Évolution Des Syndromes De Pollinisation Et Des Niches Bioclimatiques Au Sein Des Genres Antillais Gesneria Et Rhytidophyllum (Gesneriaceae)

Université de Montréal

ÉVOLUTION DES SYNDROMES DE POLLINISATION ET DES NICHES BIOCLIMATIQUES AU SEIN DES GENRES ANTILLAIS GESNERIA ET RHYTIDOPHYLLUM (GESNERIACEAE)

Hermine Alexandre

Institut de Recherche en Biologie Végétale Département de Sciences Biologiques Faculté des Arts et Sciences

Thèse présentée en vue de l’obtention du grade de PhilosophæDoctor en sciences biologiques

Avril 2016

c Hermine Alexandre, 2016 Université de Montréal Faculté des études supérieures et postdoctorales

Ce mémoire intitulé:

Évolution des syndromes de pollinisation et des niches bioclimatiques au sein des genres Antillais Gesneria et Rhytidophyllum (Gesneriaceae)

Présenté par:

Hermine Alexandre

a été évalué par un jury composé des personnes suivantes:

Simon Joly, directeur de recherche Virginie Millien, évaluatrice externe Sébastien Renaut, membre du jury Thimothée Poisot, président du jury Résumé

Contexte : Gesneria et Rhytidophyllum (Gesneriaceae) sont deux genres de plantes An- tillais aillant subi une forte diversification et qui présentent une forte variabilité de modes de pollinisation associés à des traits floraux particuliers. Les spécialistes des colibris ont des fleurs tubulaires rouges, alors que les spécialistes des chauves-souris et les généralistes présentent des fleurs campanulées de couleur pâle. La capacité d’être pollinisé par des chauves-souris (en excluant les colibris ou en devenant généraliste) a évolué plusieurs fois indépendamment au sein du groupe. Ces caractéristiques font de ces plantes un bon modèle pour étudier les relations entre l’évolution des modes de pollinisation et la diversification spécifique et écologique. Pour ceci, nous avons étudié les bases génétiques des changements de mode de pollinisation et les liens entre ces modes de pollinisations et la diversification des niches bioclimatiques. Méthodes : Nous avons réalisé une étude de QTLs pour caractériser les régions géno- miques associées à la transition de syndrome de pollinisation entre une espèce à stratégie de pollinisation mixte (Rhytidophyllum auriculatum) et une espèce spécialiste des colibris (Rhytidophyllum rupincola). Nous avons parallèlement analysé les relations entre les changements de modes de pollinisation (dimension biotique de la niche écologique) et l’évo- lution des niches bioclimatiques chez ces plantes. Enfin, d’un point de vue théorique, nous avons testé l’effet de la fréquence et de l’amplitude des changements environnementaux sur les patrons d’évolution des niches écologiques. Résultats : L’étude des QTLs a montré que la couleur et le volume de nectar sont basés chacun sur un QTL majeur, alors que la forme de la corolle a une base génétique plus complexe. Par ailleurs ces différents QTLs ne sont pas liés physiquement dans le génome.

iii L’analyse des niches bioclimatiques a montré que ces Gesneriaceae antillaises sont caracté- risées par un conservatisme phylogénétique de niche bioclimatique (PNC) et que l’évolution de ces niches est indépendante des stratégies de pollinisation. Les plantes semblent aussi être relativement généralistes du point de vue de leur niche abiotique. Finalement, nous avons testé l’hypothèse selon laquelle l’adaptation à un environnement temporellement hétérogène pourrait expliquer à la fois le caractère généraliste des plantes et leur patron de PNC. Cette hypothèse s’est trouvée partiellement vérifiée. Conclusion : Si l’indépendance génétique des traits floraux a pu faciliter l’émergence des syndromes de pollinisation en réduisant les contraintes génétiques, il semble que la ré- partition largement chevauchante des colibris et des chauves-souris ne représente pas une opportunité écologique suffisante pour expliquer les évolutions répétées vers la pollinisation par les chauves-souris. En revanche, les perturbations environnementales causant réguliè- rement des déclins dans les populations de pollinisateurs pourraient expliquer l’avantage des plantes qui ont une stratégie de pollinisation mixte.

Mots-clés :Gesneria, Rhytidophyllum, syndromes de pollinisation, niche bioclimatique, QTL, modèle de Brownian Motion, Ornstein-Uhlenbeck, variation environnementale. Abstract

Background: Gesneria and Rhytidophyllum (Gesneriaceae) are two genera endemic to the Antilles that underwent an important diversification and that present a great variability in pollination modes with regard to specific floral traits. Hummingbird specialists harbour red tubular flowers while bat specialists and generalists have campanulate (i.e., bell shaped) flowers with pale colours. Bat pollination (excluding or not hummingbirds) evolved multiple times independently in this group. These plants are thus a good model to study the relationship between the evolution of pollination mode and ecological and species diversification. To understand these relationships, we studied the genetic basis of pollination mode transition and the link between pollination mode and bioclimatic niches diversification. Methods: We performed a QTL analysis to detect genomic regions underlying the floral traits involved in the pollination syndrome transition between Rhytidophyllum auriculatum (a generalist species) and Rhytidophyllum rupincola (a hummingbird specialist). Also, we analysed the consequence of pollination mode transitions (which represent the biotic part of ecological niches) on bioclimatic niches evolution in Gesneria and Rhytidophyllum. Then, we tested whether environmental changes can result in patterns of phylogenetic bioclimatic niche conservatism through time. Results: The QTLs analysis showed that corolla colour and nectar volume are both based on one major QTL, while corolla shape is determined by a more complex genetic architecture involving several unlinked QTLs. These Antillean Gesneriaceae were found to have a pattern of phylogenetic (bioclimatic) niche conservatism (PNC) and their niche evolution was found to be independent from pollination strategies. Overall, the plants were

v found to have relatively widespread bioclimatic niches. Finally, we partially conﬁrmed the hypothesis that adapting to temporally variable environment might cause both species generalization and PNC pattern. Conclusion: Genetic independence of ﬂoral traits might have facilitated pollination syndromes evolution by reducing genetic constraints. However, the overlapping distribution of hummingbirds and bats do not represent an ecological opportunity that could explain repeated evolutions toward bat pollination. However, environmental perturbations causing regular pollinator populations collapses could explain the advantage for plants to favour generalist strategies.

Key-words:Gesneria, Rhytidophyllum, pollination syndromes, bioclimatic niche, QTL, Brownian Motion model, Ornstein-Uhlenbeck, environmental variation. Table des matières

Résumé iii

Abstract iv

Table des matières x

Liste des tableaux xi

Liste des ﬁgures xiii

Liste des abréviations xiv

Remerciements xv

1 Introduction 1 1.1 Introduction générale ...... 1 1.1.1 Les syndromes de pollinisation ...... 1 1.1.2 Les niches écologiques ...... 3 1.1.3 Eﬀets de l’hétérogénéité environnementale ...... 5 1.2 Présentation du modèle d’étude ...... 9 1.3 Objectifs spéciﬁques de la thèse ...... 11 1.3.1 Bases génétiques des traits associés à l’isolement reproducteur . . . 11 1.3.2 Relations entre l’évolution de plusieurs composantes de la niche écologique ...... 14 1.3.3 Hétérogénéité temporelle et patrons d’évolution des niches écologiques 15

vii 2 Genetic architecture of pollination syndrome transition between hummingbird- specialist and generalist species in the genus Rhytidophyllum (Gesneri- aceae) 18 2.1 Abstract ...... 18 2.2 Introduction ...... 19 2.3 Material and Methods ...... 22 2.3.1 Study System ...... 22 2.3.2 Phenotypic measurements ...... 22 2.3.3 Genotyping ...... 26 2.3.4 Linkage map construction ...... 28 2.3.5 QTL detection ...... 29 2.3.6 Pleiotropy and epistasy detection ...... 30 2.4 Results ...... 30 2.4.1 Correlation between traits and morphological variation ...... 30 2.4.2 Molecular data and Linkage map ...... 31 2.4.3 QTL analysis ...... 35 2.5 Discussion ...... 40 2.5.1 Detection of moderate QTLs involved in pollination syndrome transition ...... 40 2.5.2 Pollination syndrome evolution in the genus is summarized by morphological transition between R. auriculatum and R. rupincola ... 43 2.5.3 The role of selection in pollination syndrome transition ...... 44 2.5.4 conclusion ...... 46 2.6 Acknowledgements ...... 46

3 Bioclimatic niches evolved independently from pollination syndromes in Antillean Gesneriaceae 47 3.1 Abstract ...... 47 3.2 Introduction ...... 48 3.3 Material and Methods ...... 51 3.3.1 Species occurrences and bioclimatic data ...... 51 3.3.2 Species bioclimatic niche evaluation ...... 52 3.3.3 Bioclimatic niche overlap ...... 52 3.3.4 Bioclimatic niche evolution among plants ...... 53 3.4 Results ...... 54 3.4.1 Bioclimatic niches overlap ...... 54 3.4.2 Bioclimatic niche evolution among plants ...... 58 3.5 Discussion ...... 60 3.5.1 Bioclimatic niches evolve independently from pollination modes . . 61 3.5.2 Phylogenetic bioclimatic niche conservatism ...... 62 3.5.3 Evolution of pollination mode ...... 63 3.5.4 Conclusion ...... 63 3.6 Acknowledgements ...... 64

4 Testing phylogenetic niche conservatism under temporally variable environments with Brownian-motion based models. 65 4.1 Abstract ...... 65 4.2 Introduction ...... 66 4.3 Material and Methods ...... 68 4.3.1 Simulation Model ...... 68 4.3.2 Temporal variation frequency vs. amplitude effect on generalization (single species focus) ...... 72 4.3.3 Detection of PNC in fixed and variable environment contexts (multispecies focus) ...... 72 4.4 Results ...... 74 4.4.1 Temporal variation frequency vs. amplitude effect on generalization 74 4.4.2 Detection of PNC in fixed and variable environment contexts . . . . 74 4.4.3 Bias in ancestral trait reconstruction ...... 76 4.5 Discussion ...... 76 4.5.1 Evolution of generalization ...... 79 4.5.2 PNC detection under climate variation ...... 80 4.5.3 Intrinsic or measured traits? ...... 81 4.5.4 Conclusion ...... 82

5 Conclusion 83 5.1 Résumé des principaux résultats ...... 83 5.2 Discussion et perspectives ...... 84

Références bibliographiques 111

A Recombination fraction and LOD scores for each pair of markers i

B Proﬁles of LOD scores for linkage groups with QTLs detected ii

C Information about species used for the PCA performed on the genus iii

D Details on the species studied iv

E Scores of environmental variables over the principal axes ix

F Mean AICc values of the evolutionary models ﬁtted on the niche components x Liste des tableaux

II.I Information about linkage groups...... 38 II.II Position and eﬀects of QTLs...... 39

III.I AICc of PGLS models with identity and size depending on (first model) or independent of (second model) the pollination mode, among the two first principal axes...... 60 III.II Mean AICc values of the evolutionary models fitted on the niche components. 60

IV.I Summary of model parameters and values used for simulations ...... 73

xi Liste des ﬁgures

1.1 Illustration de deux syndromes de pollinisation...... 3 1.2 Niche écologique des plantes à pollinisation spécialiste et généraliste. . . . .6 1.3 Phylogénie du groupe Gesneria-Rhytidophyllum ...... 12

2.1 Measure of shape variation in the hybrid population and parents...... 24 2.2 Diagram presentation of the four morphological measurement approaches. . 26 2.3 Principal component analyses of shape...... 32 2.4 Shape variation associated with each principal component...... 33 2.5 Distribution and correlation among traits in the hybrid population. . . . . 34 2.6 Linkage map and position of QTLs...... 37

3.1 Species phylogeny of the studied plants, with pollination mode probabilities represented as pie charts at the tree tips...... 55 3.2 Distribution of bioclimatic niches of pollinators (a) and plant species of Gesneria and Rhytidophyllum (b) ...... 57 3.3 Overall bioclimatic niches overlap...... 58 3.4 Bioclimatic niches overlap between plants and pollinators...... 59

4.1 Effect of fixed (a) and variable (b) climate over time on species fitness curve. 71 4.2 Corrected survival probability for different scaling parameter (σ) values... 71 4.3 Effect of amplitude (a) and frequency (b) of climate temporal variation on single species tolerance after 10000 generations...... 75 4.4 Effect of interacting amplitude and frequency on species final tolerance. . . 76

xii 4.5 ∆AICc of evolution models ﬁtted over intrinsic (a) and measured (b) optimum and for intrinsic (c) and measured (d) tolerance, for diﬀerent amplitude and frequency of climate variability. Positive values of ∆AICc favours OU model...... 77 4.6 Distance between true and assumed ancestral value ...... 77 4.7 Phenotypic trait evolution, as assumed in evolution models and as it evolved in the simulations, under temporally variable environment...... 78 Liste des abréviations

2D Two dimensional AIC Akaike Information Criterion AICc Akaike Information Criterion corrected for sample size BM Brownian Motion cM Centimorgan DNA Desoxyribonucleic acid F1 First generation hybrids F2 Second generation hybrids FDR False Discovery Rate GBS Genotyping By Sequencing LG Linkage Group OU Ornstein-Uhlenbeck PC Principal component PCA Principal Component Analysis PGLS Phylogenetic Generalized Least Squares PNC Phylogenetic Niche Conservatism pval P-value QTL Quantitative Trait Loci RBN Realised Bioclimatic Niche SNP Single Nucleotide Polymorphism

xiv Remerciements

Je m’estime très chanceuse d’avoir eu un directeur de thèse comme Simon Joly, sans qui j’aurais certainement eu du mal à aller jusqu’au bout. Je le remercie avant tout pour avoir gardé son optimisme pendant quatre ans ; face à une telle adversaire défaitiste, le combat n’était pas gagné d’avance. Je le remercie pour avoir toujours laissé grande ouverte la porte de son bureau, quelque soient les raisons de mes irruptions. Merci aussi d’avoir été présent à chaque instant, même quand j’étais de l’autre côté de l’Atlantique. Ces quatre années de travail a ses côtés ont été super enrichissantes, scientifiquement et humainement, merci pour tout ça! Je remercie Brigitte Mangin pour m’avoir accueillie en stage et m’avoir fait rentrer dans le monde merveilleux des QTLs. Ma thèse a fait un grand bon en avant pendant ce stage et je lui en suis infiniment reconnaissante. Un grand merci aussi à Stéphane Muños qui a été patient et attentionné et qui m’a aidé à voir qu’il y avait vraiment des trucs pas nets dans mes données. Mon stage au sein de l’équipe tournesol a été rendu des plus agréables aussi grâce à ses nombreux amateurs de pâtisseries. Merci à tous pour les gâteaux, mousses au chocolat et autres douceurs (on travaille toujours mieux après une pause sucrée!). J’ai eu la chance, pendant mes quatre années de thèse, de pouvoir travailler avec des stagiaires extra. Merci à Justine Vrignaud, Pierre-Antoine Bourdon, Louna Fronteau et Julie Faure pour leur super travail et pour leur indulgence envers mes explications souvent incompréhensibles. Merci aussi à Delase Amesefe avec qui je n’ai pas travaillé directement mais dont l’efficacité au travail a suscité un grand élan de motivation chez moi. Merci beaucoup à Nancy Robert qui a réussi à maintenir en vie une population

xv d’hybrides franchement pas très vigoureux. Sans son travail ils seraient certainement tous morts très vite; toutes les personnes qui m’ont un jour demandé de m’occuper de leurs plantes comprendront de quoi je parle. Merci à Janique Perreault pour m’avoir toujours accueillie dans sa serre des Gesneriaceae avec le sourire. Je remercie Julie Marleau qui a guidé mes premiers pas au laboratoire. Travailler avec elle a été un vrai plaisir. Merci à Manu Gonzalez, qui a prêté ses talents en bioinfo pour nous assembler quelques transcriptomes sans lesquels bien des manips auraient été impossibles. Si Simon a déployé plein d’énergie et d’enthousiasme pour mettre en place un lab meeting-journal club hebdomadaire (ou presque), il faut remercier particulièrement Luc Brouillet et Sébastien Renaut pour être toujours présents au rendez-vous et nous mener vers de super discu. Ce journal club, je le trouve top ! Je remercie John Clark pour nous avoir fait partager ses données et ses connaissances sur les Gesneriaceae. Je remercie aussi Steve Ginzbarg pour toutes ses heures passées à visiter les Caraïbes par satellites pour géoréférencer nos plantes; même si ces visites virtuelles ont un petit côté sympa, on s’en sort vraiment mieux à deux que tout seul. Merci à Boris, Tahiana, Gonzalo, Fanny, Raymond, Ivanna et Charlotte pour avoir mis du soleil au centre. Partager tous ces lunchs avec vous a été un régal, même si les échanges ont souvent été unidirectionnels... il parait que tout le monde n’apprécie pas la semoule nature. Merci à Benjamin pour les cafés, les bières, les ping-pongs, mais aussi pour ses relectures et ses encouragements qui m’ont aidée à finir dans les temps. Merci pour toute son énergie communicative. Merci à mes parents et Elsa et Quentin pour leur soutien pendant toutes ces années. Merci, merci, merci! Enfin, merci à Quentin pour m’avoir donné envie de pousser la porte de la fac de bio (finalement, c’était pas une mauvaise idée...), et puis merci d’être là, tous les jours. 1 Introduction

1.1 Introduction générale

Les espèces évoluent continuellement en réponse à la sélection exercée par leur environnement qui peut être sujet à des variations dans le temps. Il semble que la généralisation soit un des processus permettant aux espèces de faire face aux variations environnementales. Gesneria et Rhytidophyllum, deux genres antillais de la famille des Gesneriaceae présentent des évolution répétées et indépendentes vers un mode de pollinisation général- iste (Marten-Rodriguez et al. 2010). À partir de cette observation, nous nous sommes demandé dans quelles conditions ces modes de pollinisation ont pu évoluer si rapide- ment et plusieurs fois vers la généralisation, et si d’autres axes de la niche écologique ont aussi évolué vers plus de généralisation, parallèlement au mode de pollinisation. Dans un premier temps nous allons déﬁnir les concepts de syndrome de pollinisation et de niche écologique pour ensuite aborder les études qui ont traité de l’eﬀet de la variation environnementale sur les niches écologiques.

1.1.1 Les syndromes de pollinisation

Une idée couramment admise est que la spécialisation envers certains types de pollinisateurs a permis aux angiospermes une extraordinaire diversiﬁcation (radiation adaptative). Ce succès évolutif peut être lié à un taux d’extinction réduit (par exemple parce que la

1 Chapitre 1 1.1. Introduction générale pollinisation par les animaux permet une meilleure dispersion et améliore la persistance des populations) ou à une augmentation du taux de spéciation (l’adaptation pour différents pollinisateurs entrainant un isolement reproducteur) (Armbruster, 2014). Bien que des preuves de l’impact de la spécialisation dans ce succès évolutif soient rares (Kay and Sar- gent, 2009), il existe des cas où le lien entre la diversification et la spécialisation de la pollinisation a été établi, comme par exemple chez les orchidées, où la richesse spécifique est plus forte dans les clades qui ont un nombre réduit de pollinisateurs (Schiestl and Schlüter, 2009). L’isolement reproducteur entre deux espèces est favorisé par l’adaptation des fleurs à différents pollinisateurs. Ces adaptations spécifiques aux pollinisateurs résul- tent souvent en ce que l’on appelle syndromes de pollinisation. Un syndrome de pollinisation correspond à un ensemble de traits qui évoluent conjointement en réponse à la pression de sélection exercée par un groupe fonctionnel de pollinisateurs (Fenster et al., 2004), comme par exemple la couleur et la forme des pétales, la quantité et la concentration du nectar, etc. (Figure 1.1). Historiquement la spécialisation du point de vue de la pollinisation a d’abord été considérée comme la spécialisation pour une espèce précise de pollinisateur. Ceci s’illustre par les cas de fort mutualisme par exemple chez certaines orchidées (Tremblay, 1992) ou aussi dans les cas de coévolution entre les plantes et leurs pollinisateurs, comme pour les Yucca et leurs papillons pollinisateurs (Pellmyr, 2009). Par la suite Fenster et al. (2004) ont élargi le concept de spécialisation pour un groupe fonctionnel de pollinisateur (par exemple spécialisation pour les abeilles à proboscis long, et non pas simplement pour une seule espèce d’abeille). Il existe notamment des syndromes de pollinisation associés à la pollinisation par les insectes, les chauves-souris ou les colibris (Faegri and Van Der Pijl, 1979). Chez les espèces généralistes, les fleurs n’évoluent pas simplement en réponse à une pression de sélection exercée par un seul type de pollinisateur mais par un ensemble de pressions de sélections (qui peuvent être antagonistes) exercées par une multitude de pollinisateurs, ce qui amène certains auteurs à considérer que le concept de syndrome de pollinisation ne s’applique pas aux espèces généralistes (Gomez et al., 2014a,b).

2 Chapitre 1 1.1. Introduction générale

Figure 1.1 – Illustration de deux syndromes de pollinisation.

À gauche, Rhytidophyllum auriculatum présente un syndrome de pollinisation mixte (colibris et chauve-souris), avec des ﬂeurs jaunes, à large ouverture et produisant une grande quantité de nectar. À droite, Rhytidophyllum rupincola présente un syndrome de pollinisation spécialiste des colibris avec des ﬂeurs tubulaires, rouges et produisant une plus faible quantité de nectar.

1.1.2 Les niches écologiques

Niche Eltonienne, Grinnellienne et Hutchinsonienne

La niche écologique correspond à l’ensemble des environnements dans lesquels une population peut persister (Poisot et al., 2011a). On peut distinguer plusieurs types de niches écologiques, en fonction du type de variables utilisées pour les définir. Ainsi, la niche Eltonienne prend en compte les variables biotiques, qui sont considérées comme des ressources et qui peuvent être impactées (Soberon, 2007; Elton, 1927). En revanche, la niche Grinnellienne tient compte des variables abiotiques, qui sont des conditions et pour lesquelles les individus ne sont pas en compétition (Grinnell, 1917). Contrairement à ces deux définitions, Hutchinson (1957) définit la niche écologique d’une espèce comme un espace multidimensionnel dans lequel chaque point correspond à un état de l’environnement où l’espèce peut exister indéfiniment, sans faire de distinction entre les variables biotiques et abiotiques. L’étude de la niche écologique dépend aussi de l’échelle spatiale: souvent les interac-

3 Chapitre 1 1.1. Introduction générale tions biotiques ont lieu à petite échelle et sont donc intéressantes à étudier au niveau des populations alors que les variables abiotiques peuvent conditionner la distribution d’une espèce sur de larges échelles. Mais Soberon (2007) insiste sur le fait que les variables constituant la niche écologique peuvent être considérées à la fois comme scenopoetiques (conditions, qui ne sont pas impactées par les espèces, par exemple la pluviométrie ou la température) et bionomiques (ressources, qui peuvent être impactées, par exemple la densité d’une proie) selon l’échelle à laquelle on se place. Il donne ainsi l’exemple de la luminosité, qui à large échelle dépend de la latitude et est considérée scenopoetique alors qu’à petite échelle, par exemple dans un sous-bois les individus sont en compétition pour la luminosité.

Niche fondamentale et réalisée

Dans la définition de Hutchinson, il existe une distinction entre la niche fondamentale (les points où l’espèce devrait pouvoir persister en théorie) et la niche réalisée qui est une réduction de la niche fondamentale dues au interactions biotiques comme la compétition. Ainsi, même dans des conditions de température idéales, une espèce ne pourra pas s’établir si un compétiteur est présent. Mais d’autres processus peuvent expliquer l’absence d’une espèce dans des endroits qui lui sont en théorie favorables. Holt and Barfield (2009) proposent que d’autres distinctions devraient être faites en raison de l’effet de certains processus écologiques qui agissent sur la niche fondamentale, comme l’effet Allee qui rend la croissance d’une population densité-dépendante. Ainsi, dépendamment de la densité de la population, deux niches devraient être définies: la niche d’établissement correspond aux conditions dans lesquelles une population peut croitre à faible densité alors que la niche de persistance correspond aux conditions dans lesquelles une population ayant franchi un certain seuil de densité peut se maintenir.

Niche comme fonction ou comme habitat

L’une des diﬀérences aussi entre la déﬁnition d’Elton et celle de Hutchinson, est qu’Elton considère plus la niche comme une fonction alors que Hutchinson considère la niche comme

4 Chapitre 1 1.1. Introduction générale une réponse à l’environnement (Leibold, 1995). Dans le cas de la pollinisation, du point de vue de la plante, les pollinisateurs peuvent correspondre à la fois à une condition (niche grinnellienne, à large échelle) et à des organismes en interaction, impactés positivement par le fait de consommer le nectar (niche eltonienne, à petite échelle). Cependant, il est aussi important de noter que dans le cas des réseaux de pollinisation, les interactions sont souvent asymétriques où les plantes sont généralement beaucoup plus dépendantes des pollinisateurs que l’inverse (Bascompte et al., 2006). Ainsi, dans la majeure partie des cas, l’impact de la plante sur le pollinisateur sera négligeable par rapport à l’impact du pollinisateur sur la plante et le pollinisateur pourra être considéré principalement comme une condition. Ainsi on pourra étudier l’interaction entre la plante et ses pollinisateurs po- tentiels à partir de la déﬁnition de Hutchinson, en considérant la disponibilité des pollinisateurs comme un des axes de la niche. Dans ce sens, Pellissier et al. (2012) proposent que la niche de la plante correspondra au chevauchement entre sa niche biotique (les pollinisateurs) et sa niche abiotique (l’habitat). La niche biotique (la ressource en pollinisateurs) peut être projetée dans l’espace des variables abiotique (qui correspond à la niche abiotique des pollinisateurs), et ainsi la niche de la plante correspond au chevauchement entre sa niche propre abiotique et la niche abiotique de ses pollinisateurs (Figure 1.2).

1.1.3 Eﬀets de l’hétérogénéité environnementale

Généralisation de la niche Grinnellienne

La spécialisation correspond au processus d’adaptation à une fenêtre de plus en plus étroite de conditions environnementales (Poisot et al., 2011a). La généralisation peut être vue comme le processus inverse. Ainsi, lors de perturbations environnementales, les individus plus tolérants aux perturbations environnementales devraient être favorisés (généralisa- tion, comme cela a été montré chez des drosophiles soumises à un environnement ﬂuc- tuant dans le temps; Condon et al., 2014). Les études sur les eﬀets des changements climatiques et des perturbations anthropiques (e.g. morcellement de l’habitat) montrent que les espèces très spécialisées sont plus susceptibles de s’éteindre, dans les environnements perturbés, que les espèces généralistes. Des chercheurs constatent et prédisent

5 Chapitre 1 1.1. Introduction générale

Figure 1.2 – Niche écologique des plantes à pollinisation spécialiste et généraliste. La niche abiotique fondamentale des plantes est représentée en orange (c’est la même pour les deux types de plantes) et leur niche biotique (qui correspond à la projection de la niche abiotique des pollinisateurs) est représentée en vert. La niche biotique des espèces avec un mode de pollinisation généraliste (qui se font polliniser par plusieurs groupes fonctionnels de pollinisateurs) est plus grande que celles qui ont une pollinisation spécialiste (pollinisées par un seul groupe fonctionnel). La niche réalisée des plantes est représentée en pointillés bleus et correspond au chevauchement entre les niches fondamentales biotique et abiotique. Les axes e1 et e2 représentent des ressources abiotiques. Cette ﬁgure est inspirée de Pellissier et al. (2012).

6 Chapitre 1 1.1. Introduction générale une augmentation de la proportion d’espèces généralistes en réponse aux perturbations environnementales passées et actuelles (Clavel et al., 2010). L’émergence d’espèces spécialistes devrait advenir plus souvent dans les environnements stables, alors que les généralistes seront avantagées dans des environnements perturbés ou hétérogènes (Clavel et al., 2010). Plusieurs exemples ont montré que lors des extinctions massives passées, la proportion d’espèces spécialistes parmi les espèces éteintes était beaucoup plus importante que celle d’espèces généralistes (Mckinney, 1997; Smith and Jeffery, 1998). En effet, de fait de leur niche plus large, les généralistes sont plus flexibles par rapport aux conditions dans lesquelles ils peuvent se maintenir et sont donc aussi plus susceptibles de s’établir dans des conditions différentes (changeantes) (Clavel et al., 2010). En plus des changements climatiques actuels et des extinctions massives, des perturbations cycliques affectent le degré de généralisation des espèces. Ainsi Dynesius and Jansson (2000) ont montré que les cycles de Milankovitch (changements dans les paramètres de rotation de la Terre qui ont pour conséquence l’oscillation du climat) favorisaient la dispersion et la généralisation des espèces, conduisant à de larges répartitions pour les espèces généralistes. Enfin, si l’hétérogénéité environnementale augmente avec la taille des aires géographiques, les espèces généralistes seraient donc capables de s’établir sur des aires géographiques plus larges que les spécialistes, comme c’est le cas chez Mimulus, dans le nord-ouest des États-Unis où les espèces qui ont une distribution géographique plus large ont aussi une plus grande fenêtre de tolérance de température (Sheth and Angert, 2014). Mais tous ces exemples portent principalement sur les variables abiotiques de la niche écologique; et la niche biotique pourrait évoluer différemment. En effet, toutes les dimensions de la niche écologique ne sont pas affectées par les mêmes pressions de sélection. Par exemple, Litsios et al. (2014) proposent qu’il existe des compromis évolutifs entre les différentes variables constituantes d’une niche écologique et que la généralisation pour certaines variables « doit » s’accompagner de la spécialisation sur d’autres.

7 Chapitre 1 1.1. Introduction générale

Généralisation des interactions plante-pollinisateur

Au niveau de la pollinisation, on peut s’attendre à ce que des plantes deviennent général- istes quand la disponibilité des pollinisateurs varie dans le temps et l’espace (Waser et al., 1996). Ainsi, si de fortes perturbations environnementales font varier (de façon plus ou moins cyclique) la densité des populations de pollinisateurs, les espèces végétales général- istes devraient être favorisées par rapport aux spécialistes et mieux se maintenir. Il a été montré que le degré de généralisation des réseaux de pollinisation entre plantes et colibris était positivement corrélé à la fréquence des changements climatiques pendant le quaternaire (Dalsgaard et al., 2011). Ainsi la structure (spécialiste-généraliste) des réseaux biotiques actuels est influencée par les variations abiotiques passées. Aussi, lorsque plusieurs groupes de pollinisateurs sont présents dans une aire géographique, des stratégies général- istes peuvent évoluer chez les plantes : bien que la valeur sélective (ou fitness) associée à un phénotype intermédiaire entre deux syndromes spécialistes soit plus faible que celle des spécialistes quand un seul type de pollinisateur est présent, celui-ci devient le plus ef- ficace lorsque les deux types de pollinisateurs sont là (Aigner, 2001). Aussi, une stratégie généraliste est avantageuse pour les fleurs quand la disponibilité des différents types de pollinisateurs fluctue dans le temps (Waser et al., 1996). Un bel exemple du succès évo- lutif des généralistes est celui du genre Dalechampia. Dans ce genre originaire d’Afrique les espèces sont très spécialistes, et se font polliniser par des abeilles collectrices de résine, mais lors de la colonisation de Madagascar, les pollinisateurs Africains étant absents dans la nouvelle aire géographique, ces plantes se sont adaptées à de nouveaux pollinisateurs en perdant leurs glandes productrices de résine. Elles sont ainsi devenues généralistes, ce qui a permis une diversification de ce genre à Madadgascar (Armbruster and Baldwin, 1998). Les généralistes peuvent aussi faciliter l’établissement de nouvelles espèces dans une zone géographique. Olesen et al. (2002) ont montré sur des réseaux de pollinisation dans deux îles océaniques que les espèces colonisatrices interagissaient plus avec les es- pèces natives qu’avec d’autres colonisatrices. Ils suggèrent que ce sont les espèces natives « super-généralistes » qui permettent aux colonisatrices de s’établir, en les incluant parmi leurs pollinisateurs (ou plantes sources).

8 Chapitre 1 1.2. Présentation du modèle d’étude

Spécialisation-généralisation et conséquences macro-évolutives

La spécialisation, tant pour les pollinisateurs que pour des variables physiques, est parfois montrée comme un moteur de diversification et une cause du succès évolutif de certains groupes. Tel que mentionné précédemment, la spécialisation vis-à-vis des pollinisateurs a déjà été associée à une forte diversification spécifique chez les angiospermes (Kay and Sargent, 2009; Johnson and Steiner, 2000). Pour le cas des niches abiotiques, Joly et al. (2014) ont montré que les transitions entre niches écologiques spécialisées et distinctes étaient associées à une radiation adaptative chez Pachycladon. De même, la diversité des niches climatiques semble être associée à une forte diversification spécifique chez Oenothera (Evans et al., 2009). Cependant, une forte spécialisation pourrait aboutir à un cul-de-sac évolutif. Chez des bactéries, l’adaptation (via la spécialisation) à un nouveau milieu est corrélée à une diminution des capacités de diversification (Buckling et al., 2003). Ceci peut expliquer le maintient d’espèces généralistes, puisqu’il s’agit d’une stratégie permettant une meilleure résistance face aux perturbations environnementales.

1.2 Présentation du modèle d’étude

Les genres Gesneria et Rhytidophyllum appartiennent à la famille des Gesneriaceae. Ils se sont diversifiés dans les Antilles depuis 8 millions d’années (Roalson et al., 2008). Skog (1976) a déterminé 46 espèces dans les genre Gesneria et 20 dans le genre Rhytidophyllum; cependant des réévaluations taxonomiques sont en cours et le nombre d’espèces varie par rapport à ces estimations anciennes (travaux de François Lambert, non publiés). La phylogénie moléculaire la plus complète et la plus récente du groupe contient 42 espèces (Joly et al., 2016). Ces deux genres sont caractérisés par une grande variabilité interspécifique de morphologies florales, corrélées à la variabilité du type de pollinisation (Martén-Rodríguez et al., 2010). Bien que tous les modes de pollinisation n’aient pas été confirmés pour toutes les espèces, ces genres représentent un groupe intéressant pour étudier l’évolution des modes de pollinisation car beaucoup d’information est disponible concernant la pollini-

9 Chapitre 1 1.2. Présentation du modèle d’étude sation de ces espèces (Martén-Rodríguez and Fenster, 2008; Martén-Rodríguez et al., 2009, 2010). Bien qu’il existe des modes de pollinisation mineurs comme la spécialisa- tion pour les papillons de nuits chez une espèce (Marten-Rodriguez et al., 2015), il existe trois syndromes de pollinisation principaux dans ce groupe. Premièrement les espèces pollinisées par les colibris ont principalement des fleurs de type tubulaire rouge, celles pollinisées par les chauves-souris ont des fleurs vertes ou blanches et campanulées (voir Figure 1.1), enfin les espèces à stratégie mixte (qui peuvent être pollinisées par des colibris, des chauves-souris et parfois des insectes), présentent des fleurs campanulées et de couleur variable (voir Figure 1.3) (Martén-Rodríguez and Fenster, 2008; Martén-Rodríguez et al., 2009). Le caractère ancestral est probablement la pollinisation spécialisée par les colibris, suivie d’évolutions répétées indépendantes vers d’autres modes de pollinisation (Martén-Rodríguez et al., 2010). L’ancêtre commun du genre Rhytidophyllum semble être généraliste, et certaines espèces ont subi des réversions vers l’état ancestral du groupe Gesneria-Rhytidophyllum (spécialiste des colibris), c’est le cas notamment de R. rupincola (voir Figure 1.3 et Martén-Rodríguez et al., 2010).Ce groupe d’étude est un bon exemple où la spécialisation n’est pas un cul-de-sac puisque la spécialisation pour les colibris semble avoir donné naissance à des modes de pollinisation plus généralistes (voir Figure 1.3 et Marten-Rodriguez et al. 2010). Les espèces de ces genres sont endémiques des Antilles. Le contexte insulaire est par- ticulièrement propice aux fortes diversifications spécifique. Particulièrement, les Antilles sont connues pour être un terrain favorable au développement de radiations adaptatives : plusieurs exemples de radiations sont connues dans ces îles, la plus emblématique étant celle des lézards du genre Anolis (Knouft et al., 2006; Ricklefs and Bermingham, 2008; Losos, 2010). D’autres groupes ont aussi subi des radiations dans les Antilles: d’autres lésards, des grenouilles, rongeurs et plantes (Ricklefs and Bermingham, 2008). Dans le cas des îles, l’hétérogénéité temporelle peut potentiellement jouer un rôle plus important dans l’évolution des niches écologiques comparativement aux continents car les possibil- ités d’échappement par migration sont plus limitées dans les îles du fait de leur superficie restreinte (Harter et al., 2015). Les Antilles se trouvent en zone intertropicale, donc de-

10 Chapitre 1 1.3. Objectifs spécifiques de la thèse vraient a priori être moins affectées que les régions de haute latitude par les cycles de Mi- lankovitch et héberger ainsi plutôt des espèces spécialistes (Dynesius and Jansson, 2000). Mais d’autres perturbations abiotiques cycliques à d’autres échelles temporelles (comme par exemple les ouragans) peuvent avoir de forts impacts sur les populations insulaires (Dalsgaard et al., 2009). Ainsi émerge la question de savoir quel est l’effet de la fréquence mais aussi de l’amplitude des variations climatiques sur l’adaptation et l’évolution de la tolérance par rapport aux changements climatiques. De même que pour les modes de pollinisation, nous disposons d’un échantillonnage relativement important concernant la distribution géographique de chaque espèce, notamment par l’intermédiaire d’une base de donnée géoréférencée de spécimens d’herbiers. Les espèces sont majoritairement réparties dans les grandes Antilles ( Cuba, Hispaniola, Porto Rico et la Jamaïque, Figure 1.3) à l’exception de Gesneria ventricosa qui est présente en Jamaïque et dans les petites Antilles et de Rhytidophyllum caribaeum pour laquelle nous n’avons pas de données moléculaires qui se trouve uniquement dans les petites Antilles. De plus la majorité des espèces sont isolées dans une seule île, sauf quatre espèces qui se trouvent dans deux îles. Cette ré- partition géographique donne l’opportunité d’étudier l’impact du contexte particulier de l’insularité sur les stratégies évolutives associées aux perturbations environnementales, et sur les relations entre le mode de pollinisation et l’évolution des habitats.

1.3 Objectifs spéciﬁques de la thèse

La thèse a été divisée en trois sous-objectifs. Le premier objectif est de déterminer les bases génétiques d’une transition d’un mode de pollinisation spécialiste vers un mode plus généraliste. Le deuxième objectif est de déterminer si les axes abiotiques de la niche écologique ont évolué vers plus de généralisation parallèlement aux syndromes de pollinisation. Enﬁn, nous nous demanderons comment peuvent être aﬀectés les patrons d’évolution des niches par une variation temporelle de l’environnement.

11 Chapitre 1 1.3. Objectifs spéciﬁques de la thèse

Figure 1.3 – Phylogénie du groupe Gesneria-Rhytidophyllum Chapitre 1 1.3. Objectifs spéciﬁques de la thèse

Figure 1.3 suite – À droite de la phylogénie la première colonne correspond au mode de pollinisation: les espèces dont le mode de pollinisation est incertain(gris), par les insectes (jaune), spécialiste de chauve-souris (vert) généraliste (violet) et spécialiste de colibris (rose; dans ce cas si il y a si la pollinisation n’a pas été conﬁrmée sur le terrain il y a une étoile). La deuxième colonne représente le phénotype ﬂoral des espèces. La troisième colonne représente la distribution géographique des espèces, la présence des espèces étant codée en noir.

1.3.1 Bases génétiques des traits associés à l’isolement reproducteur

Étant donné le caractère labile du mode de pollinisation dans le groupe Gesneria - Rhyti- dophyllum, nous posons l’hypothèse que les transitions d’un mode de pollinisation à l’autre sont associées à des évènements de spéciation écologique. Dans le contexte de la spéciation écologique, deux populations subissent un isolement reproducteur via une adaptation à des environnements différents (sélection disruptive). Ce type de spéciation suppose l’existence d’une corrélation entre l’isolement reproducteur et l’adaptation. Il existe plusieurs pos- sibilités pour que cette corrélation émerge : (i) si un seul caractère sous-tend à la fois l’isolement et l’adaptation (caractère qui peut être déterminé par un ou plusieurs loci) ou (ii) si plusieurs traits sont impliqués, dans le cas de la sélection sexuelle (Gavrilets, 2004). Gavrilets et al. (2007) ont montré que la spéciation écologique est possible quand un faible nombre de loci contrôlent le trait adaptatif. Dans le cas des plantes à fleurs, un changement de syndrome de pollinisation peut être directement à l’origine de l’isolement reproducteur entre deux populations, conduisant à la spéciation. Cet ensemble de traits est donc particulièrement intéressant à étudier dans le contexte de la génétique de la spécia- tion. Un changement phénotypique floral sous tendu par un seul locus peut avoir des effets important sur le comportement des pollinisateurs, induisant un isolement reproducteur po- tentiel chez les plantes. C’est le cas du changement de couleur causé par le locus YUL chez Mimulus (Bradshaw and Schemske, 2003). Le cas du syndrome de pollinisation peut être vu comme un « magic trait » (Gavrilets, 2004) puisqu’il est à la fois lié à l’adaptation à une variable environnementale (une espèce ou un groupe fonctionnel de pollinisateurs) et à l’isolement reproducteur. Les modes de pollinisation sont des traits qui évoluent beaucoup

13 Chapitre 1 1.3. Objectifs spéciﬁques de la thèse

(c’est-à-dire de façon répétée et indépendante, multidirectionnelle, avec des réversions, et des transitions qui peuvent être très rapides...). Par exemple, la pollinisation par les colibris semble être apparue plus de cents fois indépendamment en Amérique du Nord (Thom- son and Wilson, 2008). De nombreuses études ont cherché à détecter les bases génétiques des transitions de syndromes de pollinisation (Slotte et al., 2012; Wessinger et al., 2014; Galliot et al., 2006a,b; Stuurman et al., 2004; Martin et al., 2008) avec des approches de detection de QTLs (quantitative trait loci ou locus de caractères quantitatifs). Un QTL correspond à une region du génome qui peut contenir un ou plusieurs gènes qui affecte la variation d’un trait quantitatif, détectée avec des mesures de liaison entre le caractère et des marqueurs génomiques polymorphes (Mackay et al., 2009). Cependant, ces études se placent rarement dans un contexte de transition généraliste-spécialiste. Les traits majeurs qui sont étudiés sont en général la forme et la taille des fleurs, la quantité de nectar et la couleur. L’architecture génétique de ces transitions varie beaucoup d’un modèle d’étude à l’autre. Par exemple, chez Petunia axillaris et P. integrifolia, la taille des fleurs et le volume de nectar sont des traits hautement polygéniques (Galliot et al., 2006a), ce qui laisse penser d’après les auteurs que ces changements phénotypiques n’ont pas été des mo- teurs dans l’isolement entre ces deux espèces. Le même type de patron est retrouvé dans les transitions de mode de pollinisation chez Ipomopsis tenuifolia et I. guttata (Nakazato et al., 2013). En revanche, chez Penstemon neomexicanus et P. barbatus, un faible nombre de loci est responsable des changements pour le nectar, la couleur et la forme, et ces loci colocalisent, ce qui laisse penser que des contraintes génétiques ont pu jouer un rôle important dans l’évolution rapide des syndromes de pollinisation (Wessinger et al., 2014). De même, un faible nombre de QTLs pour chaque trait de syndrome de pollinisation avec une forte colocalisation a été trouvé entre Capsella grandiflora (auto-incompatible) et C. rubella (auto-compatible) (Slotte et al., 2012). De ces exemples, il semble qu’il n’y a pas de règle concernant l’architecture génétique des transitions de syndromes de pollinisation. En revanche décrire cette architecture peut être un point de départ pour mieux comprendre comment se sont faits les changements de mode de pollinisation : ont-ils été graduels et ont permis un renforcement de l’isolement reproducteur après la spéciation ou bien

14 Chapitre 1 1.3. Objectifs spéciﬁques de la thèse ont-ils été très rapides et potentiellement impliqués dans le processus de spéciation? De façon analogue, si peu de gènes sont impliqués dans les changements morphologiques, on peut s’attendre à ce que les changements soient plus fréquents d’un point de vue évolutif. La première étude de cette thèse (chapitre 2) consiste en une analyse de QTLs carac- térisant les régions génomiques associées à la transition de syndrome de pollinisation entre une espèce a stratégie de pollinisation mixte (Rhytidophyllum auriculatum) et une espèce spécialiste des colibris (Rhytidophyllum rupincola) (Figure 1.1)

1.3.2 Relations entre l’évolution de plusieurs composantes de la niche écologique

La niche écologique est constituée à la fois des conditions abiotiques dans lesquelles une es- pèce peut s’établir (température, salinité, pluviométrie, etc.) et des interactions biotiques (avec des mutualistes, des compétiteurs, des prédateurs). Si l’on étudie la spécialisation et la généralisation au niveau de la niche écologique, il faut noter qu’une espèce peut être généraliste dans certaines dimensions de sa niche (par exemple supporter de fortes amplitudes thermiques) et spécialistes dans d’autres (être pollinisée par une seule espèce de pollinisateurs). Litsios et al. (2014) ont étudié la relation entre la spécialisation au niveau de l’hôte et au niveau environnemental chez des poissons clowns. Ils ont montré qu’il existait une corrélation négative entre le degré de spécialisation pour l’hôte et le degré de spécialisation environnementale. Ils expliquent cette corrélation négative par un compromis évolutif (trade-oﬀ) entre les diﬀérents axes de la niche et émettent l’hypothèse que ce mécanisme peut expliquer la coexistence d’espèces spécialistes et généralistes. Mais d’autres types de relations peuvent exister entre la niche biotique et la niche abiotique. Si l’on prend le cas des relations entre plantes et pollinisateurs, une espèce de plante peut avoir une grande tolérance environnementale, mais être restreinte par la présence de son pollinisateur. Dans ce cas, bien que la niche fondamentale de la plante soit large (généraliste), sa niche réalisée sera beaucoup plus petite (Pellissier et al., 2012) et pourra être considérée comme spécialiste (Figure 1.2). Dans ce cas, la plante ne pourra s’établir qu’aux endroits où il y a un recouvrement entre sa niche fondamentale et la présence de

15 Chapitre 1 1.3. Objectifs spécifiques de la thèse ses pollinisateurs. Ceci a d’ailleurs des impacts sur les patrons de distribution des espèces à large échelle. En effet, quand deux espèces interagissent de façon positive (mutualisme ou commensalisme), ces deux espèces devraient avoir une distribution géographique chevauchante aussi bien sur des petites que des grandes échelles, alors que pour les interactions négatives (amensalisme ou compétition) les effets sur la distribution géographique des espèces sont seulement apparents à des petites échelles (Araújo and Rozenfeld, 2014). Ainsi, il est intéressant d’analyser les liens entre les axes biotiques et les axes abiotiques des niches écologiques, afin de permettre une meilleure compréhension des patrons de distribution spatiale des espèces et des assemblages de communautés. Ceci permettrait aussi de mieux comprendre l’impact des stratégies de reproduction spécialiste ou généraliste sur la niche des espèces de plantes. La deuxième étude (chapitre 3) teste un lien entre les modes de pollinisation (dimension biotique de la niche écologique) et l’évolution des niches bioclimatiques au sein des genres Gesneria et Rhytidophyllum.

1.3.3 Hétérogénéité temporelle et patrons d’évolution des niches écologiques

Comprendre l’impact de l’hétérogénéité environnementale, tant à l’échelle spatiale que temporelle est un enjeu majeur dans le contexte de changements climatiques actuel. Si l’on sait que les espèces généralistes semblent être avantagées face aux perturbations climatiques (Clavel et al., 2010), les variations spatiales et temporelles ont des effets antagonistes, puisque l’hétérogénéité spatiale favorise l’émergence de spécialistes alors que l’hétérogénéité temporelle favorise celle des généralistes (Kassen, 2002). Les variations temporelles de l’environnement ont ainsi des conséquences sur les traits des espèces (Parmesan and Hanley, 2015), sur la structure des communautés (Blois et al., 2013) et sur les patrons macro-évolutifs (Willis and Niklas, 2004). Par ailleurs, comprendre l’effet des changements climatiques passés sur les caractéristiques actuelles des espèces est une des stratégies utilisées pour prédire l’effet des changements actuels sur le devenir des espèces. Lavergne et al. (2013) ont ainsi montré une association entre un faible taux d’évolution des niches passées et le déclin de certaines espèces. Les changements abiotiques peuvent

16 Chapitre 1 1.3. Objectifs spéciﬁques de la thèse aussi avoir des répercussions sur les interactions biotiques. Une étude sur les interactions entre plantes et colibris a montré que la fréquence des changements climatiques pendant le Quaternaire était corrélée négativement au degré de spécialisation dans les réseaux entre plante et pollinisateur ; et par ailleurs ces changements passés sont plus important que le climat actuel pour prédire les degrés de spécialisation dans ces réseaux (Dalsgaard et al., 2011). Au niveau macro-évolutif, le niveau d’hétérogénéité environnementale peut être corrélé au taux d’évolution des niches, comme c’est le cas chez les lézards Anolis dans les Antilles (Algar and Mahler, 2015). Par ailleurs, étudier l’évolution des niches écologiques au niveau d’un groupe phylogénétique consiste souvent à analyser dans quelle mesure les niches écologiques évoluent rélativement à la phylogénie. Ce questionnement est illustré par le concept de conservatisme phylogénétique de niche (phylogenetic niche conservatism; PNC) qui correspond à un patron dans lequel les espèces apparentées ont des niches écologiques plus similaires que ce à quoi l’on devrait s’attendre compte tenu de leurs relations phylogénétiques (Losos, 2008). Dans le cas des radiations adaptatives, les niches devraient évoluer indépendamment de la phylogénie et ne pas montrer de patron de PNC (Joly et al., 2014) : l’émergence d’un patron de PNC serait plutôt associée à un manque d’opportunités écologiques ou à une faible évolvabilité intrinsèque. Parallèlement, le PNC peut être vu comme un processus capable de produire d’autres patrons évolutifs. Ainsi Wiens (2004) propose que le conservatisme de niche peut être un moteur de spé- ciation : dans un environnement hétérogène, les populations étant dans l’incapacité de s’adapter à de nouvelles conditions (à cause du PNC) se retrouvent isolées dans des zones écologiquement semblables et divergent par spéciation allopatrique. Si le conservatisme phylogénétique de niches peut être à l’origine d’évènements de spéciation allopatrique, nous aimerions comprendre les relations entre hétérogénéité environnementale, généralisa- tion et PNC. Pour la troisième étude de cette thèse (chapitre 4), j’ai utilisé une approche de simulation pour tester l’eﬀet de la fréquence et de l’amplitude des changements environnementaux sur l’évolution des niches écologiques (réduites à une seule dimension) d’un point de vue théorique.

17 Genetic architecture of pollination syndrome transition between hummingbird-specialist and generalist species in the genus 2 Rhytidophyllum (Gesneriaceae)

H. Alexandre, J. Vrignaud, B. Mangin and S. Joly 1

2.1 Abstract

Adaptation to pollinators is a key factor of diversification in angiosperms. The Caribbean sister genera Rhytidophyllum and Gesneria present an important diversification of floral characters. Most of their species can be divided in two major pollination syndromes. Large-open flowers with pale colours and great amount of nectar represent the generalist syndrome, while the hummingbird-specialist syndrome corresponds to red tubular flowers with a less important nectar volume. Repeated convergent evolution toward the generalist syndrome in this group suggests that such transitions rely on few genes of moderate to large effect. To test this hypothesis, we built a linkage map and performed a QTL detection for divergent pollination syndrome traits by crossing one specimen of the generalist species Rhytidophyllum auriculatum with one specimen of the hummingbird pollinated R. rupincola. Using geometric morphometrics and univariate traits measurements, we found that floral shape among the second-generation hybrids is correlated with morphological variation observed between generalist and hummingbird-specialist species at the genus

1article publié dans PeerJ, 3:e1028, juin 2015

18 Chapitre 2 2.2. Introduction level. The QTL analysis showed that colour and nectar volume variation between syndromes involve each one major QTL while ﬂoral shape has a more complex genetic basis and rely on few genes of moderate eﬀect. Finally we did not detect any genetic linkage between the QTLs underlying those traits. This genetic independence of traits could have facilitated evolution toward optimal syndromes.

Key-words: Pollination syndrome, Geometric morphometrics, QTL, Genotyping by sequencing, Plant mating systems, Floral Evolution .

2.2 Introduction

Flower is a key innovation often invoked to explain the radiation and evolutionary success of angiosperms (Stebbins, 1970). Flowers present variable traits such as shape, colour, flowering time from which it is often possible to distinguish groups of traits that evolve jointly for the flower to be effectively pollinated by a given type of pollinator. These groups of traits are called pollination syndromes (Fenster et al., 2004). The selection for these syndromes is often so strong that it is possible to predict which type of pollinator a given plant species relies on via the observed syndrome. For instance, flowers can harbour very different traits depending on whether they are pollinated by wind or animals (Friedman and Barrett, 2009). In animal-pollinated species, major traits involved in pollination syndrome include corolla shape and colour, floral scent, as well as the amount and concentration of nectar produced, and variation in these traits enable species to be distinguish by different groups of pollinating animals. Rosas-Guerrero et al. (2014) reviewed floral traits of 417 species and showed that the concept of pollination syndrome can be very effective at predicting the pollinators of animal pollinated flowers, more so than for non-animal syndromes. Interestingly, syndrome predictability is more effective for tropical plants, probably because of lower pollinator population densities in the tropics that increase selection pressure (Rosas-Guerrero et al., 2014). Pollination syndrome is a set of very dynamic and rapidly evolving characteristics, pro-

19 Chapitre 2 2.2. Introduction viding numerous examples of convergent evolution in many groups. In Penstemon (Plan- taginaceae), for example, ornithophilous pollination evolved multiple times from insect pollinated flowers (Wilson et al., 2007). In Ruellia, insect pollination evolved repeatedly from the ancestral hummingbird pollination (Tripp and Manos, 2008). In Gesneria and Rhytidophyllum (Gesneriaceae), generalist and bat pollinated species evolved several times from a hummingbird syndrome (Martén-Rodríguez et al., 2010). The tribe Sinningieae of the Gesneriaceae also shows an important lability of pollination modes, associated with evolution of traits such as corolla shape and colour (Perret et al., 2007). Because such transitions between syndromes are often linked with species diversification (reviewed in Van der Niet and Johnson, 2012), understanding how these transitions occur is critical for understanding angiosperms evolution. Observations of such an important lability of flower characteristics, combined with the fact that flower diversification is often linked to species diversification, led us to wonder about the genetic basis of these traits. Studies of the genetic basis of phenotypic evolution are often focused on determining (i) if parallel phenotypic changes rely on parallel genomic evolution and (ii) if these major phenotypic transitions result from major changes at a limited number of genes or from minor changes at multiple genes (reviewed in Hendry, 2013). In addition, developmental constraints such as genetic interactions (epistasy) could be important to explain the convergence of different traits to form a particular syndrome. Similarly, there are potentially important roles for genetic correlations between traits and ecological factors - such as pollinator pressures - in the redundant evolution of floral phenotypes among different species. Indeed, the speed at which a population reaches its fitness optimum greatly depends on whether traits composing the pollination syndrome are genetically independent or linked. Three scenarios can be envisaged: (i) if traits are positively correlated, selection on one trait will affect variation on other traits in a positive way and the general fitness optimum should be reached rapidly; (ii) if traits are genetically independent, no developmental constraints should affect the evolution towards the optimum and the speed of adaptation will solely be influenced by the intensity of the selective pressure; and (iii) if traits are negatively correlated, selection at one trait will

20 Chapitre 2 2.2. Introduction pull variation at other traits further from the fitness optimum, hence reducing the pace at which this optimum can be reached. Deciphering the degree of genetic correlation among traits is thus a first step toward understanding the relative role of selection versus intrinsic constraints in the evolution of phenotypes (Ashman and Majetic, 2006). To answer these questions, a popular approach is to perform QTL detection on a hybrid population generated from parents with different pollination syndromes. Previous studies have shown that colour transition is generally explained by one major QTL (Quattrocchio et al., 1999; Yuan et al., 2013; Wessinger et al., 2014). In contrast, nectar volume and concentration frequently rely on numerous genomic regions each having a small to moderate effect on phenotype (Goodwillie et al., 2006; Galliot et al., 2006a; Nakazato et al., 2013). Flower shape variation was also shown to be generally caused by several QTLs with small to moderate effects, with frequent colocalization of those QTLs (reviewed in Hermann and Kuhlemeier, 2011). Over the past several years, emerging next generation sequencing technologies have enabled the study of the genetic basis of adaptation in non- model species. Also, improvements of methods to study morphology (e.g. with geometric morphometrics) now enable to study the genetic basis and evolution of these complex characteristics (Klingenberg et al., 2001; Langlade et al., 2005; Klingenberg, 2010; Rogers et al., 2012; Franchini et al., 2014; Liu et al., 2014). The closely related genera Gesneria and Rhytidophyllum consist of approximately 75 species and have rapidly diversified in the Antilles from a common ancestor that existed approximately 8 to 11 mya (Roalson et al., 2008). During this rapid species diversification, the group also simultaneously experienced a rapid diversification of floral traits. Floral shape, colour and nectar production have evolved jointly into three evolutionarily labile pollination syndromes (Martén-Rodríguez et al., 2009, 2010): (i) species pollinated by hummingbirds that have red tubular flowers with diurnal nectar production, (ii) species pollinated by bats harbouring large pale flowers with a bell shape corolla and harbour nocturnal nectar production, and (iii) generalist species that can either be pollinated by hummingbirds, bats or moths, have generally pale flowers (although often with various spots) with large openings but with a constriction in the corolla, and can have noctur-

21 Chapitre 2 2.3. Material and Methods nal and diurnal nectar production. It has been inferred that the hummingbird syndrome is the ancestral pollination mode whereas the bat and generalist syndromes evolved independently several times (with reversals back to the ancestral hummingbird syndrome having been tentatively identified) (Martén-Rodríguez et al., 2010). We intend here to identify the genetic basis of the pollination syndrome transition between the generalist and hummingbird-specialist species in Rhytidophyllum using QTL detection in a second- generation hybrid population. Rhytidophyllum auriculatum is a typical generalist species from Hispaniola and Puerto Rico, and harbours opened yellow flowers producing large amount of nectar. The second species, R. rupincola, is a hummingbird specialist with red and tubular flowers that produces only small quantities of nectar. Its endemism to Cuba (Skog, 1976; Martén-Rodríguez et al., 2010, pers. obs.) eliminates all potential for natural hybridization with R. auriculatum. According to Marten Rodriguez et al. (2010), R. auriculatum most likely belongs to a group of generalist that evolved from an ancestral hummingbird syndrome, whereas R. rupincola likely represents a reversion to the ancestral hummingbird syndrome; the two species being closely related but not sister species. In this study, we obtained anonymous genetic markers via next generation sequencing (NGS) and built a linkage map from a second generation (F2) hybrid population between R. rupincola and R. auriculatum. We then used geometric morphometrics to study floral shape and test whether QTLs underlying floral trait evolution are few or numerous and whether they are linked or not.

2.3 Material and Methods

2.3.1 Study System

Rhytidophyllum auriculatum (female parent) was crossed with R. rupincola (male parent) from specimens from the living collection of the Montreal Botanical Garden (Canada) in 2010 to obtain ﬁrst-generation (F1) hybrids. An F1 individual was self-fertilized in 2011 to give a second-generation (F2) population of 177 individuals. Simultaneously, both parents were self-pollinated and gave several viable individuals.

22 Chapitre 2 2.3. Material and Methods

2.3.2 Phenotypic measurements

Phenotypic measures were performed from June of 2013 to April 2014 for morphological and colour traits because of a great heterogeneity of developmental rate in the population, and between November and December 2014 for nectar volume. The hybrid population was composed of 141 F2 individuals but not all of them could be used for every trait analyses.

Corolla shape

Corolla shape was analysed with geometric morphometrics methods designed to capture morphological characteristics of pollination syndromes without a priori hypotheses. In addition, of allowing the determination of shape that is representative of a particular pollination mode, geometric morphometric methods have also been shown to be very efficient at revealing the genetic basis of complex morphological changes (Klingenberg et al., 2001). Among the 141 individuals that gave flowers, four F2 individuals with abnormal flowers (disjoint petals or different flower shapes within an individual) and seven individuals presenting flowers with more or less than 5 petal lobes were discarded from the phenotypic measures, leaving 130 individuals for shape analysis. For each individual, between one and three flowers were photographed. Each flower was positioned on a gridded base (enabling to have a scale) and photos were taken with a camera at fix distance from the base, under day light. Each photo was analysed twice with the software TpsDIG2 (http://life.bio.sunysb.edu/morph/soft-dataacq.html), to evaluate variance due to han- dling errors in our analyses. The gridded base enabled to scale photos to compare flower size. Photographs from a different study (Joly et al. in prep) were also included to quantify shape variation in the whole Gesneria/Rhytidophyllum clade. This was done to charac- terize the aspects of shape that were the most significant to differentiate generalists from hummingbird specialists (see below). For these photographs, a single flower per individual was included. However, as these photos didn’t include any scale, we couldn’t compare the size of flowers from the hybrid population (F2, F1 and both parents) with those from other species. Six landmarks and 24 semi-landmarks were placed on each photo. Two

23 Chapitre 2 2.3. Material and Methods

Figure 2.1 – Measure of shape variation in the hybrid population and parents. (A) Flowers from both parents (top row), the self-pollinated F1 and samples from the F2 population; (B) position of landmarks on corolla pictures- red stars represent landmarks and small orange stars are semi-landmarks.

24 Chapitre 2 2.3. Material and Methods landmarks were placed at the extremity of the petal lobe (L1, L2), two at the base of the petal lobes (L3, L4) and two at the base of the corolla (L5, L6). Semi-landmarks were evenly dispersed on the contour of the corolla between L3-L4 and L5-L6 (Fig. 1). Geometric morphometrics analyses were then performed in R (R Core Team, 2014) with packages shapes (Dryden, 2014), geomorph (Adams and Otárola-Castillo, 2013) and ade4 (Dray and Dufour, 2007). A general Procrustes superimposition of all the photos was performed with the function gpagen allowing for sliding semi-landmarks in the superimposition, and the mean coordinates of the landmarks and semi-landmarks per individual were extracted to obtain only one shape per individual. Morphology was then measured using four complementary approaches (see Figure 2.2 for more details): (i – Pollination syndrome differences) A PCA (function dudi.pca) of nine generalist and nine hummingbird specialist species from the genera Gesneria and Rhytidophyllum (see supplementary Table 1(Annexe C) for details) was performed, and the F2 individuals were projected (function suprow) on the first PC that represents the shape difference between hummingbird-specialists and generalists. This approach estimates how much each F2 individual resemble to hummingbird specialists or to generalists. (ii – Parental differences) A PCA was performed on the two parents, giving only one principal component upon which the F2 individuals were projected. This approach measures how much each F2 individual resemble each parent. (iii – Morphological variation in the hybrid population) A PCA of the F2 population was performed (including the self-pollinated F1, both parents and three progenies of the self- pollinated parents), from which the scores of the F2 individuals were directly obtained. This approach allows investigating the genetic bases of the morphological variation observed in the F2 hybrid population. (iv – Univariate traits) Two univariate traits were extracted from the landmarks data before the Procrustes superimposition: corolla tube opening corresponds to the distance between L3 and L4, and corolla curvature as the angle formed by the lines (L1-L2) and (L5-L6) (Figure 2.1). Pictures from wild specimens were used to analyse shape only, without any size component because photos did not include a scale.

25 Chapitre 2 2.3. Material and Methods

Morphological question adressed Input data method Output data, used for QTL analyses

How much each F2 individual Landmark data of PCA on generalist Projection of F2s Coordinates of (i) – Pollinaon shape is generalist-like or generalist and and specialists on this PCA F2s on the ﬁrst PC syndrome diﬀerences hummingbird-specialist-like specialist species shape data

How much each F2 individual Coordinates of Landmark data of PCA on parents Projection of F2s (ii) – Parental shape is R. auriculatum-like F2s on the unique both parents shape data on this PCA diﬀerences or R. rupincola-like PC

How each F2 shape is Landmark data of positioned compared to all the Coordinates of F2s, selfed F1, PCA on all shape (iii) – Morphological morphological F2s on PC1, PC2 parents’ progenies data variaon in the hybrid variance in the hybrid and PC3 and both parents populaon population

What is the corolla opening Univariate Landmark data of Measurement of angle and (iv) – Univariate traits size and curvature of each measure for each F2s distance between landmarks F2 individual F2

Figure 2.2 – Diagram presentation of the four morphological measurement approaches.

Corolla colour

Flower colour was treated as a binary trait: orange or yellow. Given the large variation in intensity and distribution of the orange colour on the corolla (Figure 2.1), individuals were considered “orange” when some orange colour was observed on them. For this analysis, all the 141 F2 were analysed.

Nectar volume

Nectar was sampled in early afternoon after flower opening, which generally occurs two days after flower opening. This time was chosen because nectar is released mainly at dawn and dusk in Gesneria and Rhytidophyllum (Martén-Rodríguez and Fenster, 2008), and because no nectar production was observed during the day for the parental species. To sample nectar, the flowers were removed from the plant, and the volume was measured with a graduated 50 µL syringe. Only 62 individuals could be analysed for this trait because of an important mortality rate.

26 Chapitre 2 2.3. Material and Methods

2.3.3 Genotyping

Plant leaves were sampled and dried in silica gel, and DNA was extracted with the Qiagen (Mississauga, Canada) DNeasy Plant Mini Kit. 300 ng of DNA was used to genotype individuals using a Genotyping By Sequencing approach, following the protocol developed by Elshire et al. (2011). Library preparation was performed at Laval University (IBIS plateform, Quebec city, Canada) using the restriction enzymes PstI and MspI. We sequenced 177 F2s, duplicating ten individuals to assess genotyping repeatability: four F2s, both parents, the self-pollinated F1 and three other F1s, and two progenies of the self- pollinated parents. Individuals were multiplexed in pools of 96 samples, and sequenced on two lanes on an Illumina Hiseq 2500 at McGill University and Genome Quebec Innovation Centre (Montreal, Canada). Stacks pipeline version 1.20 Beta was used to extract genotypes from raw reads (Catchen et al., 2011). Reads were first demultiplexed and trimmed to 82 basepairs with the function process-radtags. Then, unique stacks were generated with the function ustacks, constraining for a minimum read depth (-m) of 2 to create a stack, and a maximum inter-read Single Nucleotide Polymorphism (SNP) distance (-M) of 5. The catalog was created with both parents, and SNP calls were first performed with default parameters in sstacks. Then, the error correction module rxstacks was run to perform automated corrections using the bounded SNP model and a cutoff ln likelihood value of -10 to discard unlikely genotypes. The cstacks and sstacks were then repeated with the corrected data, and genotypes data were obtained with the function genotypes. After running genotypes with the -GEN output format and allowing automatic corrections with default parameters, a R script was run to translate those data in an A (parent R. auriculatum allele), B (parent R. rupincola allele), H (heterozygous) format needed for subsequent analyses. In this script, using the information available from the self-pollinated F1, markers that are aaxab in the parents, and for which the F1 is ab were typed in the F2 population, an option not available in the Stacks pipeline. Because mutations in some TCP genes are known to be involved in the determination of flower symmetry and in the size and shape of corollas (Hileman and Cubas, 2009), the genes RADIALIS and CYCLOIDEA were included in the linkage map to test if they

27 Chapitre 2 2.3. Material and Methods could be involved in the variation in flower morphology between the two species. Gene sequences acquired from GenBank (sequence AY363927.1 from R. auriculatum for Gcyc and sequence AY954971.1 from Antirrhinum majus for RADIALIS) were compared to the parents’ transcriptomes (unpublished data) using BLASTn (Camacho et al., 2009) and primers were designed using software Primer3 (Koressaar and Remm, 2007). Gene sequences were deposited in Genbank (accession numbers KP794058, KP794059, KP794060, and KP794061). CYCLOIDEA was genotyped with the CAPS method (Konieczny and Ausubel, 1993). Around 1 ng of DNA was added to a master mix containing 0.375 U of DreamTaq (Ter- moscientific, Waltham, MA, USA), 1.5 µL of 10X DreamTaq Buffer, 0.6 µL of each 10 µM primer and 0.3 µL of 10 mM dNTPs in a total reaction volume of 15 µL. Primers used to amplify CYCLOIDEA were gcycf2 (AAGGAGCTGGTGCAGGCTAAGA) and gcycr2 (GGGAGATTGCAGTTCAAATCCCTTGA), amplification conditions were 2 min at 94◦C, followed by 40 cycles of 94◦C 15 sec, 54◦C 15 sec, 72◦C 30 sec, and then a final extension step of 1 min at 72◦C. Circa one µg of PCR product was then digested with AflII (New England Biolabs, Ipswich, MA, USA) in a 15 µL volume according to the company’s recommendations. The total volume of digestion products was visualized on agarose gel. RADIALIS was genotyped with KASPAR (LGC genomics, Teddington, UK), with protocol tuning done by LGC genomics. DNA amplification was done with 75 ng of DNA, 2.5 µL of KASP master mix, and 0.07 µL of KASP primer mix in a total volume of 5 µL. The specific primer for the first parental allele was labelled with a FAM fluorochrome while the second specific primer was labelled with a HEX fluorochrome. Amplification conditions were a first step of 94◦C for 15 min, followed by 10 cycles of 94◦C 20 sec, 61◦C decreasing of 0.6◦C at each cycle 1 min, and then another 29 cycles of 94◦C 20 sec 55◦C 1 min. Genotypes were visualized by fluorescence after the amplification procedure on viia7 system (Applied Biosystems, Foster city, CA, USA) with the genotyping protocol.

28 Chapitre 2 2.3. Material and Methods

2.3.4 Linkage map construction

GBS markers were filtered to keep only those with less than 25% data missing, and no segregation distortion (Chi2 p-value > 0.05 after Bonferonni correction). A linkage map was built with Carthagene (de Givry et al., 2005). Linkage groups were detected with a maximum two points distance of 30 cM measured with Haldane function and a minimum LOD of 3. Marker ordering in each linkage group was done with the function lkhd, which imple- ments the Lin-Kerninghan heuristic research algorithm to resolve the travelling salesman problem, optimising the 2 points distances along the linkage group. Once the first map was obtained, manual corrections were made for double-recombinants occurring within 10 cM. Because SNP calls can be erroneous if read depth is small, double recombinants scored as either A or B (homozygous) were replaced into H (heterozygous) if read depth was less than 10 reads. If read depth was more than 10, homozygous double recombinants were replaced by missing data as proposed by Kakioka et al. (2013), because those genotypes have a great probability of being erroneously typed. H (heterozygous) double recombinants were not replaced if both alleles were effectively detected in the sequencing data, but were replaced into A or B if only one allele was detected in the data (this case occurred because of mistakenly corrected calls from automatic correction in Stacks). Remaining markers were then filtered again for missing data and segregation distortion, and a new map was built. This was repeated until no double-recombinants within 10cM were found in the linkage map. After these cleaning steps, genotypes of both candidate genes were included in the dataset, and a final linkage map was built.

2.3.5 QTL detection

Before performing QTL detection, correlation between colour, nectar volume and shape traits was tested in the F2 population using pearson coeﬃcient for quantitative traits correlation and F-tests for colour. Among the 177 individuals, 141 gave ﬂowers and were kept for colour tests. One hundred and thirty individuals were kept for shape QTL detection after inappropriate data was removed (see Phenotypic measurements section). Nectar vol-

29 Chapitre 2 2.3. Material and Methods ume was transformed into a binary trait for QTL detection given its large intra-individual variation and non-normal distribution in the F2 population (difference between the maximum and minimum volume for each individual ranged from 4 to 64 µL with a mean difference value of 25 µL). Individuals with mean volume inferior to 15 µL were classified as “0” and those with a mean volume superior to 25 µL as “1”, leaving 67 individuals to detect QTLs for nectar volume. QTL detection was performed with R/qtl version 1.33-7 (Broman et al., 2003). Genotypes probabilities were calculated every 1cM with the function calc.genoprob. QTLs were looked for with scaneone with the normal model and the Haley-Knott method for the quantitative traits whereas the binary model and the EM method were used for nectar volume and colour. LOD scores were compared to the LOD threshold value obtained with 10000 permutations. Then, if a QTL was detected, it was added as an additive covariate and the procedure rerun to detect minor QTLs. For non-binary variables, percentage of variance explained by the QTLs and size effects were checked with fitqtl, adding in the model one QTL at a time. Given the limited number of individuals scored for nectar volume, a supplementary Spearman correlation test between nectar volume (codes 0/1) and genotypic data for each marker (codes 1/2/3) was performed to confirm the QTL results.

2.3.6 Pleiotropy and epistasy detection

Pleiotropic QTLs were searched by considering the principal axes of the PCA performed on the hybrid population as proposed by Mangin et al. (1998). The computation of the pleiotropic test statistics was limited to the first three principal axes, which explained most of the variance, as suggested by Weller et al. (1996). Briefly, the test was obtained by computing the LOD scores for each principal component and summing the result of all the three principal components. To access the threshold value of the pleiotropic test statistics,10000 permutations were performed (Doerge and Churchill, 1996) with the three principal components being permutated all together in order to get a null distribution, while preserving the initial intra-individual relation between phenotypic traits. QTL detection was based on the 95th quantile. Confidence regions were estimated with a 2-LOD

30 Chapitre 2 2.4. Results support, as suggested by Van Ooijen (1992). Epistasy among QTLs as well as among QTLs and other markers were tested using MCQTL (Jourjon et al., 2005).

2.4 Results

2.4.1 Correlation between traits and morphological variation

For the PCA performed on the 18 species with divergent pollination syndromes (approach i), the first principal component (PC1: 68.55%) discriminated hummingbird specialist species from generalists (Figure 2.3a). Both parents were positioned within their respective pollination syndrome group while the self-pollinated F1 and the F2 population were intermediate between both syndromes for the first principal component. As only the first principal component separated the two syndromes, only this component was used for QTL detection. The first three principal components of the PCA performed on the hybrid population (approach iii) explained the majority of morphological variability found in the hybrid population (PC1: 35%, PC2: 22.7%, PC3: 14.2%, total=71.9%; Figure 2.3b). For this PCA, parents were at the extremities of the distribution, while the self-pollinated F1 and the F2s were intermediate between parents. Interestingly, the F2 individuals were closer to R. rupincola than R. auriculatum (Figure 2.3b). The correlation between the morphological principal components, two univariate traits (constriction size, the corolla curvature) and two binary traits (corolla colour, nectar volume) were measured. Traits corresponding to different pollination syndrome components (shape, colour, nectar) were not correlated among individuals of the F2 population (Figure 2.5). However, the first principal component of each PCA (performed on the genus, both parents or the hybrid population) were correlated with each other with high correlation coefficient (first PC on the genus – first PC on the hybrid population: r = 0.98; first PC on the genus – PC on the parents: r = 0.901; first PC on the hybrid population – PC on the parents: r = 0.811, Figure 2.5). Principal components of PCA were also sometimes correlated with univariate shape measures (second PC on the hybrid population-corolla curvature: r = −0.92; constriction size-first PC on the genus: r = −0.633, Figure 2.5), and this correlation is

31 Chapitre 2 2.4. Results

a b

0.1

0.05

F2 R.auriculatum progeny

0.0 generalist F2 0.00 hummingbird Parent R.auriculatum Parent R.auriculatum Parent R.rupincola

pc2 (8.66%) pc2 Parent R.rupincola (22.7%) pc2 R.rupincola progeny self-pollinated F1 −0.05 self-pollinated F1 −0.1

−0.10

−0.2 −0.2 0.0 0.2 −0.1 0.0 0.1 pc1 (68.56%) pc1 (35%)

Figure 2.3 – Principal component analyses of shape. (A) PCA performed on wild specimens from species with different pollination syndromes (method i–Pollination syndrome differences); Large and small dots represent species mean shapes and individual shapes, respectively, and individuals that belong to a given species are linked to it with a line. (B) PCA performed on the hybrid population (method iii–Morphological variation in the hybrid population) where triangles represent self-pollinated parents’ progeny. Numbers between brackets are percentage of shape variance represented by each axis. also visible on Figure 2.4 as flowers at the extreme of PC1 harbour different opening size and flowers at the low extreme of hybrids PC2 are more incurved than flowers at the high extreme.

2.4.2 Molecular data and Linkage map

Starting from ca. 422 millions raw reads, the stacks pipeline initially gave 2,257 markers. After removing markers with more than 25% missing data and with segregation distortion, 845 markers remained to construct a genetic map. Then, with a third step of iterative map building, following correction for double recombinants and ﬁltering for missing data, we ﬁnally obtained 557 clean GBS makers plus the two candidate genes. With a maximum distance of 30cM between consecutive markers and a minimum LOD score of 3, 16 linkage

32 Chapitre 2 2.4. Results

mean -c sd mean mean +c sd

hybrids PC1

hybrids PC2

hybrids PC3

genus PC1

parents PC1

Figure 2.4 – Shape variation associated with each principal component. Each point represents a landmark (or semi-landmark) position on the proﬁle of the corolla, as shown in Figure 2.1b. Sd, standard deviation, c = 1 for hybrid population PCA, 0.5 for between syndrome PCA and 0.2 for between parents PCA.

33 Chapitre 2 2.4. Results PC3 PC2 PC1 / PC1 di ﬀ erences tube opening � on varia Morphological � on varia Morphological � on varia Morphological curvature corolla colour corolla popula � on in the hybrid popula � on in the hybrid popula � on in the hybrid volume nectar di ﬀ erences Parental � on syndrome Pollina

Pollina�on syndrome diﬀerences / PC1 0.98 0.164 −0.09 0.901 −0.633 −0.149 −0.0815 p > 0.05

Morphological varia�on in the hybrid popula�on 1.33e−16 1.47e−15 0.811 −0.631 0.012 −0.0629 p > 0.05 PC1

Morphological varia�on in the hybrid popula�on 8.85e−17 0.54 0.000785 −0.92 −0.0949 p > 0.05 PC2

Morphological varia�on in the hybrid popula�on −0.201 −0.0753 0.0779 0.053 p > 0.05 PC3

Parental diﬀerences −0.503 −0.49 −0.144 p > 0.05

tube opening −0.0408 0.206 p > 0.05

corolla curvature 0.0863 p > 0.05

nectar volume p > 0.05

corolla colour

Figure 2.5 – Distribution and correlation among traits in the hybrid population. Diagonal: traits distribution, the vertical lines correspond to the values of parent R. rupincola (red), parent R. auriculatum (green) and the self-pollinated F1 (purple). For corolla colour, yellow an red boxes represent yellow and orange flowers, respectively. Lower left triangle, correlation among traits, if covariation is significant after Sidak correction, the regression line was plotted; Upper right triangle, regression coefficient, in bold if correlation is significant.

34 Chapitre 2 2.4. Results groups were identiﬁed. Groups remained stable even if the LOD threshold was changed from 1 to 10, which is suggestive of relatively good stability of our linkage groups. The linkage map represents a total length of 1650.6 cM with an average distance between adjacent markers of 3.39 cM and relatively heterogeneous linkage group size (Table II.I and Figure 2.6). Recombination fractions and 2-points LOD scores can be visualised on supplementary Figure 2 (Annexe B).

2.4.3 QTL analysis

QTLs for simple traits

The ratio of yellow to orange flowered individuals in the F2s was of 42:99, which is not significantly different from a 1:3 ratio expected for a dominant Mendelian marker (Chi2 test : Chi2 = 1.7234; d.f=1; p-value = 0.1893). A single QTL, on linkage group LG16, was found to explain colour variation in the F2 population (Figure 2.6). One QTL explaining nectar volume differences was detected on LG12, with a very large confidence region (123.4 cM). These results were confirmed by correlation between the traits and markers as only two markers, both on LG12, were significantly correlated to nectar after a Bonferonni correction (position 46.1, p-value = 2.78e-06; position 56.3, p-value = 4.19e-05). As for colour, the amount of variance explained by this QTL couldn’t be measured because the data were transformed (binary model). Shape was analysed with geometric morphometrics and with univariate measures. For the shape variation between pollination syndromes (approach i) three distinct QTLs on LG1, LG11, and LG14 were detected and explained respectively 12.8%, 13.6% and 8.8% of variance (Figure 2.6; Table II.II). For the shape variation between parents (approach ii) also three QTLs were detected on LG13, LG11 and LG14 explaining 6.7%, 10.2% and 12.8% of the variance (Figure 2.6; Table II.II). When measuring morphological variation in the F2 hybrids (approach iii), one QTL was identified as controlling the first component on LG1 and explained 15.1% of the variance, another QTL on LG2 explained 14% of the variance for the second component, and a third QTL on LG9 explained the 14.9% of variance for the third component (Figure 2.6; Table II.II). Corolla tube opening variation was explained by 2 QTLs on LG1 and LG16,

35 Chapitre 2 2.4. Results explaining 12.5% and 12.4% of the variance, respectively. Corolla curvature was underlain by one QTL on LG2 explaining 12.8% of the variance. Interestingly, the same QTLs were detected irrespective of the way morphology was quantified (Figure 2.6), that is, co- localizing QTLs were detected for co-varying traits. For instance, the QTL on LG1 was detected with the different methods used to measure shape. Specifically, it was detected using the principal component that distinguished generalists and specialists as well as using corolla tube constriction. Considering all shape analyses together, a total of seven different QTLs were detected, which explained a small to moderate part of morphological variance (Table II.II). We found that one candidate gene for floral shape, CYCLOIDEA (at position 76.2 cM on LG16), co-localized with a QTL confidence region for corolla constriction, although the position of the gene does not correspond to the maximum LOD value (which corresponds to position 85 cM, Figure 2.6 and Table II.II). RADIALIS did not co-localize with any QTL.

Pleiotropic and epistatic QTLs

When analyzing QTLs acting pleiotropically on the ﬁrst three shape components obtained from the PCA on the hybrid population, one QTL was detected on LG1, co-localizing with QTLs for simple traits. Epistasy analysis was conducted with MCQTL and no epistatic interaction was detected among QTLs and neither among QTLs and other markers.

36 Chapitre 2 2.4. Results

LG 1 LG 6 LG 13 LG 11 LG 7 LG 14 LG 9 LG 15

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.2 1.0 1.3 4.8 1.4 6.6 14.1 6.1 4.7 8.1 16.3 6.5 SH1.1 O1 SHP.1 SG1.1 2.5 5.1 4.2 6.0 3.1 5.5 SP1.2 16.9 6.4 5.6 8.6 16.9 6.6 5.0 5.8 18.4 7.3 11.7 13.5 19.1 6.8 SG1.2 8.6 6.3 27.5 SP1.1 9.4 13.7 27.1 27.2 8.2 8.9 9.2 32.6 9.7 16.6 28.8 29.8 13.3 9.6 10.8 33.9 12.7 19.4 37.6 30.9 15.1 11.3 11.2 35.8 13.3 34.2 40.3 31.4 19.2 15.3 11.7 44.8 15.1 39.8 51.7 SH3.1 32.7 20.8 11.9 46.3 15.5 46.4 53.2 38.5 21.4 12.0 51.5 18.8 50.1 53.6 41.5 24.5 51.0 54.2 48.9 25.0 13.0 53.3 26.7 SG1.3

LG 8 SP1.3 13.7 56.0 32.5 51.8 56.4 52.7 25.5 0.0 14.7 58.6 33.2 58.3 57.2 57.5 25.8 6.3 20.0 63.4 35.0 58.5 62.7 66.8 27.5 13.2 25.2 74.7 37.6 58.7 69.3 67.6 33.8 radialis 14.3 30.5 78.0 38.5 59.0 71.3 68.0 35.1 18.7 42.2 81.7 40.2 60.0 73.8 68.4 36.2 21.4 45.7 83.5 41.5 60.7 74.8 71.3 40.9 22.5 55.3 84.2 46.3 61.6 75.7 74.6 41.9 58.6 85.1 51.7 62.1 79.4 81.4 44.9 60.0 95.5 53.7 63.6 80.2 90.1 49.0 62.0 97.9 57.0 64.3 80.5 92.9 50.0 LG 5 71.1 98.9 62.5 68.2 81.5 94.3 52.2 76.7 99.3 69.1 71.2 84.8 103.3 52.7 0.0 85.8 102.8 70.5 72.3 86.3 104.9 57.3 4.1 88.1 103.4 70.8 73.6 95.2 106.9 65.3 8.1 89.3 105.4 72.1 78.9 99.3 65.6 10.7 81.0 101.1 122.6 65.9 93.9 106.8 81.9 129.0 14.5 94.4 107.2 84.5 86.9 102.9 66.2 20.9 87.6 103.3 136.8 67.2 95.3 108.2 88.4 140.8 22.9 90.8 89.3 105.1 72.6 26.0 90.1 106.5 142.2 78.9 91.5 142.8 27.3 LG 2 93.0 92.5 108.1 79.8 28.9 LG 16 95.5 111.9 145.1 81.9 0.0 100.1 149.4 110.5 101.1 115.9 82.8 11.8 105.1 118.4 150.5 86.6 14.4 124.2 155.8 0.0 111.4 119.1 86.9 18.2 126.5 159.7 5.8 120.1 121.5 87.2 31.6 126.9 LG 10 11.6 124.1 121.9 90.8 32.7 129.2 21.1 124.7 123.7 94.3 33.8 130.6 0.0 27.4 125.6 124.7 96.7 36.3 28.6 LG 12 127.2 128.6 105.5 37.3 30.4 131.3 133.1 107.4 39.5 0.0 30.7 133.6 133.4 107.6 A1 39.6 4.6 135.4 134.7 107.7

SH2.1 37.9 39.8 5.4 39.4 148.4 144.3 113.2 22.7 41.5 10.6 40.3 149.6 151.2 113.8 43.7 15.8 42.7 150.0 153.8 117.8 45.4 N1 17.5 42.9 47.0 151.2 156.3 128.3 48.0 18.9 45.0 57.1 153.9 130.0 49.1 22.9 50.6 58.9 132.2

61.5 C1 30.3 52.1 60.7 133.8 69.7 37.7 59.0 61.1 134.5 63.5 74.2 41.6 139.1 64.9 44.7 64.3 65.8 140.0 64.6 46.8 147.6 66.4 56.3 64.9 66.6 148.0 66.4 64.3 151.9 98.0 66.7 65.0 68.1 102.0 O2 70.0 154.0 74.3 155.2 70.9 74.7 LG 44LG 71.8 162.0 LG 3 75.2 169.1 72.8 76.2 73.8 174.2 0.0 78.4 0.0 76.2 cycloidea 174.9 1.5 79.5 1.5 82.6 175.5 1.8 81.8 2.4 89.1 177.6 4.2 91.8 4.2 90.5 178.9 12.4 93.4 5.3 91.5 181.1 28.1 6.6 96.4 94.3 96.7 32.4 8.0 98.1 41.3 15.0 98.7 101.1 100.0 48.1 19.4 102.0 48.6 25.6 100.7 102.4 101.0 50.0 38.8 102.8 51.4 39.2 104.6 111.1 112.6 52.0 39.9 114.7 Morphological variation in the hybrid population: PC1 54.8 41.0 124.0 63.9 42.0 129.2 Pollination syndrome differences: PC1 64.7 44.3 130.0 Parental differences: PC1 65.5 46.8 136.6 67.5 48.4 137.4 corolla tube opening 69.7 57.9 73.4 72.0 corolla curvature Morphological variation in the hybrid population: PC2 93.6 Morphological variation in the hybrid population: PC3 99.8 101.3 shape pleiotropy 103.2 nectar volume colour

Figure 2.6 – Linkage map and position of QTLs. QTLs positions are marked with 2-LOD conﬁdence region, numbers right to the linkage groups represent markers position in cM. Unique names for QTLs correspond to the names given in Table 2.

37 Chapitre 2 2.4. Results

Table II.I – Information about linkage groups.

linkage group number of markers size (cM) average distance between markers (cM) LG1 12 15.3 1.7 LG2 23 102 4.86 LG3 22 73.4 3.86 LG4 26 103.2 4.49 LG5 11 28.9 3.21 LG6 36 95.3 3.07 LG7 59 153.9 3.02 LG8 7 22.5 3.75 LG9 46 159.7 4.2 LG10 13 68.1 5.68 LG11 48 130.6 3.19 LG12 40 137.4 3.71 LG13 34 108.2 3.49 LG14 57 156.3 3.13 LG15 81 181.1 2.62 LG16 44 114.7 2.94 Total 559 1650.6 3.39

Chapitre 2 2.4. Results

value t

eaiehmzgu eﬀect homozygous Relative

diieeﬀect Additive

aine%explained % Variance

oﬁec region Conﬁdence

Position and eﬀects of QTLs. Position ikg group Linkage

Table II.II – T name QTL SP1.2SP1.3 LG13 LG14 18SG1.2 91 LG11SG1.3 1.3-70 LG14 29 29-105O1 86.3 0-46 6.68 27.1-109 LG1 12.88O2 15.3 0.013 8.85 LG16 0.015 13.60 0-15.3 85 0.04 0.05 0.015 0.023 72-97 12.48 3.493 0.06 4.347 0.09 -0.061 12.36 3.625 4.938 0.14 -0.066 -4.591 0.15 -4.119 Positions are given in centimorganlinkage (cM) group; from confidence the regions beginning are ofdecrease. calculated the QTL names with are 2 the same LODmozygous as score effect in is Figure the 2.6. additive effect The divided relative bydifference ho- the between mean both phenotypic parents.

pc1pc2pc3 SH1.1Pleiotropy LG1 SHP.1 SH2.1 11.3 LG1 SH3.1 LG2 0-15.3 LG9 0 57 19 45.4-80 0-15 15.13 38.63 0.023 14.06 – 14.90 0.10 0.020 -0.017 0.11 – 4.487 0.19 4.120 – -4.705 – Trait ColourNectar volumePCA onpopulation hybrid PCA on parents N1 C1 pc1 LG12PCA on genus 41.6 LG16 43 14-137.4 pc1 SP1.1Corolla curvatureCorolla LG11 40.3-46 tube open- – 28ing SG1.1 – 0-40 LG1 – 11.3 A1 – 0-15.3 10.26 LG2 – 54 0.017 12.76 – 43.7-90 0.05 – 0.020 – 12.84 4.209 0.08 -5.987 4.698 0.10 -3.684

39 Chapitre 2 2.5. Discussion

2.5 Discussion

2.5.1 Detection of moderate QTLs involved in pollination syndrome transition

Our linkage map construction was able to recover 16 linkage groups. This is two more than the haploid chromosome number (n=14) for Rhytidophyllum (Skog, 1976). However, while karyotype information exists for R. auriculatum (Skog, 1976), none exist specifically for Rhytidophyllum rupincola. Yet, an n=14 for R. rupincola appears likely because all Rhytidophyllum species studied so far are n=14. In addition, differences in chromosome number between the parents seem unlikely given the viability of second generation hybrids. Finding more linkage groups than chromosomes might result from low genome coverage, however, we do not favour this hypothesis as the average distance between consecutive markers is of 3.39 cM. The parents of the cross are from distinct species and chromosomal rearrangements could have occurred between them. These could create difficulties in assigning some chromosomal segments to the rest of the chromosome; the smallest linkage groups could thus correspond to rearranged chromosomal regions between both species.

Colour diﬀerences QTL

We detected one QTL explaining colour transition between R. auriculatum and R. rupincola. The results presented here are consistent with previous studies on pollination syndrome transitions that investigated the genetic basis of colour variation. Wessinger et al. (2014) found one QTL for colour, corresponding to a gene involved in anthocyanin biosynthesis pathway. Similarly, the well-known case of colour transition between Mimulus lewisii and M. cardinalis showed that a single mutation at the YUP locus can both affect flower colour and pollinators behaviour (Bradshaw and Schemske, 2003). However, an important variation of colour patterns among orange flowers was observed in the hybrid population, both in terms of intensity and localisation of pigments (Figure 2.1). This suggests that other genes could be involved in the intensity and distribution pattern of pigments, proba-

40 Chapitre 2 2.5. Discussion bly through diﬀerential gene expression over the corolla. Other studies on the genetic basis of colour transitions suggests that colour transitions generally involves down-regulation of genes of pigment biosynthesis pathway, often via the action of transcription factors (Galliot et al., 2006b). Future work will then involve the study of the association between colour pattern and the expression of major genes in the anthocyanin biosynthesis pathway.

Nectar volume QTL

Categorizing individuals as “low producing” or “high producing”, and using a binary QTL detection model, permitted the detection of one QTL. We also tried to study sugar concentration in nectar (using a Hand Held Brix Refractometer 0-32◦, Fisher) but faced the same variability problems as for volume and did not succeed in detecting any QTL (data not shown). The conﬁdence region of the QTL for nectar volume was very large. Other QTLs could likely be detected with a larger sample size and stricter growing conditions to decrease intra individual variation. Indeed, similar studies generally detected several QTLs explaining nectar volume variation. Bradshaw et al. (1998) detected two QTLs for nectar volume explaining together 63.4% of total variance. Similarly, Stuurman et al. (2004) also detected two QTLs associated with nectar volume in Petunia pollination syndromes. In contrast, Wessinger et al. (2014) detected only one QTL for nectar volume variation.

Multiple QTLs for corolla shape

Floral shape was measured in order to first understand the genetic basis of the component of corolla shape associated with pollination syndrome transition and second, to understand the genetic basis of the components of corolla shape that are representative of differences between both parents, but not necessarily important for pollination syndrome identity. For the shape component defined by pollination mode differences, three independent QTLs were detected. While only few QTLs were expected for this shape component, to our knowledge, no other studies have successfully identified QTLs for pollination syndrome with geometric morphometrics and PCA methods. However we can compare our results with studies analysing shape differences in divergent environments in other organisms. For

41 Chapitre 2 2.5. Discussion instance, Franchini et al. (2014) used the same method to study the relationship between body shape evolution and trophic ecology between two fish species. Their results are similar to ours in that they also detected relatively few QTL (4), each one explaining less than 8% of variance. Regarding shape differences that are not necessarily associated with pollination syndromes (obtained with the PCAs on the parents and on the hybrid population), seven distinct QTLs were detected. Only four shape QTLs remained when removing those that co-localized with QTLs found with shape differences associated with the pollination syndromes. Conceivably, shape may have initially evolved dramatically during the process of pollination syndrome transition, followed by gradual, small changes along the evolutionary tree (the two species studied are not sister species). Such an hypothesis could be tested with repeated QTL studies involving closely related species and phylogenetic comparison methods (Moyle and Payseur, 2009). These results suggest that the genetic basis of shape evolution is more complex than those of colour and nectar volume. Other studies of floral morphology detected several QTLs with small to moderate effects explaining morphological changes linked to pollination syndrome evolution, supporting this idea of increased complexity. For example, Hodges et al. (2002) detected two QTLs for spur length and two QTLs for flower orien- tation differences between two Aquilegia species. Wessinger et al. (2014) detected seven idependent QTLs between two Penstemon species associated with morphological differences (explaining between 7.3 and 24.3% of the shape variance). Galliot et al. (2006a) detected six QTLs explaining several component of flower size in Petunia representing each 2.7 to 41.6% of the variance, as well as four QTLs for nectar volume (explaining 4.2 to 39.1% of the variance). The same was detected for five morphological traits in Leptosiphon (each one represented by two to seven QTLs explaining two to 28% of the variance) (Goodwillie et al., 2006). A candidate gene approach is always interesting as it can provide clues as to which genes might be involved in the morphological variation observed in a system. RADIALIS and CYCLOIDEA are two genes thought to be involved in the determination of floral

42 Chapitre 2 2.5. Discussion zygomorphy (Preston et al., 2011). More specifically, their localized expression during flower development determines petal size and shape (Feng et al., 2006; Luo et al., 1999; Wang et al., 2008). As such, they represented strong candidate genes in the present study for controlling floral shape, especially for the curvature of flowers. Neither were found to be clearly linked to the morphological differences between R. auriculatum and R. rupincola. Indeed, although CYCLOIDEA is situated within the confidence region of one QTL explaining corolla tube opening, it had a LOD score that did not pass the rejection threshold (Figure 2.6 and supplementary Figure 2 (Annexe B)) and is therefore unlikely to be highly involved in trait variation. This suggests that these candidate genes are not directly responsible for corolla shape variation in our system, at least not for the major shape differences between the pollination syndromes or in the hybrid population. However, this does not mean that they are not involved at all as critical changes could involve the regulation of their expression. If these candidate genes are trans-regulated, then the QTL would not be expected to localize with them. Clearly, further studies are needed to better understand the genetic basis of flower shape variation in this system.

2.5.2 Pollination syndrome evolution in the genus is summarized by morphological transition between R. auriculatum and R. rupincola

Our results showed that generalist and hummingbird specialist species can be diﬀerentiated with only one shape component in Rhytidophyllum and Gesneria, which concurs with a broader study of the group (Joly et al. in prep). This shape component correlates with corolla tube opening in our hybrid population and discriminates the columnar shape of hummingbird pollinated species from the cup shape of generalists (Martén-Rodríguez et al., 2009). The strong correlation between the shape components obtained with the PCA at the genera level, among the parents and on the F2 hybrid population suggest that similar drivers may lie behind ﬂower shape variation. This implies that morphological transition between R. auriculatum and R. rupincola is representative of the major morphological

43 Chapitre 2 2.5. Discussion disparity between pollination syndromes at the genus level. To compare the genetic basis of simple traits and global shape, both univariate traits and multivariate traits were measured. Our results showed that univariate morphological traits were strongly correlated with geometric morphometric shape components, suggesting that the information contained in simple traits is generally contained in geometric morphometrics data. Moreover, geometric morphometric approaches allowed the detection of more QTLs, demonstrating that they contain more information than simple traits. However, one QTL obtained with an univariate trait (QTL on LG16 for corolla tube opening) was not detected with geometric morphometric approaches. This could suggests that due to their complexity, geometric morphometric traits may not catch exactly the same variation as univariate traits, but in the present study these results could also be caused by lack of statistical power due to the small segregating population size. Our results are similar to those of Franchini et al. (2014) who also detected similar QTLs for geometric morphometric and simple traits measured with inter-landmark distances. Their study, however, also showed that additional QTLs were identiﬁed for univariate characteristics obtained independently from landmark data. Altogether, although these observations strongly favour the use of geometric morphometric in QTL studies, they reveal that it could also be beneﬁcial to include univariate traits when analysing genetic architecture of shape evolution, particularly when using small population sizes.

2.5.3 The role of selection in pollination syndrome transition

For most traits measured, only one major QTL was detected. However, for corolla tube opening, inter-parents PC and inter-syndromes PC, two, three, and three QTLs were detected, respectively. All the eﬀects of these QTLs had the same direction. While such a result cannot be validated statistically because of the small number of QTLs, it suggests that those traits evolved under directional selection rather than by drift. The existence of relationships between traits could impact the rate of adaptation. While some authors argue that genetic correlation between traits could slow down adaptation, other showed that it can facilitate it (reviewed in Hendry, 2013). Several studies found more or less

44 Chapitre 2 2.5. Discussion important correlations between some traits involved in pollination syndrome transitions. Wessinger et al. (2014) found no correlation between shape components, flower colour or nectar volume, although they found weak but significant correlations between nectar concentration and some morphological traits. Galliot et al. (2006a), in their segregating Petunia population, detected a correlation between nectar volume and floral tube width. In a study of monkeyflowers, Bradshaw et al. (1998) detected epistatic interactions between the locus YUP involved in flower colour via carotenoid concentration and two other puta- tive QTLs. Hermann et al. (2013), who studied QTLs of pollination syndromes in Petunia, found that QTLs involved in flower scent, colour and morphology were tightly clustered in one genomic region. In our study, flower shape was found to be totally independent from colour and nectar characteristics. Detected QTLs for nectar, colour and shape are localized on different linkage groups or on different regions of the same group, suggesting these traits are genetically independent. However, it is not possible to completely rule out genetic correlations between traits for two reasons. Firstly, some correlations among floral characters might exist in our study system, but we didn’t measure components that are linked with each other (such as nectar concentration, pigments intensity and patterning on the corolla, style and stamen length, etc.). Secondly, correlations could involve minor QTLs that were not detected because of our small F2 population. It is also possible that strong correlations do not exist in our system. In such a case, we could consider that neither genetic constraints nor canalization played an important role in the pollination syndrome transition between R. rupincola and R. auriculatum. This would tend to show that selection pressure exerted by pollinators – that is, extrinsic factors – played a greater role in pollination syndrome evolution than intrinsic factors. Indeed, selection could have been exerted independently on each trait, and no developmental mechanism seems to have forced concerted evolution of pollination syndrome traits. However, we still wonder if the same sequence of trait evolution could have taken place with replicated evolutions in the whole group? This question could be answered with replicated QTL studies on independent transitions and with the help of phylogenetic comparative methods. Accordingly, we agree with Moyle and Payseur (2009) that propose a combination of comparative methods

45 Chapitre 2 2.6. Acknowledgements with QTL analysis to better understand evolutionary patterns of reproductive isolation or evolution at a larger scale.

2.5.4 conclusion

The present study enabled the detection of major QTLs underlying the three major traits composing divergent pollination syndromes between two Rhytidophyllum species. Even if several minors QTLs potentially remain undetected, few major and independent regions for pollination syndrome transition were identified. The hypothesis raised by our study is that directional selection pressure exerted by different pollinators, rather than developmental constraints, was strong enough to make the different traits converge on a pollination syndrome.

2.6 Acknowledgements

We thank Nathalie Isabel who helped us in designing the experiment and François Lambert for sharing Gesneria and Rhytidophyllum shape data. We are also thankful to Stéphane Muños for his help with linkage map building and to Nancy Robert and Janique Perrault for maintaining the living plant collections at the Montreal Botanical Garden. We thank Sebastien Renaut, Nicholas Brereton, Mark Rauscher and an anonymous reviewer for comments on a previous version of the manuscript.

46 Bioclimatic niches evolved independently from pollination syndromes in Antillean 3 Gesneriaceae

H. Alexandre, J. Faure, S. Ginzbarg, J. L. Clark and S. Joly

3.1 Abstract

The interaction with pollinators is a determinant of flowering plant evolution. For instance, pollination mode is an important aspect of the ecological niche of plants, and modifications of the biotic part of the niche can affect the evolution of the abiotic niche. The Antillean genera Gesneria and Rhytidophyllum show an important lability of pollination strategies with hummingbird specialists, bat specialists and generalists. We studied the potential link between pollination mode and bioclimatic niche evolution among this plant group. We tested (i) whether species sharing the same pollination mode also presented more similar realised bioclimatic niches, (ii) whether plant species presenting a mixed mating strategy had wider bioclimatic niches than their pollination-specialist relatives and (iii) whether the evolution of bioclimatic niches was influenced by pollination mode. We characterised the realised bioclimatic niches of plants and pollinators in the environmental space, analysed niche overlap and then fitted four evolution models to test whether the evolution of bioclimatic niches of plants were influenced by pollination mode. While taking account phylogenetic relathionships among the plant genera we found that (i) plants with the same pollination mode did not present more similar bioclimatic niches,

47 Chapitre 3 3.2. Introduction

(ii) being more generalist for pollination did not enable plants to occupy wider bioclimatic niches, and (iii) the evolution of bioclimatic niches was not inﬂuenced by pollination mode. Rather, Antillean Gesneriaceae are characterised by phylogenetic bioclimatic niche conservatism (PNC). Interestingly, both functional groups of pollinators and plants are generalist for bioclimatic niche components, which we hypothesize to be a cause of PNC in plants. Furthermore, as both pollinators groups do not occupy distinct regions of the environmental space, plants with one or the other specialist pollination mode cannot be constrained to a restricted part of the environment by their pollinator functional group. We hypothesize that the bioclimatic niche conservatism of plants should be related to the important climatic oscillations they uderwent during Pleistocene. Key-words: Realised ecological niche, biotic interaction, niche conservatism, Ornstein- Uhlenbeck models, phylogenetic comparative analyses, environmental space.

3.2 Introduction

Flowers are thought to be a key innovation that enabled angiosperms to colonise and diver- sify throughout most biomes. The determinant of this evolutionary success is pollination. Among animal pollinated plants, numerous examples have illustrated the importance of specialisation or shift between specialised pollination modes in plant diversification (Whit- tall and Hodges, 2007; Kawakita and Kato, 2009; Harder and Johnson, 2009; Kay and Sargent, 2009). However generalisation now appears to also be a speciation driver in some instances (Waser et al., 1996; Johnson and Steiner, 2000). Indeed, some specialized pollination systems are evolutionary dead ends (Tripp and Manos, 2008), while generalisation can be a driver of plant diversification (Armbruster and Baldwin, 1998). Pollination mode - the degree of specialisation or the identity of pollinators - constitutes an important aspect of the ecological niche of plants. According to Hutchinson (1957), the ecological niche is an n-dimensional hypervolume in which each point corresponds to a state of the environment where the species could persist indefinitely. This niche is defined

48 Chapitre 3 3.2. Introduction by abiotic (e.g. climatic variables) and biotic factors (i.e. interaction with other species). The interaction between biotic and abiotic factors determines the range of the realised niche. Competition, which negatively impacts both interacting species (-/-), trophic interactions (+/-) or mutualism (+/+) are known to play an important role in limiting species distributions (Holt and Barfield, 2009; Price and Kirkpatrick, 2009; Moeller et al., 2012; Roux et al., 2012). Also, modifications of the biotic niche can be linked to modifications in the abiotic part of the niche. For example, host specialisation is negatively correlated with abiotic niche width in clownfishes (Litsios et al., 2014), while trophic specialisation is correlated with different abiotic niche evolution rates in damselfishes (Litsios et al., 2012). In some Caribbean plants pollinated by hummingbirds, the degree of specialisation on hummingbirds is correlated with specialisation for habitat characteristics such as temperature and rainfall (Dalsgaard et al., 2009). Additionally, in Cape flora of South Africa, sympatric sister species differing in pollination mode also adapted to diverging edaphic conditions (Van der Niet et al., 2006). Mechanisms underlying such correlations can be quite diverse, going from indirect selection in the case of damselfishes, where the environmental requirement of food resources potentially constrain the niche evolution of the consumers (Litsios et al., 2012), to reinforcement processes in the case of Cape flora speciations (Van der Niet et al., 2006). Litsios et al. (2014) also highlighted a potential trade-off between all niche axes to explain the negative correlation between biotic and abiotic degree of generalisation. Shifts of pollination modes and abiotic niche transition are two of the main factors associated with speciation in plants (Evans et al., 2009; Nakazato et al., 2010; Van der Niet et al., 2014). But maintaining species differences generally involves more than one form of reproductive isolation (for example diverging habitat and pollinator shift Kay and Sargent, 2009). Studying concomitantly abiotic and pollination aspects of the niche can provide a framework for understanding the role of the interaction between both factors in diversification. For example, the realised abiotic niche of a plant corresponds to the overlap between the abiotic niche of its pollinators and its own fundamental abiotic niche. Also, the degree of specialisation for pollinators can determine the proportion of its fundamental

49 Chapitre 3 3.2. Introduction abiotic niche that a plant can effectively fill (Pellissier et al., 2012). In case where different pollinators occupy different abiotic niches, one expects that (i) plant species adapted to different functional pollinators will have distinct realised abiotic niches, (ii) the realised abiotic niche of plants will be nested in the abiotic niche of their pollinators and (iii) plants with increased generalisation in pollination mode should have a realised abiotic niche that represents a larger part of its fundamental abiotic niche compared to specialists. Pellissier et al. (2012) also suggested that invasive species, which colonize new areas, should have a generalist pollination strategy, which they propose as an enlargement of Baker’s law. In agreement with this hypothesis, a recent study in Gesneriaceae showed that the rate of pollinator visits per flower is greater in mainland hummingbird specialists than in their island relatives (Marten-Rodriguez et al., 2015). Moreover, it was shown that pollination generalisation is far more frequent for island Gesneriaceae than for mainland ones (Marten- Rodriguez et al., 2015). Pollination generalisation in oceanic islands can be explained by the lower density of pollinators or the absence of certain pollinator functional groups on islands compared to mainland (Armbruster and Baldwin, 1998; Marten-Rodriguez et al., 2015). The closely related genera Gesneria and Rhytidophyllum are nearly endemic to the Caribbean and together consist of approximately 75 species that rapidly diversified around 8 to 11 mya (Roalson et al., 2008). This rapid species diversification was accompanied by a diversification in floral traits associated with pollination mode transitions (Martén- Rodríguez et al., 2010). In this group, three different pollination modes exist: (i) hummingbird specialists; (ii) bat-specialists and (iii) a mixed pollination strategy corresponding to pollination either by hummingbirds or bats and sometimes insects (Martén-Rodríguez et al., 2009). Here, we will focus on the specialisation for functional pollination group rather than for individual species, according to the definition of Fenster et al. (2004). The ancestral pollination mode of the group was specialised on hummingbirds and further evolution involved multiple independent transitions from hummingbird specialist to the capability of being pollinated by bats (evolving either toward bat specialist or a mixed- strategy) (Martén-Rodríguez et al., 2010). Some reversals toward the ancestral humming-

50 Chapitre 3 3.3. Material and Methods bird specialist mode were also detected but to a lesser extent (Martén-Rodríguez et al., 2010; Joly et al., 2016). The labile nature of this trait gives the opportunity to study biological replicates of pollination transitions, making it easier to test for a link between pollination and abiotic niche evolution. We here are particularly interested in bioclimatic conditions, which are known to be quite spatially variable in the Caribbean (Gamble and Curtis, 2008). Documenting a potential link between pollination and bioclimatic habitat evolution is a required first step toward understanding the ecological conditions that led to the important species diversification of Gesneria and Rhytidophyllum. In this study, we first characterised the bioclimatic niches of the species of the genera Gesneria and Rhytidophyllum in the Antilles, as well as those of their main pollinator functional groups: hummingbirds and bats. Then, we tested (i) if plants with identical specialised pollination mode (either bat or hummingbird) shared more similar bioclimatic niches, (ii) whether plant species presenting a mixed pollination strategy (involving both bats and hummingbirds) had wider bioclimatic niches than pollination specialists, and (iii) whether the evolution of bioclimatic niches in the group was influenced by pollination mode of species.

3.3 Material and Methods

3.3.1 Species occurrences and bioclimatic data

The study area consists of the Lesser and Greater Antilles, which corresponds to the range limit of the plant species group, although some pollinators have wider distributions. Plant occurrence data were obtained from herbarium specimens. Some of them were GPS- referenced during ﬁeld collection, but the majority were post facto referenced using the geolocate web application (Rios and Bart, 2010). Specimens with uncertain location or with poor coordinates precision (> 10 km) were discarded, leaving 1262 unique and reliable occurrences (each species being represented by ﬁve to 116 points, Supplementary Table S1 (Annexe D)). Hummingbird and nectarivorous bat occurrence data were obtained from Gbif that contain data from, amongst other sources, MANIS, VertNet, and

51 Chapitre 3 3.3. Material and Methods e-bird. Unrealistic data (such as points in the sea) and duplicates were ﬁltered, leaving 311 unique points for bats (each species being represented by seven to 112 points) and 7432 unique points for hummingbirds (each species having nine to 1238 points). Altitude and 19 bioclimatic variables were extracted from the Worldclim database (Hijmans et al., 2005), only for the Antilles, with a resolution of 5 arc-minutes, representing squares of 10km-long side at the equator. The 19 bioclimatic variables are deﬁned in appendix E. This resolution was chosen to match the precision of our occurrence data. We did not include soil characteristics in our analyses as they did not improve models performance in a preliminary species distribution model analysis (data not shown).

3.3.2 Species bioclimatic niche evaluation

We chose to base our study on the environmental space rather than geographical space, as we were interested in adaptation to the environment and not to range distribution. It is also closer to the niche concept of Hutchinson (1957). We thus first used a method to describe and summarize the environmental conditions available in the study area. A principal component analysis (PCA) based on a correlation matrix (i.e., variables standardized to mean=0 and variance=1) was performed on the 3458 pixels of the raster maps containing data in the study area (i.e., land pixels) which correspond to the Caribbean. Only the first two principal components were considered in the analysis as they explained most of the bioclimatic variation over the Caribbean. Using this 2D ordination of the environmental space, we then characterised the niches of plant and pollinator species. The presence points of species were projected on the first two axes of the environmental space, keeping only one presence record per pixel per species to account for sampling bias. This is important as plant collectors often return to the same localities. Species with less than five data points were discarded from the analyses, removing species with too little data or with a very restricted range. Niche position and breadth was estimated for each environmental axes. The niche position consists of the mean score value of all the points for each species along the axis, while the niche breadth was measured as the standard deviation of these scores (this measure is not influenced by the number of individuals per species). This approach

52 Chapitre 3 3.3. Material and Methods assumes that distribution of individuals is normal over both environmental axes.

3.3.3 Bioclimatic niche overlap

Niche overlap for each species pair was measured according to the method developed by Broennimann et al. (2012), using the D index of Schoener as described by Warren et al. (2008). The D index is based on a comparison of two species niche value pixel by pixel, varying between 0 (no overlap) and 1 (total overlap of ecological niches). The method of Broennimann et al. (2012) compares smoothed density distribution of species, enabling the comparison to be independent of spatial resolution. This permutation-based method enabled us to test for a significantly greater or lesser overlap than expected by chance. We measured niche overlap for (i) pollinator species pairs, (ii) plant species pairs and (iii) plant-pollinator pairs. Then, the relationship between niche characteristics and pollination mode was tested with a PGLS using the function pgls in the R package CAPER (version 0.5.2). This method fits a linear model on the data forcing a structured covariance between species. As we assume that niche evolves under Brownian motion, traits covariance decrease linearly through time between species. This method allow to measure trait correlation while accounting for phylogenetic non-independence between samples (Freckleton et al., 2002). For this analysis, we used only the 22 species for which pollination mode has been validated in the field (Martén-Rodríguez et al., 2009, 2010). We tested for an effect of pollination type by comparing the Akaike Information Criterion corrected for sample sizes (AICc) of this model with the AICc of a model with no explanatory variable (i.e., a model with only an intercept). The phylogeny used for these tests is a species tree built with five single copy nuclear genes (CYCLOIDEA, GAPDH, CHI, F3H and UF3GT) with *BEAST algorithm; the same phylogeny was used for another study and this construction is detailed elsewhere (Joly et al., 2016). To account both for niche and phylogenetic uncertainties, the measures were performed over 1000 resampled niches values and over 1000 trees sampled from the posterior distribution of the *BEAST analysis. The 1000 resampled niche values were measured sampling half of the presence points.

53 Chapitre 3 3.3. Material and Methods

3.3.4 Bioclimatic niche evolution among plants

We tested whether the evolution of bioclimatic niches of plants was influenced by their pollination mode. To do this, we first reconstructed ancestral pollination modes with stochastic mapping (Bollback, 2006) with the function make.simmap of the package PHY- TOOLS version 0.3-72 (Revell, 2012). We performed both the analysis for species with confirmed pollination mode (22 species) and with pollination mode inferred from floral shape (Joly et al., 2016, 35 species). Pollination mode probability was assigned as follows: species with known mode had a probability of 1, species with uncertain mode but for which floral shape enabled classification into a pollination syndrome (see Joly et al., 2016) was assigned a probability 2/3 for inferred pollination mode and 1/6 for both other modes. A probability of 1/3 was assigned for species with no shape data or for which shape didn’t enable a predictable pollination mode. Then, the data was fitted on four evolution models: (i) a Brownian Motion model with one rate of niche variation for the whole phylogeny (BM1); (ii) a Brownian Motion model in which each pollination mode has its own rate of niche variation (BM3); (iii) an Ornstein-Uhlenbeck model with one selective regime over the phylogenetic tree (OU1); and (iv) an Ornstein-Uhlenbeck model with a different selective regime for each pollination mode (OU3). The OU model differs from the BM model by having an optimal trait value (for each regime) and a parameter that determines the strength of selection to bring the variation closer to the optimum. When the selection parameter equals to 0, the OU model becomes a BM model (Butler and King, 2004). The models were fitted on the niche identity for environmental axes separately and simultaneously (multivariate model). Model fitting was done with the functions mvBM and mvOU of the package mvMORPH (version 1.0.3 Clavel et al., 2015). All models were fitted over 1000 phylogenies sampled from the stationary phase of the *BEAST analysis, for 1000 resampled (half the sampling points) niche values. Models were compared using the AICc and posterior probability in favour of the best model was estimated as the proportion of the phylogenies for which the best model had a smaller AICc score.

54 Chapitre 3 3.4. Results

3.4 Results

The species phylogeny we used was concordant with previous studies (Martén-Rodríguez et al., 2010) and is described in more details elsewhere (Joly et al., 2016). Some branch support values were quite high although some relationships among closely related species were ambiguous (Figure 3.1), indicating the importance of considering phylogenetic un- certainty in our analyses.

3.4.1 Bioclimatic niches overlap

The ﬁrst two principal axes of the PCA represented together 70.16% of the environmental variation over the islands and the pollinator and plant species occurred over a large part of the available environment (Figure 3.2). Although no bioclimatic variable is clearly linked to only one principal axis, the ﬁrst axis corresponds to a gradient from high temperatures and low rainfall areas to areas with lower temperatures and higher rainfall. The second axis is more related to seasonality (see supplementary Table S2 (Annexe E)). The third and fourth axes represented 13.81% and 6.98% of the variance but were not used in further analyses.

Pollinators overlap

For hummingbirds and bats, between-group overlap is not more diﬀerent than within group overlaps (Kruskal-Wallis p-value= 0.4204, Figure 3.3a), which means that bioclimatic niches of both functional groups are similar.

Plants overlap

There is a clear niche separation among species in the study group. For niche equivalency, among the 44 species present in at least 5 geographic pixels (990 comparisons), 124 species pair comparisons involved species whose niche were not signiﬁcantly diﬀerent from each other (p-value corrected with FDR from 0.063 to 0.990), 860 comparisons involved

55 Chapitre 3 3.4. Results

0.28 G. bracteosa 0.31 G. nipensis 0.22 G. viridiflora 0.95 G. acrochordonanthe

0.93 0.9 G. quisqueyana * G. sintenisii G. ekmanii

0.76 0.8 * G. aspera 0.5 G. cubensis 0.7 G. pulverulenta

0.31 0,.6 G. pedunculosa G. ventricosa G. fruticosa 0.59 0.71 G. cuneifolia 0.49 G. reticulata

0.61 G. shaferi 0,7 G. purpurascens 0.87 0.69 * G. yamuriensis G. acaulis 0.86 0.65 1 G. pedicellaris G. christii G. humilis 0.99 * G. clarensis 0.25 * G. ferruginae 0.63 G. citrina

0.92 G. salicifolia 0.65 R. bicolor 0.92 * G. sp G. pumila 0.15 * R. sp 0.63 0.19 R. berteroanum 0.39 R. bullatum 0.67 R. auriculatum 0.76 R. vernicosum 0.33 0.75 R. grandiflorum R. leucomallon 0.76 R. lomense 1 0.96 R. rupincola 0.96 * R. earlei 0.6 R. tomentosum 0.65 R. crenulatum 1 0.96 R. exsertum 0.32 * R. intermedium R. minus * R. onacaensis * Bellonia spinosa 1 * Henckelia malayana * Kohleria trinidad

Figure 3.1 – Species phylogeny of the studied plants, with pollination mode probabilities represented as pie charts at the tree tips. Red: hummingbird specialist, green: bat-specialist, blue: mixed- strategy. The species preceded by an asterisk were not used in the niche analysis. Numbers over the branches represent posterior probability of clades.

56 Chapitre 3 3.4. Results species significantly different from each other (p-value from 0.022 to 0.043) and 6 comparisons involved species that were more similar than random (p-value from 0.022 to 0.043). We compared niche overlap (D) of species pairs involving only bat specialists, only hummingbird specialists and only mixed-pollination species. This comparison highlighted that bat-specialist species were significantly more similar to each other than mixed-pollination species or hummingbird specialist species (Wilcoxon p-value= 0.00352) (Figure 3.3b).

Overlap between plants and pollinators

Bat specialist plants had bioclimatic niches that were signiﬁcantly more similar to the niches of their functional pollinators than other plants groups (Wilcoxon tests p-values <0.05) (Figure 3.4). Plants with a mixed pollination strategy harbour wider niches than hummingbird specialists, and bat specialists have the smallest niches (see supplementary Table S1 (Annexe D)).

57 Chapitre 3 3.4. Results Bat specialist Mixed strategy Hummingbird specialist unknown pollination mode (b) 15 Rhytidophyllum and 5 10 PC1 (40.77%) Gesneria b − 5 0

5 0

− 5 C2(93%) (29.39% 2 PC bat hummingbird 15 Points are the mean value forthe each species pollinator and the group colour (a) represents indicate or the pollination standard mode deviation of ofrepresent the each plants available species. conditions (b). throughout the The Bars Antillespixels). grey (all island squares 5 10 PC1 (40.77%) Distribution of bioclimatic niches of pollinators (a) and plant species of − 5 0 a Figure 3.2 –

5 0

− 5 C2(93%) (29.39% 2 PC

58 Chapitre 3 3.4. Results

a A b A AB B A 0.8

0.75 A

0.6

0.50 D D 0.4

0.25 0.2

0.00 0.0 bat_bat bat_hummingbird hummingbird_hummingbird B_B G_G H_H

Figure 3.3 – Overall bioclimatic niches overlap. (a) between pollinators, green: bat species, pink: hummingbird species, grey: comparison between bat and hummingbirds. (b) between plants, green: between bat specialists, purple: between mixed- pollination strategy species, pink: between hummingbird specialists. Letters behind the boxes represent signiﬁcant diﬀerences.

3.4.2 Bioclimatic niche evolution among plants

Diﬀerences in niche position and breadth between plant pollination modes

The PGLS model with pollination mode as an explanatory variable did not explain niche identity nor niche size (measured with standard deviation) better than the null model without it, as 0 was included in the 95% interval of diﬀerence of AICc values between models (Table III.I). This suggests that pollination mode does not inﬂuence the identity or size of the bioclimatic niches of plants.

Fit of evolutionary models on plant bioclimatic niches

Results were similar with the equal rate (ER) and the all rate different (ARD) transition rates models. The results with the ARD model are reported below, but those with the ER models are given in supplementary material (Annexe F). The analyses of bioclimatic niches (identity) evolution performed on 35 species from which 13 had predicted pollination type (not confirmed with field observations) selected the OU1 model as the best evolutionary model (Table III.II). Results on only species with confirmed pollination mode gave the

59 Chapitre 3 3.4. Results

a bat b hummingbird 0.8 B B A CA B 0.6

0.4 D

0.2

0.0

BG H BG H

Figure 3.4 – Bioclimatic niches overlap between plants and pollinators. (a) overlap between plants and bats, (b) overlap between plants and hummingbirds. Letters behind the boxes represent signiﬁcant differences . Colours represent plants pollination strategy and are the same as in Figure 3b.

60 Chapitre 3 3.5. Discussion

Table III.I – AICc of PGLS models with identity and size depending on (ﬁrst model) or independent of (second model) the pollination mode, among the two ﬁrst principal axes.

model niche identity niche position PC1 PC2 PC1 PC2 ellipse area niche component ~pollination mode 94.25 88.63 98.43 83.69 196.27 niche component ~1 91.46 85.36 95.31 80.34 193.12 ∆AICc (95% CI) -7.06:5.12 -4.61:5.12 -6.37:5.12 -4.30:5.12 -6.14:5.12

Table III.II – Mean AICc values of the evolutionary models ﬁtted on the niche components.

evolution model OU1 OU3 BM1 BM3 PC1 mean AICc 116.67 119.79 171.89 164.57 mean ∆ AICc -3.12 -55.22 -47.90 post. proba favouring OU1 0.906 0.999 0.998 PC2 mean AICc 105.54 108.60 160.87 153.58 mean ∆ AICc -3.06 -55.33 -48.04 post. proba favouring OU1 0.909 1 1 PC1 and 2 mean AICc 227.13 239.40 323.29 314.72 mean ∆ AICc -12.27 -96.16 -87.59 post. proba favouring OU1 0.996 1 1 same result (Annexe F). This supports the presence of a selection pressure that keeps the species bioclimatic niches more similar to each other than what is expected under a null (Brownian) model of evolution. Moreover, because the models with three regimes had a worse ﬁt than models with one regime, the hypothesis that plants with diﬀerent pollination modes have distinct niche preference can be rejected.

3.5 Discussion

Our study aimed at documenting the evolution of bioclimatic niches in relation to pollination mode in a group of insular plants. Both the bioclimatic niche and the pollination mode are known to be of primary importance in the evolution of angiosperms, and many

61 Chapitre 3 3.5. Discussion studies highlighted the link between plant diversiﬁcation and either pollination mode (reviewed in Armbruster, 2014) or bioclimatic niches (Boucher et al., 2012; Joly et al., 2014). However, addressing both traits concomitantly is rare (cf. Livshultz et al., 2011). Yet, investigating the interactions between these is important to understand what drives the colonisation of new niches in plant evolution because pollination mode directly aﬀects biotic interactions between the plant and its pollinators, and because biotic interactions directly impact the ecological niche of species.

3.5.1 Bioclimatic niches evolve independently from pollination modes

Our three expectations were that: (i) plant species with the same pollination mode should occupy similar bioclimatic niches, (ii) pollination mode generalisation would enable plant species to occupy a wider part of their fundamental bioclimatic niche making the bioclimatic niche of mixed mating strategy species wider than those of plants with specialised pollination, and (iii) pollination mode should influence evolution of plants bioclimatic niches. Those expectations relied on two assumptions: (1) hummingbirds and bats are characterised by different bioclimatic niches and (2) bioclimatic niches of plants are constrained by the availability of their pollinators (that is the realised bioclimatic niche of plants is nested in their pollinators bioclimatic niche). Such kinds of relationship have been demonstrated in alpine deceptive orchids. The fitness of theses plants depend on the availability of "naive" pollinators, which decreases with altitude. As a result Pellissier et al. (2010) showed that the proportion of food-deceptive orchids compared to that or rewarding orchids decrease with altitude. Another example is that of reproductive system shifting with altitude in other orchids in la Reunion island, with commuties harbouring more self-pollinating species at higher altitude (Jacquemyn et al., 2005). However, current results do not corroborate our assumptions. Indeed, the bioclimatic niches of hummingbirds are not significantly different from those of bats (Figure 3.2a). Given these results, it is perhaps not surprising that we did not observe different niche identity or breadth between plants with different pollination modes, neither wider bio-

62 Chapitre 3 3.5. Discussion climatic niche for plants with a mixed pollination strategy. Similarly, our results from the evolutionary models suggest that the evolution of bioclimatic niches of plants is not inﬂuenced by the pollination mode. This result is comparable to that of Serrano-Serrano et al. (2015) who worked on other Gesneriaceae tribes and showed independent evolution pattern between ﬂoral traits and bioclimatic niches.

3.5.2 Phylogenetic bioclimatic niche conservatism

Among the four evolution models we tested, the model best explaining niche evolution is the Ornstein-Uhlenbeck (OU) model with one selective regime (OU1, Table III.II). The OU model differs from the Brownian Motion (BM) model in that there is a global optimum and a selection pressure that acts to bring species values closer to this optimum. The biological interpretation of the fit of evolution models can vary depending on the specific situation, but common interpretation is that Brownian motion can result from drift or fluctuating selection (Hansen and Martins, 1996), whereas OU models are often interpreted as evidence of either balancing or directional selection, depending on the ancestral value. The selection of the OU1 model, with one selective regime across the whole tree, is concordant with a hypothesis of niche conservatism (Wiens et al., 2010). Indeed, it means that species are more similar to each other than what would be expected under a null model (BM) in which species were only constrained by phylogenetic relationships (Losos, 2008). Two hypotheses may explain a better fit of OU model. First, Boucher et al. (2014) showed that in a bounded landscape, boundaries restrain the range of available conditions, which artificially result in the niche evolution to better fit an OU model than a BM model when the bounds are reached, and this even if species evolve independently from climate. However, our results show that while the plant group is present in a broad range of conditions, some parts of the environment remain unoccupied by our species (Figure 3.2b). This suggests that the group has not reached landscape bounds and also that species niches do depend on climatic conditions in their distributions. Thus we favour a second hypothesis. Although we observed interspecific differences in bioclimatic niche identities, at functional levels both pollinators and plants are present in a broad range of available environments.

63 Chapitre 3 3.5. Discussion

Therefore, both plants and pollinators guilds can be regarded as generalists in terms of bioclimatic preferences in the Caribbean islands. Those islands experience important climatic variability both in temporal and spatial scales (Jury et al., 2007) and were also subject to high climate variability during the Pleistocene (Bonatti and Gartner, 1973). Such an important temporal variability associated with limited migration possibilities in the islands could exert a selection pressure toward bioclimatic niche generalisation, and could also explain a certain degree of phylogenetic niche conservatism.

3.5.3 Evolution of pollination mode

We noted that bats and hummingbirds do not occupy distinct parts of environmental space. In this context, it is possible to interpret the numerous pollination mode transitions from specialists to generalists in Caribbean Gesneriaceae. Indeed, the evolution of pollination mode in this group, while independent from the evolution of the bioclimatic niche, may nevertheless be linked to temporal variability in the Caribbean islands. It is known that pollinators are less abundant on oceanic islands than on the mainland. Particularly, in the Caribbean islands, frequent hurricanes cause dramatic decreases in population sizes in hummingbirds and bats. Consequently, Dalsgaard et al. (2009) suggested that becoming a generalist can ensure pollination when temporal availability of pollinators is heterogeneous. Moreover, Fleming et al. (2009) showed that evolution toward bat pollination can be very beneﬁcial for plants as bats generally enable dispersal over longer distances than other pollinators. Bats might also be more eﬀective pollinators, giving an advantage to the individuals that have the capacity to be bat pollinated. However, Antillean Gesner- iaceae are perennial species, and thus they are not required to reproduce every year for populations to persist. This strategy could compensate for a decline in pollinators and explain why specialist pollination modes persist. Indeed, hummingbird pollination remains a dominant syndrome in the group, showing that a specialist strategy is still highly successful.

64 Chapitre 3 3.6. Acknowledgements

3.5.4 Conclusion

From this study, it appears that the evolution of bioclimatic niches is not inﬂuenced by pollination mode in Gesneria and Rhytidophyllum, and we showed that main functional pollinator groups do not diﬀer in their bioclimatic niches. Rather, bioclimatic niches of plants seem to have evolved under phylogenetic niche conservatism. From this, we propose a new hypothesis about bioclimatic niche evolution in Caribbean Gesneriaceae. That is, temporal environmental variability in an island context favoured ecological niche generalisation in plants, and this independently from the pollination mode. This can be tested in more detail in the future.

3.6 Acknowledgements

We thank François Lambert whose ﬁeldwork enabled us to add plant occurrences to our analyses and Pedro Peres-Neto for constructive comments on the manuscript.

65 Testing phylogenetic niche conservatism under temporally variable environments with 4 Brownian-motion based models.

H. Alexandre and S. Joly

4.1 Abstract

Climate change affects all biological levels, from the physiology of individuals to community composition. Understanding those effects is important both for fundamental scientific issues and conservation plans. The strategies species use to escape climate change can lead to different macro-evolutionary patterns, which might be detected using evolutionary models. The objective of this study is to investigate whether the effect of changing climate on species traits can lead to a specific macro-evolutionary pattern. To do this, we first used a simulation approach to investigate the effect of amplitude and frequency of a temporally oscillating climate on species generalization. Second, allowing speciation in our simulations, we test the hypothesis that species generalization under climate change results in a pattern of phylogenetic niche conservatism (PNC) detectable with evolutionary models (Brownian Motion and Ornstein-Uhlenbeck) fitting. The results show that under temporally oscillating climate, species become more tolerant (i,e. generalist) than under fixed conditions. Secondly, with increasing oscillations frequency and amplitude, there is a tendency toward a PNC signal, although this is rarely strongly supported. Interestingly, we show that under variable environment, the assumptions of classic Brownian motion

66 Chapitre 4 4.2. Introduction based evolution models are unrealistic, i.e. phenotypic evolution is not necessarily linear and ancestral states might be more extreme than present states. Such models thus have to be used cautiously, particularly when studying groups that evolved in areas known to have experienced important past climatic oscillations.

Key-words: Phylogenetic Niche Conservatism, Generalization, Climate change, Brown- ian motion, Ornstein-Uhlenbeck

4.2 Introduction

Changing climate impacts the structure of biodiversity. Environmental upheavals can affect multiple biological levels, from individual traits (via acclimation or adaptation) to community structure by modifying trophic networks (Walther et al., 2002; Poisot et al., 2011b). Climate oscillations affect species range size (Davies et al., 2011) as well as specialization degree of plant-hummingbird networks (Dalsgaard et al., 2011). Species can adopt different strategies to cope with climate change (Hoffmann and Sgro, 2011). First, they can migrate to track the climate they are adapted to; this has been the case for a lot of species during glacial and interglacial Pleistocene stages (e.g. Magri et al., 2006; Cheddadi et al., 2006) as well as under current changes (e.g. Moritz et al., 2008; Lenoir et al., 2008). Second, they can acclimatize to new conditions via plasticity mechanisms. Third, they can adapt to current conditions by shifting their climate optimum and thus sliding their tolerance range. While migration has been clearly demonstrated as an escape strategy, real demon- strations of adaptation to climate change are scarce (but see Merilä and Hendry, 2014, for a review on the subject). In contrast, plastic responses of species often appear to be associated to climate variability (Merilä and Hendry, 2014; Teplitsky and Millien, 2014). Plastic responses are associated to the degree of species tolerance (i.e generalization), with the tolerance increasing with plastic reaction norm (Chevin and Lande, 2010). Species environmental tolerance should greatly be influenced by environment hetero-

67 Chapitre 4 4.2. Introduction geneity at both spatial and temporal scales, but temporal variations are expected to influence species tolerance in a different way than spatial heterogeneity. While spatial variation can favour the emergence of specialists (if migration is sufficiently low), temporal variation of climatic conditions favours generalists (Kassen, 2002). During past mass extinction, generalist species survived better than specialist ones and current environmental perturbations have also been shown to favour generalists (Clavel et al., 2010). The frequency and amplitude of climatic heterogeneity are both determinants of species generalization. Species that have important climatic variability over their spatial range generally have broader intrinsic tolerance (Sheth and Angert, 2014). The amplitude of climate seasonal variation is responsible for the width of climatic niche in some amphibians (Quintero and Wiens, 2013), while the frequency of environmental variation is associated with specialization on plant-hummingbird networks (Dalsgaard et al., 2011). The context of the changes can also be important such as in an island context where migration opportunities are limited. If temporal climate variation is wider than the climate range in space at time t, species must adapt either by changing their optimal trait value or by increasing their tolerance. Becoming generalist is a way to adapt to temporally variable environments, particularly when migrations opportunities are limited. While some studies showed that temporal variability affect the degree of generalization of species (Dalsgaard et al., 2011; Condon et al., 2014), little is known about the respective effect of amplitude vs. frequency of temporal variation on the evolution of generalization. Environmental instability can have an impact on macro-evolutionary patterns through its influence on speciation and extinction (Bennett, 2004; Willis and Niklas, 2004). How- ever the effect of climate variability on species trait levels might also create specific patterns at macro-evolutionary scales. At such scale, we are often interested in discerning whether some global trends affect similarly groups of related organisms. According to the “Plus ça change” model, species traits are supposed to remain more stable under variable environment than under fixed conditions (Sheldon, 1996). This is also expected at clade level: climatic oscillations are expected to decrease both cladogenesis and anagenesis (Jansson and Dynesius, 2002). Given this curbed anagenesis, we can expect species to show pat-

68 Chapitre 4 4.2. Introduction terns of phylogenetic niche conservatism (PNC) under environmental oscillation. PNC is the tendency of species to maintain the same niche traits over time, niche trait here being considered as the climate adaptation trait. Thus, according to the model presented above, climatic fluctuation would be expected to create a pattern of PNC over time in a group of species, and more so if these are geographically constrained. Losos (2008) defined PNC as a pattern where species traits are more similar than what we can expect given their phylogenetic relationships, because of a constrained evolution. In a landscape with varying temperature, thermal optimum can be considered as a one-dimension species niche. Evolution models such as Brownian motion (BM) or Orstein-Uhlenbcek (OU) can be used to identify patterns of traits evolution along phylogenies. BM models a drifting trait while OU is used to model traits evolving under stabilizing selection, that is if species are more similar than expected under neutrality (Butler and King, 2004). Given the definition of Losos (2008) outlined above, these models could be used to test PNC: a better fit of OU model than BM to niche optimum evolution could support a pattern of PNC (but see Wiens et al., 2010). Patterns of PNC should occur under stabilizing selection, or indirectly because of temporally variable environments. In this paper, we use a simulation approach to test the hypothesis that species generalization under climate change should be associated with PNC for niche optimum at macro-evolutionary levels. However, the way we measure climatic niches can impact PNC pattern (Boucher et al., 2014). For instance, Grandcolas et al. (2011) argued that while theoretical climatic niches can be seen as a heritable intrinsic trait, when we model them from distributional data a lot of extrinsic factors can affect this modeled niche, making it a non-heritable extrinsic trait and making meaningless the study of PNC on such traits. Given this bias we chose to use two methods to measure climatic niche (i) considering climatic niche as a heritable intrinsic trait and (ii) measuring climatic niche optimum and tolerance from geographic distribution data.

69 Chapitre 4 4.3. Material and Methods

4.3 Material and Methods

4.3.1 Simulation Model

Climate heterogeneity over space and time

The environment was modelled to be variable over space and time. The spatial structure of the environment was modelled using Moran’s eigenvector maps (Dray et al., 2006) on a two dimensional landscape of p × p pixels, keeping the Espat first eigenvectors. This method first built a distance matrix over every pixels from which a truncated connectivity matrix is extracted. Then a principal coordinate analysis is performed on the weighted connectivity matrix giving eigenvectors that can be used to model spatial structure of the environment. These eigenvectors represent sinusoidal curves of different frequency (increasing in frequency with higher eigenvectors) and their combination allows modelling a landscape with hills and valleys of different heights and depths, respectively. This spatial structure is fixed in the simulations. Through time, the climate oscillates in a sinusoidal way. The frequency of temporal change of climate is determined using one eigenvector

(Etemp) from Moran’s eigenvector maps on one dimension (T ). Increasing values of Etemp increase climate change frequency. The amplitude of temporal climate change is controlled by multiplying a parameter A to standardized eigenvector values; an increase in A thus results in an increased climatic amplitude. The climate structure over geographical space remains stable over time; at each time step, every pixel increases or decreases of the same value in temperature.

Reproduction, death, migration and selection

Populations of n individuals are simulated. For simplicity, the size of populations is kept constant through time. This means that there are no population expansions, but also that there are no population extinctions. More complex demographic models could eventually be integrated in this simulation framework. However, these conditions represent a good starting point to understand the evolution of generalization. The ﬁtness of each individual

70 Chapitre 4 4.3. Material and Methods over the range of climate values follows a normal distribution determined by two traits, named optimum and tolerance. The optimum is the climate value for which individual fitness is maximal and thus represents the mean of the fitness normal distribution. The tolerance is a measure of individual degree of generalization and determines the standard deviation of the fitness distribution. Thus the fitness of each individual over climate values follows a normal distribution with mean = optimum and standard deviation = tolerance (Figure 4.1). For each species present at time t, n couples are randomly sampled and give birth to d individuals. Individuals are hermaphrodites and each one can reproduce more than once every generation. Generations are non-overlapping, which means that all individuals die at the end of the generation and are replaced by the descendants. The optimum and tolerance traits of descendants are sampled from a normal distribution with mean being the mean of the parental traits (either optima or tolerance) and standard deviation being the standard deviation of the trait in the parental generation (sdfit or sdtol) plus a minimal threshold for the tolerance (mintol). This mimics a quantitative genetic model where the descendants are assumed to be intermediate between their parents on average but with a standard deviation parameter (sdfit or sdtol). The mintol was added to prevent individuals from becoming completely specialized; it could be interpreted as new variation entering the population due to mutation. In each couple the first parent is considered the female and the descendants migrate from the female parent according to a 2D Poisson distribution with mean and variance equal to λ. To maintain a constant population size, n × d − n individuals die before the next reproduction step. The raw survival probability of each individual Ri is determined by its fitness value at its geographical position. This is obtained from the probability density function with a mean equal to the individual’s optimum and standard deviation equal to its tolerance at the environmental value that corresponds to its geographic position. The relative survival probability of each individual (Pi) is then estimated by considering a scaling parameter σ: R − R R − R P i = i min + med i (4.1) Rmax − Rmin σ where Rmin (resp. Rmax) is the minimal (resp. maximal) raw survival probability among every descendants, and Rmed is the median of the raw survival probabilities of every indi-

71 Chapitre 4 4.3. Material and Methods

a c

selec�on toward specialisa�on climate fitness

time climate

b d

selec�on toward generalisa�on fitness cliamte

time climate

Figure 4.1 – Effect of fixed (a) and variable (b) climate over time on species fitness curve. When climate is stable, species become specialised (c) while they should become generalized when climate is variable (d). viduals. This enables to limit survival probabilities, keeping selection intensity constant over generations (Figure 4.2). When σ=1 all individuals have the same Pi and selection is replaced by drift. Note that there is no interspecific selection. Then, n individuals are sampled from the n × d descendants according to their Pi value.

Speciation

At each generation, each species has a probability s to give birth to a new species. When speciation occurs, the individuals of the species are all duplicated to give rise to two identical daughter species. We chose this procedure to correspond to the implicit evolution of traits assumed in common evolution models (such as BM or OU).

72 Chapitre 4 4.3. Material and Methods 0.8 1.0

survival probability raw survival probability σ =5 σ =2 σ =1.5 σ =1 0.0 0.2 0.4 0.6 individuals

Figure 4.2 – Corrected survival probability for diﬀerent scaling parameter (σ) values.

4.3.2 Temporal variation frequency vs. amplitude eﬀect on generalization (single species focus)

Because both frequency and amplitude of temporal climatic variation can impact species adaptation, we wanted to test both effects separately. We started with two species and allowed no speciation (s=0), evaluating different evolutionary rate values for optimum and tolerance traits (sdfit= 0.000005, 0.00001, 0.00005, 0.0001, 0.0005 and sdtol= 0.000005, 0.00001, 0.00005, 0.0001, 0.0005). We ran simulations for fixed and variable climate. For the variable climate, we evaluated four values for frequency (Etemp = 4, 10, 20, 60) and four values for amplitude (A= 0.005, 0.01, 0.02, 0.03). We ran 50 simulations for each parameter combination (Table IV.I), with 2 species evolving independently over 10000 generations. To estimate the effect on generalization, we measured the final intrinsic tolerance.

73 Chapitre 4 4.4. Results

4.3.3 Detection of PNC in ﬁxed and variable environment contexts (multispecies focus)

We then wanted to test whether adaptation to oscillating climate through increased generalization could lead to PNC. For this, we ran simulations with the speciation probability s set to 0.00039 as it generates a mean number of species of 100 with a birth-only model over 10000 generations. Evolutionary rates were set at intermediate values, sdfit= 0.00005 and sdtol= 0.00001, as these intermediate values are representative of the results obtained with all parameter combinations (see results). The parameters values for climate variability were the same as in section 4.3.2. We ran 100 simulations for each parameter value combinations (Table IV.I). We then extracted optimum and tolerance means for each species at ﬁnal step (time = 10000) to be ﬁtted to evolution models. As it is often argued that the way we generally measure niche optimum and tolerance values - according to geographic position of individuals - may bias interpretations of niche evolution (Grand- colas et al., 2011; Boucher et al., 2014), we chose to also use geographical measures and compare analyses with the intrinsic traits values. Fit of BM and OU were compared with ∆AICc values.

4.4 Results

4.4.1 Temporal variation frequency vs. amplitude eﬀect on generalization

Both amplitude and frequency of temporal heterogeneity of the environment had significant positive eﬀects on species tolerance, regardless of the sdtol and sdfit values (linear models for amplitude: R2 from 0.35 to 0.93, p-value<1.5 e-25; for frequency R2 from

0.05 to 0.90, p-value<8.1e-05 non-significant for three sdfit–sdtol combinations) (see Fig- ure 4.3a for amplitude and 4.3b for frequency). The interaction between amplitude and frequency was also significant (linear model, p-value<2e-16 for both amplitude, frequency and interaction), with the difference of final tolerance with increasing frequency becom-

74 Chapitre 4 4.4. Results

Table IV.I – Summary of model parameters and values used for simulations

parameter deﬁnition single species simu- mutlispecies simu- name lations lations λ λ parameter of a 2D poisson distribution 0.5 0.5 p width of the gridded geographical space, com- 100 100 posed of p ∗ p pixels T number of time steps 10000 10000

Espat number of eigenvalues kept to model the vari- 10 10 ation of the environmental variable in the geographical space

Etemp number of the eigenvalue kept to model the 4/10/20/60 4/10/20/60 variation of the environmental variable in time A amplitude of cyclic variation 0.005/0.01/0.02/0.03 0.005/0.01/0.02/0.03 n number of individuals for each species 50 50 d number of descendants each couple produce at 2 2 each generation

sdfit standard deviation for sampling ﬁtness trait 5e-06/1e-05/5e- 5e-05 of descendants 05/1e-04/5e-04

sdtol standard deviation for sampling tolerance 5e-06/1e-05/5e- 1e-05 trait of descendants 05/1e-04/5e-04 σ scaling parameter of survival probability 1.5 1.5 s probability for each species to speciate be- 0 0.00039 tween time t and t+1

75 Chapitre 4 4.4. Results

a 0.015 5e‐06 1e‐05 5e‐05 1e‐04 5e‐04

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● 5e‐06 0 0.005 0.01 0.02 0.03 0 0.005 0.01 0.02 0.03 0 0.005 0.01 0.02 0.03 0 0.005 0.01 0.02 0.03 0 0.005 0.01 0.02 0.03 amplitude ● 1e‐05 b ● 5e‐05 ● ● 1e‐04 ● ● ● ● ● ● ● ● ● ●●●● ● ●● ●● ●●●● ●● ● 0.0100 ●●●●● ●●●● ● ●●●●●●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ● ● ● ●● ●● ● ● ●●● ● ●●●●● ● ● ● ● ● ●● ● ●● ●● ● ● ●● ● ● ●●● ● ●●●●●● ●● ●●●●● ● ● ● ● 5e‐04 ● ● ● ● ●●● ● ●●● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ● ● ●● ● ● ● ● ●●●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ●●●●●● ●● ●● ● ● ● ●● ●● ●●●● ●● ● ●●●●●● ●●●● ●●● ●●● ●●● ● ● ●●●●● ●●● ●●●● ● ● ● ● ●●● ● ●●● ● ●●● ● ● ● ●●● ● ● ●●● ●●● ● ●●●●●● ● ●●● ● ●● ●● ●●● ● ● ●●●●●●● ●●●● ● ● ●●●●●● ●●● ● ●●● ● ●● ●●●● ● ● ●● ● ● ● ● ●●● ●● ●● ● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ●●● ●●● ● ● ●●●● ● ●● ●●●● ●●●●●●●● ●● ● ●● ● ● ●● ● ● ●●●●●● ●● ● ●●●●● ● ● ●● ● ● ● ● ● ● ●●●● ●●● ●●● ● ● ● ●●●●● ●● ● ● ● ● ● ● ●● ●● ● ●● ● ●● ● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ●● ●● ●● ● ● ● ●● ●● ●● ● ● ●● ● ● ●● ● ●●●●● ● ●●●● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ●●●●● ●● ●● ● ●● ●● ●● ● ●●●●● ●●●● ●●●● ● ● ●●●●● ● ●●●●●●● ●●● ●●●●● ●● ●● ● ● ●●●●● ●●●● ● ●● ● ●●● ● ●● ●●● ●●●●●●●●● ●● ●●● ●●● ● ●●●●●●● ●●●●● ● ● ●●●●●●● ●● ●●●●● ● ●●●●●●●● ●● ● ● ● ●●● ●● ● ● ●● ● ●●●● ● ● ●●● ●● ●●● ● ● ●●● ●●●● ● ●●●● ● ● ●● ●●● ●● ● ●●● ●●● ● ●●● ● ●● ●●● ●●● ● 0.0025 ●●●● ● ●● ●●● ●●● ● ●●●●●● ●●● ●● ●●● ● ●●●● ● ● ● ●● ●●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ●●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ●●●●●● ●●●●● ●●●●● ●●●●●●●●● ●●●●● ●● ● ●●● ●●● ●● ● ●●●●●●●●●●●●● ●●●●●●● ●● ●● ● ●●●● ● ●● ●●●●●● ●●●●●●● ●●●●●●●● ●●● ● ●●● ●●●●●●●●●●● ● ●●●●●● ●●● ● ●●● ●● ●●●●●●●●●●●● ●●● ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●● ●● ●●●●●●●●● ●●●● ●●●●●●●●●●●● ●●●●●●●● ●●●●●●●●●●●●●●● ●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●● ● ●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●● ●●●●●●●●●●●●●●●●● ● ● ●●●● ●●●●●●●●● ●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●● ●●● ●●● ●●

0 4 10 20 60 0 4 10 20 60 0 4 10 20 60 0 4 10 20 60 0 4 10 20 60 frequency

Figure 4.3 – Eﬀect of amplitude (a) and frequency (b) of climate temporal variation on single species tolerance after 10000 generations.

Colors represent diﬀerent values of sdtol and facets correspond to

diﬀerent values of sdfit. ing less pronounced for high amplitude values (negative interaction: estimate= -2.1e-03, Figure 4.4).

4.4.2 Detection of PNC in ﬁxed and variable environment contexts

We fitted evolution models on niche optimum and niche tolerance, for intrinsic and measured traits. In a fixed environment, after 10000 generations, BM is not systematically the best-fitting model (Figure 4.5). Over 100 simulations, the 95% interval of ∆AICc values

(AICcBM − AICcOU ) obtained over the 100 simulations includes 0. When climate varies temporally, OU is the best ﬁtting model only when both amplitude and frequency are high, and when niche optimum is measured with the geographical positions of individuals. For

76 Chapitre 4 4.4. Results

0.008

frequency 4 0.006 10

final tolerance 20

0.004

0.002

0.005 0.01 0.02 0.03 amplitude

Figure 4.4 – Eﬀect of interacting amplitude and frequency on species ﬁnal tolerance. the “true” optimum value, there is no clear distinction between OU and BM; the pattern is the same for the intrinsic tolerance.

4.4.3 Bias in ancestral trait reconstruction

Fitting of evolution models assumes a given model of evolution along the phylogeny, with ancestral values being always intermediate between descendant values, and evolution being globally linear. When the environment is variable and species can adapt rapidly by changing their optimal value (i.e. with increasing sdfit), phenotypic evolution depart from linearity. Thus, the assumed ancestral values of evolution models depart from the truth with increasing amplitude and frequency of climate variation as well as with sdfit (Figure 4.6), while under a ﬁxed environment, real and assumed trait evolution are quite similar (Figure 4.7).

77 Chapitre 4 4.4. Results

a b 400

300

25 200 Δ AICc BM ‐ OU 100 0

0 0 0 0.005 0.01 0.02 0.03 0 0.005 0.01 0.02 0.03 4 10 500 c 600 d 20 60 frequency

400

400 300

200 200

Δ AICc BM ‐ OU 100

0 0

0 0.005 0.01 0.02 0.03 0 0.005 0.01 0.02 0.03 amplitude amplitude

Figure 4.5 – ∆AICc of evolution models ﬁtted over intrinsic (a) and measured (b) optimum and for intrinsic (c) and measured (d) tolerance, for diﬀerent amplitude and frequency of climate variability. Positive values of ∆AICc favours OU model

0.020 0.020 a b

0.015 0.015 sdﬁt

5e‐06 0.010 0.010 1e‐05 5e‐05 1e‐04 5e‐04 0.005 0.005

0.000 0.000 true and between distance value trait ancestral es � mated 0 0.005 0.01 0.02 0.03 0 4 10 20 60 amplitude frequency

Figure 4.6 – Distance between true and assumed ancestral value for increasing values of amplitude (a) and frequency (b) of climate

variation and for diﬀerent values of sdfit (colors).

78 Chapitre 4 4.4. Results species 9 species 25 species 26 species 1 species 8 species 13 species 14 species 7 species 21 species 22 species 33 species 34 species 6 species 2 species 4 species 41 species 42 species 39 species 19 species 46 species 47 species 12 species 10 species 5 species 35 species 36 species 18 species 27 species 28 species 11 species 23 species 24 species 37 species 40 species 44 species 45 species 43 species 38 species 20 species 15 species 17 species 29 species 30 species 31 species 32 species 16 species 3

Assumed trait evolution True trait evolution

+ 0.003

Fixed environment trait value trait length=4999.5

- 0.002

Variable environment

Figure 4.7 – Phenotypic trait evolution, as assumed in evolution models and as it evolved in the simulations, under temporally variable environment. colors over the branches represent mean optimum value for each species at each generation with a gradient from red (low values) to

blue (high values). Parameters were set as follows: sdfit = 5e − 05,

sdtol = 1e−05 and for variable environment Etemp = 20 and A = 0.03 79 Chapitre 4 4.5. Discussion

4.5 Discussion

Understanding the impact of varying climate on macro-evolutionary patterns on species ecological niche is important to understand and perhaps predict the possible reactions of species under the current global changes. While multiple studies highlighted the effect of climate heterogeneity on species traits (i.e. generalization or migration, Walther et al., 2002), or communities structure (Ortega-Rosas et al., 2008), none investigated whether climatic fluctuation could have effects on macro-evolutionary patterns such as PNC. For instance, if climatic fluctuations result in an increase in generalization (or plasticity), then one might expect that long-term climatic variation could result in patterns of phylogenetic niche conservatism. If this were the case, it would allow the study of past adaptation of species to climate change in groups that have been affected by climatic variation as those groups would be characterised by stasis of their optimum fit to the environment value. This could nicely complement the experimental approaches that study the impact of climate on species (reviewed in Merilä and Hendry, 2014). While our study showed a tendency toward more niche conservatism with increasing frequency and amplitude of climate change, these results were not significant. We discuss these results and potential explanations below.

4.5.1 Evolution of generalization

Our simulation framework confirmed that both amplitude and frequency result in species generalization, supporting the findings of previous studies (Dalsgaard et al., 2011; Quintero and Wiens, 2013). Studies on plasticity showed also that the increase of plasticity evolves with an increase of spatial and temporal environmental variation, as well as with dispersion and genetic variation for plasticity (reviewed in Hendry, 2016). As tolerance curves are related to plasticity and the slope of reaction norms (Chevin and Lande, 2010), we consider that generalisation and plasticity are linked. Experimental studies showed that both frequency and amplitude of climate change had effect on insect reproduction (Banfield- Zanin and Leather, 2015). Interestingly, in our study, we found an interaction between frequency and amplitude, with the effect of frequency becoming less important when

80 Chapitre 4 4.5. Discussion amplitude increases (Figure 4.4). This interaction might be important to take into account, as it is known that current climate changes involves changes in amplitude as well as in frequency of climatic events (Meehl et al., 2000; Cai et al., 2014). While our study shows selection toward generalization under climate oscillations, the generalization degree remains limited by physical and genetic constraints (but see Berger et al., 2014; Murren et al., 2015), rendering other species capabilities (such as migration) important to survive climate change. With our model, when we set the evolutionary rates for tolerance (sdtol) too low, generalization could not evolve quickly enough to follow the environment variability. In such a context, under real conditions (i.e with interspecific competition and population size fluctuation), this kind of constraint on tolerance could lead to extinction, which was not allowed in the present simulations. In our simulations, even when generalization degree was quite important, individuals mi- grated between the warmer and cooler places, following temporal climate variation, even when they became generalists (supplementary animation Figure S3.1 available at http: //www.plantevolution.org/media/simul_env_HermineAlexandre.html). This can be illustrated by migration in glacial refugia during pleistocene ice ages (Ravazzi, 2002). We used fixed values for migration parameter, but we acknowledge that migration might act concomitantly with adaptation to allow species to escape changing environment, that is multiple species traits might be important to escape climate change. Accordingly, Büchi and Vuilleumier (2016) showed a relationship among the evolution of multiple life history traits (i.e. specialisation, reproduction, dispersion) under perturbed environments.

4.5.2 PNC detection under climate variation

Under variable climate conditions, OU was not significantly better than BM, as the 95% ∆AICc comprised 0. But as amplitude and frequency increased, the mean difference in fit between both models increased, suggesting that there is a tendency for more stable ecological niches of species across the phylogeny with a more variable climate. When climate is fixed over time, our expectation was that BM would be the best fitting model. However, OU was often favoured, which could be due to the bounded nature

81 Chapitre 4 4.5. Discussion of the environment (Boucher et al., 2014). Nevertheless, these authors systematically detected OU as the best model, which is not the case in our analyses. We suggest that the difference between both studies may rely on the non-inclusion of competition processes in our model and to the different speciation modes between studies. While their model is more biologically relevant in terms of ecological processes, ours was designed to fit more closely evolutionary models in order to be able to ignore these processes as potential factors in the results. We think that when taking into account climate evolution through time and species adaptation capacities, the limits of such kinds of models are reached. Indeed, evolutionary models rely on strong assumptions, particularly the fact that ancestral values are intermediate between descendent values and that the expected trends in phenotypic evolution is linear. We demonstrated that when evolution is non-linear, ancestral values (at the nodes of the phylogeny or along the branches) can be more extreme than that of their descendents (illustrated in Figure 4.7). We also showed that real ancestral values depart increasingly from estimated values with increasing climate variability and optimum evolutionary rates. Our model is clearly unrealistic in its extreme values, as trait genetic evolution is limited by time and physical possibilities. Indeed, under too rapid climate change, species should become extinct, because they will lack time to adapt (Lindsey et al., 2013). Also, they cannot evolve indefinitely toward higher (or lower) trait values, because evolution is contingent on genetic variability (Bradshaw, 1991) and traits interactions (Etterson and Shaw, 2001). Finally, in our model, the evolution of tolerance and optimum were independent, while it has been shown that with increasing plasticity, genetic adaptation to evolving climate is slowed down (Nunney, 2016). Nevertheless, as explained above, we think that our simple model represented a good starting point to understand the relationship between climatic oscillations and patterns of niche conservatism at macro- evolutionary scales, in part because the assumptions were set to be close to those of the evolutionary models fitted.

82 Chapitre 4 4.5. Discussion

4.5.3 Intrinsic or measured traits?

The results of phylogenetic model fitting differed when we used the real trait value (anal- ogous to a physiological trait) and when we estimated this trait from presence data. This result is both surprising and quite problematic. Indeed, it confirms that the present distribution of species is more reflective of a lack of opportunities (absence of ideal conditions in the landscape, not sufficiently rapid adaptation, limited migration, etc.) than of a per- fect overlap between individual traits and their habitats. Thus, our results support the warning previously mentioned by other studies, that is studying PNC from presence data is a hard and risky task (Grandcolas et al., 2011; Boucher et al., 2014).

4.5.4 Conclusion

In this study, we demonstrated that temporal climate variation can influence species generalization by inducing macro-evolutionary patterns similar to that of PNC. However, these patterns are not significant unless both climatic oscillations amplitude and frequency are important. In addition, we showed that the assumptions of evolution models as BM and OU are violated when species can adapt to a temporally variable environment through time. We thus recommend using them with caution in such situations. Using fossil data in ancestral trait reconstruction could be useful to test for a correlation between true and assumed ancestral value before testing evolution hypotheses with models fitting. Also, developing new evolution models, taking account of ancestral values uncertainties and allowing ancestral states to be more extreme than present states would permit to obtain more accurate conclusions.

83 5 Conclusion

5.1 Résumé des principaux résultats

En se basant sur l’étude d’un groupe de plantes des Antilles présentant une forte diver- sité spécifique et une grande variabilité de traits floraux, l’objectif de la thèse était de caractériser les conditions écologiques et génétiques des transitions entre spécialistes et généralistes vis à vis des caractéristiques biotiques et abiotiques constitutives de la niche écologique. La première étude (chapitre 2) a permis de déterminer les bases génétiques im- pliquées dans les changements phénotypiques associés à une transition de mode de pollinisation (pollinisation mixte vers pollinisation spécialiste des colibris). Cette étude a montré que, si le changement de couleur semble être associé à un seul locus, les changements morphologiques eux, semblent beaucoup plus complexes et sous-tendus par plusieurs zones génomiques distinctes. Cependant, nous n’avons pas pu déterminer clairement les loci responsables du volume de nectar produit, probablement en raison d’une forte plasticité phénotypique, avec une production plus importante dans des conditions plus humides. En effet, les conditions de culture des plantes qui avaient différentes tailles et donc différents besoins en eau n’ont pas permis de récolter des données suffisamment précises pour bien élucider les bases génétiques de ce trait. Parallèlement (chapitre 3), nous avons vu que les changements au niveau biotique (changement de pollinisateurs ou incorporation de nouveaux pollinisateurs dans la niche) sont indépendants de l’évolution des caractéristiques

84 Chapitre 5 5.2. Discussion et perspectives abiotiques de la niche (habitat). Plus spécifiquement, les plantes spécialisées pour un groupe fonctionnel de pollinisateur ne possèdent pas des niches écologiques plus similaires à ces pollinisateurs qu’aux pollinisateurs de l’autre groupe fonctionnel. Nous avons aussi montré que ceci pouvait résulter du fait que les colibris et les chauves-souris ont des niches écologiques similaires sur l’ensemble de la répartition géographique des espèces de Gesneria et Rhytidophyllum. De plus, cette étude a montré que le groupe Gesneria-Rhytidophyllum semble caractérisé par un conservatisme phylogénétique de niche, c’est à dire que les es- pèces se retrouvent dans des endroits plus similaires que ce qui pourrait être purement prédit par leurs relations phylogénétiques. Au vu des résultats de cette étude, nous nous sommes demandé dans un dernier temps quelles caractéristiques de l’environnement pouvaient expliquer un tel patron de PNC. Dans la dernière étude (chapitre 4), nous avons donc exploré de quelle façon les variations environnementales pouvaient affecter la niche des espèces et comment ces patrons se reflétaient dans un contexte macro-évolutif. Les ré- sultats ont montré que l’hétérogénéité temporelle de l’environnement est associée à un plus haut niveau de généralisation que dans un milieu fixe. Dans un contexte évolutif, les résul- tats, bien que généralement non significatifs, suggèrent que plus le niveau d’hétérogénéité temporelle est fort, plus le signal en faveur d’un conservatisme phylogénétique de niche est important.

5.2 Discussion et perspectives

Cette thèse présente une première cartographie des traits floraux impliqués dans la pollinisation chez les Gesneriaceae. Ce premier aperçu des bases génétiques des traits floraux est particulièrement important pour cette famille qui présente une très forte variabilité de morphologies florales (Harrison et al., 1999; Roalson et al., 2002, 2003; Perret et al., 2007; Serrano-Serrano et al., 2015). Chez Rhytidophyllum, la forme de la corolle semble avoir une base génétique relativement complexe, avec plusieurs QTLs sur des groupes de liaison distincts. Ce résultat est assez similaire aux bases génétiques de la forme de la fleur chez d’autres familles, par exemple chez Penstemon (Plantaginaceae), où plusieurs

85 Chapitre 5 5.2. Discussion et perspectives

QTLs sont impliqués dans les changements de formes associés à la transition entre les syndromes abeille et colibris (Wessinger et al., 2014). Le même patron a aussi été observé entre Petunia axillaris et P. integrifolia (Solanaceae), même si leurs modes de pollinisation – papillons de nuit et abeilles, respectivement – sont plus éloignés de notre système d’étude (Galliot et al., 2006a). Par ailleurs, CYCLOIDEA, un gène dont l’expression est généralement impliquée dans la symétrie bilatérale et l’asymétrie dorso-ventrale des fleurs (Feng et al., 2006; Preston et al., 2009) se situe dans la région de confiance d’un QTL associé à la forme de la corolle (Figure 2.6). Il est possible qu’une mutation affectant l’expression de ce gène soit responsable de changements morphologiques. D’autant plus que des changements d’expression de ce gène sont probablement impliqués dans l’évolution vers la pollinisation par les oiseaux chez d’autres espèces (Kay and Sargent, 2009). En revanche, un autre trait constituant le syndrome de pollinisation – la couleur de la corolle – a clairement une base génétique simple, avec un seul QTL (Figure 2.6). Ce résul- tat correspond lui aussi au patron généralement trouvé dans ce type d’études (Wessinger et al., 2014; Bradshaw and Schemske, 2003). Une étude par approche gène candidat a été entamée, pour caractériser le gène responsable du changement de couleur, avec l’aide de deux étudiants-stagiaires, Pierre-Antoine Bourdon et Louna Fronteau. Les résultats préliminaires suggèrent qu’un facteur de transcription MYB-R2R3 soit à l’origine de la perte de couleur rouge chez R. auriculatum. Si ce résultat est confirmé, notamment avec des mesures d’expression au niveau des transcrits régulés par ce facteur de transcription, ce serait intéressant puisque ce gène est celui qui est le plus fréquemment impliqués dans les changements de couleurs des fleurs chez les espèces ou la base génétique de ce trait a été caractérisé (notamment dans les genres Mimulus, Ipomoea, Antirrhinum et Petunia, Sobel and Streisfeld, 2013). Parallèlement, les transcriptomes de fleurs de trois espèces ont été séquencés : R. rupincola, R. auriculatum et R. vernicosum (cette troisième espèce ayant une stratégie de pollinisation mixte, comme R. auriculatum). L’obtention de ces transcriptomes a permis de débuter une étude, réalisée par Delase Amesefe lors de sont stage de Master I en bioinformatique, pour identifier des gènes candidats qui auraient pu être impliqués dans

86 Chapitre 5 5.2. Discussion et perspectives les changement de forme florale entre les espèces. Ces candidats devront aussi être validés, puis placés sur la carte génétique et comparés aux QTLs. Tout ceci nous permettra de mieux comprendre les mécanismes de régulation géniques dont dépendent les phénotypes floraux. Dans la deuxième partie de ma thèse (chapitre 3), nous avons pu établir un patron de conservation phylogénétique de niche (PNC) pour l’évolution des niches bioclimatique des plantes. Ceci a en outre été rendu possible grâce à une amélioration considérable des hypothèses phylogénétiques du groupe Gesneria-Rhytidophyllum. En comparaison aux études précédentes, notamment celle de Martén-Rodríguez et al. (2010), la phylogénie effectuée dans cette thèse comprend plusieurs nouvelles espèces et est basée sur quatre gènes nucléaires additionnels. Ce patron de conservation de niche phylogénétique peut nous mener vers de nouveaux questionnements. Par exemple en reprenant l’idée formulée par Wiens (2004) nous pouvons poser l’hypothèse que le PNC est un processus à l’origine de la diversification spécifique au sein du genre. Cette hypothèse pourrait être testée en mesurant la corrélation entre le recouvrement des niches bioclimatiques et le recouvrement des aires géographiques : une corrélation négative irait dans le sens de cette hypothèse. En effet, l’idée de Wiens est que si l’environnement est écologiquement hétérogène, les populations d’une même espèce, contraintes par le PNC seront isolées dans des zones écologiquement semblables et éloignées géographiquement, ce qui les conduira à diverger par spéciation allopatrique. De plus, nous avons montré qu’il n’y a pas de lien direct entre la niche abiotique (climatique) et la niche biotique (groupe fonctionnel de pollinisateurs) chez Gesneria et Rhytidophyllum. Ceci montre que la taille de la niche bioclimatique réalisée n’est pas contrainte par le type de pollinisation (spécaliste ou généraliste, Figure 1.2) et donc infirme notre hypothèse de départ. Plusieurs hypothèses peuvent expliquer ce résultat. D’abord, les espèces de plantes pourraient être spécialisées pour certaines espèces de pollinisateurs et non pas un groupe fonctionnel au complet. Par exemple, comme la plupart des espèces chez Gesneria et Rhytidophyllum ne se retrouvent que dans une seule île (sauf quand elles sont dans les petites Antilles) (Martén-Rodríguez et al., 2010), elles ne peuvent pas être associées à toutes les espèces de colibris ou de chauves-

87 Chapitre 5 5.2. Discussion et perspectives souris, qui elles non plus ne sont pas présentes partout. Ceci pourrait aussi expliquer pourquoi nos résultats sont contradictoires avec ceux de Litsios et al. (2014) qui montraient une corrélation négative entre la spécialisation au niveau biotique et abiotique chez les poissons clowns. Par ailleurs, une étude semblable à la notre, sur d’autres Gesneriaceae, a aussi montré une indépendance entre l’évolution des modes de pollinisation et l’évolution des préférences climatiques; l’évolution des préférences climatiques étant influencée par la localisation dans des biomes différents (Serrano-Serrano et al., 2015). Dans leur étude, les facteurs géographiques ont donc plus d’impact sur l’évolution des niches bioclimatiques que les interactions avec les pollinisateurs. Notre modèle d’étude présente un aspect géographique intéressant que nous n’avons pas étudié au cours de cette thèse à savoir l’isolement dans des îles différentes. Il serait donc possible de tester si la localisation dans des îles différentes a une influence sur l’évolution des niches bioclimatiques chez Gesneria et Rhytidophyllum. Cependant, contrairement à l’étude de Serrano-Serrano et al. (2015), dans notre cas il existe un fort recouvrement des îles dans l’espace environnemental. Aussi, il semble que les deux groupes fonctionnels de pollinisateurs aient des niches bioclimatiques largement chevauchantes (Figure 3.2a). Ainsi la niche bioclimatique de l’ensemble des pollinisateurs (colibris et chauves-souris) n’est pas plus grande que celle d’un seul groupe fonctionnel (particulièrement pour les colibris). Ceci pourrait expliquer que généraliser son mode de pollinisation, pour une plante, ne permette pas d’augmenter la taille de sa niche réalisée. Au vu de ces résultats, il semble intéressant de poursuivre les travaux débutés dans cette thèse en étudiant l’impact de l’isolement géographique dans des îles distinctes sur l’évolution des niches biotiques (mode de pollinisation), mais surtout sur la diversification spécifique des plantes. Ceci pourra être effectué grâce au géoréférencement d’un grand nombre de spécimens qui a été effectué durant ma thèse. Parallèlement, il serait intéres- sant d’étudier plus en détail les relations entre les plantes et leurs pollinisateurs. En effet, tout au long de ma thèse j’ai considéré que les espèces englobées dans les trois mode de pollinisation (à savoir spécialiste des colibris, spécialiste de chauve-souris, et à stratégie mixte) représentaient des groupes uniformes, mais deux espèces partageant un même mode

88 Chapitre 5 5.2. Discussion et perspectives de pollinisation (par exemple toutes deux pollinisées par les colibris), même si elles sont en sympatrie, peuvent être en fait reproductivement isolée si elles sont uniquement pollinisées par des espèces de colibris différentes. De plus, comme nous l’avons étudié dans le dernier chapitre, les variations temporelles de l’environnement ont des influences sur l’évolution des traits. Ainsi, étudier la dynamique temporelle des populations de chauve-souris et de colibris permettrait de comprendre les conditions d’émergence de modes de pollinisation généralistes chez les Gesneriaceae antillaises. C’est-à-dire quelle est la proportion de la pollinisation qui est effectuée par des colibris ou par des chauves-souris chez les espèces généralistes ; est-ce que cette proportion varie dans le temps, de façon concomitante à des évènements climatiques ; est-ce que cette proportion varie entre les espèces de plantes et est-ce que cette variation a un impact sur les traits floraux (et sur le degré d’intégration des traits floraux), comme c’est le cas chez d’autres plantes généralistes du genre Erysi- mum (Gomez et al., 2008, 2014b). Ceci pourrait permettre de comprendre, au niveau populationnel le lien entre les changements individuels (changements génétiques associés à des changements de traits), la stochasticité environnementale (variation spatio-temporelle des conditions biotiques et abiotiques) et les patrons macro-évolutifs. Les résultats du troisième chapitre ont permis de poser l’hypothèse selon laquelle l’adaptation à un climat temporellement variable pourrait causer un patron de PNC. Cette hypothèse a été testée dans le chapitre 4, mais n’a pu être vérifiée qu’en partie. En effet, même si les résultats montrent une tendance dans le sens de cette hypothèse, ils ne sont pas significatifs. Quoiqu’il en soit, cette troisième étude a permis de montrer que l’évolution des niches dans un contexte d’environnement variable dans le temps ne correspondent pas nécessairement au mode d’évolution implicite assumé dans les modèles type BM et OU. Cette faiblesse des modèles d’évolution classiques devrait nous pousser à développer de nouveaux modèles dans lesquels notamment, les valeurs de trait ancestrales ne soient pas systématiquement intermédiaires entre les valeurs actuelles. Cette thèse a permis de comprendre en partie les causes génétiques et environnementales de l’évolution de la généralisation biotique et abiotique. En plus de continuer les études amorcées dans cette thèse, il semble que la prochaine étape devrait être de com-

89 Chapitre 5 5.2. Discussion et perspectives prendre comment l’évolution des syndromes de pollinisation et des niches écologiques ont pu jouer un rôle dans la forte diversiﬁcation spéciﬁque qu’ont subi les genres Gesneria et Rhytidophyllum.

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113 A Recombination fraction and LOD scores for each pair of markers

Markers are in the same order as in the linkage map of Figure 2.6; LOD scores are in the upper triangle and recombination fraction in the lower one. Colours represent a gradient from low LOD score and great recombination fraction (blue) to large LOD scores and small recombination fraction (red).

i B Proﬁles of LOD scores for linkage groups with QTLs detected

Colour Nectar volume Corolla curvature

Corolla tube opening LG1 Corolla tube opening LG16 Pleiotropy

Hybrids PC1 Hybrids PC2 Hybrids PC3

Genus PC1 LG1 Genus PC1 LG11 Genus PC1 LG14

Parents PC1 LG11 Parents PC1 LG13 Parents PC1 LG14

Abscises are marker position along the linkage group. Red lines are detection threshold corresponding to a type 1 error of 5%, and green rectangles correspond to the 2-LOD conﬁdence region.

ii C Information about species used for the PCA performed on the genus

H, hummingbird specialist; G, generalist; (+1) correspond to parental individuals of the hybrid population but were not used to do the PCA. species number of individuals pollination type G. acaulis 3 H G. citrina 2 H G. cuneifolia 12 H G. pedicellaris 6 H G. pulverulenta 2 H G. reticulata 3 H G. ventricosa 4 H G. viridiﬂora 9 G R. auriculatum 5 (+1) G R. berteroanum 3 H R. bicolor 2 G R. crenulatum 4 G R. exsertum 11 G R. grandiﬂorum 3 G R. leucomallon 1 G R. rupincola 5 (+1) H R. tomentosum 3 G R. vernicosum 1 G

iii D Details on the species studied

H=Hispaniola, C=Cuba, J= Jamaica, PR= Porto Rico, LA= Lesser Antilles. The last four columns represent niche size calculated over the first two principal components. Sd1= standard deviation over the first axis. Sd2= standard deviation over the second axis. Min-max1= maximum distance between two individuals on the first axis. Min- max2= maximum distance between two individuals on the second axis, b.s= bat specialist, h.s= hummingbird specialist, m.p= mixed pollination, n.a= non-available.

iv species nb nb pixels islands group pollination sd1 sd2 min- min- points mode max1 max2 Erophylla bombifrons 8 8 H,PR bat - 1,795 0,943 4,428 2,546 Erophylla sezerkoni 81 67 C,H,J,PR bat - 3,053 1,945 13,874 9,788 Glossophaga longirostris 29 11 H,LA bat 2,617 1,986 10,625 7,073 Glossophaga soricina 20 18 J bat - 2,587 1,581 8,850 6,617 Monophyllus plethodon 16 12 LA bat - 2,751 0,924 9,131 3,035 Monophyllus redmani 112 86 C,H,J,PR bat - 3,416 2,314 21,838 15,498 Phyllonecteris aphylla 9 9 J bat - 2,020 0,885 5,629 2,215 Phyllonycteris poeyi 29 25 C,H bat 1,322 2,262 4,651 7,402 2,445 1,605 9,878 6,772 Anthracothorax dominicus 1141 308 H,PR hummingbird - 3,705 2,763 23,997 14,163 v Anthracothorax mango 295 96 J hummingbird - 3,002 1,493 12,764 7,109 Anthracothorax viridis 328 93 PR hummingbird - 2,821 1,386 12,529 4,743 Archilocus colubris 9 9 PR,H,C hummingbird - 1,587 2,171 5,029 5,716 Chlorostilbon maugaeus 448 100 PR hummingbird - 2,868 1,391 12,896 4,734 Chlorostilbon ricordii 922 223 C hummingbird - 1,853 1,304 10,662 10,039 Chlorostilbon swainsonii 238 103 H hummingbird - 5,029 3,873 23,561 13,876 Cyanophaia bicolor 61 20 H,LA hummingbird - 3,134 3,016 9,162 15,218 Eulampis holosericeus 841 164 LA,PR hummingbird - 2,986 1,580 13,771 8,954 Eulampis jugularis 525 102 H,LA hummingbird - 3,194 1,647 13,420 15,218 Mellisuga helenae 139 53 C hummingbird - 1,254 1,487 8,570 7,595 Mellisuga minima 723 265 H,J hummingbird - 3,645 2,894 20,071 16,091 Orthorhyncus cristatus 1238 175 H,LA,PR hummingbird - 2,905 1,538 13,528 8,946 Trochilus polytmus 523 114 J hummingbird - 2,941 1,437 12,309 6,997 2,923 1,999 13,734 9,957 species nb nb pixels islands group pollination sd1 sd2 min- min- points mode max1 max2 G. calycosa 11 10 J plant b.s 1,726 1,002 5,439 3,220 G. fruticosa 12 9 C,H plant b.s 1,283 3,075 3,909 9,844 G. pedunculosa 28 18 PR plant b.s 2,187 1,234 7,625 4,234 G. viridiflora subsp. 16 13 H plant b.s 1,742 1,302 6,277 4,181 quisqueyana 1,735 1,653 5,813 5,370 G. acaulis 69 42 J plant h.s 2,486 1,593 10,528 6,990 G. citrina 10 7 PR plant h.s 1,516 0,315 4,916 0,838 G. cubensis 30 27 C,H plant h.s 3,000 3,220 12,197 12,265 G. cuneifolia 16 13 PR plant h.s 2,095 0,828 7,410 3,364 vi G. decapleura 6 5 H plant h.s 3,496 3,925 9,526 10,325 G. pedicellaris 10 10 H plant h.s 0,532 1,486 1,684 3,810 G. pulverulenta 6 5 H plant h.s 5,557 2,366 14,641 6,364 G. reticulata 43 36 C,H plant h.s 2,343 2,578 11,014 12,106 G. ventricosa 42 23 J,LA plant h.s 2,941 1,496 11,929 7,489 R. berteroanum 37 22 H plant h.s 3,549 2,022 12,675 7,148 R. rupincola 19 13 C plant h.s 0,612 0,451 1,789 1,627 2,557 1,844 8,937 6,575 G. viridiflora subsp. acro- 10 8 H plant m.p 3,477 2,954 11,040 7,645 chordonanthe G. viridiflora subsp. viridi- 39 28 C plant m.p 2,301 2,419 8,757 9,701 flora R. auriculatum 116 95 H,PR plant m.p 3,785 2,813 17,764 11,769 species nb nb pixels islands group pollination sd1 sd2 min- min- points mode max1 max2 R. crenulatum 7 6 C plant m.p 0,787 0,499 2,330 1,286 R. exsertum 78 69 C plant m.p 2,205 2,081 9,559 10,039 R. grandiflorum 51 32 H plant m.p 4,821 2,371 20,258 9,990 R. leucomallon 31 20 H plant m.p 3,926 2,192 13,629 8,590 R. tomentosum 63 44 J plant m.p 2,461 1,465 12,051 6,990 R. vernicosum 8 6 H plant m.p 4,970 2,236 14,590 6,589 3,193 2,114 12,220 8,067 G. bracteosa 24 14 C plant n.a 1,890 2,496 7,555 8,047 G. christii 7 7 H plant n.a 4,018 2,269 11,037 5,785 G. duchartreoides 50 34 C plant n.a 1,775 2,235 7,555 8,756 vii G. ekmanii 13 9 H plant n.a 3,819 1,968 12,132 5,527 G. exserta 32 16 J plant n.a 3,096 0,837 11,645 2,674 G. heterochroa 8 8 C plant n.a 1,940 0,578 5,101 1,836 G. humilis 74 54 C plant n.a 1,755 1,328 9,306 9,701 G. libanensis 12 12 C plant n.a 1,845 1,041 5,629 3,683 G. nipensis 6 6 C plant n.a 1,454 1,994 4,068 5,208 G. pumila 21 14 J plant n.a 2,047 1,202 7,060 4,329 G. purpurascens 11 9 C plant n.a 1,924 2,446 6,686 7,263 G. salicifolia 16 11 C plant n.a 1,285 2,108 4,004 6,484 G. scabra 16 11 J plant n.a 2,080 0,365 6,779 1,393 G. shaferi 16 10 C plant n.a 1,111 2,858 3,688 8,289 R. bicolor 12 9 H plant n.a 2,072 2,281 7,832 7,795 R. bullatum 7 6 H plant n.a 4,913 2,154 14,641 6,364 R. caribaeum 8 5 LA plant n.a 0,701 0,237 1,691 0,634 species nb nb pixels islands group pollination sd1 sd2 min- min- points mode max1 max2 R. grande 38 23 J plant n.a 2,407 1,210 8,855 4,329 R. lanatum 9 7 H plant n.a 4,114 1,552 11,261 4,340 R. lomense 12 11 C plant n.a 2,653 0,712 8,428 2,783 R. minus 9 7 C plant n.a 2,090 0,693 4,889 1,818 viii E Scores of environmental variables over the principal axes bioclimatic variable normed score over normed score over the 1st PC the 2nd PC altitude (alt) 0.302 0.144 Annual Mean Temperature (bio01) -0.310 -0.175 Mean Diurnal Temperature Range (bio02) 0.025 0.289 Isothermality (bio03) 0.142 -0.097 Temperature Seasonality (bio04) -0.131 0.217 Max Temperature of Warmest Month (bio05) -0.318 0.007 Min Temperature of Coldest Month (bio06) -0.215 -0.315 Temperature Annual Range (bio07) -0.054 0.340 Mean Temperature of Wettest Quarter (bio08) -0.318 -0.085 Mean Temperature of Driest Quarter (bio09) -0.246 -0.261 Mean Temperature of Warmest Quarter (bio10) -0.325 -0.106 Mean Temperature of Coldest Quarter (bio11) -0.263 -0.247 Annual Precipitation (bio12) 0.239 -0.203 Precipitation of Wettest Month (bio13) 0.233 -0.086 Precipitation of Driest Month (bio14) 0.186 -0.314 Precipitation Seasonality (bio15) -0.114 0.3015 Precipitation of Wettest Quarter (bio16) 0.210 -0.114 Precipitation of Driest Quarter (bio17) 0.190 -0.314 Precipitation of Warmest Quarter (bio18) 0.143 -0.030 Precipitation of Coldest Quarter (bio19) 0.165 -0.309

ix F Mean AICc values of the evolutionary models ﬁtted on the niche components

(a) with ARD on 22 species evolution model OU1 OU3 BM1 BM3 pc1 mean AICc 71.46299 75.19805 93.68302 91.14571 mean delta AICc -3.735066 -22.22003 -19.68272 PP in favor of OU1 0.901 0.983 0.987 pc2 mean AICc 64.80137 68.69705 87.74205 84.88343 mean delta AICc -3.895679 -22.94068 -20.08206 PP in favor of OU1 0.934 0.967 0.984 pc1 and 2 mean AICc 145.437 171.4662 179.313 192.4982 mean delta AICc -26.0292 -33.876 -47.06117 PP in favor of OU1 1 0.976 0.993

x (b) with ER on 35 species evolution model OU1 OU3 BM1 BM3 pc1 mean AICc 116.6901 119.705 174.535 165.9093 mean delta AICc -3.014869 -57.84488 -49.21921 PP in favor of OU1 0.902 1 0.998 pc2 mean AICc 105.39 108.5548 160.1908 153.1807 mean delta AICc -3.164834 -54.80089 -47.7907 PP in favor of OU1 0.918 1 0.999 pc1 and 2 mean AICc 226.9656 239.1788 323.3153 314.6065 mean delta AICc -12.21316 -96.34962 -87.64089 PP in favor of OU1 0.994 1 1

(c) with ER on 22 species evolution model OU1 OU3 BM1 BM3 pc1 mean AICc 71.42544 75.23356 94.85502 91.60905 mean delta AICc -3.808118 -23.42958 -20.18361 PP in favor of OU1 0.911 0.976 0.984 pc2 mean AICc 64.89916 68.78401 86.44703 84.30163 mean delta AICc -3.884853 -21.54787 -19.40247 PP in favor of OU1 0.927 0.975 0.991 pc1 and 2 mean AICc 145.307 171.2861 178.123 191.5372 mean delta AICc -25.97905 -32.81595 -46.23018 PP in favor of OU1 1 0.968 0.989