nuclear SSRs (for details see Lander et al. 2011). On the northern gradient, 58 of the 71 individuals sequenced for candidate genes were also genotyped at SSR, while on the southern gradient 58 individuals genotyped at SSRs included the 25 sequenced for candidate genes and 33 additional trees.

Observed and expected heterozygosity (H o and H e ), inbreeding coefficients (F IS ) within each plot and genetic differentiation among pairs of populations (F ST ) were computed using Arlequin v.3.5.1.3. R ST statistics were not considered as values were not significantly different from F ST (Lander et al. Pers. comm.), suggesting that stepwise mutations contribute little to genetic differentiation at SSR markers in the studied populations.

Frequentist outlier approach

We first used the F ST outlier approach adapted from Beaumont and Nichols (1996) and implemented in Arlequin v.3.5.1.3. The neutral expected F ST distribution was obtained by simulating 1.0 × 10 5 SNPs 4 for parameters values.

The first scenario (labelled split, Fig. 2A) corresponded to the simplest demographic history where the ancestral population diverged at t 4 =20 generations ago into two populations (high and low elevation populations) without population size change. During the next 17 generations (from t 4 to t 1 ) the two populations evolved without population size change and exchanging migrants at a rate m. Then, during the last 3 generations (from t 1 to t 0 ) the two populations expanded while exchanging migrants at a rate m. This scenario is directly supported by SSR data in Lander et al. (2011).

The second scenario (bottleneck, Fig. 2B) was similar to the split model except that an initial bottleneck occurred simultaneously with divergence t 4 =20 generations ago. The strength of the simulated bottleneck (as expressed by the ratio of population size before and after bottleneck) varied from 3.3 to 5, consistently with the estimated strength of 5.6 reported in Lander et al. (2011; Table 3).

In the third scenario (bottleneck stepwise, Fig. 2C), the ancestral population diverged t 4 =20 generations ago, with a reduction in effective population size, into two populations. Subsequent splits at t 3 = 19 generations ago and then at t 2 = 18 generations ago led up to four populations. Then the four populations evolved from t 2 to t 1 without population size change and exchanging migrants at a rate m. Finally, during the 3 last generations (from t 1 to t 0 ) the four populations expanded while exchanging migrants at a rate m.

We used the ms software (Hudson 2002) to simulate 1.0 × 10 6 SNPs under each of these three demographic scenarios for each gradient, assuming an infinite-site model of mutations. Moreover, the migration rate m was determined based on the observed F ST -value successively at SSR and SNP markers and assuming F ST =1/(4Nm +1). The other parameter values (t 1 , t 2 , t 3 , N 0 , N 1 , N 2 ) were chosen according to the bounding values estimated in Lander et al. (2011, Table 4). Only N 1 , the effective population size before expansion was adjusted by an iterative procedure in order to get the simulated F ST at final step within the 5% of the observed F ST value. The sampling size of the present populations was set to 70/72 gametes both for North and South populations because sampling size as low as 34/36 gametes like the southern populations lead to unrealistic results. Command lines for ms are provided in the online appendix 3. A modified form of the msstats software developed within the libsequence library [START_REF] Thornton | libsequence: a C++ class library for evolutionary genetic analysis[END_REF] was used to estimate F ST and He from the ms output files.

Then, the 95% confidence intervals of the distribution of F ST vs. expected heterozygosity were calculated with the quantile function in R (package stats) for both the finite island model and the three demographic models simulations. SNPs outside the 95% confidence interval of the distribution were identified as outlier SNPs. 97 4.6. Bayesian outlier approach We used the hierarchical Bayesian method implemented in BAYESCAN 2.1 (Foll and Gagiotti 2008).

This method is based on the decomposition of the logit value of F ST into a locus specific effect (a i ) shared by all populations and a population specific effect (b j ) shared by all loci (Beaumont and Balding 2004). For each locus, BAYESCAN calculates posterior probabilities for the model including selection or not. Then model choice decision is made on Bayes factors, which can be also easily converted into a

False Discovery Rate (FDR). The last version of BAYESCAN calculates the q-value (the FDR analogue of the p-value) at each locus, which is the minimum FDR at which this locus may become significant. Foll and Gaggiotti (2008) showed through simulations that bi-allelic markers, like SNPs, have lower power to detect selection than multi-allelic markers like SSRs. Since our analyses were exploratory we looked at each locus Bayes factor and q-value rather than considering an arbitrary cutoff for regarding loci as potential outliers. We ran 20 pilot runs of 5,000 iterations and then a burn-in of 50,000 iterations followed by 150,000 iterations (thinning interval of 30).

Relation between outlier SNP and traits

Individuals sampled along the northern gradient were also phenotyped in situ for different traits (Bontemps et al. com.pers). The phenology of vegetative budburst was characterised during 2010 in both NL and NH populations (71 individuals in total) by 10 observations from dormant-bud to spreadout-leaves stages which were integrated into a Phenology Score Sum (PSS, the highest PSS indicating the earliest flushing). In addition, in population NL only (35 individuals), water use efficiency (WUE)

was measured using carbon isotope (delta 13 C) discrimination and leaf mass per unit area (LMA) was estimated as the ratio of dried mass and area of ten terminal leaves.

The effects of genotypic classes (AA, AB, BB) for detected outliers were first tested independently for each trait using one-way ANOVA (independently for each SNP) and then multivariate ANOVA were used that combined several SNPs. The multivariate ANOVA model was fitted on a reduced data set without missing data (less individuals and less SNPs), and excluding SNPs in strong LD (r²>0.2); SNPs were selected using a forward stepwise method (step function from R). For the trait PSS, measured in two populations (NL and NH), two types of ANOVA models were fitted (with and without a population effect). All analyses were performed using R. 3. Population diversity, differentiation and linkage disequilibrium estimates based on SNP markers.For each gradient, average multilocus estimates and standard deviation (sd) of nucleotide diversity within population (θπ), observed heterozygosity (Ho), expected heterozygosity (He) genetic differentiation among populations (F ST ), and between-population component of linkage disequilibrium (Zg) are reported. These estimates were successively averaged across all SNPs, or only across outlier SNPs (see Table 5).

Frequentist outlier approach

We first used the FDIST approach implemented in Arlequin v.3.5.1.3, using in turn SSR and SNP F ST estimates to simulate the distribution of neutral F ST. Between populations of the northern gradient, F ST values estimated from SSRs and SNPs were similar (F ST-SSR =0.021 and F ST-SNP =0.018), leading to similar neutral expected F ST distributions under a constant size finite island model (Figure 3A). Using the SSR-based (IslandSSR) and the SNP-based (IslandSNP) neutral F ST distributions, we detected 9 and 10 outlier SNPs at the 5% level, respectively, representing 4% of the SNPs used in the analysis (Table 5). In the southern gradient, using the IslandSSR neutral F ST distribution (simulated with target F ST-SSR = 0.041), we detected 4 outlier SNPs at the 5% level, representing 2% of the SNPs used in the analysis (Figure 3B). Using the IslandSNP neutral distribution (simulated with target F ST-SNP = 0.022), three additional SNPs (7 in total) were detected as outliers representing 3% of the SNPs used in the analysis.

Neutral expected F ST distributions were also simulated under three different demographic scenarios of population size changes and population divergence (split, bottleneck and bottleneck stepwise models, Figure 2). Though the simulated distributions always included the observed F ST-SNP value among real populations, significance thresholds were always lower than the distribution simulated under the island model (Figures 3C,D) resulting in less conservative threshold values for outlier detection.

Consequently, with these models, 5 and 21 additional SNPs were outliers at the 5% level on the northern and southern gradients, respectively. On the southern gradient, the strong increase in the number of outlier SNPs detected using realistic demographic scenarios as compared to island model also results from the fact that the sampling size at the end of ms simulations was considered to be 142, whereas for simulations with the island model (Arlequin) we assumed a sample size of 50. With ms, a sampling size of 50 was found to be limiting for the estimation of F ST under consistent scenarios.

To summarize, with the less stringent neutral envelopes (obtained with the demographic scenarios), up to 15 and 28 SNPs were detected as outliers at the 5% level on the northern and southern gradients, respectively. Only two outlier SNPs (171 and 711) were shared between the two gradients. However, considering a gene as outlier if it carries at least one outlier SNP, five genes were found to be outliers for both the northern and southern gradients (genes in bold in Table 5)

DISCUSSION

In this study, we used a combination of approaches to investigate the molecular basis of adaptation to climate in Fagus sylvatica at small spatial and temporal scales. We found two well-supported outlier SNPs (SNP 1106 and SNP 172) that share the lowest q-values and highest F ST and also explained phenotypic variation at two adaptive traits (LMA and budburst phenology). In total, 15 and 28 outlier SNPs were detected in the northern and southern gradients, respectively, by the frequentist approach but not by the stricter Bayesian approach. We cannot exclude the possibility that some of these outliers are false positives. However, Le [START_REF] Corre | The genetic differentiation at quantitative trait loci under local adaptation[END_REF] showed that under strong selection and high gene flow, SNPs under selection can show low differentiation but high correlation of allelic effects between populations (θB) and suggested that the correlation of allelic frequencies should be estimated as a surrogate of θB when allelic effects cannot be measured. In line with this expectation, we showed a higher correlation of allelic frequencies (Zg) among the outlier SNPs than among all SNPs, supporting the hypothesis that at least some of these outlier SNPs are under selection and are not false positives.

Overall, our results confirmed that, to detect outlier patterns potentially due to selection, it is important to integrate both estimates of population differentiation and additional information, such as associations with phenotypic variation or correlation of allelic frequencies (Zg) among SNPs (Ma et al. 2010). In particular, Zg estimates could be used not only to confirm outlier SNPs as in this study, but to reveal SNPs under selection in the case of low overall genetic differentiation.

Though environmental conditions varied among northern and southern gradients (Table 1), we used them as replicates to test whether variation in temperature or humidity were associated with genetic differentiation among populations. SNP 172, characterised by the highest F ST and lowest q-value, belongs to a gene for which several other SNPs were outliers either for the northern or for the southern gradient. Only one (less supported) SNP on this gene (SNP 151) was detected as an outlier on both gradients. On a different gene, SNP (711) (less supported) was found to be an outlier in both gradients.

Overall, only two SNPs were more differentiated than expected both between populations of the northern and the southern gradients. This weak consistency of outlier signal between gradients could be due to (1) the globally low power to detect outlier loci under strong divergent selection and high gene flow, (2) sampling bias as the size of the southern populations was two-fold smaller and/or (3) differences in the environmental conditions between gradients. In theory, replicates should allow separation of the outliers that reveal adaptive response to the ecological variable under consideration from those involved in response to other ecological variables specific to each replicate. In our case, the low-elevation southern population SL clearly corresponds to more stressful conditions, with higher temperature and lower relative humidity than the low-elevation northern population NL (Table 1), while climatic conditions in the high elevation populations (SH, NH) are more similar. The higher number of outlier loci (28 vs. 15) detected in the southern versus northern gradient may therefore reflect the higher environmental differences between low and high altitude in the southern gradient.

Moreover, differences in environmental conditions between high and low elevations among gradients could result in different overall adaptive responses. Consistent with our results, published experiments using replicates in natural conditions revealed very few outliers in common among replicates in a variety of organisms. In fact, only one outlier is common to four out of the 12 replicates in [START_REF] Bonin | Explorative Genome Scan to Detect Candidate Loci for Adaptation Along a Gradient of Altitude in the Common Frog (Rana temporaria)[END_REF], three outliers between the three replicates in Pluess and Weber (2012), none between the three replicates in [START_REF] Williams | Signatures of selection in natural populations adapted to chronic pollution[END_REF], four in [START_REF] Poncet | Tracking genes of ecological relevance using a genome scan in two independent regional population samples of Arabis alpina[END_REF], and one between two of the six comparisons in [START_REF] Coyer | Genomic scans detect signatures of selection along a salinity gradient in populations of the intertidal seaweed Fucus serratus on a 12 km scale[END_REF]. This highlights the difficulty of setting up replicates in natural conditions, even though replication is one of the main principles of the scientific method. However, an encouraging result of this study is that despite the absence of well-supported outlier SNPs shared among replicates, the "outlier genes", i.e. those carrying some of these outlier SNPs, are generally shared between northern and southern gradients. This confirms the importance of sequencing long fragments, ideally full-length genes, containing many polymorphic loci rather than genotyping few SNPs per gene. Gene sequencing, alignment editing and SNP verification are long and meticulous tasks but may reveal interesting genes for specific populations.

The outlier SNPs detected in this study belonged to different genes. SNP 172 belongs to a gene of the stress category and codes for an ubiquinol-cytochrome c reductase, a protein shown to be differentially expressed under heat and drought stress in Arabidopsis [START_REF] Rizhsky | When defense pathways collide: The response of Arabidopsis to a combination of drought and heat stress[END_REF]. Despite being highly supported, outlier SNP 1106 belongs to a gene coding for a protein that could not be defined with confidence. This finding shows that using candidate genes described in model species to study variation in non-model species may cause interesting species-specific polymorphisms to be missed.

Therefore we recommend not being too selective when choosing candidate genes in non-model species.

SNP 931 (the best supported outlier of the southern gradient) belongs to a gene coding for a chlorophyll a/b-binding protein of the light-harvesting complex of photosystem II and reported as differentially expressed during budburst in Quercus petraea (Derory et al. 2006). SNP 711 (outlier on both gradients) belongs to a gene coding for a pectin methylesterase (PME). PMEs influence plant development and stress response by controlling the degree of methylesterification of pectins which are major components of plant cell wall (Pelloux et al. 2007). In Fagus sylvatica, PMEs with calcium may impact the mechanical properties of pit membranes and thus the vulnerability to cavitation (Herbette and Cochard 2010).

Despite low sample sizes, the exploratory association analysis (using ANOVA) between genotype at outlier SNPs and phenotypic traits revealed interesting patterns, with in particular SNP 1106 explaining 30% of the variation of LMA within population. At the local scale studied here, we consider the observation of an outlier locus contributing to both phenotypic variation within population and genetic differentiation among population as a strong evidence of selection. . By contrast, the case of outlier loci contributing only to phenotypic variation among populations (e.g. outlier SNPs 172 and 119 for budburst phenology) is more difficult to interpret because of the sampling design with only 2 populations per gradient. Populations NH and NL differed for budburst phenology (with a difference of 8 units in average PSS between populations in situ), both because of the temperature differential (2.6°C difference in average temperature) and of genetic differentiation (with a difference of 0.6 to 1 units in average PSS among populations in common garden and Q ST =5%, p-value=0.006, Gauzere et al. comm. pers). Even though SNP 172 and SNP 119 may directly contribute only to between-(and not to within)population variation at PSS, we cannot exclude that their significant association with PSS just reflects their ability to differentiate populations (since they were selected based on high F ST and thus contrasting allelic frequencies). Interestingly, non-outlier SNPs were found to contribute to the within-population component of the variation at PSS, suggesting that different loci may contribute to phenotypic variation within and among populations.

Connections between gene function and associated phenotypic traits were not always straightforward.

SNP 172 belongs to a stress-response gene but explained a significant part of the observed variation in budburst phenology; conversely, SNP 1106 belongs to a phenology gene but was associated with LMA, a trait known to be sensitive to water stress. These counter-intuitive relationships between genetic and phenotypic variation could result from genetic correlation among traits. Many important traits are correlated because they are developmentally or structurally related, or because of correlated selection exerted on trait syndromes. Counter-intuitive association of candidate genes and phenotypic variation may also result from physiological complexity. For instance, gene expression for light-harvesting complex (outlier SN931), a phenology-related product, seems to be regulated by environmental factors and developmental signals such as light [START_REF] Yang | Induction of acclimative proteolysis of the lightharvesting chlorophyll a/ b protein of photosystem II in response to elevated light intensities[END_REF], oxidative stress and phytohormone abscisic acid (ABA) [START_REF] Staneloni | Abscisic acid, high-light, and oxidative stress downregulate a photosynthetic gene via a promoter motif not involved in phytochrome-mediated transcriptional regulation[END_REF]. While the photoperiod is the same between the high and low elevation populations, abiotic or biotic factors (e.g. insects and fungi) may cause oxidative stress and physiological response through ABA, which is known to modulate plant response to stressful conditions [START_REF] References Adie | ABA is an essential signal for plant resistance to pathogens affecting JA biosynthesis and the activation of defenses in Arabidopsis[END_REF]).

This study confirmed that the Bayesian outlier approach (here that of Foll and Gagiotti 2008) identifies a lower number of outlier loci than the frequentist approach. In this case, no outlier loci were detected by BAYESCAN at the usual threshold of Bayes factor BF=3. However, the well supported outlier SNPs 1106 and 172 showed BF substantially higher than 1, and low q-values (q-val=0.52 and 0.60 respectively). Regarding the frequentist approach, using different demographic scenarios to simulate the neutral expected F ST distributions provided two main results: first, little variation of the neutral envelope was observed between demographic scenarios, and second, the upper bound of the neutral F ST distribution was lower than when using a simple island model. By contrast, simulating a bottleneck that occurred 1940 generations ago at the glacial era, Eckert et al. (2010) obtained an increased upper bound of the neutral envelope, thereby reducing the number of detected outliers. In our case, 20 generations are certainly not enough to produce strong differences among scenarios but this temporal frame fits the demographic history in the study area (Lander et al. 2011). Indeed, [START_REF] Nordborg | Linkage disequilibrium: what history has to tell us[END_REF] showed that a change in population size does not have much effect on the coalescent unless the change lasted for a number of generations of the same order of magnitude as the new population size; translated to our case study, this would require much more than 20 generations, considering the high effective population density (Lander et al. 2011). Accordingly, in beech populations of the Mont Ventoux, using SSRs, a larger number of individuals and a Bayesian approach, Lander et al. (2011) did not find evidence of past bottlenecks (despite the evidence of the historical record) but only signals of expansion over the three last generations. Overall, theoretical arguments based on the coalescent and simplified population models are reassuring and indicate that SNPs detected as outliers are unlikely to be false positives. However, in the ms simulations, we assumed a subdivided population model with two to four finite populations, while the whole mountainside is covered by beech. At such a small scale, demographic models in continuous populations using realistic dispersal kernels may result in a larger variance of neutral F ST than the one simulated here.

Neighbouring beech populations from different elevations in Mont Ventoux show strong phenotypic differentiation for various functional traits and preliminary results from common garden experiments suggest that a significant part of this differentiation is genetic despite the recent origin of populations.

In this study, using various classical tools to analyse the genetic variation at candidate genes (differentiation, linkage disequilibrium, association) we found new evidence for potential selection effects at candidate genes. Our results confirm the hypotheses of recent local adaptation of beech populations at small spatial scale, and highlight the potential important role of micro-evolution in natural populations. Detailed analyses of the sequence variation at candidate genes should allow refined inference of the rate of adaptive molecular evolution, and of the effects of demographic processes.

APPENDICES

For appendix 1 see appendix 3 Chapter 2

Online

CONTEXT

The objective of this chapter is to better understand the building of genetic differentiation and the covariance of alleles among populations over small spatial and temporal scales and to integrate the main trees' traits of life history.

Box 2 : Adaptive differentiation for complex multilocus traits and their underlying genes

The comparison of the differentiation at phenotypic traits (measured by Q ST ) with the differentiation at neutral markers (measured by F ST ) as long been used to infer the action of natural selection. This comparison is based on the assumption that F ST values at neutral markers estimate only the effect of the interplay between genetic drift and migration, mutation rate being negligible (Spitze 1993).

If the quantitative traits are controlled by purely additive genes and exposed to the same neutral evolutionary forces, then their level of differentiation between populations would be expected to be equal for that neutral loci ( Q ST = F ST ). Any discrepancy between these two estimators may be used to detect the effects of selection [START_REF] Whitlock | Evolutionary inference from QST[END_REF]. But while differences between Q ST and F ST were commonly observed (Le Corre and Kremer 2012), no correlation have been found between molecular markers and genetic variability at adaptive traits [START_REF] Lynch | The quantitative and molecular genetic architecture of a subdivided species[END_REF]McKay and Latta 2002). Traditionally the molecular marker are usually analyzed as single locus trait despite that quantitative traits are controlled by multiple loci (QTLs) that may be in disequilibrium. Latta (1998) investigated the he link between differentiation at neutral genetic markers (F ST ), differentiation at QTLs (F STq ) and differentiation at adaptive trait (QST) through simulation; he demonstrated that "as selection acts on quantitative trait variation, it results not in changes of allelic differentiation at the underlying loci but, rather, creates covariances (linkage disequilibrium) across loci". Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF] extended this approach by comparing both within deme variability and between deme differentiation to estimate the impact of disequilibrium on these two parameters. They introduced the parameter θ that is the component of trait variance due to the effect of allelic association among QTLs relative to the variation due to individual allelic effects at each QTLs. The QST may be expressed as :

where θ B is the covariance of gene at the between population level and θ W is the covariance of genes at the within population level. By simulating loci with two alleles of symmetric additive effects without dominance or epistatis they demonstrated that:

when θ B = θ W , then Q ST = F ST q 125 Q S T = ( 1 -θ B ) F S T q ( θ B -θ W ) F S T q + 1 + θ W when θ B > θ W , then Q ST > F ST q when θ B < θ W , then Q ST < F ST q Box 2 (continued)
Each polymorphic QTL contribute to the between-deme genetic variance of the trait through two components: the between-deme genic variance at the locus and the betweendeme genetic covariance with other QTL. Under high gene flow (Nm=10) most QTL have low F STq values but few QTL display high F ST and high between-deme genic variance. Under such circumstance the detection of outlier loci by genome scans would be successful but only a subset of genes would be identified (Figure 4.1).

The authors, through numerous simulations (see also [START_REF] Kremer | Decoupling of differentiation between traits and their underlying genes in response to divergent selection[END_REF] came to the conclusions that under divergent selection θ B is the major driver of Q ST . Le [START_REF] Corre | The genetic differentiation at quantitative trait loci under local adaptation[END_REF] The inter-genic alleles association theoretically occurred from the first generation. During the first 10-40 generations the Q ST strongly increased mainly due to a θ B increase. The number of loci involved in the trait is of great importance in the relation between θ, Q ST , F STq and F ST . For a small number of loci, F STq value should be closer to Q ST than to F ST whereas for a high number of loci, F STq should be closer to F ST and frequently undifferentiated [START_REF] Kremer | Decoupling of differentiation between traits and their underlying genes in response to divergent selection[END_REF]) But a larger number increases the number of opportunnities for favorable associations. This may cause θ B to reach even higher values.

INTRODUCTION

Current climatic models predict important changes in mean of climatic parameters over the next century (e.g., an increase in temperature), more frequent and more intense extreme climatic events (e.g., drought or flooding), and other related changes, such as parasite outbreaks (IPCC 2007). Because they are long-lived, forest tree species will need to respond to these changes within one to ten generations. As for other species, trees can survive by changing their ecological niches in space though migration (without adaptation) and/or by adapting locally (without migration) to the new conditions [START_REF] Aitken | Adaptation, migration or extirpation: climate change outcomes for tree populations[END_REF]. Recent studies suggest that migration abilities of most forest trees are unlikely to match the velocity of climate change, especially when accounting for habitat fragmentation induced by human activities [START_REF] Anderson | Evolutionary and Ecological Responses to Anthropogenic Climate Change[END_REF][START_REF] Nathan | Spread of North American wind-dispersed trees in future environments[END_REF][START_REF] Zhu | Failure to migrate: lack of tree range expansion in response to climate change[END_REF]. Alternatively, there is increasing experimental evidence (though not directly for tree species) that adaptation to new climatic conditions may occur within a few generations, for instance, in mammals [START_REF] Reale | Genetic and plastic responses of a northern mammal to climate change[END_REF][START_REF] Franks | Rapid evolution of flowering time by an annual plant in response to a climate fluctuation[END_REF] or annual plants [START_REF] Antonovics | Evolution in closely adjacent plant populations X: long-term persistence of prereproductive isolation at a mine boundary[END_REF][START_REF] Franks | Rapid evolution of flowering time by an annual plant in response to a climate fluctuation[END_REF]Gonzalo-Turpin and Hazard 2009).

Moreover, these studies suggest that in addition to plasticity, the ecological success a species facing climate change can rely on genetic adaptation, i.e., the genetic change in a population responding to selection. Underestimating the potential for rapid genetic adaptation of populations may thus lead to unnecessary, if not damaging, recommendations (for instance the use of non-native populations for restoration), whereas overestimating this potential may lead to the rapid decline of population before mitigating measures can be taken. Therefore, monitoring the ability of populations to respond to the selection that is induced by climate change has become a major area of research in ecological genetics (Hansen et al. 2012).

Tracking adaptive response of populations requires monitoring, i.e., analyzing either phenotypic traits or genetic markers over time (Hansen et al. 2012). A common approach in ecology is to trade space for time, i.e., to consider that the spatial shift in climatic conditions along a gradient can be considered to be equivalent to the temporal shift that each local population will face in a changing climate. This is a gross simplification, since climate change (CC) may result in new combinations of climatic parameters, imposing entirely new selection pressure. Thus, the space-for-time simplification should be used with caution in predicting the future response of populations. However, it may still be useful as a working subject to selection. This method relies on the Bayesian model of allelic frequencies within each deme based on a multinomial-Dirichlet likelihood that was initially proposed by Beaumont and Balding (2004). This method (implemented in the software BAYESCAN) is based on a logistic regression model in which each logit value of F STij for locus i in population j is decomposed into a locus-specific effect (a i ) that is shared by all of the populations and a population-specific effect (b j ) that is shared by all of the loci. The posterior probability of locus i being under selection as estimated by defining two alternative models, one that includes the a i subject and another that excludes it. This Bayesian approach has the advantage of circumventing the problem of using a simulated neutral distribution, and of resolving the problem of multiple testing of a large number of loci (Beaumont and Balding 2004;Foll and Gaggiotti 2008;[START_REF] Gautier | A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets[END_REF].

Several simulation studies investigated the possible limitations of these refined outlier approaches, including (1) false positives due to population stratification [START_REF] Excoffier | Detecting loci under selection in a hierarchically structured population[END_REF] or the absence of correction for multiple testing [START_REF] Perez-Figueroa | Comparing three different methods to detect selective loci using dominant markers[END_REF], (2) limited sensitivity (false negatives) due to the polygenic architecture of the traits under selection (Le [START_REF] Corre | The genetic differentiation at quantitative trait loci under local adaptation[END_REF] or the variation of selection among population pairs [START_REF] Vitalis | Interpretation of variation across marker loci as evidence of selection[END_REF]) and (3) the effect of linkage disequilibrium between the detected outlier marker and the nearest loci under selection [START_REF] Vilas | A simulation study on the performance of differentiation-based methods to detect selected loci using linked neutral markers[END_REF]. These studies primarily relied on simulations of structured populations in a heterogeneous environment under different evolutionary scenarios (intensity of selection, gene flow and mutation rate) and a varying number of loci coding for traits under selection. Two principle results are that the performance of the different outlier approaches can be very different depending on the evolutionary scenario and that their power to detect loci under selection is generally low when gene flow is high, even in the case of strong divergent selection among populations.

These results have important implications for the abilities of F ST outlier scans to detect loci that are under selection at a local spatial scale and/or when the selection is recent. Latta (1998) reported that, under strong gene flow when the number of loci is high, high levels of genetic differentiation among populations for the trait (Q ST ) could be obtained through an increased covariance of allelic effects among demes rather than through changes in allelic frequencies at QTLs (i.e., low F STq ). Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF] further derived the analytical relation between Q ST , F ST and the covariance of allelic effects within and among demes under and island model of population structure. These authors reported that the covariance of allelic effects among demes (θ B ) is the predominant component of Q ST under highly diversifying selection and high gene flow. Using simulations of the transient dynamics of θ B for an outcrossing species under high gene flow, [START_REF] Kremer | Decoupling of differentiation between traits and their underlying genes in response to divergent selection[END_REF] reported that θ B increased very rapidly during the first 5-10 generations of selection, although the levels of genetic differentiation at QTLs remained very low. These results suggest that the covariance of allelic effects of QTL among populations could provide early molecular signatures of natural selection. Whereas QTLS effects are generally unknown in natural populations, it is in contrast quite a simple matter to estimate LD among populations as it is a component of θ B . Yet, to our knowledge, only two experimental studies have investigated the among-population component of LD as indicator of selection. Storz and Kelly (2008) proposed a new statistic (Zg) to measure the between-population (or between-allele class) component of LD and estimated Zg-values among two α-globin genes that are known to contributes to fitness variation in deer mouse populations at high and low altitudes. Unexpectedly, the estimated Zg-values at the selected loci did not exceed the simulation-based neutral expectations. In contrast, Ma et al. (2010) investigated patterns of F ST and Zg for 25 genes from the photoperiod pathway in black poplar and 21 putatively neutral genes in 12 populations along a latitudinal gradient. Whereas these authors did not identify any loci with unusually high F ST levels among the populations, they did observe large covariance in allelic effects across populations for genes that are involved in growth cessation.

The objective of the present study is to investigate the power of various approaches to detect loci under recent selection due to climate variations and high gene flow. Unlike most simulation studies that consider long-term evolution (>1000 generations), we wish to specifically address the power of different approaches to detect early signatures of natural selection (i.e., fewer than 10 generation) to provide markers to monitor population response to CC. Instead of considering structured population along a simplified environmental gradient, we also wish to investigate a more realistic case of climateinduced selection. Recently, developed eco-genetic simulation models can be used for these purposes (e.g., Kramer et al. 2008). Among these models, Oddou-Muratorio and Davi (in prep, OM&D hereinafter) recently proposed an individual-based physio-demo-genetic model (named PDG) that allows for simulating climate-driven evolutionary dynamics in a continuous tree population at a brief time scale. The authors used this model to investigate the patterns of climate-driven phenotypic and genetic divergence along an altitudinal gradient of European beech populations. OM&D reported that a small number of generations are sufficient for natural selection to result in genetic differentiation for the Timing of BudBurst (TBB). The primary value of this model is that the natural selection resulting from climatic variables (temperature, precipitations) is not modeled by the user but dynamically emerged from the biophysical model. In contrast, classical population genetic models generally model the adaptation process as driven by fitness and assume that individual fitness decreases with the distance between the individual phenotype and the optimal phenotype.

Here, we use data sets that were simulated using PDG (OM&D) to compare the performance of i) two classical outlier methods that are based on population differentiation (Beaumont and Nichols 1996;Foll and Gagiotti 2008), ii) allelic frequency clines and iii) a method that is based on the between-sample component of linkage disequilibrium (Storz and Kelly 2008).

MATERIALS AND METHODS

The model and the simulated data sets

The simulations were performed using PDG, a process-based, individual-based model that was used to simulate the evolutionary dynamic over 5 generations of Fagus sylvatica populations along an altitudinal gradient from 700 to 1700 m on Mont Ventoux (OM&D). PDG couples, including i) a physiologically process-based module (CASTANEA, [START_REF] Dufrene | Modelling carbon and water cycles in a beech forest Part I: Model description and uncertainty analysis on modelled NEE[END_REF]) that simulates the water and carbon balance at the level tree as a function of tree local climate and soil; ii) a quantitative genetics module that relates genotype to phenotype for an adaptive trait; and iii) a demographic module that converts tree reserves into seed productions and tree mortality and that models fecundation and seed dispersal and establishment. This model was parameterized for the F. sylvatica on Mont Ventoux as described in the details of OM&D. Here, we detail only the model component that is related to the quantitative genetic module. A single trait was considered to be genetically variable among individuals, namely, the Timing of BudBurst (TBB). TBB was modeled as a quantitative trait depending both on the environment (here, the temperature), which varied along the altitudinal gradient and on the individual's genotype and that determined directly the number of degree days required for budburst (F critBB ). F critBB is one of the >120 parameters of CASTANEA. F critBB was encoded by ten additive bi-allelic QTLs.

Following the notation given in [START_REF] Bost | Genetic and nongenetic bases for the L-shaped distribution of quantitative trait loci effects[END_REF], the contribution of a given locus l to the genetic value of F critBB was given by m l +a l, m l , and m l -a l , respectively, for the homozygote A1A1, the heterozygote A1A2 and the homozygote A2A2. As in [START_REF] Bost | Genetic and nongenetic bases for the L-shaped distribution of quantitative trait loci effects[END_REF], all of the m l are identical and equal to µ FcritBB /L; µ µ FcritBB (the mean F critBB value in the simulated population was 190°). The allelic effects (al) were additive corrections to the contribution of locus l to the parameter for the homozygotes (without dominance). In the metabolic flux model, [START_REF] Bost | Genetic and nongenetic bases for the L-shaped distribution of quantitative trait loci effects[END_REF] reported that for L loci that all have the same contribution (m l =µ/L) to the character, an L-distribution of QTL effects can be simulated with allele effects that are randomly drawn from a Gaussian distribution of mean µ/2L and with a standard deviation σ that is sufficiently small to ensure that a l belongs to [0; µ/L] (here σ = µ/8L). The theoretical heritability of F critBB (at initiation) was fixed to h² FcritBB =1. The allelic effects were constant in time and across repetitions. Additionally, 410 neutral biallelic loci were simulated.

First, a single population of 500 trees was generated by drawing the initial allele frequencies between 0 and 1 from a uniform distribution. Next, the 500 trees were split into five sub-populations at five altitude levels (from 700 to 1700 m in elevation), and a neutral pre-evolution phase was run during five non-overlapping generations at a constant population size. During this phase, only genetic drift and dispersal shaped the changes in allelic frequencies in space and time. Lastly, adaptive evolution was simulated over five additional non-overlapping generations, with spatial expansion of the population along the gradient. We considered scenario A (adaptive evolution with mortality) in OM&D. Five repetitions were run. The initial allele frequencies, the individual genotypes and the positions of the initial 500 founder trees differed for the repetitions. However, the allelic effect at each QTL was kept constant.

Measures of population differentiation

The continuous altitudinal gradient was split into 5 discrete populations, such that each population occupied an altitudinal range of 200 m (e.g., the first population between 700 and 900 m, the second between 900 and 1100, etc.). For each repetition and generation, we computed the genetic differentiation among all pairs of populations for F critBB (Q ST ), both for QTLs (F STq ) and neutral loci (F ST ). We consider here the simple case where the parameter F critBB has a heritability of 1 and is a purely additive trait. Q ST can thus be simply computed with the following formula:

Q ST = V B /(V B + 2V W )
where V B is the variance of F critBB between populations, and V W the average variance within population.

The F ST values were estimated at each locus using Arlequin v. 3.5.1.3 (Excoffier and Lischer 2010).

Patterns of genotypic and allelic frequencies across elevation

We plotted the distribution of individual dose D of alleles decreasing F critBB against altitude at the first (G0) and final generation (G5). For each individual j, D j was computed as the sum across all of the QTLs of alleles, with a negative allelic effect a i (A1 allele), using the following formula: D j =Σ i d ji for a single QTL loci, with d ji = 1 if j is homozygote A1A1, d ji = 0.5 if j is heterozygote A1A2 and d ji = 0 if j is homozygote A2A2.

We then searched for the loci exhibiting a significant clinal variation in genotypic frequencies at generation G5 between 900 m (bottom of Pop2) and 1300 m (top of Pop3) in elevation. To do so, we first used the following one-way ANOVA model at each locus i:

Alt j =d ji + ε j
where Alt j is the altitude of individual j, and d ji is defined above. To account for the neutral component of the cline in allelic frequencies generated by gene flow, among the loci exhibiting a significant pvalue with the ANOVA, we removed those also exhibiting a significant clinal variation at G0. This yielded the rate of loci exhibiting clinal variation for both categories of loci (i.e., QTL and neutral). All of the ANOVAs were performed using R.

FST -based outlier approaches

The genotypes from simulated data sets were used to evaluate the capacities of F ST histograms and most frequent and Bayesian outlier approaches to detect candidate markers for selection. For all of these tests, we considered only Pop2 (between 900 and 1100 m) and Pop3 (between 1100 m and 1300 m).

Simple histograms of F ST were used as one of the initial outlier identification methods and are commonly used in high-density genome scans to detect regions with multiple markers with high F ST (e.g., [START_REF] Akey | A high-density SNP allele frequency map reveals genomic signatures of natural selection[END_REF]. With respect to the method of Beaumont and Nichols (1996), we used the version (referred to as FDIST hereinafter) implemented in Arlequin v.3.5.1.3 (without hierarchical structure). The neutral expected F ST distribution was obtained by simulating 1.0 × 10 6 SNPs under a constant-size finite island model. Next, the 95% and 99% confidence intervals of the distribution of F ST vs. expected heterozygosity were calculated using the quantile function in R (package stats). Loci outside of the 95% and 99% confidence intervals of the distribution were identified as outlier loci.

We also used the Bayesian method implemented in BAYESCAN 2.1 (Foll and Gagiotti 2008). For this method (referred to as BAYESCAN hereinafter), we ran 20 pilot runs of 5,000 iterations followed by a

Patterns of genotypic and allelic frequencies across elevation

At initiation (before drift), the distribution of the dose of alleles decreasing F critBB was identical across altitudes. In contrast, there was a clear shift in the distribution at G5: the number of alleles with decreasing F critBB values was at minimum at 1000 m (Pop2) and at a maximum at 1200 m (Pop3); this number then decreased slowly (Figure 4.5). This distribution fitted well the distribution of F critBB values across altitudes.

We investigated the slope in genotypic frequencies between 900 m and 1300 m at each locus. The rate of loci exhibiting a clinal variation between 900 m and 1300 m at generation G5 was 71% for the neutral loci versus 95% for the QTLs. We accounted for the neutral component of the cline in allelic frequencies that were generated by gene flow by considering slope in the genotypic frequencies at generation G0. When removing the loci exhibiting a clinal pattern at generation G0 due to gene flow, the rate of loci exhibiting a clinal variation at generation G5 only fell to 42% for the neutral loci versus 43% for the QTLs.

Inter-genic allelic correlation among populations Pop2 and Pop3

The mean Zg --value for all of the QTLs was Zg-QTL = 0.052 (s.d.=0.022), while for all of the neutral loci it was Zg-Neut = 0.007 (s.d.=0.002). We also computed the mean Zg value for all of the loci that were detected as outliers with Arlequin 3.5: Zg-Out = 0.006 (s.d.=0.003), and those that were not detected as outliers: Zg-NoOut = 0.008 (s.d.=0.002). Based on the variation among the 5 repetitions, only the difference between Zg-QTL and all of the other Zg-values was significant (Figure 4.6,Figure 4.7).

The reverse process was used to discover the QTLs from 100,000 combinations of 10 of the 420 SNPs.

Among the 10 most frequent loci associated with the highest Zg values, on average, of 22% were QTLs versus 78% of neutral loci (Figure 4.8, Table 1). Three QTLs where detected in two repetitions, 2 QTLS in two repetitions and one QTLs in one repetition. The QTLS with the largest effects (QTL 6 and 9) were the most frequently detected QTLS. Lastly, Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF] considered diversifying selection at the between-deme level, with the diversifying action of selection scaled by V ZOPT , i.e., the variance of optimum phenotypic values across the demes. These authors examined cases where V ZOPT varied between 0 (uniform selection) and 10 (strong diversifying selection). To be able to compare this situation with our case, we again ran simulation until generation G8. We took the mean values of F critBB that were reached in Pop2 (F critBB-pop2 =190) and Pop3 (F critBB-pop3 =180) as proxies of the optimum F critBB . Assuming a linear variation of optimum value between Pop2 and Pop3, this would correspond to V ZOPT =12.5, which is strong diversifying selection.

The simulated situation that was considered in the present study would thus be the closest to the case of combining high gene flow, moderate to strong stabilizing selection within demes and strong diversifying selection among demes in the study of Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF]. This is the situation in which these authors obtained a maximum decoupling between differentiation at the trait (Q ST reaching values as high as 0.8) and differentiation at neutral loci (G ST remaining below 0.2), whereas genetic differentiation at QTLs was close to Q ST (G ST q reaching values as high as 0.6). In contrast, in our simulation at generation G5, the average levels of genetic differentiation were globally lower for the traits (Q ST =0.22), QTLs (F STq = 0.084) and neutral loci (F ST = 0.042), an observation that is likely to have resulted from the far lower number of generations simulated here (5) versus the 3000 generations that were simulated in Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF]. The study of transient dynamics that was performed in [START_REF] Kremer | Decoupling of differentiation between traits and their underlying genes in response to divergent selection[END_REF] supports this hypothesis, as after only 100 generation, G ST q was still below 0.1 (see Figure 5 in this previous study). Despite the globally lower differentiation, we also observed a strong decoupling between Q ST and F ST . The decoupling of F STq and F ST was more limited in the present study that in Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF] but was still significant on average.

This result likely confirms that the covariance of allelic effects among demes (θ B ) is the predominant component of Q ST under highly diversifying selection and high gene flow. We lastly also confirm the result of Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF] regarding the high variance of F ST q across QTLs: only 1 to 2

QTLs out of 10 exhibited the highest F ST q -values , whereas most QTLs exhibited F ST q -values that were close to F ST at neutral loci.

Global performance of F ST -based outlier approaches

Previous studies by Narum and Hess (2011) In the present study, most F ST -values at QTLs were close to F ST at neutral loci, both being below 0.1.

As expected, both BAYESCAN and FDIST exhibited a very limited ability to detect QTL. BAYESCAN had the advantage of producing the lowest rate of false positives, even if it often failed to detect highly differentiated QTLs with large allelic effects (high false negative rates). FDIST had a lower false negative rate than BAYESCAN; however, the rate of false positive was extremely high. Perez-Figueroa et al. ( 2010) also observed that most loci that were detected exclusively by DFDIST (modification for dominant markers of FDIST) were false positives. This likely resulted from the absence of correction for multiple testing in FDIST compared to BAYESCAN. There are several other reasons that may explain the low performances of BAYESCAN and FDIST. First, only two populations were scanned in the present study, which may, for instance, decrease the estimation accuracy of the population-specific effects (b j ) that are shared by all of the loci in BAYESCAN. Moreover, Foll and Gaggiotti (2008) reported through simulations that bi-allelic markers, such as those simulated here, have lower power to detect selection than multi-allelic markers. This may be particularly true, at a short time scale.

In our simulated data sets, all of the loci were independent, which is an unrealistic assumption when using a large number of markers. The study by [START_REF] Vilas | A simulation study on the performance of differentiation-based methods to detect selected loci using linked neutral markers[END_REF] investigated the sensitivity of BAYESCAN and FDIST to the level of linkage disequilibrium between the marker loci and the selected loci. Their results were not particularly encouraging given that all of the methods exhibited a substantial uncertainty regarding the genetic association between markers and selected loci and tended to detect false positive. BAYESCAN provided somewhat lower numbers of type I and II errors than DFDIST/FDIST. Although this was beyond the scope of the present study, it would be interesting to evaluate the effect of LD in the case of realistic data sets, as simulated here.

Performance of methods that are based on clines of genotypic frequencies

Our results indicate that the pattern of allelic frequency clines of genotypic frequencies across elevation failed to identify QTLs with confidence given that many significant clines also emerged for neutral loci. Allelic frequency clines have often been considered to be evidence of local adaptation [START_REF] Endler | Geographic variation, speciation, and clines[END_REF][START_REF] Ducousso | Latitudinal and altitudinal variation of bud burst in western populations of sessile oak (Quercus petraea (Matt) Liebl)[END_REF][START_REF] Storz | Natural selection drives altitudinal divergence at the albumin locus in deer mice, Peromyscus maniculatus[END_REF]. Indeed, at local scales, we expect that selection favoring a particular allele, one that is positive directional selection, would lead to local selective sweeps [START_REF] Excoffier | Detecting loci under selection in a hierarchically structured population[END_REF]). However, gene flow and demographic events can leave similar signals in the genome [START_REF] Depaulis | Power of neutrality tests to detect bottlenecks and hitchhiking[END_REF]. For instance, in the case of colonization, using a 2D steppingstone model of a small population with stochastic demography and genetic drift, Edmonds et al. (2005) reported that neutral mutations can have two distinct fates. The mutant is either transported with the front, leading to an embolism effect, or overstepped by the front, remaining at low frequency near the location where it first appeared. In the first case, which is referred to as "gene surfing", we expect allele frequency clines that are parallel to the colonization front to emerge (see also [START_REF] Excoffier | Surfing during population expansions promotes genetic revolutions and structuration[END_REF][START_REF] Hallatschek | Gene surfing in expanding populations[END_REF]). This may be the case in our simulated data sets and highlights the importance of accounting for the demography history experienced by the population to accurately distinguish between the genome-wide effects of demography and the locus-specific effects of selection [START_REF] Kujala | Sequence variation patterns along a latitudinal cline in Scots pine (Pinus sylvestris): signs of clinal adaptation?[END_REF]). However, neutral markers are scarce in trees. With the majority of the developed markers coming from functional genes, it is not possible to examine genome-wide patterns of noncoding markers, as in Drosophila [START_REF] Glinka | Demography and natural selection have shaped genetic variation in Drosophila melanogaster: A multi-locus approach[END_REF].

Performance of the between-population component of intergenic linkage disequilibrium ( Z g )

Interestingly, the between-population component of LD among loci was much higher for the QTLs than for the neutral loci. The Zg value at the QTLs was also higher than for the loci that were considered to be outliers by FDIST. Lastly, the loci involved in subset of loci exhibiting the highest Zg values were not those with the highest F ST values, confirming that Zg scans bring a significantly new information compared to F ST scans. This result confirms the theoretic expectations that selection effects on each QTL during the first generations would be very small; thus, the allelic frequencies would not be expected to change strongly (Latta et al. 1998). In contrast, the emergence of linkage disequilibrium among QTLS can cause rapid and significant changes in the additive genetic variance [START_REF] Bulmer | The stability of equilibria under selection[END_REF][START_REF] Bulmer | Linkage disequilibrium and genetic variability[END_REF]. As was suggested by [START_REF] Kremer | Decoupling of differentiation between traits and their underlying genes in response to divergent selection[END_REF], the intergenic disequilibrium could reach higher values when the trait is controlled by a larger number of loci as the number of opportunities for favorable associations increases. This study confirmed that genetic covariance is the principal driver of genetic differentiation in cases of high gene flow and high selection at short time-and spatial scales.

Nevertheless, we did not identify with confidence the QTLs by screening the Zg values for loci combinations. This result highlights the difficulty of identifying a locus when it is an interaction between numerous loci that drive phenotypic variation.

CONCLUSION

There is an increasing concern regarding the rate of response of natural populations to ongoing climate change [START_REF] Aitken | Adaptation, migration or extirpation: climate change outcomes for tree populations[END_REF]. Indeed, the range of temperature increase that was simulated by IPCC is dramatically high for the next 100 years, threatening the majority of the broadleaf species in Europe. As a consequence, there are substantial advantages of putting more effort into evaluating genetic responses at a short timescales e.g., five generations of trees, representing at least 250 years. Five generations is brief at an evolutionary time scale is a long period of time for foresters and environmental policies.

OM&D showed that a significant response to climate-induced selection can be achieved within a timescale of 5 generations. In the present study, we investigated whether genetic markers could be of use to monitor such adaptive changes. Our results demonstrate that only a combination of several genetic indicators (F ST , Zg) can capture the signal of selection at short time-scales. Moreover, the power of genetic indicators that are based on markers appears too low to not use phenotypic indicators. In this PhD, I investigated the variation of 58 candidate genes along two attitudinal gradients of Fagus sylvatica on Mont Ventoux. My objective was to disentangle the signal of adaptation to climate from neutral evolutionary forces and demography. The structure of this discussion is the following. First I will draw conclusion regrading the methodology, in particular, regarding the difficulties when developing and sequencing candidate genes in a non-model species. Second, I will discuss how and what this new data set helped to learn about the response of Fagus sylvatica to climate. Third, I will broaden the debate on the use of molecular markers in the context of climate change.

EXPERIMENTAL GENOMIC APPROACH IN NON-MODEL SPECIES : METHODOLOGICAL INSIGHTS

1.1. Is Sanger-sequencing appropriate for population genomic studies ? Sanger sequencing is a well-known and still cost effective method. It produces long DNA fragments, in average 800b in length [START_REF] Hert | Advantages and limitations of next-generation sequencing technologies: A comparison of electrophoresis and non-electrophoresis methods[END_REF]) and among the diverse method available it has the lowest average error rate [START_REF] Kircher | High-throughput DNA sequencing -concepts and limitations[END_REF]. After sequence end trimming, an error occur every 10,000-100,000b (Ewing and Green 1998). The sequencing error observed is mainly due to errors in the amplification step, contamination in the sample used and/or polymerase slippage at low complexity High-quality Sanger sequencing is now commonly used to generate low-coverage sequencing of individual positions and regions as for example diagnostic genotyping [START_REF] Kircher | High-throughput DNA sequencing -concepts and limitations[END_REF].

However, the technology is expensive and too slow for sequencing numerous genes on a large sample.

Other method than Sanger sequencing should be preferred for studies requiring more than 10-15 DNA fragments, particularly when the genome or sequence of reference is non available. The last highthroughput sequencing methods (e.g. 454/Roche and Illumina), despite the drawback to produce smaller fragments than Sanger, produce high coverage and so have very low rates of misidentifying individual bases, making them perfectly suited for the identification of SNPs [START_REF] Kircher | High-throughput DNA sequencing -concepts and limitations[END_REF].

the high coverage allowed may easily produce a high coverage making easier to check through automatic filter. 154 155 Many forest tree species display significant genetic differentiation for adaptive traits across their geographic range (Savolainen et al. 2007), molecular patterns of differentiation at large scales were naturally investigated in order to detect the imprints of local adaptation and to identify loci under selection (e.g. [START_REF] Heuertz | Multilocus patterns of nucleotide diversity, linkage disequilibrium and demographic history of Norway spruce [Picea abies (L.) Karst][END_REF][START_REF] Eveno | Contrasting patterns of selection at Pinus pinaster Ait. drought stress candidate genes as revealed by genetic differentiation analyses[END_REF]Prunier et al. 2011).

Detecting the signature of selection in natural populations has long been of interest. The idea behind tests for selection is that a selective process would only affect certain regions of the genome, while demographic processes would affect the whole genome uniformly; a simple idea, which was originally proposed by [START_REF] Cavalli-Sforza | Population structure and human evolution[END_REF]. Later, a formal test of neutrality was proposed by Lewontin and Karauker (1973) based on the comparison of the variance of F statistics across populations. This test had been criticized (Nei and Maruyama 1975;Robertson 1975a,b;[START_REF] Nei | Mean and Variance of Fst In A Finite Number of Incompletely Isolated Populations[END_REF]) and re-discovered several times, which has lead a development of several methods based more or less on the same original idea. FDIST (Beaumont and Nichols 1996) and BAYESCAN (Foll and Gaggiotti 2008) are two popular methods to identify patterns of genetic differentiation driven by selection. Table 9 provides a summary of the findings of several studies studies that used F ST -based methods in natural populations of trees. Concerning the number of outliers, both frequentist and Bayesian methods detected roughly the same proportion of outliers (~3.5%). An exact proportion would be hardly accessible as the authors do not report the results for the same α cutt-off value and Bayes Factor. However, for the studies that used both methods, the frequentist method generally detected more outliers than the Bayesian method.

For instance, the frequentist method detected 10 times more outliers than Bayesian method [START_REF] Namroud | Scanning the genome for gene single nucleotide polymorphisms involved in adaptive population differentiation in white spruce[END_REF] and Prunier et al. (2011), 5 times more [START_REF] Tsumura | Genome scanning for detecting adaptive genes along environmental gradients in the Japanese conifer, Cryptomeria japonica[END_REF] or only 1.2 times more in [START_REF] Eveno | Contrasting patterns of selection at Pinus pinaster Ait. drought stress candidate genes as revealed by genetic differentiation analyses[END_REF]. Interestingly, the difference in the number of outlier detected by both methods was the highest in studies investigating weakly differentiated populations (F ST from 0.006 to 0.04) while in the study of [START_REF] Eveno | Contrasting patterns of selection at Pinus pinaster Ait. drought stress candidate genes as revealed by genetic differentiation analyses[END_REF] populations were much more differentiated (F ST =0.14). Beaumont and Nichols (1996) tested the robustness of their method by simulating differentiation among populations of F ST =0.17 which is in the same order of magnitude as in Narum and Hess (2011).

Many of the studies listed in Table 9 compared populations distant by more than 100km. There are only two studies (Jump et al, 2006;Pluess and Weber 2012) dealing with populations distant by less than 10km. Interestingly these two studies were conducted in F. sylvatica and both detected outliers but studied experimental situations. Simulation results suggest that climate variation along F. sylvatica altitudinal gradient in Mont Ventoux generated a strong divergent selection along the gradient together with strong stabilizing selection within population and high gene flow between populations. As expected from the simulations of Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF], the F ST -based genome scan methods failed to accurately discriminate the QTLs from neutral loci.

Previous studies of genomic patterns of population differentiation over large scales concluded that it is both crucial and difficult to account for the confounding effects of demographic processes, which can leave molecular signatures similar to those of than selection [START_REF] Vasemägi | The adaptive hypothesis of clinal variation revisited: single-locus clines as a result of spatially restricted gene flow[END_REF][START_REF] Savolainen | Adaptive Potential of Northernmost Tree Populations to Climate Change, with Emphasis on Scots Pine (Pinus sylvestris L.)[END_REF]Chen et al. 2012). In this context, altitudinal gradients emerged as valuable experimental designs to avoid confounding signatures from demographical events. Indeed, altitudinal gradient display steep environmental variations (temperature, air and soil humidity, frost risk) over short spatial distances, which can result in strong divergent selection pressure and enhance local adaptation, despite extensive gene flow (Gonzalo-Turpin and Hazard 2009[START_REF] Alberto | Adaptive responses for seed and leaf phenology in natural populations of sessile oak along an altitudinal gradient[END_REF], Montesinos-Navarro et al. 2011). In such systems, patterns of genetic diversity and differentiation both at adaptive traits and their underlying genes are expected to be driven mainly by the balance between selection and gene flow, while the effect of mutation, genetic drift and different history of the populations are expected to be minimized. But the drawback is a lack of efficiency of the genome scan methods when the population are slightly differentiated.

In conclusion, the results of this combined study of experimental and simulated data sets are not particularly encouraging for the use of outlier detection tests at local spatial scale. One one hand, the frequentist approach of Beaumont and Nichols (1996) was confirmed to lead to a high rate of false positives, and need to be corrected. On the other hand, the Bayesian approach was confirmed to have a lower rate of false positives, but the method is also more conservative, i.e. loci under selections are more likely to remain undetected. 1.3. Towards integrated approaches to detecting selection at local spatial scale and short evolutionary time scales ? "In artificial selection experiments it seems likely that the effects of linkage disequilibrium will be more important than the slower and less dramatic effects due to changes in gene frequencies" [START_REF] Bulmer | Linkage disequilibrium and genetic variability[END_REF] The results of this PhD thesis confirmed that besides population differentiation estimated by F ST , the correlation of allelic frequencies among loci among populations (estimated by Zg) can capture molecular signature of divergent selection among populations. In the simulation study performed in chapter IV, the Zg value was significantly higher for the QTLs (Zg-QTL = 0.052) than for the neutral loci (Zg-Neut = 0.007 ). Moreover, the loci exhibiting the highest Zg values were not those with the highest F ST values, confirming that Zg scans bring a significantly new information as compared compared to F ST scans. This was expected considering that the Zg estimated provide by Storz and Kelly is standardized by the variance of allelic frequencies. In the experimental study performed in chapter 3, the Zg value was significantly higher for outlier SNPs than for all SNPs together.

Overall, the results of this PhD study thus advocate towards the simultaneous use of various indicators of the action of divergent selection, at least simultaneously F ST and Zg, and probably other statistics used in neutrality tests. Recently, ABC (Approximate Bayesian Computation) approaches have received much attention due to their capacity to integrate various summary statistics to estimate demographic and genetic parameters in population with complex history and structure [START_REF] Csillery | Approximate Bayesian Computation (ABC) in practice[END_REF]). It would be quite interesting to test whether such approaches could perform better than the one used during this PhD thesis to detect molecular signature of recent selection at local spatial scale.

TOWARD A BETTER KNOWLEDGE OF F. SYLVATICA ADAPTIVE RESPONSE TO CLIMATE

A low diversity, low differentiation among populations and strong LD

Beginning with nucleotide diversity, the values estimated in F. sylvatica (θπ -total =2. 6, Seifert et al. 2012; θπ -total =2.2 this study) are among the lowest compared to other tree species (Savolainen et al. 2007) These values appear even lower when compared to Quercus species which have on average a θπ -total = 9.39 [START_REF] Kremer | Genomics of Fagaceae[END_REF]. [START_REF] Seifert | DNA sequence variation and development of SNP markers in beech (Fagus sylvatica L.)[END_REF] The current genetic diversity of European tree species over their range is likely to have been shaped by successive range contractions during the last glaciations [START_REF] Petit | Glacial refugia: Hotspots but not melting pots of genetic diversity[END_REF]. Phylogeographic studies of Quercus species revealed that the current European populations originate from three main refugia, the Iberian and Italian peninsulas and the Balkans [START_REF] Petit | Identification of refugia and post-glacial colonisation routes of European white oaks based on chloroplast DNA and fossil pollen evidence[END_REF]. Admixture 6 between lineages originating from these three main refugia may SSR markers was observed while grater differentiation (F ST =0.261) for a gene previously demonstrated to be under selection [START_REF] Pampoulie | The genetic structure of Atlantic cod (Gadus morhua) around Iceland: insight from microsatellites, the Pan I locus, and tagging experiments[END_REF]. Another example in European flounder (Platichtchys flesus) where differentiation for a heat shock protein locus was much greater than at neutral markers, F ST =0.45 against F ST =0.02 respectively [START_REF] Hemmer-Hansen | Adaptive divergence in a high gene flow environment: Hsc70 variation in the European flounder (Platichthys flesus L.)[END_REF]). Forest trees are known for their large population size and similar results were observed by [START_REF] Namroud | Scanning the genome for gene single nucleotide polymorphisms involved in adaptive population differentiation in white spruce[END_REF]. The authors identified by genome scan 20 SNPs with differentiation around around ten fold higher than the differentiation at neutral loci (F ST =0.006, other examples in Table 9). main threaten is the cavitation by which air bubbles disrupt the hydraulic conductance, thus exacerbating plant water deficit [START_REF] Zimmermann | Xylem structure and the ascent of sap.. Xylem structure and the ascent of sap[END_REF][START_REF] Choat | Global convergence in the vulnerability of forests to drought[END_REF]. Cavitation may be avoided by two strategies. First, the tree may reduce its evapotranspiration [START_REF] Meinzer | Xylem hydraulic safety margins in woody plants: coordination of stomatal control of xylem tension with hydraulic capacitance[END_REF], second by increasing the the xylem conduits resistance. Although xylem structure can acclimate to environmental variation during growth and development, subsequent acclimation of embolism resistance to environmental stress is not possible because xylem conduits are dead at maturity. Adjacent conduits are connected by pit that from pores in the walls. An enzyme, the pectin methyesterase (PME) determines the number of sites where pectin chains are potentially cross-linked by calcium, and may thus strongly impact the mechanical properties pit membranes (Pelloux et al. 2007;Herbette and Cochard 2010) . In woody species, xylem vulnerability to cavitation correlates tightly with species-specific drought tolerance [START_REF] Pockman | Vulnerability to xylem cavitation and the distribution of Sonoran desert vegetation[END_REF][START_REF] Tyree | Desiccation tolerance of five tropical seedlings in Panama. Relationship to a field assessment of drought performance[END_REF][START_REF] Maherali | Adaptive variation in the vulnerability of woody plants to xylem cavitation[END_REF]). Moreover substantial variations have been found between genotypes of a different species [START_REF] Cochard | Xylem vulnerability to cavitation varies among poplar and willow clones and correlates with yield[END_REF][START_REF] Dalla-Salda | Genetic variation of xylem hydraulic properties shows that wood density is involved in adaptation to drought in Douglas-fir (Pseudotsuga menziesii (Mirb.))[END_REF][START_REF] Lamy | Micro-evolutionary patterns of juvenile wood density in a pine species[END_REF]. Recently, by measuring the cavitation resistance of 17 F. sylvatica populations representative of the full range of the species in Europe, [START_REF] Wortemann | Genotypic variability and phenotypic plasticity of cavitation resistance in Fagus sylvatica L. across Europe[END_REF] found a high genetic variability for cavitation resistance among populations. In consequence, cavitation resistance can be suspected to be a functional traits with major effect on the individual fitness.

Interestingly, the SNP 711 coding for a pectin methylesterase was detected by FDIST outlier on both gradients. Therefore this encouraging result suggest more studies on the PME activity and its genetic component.

The timing of budburst phenology is an important life history trait determining the vegetation length in F. sylvatica [START_REF] Nielsen | Phenology and diameter increment in seedlings of European beech (Fagus sylvatica L.) as affected by different soil water contents: variation between and within provenances[END_REF]. Specifically on the Mont Ventoux, the PhD thesis of [START_REF] Bontemps | Potentiel évolutif d'une population de hêtre commun sur le Mont Ventoux[END_REF] demonstrated that the timing of budburst is variable and heritable trait (narrow-sense h²=0.29) and that is is submitted to a significant ongoing directional selection towards earlier budburst.

This may be either because earlier budburst increase the length of the vegetative season (suggesting that the risk of late frost damages are not limiting) or because it allows maximizing growth by avoiding drought stress. Indeed, the PhD thesis of Bontemps (2012) also showed a negative phenotypic and genetic correlation between the timing of budburst and water use efficiency in the bottom population of Mont Ventoux altitudinal gradient. Secondly, a common garden experiment showed a low but significant patterns of genetic differentiation for the timing of budburst between populations NL and NH, NH showing earlier budburst in controlled conditions (Q ST ≈ 5%, Gaüzère 2011). This suggest that past selection for earlier budburst was stronger in NH than in NL, and/or that selection towards earlier budburst in NL may be very recent. Thirdly, using the PDG model, where only the timing of budburst (but not water used efficiency) was considered to be variable and heritable, this trait was showed to evolve towards different values along the altitudinal gradient. We analyzed simulations in which the risk of late frost was not considered, and where one population (Alt 2) evolved later timing of budburst, which resulted in a strong pattern of differentiation at this traits (with Q ST ~ 25%) The simulations, consistently with the theoretical expectation of Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF] revealed that the 2 QTLs (over 10) with the highest allelic effect were highly differentiated and detected as outlier with the FDIST approach. By contrast, in this PhD study, the genes putatively involved in phenology did not show any strong signal of selection. Three SNPs located in phenology genes were detected as outliers with FDIST (with more stringent null model) in one of the two gradient, but were not among the 4 strongly supported SNPs. The among-population variation of budburst phenology was found to be significantly explained by one SNP, but it belonged to a gene from the stress category. In the light of the simulations the probability that those SNPs are false positive is thus very high. Therefore further investigations through multiple approaches like in the study of Chen et al. (2012) Leaves are primarily the photosynthetic organs of the plant. One leaf proxy commonly used is the leaf dry mass per area (LMA). LMA has the particularity to vary more than 100-fold among species.

Although a large part of this variation can be ascribed to differences between functional groups, environmental conditions (e.g. light, CO 2 ) also have a major impact on LMA [START_REF] Poorter | Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis[END_REF].

Despite high phenotypic plasticity controlled by environmental conditions, significant genetic differentiation have been observed between population of Quercus suber [START_REF] Ramirez-Valiente | Phenotypic plasticity and local adaptation in leaf ecophysiological traits of 13 contrasting cork oak populations under different water availabilities[END_REF]. In the population of the Mont Ventoux, one outlier SNP (belonging to a phenology gene) was correlated to LMA.

In conclusions the three genes containing highly supported outlier SNPs appear as good candidates for further investigations of the response to climate in F. sylvatica.. Moreover, the study of [START_REF] Bontemps | Potentiel évolutif d'une population de hêtre commun sur le Mont Ventoux[END_REF] investigating selection on functional trait in F. sylvatica highlight the need to consider multitrait response to selection; Indeed, major functional traits were shown to be correlated. This may explain why variation for burdburst was found to be associated to a gene belonging the "stress gene" category and why in contrast the variation in LMA was found to be associated to a "phenology gene".

straightforward. Changes in a particular gene may be restricted to a specific life cycle. For instance, in Drosophila melanogaster, hsp70 7 expression correlates with heat resistance at the larvae stage but not at the adult stage [START_REF] Jensen | Adult Heat Tolerance Variation in Drosophila melanogaster is Not Related to Hsp70 Expression[END_REF].

Even if markers with large effect on the fitness had been detected, it is important to not restrict further investigation to them. Firstly because recent polygenic models tend to incorporate a maximum of markers and not merely the statistically significant ones into the estimation of the the trait values [START_REF] Rockman | The QTN program and the alleles that matter for evolution: all that's gold does not glitter[END_REF]). Secondly, one locus under selection in one environment condition may act as neutral in another,. In addition, climate change is not a mere modification of climatic parameters but through emergent properties of the ecosystem, is also a change in species composition and abundance.

Therefore the future environmental conditions and threatens that tree species will face are almost unknown. Microevolutionary processes seem to occur within short geographic distances and that 'preadaptive' genes can easily spread across the landscape (Pluess and Weber 2012). Therefore, in the short-time we may favor the evolution toward a local optimum for a specific functional trait. But, in the mean time, we should also try to keep enough genetic diversity for all the other traits in order to allow any further adaptation [START_REF] Lefevre | Considering evolutionary processes in adaptive forestry[END_REF].

In conclusion, a better understanding of the ultimate and proximate mechanisms that drive adaptation would be reached through a trans-disciplinary approach and by the integration of their different methods (Figure 2, [START_REF] Barrett | Molecular spandrels: tests of adaptation at the genetic level[END_REF].

7

Protein that play an important role in protein-protein interactions such as folding and assisting in the establishment of proper protein conformation and prevention of unwanted protein aggregation. They are often up-regulated during stress response.

168

Figure 2. Connections between various approaches for studying the genetics of adaptation. Ideally, an allele is designated as adaptive only when connections are made between its genotype, phenotype and fitness. From [START_REF] Barrett | Molecular spandrels: tests of adaptation at the genetic level[END_REF].

9

  ContentsIntroduction ...........................................................................................................................................17 1.Speed of adaptation of forest trees to climate change........................................................................19 2.Overview of the concept of local adaptation......................................................................................21 3.Main approaches by which to demonstrate local adaption................................................................22 3.1.Approaches to searching for signs of selection among populations............................................25 3.2.Approaches for searching for the signal of selection within a population...................................27 4.Disentangling the role of demography and adaptation in the interpretation of patterns of local adaptation...........................................................................................................................................28 5.Latitudinal versus altitudinal gradient as models for investigating local adaptation.........................31 6.Objectives of the thesis......................................................................................................................32 Chapter 1. Species and sites ..................................................................................................................35 1.Fagus sylvatica...................................................................................................................................37 1.1.Phylogenetic origin .....................................................................................................................37 1.2.The post-glacial distribution of Fagus sylvatica..........................................................................39 1.3.Main physiological and ecological properties..............................................................................40 1.4.Bud burst phenology ...................................................................................................................41 1.5.The genome of F. sylvatica .........................................................................................................42 2.Mont Ventoux.....................................................................................................................................44 3.The history of Fagus sylvatica on Mont Ventoux..............................................................................46 Chapter 2. Candidate genes selection and SNP discovery......................................................................49 1.Context ..............................................................................................................................................51 2.Abstract..............................................................................................................................................55 3.Introduction........................................................................................................................................56 4.Material and methods.........................................................................................................................59 4.1.Plant material...............................................................................................................................59 4.2.EST databases and annotation......................................................................................................59 4.3.Candidate gene selection..............................................................................................................59 4.4.Primer design and gene amplification..........................................................................................60 4.5.Candidate gene sequencing..........................................................................................................60 4.6.Data analysis................................................................................................................................61 4.6.1.Phase reconstruction...........................................................................................................61 4.6.2.Nucleotide diversity, LD patterns and recombination.........................................................61 5.Results................................................................................................................................................62 5.1.Gene selection, amplification and sequencing.............................................................................62 5.2.Genomic diversity........................................................................................................................67 5.3.Linkage disequilibrium (LD).......................................................................................................67 6.Discussion..........................................................................................................................................69 7.Appendices.........................................................................................................................................73 Chapter 3. Detection of loci under selection in Fagus Sylvatica along an altitudinal gradient: an outlier experimental approach............................................................................................................................

Chapter 4 .

 4 ......................................................................................................................................107 7.References........................................................................................................................................112 8.Appendices.......................................................................................................................................116 Interaction between selection and gene flow in the building of genetic differentiation along an altitudinal gradient: lessons from an Eco-Physio-Demogenetic simulation model..........................123 1.Context.............................................................................................................................................125 2.Introduction......................................................................................................................................128 3.Materials and Methods.....................................................................................................................132 3.1.The model and the simulated data sets.......................................................................................132 3.2.Measures of population differentiation......................................................................................133 3.3.Patterns of genotypic and allelic frequencies across elevation..................................................134 3.4.FST -based outlier approaches...................................................................................................134 3.5.Patterns of covariance among populations.................................................................................135 4.Results..............................................................................................................................................137 4.1.Patterns of genetic differentiation for the trait (FcritBB), the QTLs and the neutral loci..........137 4.2.FST -based genome scan between populations Pop2 and Pop3................................................137 4.3.Patterns of genotypic and allelic frequencies across elevation..................................................141 4.4.Inter-genic allelic correlation among populations Pop2 and Pop3............................................141 5.Discussion........................................................................................................................................144 5.1.Comparison with theoretical expectations.................................................................................144 5.2.Global performance of FST -based outlier approaches..............................................................146 5.3.Performance of methods that are based on clines of genotypic frequencies..............................147 5.4.Performance of the between-population component of intergenic linkage disequilibrium (Zg) ..............................................................................................................................................................

Figure

  Figure 1. IPCC report 2007

Figure 4 .

 4 Figure 4. Distribution of the observed statistics (θ, π, Tajima's D, …) under the neutral model (in blue) and the the true model (in red). The demography can influence the distribution of the neutral model and therefore lead to high proportion of false positive (modified from Siol et al. 2010)

Chapter 3 :Chapter 4 :

 34 Figure 1.1. Phylogenetic relationships in extant Fagus based on morphological and molecular data. Two potential roots are indicated by arrows. Colored edges represent alternative (in-congruent) relationships indicated by the underlying data.Modified from[START_REF] Denk | The biogeographic history of beech trees[END_REF]

Figure 1 . 4 .

 14 Figure 1.4. Beech phenological stages: 1) buds are dormant; 2) buds are swelling; 3) bud scales are broken; 4) leaves are emerging; 5) leaves are spread out (modified from (Malaisse 1964).

Figure 1

 1 Figure 1.5. a Altitudinal effect on mean annual temperature (grey circles) and on the number of spring frost days (black crosses) in 2007 and 2008 along the gradient studied on the north-facing slope of Mont Ventoux. The number of spring frost days was calculated as the average of the number of frost days between April and June for the years 2007 and 2008. The slope of the linear regression between mean temperature and altitude is -0.71°C/100 m. b Elevational effect on spring and summer (March-August) rainfall on the Mont Ventoux for the period 2000-2005. The slope of the linear regression between rainfall and altitude is +14 mm/100 m (from (Cailleret and Davi 2011).

  Figure 1.6), Lander et al. (2011) used approximate Bayesian (ABC) computation on microsattellite markers to reconstruct the demographic scenarios of beech on Mont Ventoux. ABC analysis selected three populations as the most likely demographic scenario, with two remnant populations and one being recently expanded (Figure 1.7). The most supported scenario is scenario 7, with the South population diverging from the other populations twenty generations ago

Figure 1 . 6 .

 16 Figure 1.6. Areas of consistent beech presence since 1845 are indicated by polygons filled with black stripes. Areas where beech forest appears to have expanded between 1845 and 2000 are indicated in the east and south by polygons filled with points or solid colour (from (Lander et al. 2011).

Figure 1 . 7 .

 17 Figure 1.7. The scenarios used in the DIYABC analysis. All eight scenarios assume that at the present time (0 years), there are three populations, East, South and West (E, S, W), and these three populations diverged from a single population in the past. Colour or pat-tern change in the branches of the coalescent tree indicates the possibility of population size change. The time scale is shown on the right (from Lander et al. 2011).

Figure 2 . 1 .

 21 Figure 2.1. Proportion of proteins cited from one to five times.

Figure 2

 2 Figure 2.2. Location of A) Mont Ventoux superimposed on beech distribution area (Von Wuehlisch G. 2008 and B) of the 4 studied populations superimposed on a digital elevation model

  Our results, unsurprisingly, agree with those ofItsik et al. (2006), who performed a validation and reconciliation of LD in six large human SNP genotype data set. It is well established that SNPs of different MAFs, on average, have different LD properties[START_REF] Pritchard | Linkage disequilibrium in humans: Models and data[END_REF], for both biological (common alleles are generally older than less common alleles) and statistical reasons (sampling). SNPs of small MAF (rare variants) tend to have higher pairwise D' values and lower pairwise r² values than do common SNPs. Sample size primarily affects the D' measure; smaller samples miss out the gamete(s), inflating D'[START_REF] Jorde | Linkage disequilibrium and the search for complex disease genes[END_REF].

Figure 3

 3 Figure 3.1. Alleles permutation test. *(p < 0.05)

  because they display steep environmental variations (temperature, air and soil humidity, frost risk) over short spatial distances. These variations can result in strong divergent selection pressure and enhance local adaptation, despite extensive gene flow. Published reciprocal transplantation studies on plants suggests that local adaptation can occur along altitudinal gradients at a short spatial scale (Gonzalo-Turpin and Hazard 2009, Montesinos-Navarro et al. 2011), because there is a significant genotypic component in the expression of the phenotypic differentiation.

  . Nowadays, beech stands occur at elevations from 750 m to 1695 m on the northern side (characterised by a steep slope) and from 840 m to 1615 m on the southern side (characterised by a gentle slope) of Mont Ventoux. A total of 96 adult individuals was sampled in four populations along two altitudinal gradients (see Chapter 2, Figure 2.2 and Table

  and A. Kremer). Then we searched for homology among 48110 Fagus sylvatica Expressed Sequence Tags (ESTs) developed within EVOLTREE Network of Excellence (http://www.evoltree.eu/). The genes were amplified from diploid genomic DNA and Sanger sequenced. Sequences were base-called and assembled with PHRED, PHRAP and Polyphred programs included in the pipeline SeqQual (Lang et al. unpublished, available upon request to the authors), which additionally deals with individual base quality (minimum PHRED score of 20). Alignments and sequences at polymorphic sites were visually checked and heterozygous indels were recoded as SNPs (see online appendix 1).

Figure 2 .

 2 Figure 2. Demographic scenarios simulated with ms to obtain neutral expectation for F ST distributions, with m, migration rate; N 2 , ancestral population size; N 1 , population size before expansion; N 0 , present population size; t, time in generations. A) split scenario with divergence without population size change at t 4 , evolution without population size change up to t 1 and then expansion B) bottleneck scenario similar to the split scenario except that an initial bottleneck occurred simultaneously with divergence at t4. C) The bottleneck stepwise scenario, with three divergence events at t 4 , t 3 ,t 2 , evolution without population size change up to t 1 and then expansion. See Table4for parameters values.

Figure 3 .Figure

 3 Figure 3. Distribution of FST as function of expected heterozygosity within population for each of SNPs and the 5% limits of simulated distributions based on A) Island models in northern gradient, B) Island models in southern gradient, C) split, bottleneck and bottleneck stepwise models in northern gradient, D) split, bottleneck and bottleneck stepwise models in southern gradient.

  Appendix A2: Selection of 12 neutral SSR from Lander et al. (2011). The 12 SSRs markers used for computing the mean neutral F ST . The 12 SSRs were chosen based on the neutral expectation of F ST simulated through 5.0 × 10 4 realizations assuming 100 demes and a stepwise mutation model (no difference with IAM was observed). The microsatellite sfc1063 was detected as outlier at the 5% level. We removed this marker and ran again the same analysis with the adjusted mean F ST . The 12 microsatellites were inside the neutral envelop and close to the mean F ST value. A) neutral envelope simulated assuming a stepping stone model; B) neutral envelope simulated assuming an infinite allele model. Bottleneck & stepping stone scenario ./ms 142 1000000 -s 1 -I 4 70 0 0 72 -ma x 44.

  illustrated the role of θ B in a simple example. They considered an additive trait controlled by two loci with α and β their genotypic values within populations and αmean and βmean their mean within within-population genotypic values; the trait's value (Z) is defined by Z= α+β. In two different environments the trait value leading to the highest fitness is different. Within population selection drive phenotypic values to the local optimum Z OPT (Figure 4.2a) and leads to negative values of θ W in each population (negative slope of the blue bands Figure 4.2b). Between populations, the divergent selection will create positive values of θ B regardless the strength of stabilizing selection (positive slope of the black dash Figure 4.2b).

Figure 4 .

 4 Figure 4.1. Contribution of each polymorphic QTL to the between-deme (tight hatching) genic variance and (spaced hatching) genetic covariance for the trait under weak selection and high gene flow (Nm=10). Modified from Le[START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF]

Figure 4 .

 4 Figure 4.2. Expected values of θW and θB under stabilizing and divergent selection. a) Phenotypic values distribution at initial stage. The local selection will screen individuals having the sum of their genotypic values the closest to ZOPT. b) Tradeoff between α and β , the blue bands represent the combinations that lead to trait values close to ZOPT. Modified from Le Corre and Kremer (2012).

Figure 4 . 3

 43 Figure 4.3 Patterns of genetic differentiation across generations for the trait FcritBB (as measured by QST), fo the QTLs (as measured by FSTq) and for the neutral loci (as measured by FST).

Figure 4 . 4

 44 Figure 4.4 Histogram of pairwise FST between the Pop2 and Pop3 at the generation G5 for neutral loci (FST, grey bars) and QTLs (FSTq, black bars).

Figure 4 . 5

 45 Figure 4.5 Evolution of the distribution of the dose of alleles decreasing FcritBB (cumulated across all the 10 QTLs) against altitude (variation=variation among the 5 repetitions).

  140

Figure 4 . 6

 46 Figure 4.6 Median distribution of the Zg computed for the different categories of SNPs from 5 repetitions.Figure 4.7 Test for the differences in mean Zg among categories of loci from 5 repetitions.

Figure 4 . 7

 47 Figure 4.6 Median distribution of the Zg computed for the different categories of SNPs from 5 repetitions.Figure 4.7 Test for the differences in mean Zg among categories of loci from 5 repetitions.

Figure 4 . 8

 48 Figure 4.8 Rate of occurrence of neutral loci (black, empty dot) and QTL (red, full dot) in the 5,000 subset of 10 loci showing the highest value of Zg among 100,000 random samples of loci, per repetition and as fubction of the frequency of the first allele in population N2. The horizontal line separate the 10 most frequent loci from all the others (test for outliers).

  and Perez-Figueroa et al. (2010) also explored the performance of BAYESCAN and FDIST to detect loci under selection. Perez-Figueroa et al. (2010) considered a subdivided population at equilibrium and concluded that BAYESCAN was the most efficient method, detecting both a high proportion of the loci under selection and minimizing the rate of false positives. After 2000 generations and under strong selection, Narum and Hess (2011) reported that FDIST and BAYESCAN were able to detect the majority of QTLs, with the later having globally the lowest rate of false positive and false negatives. Interestingly, those QTLs also had the highest F ST values (Fig. 3, Narum and Hess 2011). According to Kremer and Le Corre (2011) such high differentiation at QTLs is unexpected to occur within 10-20 generations except for the very few QTLs with the largest effects. Moreover, Perez-Figueroa et al. (2010) clearly advise not to use outlier approaches to detect diversifying selection in cases where the mean genetic differentiation among the population is larger than ~0.2.

Figure 1 .

 1 Figure 1. Modified flow chart from the chapter II. The font size is roughly proportional to the time spent at the particular step.

sequences 4 .

 4 Further, lower intensities and missing termination variants tend to lead to sequencing errors accumulating toward the end of long sequences. Fragment reduced separation by the electrophoresis, base miscalls, deletions as well lower intensities and missing termination variants increase with read length (Ewing et al. 1998) Despite these advantages, two main technical drawbacks should be mentioned. First only two reads per individuals are produced. Sanger sequencing requires the forward and reverse primer of the amplicon 5 . The forward sequences are sequenced from the forward primer and vice versa. Therefore, both forward and reverse sequences should reverse complement each other. If there is no overlapping between the two sequences, the SNPs identification is solely based either by the forward or the reverse sequences and in consequence must be confirmed by a careful check of the chromatograms. Second, when applied to diploid tissue, both alleles are read at the same time. In such situation, the presence of heterozygous insertion-deletion (INDEL) induce a shift between the two alleles and blur the optic signals (see supplementary material in the manuscript of chapter 2). For most sequences where INDELs occur, long 4 region made of short variable number tandem repeats and homopolymers (stretches of the same nucleotide) 5 There is different meaning but here amplicon means a piece of DNA resulting from artificial amplification 153 DNA fragments are lost. Moreover the INDELs presence greatly complicates the alignment process and requires frequent adjustments that doomed advantages of automatic alignment pipelines (Appendix 3, chapter 2). Automatic software such as OLFinder (Dixon 2010) for recovering the haplotype sequences works only if the sequences are of very high quality after the INDEL site which is rarely the case.

  .81 1.Context...............................................................................................................................................83 2.Abstract .............................................................................................................................................88 3.Introduction........................................................................................................................................89 4.Materials and methods ......................................................................................................................92 4.1.Study species and site .................................................................................................................92 4.2.Candidate gene selection, sequencing and SNP validation..........................................................93 4.3.Genetic diversity, differentiation and linkage disequilibrium at candidate genes........................93 4.4.SSR data.......................................................................................................................................94 4.5.Frequentist outlier approach.........................................................................................................94 4.6.Bayesian outlier approach............................................................................................................98 4.7.Relation between outlier SNP and traits......................................................................................98 5.Results................................................................................................................................................99 5.1.Genetic diversity and differentiation at SNP and SSR markers ..................................................99 5.2.Frequentist outlier approach ...

...................................................................................................102 5.3.Bayesian outlier approach .........................................................................................................106 5.4.Patterns of between-population correlations for SNP pairs.......................................................106 5.5.Relation between outlier SNPs and functional traits..................................................................106 6.Discussion .

  Experimental genomic approach in non-model species : Methodological insights.........................153 1.1.Is Sanger-sequencing appropriate for population genomic studies ? ........................................153 1.2. Are genome-scan approaches appropriate for detecting selection along local environmental gradients ?..................................................................................................................................156 1.3. Towards integrated approaches to detecting selection at local spatial scale and short evolutionary time scales ?..........................................................................................................159 2.Toward a better knowledge of F. sylvatica adaptive response to climate........................................160 2.1.A low diversity, low differentiation among populations and strong LD ...................................160 2.2.Genes and traits involved in adaptation of F. sylvatica to climate.............................................163 3.Genetic markers as tools

..148 6.Conclusion.......................................................................................................................................148 Discussion..............................................................................................................................................151 1.

Table 1 .

 1 Summary of the five primary classes of neutrality tests (modified from[START_REF] Eveno | L'adaptation à la sécheresse chez le pin maritime (Pinus pinaster Ait.) :patrons de diversité et différenciation nucléotidiques de gènes candidats et variabilité de caractères phénotypiques[END_REF]

	Signals natural selection	Examples of statistics	Level of comparison
	Linkage disequilibrium	ZNS, LRH	intra-population
	increase
	Increase / descrease of	F	between populations /
	genetic differentiation		species
	Increase of the derived	Fay and Wu's H	intra-population
	allele
	Increase / decrease of	Tajim's D, Fu's Fs, Fu and	intra-population
	genetic diversity	Li's F, Fu and Li's D, HKA
	Increase of the ratio non-	DN/DS, KA/KS, MK	between species
	synonymous / synonymous
	ratio

. DISENTANGLING THE ROLE OF DEMOGRAPHY AND ADAPTATION IN THE INTERPRETATION OF PATTERNS OF LOCAL ADAPTATION

	During the 1960s and 1970s, the Japanese geneticist M. Kimura discovered in a series of studies a new
	model to explain the numerous mutations that can be observed in a given organism's genome (Kimura
	1983). In his model, the vast majority of mutations are divided into two groups. The first group is
	composed of selectively neutral (or nearly neutral) mutations that become fixed in a species by genetic
	drift. These changes account for nearly all of the observable nucleotide changes between two species
	and became the name of a model widely used in population genetics, the standard neutral model
	(SNM). The second group of mutations is composed of selectively deleterious mutations that arise
	continuously and that are eliminated over time by natural selection. Deleterious mutations are rarely
	observed when comparing the genomes of two species but are the basis a substantial fraction of
	population diversity within a species. As they cause mutant phenotypes, deleterious mutations are well
	known by functional geneticists.
	28

  Fagus in the north Pacific region is likely in the early Eocene. The genus crossed the Bering channel and colonized Eurasia. A second phase of radiation began in the late Oligocene-early Miocene. At this time, the subgenus Engleriana emerged. In the Middle and Upper Miocene, the first differentiation occurred within the Eurasian populations, gradually isolating ancestors of modern lineages (including F. sylvatica). The separation between F. crenata and F. sylvatica is assumed to have begun with the fragmentation of the ancestral population between Europe and northeastern Asia after central Asia became an unsuitable habitat for Fagus during the Miocene (Denk and Grimm 2009).

	1.2. The post-glacial distribution of Fagus sylvatica 1.3. Main physiological and ecological properties
	Beech was present in Europe before the Quaternary (Denk et al. 2002); however, the data are highly Beech is a long-lived species (up to 250 years or more) but is normally harvested at 80-120 years of
	imprecise due to the limited availability of fossil remains. Beech repeatedly appeared during the age. Beech is generally 30-35 m tall, although it may rarely grow as high as 40-45 m. Beech has
	majority of the temperate periods of the previous 500 ka. At the end of the last interglacial period (the separate male and female flowers on the same tree and is wind pollinated (anemophily). Its fecundation
	Eemian, 127-112 ka BP), beech is well represented in southern Europe; pollen is observed at numerous is predominantly based on allogamy, although autogamy has been observed in natural conditions (Piotti
	sites in central and northern Europe but in limited amounts. During the last glacial period (hereafter, the et al. 2012). Seed production begins between 40 and 50 years of age in open area and between 60 and
	last glaciation) (110 ka BP to 10 ka BP), beech populations expanded in the southern part of the Italian 80 years of age in the case of dense canopy. The seed abundance is highly variable, and the year of
	peninsula and in the Balkan Peninsula (Figure 1.3), in southeastern France as well in the Iberian masting 1 occurs with a frequency of 5-10 years (von Wuehlisch et al. 1995). The large seeds are first
	Peninsula along the Pyrenees and the coast of the Cantabrian Sea. In contrast, beech was a minor dispersed by gravity (barochory) and then in certain cases by animals (zoochory), which collect and
	component of the forest vegetation in central Europe. At the end of the last glaciation, between 10 ka hide seeds
	and 9 ka BP, the presence of beech pollen increased rapidly in central Italy and in the Alps, suggesting
	Figure 1.2. Distribution map of beech (Fagus sylvatica) EUFORGEN 2009, www.euforgen.org the migration and expansion of beech from a small population that persisted during the glacial period.
	In southern France, beech is found at low elevations, near to the estuary of the Rhône river. The results
	from anthracological investigations along the Rhône valley indicate that beech could have survived the
	last glacial period both in the Mediterranean coastal area and in the eastern Alps (Delhon and Thiebault
	2005). The outer Alpine chain was rapidly colonized between 7 ka and 6 ka, but the paleo-botanical
	data do not allow for the determination of whether these populations originated from Austria-
	Switzerland or from sites near the Rhône valley. Although no paleobotanical data are available from
	Mont Ventoux, it is likely that its colonization began from refugia located in the southern Rhône valley
	rather than in Austria or Italy. A mix of these refugia is unlikely to have occurred because of the
	absence of beech between Mont Ventoux and the Alps or Northern Spain. During the last 5 ka BP,
	beech rapidly expanded in Germany, crossed the Channel to England, colonized Scandinavia and
	disappeared from the Rhône valley lowlands. The distribution of beech in Europe of 1 ka BP is very
	similar to its current distribution (Magri et al. 2006). Beech forests now cover approximately 17 million
	hectares in Europe (Comps et al., Figure 1.2). In the northern part of its range, beech grows at low
	elevations, whereas it is found at altitudes above 1000 m asl in the southern part of its range.
	Figure 1.3. Tentative location of refuge areas for Fagus sylvatica during the last
	glacial maximum and hypothesized main colonization routes during the post-glacial
	period (from (Magri et al. 2006)
	38

  ,González-Martínez et al. 2006[START_REF] Krutovsky | Nucleotide diversity and linkage disequilibrium in coldhardiness-and wood quality-related candidate genes in Douglas fir[END_REF], Pyhäjärvi et al. 2007). However, many of these early studies were based on relatively short candidate gene sequences (maximum size of approx. 500 bp). When

longer sequences

[START_REF] Larsson | Population Genetics and Genome Organization of Norway Spruce[END_REF]

or intergenic regions

[START_REF] Moritsuka | Extended Linkage Disequilibrium in Noncoding Regions in a Conifer, Cryptomeria japonica[END_REF]

are analysed, LD decays less rapidly, which is consistent with the observation that conifers have relatively low recombination rates (Jaramillo-Correa et al. 2010). Our knowledge of LD patterns in broadleaves is limited to Populus species. In Populus tremula, the recombination rate is much higher than in conifers (Jaramillo-Correa et al. 2010), and LD consistently decays more rapidly

Table 2

 2 Climatic data and location of the 4 studied populations. N is the number of individuals sampled for candidate gene sequencing. Climatic data include the mean (Tmean), maximum (Tmax) and minimum (Tmin) yearly temperature and the mean (RHmean%) and minimum (RHmin%) relative air humidity averaged from 2008 to 2010.

	Climatic data

Table 2

 2

	, Figure 2.2). Two plots were sampled on the northern

4.2. EST databases and annotation

Three F. sylvatica EST libraries were produced within the course of the EVOLTREE Network of Excellence (http://www.evoltree.eu) using i) dormant buds (3 genotypes, 15000 clones), ii) adult tree leaves (1 genotype, 768 clones) and iii) early swelling buds (3 genotypes, 19968 clones). The initial EST assemblies were performed at Laboratoire Bordelais de Recherche en Informatique (LaBRI, Bordeaux, France, http://www.labri.fr). The resulting contigs were cleaned using the RepeatMasker

Table 3 .

 3 Diversity estimates for the 16 genes with a length higher than 800bp and displaying at least 10 SNPs. a Consensus sequence length in base pairs, b Number of insertions-deletions events, c Number of segregating sites, d Nucleotide diversity(θ ; per site x 10 -3 ), e Tajima'D index, f Number of haplotype, g Frequency of the most common haplotype, h Haplotype diversity, i Composite-likelihood estimate of the population-scaled recombination rate (ρ ; per site x 10 -3 ). For the complete table with the 58 genes see appendix 4.

	Gene.cat.	Seq.Name	Seq.Description	L a indel b S c	θπ d	D e hap f	H h	ρ i
	Stress	10	adp-glucose pyrophosphorylase large subunit	1177	2	22 2.98 -0.37 18	0.97 7.43
	Stress	23-1	ubiquinol-cytochrome c reductase iron-sulfur	1299	1	35 6.35 1.18 16	0.92 20.63
	Stress	30-2	caffeic acid o-methyltransferase	1331	4	38 0.95 -1.59 10	0.86 6.44
	Stress	51-2	isoflavone reductase homolog	1091	1	17 2.38 -0.47 7	0.72 3.78
	Stress	58	cold regulated 413 plasma membrane 1 1135	1	11 1.43 -0.45 10	0.58 5.18
	Stress	66	s-adenosylmethionine decarboxylase 1091 NA 12 2.25 0.42	8	0.83 3.31
	Stress	73	formate dehydrogenase	1308	1	13 2.38 0.04	9	0.82 3.76
	Stress	91-2	catalase	1503 NA 18 2.33 -0.15 5	0.56 0.01
	Stress	154-1	probable pectinesterase pectinesterase inhibitor 40-like	1234 NA 11 2.5	0.98	7	0.83 0.82
	Stress	155-2	polygalacturonase-1 non-catalytic subunit beta	1010 NA 32 4.73 -0.82 17	0.91 11.55
	Pheno	98-1	catalase	1452	1	17 2.25 0.16	6	0.61 0.17
	Pheno	133	NA	918	1	11 2.4	0.27	9	0.84 0.01
	Pheno	134-2	type 2 metallothionein	993	2	12 0.95 -0.59 20	0.91 20.02
	Pheno	145-2	proteasome subunit alpha type-7	1254	2	26 4.94 0.75 12	0.84 0.9
	Pheno	148-1	skp1-like protein	1498	4	19 2.66 0.74 12	0.87 1.4
	Pheno	150-2	auxin response factor 6	1304	3	16 4.01 1.06	9	0.8 1.14

Table 4 .

 4 Mean linkage disequilibrium (r²) and mean recombination rate for the 16 genes with a length higher than 800bp and with at least 10 SNPs. j Number of pairwise linkage used for r², j Number of significant pairwise r², l Distance in bp by which the r² is reduced by half. For the complete table with the 58 genes see appendix 4.

	gene.name	Num. pw.comp j Prop sig.ld j	mean.r2	half.dist	Mean log10rec
	10		37	0.12	0	-2.3
	23-1		47	0.12	30	-1.6
	30-2		43	0.19	188	-2.2
	51-2		57	0.28	489	-2.5
	58	55	33	0.15	60	-2.4
	66	66	41	0.1	53	-2.2
	73	78	64	0.18	149	-2.5
	91-2		54	0.43	1384	-6.6
	154-1	55	87	0.27	376	-1.9
	155-2		30	0.1	43	-2
	98-1		96	0.46	1365	-6.4
	133	55	40	0.18	135	-5.6
	134-2	66	24	0.11	0	-1.5
	145-2		42	0.16	108	-3.5
	148-1		57	0.18	187	-2.7
	150-2		78	0.3	666	-2.8

l Figure 2.3. top: gene r² decay, dark line is the average; middle: effect of MAF on r²; bottom: effect of MAF on D'

5.2. Genomic diversity

A total of 572 SNPs was detected from the 56 gene sequences, giving approximately one SNP per 86

  polymorphism (an average of one SNPs per 86 bp) For 10 different, independently developed, F. sylvatica candidate genes,Siefert et al. (2012) reported that the SNP frequency was slightly lower than what we observed, with one SNP per 129 bp. From our 56 F. sylvatica candidate gene sequences, we estimated that the mean nucleotide diversity was 0.0026, which is identical to estimates from 10 candidate genes in three German populations (θπ = 0.0026,[START_REF] Seifert | DNA sequence variation and development of SNP markers in beech (Fagus sylvatica L.)[END_REF]. These two independent studies, screening a total of 66 genes, suggest that nucleotide diversity in F. sylvatica is lower than that of other Fagaceae, such as Quercus petraea (θπ = 0.0218,[START_REF] Vornam | Characterisation and natural variation of a dehydrin gene in Quercus petraea (Matt.) Liebl[END_REF], θπ = 0.0048, Gailing et al. 2009; θπ = 0.0061, Derory et al. 2010) and Quercus mongolica (θπ = 0.0069;[START_REF] Quang | Nucleotide variation in Quercus crispula Blume[END_REF]. F.

sylvatica nucleotide diversity levels are also low in comparison to other broadleaves, e.g., Populus species (θπ = 0.0072 for Populus nigra in Chu et al. 2009; θπ = 0.0042 for Populus tremula in Ingvarsson 2008) and conifers (between 0.0024 and 0.0082 from a total of 8 species; Jaramillo-Correa

  Details of the PCR reaction mixture and the thermal profile for the amplification of the selected genes (see also

	Gene cat. GeneBank ID Stress JX406436 Stress JX406437 Stress JX406438 Stress JX406485 Stress JX406439 Stress JX406486 Stress JX406440 Stress JX406441 Stress JX406442 Stress JX406443 Stress JX406444 Stress JX406445 Stress JX406446 Stress JX406447 Stress JX406448 Stress JX406487 Stress JX406449 Stress JX406450 Gene cat. GeneBank ID Stress JX406459 Stress JX406460 88-2-1 2 4 7 10 14 17 19 20 21 23-1 24 27 30-2 33 39 47-1 50 51-2 Seq. Name 88-1 Stress JX406461 88-2-2 Stress JX406491 91-2 Stress JX406462 92 Stress JX406480 154-1 Stress JX406481 154-2-1 Stress JX406482 155-2 Stress JX406483 155-3 Stress JX406484 0156C Pheno JX406463 98-1 Pheno JX406464 99 Pheno JX406465 100 Pheno JX406492 102 Pheno JX406466 110-1 Pheno JX406467 110-3 Pheno JX406468 123-1 Pheno JX406469 123-2 Pheno JX406470 125 Pheno JX406471 129 Pheno JX406472 130 JX406459 88-1 JX406458 80 step3 Cycle 3 x35 Stress Stress step1 step2 step3 Cycle 4 x1 step1 step2 step1: denaturation, step2: annealing, step3: extension c2-h2 zinc finger protein Seq. Description subtilisin-like protease adp-glucose pyrophosphorylase large subunit glutamate dehydrogenase 2 short chain alcohol dehydrogenase histone h3 beta-ketoacyl-coa synthase family protein s-adenosylmethionine synthetase ubiquinol-cytochrome c reductase iron-sulfur plastocyanin chloroplast probable -trehalose-phosphate synthase caffeic acid o-methyltransferase histone h4 potassium transporter 2 adenylate kinase crt dre binding factor isoflavone reductase homolog probable xyloglucan endotransglucosylase hydrolase protein 23 Seq. Description light-harvesting complex ii protein lhcb2 chlorophyll a b-binding protein chlorophyll a b-binding protein catalase 1-aminocyclopropane-1-carboxylate oxidase pectin methylesterase polygalacturonase-1 non-catalytic subunit beta polygalacturonase udp-glucose dehydrogenase catalase zinc finger (ccch-type) family protein cryptochrome 1 protein 2 histone h3 cytosolic class i small heat-shock protein heat shock protein chlorophyll a b-binding protein chlorophyll a b-binding protein na glyceraldehyde-3-phosphate dehydrogenase ultraviolet-b-repressible protein light-harvesting complex ii protein lhcb2 protein phosphatase 2c 72° 1 min 94° 30 sec Cycle 4 x1 55° 30 sec 72° 1 min 72° 7 min 7° ∞ Stress JX406450 51-2 isoflavone reductase homolog Stress JX406451 52-1 adenosylhomocysteinase s-adenosyl-l-homocysteine hydrolase Stress JX406452 52-2 s-adenosyl-l-homocysteine hydrolase Stress JX406488 58 cold regulated 413 plasma membrane 1 Stress JX406453 60 eukaryotic translation initiation factor 5-like Stress JX406454 61 heat shock protein 70 Stress JX406489 62-1 heat shock Stress JX406455 66 s-adenosylmethionine decarboxylase Stress JX406456 68-2 glyceraldehyde 3-phosphate partial Stress JX406457 70 pathogenesis-related thaumatin-like protein Stress JX406490 73 formate dehydrogenase average JX406479 150-2 auxin response factor 6 Pheno JX406478 148-1 skp1-like protein Pheno JX406477 145-2 Pheno proteasome subunit alpha type-7 JX406476 142 Pheno membrane protein cov JX406493 134-2 Pheno type 2 metallothionein JX406475 133 Pheno na JX406474 132-1 Pheno histone h4 JX406473 131 Pheno histone h3 JX406472 130 Pheno ultraviolet-b-repressible protein Stress JX406449 50 crt dre binding factor JX406471 129 Pheno glyceraldehyde-3-phosphate dehydrogenase Stress JX406487 47-1 adenylate kinase JX406470 125 Pheno na step2 65-55° 30s Stress JX406448 39 potassium transporter 2 JX406469 123-2 chlorophyll a b-binding protein Pheno Cycle 1 x1 94° 5 min Cycle 1 x1 Cycle 2 x10 step1 94° 30 sec Cycle 3 x35 Stress JX406447 33 histone h4 JX406468 123-1 chlorophyll a b-binding protein Pheno Stress JX406446 30-2 caffeic acid o-methyltransferase JX406467 110-3 heat shock protein Pheno Stress JX406445 27 probable -trehalose-phosphate synthase JX406466 110-1 Pheno cytosolic class i small heat-shock protein Seq. Name probable pectinesterase pectinesterase inhibitor 40-like Appendix 4. (continued) Appendix 2. Appendix 1) PCR protocol 1 PCR protocol 2 DNA 1µl (20ng/µl) DNA Buffer 5x GoTaq 3µl Buffer 5x GoTaq 10mM dNTP 0,3µl 10mM dNTP 10µM primers F 0,3µl 10µM primers F 10µM primers R 0,3µl 10µM primers R 0,15µl 9,95µl TAQ (MgCl 2 1,5mM) TAQ (MgCl 2 1,5mM) H 2 O H 2 O Stress JX406444 24 plastocyanin chloroplast JX406492 102 Pheno histone h3 Stress JX406443 23-1 ubiquinol-cytochrome c reductase iron-sulfur JX406465 100 Pheno cryptochrome 1 protein 2 Gene.cat. GeneBank Seq.Name Seq.Description Stress JX406436 2 c2-h2 zinc finger protein Stress JX406437 4 subtilisin-like protease Stress JX406438 7 probable xyloglucan endotransglucosylase hydrolase protein 23 Stress JX406485 10 adp-glucose pyrophosphorylase large subunit Stress JX406439 14 glutamate dehydrogenase 2 Stress JX406486 17 short chain alcohol dehydrogenase Stress JX406462 92 Stress JX406480 154-1 Stress JX406481 154-2-1 Stress JX406482 155-2 Stress JX406483 155-3 Gene.cat. GeneBank Seq.Name Seq.Description 1-aminocyclopropane-1-carboxylate oxidase probable pectinesterase pectinesterase inhibitor 40-like DETECTION OF LOCI UNDER SELECTION IN FAGUS min. eValue Forward (5'-3') 7.4E-65 66.4% GATTGGAAATGGATCACAAG 8.0E-100 72.75% TTTGCTAGCTTTGCTTTTCT 5.7E-153 72.85% AGTCTCTTCCATGTCTACGC 4.5E-11 87.1% GAGCATTCCTGCTAATGTTT 2.5E-49 94.2% AATGCACTGGCTCAACTAAT 1.9E-25 62.4% AGTAAAGACTTCCCCTCGAC 4.3E-58 64.9% AACACCAGTTTGAGATCCAC 4.3E-153 95.0% GTCTGCTTTCAATTGCTCTT 9.2E-97 98.95% ACTAAGAGGCCTGAGGAGAT 1.2E-38 90.55% TCAAAAGATGCTTAGAGTTGC 8.4E-80 86.0% ATCCTTATCCCATGGCTACT 2.1E-79 90.65% AGGCTCAGCCATCTGTATC 2.1E-57 94.6% TGTCTCGAAATACCCCTCTA 7.9E-45 100.0% AAATCATCGAAAATGTCTGG 8.7E-86 83.5% ACCTAGTGGTTCCATCTCAA 1.3E-19 87.4% TCTTTTCCTTACCCACAAAA 8.1E-20 82.85% AGGCGAAGGATATTCAGAG 3.3E-62 68.2% CGCTGGCTATTTTCTACCTA mean Similarity min. eValue Forward (5'-3') 1.2E-152 97.9% CATCCAACAATCAGCATTC 1.6E-148 97.1% CTCTCTCTTCCCCATCTCTT 1.2E-162 96.85% GCTCTCTCCTCTCCATCTTT 1.4E-92 98.35% GGAGGAAGAGGCTATTAAGG 1.9E-54 67.1% GCAAAGCATAAGAAAGTGAGA 6.2E-107 66.1% TACTCTCTTGCCCATCTCAG 8.4E-141 90.3% TGACTAGTTCAAAAAGGTTGTG 7.0E-162 81.2% TCTCAAGCTATAGAAATAAAGCA 6.9E-78 56.30% GTGCGAGAACTTCAAAGGTA 3.9E-174 95.74% GATCTTTTCAACCCCGATA 7.0E-77 98.6% ATGCCACACAGGATCTCTAC 2.2E-82 89.45% CTGAAAGGCGATAATTCAGT 1.0E-33 75.6% GAGACTCTGCTGAATCAACC 2.5E-54 64.7% CCGAAGACAAAGAAGAAAGA 2.4E-64 88.05% CTCTAAAATGGCGCTCAG 3.3E-73 89.76% CTCTTTCAAGCTTTCAGTGC 8.1E-161 97.2% CTCTCTCTTCCCCATCTCTT 8.8E-165 97.0% GCTCTCTCCTCTCCATCTTT na na GGGCACACTTAATTGAAAAG 4.8E-74 91.5% AGGGTGACATCCCTACCTAC 4.8E-55 84.1% ATTCACATAGAGAGGGCAGA 1315 NA 4 4 0.78 0.28 0.62 0.31 0 1359 NA 9 8 0.34 3.29 1.67 0.53 0 step3 72° 1 min step1 72° 7 min Cycle 4 x1 step2 step2 7° ∞ 1091 1 17 7 0.43 3.78 2.38 -0.47 489 AACAAAACCTACTTTGCATTAAA Reverse (5'-3') CTTCACAGATGGGAGAAGAC AGCAAACCTTGTAGATACCG ATCTTGGCTAGCTCTGTTTT CTGTGATCCAAAGACACCTT TATCAATACCACTCGGGTTC ATGCACAACAAAGGAAGAAC CAAGTCCACTGGAGGAAAG TACAAGCACAATCAAGTCCA CACCTTAGTTGGGTCCTTC ACTTCACCGTAACAGTGGTT CAGAGTGTTCAAACAAGCAA CCATGAGAAGCATTACAGGT CATTCCCTTTTTCCTCTCTT TTCCTGGAATTGGTCTAGTG CAGCGACCAGTAATTCTCTC AAATTTGTACATGGCTTTGC TTACTTATTGGGTTCACGACT Reverse (5'-3') AAAAGAAAAGGCAGTGATTCT PCR protocol AGAACATAGCCAGTCTTCCA GGTCAGCAAGGTTCTCAAT CACACATAACATGCGAGTTT AAGGAGGGTAGTTGCTGACT CCCATCTCCTAAGAACATCA CTCATTTGTCAATCATCTCG AGGATTGGCACTGATTTATG GTGAGCAAAGAAGCAAACTT CCATCCCTCTCTCTCCTAAT TCCCATTAAACAAAACAACC CAAGCTTCGTAAACAAAACA AGATTCCACACAAAAGGGTA GTCCCTTTCCAGTCACAAT TGTTGGTGACTGGGTCAG CTAATATTGGGTTGGCACAT 0.36 6 100 0.88 CAAATTACAGCTGAAAGAGTGA 0.79 36 0.12 TGCCAACAGGAAGACATATT 0.72 136 0.28 522 NA 12 5 0.44 0.01 10 3.73 0 0.71 66 0.56 614 NA 16 6 0.47 0 7.09 1.52 0 0.69 120 0.44 1135 1 11 10 0.63 5.18 1.43 -0.45 60 0.58 55 0.15 756 2 14 13 0.21 1.27 3.58 0.14 0 0.87 91 0.07 920 NA 5 5 0.38 0.01 1.98 2.05 0 0.7 10 0.25 909 NA 3 2 0.91 0.02 0.2 -1.16 0 0.16 3 0.29 1091 NA 12 8 0.32 3.31 2.25 0.42 53 0.83 66 0.1 625 NA 7 5 0.42 0.01 2.98 0.46 0 0.69 21 0.18 496 NA 5 3 0.73 0.01 1.86 0.15 0 0.43 10 0.2 1308 1 13 9 0.28 3.76 2.38 0.04 149 0.82 78 0.18 860 2 10 7 0.50 3.72 2.17 0.18 93 0.63 79 0.26 1304 3 16 9 0.3 1.14 4.01 1.06 666 0.8 120 0.3 1498 4 19 12 0.24 1.4 2.66 0.74 187 0.87 171 0.18 1254 2 26 12 0.27 0.9 4.94 0.75 108 0.84 325 0.16 501 1 17 13 0.21 0.18 3.47 -0.88 0 0.89 136 0.44 993 2 12 20 0.2 20.02 0.95 -0.59 0 0.91 66 0.11 918 1 11 9 0.28 0.01 2.4 0.27 135 0.84 55 0.18 496 1 4 3 0.81 0.01 1.53 -0.16 0 0.32 6 0.49 618 NA 8 10 0.32 21.45 0.07 -1.08 0 0.8 28 0.18 518 NA 9 3 0.77 0.01 5.52 1.47 0 0.37 36 0.77 step1 616 2 8 8 0.43 24.9 3.3 0.24 0 0.74 28 0.32 978 NA 4 3 0.93 0.03 0.21 -1.31 0 0.13 6 0.43 step3 999 NA 9 8 0.66 38.38 0.22 -1.6 0 0.55 36 8 0.02 411 2 6 5 0.47 0.01 3.75 0.97 0 0.71 15 0.18 TTCAGAGACAACAGGGTCTT step2 60° 30 sec step2 480 NA 3 2 0.55 0.01 3.1 3.06 0 0.5 3 100 0.99 698 NA 6 6 0.4 0.02 1.57 0.13 0 0.7 15 0.09 ATACACCATCAGAGGGATTG step1 94° 30 sec Cycle 3 x35 520 1 4 3 0.76 0 2 0.92 0 0.38 6 0.51 674 NA 8 5 0.73 0.01 0.77 -1.44 0 0.44 28 0.13 step1 1331 4 38 10 0.31 6.44 0.95 -1.59 188 0.86 703 0.19 679 NA 2 3 0.9 0.02 0.39 -0.3 0 0.19 1 100 0.37 94° 5 min Cycle 1 x1 544 NA 5 6 0.66 0.03 1.27 -0.48 0 0.54 10 0.03 589 NA 5 2 0.57 0.01 0.02 0.99 0 0.52 10 0.58 GCGATAATCAACATTCAACA mean Similarity protocol -3.9 -2.8 -2.7 -3.5 -4.8 -1.5 -5.6 -4.8 -1.6 -5.2 -2.7 -6.4 -5 -5.2 -4.7 -5.3 PCR PCR protocol 3 1µl (20ng/µl) DNA 1µl (20ng/µl) 3µl Buffer 5x GoTaq 3µl 0,3µl 10mM dNTP 0,3µl 0,3µl 10µM primers F 0,3µl 0,3µl 10µM primers R 0,3µl 0,15µl 0,15µl 9,95µl 9,95µl 515 NA 6 7 0.28 0.01 2.84 0.86 0 0.82 15 0.05 1168 1 9 5 0.41 0.03 1.5 0.59 0 0.67 36 0.29 -4.6 H 2 O 1299 1 35 16 0.24 20.63 6.35 1.18 30 0.92 595 0.12 379 1 na na na na na na na na na na na TAQ (MgCl 2 1,5mM) half.dist Hg num.pw.comp prop.sig.ld mean.r2 mean.log10rec -4.2 -2.4 -2.5 -4.8 -4.6 -2.2 -3.9 -6 -2.4 -2.4 -3.8 -5.5 -2.5 -1.4 -0.9 -4 -4.6 -2.2 -6.2 -4.6 -1.6 498 na 0 na na na 0 na na na na na na na 711 1 5 4 0.86 0.08 0.47 -1.31 0 0.26 10 0.07 -3 924 NA 3 3 0.92 0.02 0.19 -1.06 0 0.15 3 0.33 -4.1 1177 2 22 18 0.1 7.43 2.98 -0.37 0 0.97 231 0.12 -2.3 727 NA 9 8 0.54 10.4 1.04 -1.1 0 0.67 36 0.11 -3 1320 NA 6 7 0.38 4.81 1.08 0.01 0 0.78 15 0.07 L a indel b S c hap d f.h_max e ρ f θπ g D h Hg mean.r2 mean.log10rec 774 NA 7 8 0.48 0.17 2.09 -0.2 0 0.7 21 0.11 -7.7 1234 NA 11 7 0.29 0.82 2.5 0.98 376 0.83 55 0.27 -1.9 700 NA 3 3 0.66 0.01 1.37 0.98 0 0.46 3 0.32 -5.7 1010 NA 32 17 0.24 11.55 4.73 -0.82 43 0.91 496 0.1 -2 877 NA 5 5 0.33 0.01 1.12 -0.03 0 0.73 10 0.04 -5.5 L a indel b S c hap d f.h_max e ρ f θπ g D h half.dist num.pw.comp prop.sig.ld CHAPTER 3. pectin methylesterase polygalacturonase-1 non-catalytic subunit beta polygalacturonase SYLVATICA ALONG AN ALTITUDINAL GRADIENT: AN -2.5 Stress JX406440 19 histone h3 829 1 9 6 0.44 0 3.45 1.87 0 0.66 36 0.29 -5.4 Stress JX406441 20 beta-ketoacyl-coa synthase family protein 861 1 4 3 0.41 0 1.86 2.18 0 0.66 6 100 0.44 -5.2 Stress JX406442 21 s-adenosylmethionine synthetase 467 NA 3 4 0.48 0.01 1.81 1.03 0 0.67 3 100 0.04 -3.9 Stress JX406484 0156C 967 NA 5 4 0.59 0.01 0.49 -0.52 0 0.58 10 0.24 -4.7 JX406463 98-1 1452 1 17 6 0.52 0.17 2.25 0.16 1365 0.61 136 0.46 -6.4 JX406464 99 418 NA 2 2 0.9 4.15 0.41 -0.69 0 0.18 1 0 0 -3.4 udp-glucose dehydrogenase Pheno catalase Pheno zinc finger (ccch-type) family protein OUTLIER EXPERIMENTAL APPROACH
	Stress Stress Stress	Stress Stress Stress Pheno JX406460 Pheno JX406461 Pheno JX406491	JX406451 JX406452 JX406488 JX406473 88-2-1 JX406474 88-2-2 JX406475 91-2	52-1 52-2 58 131 132-1 133	s-adenosyl-l-homocysteine hydrolase cold regulated 413 plasma membrane 1 adenosylhomocysteinase s-adenosyl-l-histone h3 chlorophyll a b-binding protein homocysteine hydrolase histone h4 chlorophyll a b-binding protein na catalase	3.6E-104 1.1E-134 1.1E-17 4.2E-87 621 6.0E-45 698 na 1503 NA 18 5 96.95% 97.35% 84.65% 98.75% NA 7 5 100.0% NA 6 5 na	GTCAAGGATATCTCCCAAGC TTGATGAGGGCTACTGATGT AATGAAAACTGACGAAGGTG GGAAACCAAGCTATTGAAAA 0.73 0.01 1.03 -1.03 CAAATCATCGAAAATGTCTG 0.47 11.86 0.03 -1.02 CCGAGAAGGTGTTTGATCT 0.56 0.01 2.33 -0.15 1384 0 0	ATCTTTGCTTCATCTTGTGG GTCCTTGGAAAGCTTGGT TGGCAAGACATATTACCACA GAACCAAACACAAACACAAA 0.44 21 0.69 15 GAATTACAAAACAAAGCCAAC ACTGGCAAGTCAATATTTGG 0.56 153	0.34 0.08 0.43	-1.9 -2.6 -6.6
		Stress Pheno	JX406453 JX406493	60 134-2	eukaryotic translation initiation factor 5-like type 2 metallothionein	1.6E-83 1.5E-34	71.25% 83.1%	AGACGACAATGATGATGACA TTCCCACATATATTATCCCATT	AAATACATGGCAAACTCGAC GGCTTGATTTTATTGTTCTCTC
		Stress Pheno	JX406454 JX406476	142	61	heat shock protein 70 membrane protein cov	1.7E-177 2.4E-18	96.35% 82.15%	AGATGGACAAGAGCACAATC GTTGTACTCAGCCAACTATGTG	CCCCTAAATGAGAGTGTCAA ATCTAAGAGCAATTGGCAAC
		Stress Pheno	JX406489 JX406477	62-1 145-2	heat shock proteasome subunit alpha type-7	1.6E-102 3.7E-39	98.15% 97.75%	CGTCTTATGTGGCTTTCAC GCAGGTAAAGAAGAAGAAGAAA	CTAGGGCTTCCAGTAATGTC CTGAAATGTTCCTGATGGAT
		Stress Stress Pheno Stress Pheno	JX406455 JX406456 JX406478 JX406457 JX406479	66 68-2 148-1 70 150-2	s-adenosylmethionine decarboxylase glyceraldehyde 3-phosphate partial skp1-like protein pathogenesis-related thaumatin-like protein auxin response factor 6	1.9E-175 4.8E-18 6.7E-41 2.2E-110 2.0E-35	83.8% 95.8% 92.5% 87.85% 94.02%	GAATCAACATCCATCACACA CATCTAGCAAGGACTGGAGA GAAAAAGGCAAAAGTAATCG ACTCCTAGACTGGACAGCAC GGAGTGCTCAATAGACCAAG	AGGCTTGAGGTATCATTCTG TACTACCAATGCCCCATC TAATCCCAACACCACTTTTC AGGCGTAGCTATAGGACTTG TTCCAAAAGCTTAGAAAGACA
		Stress	JX406490		73	formate dehydrogenase	2.8E-74	94.85%	AGAATTTGCAACTGCTTCTC	TTATTCAGGACCTTGCTGAC
						The Holy Grail Quest
									81

a Consensus sequence length in base pairs b Number of insertions-deletions events c Number of segregating sites d Population-scaled mutation rate (θ ; per site × 10 -3 ) based on the average number of nucleotide differences. e Tajima'D index f Number of haplotype g Frequency of the most common haplotype h Haplotype diversity i Composite-likelihood estimate of the population-scaled recombination rate (ρ ; per site × 10 -3 ) j Number of pairwise linkage used for r² k Number of significant pairwise r² l Distance in bp by which the r² is reduced by half

Table 5 .

 5 Allelic richness (A), observed heterozygosity (Ho), expected heterozygosity (He) and coefficient of consanguinity(FIS)

	Populations	A	Ho	s.d.	He	s.d.	F IS	P (Rand F IS Obs F ⩾
	NL	6.75	0.7087	0.1231	0.7066	0.1158	-0.0069	0.6109
	NH	6.83	0.6495	0.1600	0.6957	0.1526	0.0472	0.0870
	SL	5.92	0.6523	0.1243	0.6920	0.1227	0.0583	0.0391
	SH	5.92	0.6339	0.1689	0.6532	0.1382	0.0187	0.3441

IS

)

Table 6 .

 6 Global differentiation at microsattelite markers

	Locus	Fst	P-value individuals and genes permutations tests	Rst	P-value individuals and genes permutations tests	P-value alleles permutations tests	Mean pairwise Fst
	ALL LOCI	0.0251	0.0000	0.0246	0.0006	0.7994	0.0251
	FS1_15	0.0396	0.0000	0.0105	0.3347	0.4565	0.0393
	FS3_04	0.0106	0.3338	-0.0126	0.4678	0.0186	0.0106
	sfc0007_2	0.0343	0.0021	0.0527	0.0292	0.5967	0.0337
	sfc_0018	0.0391	0.0034	0.0235	0.1402	0.7723	0.0388
	sfc_0036	0.0222	0.0474	0.0377	0.0589	0.4494	0.0234
	sfc_1143	0.0190	0.0267	0.0900	0.0008	0.0397	0.0181
	csolfag_25	0.0255	0.0580	0.0482	0.0237	0.2280	0.0254
	csolfag_29	0.0091	0.2934	0.0116	0.3804	0.8850	0.0082
	csolfag_31	0.0180	0.0904	0.0036	0.6183	0.5702	0.0178
	csolfag_5	0.0201	0.0517	0.0713	0.0081	0.1935	0.0200
	csolfag_6	0.0222	0.0074	0.0348	0.0631	0.5552	0.0216
	mfc7	0.0314	0.0039	-0.0100	0.4978	0.1017	0.0307

  in Fagus sylvatica -Jump et al. 2006; 11-fold in Picea mariana -Prunier et al. 2011, two-fold in in Fagus sylvatica -Pluess and Weber 2012). In previous comparisons between frequentist outlier tests

Table 4 .

 4 Demographic parameters used in ms simulations under 3 different scenarios (Split, Bottleneck, Bottleneck stepwise). Parameters are: m, migration rate; N 0 , present population size; N 1 , population size before expansion; N 2 , ancestral population size; t, time in generations.

	Scenario		Split		Bottleneck	Bottleneck stepwise
	Parameter North	South	North	South	North	South
	m	0.0067	0.0056	0.0067	0.0056	0.0067	0.0056
	N 0	2000	2000	2000	2000	2000	2000
	N 1	480	380	550	430	500	400
	N 2	480	380	4000	4000	4000	4000
	t 1	3	3	3	3	3	3
	t 2	-	-	-	-	18	18
	t 3	-	-	-	-	19	19
	t 4	20	20	20	20	20	20

Table 2 .

 2 Population diversity and differentiation estimates based on SSR markers. For each gradient, average multilocus estimates and standard deviation (sd) of observed heterozygosity (Ho), expected heterozygosity (He) and inbreeding coefficient (FIS) within each population (pop) are reported, together with the differentiation among populations within gradient (F ST ) IS >Obs F IS ) significant at the 5% level Table

	Gradient	Pop	Ho	sd	He	sd	F IS	F ST
	North	NH	0.650	0.160	0.696	0.153 0.047NS 0.0212
		NL	0.709	0.123	0.707	0.116 -0.007NS
	South	SH	0.634	0.169	0.653	0.138 0.019NS 0.0415
		SL	0.652	0.124	0.692	0.123	0.058*
	* P(Rand F

  Mean diversity for genes that contains at least one outlier SNP.

							All SNPs									Outlier SNPs
	Gradient Population	θ π*	sd	H o	sd	H e	sd	F ST	sd	Z g	θ π*	sd	H o	sd	H e	sd	F ST	sd	Z g
	North	NH NL	0.0026 0.002 0.221 0.173 0.256 0.163 0.018 0.044 0.0004 0.0026 0.002 0.255 0.189 0.273 0.163	0.0030 0.002 0.244 0.152 0.306 0.167 0.138 0.049 0.651 0.0029 0.002 0.294 0.098 0.390 0.096
	South	SH SL	0.0024 0.002 0.291 0.182 0.316 0.148 0.022 0.079 0.0001 0.0026 0.002 0.280 0.190 0.321 0.153	0.0029 0.002 0.274 0.165 0.312 0.148 0.173 0.054 0.063 0.0032 0.002 0.401 0.208 0.444 0.080

*

Table 5 .

 5 SNPs detected as outliers for each method and each gradient. IslandSSR and IslandSNP refer to the frequentist outlier method implemented in Arlequin; Split, Bottleneck, and Bottleneck stepwise to frequentist outlier method using with ms to simulate neutral envelop. The SNPs and genes in common between the two gradients are reported in bold. Gene ID is the reference of the gene (see

	Chapter 2)

Table 6 .

 6 SNPs contributing significantly to trait variation. Four models were used, one way ANOVA and multivariate ANOVA with forward stepwise method, and with or without population term (for analysis including two populations). PSS, Phenology Score Sum; LMA: leaf mass per area; DF: degree of freedom; R²: adjusted R²; SNP cat.: SNP categorized as outlier or non outlier based on the frequentist method; FDR: False Discovery Rate. one way ANOVA model. Then, using a dataset without missing data and unlinked loci (reduced to 45 individuals and 3 SNPs with r²<0.2), the best multivariate ANOVA model with population term included only SNP 579 (R²= 0.13, p=0.008).

	Trait	Population ANOVA Model	SNP ID	SNP cat.	DF	R²	p-value	Bonferroni	FDR
									corrected p-
									value
	PSS	NH, NL	One-way, no pop	172	outlier	61	0.22	<0.001	0.003	<0.001
			term
		NH, NL	Multivariate, no	119, 172	outlier	43	0.28	0.001	-	-
			pop term
		NH, NL	Multivariate, with	579	non out-	43	0.13	0.008	-	-
			pop term		lier
	LMA	NL	One-way	1106	outlier	30	0.30	0.002	0.030	0.002

  7 44.7 44.7 44.7 x 44.7 44.7 44.7 44.7 x 44.7 44.7 44.7

	44.7 x -G 4291.8 -eG 0.000375 0.0 -ema 0.000375 4 x 8.96 8.96 8.96 8.96 x 8.96 8.96 8.96 8.96 x 8.96
	8.96 8.96 8.96 x -ej 0.00225 4 3 -ej 0.002375 3 2 -ej 0.0025 2 1 -eN 0.0025 2 | msstats -I 2 70 72

Table 7

 7 Rate of Type I error (false positive), well-detected QTLs, Type II error (false negative) and QTLs among the outliers

	Generation	False positive	Well detected	False negative %QTL among
			QTLs		the outliers
			FDIST
	G0	0.94	0.10	0.90	0.06
	G1	0.95	0.10	0.90	0.05
	G2	0.94	0.14	0.86	0.06
	G3	0.93	0.20	0.80	0.07
	G4	0.95	0.18	0.82	0.05
	G5	0.93	0.24	0.76	0.07
			BAYESCAN
	G0	0.00	0.00	1.00	0.00
	G1	0.00	0.00	1.00	0.00
	G2	0.00	0.02	0.98	1.00
	G3	0.00	0.02	0.98	1.00
	G4	0.40	0.06	0.94	0.60
	G5	0.20	0.08	0.92	0.80

Table 8

 8 Proportion of neutral loci (false positive), QTLs (well-detected QTLs) in the subset of 10 loci involved in the combination showing the 5% highest Zg-values. These results were obtained for generation 5.With respect to the genetic model, Le[START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF] assumed that the trait under selection was determined by 10 additive independent QTLs with arbitrary allelic effects, as was performed in the present study. With respect to selection, these authors considered first stabilizing within-deme selection, with the selection intensity ω² varying from 1 (strong) to 100 (weak). To compare this situation with our case, we ran the simulation until generation G8 and estimated the linear and quadratic selection gradient on F critBB separately for Pop2 and Pop3. In Pop 2, the linear selection gradient on F critBB was not significant, suggesting that the optimum value for F critBB was reached at this stage. The quadratic selection gradient on F critBB was significant; the intensity of the stabilizing selection was estimated as 2.17, indicating a strong stabilizing selection within Pop2. In contrast, the linear selection gradient on F critBB was significantly negative for Pop 3, suggesting that the optimum value for F critBB was not reached at this stage; the quadratic selection gradient on F critBB was significant, and the intensity of stabilizing selection was estimated as 45.5, indicating moderate stabilizing selection within Pop3.

	Repetition	False	Well
		positive	detected
			QTLs
	Repet0	0.7	0.3
	Repet1	0.8	0.2
	Repet2	0.8	0.2
	Repet3	0.9	0.1
	Repet4	0.7	0.3
	Average	0.78	0.22

Table 9

 9 Non exhaustive list of studies that used FST-based genome scan on natural population of trees. Are genome-scan approaches appropriate for detecting selection along local environmental gradients ?

	Ref	Species	Approx. scale (km)	Marker Software	Rep. Av. Fst	Av. Fst without outlier	av. Fst outlier	FstOut / Fst ratio	Nb of markers	% of outlier
	Pluess and Weber 2012	Fagus sylvatica	0.5	AFLP BayeScan	3	0.028		0.062	2.2		2.9%
	Jump et al. 2006	Fagus sylvatica	2	AFLP	Fdist	no	0.018	-	0.3	16.7		0.4%
					Fdist		0.02		0.18	9		3.0%
	Lalagüe et al.	Fagus sylvatica	1-5	SNP		1
					BayeScan							0.0%
	Cox_2011	Alnus glutinosa	50-100		BayeScan	no						3.2%
	Alberto et al. 2010	Quercus petraea	100	SSR BayeScan	2	0.023 0.02 0.043	2.2	16	12.5%
	Namroud 2008	Picea glauca	500	SNP	FDIST2 NEWFST	no	0.006					3.7% 0.4%
	Tsumura et al. 2012	Cryptomeria japonica 100-800 SNP	Fdist2 Arlequin3.5 Bayescan		0.0391				1026 10.0% 5.8% 2.0%
	Prunier et al. 2011	Picea mariana	<800	SNP	FstSNP BayesFst	no			0.053 0.064			5.1% 0.5%
	Archeré et al. 2005	Picea abies	~1000	AFLP / SSR / ESTP	FDIST2			0.015 0.02	1.3		3.3%
	Ma et al. 2010	Populus tremula	>1000 SNP BayeScan	no	0.017					0.0%
	Eveno et al. 2008	Pinus pinaster	>1000 SNP	Fdist2 FstSNP BayesFst	no	0.137		0.164 0.004 0.416	1.2 0.0 3.0		2.0% 1.7% 1.7%
				AFLP /
	Archeré 2005	Picea abies	~2000	SSR /	FDIST2			0.02 0.026	1.3		2.6%
				ESTP
	Holliday et al. 2011	Picea sitchensis	2000	SNP	BayesFst	no	0.06					4.1%
	Eckert et al. 2010	Pinus taeda	4500	SNP	Fdist2	no			0.2		7216 0.3%
	Cox et al. 2011	Alnus glutinosa	europe AFLP BayeScan	no						6.5%

Table 10 .

 10 Among population genetic differentiation (as measured by FST ) in F. sylvatica

	Marker	FST	Number of populations and locations	references
	Isozyme	0.059	389 populations across Europe combined	(Comps et al. 2001)
	Isozyme	0.031 -	389 separate stands across Europe	(Comps et al. 2001)
		0.114
	Isozyme	0.046	21 populations across Italy combined	(Leonardi & Menozzi 1995)
	Allozyme	0.054	99 populations across central and Mediterranean	(Comps et al. 1990)
			Europe combined
	SSR	0.0085 -	10 separate stands across Europe	(Buiteveld et al. 2007)
		0.0535
	SSR	0.058	10 populations across Europe combined	(Buiteveld et al. 2007)
	SSR	0.022	6 populations from 3 regions of northern Germany	Seifert, unpublished
	AFLP	0.024	3 populations from one mountain slope	(Jump et al. 2006)
	AFLP	0.028	6 populations from 3 regions of Switzerland	Pluess and Weber (2012)
	SNP	0.012	6 populations from 3 regions of northern Germany	(Seifert et al. 2012)
	SNP	0.018-
		0.022

  are necessary for confirming those candidates. On another hand, if the timing of budburst is selected towards earlier values both in top and bottom populations of Mont Ventoux gradient (as suggested by the combined experimental results of Bontemps 2012 and Gaüzère 2011), we do not expect strong signal of divergent selection among populations pairs.

Refers to the grouping of the ancestor and its extant and/or extinct descendants.

DNA regions of low compositional complexity that may cause spurious or misleading results.

Linkage disequilibrium. LD is the non random association of alleles adjacent loci

Interbreeding of individuals from previously separated populations

Acknowledgments

Acknowledgements

We thank Pauline Garnier-Géré for her help with the cleaning and the analyses of candidate gene sequences. We thank Yao Chen Lin for his help with the cleaning of the consensus sequences. We thank Norbert Turion, Olivier Gilg, Frank Rei (INRA-UEFM) for fieldwork, and Sara Torre and Federico Sebastiani for their help in the lab. All of the authors were supported by the EU Network of Excellence "EvolTree" (GOCE-016322) and by ERA-NET BiodivERsA project "LinkTree". GGV was also

ACKNOWLEDGMENTS

We thank Pauline Garnier-Géré for her help in the data mining and all comments during the manuscript writing. We thank Yao Chen Lin for his help with the ESTs alignments, Stefania de Quattro for her help in the sequencing of genes and Jonathan Safrana for his help in checking all SNPs. We thank Aurore Bontemps (INRA-URFM) for kindly providing phenotypic data part of her PhD thesis, as well as Norbert Turion, Olivier Gilg, Frank Rei (INRA-UEFM) for field work (mapping, sample collection and measurement). All of the authors were supported by the EC-supported Network of Excellence Evoltree (GOCE-016322) and by ERA-NET Biodiversa project LINKTREE. GGV was supported by a grant by the Italian MIUR project "Bioversitalia" (RBAP10A2T4). HL, SOM and BF were supported by INRA-EFPA projet innovant 2010.

In order to keep the indel polymorphic site in the set of polymorphic loci for subsequent analyses we converted the indel polymorphism into a SNP-like polymorphism using the procedure illustrated by the figures on the right (Ab, Bb, Cb). The homozygous individual for the missing cytosine (-on chromatogram Ba) was recoded as an homozygous individual (AA on chromatogram Bb). Theoretically any base but C could have been chosen to replace the indel; here we chose A because it correspond to the next base on site 8.

The heterozygous individual was then coded as follow:

-Site 7, which shows the first double peak resulting from the 1-bp shift, is coded as heterozygous M (standing for C or A).

-When the indel is longer than 1-bp, the insertions, here represented by "-", are replaced by the consensus.

-The next bases are coded as missing data (here as "?").

information regarding population structure than F ST . In addition, a non-significant permutation test suggests that F ST should be preferred over R ST [START_REF] Hardy | Microsatellite allele sizes: A simple test to assess their significance on genetic differentiation[END_REF].

Authors :

Hadrien Lalagüe 1,2,3 , Santiago C. González-Martínez 4 , Bruno Fady 3 Giovanni G. Vendramin 2* , and Sylvie Oddou-Muratorio 3 .

1 Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, 56127 Pisa, Italy 2 CNR, Plant Genetics Institute, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Firenze), Italy.

3 UR629, Écologie Forestière Méditerranéenne, INRA, Domaine Saint Paul, F-84914 Avignon, France. 4 CIFOR-INIA, Forest Research Centre, Carretera de La Coruña km 7.5, 28040, Madrid, Spain. *Corresponding author

ABSTRACT

A major goal of evolutionary ecology is to understand the genetic basis of local adaptation, and to disentangle the effects of distinct evolutionary forces on its establishment and maintenance. Altitudinal gradients displaying strong environmental heterogeneity across short spatial scales constitute appealing case studies. In these systems, patterns of genetic diversity and differentiation at genes controlling adaptive traits are expected to be driven mainly by the balance between selection and gene flow.

We investigated patterns of genetic diversity, differentiation, and linkage disequilibrium at candidate genes in four populations of European beech (Fagus sylvatica L.) along two altitudinal gradients to test whether the signature of selection could be detected despite high gene flow and recent population origin. Fifty eight candidate genes for stress and budburst phenology sequenced in 96 individuals revealed 581 SNPs, with low differentiation among populations (average F ST = 0.018 and 0.022 in the two altitudinal gradients).

Although Bayesian tests did not detect any outliers at the usual threshold of Bayes factor BF=3, frequentist detection tests combined with demographic simulations detected 15 outlier SNPs on one gradient (average F ST = 0.138) and 28 outlier SNPs on the other gradient (average F ST = 0.173). One SNP explained 20% of the variation observed in situ for budburst phenology and another SNP explained 30% of the variation observed in situ for leaf mass per area. Finally the between population component of linkage disequilibrium was much higher for outlier SNPs than for SNPs overall, indicating multi-locus signatures of selection. Key-words: outlier test, budburst phenology, abiotic stress, selection, SNP under a constant-size finite island model with 10 demes, 50 (south) to 142 (North) gametes and a mutation model accounting for the SNP bi-allelic state [START_REF] Excoffier | Detecting loci under selection in a hierarchically structured population[END_REF]. Moreover, two neutral F ST distributions were simulated, with two different target mean F ST values for the coalescent-based simulations. First, a neutral F ST distribution (IslandSSR distribution) was obtained by using as target F ST the mean value estimated from SSR markers (F ST-SSR ), considered as neutral reference loci. Second, a neutral F ST distribution (IslandSNP distribution) was obtained by using as target F ST that estimated from all SNP markers (F ST-SNP ), assuming that most SNPs behave neutrally.

Changes in population size are likely to modify the neutral expectation of F ST values as compared to that simulated under the constant size island model (Eckert et al. 2010). Although the study by Lander et al. (2011) provided valuable information on the possible demographic history of the studied populations, the precise timing and magnitude of demographic events were difficult to estimate. Here, we accounted for this uncertainty by considering three alternative demographic scenarios.

RESULTS

Genetic diversity and differentiation at SNP and SSR markers

A total of 58 gene fragments (49.5 kbp in total) was analysed in 96 individuals. The total number of SNPs (including recoded indels) was 491 in the complete data set (singleton, trinucleotide and SNPs in LD included, but only for SNP with less than 30% of missing data); 145 SNPs were found in the 20 phenology genes and 346 in the 38 drought stress genes. The mean nucleotide diversity across all genes was low (θ π = 0.0026). No differences were observed between functional categories: θ π-phenology = 0.0026 (s.d. = 0.0024 ) versus θ π-stress = 0.0026 (s.d. = 0.0022) or among populations (Table 3). Overall mean Tajima's D across genes was 0.54 (s.d. = 1.05), ranging from -1.36 to 3.52. Also for this parameter no significant differences between functional categories: D phenology = 0.26 (s.d. = 1.14) versus D stress = 0.68 (s.d. = 1.09) or among populations (Table 3) were observed.

Mean pairwise LD across SNPs within fragment was r 2 =0.33 and r 2 =0.44 on northern and southern gradients, respectively, while between genes, it was r 2 =0.16 and r 2 =0.14 on northern and southern gradients, respectively. Respectively 69% of the 49141 and 45% of the 64620 pairwise comparisons on the northern and the southern gradients were significant at 5% level using a Chi-square test. We observed strong LD blocks within amplicons and also between several pairs of them (see online appendix 4). We excluded from following analyses one of the two SNPs in each pair displaying r 2 >0.9 (i.e. 86 and 123 SNPs for northern and southern gradients, respectively). The number of SNPs retained was thus 228 and 238 for the northern and southern gradients, respectively.

Based on the retained SNPs, the mean genetic differentiation as measured by F ST across SNP pairs was 0.018 (max = 0.270) and 0.022 (max = 0.446) in the northern and southern gradient, respectively. No significant differences were observed between functional categories F ST -phenology = 0.016 (s.d. = 0.04) versus F ST -stress = 0.02 (s.d. = 0.046) for the north gradient and F ST -phenology = 0.035 (s.d. = 0.093 ) versus F ST -stress = 0.017 (s.d. = 0.073) for the south gradient.

Of the 12 microsatellites (SSRs) tested for neutrality, one outlier SSR was removed (see online appendix 2 for details). Observed (H o ) and expected heterozygosity (H e ) at the remaining 11 SSRs were similar across populations (Table 2). A significant reduction in heterozygosity due to inbreeding was found for the population SL (Table 2) while for the three other populations mean F IS across SSRs was not significantly different from zero. Differentiation between the NH and NL populations was F ST = 99 0.021; between the SH and SL populations, it was F ST = 0.042.

Using BAYESCAN 2.1 and prior odds set to 10, no SNP could be identified as outlier using the standard Bayes factor threshold of 3 for the selected versus the neutral model. However, some loci showed Bayes factors substantially higher than 1, and lower q-values than others. Among them, SNPs 1106 and 172 in the northern gradient and SNP 931 in the southern gradient were among the outlier loci identified by the frequentist approach.

Patterns of between-population correlations for SNP pairs

The between-population component of LD (Zg) was higher for outlier SNPs based on the frequentist approach (Table 3). Estimates on the northern gradient were Zg=0.0004 for all SNPs versus Zg=0.651 for the outlier SNPs; on southern gradient, Zg=0.0001 for all SNPs versus Zg=0.063 for the outlier SNPs.

Relation between outlier SNPs and functional traits

We first analysed the variation in LMA measured in 35 individuals within the NL population by fitting 15 independent models for each of the 15 outlier SNPs. After correction for multiple testing SNP 1106 was found to contribute significantly to the variation of LMA within population (R²=0.30, Bonferroni

p-value=0.03).

The variation in budburst phenology was assessed by measuring PSS in 71 individuals from the two populations NH and NL. First, 15 independent models were fitted for each of the 15 outlier SNPs (treating individuals from NH and NL simultaneously and including a population term); none of the outlier SNPs significantly contributed to variation at PSS within population. Removing the population term, and correcting for multiple testing, SNP 172 was found to be significantly associated with variation at PSS (R²=0.21, Bonferroni p-value=0.003, Table 6). We then applied a multivariate ANOVA model, after excluding missing data and linked loci from the data set (keeping 48 individuals and 3 outlier SNPs with r²<0.2); the best selected multivariate ANOVA model had R²=0.28 (p-value=0.001, Table 6), and included 2 outliers SNPs (among which SNP 172). Finally, we also tested whether other non-outlier loci contributed to variation at PSS within population: 8 non-outlier SNPs were found to be Online Appendix A3. Command lines for ms :

Northern gradient

Split scenario:

./ms 142 1000000 -s 1 -I 2 70 72 -ma x 53.3 53.3 x -G 3805.64 -eG 0.000375 0.0 -ema 0.000375 2 x 12.86 12.86 x -ej 0.0025 2 1 | msstats -I 2 70 A) hypothesis to identify the major phenotypic traits or adaptive genes that contribute to the spatial genetic variation among populations. Such traits could then be monitored over time.

Recently, genetic markers and, more generally, "-omics" tools have been increasingly used to investigate the distribution of adaptive genetic diversity within and among natural populations, through environmental association approaches [START_REF] Neale | Association genetics of complex traits in conifers[END_REF] or a wide variety of other methodologies, from allele frequency distribution to gene expression analysis (Chen et al. 2012).

Compared to trait-based approaches, genomics approaches have the major advantage of directly accessing the genetic component of the spatial adaptive variation without the confounding effect of the environment. However, one of their major drawbacks is the general difficulty of i) associating genetic variation at particular loci with the selective pressure, and ii) associating genetic variation at particular loci with the variation in phenotypic traits (Hansen et al. 2012). Although such correlations can be asserted in model species for which the loci that underlie ecologically important traits are known, it often remains a challenge for non-model species.

A common approach in non-model species is to use a genome scan (also referred to as outlier approaches) to identify genetic markers that are under diversifying hitch-hiking selection in a heterogeneous environment. A genome scan typically consists of genotyping a large number of loci in divergent populations to identify the loci that exhibit distinct ("outlier") values of a population genetic parameter compared to the neutral expectation (e.g., [START_REF] Akey | A high-density SNP allele frequency map reveals genomic signatures of natural selection[END_REF]. Outlier approaches generally use population differentiation as measured using Wright's F ST and rely on the assumptions that i) past demographic events shape the average value of F ST across the genome and ii) diversifying selection increases F ST at specific loci [START_REF] Lewontin | Distribution of Gene Frequency as a Test of the Theory of the Selective Neutrality of Polymorphisms[END_REF]. The majority of the recent developments with respect to the statistical outlier approaches based on F ST scans aimed to account for the high expected variance in F ST due to the correlation of allelic frequencies among demes that result from common demographic history, likely making the original approach of Lewontin and Krakauer unreliable. Among these developments, the frequentist approach of Beaumont and Nichols (1996) relies on coalescent simulation of the expected neutral distribution of F ST assuming an infinite island model of population structure; this analysis is conditional on heterozygosity. This approach that was implemented in FDIST (Beaumont and Nichols 1996) and Arlequin software (Excoffier and Lischer. 2010) was reported to be remarkably robust to a wide range of alternative models of population structure (e.g., colonization, stepping stone). A classical alternative is the Bayesian method that was proposed by Foll and Gaggiotti (2008) to directly estimate the posterior probability that each locus is burn-in of 50,000 iterations and then by 150,000 iterations (thinning interval of 30).

Patterns of covariance among populations

The covariance of allelic effects is 4α i α j D ij , where α i and α j are the additive allelic effects at loci i and j, respectively, and D ij is the LD between the two loci (Latta 1998). When the allelic effects are unknown, we can estimate D b(ij) , which is the between-population component of LD between loci. We used the estimator Zg of D b(ij) provided by Storz and Kelly (2008, equation 3):

Note that the numerator is squared to remove the sign of the LD (which is arbitrary). Moreover, the denominator allow for the normalization of Zg for allelic frequencies and thus to remove its dependency on F ST .

We computed the average value of Zg for the QTLs, the neutral loci, the outlier and non-outlier loci at generation G5 and considered only Pop2 and Pop3. We then tested whether the QTLs can be detected using high values of Zg. Considering all of the loci together (i.e., both QTLs and neutral loci), we computed the Zg-value for R randomly selected subsets of N loci, with R=100,000 and N=10, 20 or 30.

For each N, we kept only the subset of loci involved in the 5% highest values of Zg. For all of these subsets, we sorted the loci according to their frequency of occurrence. 135

DISCUSSION

The originality of our simulation study is that it relies on a mechanistic physio-demo-genetics model to simulate the construction of genetic differentiation for the TBB over 5 generations in a continuous population colonizing an altitudinal gradient (OM&D). The primary value of this model is to generate patterns of genetic differentiation for TBB without any assumption regarding the relationship between TBB and individual fitness (which dynamically emerged from the biophysical model); moreover, this model is calibrated for a specific species at a specific site (i.e., Fagus sylvatica on Mont Ventoux). The principal limitations of this model are the assumptions regarding trait architecture (determined by 10 additive independent QTLs with arbitrary allelic effect and heritability equal to 1 and the simulation design with non-over-lapping generations. Despite these limitations, we argue that the significant patterns of differentiation for TBB that emerged in 5 generations across the altitudinal gradient provide an interesting case study with to investigate molecular signatures of natural selection over short spatial and temporal scales. Our result suggests that in such cases, we should expect an overall very limited efficiency of classical approaches in detecting loci under selection, including two genome-scan methods (i.e., FDIST and BAYESCAN), genotypic frequency clines and a more original approach relying on the between-population component of linkage disequilibrium. Before discussing these results, we first attempt to relate the observed patterns of genetic differentiation to theoretical patterns that have been observed in previous simulation studies.

Comparison with theoretical expectations

We took as a reference the simulation study of Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF], who investigated patterns of differentiation at trait, QTL and neutral genes in a subdivided population. With respect to the population model, they considered a subdivided population of 25 demes (each of size 500). The migration rate varied from 0.0002 to 0.02, corresponding to an effective number of migrant individuals (Nm) of 0.1, 1 or 10. At generation 5, we had ~300 individuals in Pop2 versus 500 in Pop3 to Pop5.

Effective migration (i.e., dispersal over half of the spatial limit of a population) occurred only through pollen and occurred at a rate that reached m=0.01% at generation G5. Our situation thus correspond to the high gene flow situation that was considered by Le [START_REF] Corre | Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection[END_REF]. We will consider using AFLP markers which makes further investigations complicated on the DNA regions detected as outliers. The differentiation among populations was low in both studies (F ST =0.018 in Jump et al. 2006; Pluess and Weber 2012). In Jump et al. (2006) the only outlier detected (using the frequentist approach) was correlated to temperature. In Pluess and Weber (2012), 7 outlier markers identified with the Bayesian approach also changed their frequency with local moisture availability.

Both of these studies suggest that is possible to detect selection signals at small spatial scale using genome scan approaches.

In this PhD study, I used both frequentist and Bayesian approaches in order to detect outlier loci in experimental and simulated populations of F. sylvatica at local spatial scales. The experimental data sets consisted 581 SNPs from 58 candidate genes for stress and budburst phenology sequenced in 96 F. sylvatica individuals belonging to four populations along two altitudinal gradients (Chapter 3). The frequentist approach revealed between 7-12% and 2-4% outliers with a less and more stringent null model, respectively. The Bayesian approach detected no outliers. Genome scans were also used to detect loci under selection in 5 simulated data sets with 10 QTLs (coding for the timing of budburst) and 410 neutral loci for populations evolving under divergent selection during 5 generations (Chapter 4). The frequentist approach, applied on the two most divergent populations of the gradient, detected, on average, 33.4 outliers, and, on average, only 24% of the QTLs were detected. In contrast, the Bayesian approach revealed, on average, one outlier, and, on average, only 8% of QTLs were detected.

The rate of false positive was extremely high for both methods (93% with the frequentist and 20% with the Bayesian approach respectively). But the higher rate of false positive for the frequentist method highlight that correction for multiple test is necessary for attempting to isolate the 6% of true positive [START_REF] Eveno | Contrasting patterns of selection at Pinus pinaster Ait. drought stress candidate genes as revealed by genetic differentiation analyses[END_REF][START_REF] Vilas | A simulation study on the performance of differentiation-based methods to detect selected loci using linked neutral markers[END_REF]. Thus, despite the strong intra-and inter-population selection, both the frequentist and Bayesian methods failed to detect with confidence the QTLs in the simulated data sets.

The combination of simulation and experimental approaches used in this thesis shed light several methodological issues regarding the use of outlier tests, such as sample size and design. Indeed, the low sample size used in the experimental study might have contributed to the lower than expected number of outliers from a set of candidate genes. I sampled only two populations in each gradient, with an unequal number of individuals (35 individuals on the north gradient and 12 individuals on the south gradient). Concerning the frequentist approach, Beaumont and Nichols (1996) showed through simulation that the joint distributions of F ST and heterozygosity is robust to variation in sample size when both more than 50 gametes per population and more than 15 populations are sampled. But few studies fulfill these sampling criteria. In Jump et al. (2006), 70 individuals per population for a total of 3 population; in [START_REF] Namroud | Scanning the genome for gene single nucleotide polymorphisms involved in adaptive population differentiation in white spruce[END_REF], 26 individuals per population for a total of 6 populations; in Prunier et al. (2011), 6 individuals per population for a total of 26 populations and lastly in Tsumura et al. (2012), 13 individuals per population for a total of 14 populations. Concerning the the Bayesian approach, the low number of populations do not seem to drastically decrease the performance (Foll and Gaggiotti 2008). Indeed Pluess and Weber (2012) successfully identified SNPs that are outliers in the three replicates. They sampled 70 individuals per population and only 2 populations along 3 gradients (for a total of 6 populations).

In contrast, using simulated data from a physio-demo-genetic model of F. sylvatica with up to 500 individuals per population, I found that the efficiency of outlier approaches was rather poor. The effect of increasing the number of sampled populations on the efficiency of outlier approach remain to be investigated. Nevertheless, the simulation study by Oddou-Muratorio and Davi (in prep) highlighted that the optimal values for the sum of temperatures required for budburst (the genetic component of the timing of budburst) are expected to show strongly non monotonic variations along the altitudinal gradient: in their simulations, the selection for earlier budburst was stronger at intermediate elevation than at high of very high elevation, while the selection for later budburst was observed a low elevation but not at very low elevation. Additionally, the strength of the optimizing selection within population also varied. In such situation where the type and strength of selection vary within and among population pairs, it is not straightforward that an increase in the number of population used for genome scan will increase the power to detect loci under selection [START_REF] Vitalis | Interpretation of variation across marker loci as evidence of selection[END_REF]. This PhD study also shed new lights on ability of genome scans to identify patterns of genetic differentiation driven by climate-related selection at local spatial scales. The PDG simulation model s used in Chapter 4 (Oddou-Muratorio et Davi in prep) showed that climate applied a selective pressure on budburst date and lead to significant patterns of genetic differentiation for this trait, as observed based on a common garden experiments from populations of Mont Ventoux (Gaüzère 2011). The first originality of this simulation study was to rely on a mechanistic biophysic and physiological model accounting for climate variation along an altitudinal gradient of F. sylvatica to simulate "realistic" patterns of differentiation for the timing of budburst. The second originality was that it considered a continuous population with patterns of gene flow, genetic drift and history roughly similar the the explain the absence of diversity loss during the colonization of Europe. Moreover, two species, Q. robur and Q. petrae co-occur in western Europe and hybridization was shown to facilitate colonization and prevent melting of genetic diversity [START_REF] Petit | Hybridization as a mechanism of invasion in oaks[END_REF]). In addition, Quercus species produce large seeds and long dispersal events are favored by the European jay Garrulus glandarius L. [START_REF] Petit | Chloroplast DNA footprints of postglacial recolonization by oaks[END_REF]). On the reverse, for F. sylvatica, only two refugia (Iberian peninsula and Balkans) seem to have contributed to the recolonization of Europe, the Italian refugium having slightly contributed [START_REF] Demesure | Chloroplast DNA phylogeography of the common beech (Fagus sylvatica L) in Europe[END_REF]. The high level of among-population differentiation for maternally-inherited markers (cpDNA) contrast with the low level of differentiation for bi-parentally inherited markers (isozymes ; [START_REF] Comps | Allozymic Variability In Beechwoods (fagus-sylvatica L) Over Central-europe -Spatial Differentiation Among and Within Populations[END_REF]) suggesting low level of gene flow mediated by seed dispersal [START_REF] Demesure | Chloroplast DNA phylogeography of the common beech (Fagus sylvatica L) in Europe[END_REF]. This was also the case for Quercus species, but in F. sylvatica, ecological studies of contemporary abilities of seed dispersal suggest that most dispersal events are assisted by small mammals (rodents) and that long-distance dispersers (large mammals, birds) do not contribute significantly (Bontemps et al. in prep). In addition despite reported case of inter-cross with F. orientalis [START_REF] Comps | Diverging trends between heterozygosity and allelic richness during postglacial colonization in the European beech[END_REF], F. sylvatica is the only Fagus species to occur in western Europe. These three characteristics may explain why F. sylvatica have a nucleotide diversity much lower than Quercus.

Regarding patterns of genetic differentiation, F. sylvatica was found in previous studies to have low F ST values at regional and European scales when considering neutral, bi-parentally inherited markers (Table 4). Overall, F ST -values may be slightly higher for isozyme and SSR ( F ST = 0.05) than for AFLPs and SNPs ( F ST = 0.02), which is expected considering the difference in the number of allele per locus for these two categories of markers. However, this may also be due to the geographical scale and the number of populations considered. Overall, the low F ST -values reported for F. sylvatica are similar to those reported for other Fagaceae and anemophilous forest tree species [START_REF] Hamrick | Factors influencing levels of genetic diversity in woody plant species[END_REF]Kremer et al. 2012). Finally, the F ST -values measured at outlier SNPs in this study was around 7-fold higher the F ST -values measured at all SNP together (from 0.018-0.022 at all SNPs versus 0.138-0.173 at loci putatively under selection). Of course we cannot rule out that many of these outlier SNPs are false positive, but still, this range of F ST -values may provide a rough figure of quantitative difference in F STvalues expected between neutral and selected loci in F. sylvatica at local scale. Non negligible differences in F ST values between neutral and selected loci are expected in populations with large effective population size (N e ), even under fairly weak selection because the selection coefficient (s) is more likely to be greater than the mutation rate, m [START_REF] Allendorf | Genomics and the future of conservation genetics[END_REF]. Studies supporting this prediction are numerous. In Atlantic cod (Gadu morhua) almost no genetic differentiation at neutral Finally, this PhD study also shed light on the level of linkage disequilibrium (LD) in F. sylvatica. In line with a recent study (Larsson, PhD thesis 2012) the LD decay is was so fast, possibly because it was computed from sequences longer than 500 or 800bp. Contrary to previous observations (Savolainen et al. 2007;[START_REF] Neale | Forest tree genomics: growing resources and applications[END_REF] the LD decay may thus be slow in some trees species. The observed low LD decay in addition to the low differentiation between populations, suggest that whole genome scan and association mapping would be efficient strategies to understand the molecular basis of local adaptation to environmental changes in F. sylvatica. Then the recent sequencing methods so-called Next Generation Sequencing (NGS) would be worthy of being considered for non model species.

However, the candidate gene approach is not doomed, it allows to generate long fragments without missing the low frequency alleles. Even in human genetics the candidate gene approach provide some advantages [START_REF] Wilkening | Is there still a need for candidate gene approaches in the era of genome-wide association studies?[END_REF]).

Genes and traits involved in adaptation of F. sylvatica to climate

Among the respectively 15 SNPs (Northern gradient) and 28 SNPs (Southern gardient) identified as outlier through genome scan on the experimental data set in Fagus sylvatica on Mont Ventoux, 4 SNPs were considered to be likely under selection because of additional evidence based on complementary information. First, two outlier SNPs were outlier on both gradients (SNP 151 coding for an ubiquinolcytochrome c reductase iron-sulfur and SNP 711 coding for a pectin methylesterase). Also, two other outlier SNPs were shown to explain variation at functional traits within population (SNP 172 which is in the same gene than the SNP 151 and the SNP 1106 belongs to a gene coding for a protein that could not be defined with confidence). In the following, I discuss these genes strongly suspected to be under selection relation with the expected and /or observed patterns of selection on functional traits on Mont Ventoux.

First of all it is important to stress out that the association study performed in this PhD thesis (Chapter 3) was an exploratory approach. Our purpose was to investigate whether outlier loci identified by genome scan significantly contributed to the phenotypic variation within population, which would support these outliers.

Mediterranean basin is characterized by severe drought during the summer associated with high temperature. When water pressure difference between the soil and the atmosphere is high, one of the

GENETIC MARKERS AS TOOLS FOR UNDERSTANDING ADAPTIVE RESPONSE TO CLIMATE CHANGE

Despite the low number of SNP/genes identified as contributing to spatial adaptive genetic variation in F. sylvatica in this PhD study, it is interesting to wonder what use can be done from these few genes for understanding future response of F. sylvatica to climate change. Studies on genome wide polymorphisms in trees recently displayed evidences of adaptive molecular genetic variation driven by environmental heterogeneity through environmental associations (Eckert et al. 2010;[START_REF] Grivet | Molecular footprints of local adaptation in two Mediterranean conifers[END_REF]Prunier et al. 2011) sometimes completed by functional and expressional information on the detected genes [START_REF] Holliday | Widespread, ecologically relevant genetic markers developed from association mapping of climate-related traits in Sitka spruce (Picea sitchensis)[END_REF]Chen et al. 2012). There is indeed accumulating evidence that climate change can cause genetically based adaptive evolution in broad variety of species [START_REF] Franks | Genetics of climate change adaptation[END_REF]. But there is no evidence that this will commonly occur and prevent genetic diversity loss or mass extinctions. On the contrary, a recent meta-analysis predicted major losses in genetic diversity for northern plants because of the climate change [START_REF] Alsos | Genetic consequences of climate change for northern plants[END_REF].

Tracking adaptive response of populations to ongoing climate change requires monitoring, i.e.

analysing either phenotypic traits of genetic markers over time (Hansen et al. 2012). A common approach in ecology is to trade space for time, i.e. to consider that the spatial shift in climatic conditions along a gradient can be equivalent to the temporal shift that each local population will face under changing climate. In this context, loci identified as contributing to the spatial genetic variation among populations (for instance through the kind of genome scans performed here) should provide good candidate markers monitoring through time . However, the space for time hypothesis is a gross simplification, since climate change may result in new combinations of climatic parameters imposing entirely new selection pressure.

Another problem in non model species as considered here is the lack of functional and expressional information on the candidate genes. Together with tracking changes in allelic frequencies across space, it would also be interesting to investigate the level of expression of these genes. Gene expression profile in F. sylvatica have been carried out in contrasted treatments (drought stress and control) and for two provenances (Greek and German). For twelve of the 58 genes developed and sequenced during this PhD thesis. Interestingly, two of them (genes 20 and 33) displayed a significant differentiation between provenances and treatments (Dounavi, personal communication). But the approach is not always