Déterminisme environnemental de la fécondité du chêne vert Iris Le Ronce
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Iris Le Ronce. Déterminisme environnemental de la fécondité du chêne vert. Sciences agricoles. Université Montpellier, 2020. Français. NNT : 2020MONTG020. tel-03155263
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THÈSE POUR OBTENIR LE GRADE DE DOCTEUR
DE L’UNIVERSITÉ DE MONTPELLIER
Filière EFSA - Écologie Fonctionnelle et Sciences Agronomiques
École doctorale GAIA
Unité de recherche Centre d’Écologie Fonctionnelle et Évolutive
Déterminisme environnemental de la fécondité du chêne vert
Présentée par Iris LE RONCÉ Le 12 novembre 2020
Sous la direction d’Isabelle CHUINE, Jean-Marc LIMOUSIN et Samuel VENNER
Devant le jury composé de
Evelyne COSTES, directrice de recherche, INRAE Présidente de jury Georges KUNSTLER, IDAE, INRAE Rapporteur Annabel PORTÉ, directrice de recherche, INRAE Rapportrice
Isabelle CHUINE, directrice de recherche, CNRS Directrice de thèse
Jean-Marc LIMOUSIN, chargé de recherche, CNRS Co-encadrant, invité Samuel VENNER, maître de conférences, Université de Lyon Co-encadrant, invité
1
Table des matières
Avant-propos ...... 4 Remerciements...... 5 Introduction générale ...... 8 1. Fécondité et changements climatiques ...... 9
2. Déterminisme de la dynamique de reproduction des arbres forestiers ...... 10
3. Objectifs de la thèse...... 18
Matériel et méthodes général ...... 21 1. Le chêne vert ...... 22
2. Terrain d’expérience du CEFE ...... 22
3. Le site expérimental de Puéchabon ...... 23
Chapitre 1 : Impact de la limitation en ressources induite par une défoliation expérimentale sur l’allocation à la reproduction ...... 26 Chapeau ...... 27
Le Roncé et al. 2020 ...... 28
Chapitre 2 : Le développement des fruits du chêne vert inhibe la production de fleurs femelles, modifie l’architecture des rameaux et impacte les dynamiques des réserves en azote et en zinc ... 43 Chapeau ...... 44
Introduction ...... 46
Materials and methods ...... 48
Results ...... 54
Discussion ...... 60
References ...... 65
Chapitre 3 : La réduction des précipitations à long terme induit une diminution du nombre de fruits qui s’ajoute à l’effet des autres déterminants de la fécondité du chêne vert ...... 70 Chapeau ...... 71
Introduction ...... 72
Materials and methods ...... 75
Results ...... 82
Discussion ...... 89
2 References ...... 94
Chapitre 4 : Modélisation de la production de fleurs et de fruits grâce au modèle PHENOFIT, un modèle basé sur les processus ...... 101 1. Introduction ...... 102
2. Modèle PHENOFIT et concepts généraux ...... 104
3. Variables liées à la fonction mâle ...... 107
4. Variables liées à la fonction femelle ...... 109
5. Paramétrage du modèle pour le chêne vert ...... 114
6. Résultats ...... 122
7. Discussion et perspectives ...... 124
Discussion générale ...... 127 1. De la nécessité de clarifier la notion de déterminant ...... 130
2. Une fécondité dessinée par les vétos ...... 131
3. De l’importance d’étudier l’allocation entre puits avec une chronologie fine et à différentes échelles ...... 134
4. Des déterminants dont la connaissance reste à affiner ...... 136
5. Impact des changements globaux sur la fécondité des arbres forestiers ...... 137
6. Enseignements de cette thèse ...... 139
Références bibliographiques ...... 140 Annexes ...... 151 Annexe 1 : Schermer et al. 2020 ...... 151
Annexe 2 : Matériels supplémentaires du Chapitre 1 ...... 164
Annexe 3 : Matériels supplémentaires du Chapitre 2 ...... 174
Annexe 4 : Matériels supplémentaires du Chapitre 3 ...... 179
Résumé ...... 186 Abstract ...... 186
3 Avant-propos
Cette thèse est rédigée sous la forme d’une thèse sur articles comprenant quatre chapitres. Trois de ces chapitres sont rédigés sous un format d’article en anglais et le dernier est sous format libre en français. Les travaux de recherche présentés dans cette thèse ont été effectués dans le cadre d’un e formation doctorale de trois ans du ministère chargé de l’environnement et gérée par AgroParisTech. Le doctorat s’est déroulé au sein du laboratoire Centre d’ Écologie Fonctionnelle et Évolutive (CEFE) à Montpellier où j’ai fait partie de l’é quipe FORECAST (FOREts, Changements globaux, et Adaptation : Simulations et expérimenTations ). J’ai encadré deux stagiaires (une étudiante de Master 1 et une étudiante de Licence 3) et co-encadré deux stagiaires (une étudiante de Master 1 et un étudiant de Licence 3). Un des stages de Master 1 a bénéficié d’une bourse « Equipe de Recherche Junior » du Labex CeMEB et l’autre d’une gratification de l’OSU OREME. Par ailleurs, j’ai bénéficié d’une aide technique d’un mois financé e par l’ANR FOREPRO. Dans le cadre de ce travail, j’ai mis en œuvre différentes appr oches : expérimentation sur le Terrain d’E xpériences du laboratoire, analyse de données acquises avant ma thèse et un peu de modélisation, principalement dans la conception. Mon travail a beaucoup bénéficié de l’aide technique des personnels de la station expérimentale de Puéchabon, du Terrain d’Expérience du CEFE , de la Plateforme d’Analyse s Chimiques en Ecologie et de la plateforme CAPSIS de l’INRAE. Une partie des analyses chimiques ont été réalisées en collaboration avec l’équipe d’Arthur Gessle r dans le laboratoire du WSL de Zurich en Suisse.
4 Remerciements
Cette thèse aura représenté trois très belles années, intellectuellement, humainement, et comme point de départ de très belles rencontres. Avant toute chose, un énorme merci à Isabelle, Jean-Marc L. et Samuel pour votre encadrement. J’ai beaucoup appris auprès de vous. Cela a été un grand plaisir de travailler avec un tel équilibre entre indépendance et grande confiance dans votre soutien et votre regard scientifique. Merci beaucoup Isabelle pour ta confiance, ta bienveillance mais aussi ton exigence. C’était une grande chance d’être encadrée par toi sur le plan scientifique, et sur le plan personnel. Jean-Marc, merci beaucoup pour ta présence (même quand le couloir était déserté le soir !), tes rires communicatifs et ta foi inébranlable dans l’intérêt de l’expérimental et des statistiques et des théories simples. Merci également pour tes relectures si attentives. Samuel, merci beaucoup pour ton soutien et ta participation rigoureuse aux différentes étapes de cette thèse. Merci aux membres de mon comité de suivi de thèse, qui m’ont bea ucoup apporté par leurs retours : Pierre-Olivier Cheptou, Hendrik Davi, Nicolas Delpierre, François Lebourgeois et Alexia Stokes . Merci aux membres du jury, qui ont accepté d’évaluer ce travail : Evelyne Coste s, Georges Kunstler et Annabel Porté , ainsi que Maxime Cailleret qui n’a pas pu être présent le jour J . Cette thèse n ’aurait pas été la même sans l’aide cruciale de plusieurs co llaboratrices et collaborateurs : Maude, Jordane G., Jean-Marc O. et Eliane. Un grand merci à François de Coligny pour son aide pour la partie code sous Phenofit, et sa gentillesse. Merci également au groupe des autres « glandeurs et glandeuses » de France, avec qui cela aura été un grand plaisir d’interagir : Eliane (merci pour ton bel enthousiasme !), Laura T., Marie-Claude, Valentin, Thomas C., Sylvain, Vincent et Nicolas. Merci Vincent de m’avo ir aiguillée sur ce sujet de thèse. Tout seul, on va plus vite, à plusieurs, on va plus loin, mais cela implique de trouver des bonnes âmes volontaires pour le travail de terrain ! Et j’ai eu la chance énorme d’être aidée et accompagnée par de nombreuses p ersonnes, sans qui ce travail n’aurait pu être ce qu’il est. Merci à tous les copains et copines qui ont donné de quelques minutes à de nombreuses heures pour m’aider à tenir le terrain dans les temps : Anne -Sophie (oh le beau coup de soleil, désolée !), Amélie, Matthias, Jordane G., Maude, Stéphane, Laura G. Merci aux stagiaires avec qui j’ai travaillé (ma fourmilière personnelle !) Manon, Elia, Marie, Maud et Tom. Cela a été une très belle expérience de vous rencontrer et de vous encadrer, et votre aide m’a été précieuse. J’ai beaucoup appris en travaillant avec vous et j’espère que vous aussi. Merci à toute l’équipe du TE, pour votre formidable volonté d’aider, même à la dernière minute, et votre créativité sans fond : Pauline D., Thierry, David, Pierrick, Fabien. Un grand merci évidemment à l’équipe de Puéchabon pour toute votre aide bienveillante. Merci à la PACE, qui a
5 accueilli de nombreuses analyses de cette thèse. Thank you so much Leonie and Arthur for your kind welcome in WSL, and your patience despite a difficult start... Vielen Dank Doris, Ursina und Elena, es war sehr schön, ihnen zu treffen. Merci à la plateforme GEMEX, pour le prêt de pipettes qui ont vu du pays ! Merci à Clémence, Alexandre et Patrick de Genobois pour les innombrables broyages et pesées. Thank you, Elizabeth and Joshua, for being the ones that offered me my first contact with masting, tree science and field work 6 years ago! Merci à Christoph Bachofen pour les conseils de protocole NSC, à Yves Caraglio pour son expertise d’architecture des plantes et à Roselyne Lumaret pour le prêt d’ancien s travaux sur les régimes de reproduction du chêne vert. Enfin, merci Laurent de m’avoir donné la chance d’avoir un aperçu de ce qu’est l’enseignement. Un grand merci aux équipes gestion, RH, secrétariat et informatique du CEFE, en particulier Mélanie, Natacha B., Jordane C., Asma, Florence B., Véronique et Pauline B. qui ont été là quand il a fallu débrouiller des situations tendues de contrat / convention de stage / frais de mission ou comprendre un bordereau DHL. Merci Marie-Laure pour ta belle direction, en particulier pendant la crise Covid. Merci à Bruno pour ta solidarité cycliste. J’ai eu la chance d’avoir comme supérieur Alexandre Péry et comme relai Agro ParisTech Sophie Le Calvar. C’était un vrai plaisir de travailler à votre contact et de pouvoir vous faire une telle confiance. Ces 3 années ont été l’occasion de faire des rencontres : des rencontres parfois farfelues, systématiquement débiles mais toujours inattendues, souvent déglingues, occasionnellement mignonnes, mais toujours chouettes. FORECAST, c’est un peu comme l’Atlantique en Poitou- Charentes, c’est un peu dur d’y rentrer, mais quand on y est, on ne veut plus en sortir. J’ai eu beaucoup de chance d’être ainsi entourée, surtout dans les moments parfois difficiles qui surviennent pendant les 3 ans de vie que sont une thèse. Me rci tout d’abord à Jean -Marc O., Papa Ours, pour toutes ces discussions, ces découvertes, ces lectures, et ta grande générosité. Merci aux amis , j’ai eu beaucoup de chance de vivre cette aventure à vos côtés ! Les co-bureaux d’abord : à Julie merci pour ton écoute et ta gentillesse, à Swanni pour ta bonne humeur inoxydable, à Jordane G. pour ces pauses thé et notre passion chêne vert et à Wentao pour ces quelques mois de bureau partagé . Juliane, tout ça pour ça, certes, mais c’était quand même chouette de pouvoir partager cette galère avec toi ! Et graphiquement, cette thèse te doit beaucoup. Nafissa, merci pour ta douceur et ton soutien, courage, toi aussi tu y es presque ! Marianne, merci pour ta si belle finesse, même si tu quittes Paris quand j’y arrive, on se verra quand même. Merci à Marion J. pour ton optimisme contagieux et à Daphné pour ta fraîcheur et ton attention aux autres en toute circonstances. Maude, merci pour ta gentillesse et ton humour décapant à l’origine de bien des fous rires d’équipe. Merci à Thomas L. pour toutes ces pauses pas toujours très courtes ni très fines, mais si drôles. Merci à Florent, le chef de meute (que serait Forecast sans toi ?), pour avoir redécouvert mon objet d’étude avec une candeur toujours renouvelée et pour notre goût partagé des infusions et de l’espagnol. Merci à Xavier, pour tes jeux
6 de mots (fléchés) et ton sens du verbe et à bientôt pour parler sauvetage de la forêt française. Merci à Florence V. pour nos discussions, ainsi qu’à Jean, Michael, Pasc al, Ammar et Karim. Merci aux copains du CEFE et de Montpellier ! Déjà les vieilleux du 2 ème et assimilés : Nina & Loïc, Gaël, Marine, Marion C. (#gratitude), Margaux, Adrien & Evanne , c’était un plaisir de vous retrouver, vivement qu’on se revoit , à la Barbote ou ailleurs. Merci aussi à Eva, Françoise, Mathilde & Ben, pour ces dîners et ces balades qui ont beaucoup éclairé mon début de thèse, avant que vous ne voguiez vers d’autres horizons plus ou moins froids. Merci à Stéphane, Nathalie & Leia pour toutes ces chouettes soirées (garanties sans jeux collaboratifs). Merci aux copains et copines biofluxien-ne-s et ecoparien-ne-s pour ces thés et sorties : Floriane, Lauren & Robin, Pierre D. & Helena, Kévin, Florian (merci aussi pour nos covoits ariégeois), Pierre G., Lucie, Léo et Stefania. Merci Hélène, Camila, William et Agathe pour nos discussions. Merci Manon pour ton enthousiasme et ta volonté de créer du collectif. Merci à Anne-Sophie et Sylvain pour ces supers dîners (garantis sans toxine botulique). Merci en particulier à Victor, Rémi et Alexis pour toutes ces crêpes, le soutien et nos discussions dans ces derniers mois. Merci à Flora et Matthias pour l’aide pour la mise en page , et à Emma pour le soutien pré- soutenance. Merci à mes parents, ma famille et aux ami-e-s de longue et moins longue date. Pour finir, Matthias, merci pour tout, bien sûr.
7 Introduction générale
Cycle reproducteur du chêne vert.
Introduction générale
1. Fécondité et changements climatiques La réussite de la reproduction des arbres forestiers est un facteur essentiel de la démographie de leurs populations, en particulier dans les forêts en régénération naturelle. Or, les changements climatiques actuels et futurs exercent une pression croissante sur les forêts du monde entier (Bonan, 2008; Lindner et al. , 2010) et leurs impacts potentiels sur le recrutement d’individus à partir de graines sont mal compris (Morin et al. , 2007; Clark et al. , 2011). Ces incertitudes sont génératrices d’inquiétudes sur l’avenir des forêts. Elles appellent à développer notre connaissance des processus impliqués et à adapter tant que possible les pratiques de gestion (Plan recherche et innovation 2025 filière forêt-bois , 2016; Programme national de la forêt et du bois 2016-2026 , 2017).
En contexte méditerranéen, les modèles climatiques prévoient une augmentation des températures et de la fréquence des sécheresses (Meehl & Tebaldi, 2004; Lémond et al. , 2011; IPCC, 2013; Ruffault et al. , 2014). Or, prévoir les conséquences des changements climatiques actuels et futurs sur le renouvellement des forêts nécessite d’avoir une compréhension des déterminants environnementaux de l’ensemble du processus de production et de recrutement des graines, depuis la floraison jusqu’à la germination des graines (Crone & Rapp, 2014; Pearse et al. , 2016; Allen et al. , 2017). On entend ici déterminant environnemental au sens de tout déterminant qui ne soit pas lié au déterminisme génétique, qui peut donc être externe à l’individu, comme les conditions météorologiques, ou interne, comme ses réserves en nutriments. Historiquement, la majorité de la recherche scientifique portant sur l’impact des changements climatiques sur les arbres s’est concentrée sur la croissance, la survie et la phénologie foliaire des individus en réponse à la hausse des températures et aux changements de composition de l’atmosphère (CO2, polluants) (Pastor & Post, 1988; Menzel et al. , 2006; Thomas et al. , 2010; Keenan et al. , 2014; Fernández -de -Uña et al. , 2016; Fernández-Martínez et al. , 2017b). La compréhension du fonctionnement et du déterminisme de la fécondité des arbres, en particulier en contexte de changements climatiques, a fait l’objet d’un intérêt plus récemment, comme en témoignent les quatre thèses publiées en trois ans en France sur différents aspects de la fécondité des arbres forestiers (Caignard, 2018; Schermer, 2019; Journé, 2020; Touzot, 2020).
L’anticipation des impacts potentiels des changements climatiques sur la fécondité des arbres forestiers peut être compliquée par le fait que les dynamiques de fructification des arbres forestiers - la plupart pollinisés par le vent - sont souvent caractérisées par des fructifications massives, intermittentes et synchronisées à l’échelle d’une population (Silvertown, 1980; Kelly, 1994; Koenig & Knops, 2000). Ce patron de fructification, dit de « masting », est une stratégie de reproduction répandue parmi de nombreux groupes taxonomiques de plantes en milieu tempéré et méditerranéen. Les effets des changements climatiques, soit déjà présents, soit simulés
9 Introduction générale expérimentalement, montrent que les différentes facettes des changements climatiques (hausse des températures, réduction des précipitations , enrichissement de l’atmosphère en CO 2, dépôts d’azote atmosphérique, etc.) impactent déjà ou sont très susceptibles d’impacter la quantité de graines produites par les essences forestières et la fréquence des épisodes de masting . En effet, certains travaux observent ou prédisent un effet favorable des changements climatiques sur la production de graines (Overgaard et al. , 2007; Hedhly et al. , 2009; Buechling et al. , 2016; Caignard et al. , 2017), d’autres un effet défavorable, parfois du fait d’une réduct ion de la variabilité de la fécondité (Mckone et al. , 1998; Hedhly et al. , 2009; Pérez-Ramos et al. , 2010; Sánchez-Humanes & Espelta, 2011; Iler & Inouye, 2013; Bogdziewicz et al. , 2020c) , d’autres des effets contrastés selon la variable étudiée (Pearse et al. , 2017; Shibata et al. , 2020) et enfin un dernier groupe d’études prédisent une absenc e d’effet (Kelly et al. , 2013) . Aucun consensus fort ne se dégage et l’effet attendu des changements climatiques sur la fécondité des arbres reste donc relativement flou. Ce flou est certainement lié aux fluctuations très fortes de la fécondité des arbres forestiers et à la diversité des déterminants susceptibles d’avoir un effet sur la fécondité et d’interagir entre eux. Il y a donc aujourd’hui un fort enjeu à explorer davantage les mécanismes de la fécondité des arbres forestiers pour mieux comprendre notamment l’impact des changements climatiques sur les plantes se reproduisant par masting (Lindner et al. , 2010).
2. Déterminisme de la dynamique de reproduction des arbres forestiers
2.1 Causes du masting et déterminants du cycle reproducteur
Les causes évolutives, ou distales, qui ont permis l’émergence du masting font l’objet de nombreuses hypothèses qui expliquent comment, paradoxalement, ne pas produire de graines tous les ans permet de maximiser le succès reproducteur des individus (Kelly & Sullivan, 1997; Tachiki & Iwasa, 2010). Les deux principales hypothèses chez les arbres sont la satiation des prédateurs les années de forte production de graines et l’augmentation de l’efficacité de la pollinisation les années de forte floraison (Janzen, 1971; Silvertown, 1980; Kelly, 1994; Pearse et al. , 2016).
Les déterminants environnementaux induisant une reproduction réussie sont au cœur de ce travail, et sont encore très controversés chez les arbres forestiers (Crone & Rapp, 2014; Pearse et al. , 2016; Allen et al. , 2017; Vacchiano et al. , 2018), notamment par opposition aux arbres fruitiers qui ont fait l’objet d’ét udes plus poussées (Monselise & Goldschmidt, 1982; Samach & Smith, 2013; Smith & Samach, 2013; Sharma et al. , 2019). Ces déterminants peuvent également être appelés causes proximales, par opposition aux causes distales.
Avant de détailler les principaux déterminants environnementaux de la reproduction sexuelle des arbres forestiers, je ferai un rappel des principales étapes du cycle reproducteur sur
10 Introduction générale lesquelles ils sont susceptibles d’agir. Je présente ici le cycle de reproduction des chênes, un des genres dont la dynamique de fécondité est la plus étudiée, et en particulier celui du chêne vert (Quercus ilex L.), une espèce de chêne méditerranéen au feuillage persistant très répandue autour du bassin méditerranéen et qui est l’espèce modèle de cette thèse.
Figure 1 : Grandes étapes des cycles des fonctions de reproduction mâle (en bleu) et femelle (en orange) du chêne vert de l’initiation florale à la dispersion des graines. Les périodes données sont indicatives. Figure inspirée de (Schermer et al., 2016).
Le cycle reproducteur chez les chênes commence par l’initiation florale, qui se fait au cours de la conversion irréversible de méristèmes végétatifs en des méristèmes reproducteurs (contenant uniquement des fleurs, ou un ensemble de fleurs et de feuilles) (Johnson et al. , 2009; Allen et al. , 2017). Chez le chêne ver t, il semblerait que cette étape ait lieu pendant l’été de l’année t -1 (i.e. année précédant la fructification) pour les fleurs mâles, alors que l’initiation des fleurs femelles pourrait avoir lieu juste avant ou pendant le débourrement ((Sobral et al. , 2020) chez Quercus suber ), Fig. 1). Les inflorescences mâles, ou chatons, apparaissent les premières dès le d ébourrement de l’année t, le plus souvent à la base des nouveaux rameaux, et les inflorescences femelles s’observent plus tardivement et à l’aisselle des nouvelles feuilles de l’année (Yacine & Bouras, 1997). La pollinisation intervient au moment de l’anthèse en avril -mai, tandis que la fécondation a lieu fin juin-début juillet, soit 55 à 60 jours après la fixation du pollen sur le stigmate (Yacine & Bouras, 1997; Pulido & Díaz, 2005). Cette période est appelée la phase progamique (Sogo & Tobe, 2006), et induit la formation du fruit , appelée nouaison (Fig. 1). Pendant l’été et l’automne, les fruits (glands) se développent et achèvent leur maturation en novembre ou décembre en France (Yacine & Bouras, 1997), un peu plus tôt en Espagne (Perez-Izquierdo & Pulido, 2013). Chez le chêne vert comme chez les autres espèces du groupe des chênes blancs (sous-genre Lepidobalanus ), la floraison et la fructification ont lieu au cours de la même année, alors qu’elles s’étalent sur 2 ans dans le groupe des chênes rouges (sous-genre Erythrobalanus ) au sein desquels la phase progamique dure un an (Johnson et al. , 2009).
11 Introduction générale
Différents facteurs sont susceptibles d’impacter chaq ue étape du cycle reproducteur et sont liés à l’environnement biotique et abiotique des arbres et à leur état physiologique, lui -même produit de l’interaction entre l’environnement et leurs gènes. Ces facteurs, que l’on appellera des déterminants ( drivers en anglais) dans le cadre de ce travail, vont conditionner le nombre potentiel d’organes reproducteurs au début du cycle reproducteur et leur développement avant et après fécondation pour aboutir à un nombre de fruits prêts à être dispersés à la fin du cycle.
Ces déterminants du nombre de graines, ou de fruits, produits par un arbre appartiennent à deux grandes familles : les déterminants liés à la disponibilité en ressources et à leur allocation aux différents organes et fonctions de l’arbre, et les déterm inants liés aux conditions météorologiques qui vont également déterminer la phénologie de la reproduction (Pearse et al. , 2016; Allen et al. , 2017). Ces deux grandes familles ne sont bien sûr pas indépendantes, les conditions météorologiques ayant un impact sur l’acquisition et l’allocation des ressource s (Smaill et al. , 2011; Tanentzap et al. , 2012; Allen et al. , 2017; Gavinet et al. , 2019; Bogdziewicz et al. , 2020b).
L’environnement biotique impacte également la dynamique de fécondité des individus en modulant l’acquisition des ressources comme peuvent le faire les mycorhizes (Newbery, 2005; Koide, 2010) , en impactant l’état sanitaire de l’arbre comme le font les herbivores et les pathogènes (Crawley, 1985; Thomas et al. , 2002; Hochwender et al. , 2003; Pearse et al. , 2015a; Nakajima, 2015; Canelo et al. , 2018) ou en consommant directement les organes reproducteurs (florivorie, consommateurs de glands pré-dispersion) (Johnson et al. , 2009; Pulido et al. , 2010). Ces aspects ne seront qu’indirectement abordés dans le cadre de ce travail, mais peuvent avoir un impact fort sur le succès reproducteur des individus. Enfin, l’efficacité de la pollinisation, qui dépend de la disponibilité en pollen et des conditions météorologiques, est également un facteur impactant fortement le succès reproducteur chez certaines espèces (Crone & Rapp, 2014; Koenig et al. , 2015; Schermer et al. , 2019). Cependant, chez le chêne vert, la disponibilité en pollen semble être peu limitante en forêt dense (Fernández-Martínez et al. , 2012; Bogdziewicz et al. , 2017b). Elle est, au contraire, limitante chez les chênes tempérés. La comparaison entre ces deux groupes de chênes a été le sujet d’un travail en collaboration avec Eliane Schermer au LBBE (Lyon) présenté en Annexe 1 (Schermer et al. , 2020).
Différentes variables ont été identifiées comme ayant un impact sur le succès reproducteur du chêne vert, et sont résumées en Table 1. Cependant, les contributions respectives des conditions météorologiques et des ressources des individus à la fécondité ont été peu étudiées conjointement, en particulier aux différentes étapes du cycle reproducteur entre lesquelles les besoins de l’arbres peuvent varier (Fig. 2, page 18).
12 Introduction générale
Très peu de travaux portent notamment sur les déterminants de la floraison chez les arbres forestiers, comparativement aux arbres fruitiers (Samach & Smith, 2013). Par ailleurs, si les chênes ont généralement un ratio fruits/fleurs relativement bas (Sharp & Sprague, 1967; Kanazashi & Kanazashi, 2003; Johnson et al. , 2009; Tsuruta et al. , 2011), les études corrélatives travaillant sur des données de productions de biomasses de fruits ne permettent pas de discerner si les échecs de reproduction ont lieu avant la nouaison (du fait par exemple d’une faible production de fleurs femelles, d’échecs de pollinisation, ou de conditions défavorables à la fécondation) ou après la nouaison pendant le développement des fruits (Fukumoto & Kajimura, 2005; Tsuruta et al. , 2011). Les rares études qui différencient les étapes du cycle reproducteur chez les chênes montrent qu’une majeure partie de l’avortement des organes femelles a lieu entre l’anthèse et la fécondation des fleurs (Masaka & Sato, 2002; Fukumoto & Kajimura, 2005). Si la quantité en pollen est limitante dans certains cas, deux études sur Quercus lobata et Quercus ilex ont montré que la majorité de l’avortement des fleurs éta it causée par des facteurs indépendants de la limitation en pollen (Pulido & Díaz, 2005; Pearse et al. , 2015b) . Cependant, chez le chêne vert, la littérature montre que l’étape de développement des fruits noués peut également être particulièrement limitante (Pulido & Díaz, 2005; Espelta et al. , 2008; Pérez-Ramos et al. , 2010). La physiologie de la reproduction des arbres forestiers et ses détermina nts méritent donc d’être davantage étudiés.
2.2 Déterminisme par les conditions météorologiques
Le lien entre dynamiques de fructifications et conditions météorologiques a fait l’objet de nombreuses publications, pour la plupart corrélatives et basées sur des biomasses de fruits sans distinction de leur succès de développement, et souvent sur un nombre restreint de sites. Elles ont fait l’objet d’une revue de la littérature chez les chênes (Koenig & Knops, 2014) et d’une autre sur les chênes méditerranéens (Pérez-Ramos et al. , 2015). Il ressort de ces revues que dans la majorité des études, au moins une variable environnementale est identifiée comme corrélée aux dynamiques de fructification, et que dans le cas des chênes méditerranéens, 70 % des études identifient au moins une variable reliée au risque de déficit hydrique au printemps et en été (Koenig & Knops, 2014; Pérez-Ramos et al. , 2015). Cependant, aucune variable météorologique ne permet de prédire de façon récurrente les patrons de fructification chez les chênes d’espèces différentes, voire même également entre populations différentes d’une même e spèce. De plus, compte tenu de la grande diversité de conditions météorologiques possibles, il existe un risque que les corrélations détectées ne soient que des artefacts (Crone & Rapp, 2014).
13 Introduction générale
L’enjeu aujourd’hui est donc d’identifier les sensibilités aux conditions climatiques des processus clés à chaque étape du cycle r eproducteur (Fig. 2), qui ont déjà fait l’objet de différentes études dans le cas du chêne vert (Table 1).
Table 1 : Résumé des principaux déterminants liés aux conditions météorologiques et aux allocations de ressources de l’arbre identifiés dans la litté rature comme affectant la fécondité du chêne vert et pouvant être rattachés à une étape du cycle reproducteur. Les périodes associées aux étapes du cycle sont basées sur nos connaissances de la biologie reproductive du chêne vert et sur nos observations. n.s. = non significatif. Table adaptée et traduite de la Table 1 du Chapitre 3.
Étape du cycle Déterminant identifié Effet [référence] reproducteur Initiation florale femelle Production de fruits de l’année précédente - (interagit avec la (Autocorrélation négative) météo) [1] Développement des fleurs Printemps chaud + [1] mâles (Avril) Pollinisation (Avril-Mai) Humidité de l’air d’avril - [2] Concentration pollinique de l’air + [2, 3] n.s. [1, 4] Printemps chaud + [1] Température minimale de mai - [2] Phénologie tardive + [4] Fécondation et nouaison Sécheresse de printemps - [2, 4, 5, 6] (Juin) Précipitations du printemps + [3, 7] Température moyenne de juin + [3] Croissance du fruit et Sécheresse d’été et/ou de début d’automne - [5, 7, 9, 10, 11] maturation (Juillet- Température d’été et/ou de début d’automne - [12] Novembre) Précipitations d’automne + [3, 7] Pluies torrentielles - [5] Croissance secondaire d’automne - [8] [1] (Bogdziewicz et al. , 2017b); [2] (García-Mozo et al. , 2012); [3] (García-Mozo et al. , 2007); [4] (Fernández- Martínez et al. , 2012); [5] (Pérez-Ramos et al. , 2010); [6] (Fernández-Martínez et al. , 2015); [7] (Alejano et al. , 2008); [8] (Martín et al. , 2015); [9] (Carevic et al. , 2010); [10] (Espelta et al. , 2008); [11] (Liu et al. , 2015); [12] (Montserrat-Martí et al. , 2009).
Les déterminants liés aux conditions météorologiques peuvent être favorables à l’état physiologique de l’arbre de façon générale, comme les précipitations limitant le stress hydrique
14 Introduction générale est ival ou l’absence de gel au moment du débourrement. D’autres variables météorologiques sont favorables à certains processus spécifiques de la reproduction comme l’absence d’humidité ou de précipitations lessivant le pollen au printemps (García-Mozo et al. , 2007) ou comme les températures favorisant la croissance du tube pollinique avant la fécondation (Hedhly et al. , 2009). Certaines des variables peuvent avoir un effet négatif sur les processus permettant la reproduction et être très limitantes, comme un fort gel ou une forte sécheresse pendant le développement des fruits (Alejano et al. , 2008; Carevic et al. , 2010; Pérez-Ramos et al. , 2010). Nous désignerons ces déterminants dans le cadre de ce travail comme des vétos ( vetos en anglais, concept développé en particulier par Pearse et al. (2016) et par Bogdziewicz et al. (2018).
2.3 Déterminisme par les ressources de l’arbre
L’hypothèse avancée dans la littérature d’un déterminisme de la fécondité par les ressources découle de plusieurs constats. La reproduction est une fonction coûteuse pour les plantes en général (Obeso, 2002), en particulier chez les chênes dont les graines sont relativement grosses et riches en nutriments (Ishida et al. , 2005). La production de fruits pendant une année de masting peut représenter une part conséquente de la biomasse totale produite par l’arbre (Janzen, 1971; Kelly, 1994; Kelly & Sork, 2002) . Par exemple, la biomasse de fruits peut correspondre à jusqu’à 79 % et 52 % de la biomasse de bois produite une année chez le hêtre et le chêne sessile respectivement (Mund et al. , 2010; Delpierre et al. , 2016). La reproduction risque par conséquent d’être en compétition avec les autres fonctions essentielles telles que la croissance, la survie ou la résistance à des stress biotiques ou abiotiques (Jösson & Tuomi, 1994; Obeso, 2002; Barringer et al. , 2013; Redmond et al. , 2019), voire le stockage actif de réserves (Wiley & Helliker, 2012).
Par ailleurs, une année de masting n’est pratiquement jamais suivie d’une autre année de masting , mais est au contraire souvent suivie d’une année de faible fructification chez de nombreuses espèces (autocorrélation négative, (Koenig & Knops, 2000; Koenig et al. , 2016)). Cette caractéristique du masting a amené plusieurs auteurs à supposer qu’une fructification exceptionnelle ne peut avoir lieu que lorsque la plante a atteint un certain seuil de ressources après plusieurs années d’accumulation. Ces ressources, qui s’épuisent suite au masting , seraient ensuite insuffisantes pour produire une grande quantité de fleurs à nouveau l’année suivante. Cette hypothèse constitue la base des « resource budget models » (RBM) qui modélisent les patrons de fructification à l’échelle individuelle et populationnelles (Sork et al. , 1993; Isagi et al. , 1997; Crone & Rapp, 2014; Venner et al. , 2016) et qui sont développés plus bas dans cette introduction.
Enfin, certains auteurs ayant fait le constat que les conditions météorologiques étaient en général bien moins variables entre années que l’intensité de fructification des espèces se
15 Introduction générale reproduisant par masting en ont déduit que les conditions météorologiques ne pouvaient être le seul déterminant du masting , et que d’autres déterminants devaient être impliqués (Koenig & Knops, 2000, 2005; Kelly & Sork, 2002; Monks & Kelly, 2006). Cet argument est en revanche plus discutable, car rien ne garantit que la production de fruits et les variables météorologiques limitantes soient liées de façon linéaire ou logarithmique, comme l’ont montré d’autres au teurs récemment (Fernández-Martínez et al. , 2017a).
Tout comme il semble y avoir une compétition entre la reproduction d’une année donnée et cel le de l’année suivante, la compétition pour l’allocation des ressources entre la reproduction et les autres fonctions, en particulier entre la reproduction et la croissance, ont fait l’objet de nombreuses études corrélatives. Certaines de ces études montrent une relation négative entre fonctions ou biomasses d’organes produites qui est interprétée comme une compétition ( trade-off en anglais) (Sánchez-Humanes et al. , 2011; Han et al. , 2011; Martín et al. , 2015; Vergotti et al. , 2019). Toutefois, ces corrélations négatives ne sont pas universelles (Dick et al. , 1990; Yasumura et al. , 2006; Knops et al. , 2007; Alla et al. , 2012; Redmond et al. , 2019; Vergotti et al. , 2019). Si compétition entre fonctions il y a, les différentes fonctions de la plante sont certainement soumises à des règles de priorité d’allocation (Suzuki, 2001; Obeso, 2004; Wiley & Helliker, 2012), qui peuvent varier au cours des saisons. Ces règles d’allocation sont souvent étudiées à l’échelle de l’arbre ou de la parcelle, mais devraient être étudiées en considérant les arbres comme des organismes modulaires. En effet, les « modules » qui composent la couronne d’un arbre peuvent être plus ou moins indépendants, notamment en termes de carbone (Watson & Casper, 1984; Sprugel et al. , 1991), et l’échelle de l’arbre à laquelle sont étudiée s les règles d’allocation peut considérablement affecter les résultats (Sánchez-Humanes et al. , 2011; Sala et al. , 2012; Santos-del-Blanco & Climent, 2014; Funk, 2017). Ce s era l’objet d’un des chapitres de ce travail d’étudier les règles d’allocation de ressources à différentes fonctions au sein du chêne vert à différentes échelles, ainsi que de tester l’autonomie en carbone des branches.
Bien que les RBM aient été proposés il y a plus de vingt ans et aient acquis une certaine finesse dans leur capacité à reproduire les patrons du masting , encore peu d’études ont permis de valider de façon expérimentale les bases physiologiques des hypothèses qui les sous-tendent, à savoir no tamment l’hypothèse d’une accumulation jusqu’à un certain seuil de ressources en prévision d’un évènement de fructification important, la consommation des ressources limitantes lors d’un épisode de masting et la réduction de la floraison l’année suivante. En particulier, l’identité des éléments dont l’accumulation serait nécessaire est encore mal connue, et varie certainement entre espèces (Han & Kabeya, 2017) . L’azote (N), le phosphore (P) et les glucides non structuraux ont déjà été identifiés comme potentiellement impliqués dans les dynamiques de fructification. Ces
16 Introduction générale constats ont pu être mené s par l’observation d’une diminution des réserves post -masting (Sala et al. , 2012; Pearse et al. , 2016; Han & Kabeya, 2017) et par des approches expérimentales révélant le lien entre l’abondance d’un nutriment et l’intensité de l’induction flor ale (Miyazaki et al. , 2014; Satake et al. , 2019) . L’objet d’un de ces chapitres de cette thèse sera donc d’étudier de façon expérimentale l’effet du développement et de la maturation des fruits sur les réserves de l’arbre et sur la floraison femelle de l’année suivante. Par ailleurs, les RBM sont des modèles individus -centrés. Il se peut cependant que la régulation de la reproduction par ce cycle d’accumulation et de consommation de réserves ait lieu à une échelle plus locale, à l’échelle de la branche voire du rameau, ce que très peu d’études explorent (Sala et al. , 2012). Nous veillerons donc dans le cadre de notre travail à tester à quelle échelle les potentielles consommations des réserves et inhibitions de l’induction florale pourraient avoir lieu.
2.4 Quels effets des changements climatiques sur les déterminants de la reproduction ?
Les changements globaux peuvent être de nature multiple : modification des températures et des régimes de précipitations, la hausse des concentrations en CO 2 atmosphérique, changement de fréquence des évènements extrêmes de sécheresse ou de chaleur, etc. Tous ces facteurs sont susceptibles d’affecter différents processus pouvant imp acter la reproduction tels que la phénologie (Ogaya & Peñuelas, 2004; Menzel et al. , 2006; Fu et al. , 2014) , l’acquisition des ressources par les individus (Kreuzwieser & Gessler, 2010; Lukac et al. , 2010) , l’allocation de ressources à la reproduction (Gavinet et al. , 2019; Bogdziewicz et al. , 2020b), la pollinisation (Schermer et al. , 2019) et la survie des fruits (Hedhly et al. , 2009; Misson et al. , 2011; Nussbaumer et al. , 2020).
Pour mieux prévoir les effets potentiels des changements climatiques sur la reproduction, les scientifiques disposent notamment de quatre approches. La première est celle des suivis à long terme de la fécondité pour tester si les changements climatiques présents ont déjà un impact sur les dynamiques de fécondité (Pearse et al. , 2017; Shibata et al. , 2020; Bogdziewicz et al. , 2020c). La deuxième est celle de l’expérimentation manipulant l’arbre pour comprendre les déterminants physiologiques de la reproduction (Bogdziewicz et al. , 2020c). La troisième est celle d’expérimentations de type manipulations d’écosystèmes ou de climat simulant les évolutions du climat prévues à long terme par des réchauffements, des enrichissements en CO 2 ou des réductions de précipitations (LaDeau & Clark, 2001; Pérez-Ramos et al. , 2013; Chung et al. , 2013; Bykova et al. , 2018; Bogdziewicz et al. , 2020a). La dernière approche possible est celle de la modélisation, qui implique d’avoir une compréhension fine des mécanismes de la reproduction pour pouvoir les simuler correctement (Monks et al. , 2016; Vacchiano et al. , 2018). Actuellement, aucun modèle ne
17 Introduction générale permet de faire des prédictions fiables de la fréquence et de l’intensité des fructifications des arbres dans les conditions climatiques futures (Schermer, 2019).
3. Objectifs de la thèse L’objectif général de cette thèse est d’approfondir notre connaissance des déterminants de la fécondité des arbres forestiers, et de leurs interactions, afin de mieux comprendre comment la fécondité pourrait être impactée par le changement climatique.
Figure 2 : Résumé schématique des grandes questions abordées dans le cadre de cette thèse et des chapitres qui les traitent.
Ce travail a été décliné en quatre parties : Dans les trois premières parties, nous avons utilisé trois approches expérimentales, et dans la quatrième partie nous avons commencé à intégrer les résultats issus de ces expérimentations dans un modèle basé sur les processus, le modèle PHENOFIT (Chuine & Beaubien, 2001). Nous nous sommes particulièrement concentrés sur les déterminants liés aux conditions météorologiques et à la gestion des ressources par les individus au travers des quatre chapitres présentés ci-dessous et résumés dans la Fig. 2.
18 Introduction générale
Dans le cadre de ce travail, j’ai donc abordé les quatre questions suivantes :
Question 1 : La limitation des ressources des branches suite à une défoliation entraîne-t- elle des changements d’allocations entre différentes fon ctions (reproduction actuelle, croissance, production de feuilles et reproduction futures) ?
Dans le Chapitre 1, nous avons manipulé la relation source-puit en imposant différentes intensités de défoliation à des branches de chêne vert portant des fruits noués. Nous avons ensuite observé l’effet de cette défoliation sur la survie, la croissance et la germination des fruits, ainsi que sur la production d’organes (fleurs, feuilles, rameaux) l’année suivante. L’objectif était de tester si les relations d’alloc ations entre différentes fonctions étaient impactées par la limitation en ressources à l’échelle du rameau et de la branche, et de tester le niveau d’autonomie en carbone des branches . Ce chapitre fait l’objet d’un article publié dans le journal Annals of Botany .
Question 2 : Le développement et la maturation des fruits impactent-ils les réserves de l’arbre et limite nt-ils la floraison femelle de l’année suivante ?
L’objectif du Chapitre 2 était de tester les mécanismes qui sous -tendent l’autocorrélation négative dans la fécondité des arbres, et qui sont dans une certaine mesure une des hypothèses fondamentales des RBM en étudiant l’impact de la reproduction sur les réserves en différents éléments et sur la floraison future. Dans ce chapitre, nous avons retiré tous les fruits noués de neuf chênes verts et observé si ce traitement augmentait les réserves des arbres après la maturité des fruits par rapport à un groupe t émoin. Nous avons également regardé l’effet du retrait de fruits sur la floraison de l’année suivante, à l’échelle du rameau et de l’arbre. Ce chapitre fait l’objet d’un article à soumettre.
Question 3 : La diminution des précipitations modifie-t-elle la fécondité du chêne vert et ses déterminants physiologiques et climatiques ?
L’impact des changements climatiques sur la fécondité des arbres reste encore mal connu. L’objectif de ce Chapitre était de déterminer comment une augmentation de l’aridité simulée par une diminution des précipitations, une des facettes potentielles du changement climatique en milieu Méditerranéen, pouvait affecter la fécondité des arbres. Pour cela, nous avons analysé les données de suivi à long terme du site expérimental de Puéchab on qui comprend un dispositif d’exclusion des précipitations depuis 2003 afin de déterminer si la réduction des précipitations affectait la reproduction des arbres et si elle modifiait l’effet des déterminants de la fécondité sur la variabilité interannuelle de fécondité. Enfin, cette étude nous a permis de tester si les arbres soumis à une réduction des précipitations sur le long terme étaient plus résistants aux épisodes de sécheresse en
19 Introduction générale contexte Méditerranéen. Ce chapitre fait l’objet d’un article en rév ision dans le journal New phytologist .
Question 4 : Les déterminants de la reproduction du chêne vert identifiés dans les précédentes parties permettent-ils de simuler les patrons de fructification en réponse au climat ?
Peu de modèles sont actuellement capables de prédire à la fois les dynamiques de production de graines des arbres forestiers et l’impact qu’auraient les changements climatiques sur ces dynamiques (Schermer, 2019; Journé, 2020). L ’objectif du Chapitre 4 est donc de développer un modèle mécaniste de fécondité permettant d’intégrer l’impact des conditions météorologiques sur les processus impliqués dans la variabilité interannuelle de la reproduction des arbres. Pour cela, nous avons intégré dans le modèle PHENOFIT (Chuine & Beaubien, 2001) les résultats des trois chapitres précédents, en particulier ceux du Chapitre 3, avant de modéliser de façon quantitative la production de fleurs et de fruits, notamment chez le chêne vert. Le modèle PHENOFIT est un modèle basé sur les processus qui modélise la réponse quotidienne des processus physiologiques des arbres aux facteurs environnementaux tels que les conditions météorologiques et les conditions de sol. Ce chapitre est rédigé en français dans un format libre.
20 Matériel et méthodes général
Matériel et méthodes général
1. Le chêne vert Dans le cadre de cette thèse, mes travaux ont essentiellement porté sur le chêne vert ( Quercus ilex ), une essence communément répandue dont l’aire de r épartition est localisée autour du bassin méditerranéen (Fig. 3). Cette espèce comprend deux sous-espèces, Quercus ilex ssp . ilex et Quercus ilex ssp. ballota (Desf.), qui sont interfécondes mais dont les aires de distribution sont relativement distinctes : la sous-espèce ballota est localisée principalement à l’intérieur des terres (Espagne, Maghreb) et la sous-espèce ilex sur les zones côtières (France et partie Est de la distribution de l’espèce) (Roda et al. , 1999). Si la littérature sur laquelle je me suis appuyé porte sur les deux sous- espèces, mes travaux portent exclusivement sur la sous-espèce ilex .
Figure 3 : Aire de distribution du chêne vert autour du bassin méditerranéen en gris. Les croix représentent des populations isolées géographiquement. Figure issue de (Delzon et al., 2013).
Le chêne vert est une espèce anémophile, monoïque et essentiellement allogame (Yacine & Lumaret, 1988) . C’est également une espèce au feuillage sempervirent et à la croissance lente (Roda et al. , 1999), dont les individus en âge de se reproduire peuvent ne pas faire plus de quelques mètres. Cela en fait donc une essence de choix pour des travaux de manipulation à l’échelle de l’individu.
2. Terrain d’expérience du CEFE Les expériences de défoliation et de retrait des fruits présentées respectivement dans les Chapitres 1 et 2 ont été réalisées sur un groupe de chênes verts (Fig. 4) plantés en 1998 sur le terrain d’expérience du laboratoire où j’ai réalisé ma thèse, le Centre d’Ecologie Fonctionnelle et Evolutive à Montpellier (latitude : 43.64°N, longitude : 3.86°E, altitude : 76 m). Contrairement au site expérimental de Puéchabon présenté dans la partie suivante, les arbres sur lesquels j’ai réalisé ces expérimentations n’ont pas fait l’objet de suivis pouvant donner une indication de leurs comportements sur le long terme.
22 Matériel et méthodes général
Figure 4 : Photographies du travail expérimental réalisé sur le terrain d’expérience du CEFE . A) Groupe d’arbres sur lesquels les expériences des chapitres 1 et 2 ont été réalisées. B) Exemple de branche fortement défoliée dans l’expérience conduite dans le Chapitre 1. C) Mesures de l’activité photosynthétique des arbres grâce à un LICOR ®- 6400. D) Mesure de la capacité germinative des glands parvenus à maturité sur les branches défoliées du Chapitre 1 dans des étuves à germination.
3. Le site expérimental de Puéchabon Le site expérimental de Puéchabon, dans l’Hérault, est suivi par le CNRS depuis 1984 et est installé dans la forêt domaniale du village de Puéchabon (latitude : 43.74°N, longitude : 3.59°E, altitude : 270 m) sur un plateau karstique (Fig. 5A). La forêt domaniale a été gérée en taillis pendant des siècles et la dernière coupe à blanc a eu lieu en 1942. La végétation est largement dominée par le chêne vert ( Quercus ilex ) qui représente plus de 80% du couvert. L’indice foliaire est de 2.9 m².m-² et la hauteur moyenne des arbres est d’environ 5.5 m. Le site est soumis à un climat méditerranéen caractéristique, les précipitations annuelles sont de 916 mm. La forêt pousse sur un sol caillouteux composé de calcaire du Jurassique dont les fissures sont remplies par des argiles.
Figure 5 : Photographies du site expérimental de Puéchabon. A) Vue sur la canopée depuis la parcelle aménagée avec des échafaudages. B) Vue sous couvert des gouttières installées dans le trai tement d’exclusion de précipitations et des collecteurs de litière. C) Mesure du diamètre d’un gland au cours de sa croissance. Crédits A et B : CEFE- CNRS, C : I. Chuine.
23 Matériel et méthodes général
Les données analysées dans le Chapitre 3 sont issues du dispositif d’exclusion de pluie partielle du site de Puéchabon (https://puechabon.cefe.cnrs.fr/spip.php?article8), qui a été mis en place au printemps 2003. Ce dispositif est constitué de trois répétitions ou blocs comprenant quatre traitements : une parcelle sèche recevant 27 % de précipitation en moins, une parcelle témoin, une parcelle éclaircie de 30 % de sa surface terrière et une parcelle éclaircie et sèche (Fig. 6) . L’exclusion de pluie est basée sur le principe des throughfall displacement experiment (Hanson et al. , 2003) avec des gouttières installées sous le feuillage et qui couvrent 30 % de la surface de la parcelle (10 m x 10 m) (Fig. 5B) . Compte tenu des pertes par évaporation directe de l’eau interceptée par le feuillage, ce dispositif réduit la pluie incidente de 27 % (Limousin et al. , 2008). Dans les parcelles témoins, des gouttières inversées ont été mises en place pour que le microclimat et les transferts radiatifs soient identiques à ceux dans les parcelles sèches. Les données des trois blocs mais uniquement des parcelles sèches et témoin ont été utilisées dans le Chapitre 3. Dans le chapitre 4, nous avons utilisé les données des trois blocs et des quatre traitements.
Figure 6 : Plan des parcelles du dispositif MIND au sein du site expérimental de Puéchabon . Figure adaptée d’une image de J. Kempf.
Parmi les données que nous avons utilisées, la production de litière est suivie tous les mois grâce à des collecteurs (Fig. 5B) répartis aléatoirement dans les parcelles dont le contenu (feuilles, bois, chatons, glands) est trié et pesé depuis 2003 et dont le nombre de glands est compté selon leur statut depuis 2007 (avortés, piqués ou arrivés à maturité). Le diamètre des troncs de tous les arbres de chaque parcelle est mesuré annuellement, certains sont par ailleurs équipés de dendromètres automatiques. La phénologie de la feuillaison et de la floraison est suivie toutes les
24 Matériel et méthodes général semaines entre mars et juillet grâce à des échafaudages qui équipent une des trois parcelles. Une fois par an en hiver, des feuilles sont prélevées dans cette même parcelle pour faire l ’objet d’analyses de composition chimique. Le site est par ailleurs équipé d’une station météorologique et d’une tour
à flux suivant en continu les flux de CO 2, d’eau et d’énergie entre l’écosystème et l’atmosphère par la méthode des corrélations turbulentes ( eddy correlation ).
À cette grande quantité de données collectées annuellement, nous avons ajouté entre 2015 et 2018 un suivi mensuel de la croissance et de la survie des glands entre leur nouaison en juillet, et leur maturité en novembre dans la parcel le équipée d’échafaudages (Fig. 5C). Ce suivi a été mené par Isabelle Chuine en 2015 et 2016 (avec l’aide de Soline Martin-Blangy), et principalement par moi-même en 2017 et 2018.
Enfin, notre étude du Chapitre 3 et la partie modélisation du Chapitre 4 s’a ppuient largement sur le modèle de bilan hydrique du modèle de végétation SIERRA, développé par Florent Mouillot (Mouillot et al. , 2001), et calibré sur le site de Puéchabon (Cabon et al. , 2018) par des mesures régulières de potentiel hydrique.
25 Chapitre 1 : Impact de la limitation en ressources induite par une défoliation expérimentale sur l’allocation à la reproduction
Rameau de chêne vert portant une deuxième pousse d’été et deux fruits noués avortés sur la pousse de printemps. Crédit : Matthias Grenié.
Chapitre 1
Chapeau Le Chapitre 1 de cette thèse est basé sur l’article (Le Roncé et al. 2020), publié dans la revue Annals of Botany . Son objectif était de tester si les relations d’allocatio ns entre différents organes (fleurs mâles et femelles, rameaux, feuilles) étaient impactées par une limitation en ressources à l’échelle du rameau et de la branche, et d’estimer le niveau d’autonomie en carbone des branches . Dans ce chapitre, nous avons montré que la régulation de l’allocation des re ssources à la reproduction semblait pouvoir se faire à plus large échelle que celle du rameau ou de la branche, et qu’en cas de défoliation, même intense, au cours du développement du fruit, la survie et la croissance des fruits étaient très peu affectées. L’allocation à la floraison lors du printemps suivant est en revanche réduite, l’arbre privilégiant la production de feuilles pour compenser la défoliation de l’année précédente.
Ce travail a pris la forme d’une expérimentation conduite sur le terrain d’e xpérience du CEFE sur des chênes verts d’une vingtaine d’années. Elle a duré deux ans et a fait l’objet du stage de Master 1 d’Elia Dardevet pendant la deuxième année de l’expérience. Elle a été co -conçue avec Isabelle Chuine et Jean-Marc Limousin. J’ai as suré la majorité de sa mise en place et de son suivi avec l’aide ponctuelle de plusieurs personnes, en particulier Pauline Durbin et Jordane Gavinet pour la mise en p lace, et de l’aide régulière d’Elia Dardevet et Isabelle Chuine pour le suivi. J’ai réalis é la majorité des analyses statistiques, avec l’aide de Maude Toïgo sur différents points techniques. La rédaction a été une collaboration avec tous les co-auteurs.
L’article est en ligne au lien suivant : https://doi.org/10.1093/aob/mcaa137
Les « Supplementary materials » de cet article sont en Annexe 2 de cette thèse (page 164).
27 Chapitre 1
Annals of Botany XX : 1–15, 2020 doi: 10.1093/aob/mcaa137, available online at www.academic.oup.com/aob
Resource manipulation through experimental defoliation has legacy effects on allocation to reproductive and vegetative organs in Quercus ilex
Iris Le Roncé1, Maude Toïgo 1, Elia Dardevet 1, Samuel Venner 2, Jean-Marc Limousin1,a and Isabelle Chuine1,a,*
1CEFE, Univ. Montpellier, CNRS, EPHE, IRD, Univ. Paul Valéry Montpellier 3, Montpellier, France and 2Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, Université de Lyon, Université Lyon 1, CNRS, F-69622, Villeurbanne, France *For correspondence. E-mail [email protected] aCo-last authors.
Received: 18 March 2020 Returned for revision: 29 May 2020 Editorial decision: 9 July 2020 Accepted: 14 July 2020
• Background and Aims In plants, high costs of reproduction during some years can induce trade-offs in re- source allocation with other functions such as growth, survival and resistance against herbivores or extreme abiotic conditions, but also with subsequent reproduction. Such trade-offs might also occur following resource shortage at particular moments of the reproductive cycle. Because plants are modular organisms, strategies for resource al- location to reproduction can also vary among hierarchical levels. Using a defoliation experiment, our aim was to test how allocation to reproduction was impacted by resource limitation. • Methods We applied three levels of defoliation (control, moderate and intense) to branches of eight Quercus ilex trees shortly after fruit initiation and measured the effects of resource limitation induced by leaf removal on fruit development (survival, growth and germination potential) and on the production of vegetative and repro- ductive organs the year following defoliation. • Key Results We found that defoliation had little impact on fruit development. Fruit survival was not affected by the intense defoliation treatment, but was reduced by moderate defoliation, and this result could not be explained by an upregulation of photosynthesis. Mature fruit mass was not affected by defoliation, nor was seed germination success. However, in the following spring defoliated branches produced fewer shoots and compensated for leaf loss by overproducing leaves at the expense of #owers. Therefore, resource shortage decreased resource allocation to reproduction the following season but did not affect sex ratio. • Conclusions Our results support the idea of a regulation of resource allocation to reproduction beyond the shoot scale. Defoliation had larger legacy effects than immediate effects.
Key words: Defoliation recovery, allocation shifts, trade-offs, fruit production, seed germination, sex allocation, primary growth, Quercus ilex.
INTRODUCTION the reproductive structures ( Gavinet et al., 2019 ; Bogdziewicz et al ., 2020b). Understanding strategies of resource allocation Climate change is currently affecting the reproductive suc- to reproductive functions versus other functions, as well as the cess of trees (Pérez-Ramos et al., 2010 ; Sánchez-Humanes environmental determinants of reproductive success, is there- and Espelta, 2011; Caignard et al., 2017 ; Bogdziewicz et al ., fore essential to grasping and predicting how the reproductive 2020a) and their allocation of resources to reproduction success of trees and regeneration will be affected by future cli- (Monks et al., 2016 ; Gavinet et al., 2019 ). While the frequency matic conditions. of reproductive failures has been increasing in some species Trade-offs in resource allocation arise from the fact that and areas (Bogdziewicz et al ., 2020a), jeopardizing the natural when limiting resources are allocated to one function, such regeneration of the forest, fruit production has been increasing as reproduction, they become unavailable for others ( Bazzaz in others (Caignard et al., 2017 ). Climate change effects on tree et al. , 1987 ; Stearns, 1989 ; Obeso, 2002 ), thereby creating the growth can be either positive due to CO fertilization, nitrogen 2 need for priority rules of allocation ( Suzuki, 2001 ; Wiley and deposition (Fernández-Martínez et al., 2017 ) and the length- Helliker, 2012 ). Reproduction being costly for plants, it might ening of the growing season (Menzel and Fabian, 1999 ; Piao result in trade-offs with other functions, such as growth, survival, et al., 2007 ; Delpierre et al., 2009 ), or negative because of more defence against pests and herbivores, and resistance to extreme stressful conditions, especially in water-limited ecosystems, abiotic conditions ( Obeso, 2002 ; Barringer et al. , 2013 ), as well where aggravated droughts are expected ( Dai, 2013; IPCC, as with subsequent reproductive efforts. In trees, the trade-off 2013). These climate change effects are likely to affect both the between growth and reproduction, generally studied at the carbon source through photosynthesis (Luyssaert et al., 2007 ; stand scale, has been the focus of most investigations and has Biederman et al., 2016 ) and the carbon sink through cambial found some support in some cases ( Han et al. , 2011 ; Sánchez- activity (Babst et al., 2013 ; Lempereur et al., 2017 ), and to Humanes et al. , 2011 ; Martín et al. , 2015 ; Vergotti et al. , modify carbon allocation to the different organs, especially 2019 ), but not in all ( Dick et al. , 1990 ; Yasumura et al. , 2006 ;
© The Author(s) 2020. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: [email protected]. Chapitre 1
2 Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies
Knops et al. , 2007 ; Alla et al. , 2012 ; Redmond et al. , 2019 ; which suggests that whole-tree regulation or physiological in- Vergotti et al. , 2019 ). tegration among branches is sometimes necessary. Therefore, Understanding how reproduction may be limited by other it seems that the scale of the regulation of carbon allocation to functions or, on the contrary, limit them is of particular import- sexual reproduction in woody plants can range from the branch ance in mast-seeding species. Mast-seeding species show years to the whole individual (Ushimaru and Genkai-Kato, 2011). of massive and synchronous production of seeds that alternate The degree of physiological autonomy of shoots for repro- with one or more years of negligible production. Fruit produc- duction varies among species and situations ( Henriksson, 2000; tion during a mast year consumes a signi'cant amount of re- Hasegawa et al., 2003 ; Díaz et al., 2004 ; Sánchez-Humanes sources (Janzen, 1971; Kelly, 1994 ; Kelly and Sork, 2002 ) as et al., 2011 ). Therefore, costs of reproduction may vary in fruit biomass amounting up to 79 and 52 % of annual wood bio- contradictory ways if studied at the tree, the branch or the shoot mass production has been reported during a mast year in beech level ( Obeso, 1997), but few studies have examined repro- and sessile oak, respectively ( Mund et al., 2010 ; Delpierre duction costs and allocation trade-offs between reproduction et al., 2016 ). Different mechanisms of allocation to repro- and other functions at multiple hierarchical levels ( Sánchez- duction have been proposed to explain mast seeding ( Pearse Humanes et al., 2011 ; Alla et al., 2012 ; Barringer et al., 2013 ; et al., 2016 ). Among them, two have found some support in Hossain et al., 2017 ). Furthermore, very few of these studies oaks. First, the resource depletion (or storage) hypothesis pro- are explicitly related to mast seeding ( Miyazaki, 2013). Here poses that the tree’s reserves are depleted during mast years and we aimed to bring new insights to the elucidation of the mech- that the tree needs to stock resources for several years before anisms of mast seeding by investigating the investment in re- it can invest strongly in reproduction again ( Sork et al., 1993 ; production at different physiological scales. More precisely, we Sánchez-Humanes et al., 2011 ; Pearse et al., 2016 ). Second, aimed to investigate allocation to reproduction at the branch the resource-switching hypothesis proposes that resources are and shoot levels from fruit initiation to fruit initiation of the shifted from vegetative growth to reproduction in mast-seeding next season in a mast-seeding species. years (Norton and Kelly, 1988 ; Hirayama et al., 2008 ; Sánchez- One method of studying the allocation relationships be- Humanes et al., 2011 ). Whichever hypothesis, or their combin- tween different sinks is to manipulate sink–source relationships ation, is correct, the nature of the limiting resource(s) inducing (Iqbal et al., 2012 ; Bogdziewicz et al., 2020 c). Manipulation of the mast-seeding behaviour remains unknown in many species sink–source relationships by defoliation can help to determine (Han and Kabeya, 2017). whether branches or individual plants are able to compensate Studies investigating mast seeding are usually based on for the loss of photosynthetic capacity and nutrient storage to correlative analyses of fructi'cation time-series at the tree or achieve their reproduction. Compensation may happen by ei- plot scale. Understanding the physiological mechanisms re- ther changing allocation from other functions to reproductive sponsible for mast seeding, and more generally the allocation organs (growth, survival, storage, future reproduction) ( Sprugel of resources to reproduction in trees, requires a deeper under- et al., 1991 ; Obeso, 1998; Hoch, 2005) or by increasing the standing of the regulation of reproduction all along the repro- photosynthetic activity of the remaining leaves in the case of ductive cycle from bud initiation to fruit maturation ( Miyazaki carbon (Iqbal et al., 2012). et al ., 2002; Bañuelos and Obeso, 2005; Ichie et al., 2013 ; Most defoliation and herbivore exclusion experiments on Allen et al., 2017 ). The goal of this study was thus to better temperate forest trees and fruit trees have shown a negative understand how allocation to reproduction is regulated at the effect of defoliation on fruit set ( Mehouachi et al. , 1995 ; different steps of the reproductive cycle, from #owering to seed Obeso, 1998 ; Iglesias et al. , 2003 ; Frioni et al. , 2018 ) and germination. on fruit size ( Obeso, 1998 ; Hoch, 2005 ; Matsumoto et al. , A key question in understanding mast seeding, and more 2017 ). In oaks, most studies concerned natural herbivory generally the inter-annual variation in tree fecundity, is which and showed also negative impacts on fruit set and total fruit resource is most limiting to reproduction and how this resource production ( Crawley, 1985 ; May and Killingbeck, 1995 ; is allocated to the different functions. Phosphorus, nitrogen Hochwender et al. , 2003 ; Nakajima, 2015 ; Pearse et al. , and non-structural carbohydrates are known to be involved in 2015 ; Canelo et al. , 2018 ). However, other studies have found the proximate mechanisms driving mast seeding (Han et al., no effect of herbivory on fruit set, fruit size or yield in other 2011; Sala et al., 2012 ; Miyazaki et al., 2014 ; Allen et al., species ( Obeso and Grubb, 1993 ; Mehouachi et al. , 1995 ; 2017; Han and Kabeya, 2017; Fernández-Martínez et al., Tamura and Hiura, 1998 ; Ezzahouani and Williams, 2003 ; 2019). Most of the carbon used for fruit growth, either in forest Bañuelos and Obeso, 2005 ; Frioni et al. , 2018 ; Pasqualotto trees or in fruit trees, has been shown to derive from short- et al. , 2019 ). In some cases, overcompensation, i.e. a positive term photoassimilates mostly produced by leaves in the close effect of herbivory or leaf removal ( Agrawal, 2000 ; Iqbal vicinity of the fruit (Hasegawa et al., 2003 ; Volpe et al., 2008 ; et al. , 2012 ), has been observed on fruit size in Vitis vinifera Hoch et al., 2013 ; Ichie et al ., 2013; Han et al., 2016 ). This sug- (Ezzahouani and Williams, 2003 ) and on #ower production gests that foliated branches are autonomous for carbon for most in Quercus ilex ( Díaz et al. , 2004 ). However, it is dif'cult to of the growing season to produce the fruits, as proposed by the draw general conclusions from these studies because they branch autonomy theory (Watson and Casper, 1984 ; Sprugel differ in many respects: defoliation intensity, defoliation et al., 1991 ). However, other studies have also shown that in- extent (branch or whole tree), moment of the reproductive dividual fruit-bearing branches are sometimes unable to supply cycle, targeted variables (fruit number, fruit set, total seed all the carbon required for the development of their fruits biomass, yield, etc.). Moreover, most studies have looked at (Newell, 1991 ; Miyazaki et al., 2007 ; Pasqualotto et al., 2019 ), the immediate effect of defoliation on ongoing reproduction,
29 Chapitre 1
Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies 3 while few of them have explored the legacy effect of defoli- (Yacine and Bouras, 1997 ; Gómez-Casero et al., 2004 ) ( Fig. 1). ation on resource allocation to different functions in the sub- Catkins develop in the axils of lower leaves of the current-year sequent seasons ( Noyce et al. , 2016 ; Wiley et al. , 2017 ). The shoot or in separate buds bearing only catkins. Female pistil- majority of defoliation studies on forest trees have focused late #owers mature a few days after staminate #owers and are on deciduous species, which sustain lower leaf construction located on an in#orescence at the upper part of the current- cost than evergreen species ( Villar and Merino, 2001 ) and year shoot. Female in#orescences can bear one to six pistillate might be more tolerant of defoliation ( Piper and Fajardo, #owers ( Fig. 1). Fertilization occurs in late June and early July, 2014 ), but also have a shorter photosynthetic activity period. leading to fruit initiation. Fruits (acorns) achieve maturation in Determining what impact defoliation has on resource alloca- November–December ( Yacine and Bouras, 1997 ). tion in evergreen species is thus not straightforward. The experimental plot was located in Montpellier, France Here, we studied the regulation of allocation to reproduc- (latitude 43.64°N, longitude 3.86°E, altitude 76 m). The cli- tion relative to other functions in the monoecious evergreen tree mate is Mediterranean with an annual rainfall of 629 mm and Q. ilex , which is the most widespread forest tree species in the mean annual temperature of 15.2 °C. The soil of the 'eld site Mediterranean Basin, using experimental defoliation to create is a rendzina-like silty clay soil, with a pH of 8 and a depth a situation of resource limitation. The originality of our study varying from 150 to 200 cm. The trees used for the experiment is to cover the main steps of the reproductive cycle, at different were planted in 1998 from fruits collected from nearby natural scales within the tree and with a large range of defoliation in- populations. Trees were on average (± s.d.) 4.3 ± 0.3 m tall with tensities, from 0 to >80 % defoliation, and to quantify its im- a mean basal area of 124 ± 39 cm2 during the experiment. pact not only on allocation to reproduction in the same year but also on allocation to vegetative and reproductive organs the following year. More speci'cally, we aimed to answer the fol- Experimental setting lowing questions: In 2018, eight trees bearing fruits were selected for the ex- (1) Are branches and shoots able to compensate for resource periment. On each tree, six branches bearing at least 10–15 ini- limitation to maintain fruit production (fruit survival, tiated fruits (i.e. fertilized pistillate #owers) were selected in growth and germination potential) and production of vege- different and distant parts of the tree crown. The shoot is de- tative and reproductive organs the following year? 'ned here as the growth unit of the current year (or the spring (2) Are there trade-offs of allocation at the shoot scale be- and summer growth units in cases when two growth #ushes tween reproductive and vegetative organs, between sub- happened due to polycyclism). The branch refers to a rami- sequent years and between male and female reproductive 'ed structure consisting of several shoots and including all the organs, and how are these trade-offs affected by resource rami'cations above the lowest one carrying at least one fruit limitation? (Fig. 2). The number of 2018 shoots per branch varied from 18 (3) Are there some steps of the reproductive cycle that are more to 192 among all trees, and the number of fruit-bearing 2018 sensitive to resource limitation than others? shoots per branch varied from four to 17 among all trees. We applied three defoliation treatments (0 %, control; 50 %, For each tree, branches were randomly assigned to one of the moderate; 85 %, intense leaf removal) to six branches of eight three following treatments (two replicates per tree): no defoli- trees and monitored the impact it had on allocation to repro- ation (control); moderate defoliation; and intense defoliation. duction at different steps of the reproductive cycle as presented Defoliation in the moderate treatment consisted of removal of in Fig. 1. First, we expected that shoots would be less able to half of the leaves and in the intense defoliation treatment it con- compensate for loss of leaves necessary for fruit production in sisted of removal of all leaves except the uppermost leaf of each the intense defoliation treatment compared with the moderate shoot, which led to removal of 85 ± 3 % of the leaves. For this defoliation treatment. Second, we expected reduced germin- last treatment, one leaf per shoot was left to allow minimal tran- ation success for seeds produced by defoliated branches due spiration #ow in the shoot (Figs 1 and 2). to a reduced amount of reserves. Third, we expected strongly Branches were defoliated between 21 and 27 June 2018, reduced, or even suppressed, production of #owers the fol- shortly after fruit initiation and once the leaves of the spring lowing year on the defoliated branches because of priority in- #ush were mature. On 28 out of the 48 monitored branches, a vestment in leaves to compensate for defoliation, as observed second #ush of leaves (called ‘summer #ush’ hereafter) hap- by Wiley et al. (2017) in Quercus velutina. Finally, since sex al- pened in early July. These shoots underwent the same defoli- location theory predicts that female function is more expensive ation treatment as their respective spring shoot in mid-July. (Charlesworth and Morgan, 1991 ), we expected a shift towards In order to estimate the mean individual leaf area for each maleness the following year on the defoliated branches. tree, we randomly selected and scanned around 200 leaves per tree using the image analysis software ImageJ, and used the scan to calculate the total leaf area of every shoot. MATERIALS AND METHODS
Study species and area Fruit growth monitoring and germination the year of defoliation Quercus ilex is an evergreen wind-pollinated monoecious tree Fruit growth and survival between initiation and maturity that usually #owers in May in the study area. The male in#or- were followed on a total of 930 acorns that were individually escences, called catkins, bear around 20–25 staminate #owers tagged and monitored. Up to six fruits could grow at the axil
30 Chapitre 1
4 Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies
2018 Intense summer Dependent variable Effect of defoliation and defoliation shoot 2018 other covariables 2018 spring Moderate defoliation (–) shoot Fruit survival between Intense defoliation (NS) Two initiation and maturation (1) 2 or more fruits per leaf (+) One fruits Summer flush in 2018 (–) fruits per leaf per leaf Photosynthesis rate (2) Defoliation (NS)
Mature fruit mass (3) Defoliation (NS)
Seed germination Defoliation (NS) success (4) Fruit mass (+)
1 2 Intense defoliation (–) Moderate defoliation (NS) Number of 2019 spring Shoot basal area (+), shoots per 2018 shoot (5) interaction with treatment Summer flush in 2018 (+) 4 2019 Spring 3 2019 spring shoots Moderate defoliation (–) shoots 2019 Number of leaves per per 2018 Intense defoliation (–) 2019 spring shoot (6) shoot Shoot basal area (+)
Moderate defoliation (–) 2018 shoot Number of catkins per Intense defoliation (–) basal 2019 spring shoot (7) Shoot basal area (–) area Number of leaves when defoliated ( –)
Number of staminate Moderate defoliation (–) flowers per catkin (8) Intense defoliation (–)
Number of staminate Defoliation (NS) per staminate flower (9)
Moderate defoliation (–) Number of female Intense defoliation (–) flowers per 2019 Number of leaves ( +) spring shoot (10) Shoot basal area (+) 2018 acorn production when defoliated (–)
Defoliation (NS) Shoot basal area (+) vs Sex ration (11) Number of leaves ( +) interaction with treatment 2018 acorn production (–)
Fruit set (12) Defoliation (NS)
FZ[. \. Diagram summarizing the main results of this study. Each box corresponds to a statistical analysis and the numbers following dependent variables cor- respond to the model numbers in Tables 1–3 . Only defoliation effects and signi'cant covariables are shown here. The sign of the relationship between dependent variables and 'xed effects is expressed as follows: NS, no signi'cant difference from control; +, positive effect; −, negative effect.
31 Chapitre 1
Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies 5
Branch limit A containing all the BC shoots bearing at least 10 fruits that underwent defoliation or not Spring shoot 2018
Shoots localized before the first ramification carrying the selected fruits (not in the branch)
Before defoliation / control treatment After intermediate defoliation After intense defoliation
FZ[. ~. Schematic representation of the defoliation protocol. of the same leaf and this information was recorded for each area for that particular tree, and dividing by the number of fruits fruit as a single fruit or two or more fruits growing at the axil that were initiated by the shoot in July. of the same leaf. From July 2018 until November 2018, fruit survival and size was monitored six times [1 July (day 0), 23 August (day 53), 14 September (day 75), 5 October (day 96), Photosynthesis and predawn leaf water potential in the year of 19 October (day 110) and 8 November (day 130)]. Fruit size was measured with an electronic calliper. During the early defoliation stage of fruit development, we measured fruit diameter be- In order to check the treatment effect on photosynthesis, we cause its shape is round (the embryo is invisible, inside the measured leaf gas exchange on current-year leaves adjacent to acorn cup). As soon as the shell protecting the embryo pro- the fruits. We measured gas exchange on one leaf per treatment trudes from the cup, we measured fruit length from the base (i.e. three leaves per tree) of six trees, except the moderate de- of the cup to the top of the fruit excluding the remains of the foliation treatment on the sixth tree due to accessibility con- style. Fruit mass at maturity was strongly correlated to fruit straints (17 leaves in total). Leaf gas exchange was measured size ( Supplementary Data Fig. S1B ), so we assume that seed on 4 July 2018, 1–2 weeks after defoliation and after the spring growth in mass and volume occurred simultaneously. Shoots leaves had reached maturity. Measurements were carried out that had died or were broken between July and November with two portable photosynthesis systems (Li-6400, Li-Cor, (3 %) were removed from the dataset so that fruit survival Lincoln, NE, USA) equipped with a light source (6200-02B could be calculated without interference of shoot mortality LED, Li-Cor). Leaves were 'rst acclimated in the chamber for (Canelo et al. , 2018 ). After the last fruit size measurement >20 min at ambient temperature, ambient CO 2 concentration in November, we monitored fruit maturation (desiccation) (400 ppm) and a saturating photosynthetic photon #ux density twice a week. Once they started turning brown, fruits were (PPFD) of 1500 µmol m−2 s −1 . collected and weighed. Tree water stress at the end of summer was determined by In order to determine their capacity to germinate, all col- the predawn leaf water potential measured on 5 September and lected fruits were placed in closed Magenta™ boxes 'lled with 4 October 2018 with a pressure chamber (PMS 1000, PMS 18 g of vermiculite imbibed with 50 mL of distilled water and Instruments, Corvallis, OR, USA). Leafy shoots were collected kept at 25 °C in the dark in germination stoves (LMS™). Fruits before sunrise and stored in airtight bags in a refrigerator until infested by insects (19 out of 247) were discarded, and ger- measurements 2 h later. All trees were sampled, including two mination success was recorded after 4 weeks if a radicle had leafy shoots per tree, and the difference between the two shoots emerged. Holm oak acorns are recalcitrant seeds, meaning that never exceeded 0.2 MPa. they are extremely sensitive to dehydration ( Joët et al., 2013 ). For this reason, they have no dormancy and have to germinate rapidly as germination probability decreases with desiccation Shoot, leaf, %ower and initiated fruit production the year (Joët et al., 2016). following defoliation The number of leaves and the basal diameter of all the monitored 2018 spring shoots were counted and measured in On each of the 48 branches that had been manipulated in December 2018 and January 2019, i.e. after fruit maturity and 2018, we randomly selected 've 2018 shoots that had borne before the 2019 spring #ush. In addition, on each of the 48 ma- at least one fruit and on which at least one bud was starting to nipulated branches we tagged 've 2018 shoots that had not break. On each of these 2018 fruit-bearing shoots and on each borne a single fruit in 2018, on which we performed the same of the tagged 2018 non-fruit-bearing shoots (produced either leaf and basal area measurements. Although we did not count during the spring #ush or the summer #ush), we counted the the total number of leaves that 2018 shoots carried before de- number of 2019 spring shoots, the number of leaves per 2019 foliation, we checked that shoot basal area and the number of spring shoot, the number of catkins per 2019 spring shoot and spring leaves were very strongly correlated in the control treat- the number of female #owers per 2019 spring shoot. Note that ment (Supplementary Data Fig. S1A). 2019 spring shoots could be composed exclusively of catkins Mean leaf area per fruit for each 2018 shoot was obtained (in this case there is no twig), of both leaves and #owers (catkin by multiplying the number of leaves per shoot by the mean leaf or female in#orescence) or exclusively of leaves.
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6 Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies
For each 2018 shoot of seven of the trees (out of eight), we with an offset. We only included the interactions between defoli- collected two catkins (if existing) out of all the catkins pro- ation and the other covariates in the complete models. For each duced by 2019 shoots. On these catkins, we counted the total response variable, we then applied a simpli'cation of the model number of staminate #owers that they bore and we selected one by sequentially removing the insigni'cant interaction terms, staminate #ower in the middle of each to count the number of starting with the weakest and least signi'cant interaction. We stamens per #ower. considered our level of signi'cance to be P < 0.05. The sex ratio of the shoots was calculated as the proportion of We used tree, branch and shoot as random factors. For meas- female #owers produced by the shoots out of the total number urements at the shoot scale (e.g. fruit survival), we considered of in#orescences of the shoot (female #owers plus catkins). tree, branch and shoot together as nested random factors. Thus, The number of female #owers that had developed into fruits the model estimated the variability of the data due to the dif- (fruit set) was counted on 17 July 2019. We could not monitor ferences between trees, between the branches of a tree and fruit growth and maturation in summer and autumn 2019 as an between the shoots of a branch. For variables analysed at the early extreme heat wave signi'cantly damaged the leaves of the branch scale (e.g. the number of leaves per shoot in 2019), we tagged shoots on 28 June 2019. nested branches within tree as random factors. For random sampling at the tree scale (e.g. the rate of photosynthesis), we used trees as a random factor to take into account the between- tree heterogeneity (Table 1 ). Statistical analyses We 'tted GLMMs with a Gaussian distribution for the re- All statistical analyses and visual representations were sponse variables presenting a normal error structure, with a bi- conducted using the software R version 3.6.1 (2019) and the nomial distribution and its logit link function for dichotomous ggplot2 package (Wickham, 2009 ). We used the following response variables and with a Poisson distribution and its log packages for data analysis: lme4, car, multcomp and MuMIn link function for count data (Table 1 ). The signi'cance of the (Hothorn et al., 2008 ; Bates et al., 2015 ; Fox and Weisberg, studied effects was determined through an ANCOVA of type 2019; Bartoń, 2019). II using a Wald χ2 test followed by a Tukey post hoc test with We studied the effects of defoliation and additional covariates Bonferroni–Holm correction to perform pairwise comparisons. on reproductive allocation with 12 generalized linear mixed We completed our analyses by characterizing fruit length and models (GLMMs) ( Table 1 , Fig. 1 ). Four of these models are survival rate at each measurement date over the course of the dedicated to allocation to reproduction in the year of defoliation year for the different treatments of defoliation with GLMMs (2018), and eight are dedicated to allocation to reproduction (Gaussian and binomial distribution respectively, treatment as the following year ( Table 1 , Fig. 1 ). All continuous covariables the 'xed effect and tree/branch/shoot as a random effect). were standardized prior to analysis to compare model estimates We checked for dataset homogeneity ( Supplementary Data between variables. The analyses of Models (5), (11) and (12) Table S1 ) and evaluated collinearities between variables by cal- (number of 2019 shoots, 2019 sex ratio and 2019 fruit set, re- culating the variance-in#ated factor ( Dormann et al., 2013 ). spectively) took into account the number of 2018 shoots with an This index is calculated as the inverse of the proportion of vari- offset. The analyses of Models (6), (7) and (10) (number of leaves, ance speci'c to each explanatory variable. It was calculated as number of catkins and number of female #owers produced in 1/(1 − R2), where R2 is the coef'cient of determination of the 2019, respectively) took into account the number of 2019 shoots linear regression between a given explanatory variable and the
T \. Detail of the structure of GLMMs used to establish the effect of defoliation on the allocation to reproduction in the year of defoliation and the following year: response variable, distribution of the model, set of covariates and random effects, number of obser- vations and residual degrees of freedom
Model Response variable Model distribution Set of covariables 1 Random effects No. of observations (d.f.)
Allocation to reproduction in the year of defoliation (2018) (1) Fruit survival Binomial A Tree|branch|shoot 918 (907) (2) Photosynthesis rate Gaussian None tree 17 (12) (3) Mature fruit mass Gaussian A Tree|branch|shoot 247 (233) (4) Fruit germination success Binomial B Tree|branch|shoot 228 (221) Allocation to reproduction the year following defoliation (2019) (5) No. of 2019 spring shoots per 2018 shoot Poisson C Tree|branch 241 (230) (6) No. of leaves per 2019 spring shoot Poisson C Tree|branch 241 (232) (7) No. of catkins per 2019 spring shoot Poisson D Tree|branch 241 (230) (8) No. of staminate #owers per catkin Gaussian None Tree|branch 190 (184) (9) No. of stamens per staminate #ower Gaussian None Tree|branch 190 (184) (10) No. of female #owers per 2019 spring shoot Poisson D Tree|branch 241 (228) (11) Sex ratio of 2019 spring shoots Binomial D Tree|branch 241 (228) (12) Fruit set on 2019 spring shoots Binomial E Tree|branch 140 (133)
1In addition to the effect of defoliation, the set of covariates shared by the models is indicated as follows: (A) 2018 shoot basal area, mean leaf area per fruit on 2018 shoot, summer #ush in 2018 (yes/no), more than one fruit initiated per leaf (yes/no), minimum predawn potential of the tree (measured on 4 October); (B) fruit mass; (C) 2018 shoot basal area, summer #ush in 2018; (D) 2018 shoot basal area, summer #ush in 2018, number of mature acorns on 2018 shoot, number of leaves per 2019 shoot; (E) 2018 shoot basal area, number of leaves per 2019 shoot; None: no covariable added.
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Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies 7
2 2 remaining variables. Marginal ( R m) and conditional ( R c) coef- Table 2 ]. Besides, fruit-bearing branches were usually larger 'cients were calculated with the package MuMIn according to than non-fruit-bearing branches (Supplementary Data Fig. S3 ). Nakagawa’s method ( Nakagawa and Schielzeth, 2013 ) to es- timate the variance explained by 'xed effects and 'xed plus random effects, respectively. Effect of defoliation on fruit growth, fruit survival, seed germination and photosynthetic activity RESULTS The effect of experimental defoliation on fruit survival de- Fruit growth showed a linear progression in 2018, although it pended on defoliation intensity: the moderate defoliation treat- slowed down strongly from early September to early October ment had a negative effect on fruit survival compared with the when drought stress was maximum (measured predawn water control (coef'cient parameter estimate ± s.e.: −0.67 ± 0.3), potential of −3.1 ± 0.2 MPa on 5 September and −3.8 ± 0.3 MPa but not the intense defoliation treatment [ Table 2 , Model (1); on 4 October). Growth resumed after the 'rst heavy rains of au- Supplementary Data Table S2 ]. Fruit survival decreased with tumn, which occurred on 6 and 7 October ( Fig. 3A , C). Less the second summer #ush [Table 2 Model (1), −0.94 ± 0.3; than half of the fruits reached maturity whatever the treat- Supplementary Data Table S2 ] but increased with the number ment (Fig. 3B ). Like almost all the studied variables, mature of fruits growing in the axil of the same leaf [Table 2 , Model fruit mass and fruit survival varied strongly between trees, (1), 0.37 ± 0.2; Supplementary Data Table S2 ]. The probability branches (Supplementary Data Fig. S2 ) and shoots, which also of fruit survival was not affected by 2018 shoot basal area, explains the fact that the marginal R2 (only 'xed effects) was mean leaf area per fruit or minimum predawn water potential much lower than the conditional R2 ('xed + random effects, of the tree [Table 2 , Model (1)]. Defoliation did not affect the here shoot nested in branch nested in tree) of models (1) and photosynthetic rate measured between 1 and 2 weeks after de- (3) [0.07 and 0.33 for model (1), 0.07 and 0.73 for model (3); foliation [Table 2 , Model (2)]. The interaction of defoliation with the number of acorns per leaf was signi'cant [ Table 2 , Model (3); Supplementary Data Table S2 ]. Mature fruit mass increased with both moderate and 17.5 intense defoliation only in the case of more than one fruit per A * leaf (0.22 ± 0.09 and 0.21 ± 0.09, respectively). Mature fruit 15.0 Defoliation intensity ns mass was not affected by 2018 shoot basal area, mean leaf area Control per fruit, summer #ush or minimum predawn water potential 12.5 Moderate ns ns Intense ns of the tree [Table 2 , Model (3); Supplementary Data Table S2 ]. 10.0 Mean germination rate was low in the three treatments (52,
Fruit size (mm) size Fruit 52 and 56 % in the control and moderate and intense defoliation 7.5 ns treatments, respectively) and strongly positively correlated to fruit mass [ Table 2 , Model (4), coef'cient parameter estimate ± s.e., 1.00 0.84 ± 0.3]. Defoliation did not affect the seed germination rate B [Table 2 , Model (4); Supplementary Data Table S2 ]. ns 0.75 ns * * * 0.50 Effect of defoliation on the production of vegetative and reproductive organs the following year
Survival rate 0.25 Shoots and leaves. The number of shoots produced per 0 2018 shoot in spring 2019 was higher on branches that had re#ushed during the summer of 2018 [9.7 shoots on average 40 C on re#ushed branches compared with 4.0 on branches with no 2018 re#ush; Table 3 , Model (5); Fig. 4A ]. It was also 30 positively correlated with 2018 shoot basal area whatever the 20 treatment [ Table 3 , Model (5); Supplementary Data Table (mm) S3 ], although defoliation affected this relationship [ Table 3 , 10 Model (5); Supplementary Data Table S3 ]. For an equiva- Daily precipitation 0 lent 2018 basal area increment, fewer shoots were produced Jul Aug Sep Oct Nov during spring 2019, when the branch was intensely defoliated compared with control (−0.39 ± 0.08). The effect was in a Date similar direction in moderately defoliated shoots, although it FZ[. . Variation of (A) mean fruit size (± 95 % con'dence interval, raw data), was only marginally signi'cant (−0.18 ± 0.09). Overall, the (B) fruit survival rate for each treatment (all trees pooled together, raw data) number of 2019 spring shoots per 2018 shoot was lower in and (C) daily precipitation from 1 July 2018 to 7 November 2018. GLMM the intense defoliation treatment (3.9 shoots on average com- testing the effect of treatment alone as 'xed effect and with Gaussian distri- bution for (A) and binomial distribution to test the survival of each acorn for pared with 5.3 shoots in the control treatment; Fig. 4A ), while (B): *signi'cant difference between treatments; ns, no signi'cant difference the number of leaves supported by 2019 spring shoots in- (P < 0.05). creased both with defoliation [ Table 3 , Model (6); Fig. 4B ;
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8 Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies
T ~. Summary of GLMMs testing the effects of defoliation and other biological covariates on fruit survival, growth and germin- 2 2 2 ation, as well as photosynthetic rate in 2018. The table reports statistics, P-values and marginal (R m) and conditional ( R c) R for the reduced, &nal model. Bold font indicates signi&cant effects ( P < 0.05)
2 2 2 Model Response variable Predictor Wald χ ( P-value) R m ( R c) (1) Fruit survival between initiation and maturation Defoliation treatment 8.7 (0.01) 0.07 (0.33) 2018 shoot basal area 0.3 (0.6) Mean leaf area per fruit on 2018 shoot 0.2 (0.7) Summer #ush in 2018 (yes/no) 8.1 (0.004) More than one fruit initiated per leaf (yes/no) 4.0 (0.04) Minimum predawn potential 0.6 (0.4) (2) Photosynthesis rate Defoliation treatment 0.3 (0.9) 0.01 (0.44) (3) Mature fruit mass Defoliation treatment 4.1 (0.1) 0.07 (0.73) 2018 shoot basal area 1.8 (0.2) Mean leaf area per fruit on 2018 shoot 0.005 (0.9) Summer #ush in 2018 0.02 (0.9) More than one fruit per leaf 0.2 (0.6) Minimum predawn potential 0.9 (0.3) Defoliation × more than one fruit per leaf 9.5 (0.009) (4) Fruit germination success Defoliation treatment 0.4 (0.8) 0.12 (0.51) Fruit mass 11.6 (<0.001)
T . Summary of GLMMs testing the effects of experimental defoliation and other biological covariates on growth parameters in 2 2 2 2019. The table reports statistics, P-values and marginal (R m) and conditional ( R c) R for the reduced, &nal model. Bold font indicates signi&cant effects (P < 0.05)
2 2 2 Model Response variable Predictor Wald χ ( P-value) R m ( R c) (5) Number of 2019 spring shoots per 2018 shoot Defoliation treatment 12.0 (0.002) 0.40 (0.63) 2018 shoot basal area 103.3 (<0.001) Summer #ush in 2018 (yes/no) 23.7 (<0.001) Defoliation × 2018 shoot basal area 29.0 (<0.001) Defoliation × summer #ush in 2018 8.0 (0.02) (6) Number of leaves per 2019 spring shoot Defoliation treatment 22.7 (<0.001) 0.21 (0.90) 2018 shoot basal area 26.9 (<0.001) Summer #ush in 2018 3.0 (0.08) Defoliation × summer #ush in 2018 17.0 (<0.001)
Supplementary Data Table S3 ] and with the basal area of 2018 of female #owers was positively correlated to the number of shoots [ Table 3 Model (6), 0.08 ± 0.02]. leaves in all treatments [ Table 4 , Model (10); coef'cient param- eter estimate ± s.e., 0.66 ± 0.07]. The number of female #owers Male %owers. The number of catkins per 2019 spring shoot was was also positively related to the 2018 shoot basal area in the lower in the moderate defoliation treatment than in the control, control and intense defoliation treatments [0.25 ± 0.11 and and even more so in the intense defoliation treatment [ Table 4 , 0.09 ± 0.04, respectively; Table 4 , Model (10)]. The number of Model (7); Fig. 5A ; Supplementary Data Table S4 ]. Thus, de- female #owers was negatively related to the number of fruits foliation reduced the number of catkins and it also reduced the produced the year before in both the moderate and the intense number of staminate #owers per catkin [ Table 4 , Model (8), defoliation treatment [−0.46 ± 0.11 and −0.47 ± 0.12, respect- −2.8 ± 1.2 for moderate defoliation, −3.7 ± 1.2 for intense de- ively; Table 4 , Model (10); Supplementary Data Table S4 ], but foliation] but had no effect on the number of stamens per stam- not in the control treatment. inate #ower [ Table 4 , Model (9); Supplementary Data Table S4 ]. The 2019 spring shoots carried by large 2018 shoots pro- duced fewer catkins [ Table 4 , Model (7); coef'cient parameter estimate ± s.e., −0.05 ± 0.02]. Catkin number was negatively Sex ratio related to leaf number in the moderate and intense defoliation The sex ratio (de'ned here as the proportion of female treatments but not in the control treatment (−0.33 ± 0.05 for #owers produced by the shoot out of the total number of in- moderate defoliation, −0.35 ± 0.07 for intense defoliation; #orescences of the shoot, i.e. female #owers plus catkins) Supplementary Data Table S4 ). was clearly biased towards male on all trees (13, 21 and 21 % of female #owers in the control, moderate and intense Female %owers. The number of female #owers per 2019 spring defoliation treatments, respectively; Supplementary Data shoot was reduced in both the moderate and the intense de- Fig. S4A ). On average the sex ratio was not affected by de- foliation treatment [Table 4 , Model (10); Fig. 5B ]. The number foliation because both female and male #ower production
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Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies 9
Fruit set 2nd flush in summer 2018 No Yes Fruit set in 2019 (proportion of female #owers that suc- A a a b ceeded in initiating a fruit) was relatively low (33, 48 and ns* ns 30 % in the control and moderate and intense defoliation treat- 30 ments, respectively; Supplementary Data Fig. S4B). Fruit set did not differ between treatments [Table 4 , Model (12)] and was not affected by 2018 shoot basal area [ Table 4 , Model (12)]. However, fruit set was positively related to the number of 10 leaves produced in 2019 [coef'cient parameter estimate ± s.e., 0.40 ± 0.18; Table 4 , Model (11)].
3 Mean number of 2019
spring shoots per 2018 shoot Interactive effect of defoliation and reproductive status at defoliation 1 Defoliation had similar effects on shoots on defoliated branches that did not bear a single fruit in 2018 and shoots 12.5 B a b b that bore a fruit in terms of shoot production and catkin pro- duction the following year (Supplementary Data Table S5 , *ns ns 10.0 Supplementary Data Fig. S6A, C). Leaf production per 2019 shoot was higher for fruit-bearing shoots compared with non-fruit-bearing shoots in the control and moderate defoli- 7.5 ation treatments, but not in the intense defoliation treatment (Supplementary Data Table S5 , Supplementary Data Fig. S6B). Female #ower production in 2019 was higher on 2018 fruit- 5.0 bearing shoots compared with non-fruit-bearing shoots in the control treatment, but this difference disappeared in the defoli-
per 2019 spring shoots ated treatments (Supplementary Data Table S5 , Supplementary Mean number of leaves 2.5 Data Fig. S6D).
0 DISCUSSION No defoliation Moderate Intense defoliation defoliation In this study we investigated the impact of resource limitation Treatment induced by defoliation on the main steps of the reproductive cycle, from #ower bud development to seed germination FZ[. . Effects of defoliation treatment and the second growth #ush during (Fig. 1). We found that defoliation applied shortly after fruit summer 2018 on (A) number of 2019 spring shoots produced per 2018 shoot set had limited effects on fruit development, had no effect on on a log scale and (B) number of leaves per 2019 spring shoot. Black points 10 fruit 'nal mass and germination success, and did not upregulate indicate group means and grey points represent raw data. Different letters cor- respond to treatment effect in pairwise comparisons using Tukey’s post hoc test photosynthesis. In the following spring, we found that defoli- in the GLMM [Table 1 , Models (5) and (6), respectively] ( P < 0.05). Within ated branches produced fewer shoots, fewer #owers per shoot treatments: *signi'cant effect of the summer #ush in 2018; ns, no signi'cant and more leaves per shoot. We also found negative relationships difference (Tukey post hoc test with Bonferroni–Holm correction, P < 0.05). between staminate #ower and leaf production in defoliated treatments, as well as between fruit production and subsequent was decreased on defoliated branches [ Table 4 , Model (11); #ower production. Finally, we found that defoliation did not Supplementary Data Table S4 ]. The sex ratio was positively affect the sex ratio the following year. related to the 2018 shoot basal area (coef'cient parameter estimate ± s.e., 0.28 ± 0.05) and negatively related to the number of mature fruits produced in 2018 [−0.27 ± 0.07; Branches upregulate leaf production relative to %owers following Table 4 , Model (11)]. The interaction term between treat- defoliation ment and a second #ush in summer 2018 was signi'cant [Table 4 , Model (11); Supplementary Data Table S4 ], with As we expected, defoliation reduced the number of catkins an effect of intense defoliation signi'cant and positive only per shoot, the number of staminate #owers per catkin and the in the absence of a summer #ush. number of female #owers per shoot in the following year, while There was no relationship between female #ower production it increased the number of leaves. This allocation shift did not and catkin production per 2019 spring shoot in the control and impact the number of stamens per #ower, which might be more intense defoliation treatments, but a negative relationship ap- developmentally constrained. We observed a reduction, but not peared in the moderate defoliation treatment (Supplementary an interruption, of female #ower production the year following Data Fig. S5). defoliation. This contrasts with 'ndings by Wiley et al. (2017)
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10 Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies
T . Summary of GLMMs testing the effects of defoliation and of other biological covariates on %owering and fruit set parameters 2 2 2 in 2019. The table reports statistics, P-values marginal (R m) and conditional R ( R c) for the reduced, &nal model. Bold font indicates signi&cant effects (P < 0.05)
2 2 2 Model Response variable Predictor Wald χ ( P-value) R m ( R c) (7) Number of catkins per 2019 spring shoot Defoliation treatment 31.8 (<0.001) 0.43 (0.83) 2018 shoot basal area 4.8 (0.03) Summer #ush in 2018 (yes/no) 1.6 (0.2) Number of mature acorns on 2018 shoot 0.1 (0.3) Number of leaves per 2019 shoot 37.9 (<0.001) Defoliation × leaves per 2019 shoot 17.9 (<0.001) (8) Number of staminate #owers per catkin Defoliation treatment 10.5 (0.005) 0.05 (0.62) (9) Number of stamens per staminate #ower Defoliation treatment 0.01 (1.0) 0.001 (0.52) (10) Number of female #owers per 2019 spring shoot Defoliation treatment 12.6 (0.002) 0.40 (0.76) 2018 shoot basal area 3.9 (0.05) Summer #ush in 2018 1.0 (0.3) Number of mature acorns on 2018 shoot 23.4 (<0.001) Number of leaves per 2019 shoot 89.2 (<0.001) Defoliation × 2018 shoot basal area 7.9 (0.02) Defoliation × number of mature acorns 11.7 (0.003) (11) Sex ratio Defoliation treatment 2.5 (0.3) 0.12 (0.32) 2018 shoot basal area 28.4 (<0.001) Summer #ush in 2018 1.0 (0.3) Number of mature acorns on 2018 shoot 13.6 (<0.001) Number of leaves per 2019 shoot 59.8 (<0.001) Defoliation × leaves per 2019 shoot 20.1 (<0.001) Defoliation × summer #ush in 2018 8.9 (0.01) (12) Fruit set in 2019 Defoliation treatment 2.3 (0.07) 0.05 (0.35) 2018 shoot basal area 0.1 (0.5) Number of leaves per 2019 shoot 5.1 (0.02) after whole-tree defoliation in Q. velutina, probably because, signi'cantly reduced after whole-tree defoliation but that pro- in our case, defoliated branches could rely on resources from duction of #owers was suppressed the following year on the de- non-defoliated branches further away. foliated branches because of priority investment in defoliation The intense defoliation induced a reduction in the number recovery. We could hypothesize that in the event of resource of spring shoots the following year, consistent with the usu- limitation preferentially takes place before #ower initiation and/ ally observed growth reduction after experimental defoliation or development than after a signi'cant amount of resources, es- in Q. ilex and other species ( Vanderklein and Reich, 1999 ; Piper pecially nutrients, has already been invested in #owers. As both and Fajardo, 2014 ; Schmid et al., 2017 ; Wiley et al., 2017 ), carbohydrate and nitrogen availabilities have been identi'ed although an increase in shoot production after defoliation has as potentially involved in the initiation of #owers ( Miyazaki, sometimes been observed (Cherbuy et al., 2001 ). Quercus ilex 2013; Miyazaki et al., 2014 ), such a regulation might be linked has preformed buds ( Montserrat-Martí et al., 2009 ), which are to the carbohydrate and nitrogen content of the branch. already formed at the time of defoliation. Thus, our experimental To sum up, defoliated branches compensated for leaf loss the defoliation could not affect bud set, but might have affected the year following defoliation by increasing the number of leaves allocation of resources to buds during their development from per shoot consistently with defoliation intensity. The produc- the summer that followed defoliation to the next spring. The tion of shoots, staminate #owers and female #owers was, how- 2019 spring shoots bore on average more leaves in the defoli- ever, reduced compared with the control treatment. The loss of ation treatments compared with control, consistent with pre- resources induced by defoliation was compensated primarily vious studies on Q. ilex ( Cherbuy et al., 2001 ; Schmid et al., for achieving fruit development during the year of defoliation, 2017) and other species ( Iqbal et al., 2012 ; Nakajima, 2015). and for recovering the leaf area in the following year. Quercus ilex is an evergreen species in which two or three co- horts of leaves usually coexist, and which has been shown to compensate for leaf loss in the following year (Cherbuy et al., Compensation for resource limitation and branch autonomy 2001; Limousin et al., 2012 ). Therefore, a larger allocation to vegetative organs was expected to compensate for the previous Branch defoliation had contrasted effects on fruit and seed leaf loss. development during the months that followed ( Fig. 1 ). First of We observed that branches favoured the completion of fruit all, defoliation, either moderate or intense, did not decrease the development during the year of defoliation, but favoured the mass of the mature fruits, contrary to our expectations. Second, production of leaves relative to #owers the following year. fruit abortion increased with moderate defoliation but not with Our results at the branch level are consistent with the few intense defoliation. Third, defoliation had no effect on seed studies that investigated the impact of resource manipulation germination success, which was only positively related to fruit on the different reproductive steps. In Q. velutina, Wiley et al. mass, as previously observed in other oak species (Bon'l, 1998 ; (2017) observed that production of second-year acorns was not Huerta-Paniagua and Rodríguez-Trejo, 2011 ; Sánchez-Montes
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Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies 11
A a b c The branch autonomy theory suggests that foliated branches are carbon-, but not nutrient-, autonomous for most of the growing season ( Watson and Casper, 1984 ; Sprugel et al. , 1991 ). At the shoot scale, acorn growth in oaks is thought 4 to be mainly based on carbohydrates produced during their development by the adjacent leaves ( Hoch et al. , 2013 ; Ichie et al. , 2013 ; Fernández-Martínez et al. , 2015 ) and also by the photosynthetic cells of their own pericarp until it dehy- 2 drates ( Hoch and Keel, 2006 ). At the branch scale, shoot- bearing fruits are known to obtain part of their resources per 2019 spring shoot
Mean number of catkins from adjacent shoots that do not bear fruits ( Miyazaki et al. , 2007 ; Sánchez-Humanes et al. , 2011 ; Xie and Guo, 2015 ). In Q. ilex , Alejano et al. (2008) observed that acorns from the 0 southern side of trees were signi'cantly heavier than those at other positions, suggesting a local regulation of resource 2.5 B a b b allocation to fruits. Fruit growth in the intense defoliation treatment was similar 2.0 to that in the control, although we did not observe any increase in photosynthetic rate in the remaining leaves to compensate for assimilate loss, consistently with most previous studies on 1.5 oak saplings (Lovett and Tobiessen, 1993 ; Vanderklein et al., 2001; Wiley et al., 2013 ). Therefore, carbon used for fruit 1.0 'lling either originated from recent photoassimilates produced by non-defoliated branches further away ( Oitate et al., 2011 ), from local reserves in the shoot and its vicinity, or from re-
Mean number of female 0.5 serves in distant storage organs such as the stem and below- flowers per 2019 spring shoot flowers ground parts. Additional measurements of the amount of stored 0 non-structural carbohydrates in some small branches at the time of defoliation suggest that they would not be suf'cient No defoliation Moderate Intense to 'll all the acorns that reached maturity (results not shown). defoliation defoliation This suggests that the carbon contained in the mature fruits of Treatment highly defoliated branches probably came from further away, FZ[. . Effect of defoliation treatment on (A) the mean number of catkins and although girdling experiments would be necessary to strictly (B) the mean number of female #owers per 2019 spring shoot. Black points assess branch autonomy for fruit 'lling. Our experiment shows, indicate group means and grey points represent raw data. Different letters indi- however, that the higher fruit abortion rate on moderately de- cate signi'cant differences between treatments [Table 1 , Models (7) and (10), foliated branches results from a branch allocation strategy ra- respectively]. The statistical signi'cance threshold is 0.05. For each group, the shaded area represents the right side of the kernel density plot. ther than from impossibility of sustaining fruit development despite lower leaf area. The year 2018 was an intermediate year in terms of fruit production for Q. ilex in the area, i.e. neither de Oca et al. , 2018 ; Shi et al. , 2019 ). Results of the moderate a mast-seeding year nor a year with considerable reproductive defoliation treatment are consistent with the generally nega- failure (data not shown). If the same experiment had been per- tive impact of natural herbivory on oak fruit production, even formed during a mast year, the effect of defoliation might have though natural herbivory intensity is usually, but not always, been stronger because of increased competition for carbon be- less than removal of half of the leaves ( Crawley, 1985 ; May and tween fruits. More experimental work is needed to understand Killingbeck, 1995 ; Pearse et al. , 2015 ; Nakajima, 2015 ; Canelo how fruiting intensity might interact with the defoliation effect et al. , 2018 ). The absence of the effect of intense defoliation on by repeating the experiment in multiple years, and by coupling fruit production is, however, more surprising but is consistent them to chemical analysis of both carbohydrate and nutrient with the recent studies of Pasqualotto et al. (2019) and Wiley reserves in branches in order to understand the physiological et al. (2017) on hazelnut development and oak acorn produc- basis of allocation regulation. tion (initiated before defoliation) in Q. velutina , respectively. Interestingly, shoot basal area did not affect mature fruit mass However, our results contrast with those of the few experi- in 2018 and fruit set in 2019, contrary to what has been ob- ments that tested how different defoliation intensities impacted served in some fruit trees (George et al., 1996 ). The mean leaf fruit production, and either found an increasingly negative area per fruit did not affect fruit mass either, consistent with effect with increasing defoliation intensity ( Mehouachi et al. , observations at the branch scale in hazelnut trees (Pasqualotto 1995 ; Kaitaniemi et al. , 1999 ; Hoch, 2005 ) or no effect of de- et al., 2019 ). Therefore, fruit development might not be as de- foliation at all ( Obeso, 1998 ; Tuomi et al. , 1988 ; Tamura and pendent on local leaf photosynthesis as we initially expected. Hiura, 1998 ). Our results suggest that at the level of the branch The ability of the branches to rely on distant sources of carbon the compensation for resource loss in order to maintain fruit to sustain fruit development might explain the absence of a growth might differ depending on defoliation intensity. clear defoliation effect in our experiment.
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12 Le Roncé et al. — Resource manipulation has legacy effects on allocation strategies
The production of shoots and #owers the year following Sex allocation defoliation appeared to be resource-limited. The limiting re- Factors determining sexual allocation in natural tree popula- sources might have been carbon, as leaf removal prevents tions are still poorly known, especially in mast-seeding species local production of non-structural carbohydrates. However, (Kazuhiko, 2007; Knops and Koenig, 2012 ; Rapp et al., 2013 ). leaf and #ower production might depend more on nutrients, The sex allocation theory assumes the existence of a trade-off such as phosphorus and nitrogen, that are known to be in- between male and female functions, and that if there is resource volved in the proximate mechanisms driving mast seeding shortage monoecious plants should shift towards maleness be- (Han et al. , 2011 ; Sala et al. , 2012 ; Miyazaki et al. , 2014 ; cause maintaining the male function usually requires less in- Allen et al. , 2017 ; Han and Kabeya, 2017 ; Fernández- vestment than maintaining the female function ( Charlesworth Martínez et al. , 2019 ). As evergreen species store part of and Morgan, 1991 ). This hypothesis might, however, not apply their nitrogen and phosphorus reserves in their foliage to mast-seeding species, in which increased pollination ef'- (Chapin et al. , 1990 ; Cherbuy et al. , 2001 ), trees lose a sub- ciency requires synchronous investment in male and female stantial portion of their nutrients with defoliation ( Millard function during mast years (Rapp et al., 2013 ). Sex allocation et al. , 2001 ; Iqbal et al. , 2012 ), although Q. ilex also stores is very much male-biased in Q. ilex, but we found no correl- a non-negligible part of the nutrient reserves in the shoots ation between the number of female #owers and the number of (Palacio et al. , 2018 ). Defoliation treatment did not affect catkins at the shoot scale in the control treatment, as observed fruit set the following year. Besides, other factors known to in previous studies on Q. ilex ( Pulido et al., 2014 ) and other oak impact fruit set in Q. ilex , such as pollen limitation or spring species (Knops and Koenig, 2012 ). Our results provide no sup- drought ( Bogdziewicz et al. , 2017 ), probably had minor im- port for the sex allocation theory, even in a context of resource pacts on fruit set in 2019 because spring was neither particu- limitation by defoliation, as defoliation reduced allocation to larly dry nor particularly rainy during pollination. pistillate and staminate #owers similarly. Sex allocation shift towards maleness therefore might occur after a resource limi- tation imposed by a defoliation at the plant scale (e.g. Narbona Increasing resource limitation generates allocation trade-offs and Dirzo, 2010), but not when defoliation is only applied at the shoot or branch scale (e.g. Wang et al. 2016). Resource allocation trade-offs in trees may exist between vegetative growth and reproduction ( Obeso, 2002 ; Barringer et al. , 2013 ), between current and future reproduction as cur- rently assumed in mast seeding species ( Koenig and Knops, Conclusions 2000 ) and between male and female #owering in monoecious Our results strongly suggest that the regulation of resource species ( Charlesworth and Morgan, 1991 ). In this study, we allocation to reproduction occurs at a larger scale than the shoot found no clear evidence for a trade-off between vegetative growth scale, and that #ower production is more sensitive to resource and female reproduction at the shoot scale. On the contrary, we #uctuation than fruit development and seed germination suc- found that shoots that had initiated a fruit in 2018 were larger cess. Most importantly, our results also reveal the complexity of than those that had not, as already observed in Q. ilex ( Sánchez- strategies of resource allocation to the different plant functions Humanes et al. , 2011 ; Alla et al. , 2012 ), and that large 2018 over two consecutive years depending on resource availability. shoots produced 2019 shoots with more leaves and more female Climate change is currently signi'cantly modifying water and #owers. During morphogenesis, there might be a developmen- carbon availability through altered phenology and suboptimal tally constrained positive relationship between growth and fe- temperature and soil moisture conditions, subsequently modi- male function at the shoot scale, probably because large shoots fying the allocation of resources to the different organs. Our can provide more nutrients to fruits. However, the summer #ush results show that it is essential to explore more deeply the in 2018 had a negative impact on fruit growth, which suggests complexity of resource allocation to #owers to propose robust that the summer shoot growth was a competing resource sink projections of tree fecundity, and subsequently of forest regen- for fruits, and that there is a trade-off between shoot growth and eration in future climatic conditions. fruit production when the two occur simultaneously. The resource budget model hypothesis predicts that resources would be depleted after fruiting, which would limit the number SUPPLEMENTARY DATA of #owers produced the following year ( Crone et al., 2009 ). A negative correlation between seed production one year and Supplementary data are available online at https://academic. the next has indeed often been observed at the tree level in oaks oup.com/aob and consist of the following. Figure S1: relation- (Sork et al., 1993 ; Kelly and Sork, 2002 ; Pérez-Ramos et al., ships between shoot basal area and leaf number and between 2010). Consistently, we found a trade-off, at the shoot scale, fruit length and biomass. Figure S2: variation of fruit survival between mature fruit production and female #ower produc- and mass within trees. Figure S3: effect of reproductive status tion the following year in defoliated branches. This result thus of the 2018 spring shoot on its basal area. Figure S4: variation highlights the need for evaluating reproductive costs at both of sex ratio and fruit set across treatments. Figure S5: relation- the modular (shoot and branch) and the individual level, and ship between number of catkins and number of female #owers over multiple years throughout the individual’s lifespan ( Genet within treatments. Figure S6: effect of defoliation and of having et al., 2010 ; Sánchez-Humanes et al., 2011 ; Sala et al., 2012 ; borne or not borne fruits in 2018 on organ production in 2019. Bogdziewicz et al., 2019). Table S1: dataset characteristics across treatments. Tables S2,
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S3 and S4: estimates corresponding to Tables 2–4 . Table S5: Bogdziewicz M, Fernández-Martínez M, Espelta JM, Ogaya R, Penuelas J. effect of the interaction between defoliation and of bearing or 2020b. Is forest fecundity resistant to drought? Results from an 18-yr rainfall-reduction experiment. New Phytologist 227: 1073–1080. not bearing a fruit in 2018 on organ production in 2019. Bogdziewicz M, Ascoli D, Hacket-Pain A, et al. 2020c. From theory to experi- ments for testing the proximate mechanisms of mast seeding: an agenda for an experimental ecology. Ecology Letters 23 : 210–220. FUNDING Bon)l C. 1998. The effects of seed size, cotyledon reserves, and herbivory on seedling survival and growth in Quercus rugosa and Q. laurina This work was supported by LabEx CeMEB, an Agence (Fagaceae). American Journal of Botany 85 : 79. Nationale de la Recherche (ANR) ‘Investissements d’avenir’ Caignard T, Kremer A, Firmat C, Nicolas M, Venner S, Delzon S. 2017. programme (ANR-10-LABX-04-01), the ANR FOREPRO Increasing spring temperatures favor oak seed production in temperate areas. Scienti&c Reports 7: 8555. (ANR-19-CE32-0008) and Agroparistech and the French Canelo T, Gaytán Á, González-Bornay G, Bonal R. 2018. Seed loss be- Ministry of Environment to I.L.R. fore seed predation: experimental evidence of the negative effects of leaf feeding insects on acorn production. Integrative Zoology 13 : 238–250. Chapin FS, Schulze E, Mooney HA. 1990. The ecology and economics of storage in plants. Annual Review of Ecology and Systematics 21 : 423–447. ACKNOWLEDGMENTS Charlesworth D, Morgan MT. 1991. Allocation of resources to sex functions in #owering plants. 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Growth reduction after defoliation is Yacine A, Bouras F. 1997. Self- and cross-pollination effects on pollen tube independent of CO2 supply in deciduous and evergreen young oaks. New growth and seed set in holm oak Quercus ilex L (Fagaceae). Annales des Phytologist 214: 1479–1490. Sciences Forestières 54 : 447–462. Shi W, Villar-Salvador P, Li G, Jiang X. 2019. Acorn size is more im- Yasumura Y, Hikosaka K, Hirose T. 2006. Resource allocation to vegeta- portant than nursery fertilization for outplanting performance of Quercus tive and reproductive growth in relation to mast seeding in Fagus crenata . variabilis container seedlings. Annals of Forest Science 76 : 22. Forest Ecology and Management 229: 228–233.
42 Chapitre 2 : Le développement des fruits du chêne vert inhibe la production de fleurs femelles, modifie l’architecture des rameaux et impacte les dynamiques des réserves en azote et en zinc
Crédit : Juliane Daussy.
Chapitre 2
Chapeau Le Chapitre 2 de cette thèse fait l’objet d’un article qui sera soumis très prochainement. Son objectif était de tester les mécanismes qui sous-tendent l’autocorrélation temporelle négative dans la fécondité des arbres, ce qui constitue l’une des hypothèses fondamentales des « Resource Budget Model s » (RBM), en étudiant l’impact d u nombre de fruits en croissance sur les réserves en différents éléments et sur la flo ribondité de l’année suivante. Nous avons montré que les fruits en développement du chêne vert ont un impact négatif sur les dynamiques des réserves en azote et en zinc dans les branches, ainsi que sur la production de fleurs femelles l’année suivante. Ce changement d’allocation aux fleurs femelles induit par les fruits de la saison précédente étai t par ailleurs lié à un changement d’architecture des ramifications
Ce travail a pris la forme d’une expérimentation réalisée sur le terrain d’expérience du CEFE sur des chênes verts d’une vingtaine d’années et a duré deux ans. Elle a été conçue sur une id ée personnelle, et a fait l’objet du stage de Master 1 d’Elia Dardevet durant la deuxième année de l’expérience. J’ai assuré la majorité de sa mise en place et de son suivi avec l’aide ponctuelle de plusieurs personnes pour la mise en place, et en particulier Anne-Sophie Bonnet-Lebrun, et l’aide régulière d’Elia Dardevet et Isabelle Chuine pour le suivi. J’ai reçu une aide technique importante pour la préparation des échantillons et pour une partie des analyses chimiques (Elia Dardevet, Patrick Léger (INRA) , Alexandre Defossez). J’ai réalisé les analyses de sucres dans l’équipe d’Arthur Gessler (WSL Zurich, Suisse) après formation par Leonie Schönbeck. J’ai réalisé les analyses statistiques. La rédaction a été une collaboration avec tous les co-auteurs.
Les « Supplementary materials » de cet article sont en Annexe 3 de cette thèse (page 174).
44 Chapitre 2
Fruit removal up-regulates female flower production, modifies shoot architecture and induces changes in nitrogen and zinc dynamics in Quercus ilex
Iris Le Roncé, Elia Dardevet, Samuel Venner, Leonie Schönbeck, Arthur Gessler, Jean-Marc Limousin and Isabelle Chuine
Abstract
Fruit production is supposed to deplete stored resources, which would subsequently limit flower production the following year. These hypotheses have especially been proposed for mast seeding species. However, both of them have rarely been tested in forest trees. Using a fruit removal experiment, we tested whether preventing fruit development would increase nutrient storage and modify allocation to reproduction and vegetative growth the following year.
We removed all the fruits from nine adult Quercus ilex trees shortly after fruit set and compared the concentrations in nitrogen, phosphorus, zinc, potassium and starch in leaves, twigs and trunk before, during and after the development of female organs with nine control trees. The year following fruit removal, we measured the production of vegetative and reproductive organs as well as their location on the new spring shoots.
Fruit removal modified the seasonal dynamics of nitrogen and zinc concentrations which decreased in branches during fruit growth in control trees but not in trees that underwent fruit removal. Fruit removal also increased the production of female flowers the following year by modifying the architecture of new shoots.
Although changes in resource allocation to organs, both in quantity and nature, and changes in nutrient dynamics were not monitored during several years, our results suggest that resource dynamics might constrain flower production in Q. ilex more strongly than supposed in recent modelling studies using resource budget models. They also strongly encourage further experiments manipulating fruiting intensity over multiple years to confirm the causal link between variations in resource storages and/or uptake and flowering in masting species, as this one of the main hypotheses of resource budget models.
Keywords: resource allocation, nutrients, mast seeding, flowering, architecture, non-structural carbohydrates, neoformation, photosynthesis
45 Chapitre 2
Introduction Flowering, the first step of plant sexual reproduction, results from a chain from floral induction, by which a vegetative meristem is converted into a floral meristem, to floral initiation and development until anthesis. This process is the consequence of plants’ reactions to internal factors (e.g. hormones, resources levels), environmental cues (e.g. temperature, photoperiod) and environmental vetoes (e.g. frost, drought, biotic stress) (Allen et al. , 2017). However, while the determinants of flowering intensity have been well described in fruit crop trees (Monselise & Goldschmidt, 1982; Samach & Smith, 2013; Sharma et al. , 2019), they remain poorly known in forest trees although they are essential to understand what drives fecundity in general, and mast seeding in particular (Crone & Rapp, 2014). Indeed, many forest and fruit tree species reproduce following a behaviour of massive and synchronous seed production that alternate with one or more years of negligible production, also called masting or mast seeding (Silvertown, 1980; Kelly & Sork, 2002).
As reproduction is usually considered as a costly function for plants (Obeso, 2002), most hypotheses for masting patterns imply a regulation of resources allocated to reproduction. This assumption is notably based on the commonly observed pattern of negative autocorrelation between past and current intensity of seed production in forest masting species (Koenig & Knops, 2000; Koenig et al. , 2016; Moreira et al. , 2019), and in fruit crop trees (Jonkers, 1979; Monselise & Goldschmidt, 1982; Samach & Smith, 2013), that is usually attributed to resource limitation and reproductive trade-off among years.
In particular, the resource depletion (or storage) hypothesis proposes that tree's reserves are depleted during mast years and that the tree needs to stock resources for more than one year before it can invest strongly in reproduction again (Sork et al. , 1993; Sánchez-Humanes et al. , 2011; Pesendorfer et al. , 2016; Pearse et al. , 2016; Bogdziewicz et al. , 2018; Schermer et al. , 2019). Depletion of stored resources has been historically mostly shown in fruit crop species (Goldschmidt & Golomb, 1982; Brown et al. , 1995; Rosecrance et al. , 1998) that are selected and managed for increased fruit production and to avoid as much as possible irregular production . Moreover, this depletion hypothesis is a key stone in the resource budget models that generate masting behaviour within forest tree population(Satake & Iwasa, 2002; Crone & Rapp, 2014; Venner et al. , 2016; Abe et al. , 2016); together with the assumption that storage level during floral induction determines flowers abundance in the following spring (Monks et al. , 2016; Funk, 2017). However, the explicit relationships between inter-annual variability in resource supply and in floral induction and initiation have been largely unexplored (Crone & Rapp, 2014; Miyazaki et al. , 2014; Allen et al. , 2017; Han & Kabeya, 2017).
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Recent correlative studies in forest tree species added evidence of resource depletion induced by masting for nitrogen (N), phosphorus (P) and non-structural carbohydrates (NSC) depending on the species (reviewed in (Pearse et al. , 2016) and (Han & Kabeya, 2017)). In a remarkable study on Fagus crenata , Miyazaki et al. (2014) showed that the expression of genes involved in floral induction was increased under high nitrogen availability (naturally or following fertilisation). Yet, in many species, we still miss basic understanding of how resources are involved in flower and seed production, of whether resources consumed by current reproduction are indeed limiting future flowering, and of which resource or combination of resources is involved. To do so, one experimental approach can be to prevent fruit growth, which is the most resource- demanding phase of the reproductive cycle, by harvesting all fruits early in their development on some trees (Obeso, 2002; Bogdziewicz et al. , 2020) and look whether their resource levels at the end of the reproductive season are higher than control trees.
Flower and fruit thinning is a common agronomic practice in commercial fruit crops in order to reduce alternate bearing patterns and increase fruit size and quality (Coneva & Cline, 2006). Flower and fruit removal experiments have thus mainly been realised in herbaceous plants (Crone et al. , 2009) and in fruit crop species (Reig et al. , 2006; Verreynne & Lovatt, 2009; Samach & Smith, 2013; Fernandez et al. , 2018) in order to determine how it impacted resource storage and further flowering. To our knowledge, only two studies used such experiment for forest tree species. Santos- de-Blanco & Climent (2014) tested how removing developing cones in Pinus halepensis impacted reproduction and growth the following year. Sala et al. (2012) investigated how nutrient levels were impacted by mast years in Pinus albicaulis , and showed a post-masting resource depletion as well as different costs of reproduction when studied at local scale or at tree scale. Reproduction costs may indeed differ depending on the scale at which they were evaluated (Sánchez-Humanes et al. , 2011; Sala et al. , 2012; Funk, 2017; Le Roncé et al. , 2020). Our objective in this study was to investigate how preventing fruit development and maturation would impact the resources of the tree and the allocation to flowering and growth the following year in Quercus ilex , another evergreen species belonging to the commonly studied masting taxa of oaks. By removing all fruits of a group of nine Quercus ilex trees shortly after fruit set that was compared to a control group that could develop their fruits during summer, we aimed at testing the following hypotheses:
Hypothesis 1: Resources that could not be allocated to fruit development will be stored, and carbon uptake will be downregulated, because primary and secondary growth are mostly finished in Q. ilex at the time of fruit set (Chapitre 3; (Lempereur et al. , 2015)).
Hypothesis 2: Fruit removal will increase female flower production the following spring because resource stored should up-regulate floral induction. 47 Chapitre 2
Hypothesis 3: This up-regulation of female flower production will take place at the tree scale because resource spared will not be necessarily stored only in fruit bearing shoots.
Materials and methods Study species and area
Quercus ilex L. is an evergreen wind-pollinated monoecious tree that usually flowers in May in the study area (southern France). The male inflorescences, called catkins, bear around 20-25 staminate flowers (Yacine & Bouras, 1997; Gómez-Casero et al. , 2004). Catkins develop in the axils of lower leaves of the current-year shoot or in separate buds bearing only catkins. Female pistillate flowers mature a few days after staminate flowers and female inflorescence can bear one to six pistillate flowers. Fertilization occurs in late June early July, leading to fruit initiation. Fruits, called acorns, achieve their maturation in November-December (Yacine & Bouras, 1997).
The experimental plot was located in Montpellier, France (latitude: 43.64°N, longitude: 3.86°E, altitude: 76m). The climate is Mediterranean with an annual rainfall of 629 mm and mean annual temperature of 15.2°C. The soil of the field site is a rendzina-like silty clay soil, with a pH of 8 and a depth varying from 150 to 200 cm. The trees used for the experiment were planted in 1998 from fruits collected from nearby natural populations. Trees were on average (± SD) 3.6 ± 0.5 meters tall with a mean basal area of 52 ± 28 cm² (at 0.4 m) during the experiment and no difference in basal area between groups (supplementary Table S1).
Experimental setting
In spring 2018, twenty-four trees were selected for the experiment. Out of the twenty-four, six did not set any fruit in June (the naturally “No Fruit” (NF) group), and the eighteen others were randomly divided in two groups of nine trees. In the first group, all the fruits from the nine trees were removed between the 12 th and the 19 th of June 2018 (the “Removed Fruit” (RF) group) and the second group was used as control (Fig. 1). The trees of the naturally “No Fruit” (NF) group did not set female flowers in spring 2019.
48 Chapitre 2
Figure 1: Diagram summarising the experimental protocol.
On each tree of the “Removed Fruit” and control groups, 20 shoots that had set a fruit in June 2018 were randomly selected on different parts of the tree crown. On all trees from the three groups (NF, RF, control), eight shoots that had not set a fruit in 2018 were also randomly selected in the upper part of the crown of each tree. The shoot is defined here as the growth unit of the current year (or the spring and summer growth units in cases when two bud flushes occurred due to polycyclism).
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In order to estimate fruit development success in the control group, the number of fruits was counted after fruit set in June 2018 and shortly before fruit maturity (the 19 th of October or the 8 th of November depending on fruit maturation phenology) to estimate fruit development success on the twenty shoots that had set fruits.
Photosynthesis after fruit removal
In order to check fruit removal effect on photosynthesis, we measured leaf gas-exchange on current-year leaves adjacent to the fruits in control trees or that had been adjacent to one fruit before fruit removal in RF trees. We measured gas-exchanges on two leaves per tree of 14 trees (7 trees of the control group and 7 trees of the RF group, 28 leaves in total). Leaf gas exchange was measured between June 28 th 2018 and July 4 th , 2018, after the spring leaves had reached maturity and 2 weeks after fruit removal. Measurements were carried out with two portable photosynthesis systems (Li-6400, Li-Cor, Lincoln, NE, USA) equipped with a light source (6200-02B LED, Li- Cor). Leaves were first acclimated in the chamber for more than 20 min at ambient temperature, ambient CO 2 concentration (400 ppm) and a saturating photosynthetic photon flux density (PPFD) of 1500 µmol m –2 s–1.
Shoot growth and production of vegetative and reproductive organs the year following fruit removal
In order to estimate the secondary growth of 2018 shoots the year following fruit removal, shoot diameter was measured with an electronic calliper in mid-January 2019 and mid-July 2019 on the twenty shoots that had set a fruit in 2018 and on the eight shoots that had not.
On the twenty shoots per tree of the control and RF groups that had set a fruit in June 2018, we randomly selected four to fifteen 2018 shoots depending on trees on which at least one bud was starting to break. On each of these shoots and on each of the eight shoots that had not set fruits, we counted the number of 2019 spring shoots and the numbers of leaves, catkins and female flowers per 2019 spring shoot. For each of these organs, we noted the node rank on which they occurred. Note that 2019 spring shoots could be composed exclusively of catkins (in this case, there is no twig), of both leaves and flowers (catkin or female inflorescence), or exclusively of leaves. Therefore, each node could be composed either by one or more catkins only, by one or more catkins at the axil of one leaf, by one leaf only, by one female inflorescence at the axil of one leaf, or by one female inflorescence only (rarely).
On each 2018 shoot of all the trees except one, we collected two (if existing) of the catkins produced by the 2019 shoots. On each catkin, we counted the number of staminate flowers.
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The number of female flowers that had developed into fruits (fruit set) was counted on July 19th, 2019. We could not monitor fruit growth and maturation in summer-autumn 2019 as an extremely early heat wave damaged significantly the leaves and fruits in June 28th, 2019.
Tissues sampling
In order to estimate fruit removal effect on tree reserves, we sampled tree tissues before fruit removal in spring 2018 shortly before bud break (16 th of April), shortly after fruit removal in summer 2018 once spring leaves had reached maturity (4 th of July, between two and three weeks after fruit removal in the RF group), long after fruit removal in winter 2019 after last fruit fall (7 th of January 2019) and in spring 2019 shortly before bud break (8 th of April). The different sampling sessions are described in Fig. 1.
On each tree and at each sampling date, 2 small sun-exposed branches close to shoots that bore a fruit (in the control group) or had borne a fruit (in the RF group) or random shoots (in the NF group) were cut. For each branch, leaves and twigs produced during the current year (less than 1 year old) were separated from leaves produced the one or two years before, called 1- and 2-year- old leaves and 1- and 2-year-old twigs hereafter. Leaves and twigs of the two sampled branches and of the same age cohort were pooled together within each sample type. Trunk sapwood samples were taken at 30 cm above the ground with a 1-cm wide corer in spring 2018 and winter 2019 sampling dates. Sapwood was sampled in the first cm depth past the cambium and the bark and phloem were removed. We sampled sapwood of the trunk only twice in order to avoid frequent damages to the trees.
In order to estimate the nutrient content of the female organs at different stages of the reproductive cycle, fruits were collected on nearby trees after fruit set in June 2018 (3 trees, 85 ± 10 fruits collected per tree) and at fruit maturity in November 2018 (3 trees, 6 ± 1 fruits collected per tree). Female flowers were collected in May 2019 (5 trees, 128 ± 36 flowers collected per tree).
All leaf, twig, trunk, flower and fruit samples were micro-waved for 30 seconds shortly after collection to stop enzymatic activity, oven-dried at 60°C for 3 days and grinded to a fine powder prior to the chemical analyses (Quentin et al. , 2015).
Chemical analyses
The carbon (C) and nitrogen (N) concentrations were determined by thermal combustion of 2 mg of ground sample using a Flash Smart NC Soil Elemental Analyzer (ThermoFisher Scientific, USA).
The phosphorus (P) concentration was measured colorimetrically using the molybdenum blue method (Grimshaw and others 1989). Eighty mg of ground sample, 8 mL of HNO 3 and 2 mL
51 Chapitre 2
of H2O2 were mixed and the mixture was heated at 175 °C for 40 min using microwaves (Ethos One, Milestone SRL, Italy). After this mineralization step, the sample was diluted to a total of 50 mL. A hundred μL of sample, 100 μL of NaOH, 50 μL of mixed reagent ( emetic tartar and ammonium molybdate solution) and 50 μL of ascorbic acid were mixed directly in a 96 well microplate. After 30 min at 40 °C, the reaction was completed, and the P concentration was measured at 720 nm using a microplate reader (Victor Nivo S, PerkinElmer, Singapore). Following the mineralization step (i.e. the same as for P analysis), potassium (K) and zinc (Zn) concentrations were measured using an atomic absorption spectrometer (AAS, iCE 3000 series, ThermoScientific, China). Phosphorus analysis were conducted on all tissues, except on female flowers and fruit.
Non-structural carbohydrates (NSC) were analysed following the protocol described by Wong (1990) and adapted according to Hoch et al. (2002). NSCs are defined here as low -molecular- weight sugars (glucose, fructose and sucrose) plus starch. Ten to 12 mg of ground material were boiled in 2 mL of distilled water for 30 min. After centrifugation, an aliquot of 200 µL was treated with Invertase and Isomerase from baker’s yeast (Sigma -Aldrich, St Louis, MO, USA) to degrade sucrose and convert fructose into glucose. The total amount of glucose (sugars) was determined photometrically at 340 nm in a 96-well microplate photometer (HR 7000; Hamilton, Reno, NE, USA) after enzymatic conversion to gluconate-6-phosphate (hexokinase reaction, hexokinase from Sigma Diagnostics, St Louis, MO, USA). The total amount of NSC was measured by taking 500 µL of the extract (including sugars and starch) incubated with a fungal amyloglucosidase from Aspergillus niger (Sigma-Aldrich) for 15 h at 49°C to digest starch into glucose. Total glucose (corresponding to NSC) was determined photometrically as described above. The concentration of starch was calculated as NSC minus free sugars. Pure starch and glucose, fructose and sucrose solutions were used as standards, and standard plant powder (Orchard leaves; Leco, St Joseph, MI, USA) was included to control the reproducibility of the extraction. Because all samples were run in a single laboratory with no change in protocol during the processing of samples, issues with the comparison of results across methods or laboratories were avoided (Quentin et al. , 2015). NSC analysis were conducted on tissues sampled in spring 2018, summer 2018 and winter 2019.
Statistical analysis
All statistical analyses and visual representations were conducted using the software R version 3.6.1 (R Core Team, 2019) and the packages ggplot2 and interactions (Wickham, 2016; Long, 2019). We used the following packages for data analysis: lme4, car and multcomp (Hothorn et al. , 2008; Bates et al. , 2015; Fox & Weisberg, 2019). We considered our level of significance as p-value < 0.05.
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The relationship between the number of removed fruits per tree and the tree basal area within the treated group was tested with Spearman correlation.
We tested the effects of fruit removal, of the sampling date (before or after the development of female organs) and of their interaction on nutrient concentrations (N, P, K, Zn and starch) in leaves, twigs and trunk with fifteen linear mixed effect models (5 nutrients x 3 tissues). We used cohort (1 yr-old and 2-yr old) nested within tree identity as a random factor for models explaining nutrient concentrations in leaves and twigs, and tree identity as a random factor for models explaining nutrient concentrations in trunk.
The effect of experimental fruit removal on photosynthesis was tested with a linear mixed effect model with tree identity as a random effect.
The effect of fruit removal on the production of vegetative and reproductive organs by 2018 shoots and their secondary growth the year following the treatment was tested with eight generalised linear mixed effect models (GLMMs, 4 models related to shoots and leaves, 4 models related to reproductive organs) for each shoot reproductive status (shoots that set a fruit in 2018 and shoots that did not). We fitted GLMMs with a Gaussian distribution for the response variables presenting a normal error-structure (2018 shoot secondary growth, number of staminate flowers per catkin), with a negative binomial distribution and its log link function for count data (number of 2019 spring shoots per 2018 shoot, numbers of nodes, leaves, female flowers, catkins, initiated fruits per 2019 spring shoot) and with a binomial distribution and its logit link function for dichotomous response variables (fruit set). We used 2018 shoot identity nested in tree identity as random effect in models explaining the numbers of nodes, leaves, female flowers and catkins per 2019 spring shoot, and tree identity as a random effect for other models at 2018 shoot scale. We used as fixed effects the fruit removal treatment, and as covariable the 2018 shoot diameter, as well as their interaction. 2018 shoot diameter was standardized prior to analysis to compare models estimates between variables. For each response variable, we then applied a simplification of the model by sequentially removing the insignificant interaction terms. Marginal (R²m) and conditional (R²c) R-squares were calculated with the package MuMIn (Bartoń, 2019) according to Nakagawa’s method (Nakagawa & Schielzeth, 2013) to estimate the variance explained by fixed effects and fixed plus random effects, respectively.
Within treatments, the effect of the reproductive history (i.e. whether the shoots had set a fruit or not in 2018) on the numbers of leaves, female flowers and catkins per spring shoot and on the mean number of initiated fruits per spring shoot were studied with a negative binomial
53 Chapitre 2 distribution, reproductive status and 2018 shoot diameter as fixed effects, and 2018 shoot identity nested in tree identity as random effect.
Results On the nine trees from the RF group, we removed respectively 28, 82, 129, 131, 178, 213, 653, 798 and 3994 fruits. The number of fruits removed was strongly correlated to the tree basal area (Spearman ρ = 0.83, p = 0.008). On the nine trees of the control group, 56 ± 20 % of the fruits reached maturity in 2018.
Tissues nutrient content and seasonal dynamics
Before bud break in spring 2018, concentrations in N, P, K, Zn and starch did not differ for any tissue between the three groups (supplementary Table S1). At the time of fruit removal, initiated fruits weighted less than 10 % of their mean final dry mass and contained less than 25 % of their final content of C, N, K and Zn in mature fruits (supplementary Figure S1). Seasonal patterns of nutrient concentrations were generally similar in leaves and branches from different cohorts, but varied among tissues and nutrients (supplementary Figure S2).
Effect of fruit removal on reserves and photosynthetic activity
Treatment showed significant interactions with the sampling date on the concentrations of N in leaves, Zn in leaves and twigs, K in twigs, and starch in twigs (Table 1, Fig. 2), indicating that the effect of fruit removal differed between spring 2018 (before bud break) and winter 2019 (after fruit fall). These interactions are positive for N, K and Zn and negative for starch (Table 1). Note that fruit removal did not induce significant differences in winter concentrations, i.e. at the end of the reproductive cycle, for any of the nutrients (Fig. 2). While concentrations of N in leaves and Zn in leaves and branches decreased in control trees during fruit growth, these concentrations remain at the same level in RF trees (Fig. 2A, 2B and 2C). Concentrations of K and starch of the two cohorts of twigs differed between the control and RF trees prior fruit set, but this difference vanished after fruit growth (Fig. 2D and 2E).
54 Chapitre 2
Table 1: Summary of GLMMs testing the effects of fruit removal, of the sampling date (winter 2019 vs spring 2018, i.e. before and after the development of flowers and fruits) and of their interaction on nitrogen, phosphorus, zinc, potassium and starch concentrations in leaves (1 and 2-yr old), twigs (1 and 2-yr old) and trunk. We report number of observations, standardised estimates ± SE and p-values for general treatment (removed fruit (RF) compared to control (C)), date and interaction effects. Characters in bold font refer to significant effects (p < 0.05) and stars to levels of significance (0.05 < p* <0.01; 0.01 < p** < 0.001; p*** < 0.0001). Model significant interactions are plotted in Fig. 2.
Response variable Tissue N Standardised estimate ± SE of the predictor Treatment Date Treatment x (RF – C) (Winter 2019 – Date Spring 2018) (RF – C : Winter – Spring) [Nitrogen] Leaves 70 0.09 ± 0.06 -0.15 ± 0.04*** 0.13 ± 0.06 * (%) Twigs 72 -0.01 ± 0.05 0.24 ± 0.04*** 0.10 ± 0.06 Trunk 36 0.03 ± 0.16 0.35 ± 0.14 ** -0.02 ± 0.19 [Phosphorus] Leaves 69 0.03 ± 0.05 0.008 ± 0.03 0.03 ± 0.04 (mg.g -1) Twigs 70 -0.04 ± 0.1 -0.18 ± 0.06 ** 0.02 ± 0.09 Trunk 35 0.009 ± 0.08 -0.12 ± 0.06 * -0.03 ± 0.09 [Zinc] Leaves 70 0.002 ± 0.001 -0.002 ± 0.0008 0.003 ± 0.001 * (mg.g -1) Twigs 70 0.003 ± 0.003 -0.006 ± 0.001 *** 0.006 ± 0.002 ** Trunk 36 0.001 ± 0.002 0.001 ± 0.002 0.003 ± 0.003 [Potassium] Leaves 70 0.15 ± 0.26 1.31 ± 0.18 *** 0.11 ± 0.25 (mg.g -1) Twigs 69 -0.05 ± 0.07 0.03 ± 0.04 0.14 ± 0.05 * Trunk 36 0.02 ± 0.10 0.10 ± 0.06 -0.09 ± 0.08 [Starch] Leaves 70 -0.23 ± 0.61 -4.11 ± 0.30*** -0.74 ± 0.42 (%) Twigs 72 0.17 ± 0.62 -6.34 ± 0.34*** -1.42 ± 0.48 ** Trunk 36 0.47 ± 1.12 -5.97 ± 0.60*** -1.47 ± 0.84
Fruit removal did not affect the photosynthetic rate of the leaves that were (or had been) close to a fruit when measured two weeks after (N = 29, Type II Wald Anova: χ² = 2.0, p = 0.2).
55 Chapitre 2
Figure 2: Model interactions between treatment and sampling date (before and after the development of female organs) observed for concentrations of A) nitrogen (N) in leaves, zinc (Zn) in B) leaves and C) twigs, D) potassium (K) in twigs and E) starch in twigs. These figures illustrate the significant interactions detected in the GLMMs described in Table 1. Full big points and error bars represent an average value and its associated confidence interval for each date and treatment, and transparent small points the raw data. Significant differences between treatments are indicated in bla ck (p* < 0.05, “n.s.”= non -significant), significant differences between dates for each treatment are indicated in blue and orange for control and removed fruit treatment, respectively (p* < 0.05, “n.s.”= non -significant).
Effect of fruit removal on the production of vegetative and reproductive organs and on shoot secondary growth the following year
Fruit removal did not impact the number of ramifications produced the following year (spring shoots produced in 2019 per 2018 shoot, Table 2 and Table S2, protocol described in Fig. 1). The number of 2019 spring shoots was positively related to the diameter of 2018 shoots (Table 2 and Table S2), and the relationship was stronger in shoots on which fruits were removed compared to shoots on which fruits could develop (Table 2). Fruit removal increased 2018 shoots secondary growth between January 2019 and July 2019 (Table 2 and Table S2), and shoot growth was positively correlated to shoot initial diameter in RF trees but not in control trees (Table 2; coefficient parameter estimate ± SE: 0.51 ± 0.11 *** for RF; -0.24 ± 0.16 for control).
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Table 2: Summary of GLMMs testing the effect of fruit removal and of 2018 shoot diameter in shoots that had set a fruit in 2018 on: the production of spring shoots in 2019, the numbers female flowers, catkins, leaves and nodes per spring shoot in 2019, on shoot 2018 secondary growth during spring, on fruit set in 2019 and on the mean number of initiated fruits per spring shoot after fruit set in 2019. For each model, interactions between treatment and diameter were first included, and non-significant interactions were removed from the model. We report the number of observations, standardised estimates ± SE and p-values for treatment comparisons (control (C), removed fruit
(RF)), diameter and interaction effects, marginal (R² m) and conditional (R² c) R² for the reduced, final model. Characters in bold font refer to significant effects (p < 0.05) and stars to levels of significance (0.05 < p* <0.01; 0.01 < p** < 0.001; p*** < 0.0001).
Response variable N Standardised estimate ± SE of the predictor R² m (R² c) (Effect estimated) Treatment 2018 shoot Treatment x (RF – C) diameter Shoot diameter (RF – C x slope) Nb of female flowers 812 0.81 ± 0.38 * 0.21 ± 0.10 * n.s. 0.10 (0.41) Nb of catkins 812 -0.81 ± 0.54 -0.17 ± 0.07 * n.s. 0.12 (0.25) Nb of leaves 812 0.25 ± 0.29 0.23 ± 0.06 *** n.s. 0.07 (0.51) Nb of nodes 812 0.08 ± 0.10 0.11 ± 0.03 *** n.s. 0.06 (0.27) Nb of 2019 spring shoots 184 0.02 ± 0.14 0.37 ± 0.05 *** 0.21 ± 0.09 * 0.29 (0.45) 2018 shoot spring 339 1.6 ± 0.38 *** 0.26 ± 0.10 ** 0.76 ± 0.19 *** 0.13 (0.25) secondary growth (mm²) Fruit set in 2019 181 -0.04 ± 0.45 0.08 ± 0.11 n.s. 0.001 (0.14) Mean nb of initiated fruits 181 0.72 ± 0.49 0.22 ± 0.19 n.s. 0.08 (0.25) per 2019 spring shoot
Fruit removal increased the production of female flowers in 2019 whatever the reproductive status of the shoots (i.e. whether they had or not set fruits in 2018) (Fig. 3A; Table 2 and Table S2). Female flower production was also positively correlated to 2018 shoot diameter (Table 2 and Table S2). However, the number of fruits initiated in 2019 did not differ between control and RF shoots that had set a fruit in 2018 while this number was higher in RF trees compared to control trees on shoot that initially have no fruit (Fig. 3D, Table S2).
57 Chapitre 2
Figure 3: Effect of the fruit removal and of whether the 2018 shoot had set a fruit or not in 2018 on A) the number of female flowers per 2019 spring shoots on a log scale, B) the number of leaves per spring shoot, C) the number of catkins per spring shoot and D) the mean number of fruit initiated after fruit set in 2019 per spring shoot. Full black points indicate group mean. Within shoot reproductive status in 2018, significant differences between treatments are indicated in black (p* < 0.05, “n.s.” = non-significant, corresponding to the GLMMs in Table 2 and Table S2, p < 0.05). Within the control trees and within the defruited trees, significant differences shoot reproductive status in 2018 for each treatment are indicated in blue and orange for control and removed fruit (RF) trees, respectively (p* < 0.05, “n.s.” = non-significant).
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In shoots that had set a fruit in 2018, fruit removal affected neither the number of leaves, the number of nodes, nor the number of catkins (Table 2; Fig. 3B and 3C). These variables were however significantly correlated to shoot diameter (positively for the number of leaves and of nodes, negatively for the number of catkins) (Table 2). Neither fruit removal, nor 2018 shoot diameter affected the number of staminate flowers per catkin (N = 127, Type II Wald Anova: χ² = 0.01, p = 0.9 and χ² = 0.48, p = 0.5, respectively).
In shoots that initially had not set a fruit in 2018, (i.e. shoot that were thus either adjacent to shoots that had underwent fruit removal in RF trees or adjacent to shoots on which fruits could develop in control trees) fruit removal in adjacent shoots increased leaf production compared to control trees(Fig. 3B; Table S2), and decreased catkin production (Fig. 3C; Table S2).
Effect of fruit removal on subsequent spring shoots architecture
Study of shoot architecture showed that catkins were present mainly at the basal part of the shoots, on the nodes 1 to 4 (Fig. 4), and that leaves and female flowers presented a mode around the nodes 4 to 6 (Fig. 4, supplementary Figure S3). We never observed shoots bearing catkins at a higher node rank than female flowers. In both RF and control trees and whatever the shoot type, the number of female flowers was strongly correlated to the number of leaves of a shoot (supplementary Figure S4).
Figure 4: Relative frequency of the different organs (leaves, female inflorescences and catkins) at each node rank of 2019 spring shoots in control and RF trees. Increment in node rank indicates location of the node from the base toward the end of the shoot. The dotted line represents the node 5, which is approximatively the mode for leaves and female flowers. Female inflorescences frequency is also presented alone in Fig. S3 with higher precision.
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Qualitatively, changes in relative occurrences of leaves, female flowers and catkins in RF trees compared to control trees occurred mainly at between the base of the shoot and the Node 5 (Fig. 4, supplementary Figure S3).
Discussion In this study, we investigated how preventing fruit development and maturation would impact the reserves of the tree and allocation to flowering and growth the following year (Fig. 1). We found that while N concentration in leaves and Zn in leaves and twigs decreased in the control trees during fruit development, they did not change in trees on which fruits were removed. However, treatment did not induce significant differences in winter nutrient concentrations after the reproductive cycle, did not favour starch reserves accumulation and did not downregulate photosynthesis. In the following spring, fruit removal induced an increase in the production of female flowers and partly of fruits. Besides, we observed an increase in the number of leaves and nodes on the shoots and a decrease in catkins production only on shoots that had not directly underwent treatment (shoots that did not set a fruit in 2018), thus showing a regulation of allocation at a branch or crown scale rather than at shoot scale.
Fruit removal impacted moderately resource dynamics but not resource levels nor photosynthetic activity
2018 was an intermediate year in terms of fruit production for Quercus ilex in the area, i.e. neither a mast seeding year nor a year with important reproductive failure (Chapitre 3). Preventing experimentally the seeds from being filled during this year induced some changes in the dynamics of nutrient concentrations in twigs (zinc, potassium, starch) and leaves (nitrogen, zinc) but not in the trunk during female organ development.
While leaves of the control trees exhibited a decrease in nitrogen and zinc concentrations during fruit filling in summer and autumn, this was not observed in RF trees. This result is consistent with previous studies which have shown that leaves are usually the main pool of N storage in evergreen species (Cherbuy et al. , 2001; Millard & Grelet, 2010; Palacio et al. , 2018) and that foliar nitrogen content is lower in reproductive shoots than in vegetative shoots in Q. ilex (Alla et al. , 2013). Furthermore, Callahan et al. (2007) noted an increase in acorn production in N- fertilized oak forest plots, showing that nitrogen availability might limit fruit production in oaks. Foliar Zn has also been shown to be associated positively with fruit production in European forest trees, and negatively related to fruit production variability (Fernández-Martínez et al. , 2017). In fruit crop trees, leaves from fruit bearing shoots have lower Zn concentration than non-bearing shoots in almond tree (Saa et al. , 2018) and many species increase their fruit production in response to Zn fertilization (Hafeez, 2013; Zhang et al. , 2014). This might be explained by the involvement of the
60 Chapitre 2
Zn-finger transcription factors in the development of floral tissues in some plant species (Hafeez, 2013). Thus, fruits seem to be strong nitrogen and zinc sinks during their development in Q. ilex which are mainly supplied by nearby leaves and twigs in normal conditions. In stressful conditions however, such as strong nitrogen and zinc depletion following heavy defoliation, nitrogen and zinc supply to fruits might come from farther pools (Le Roncé et al. , 2020) such as stems, trunk and big roots.
We did not observe any effect of fruit removal on P, contrarily to what Sala et al. (2012) observed in reproductive branches of Pinus albicaulis on which cones had been removed during a mast year. The importance of P supply to growing fruits might be less important than that of N and Zn and become apparent in mast seeding year only.
K and starch concentrations in branches were different between RF and control trees in spring before fruit removal (in RF group, K level was lower and starch level higher compared to the control group), but the difference disappeared in winter. While there is no a priori reason, except than hazard, for a difference between RF and control trees in these two resources prior fruit removal, the difference in their dynamics after fruit removal nevertheless interesting. We did not expect a positive effect of fruit removal on starch concentration because NSC pools have previously been shown not to be affected by masting events in oaks (Körner, 2003). However, the stronger decrease in starch concentration of all tissues from spring to winter in RF trees (supplementary Figure S2) is surprising considering that fruit removal did not appear to downregulate photosynthetic activity, contrary to what has sometime been shown in peach, pistachio and olive trees (Vemmos, 1999; Proietti, 2000; Haouari et al. , 2013; Andrade et al. , 2019). Starch contents of all tissues were much lower in summer and winter compared to spring (supplementary Figure S2), as in other evergreen species (Hoch et al. , 2003). As most of the primary and secondary growth occurs before July in Q. ilex ((Lempereur et al. , 2015), Chapitre 3), the carbohydrates produced in summer and autumn that were neither used by growing fruits nor for growth have probably been translocated to other parts of the trees, and most likely to the collar and roots where important NSC reserves are stored (Loescher et al. , 1990; Kozlowski, 1992).
Although the seasonal dynamics in N and Zn concentrations differed between RF and control trees, this did not translate into significant differences in concentration after fruit fall. Nutrient consumption by fruits might thus be negligible in regard of the whole tree storage, as also suggested by the recent study of Bogdziewicz et al. (2019) which showed using resource budget models that costs of reproduction in Q. ilex were too small to impose a replenishment period. These results are quite contradictory with to the general assumption of resource budget models that reproduction depletes reserves of some nutrient. Nevertheless, the fact that fruit removal 61 Chapitre 2 during a non-mast year induced a change in the seasonal dynamics of N and Zn concentrations suggests that successful fruiting affects the dynamics of tree resources in N and Zn.
Fruit removal increased female flowers the following year
Fruit removal increased female flower production in spring shoots borne both by 2018 shoots that had set fruits which had been removed and shoots that had not. These results show that current reproduction has a cost for future reproduction (Obeso, 2002) and that allocation to reproduction is regulated at the crown scale and not only at local shoot scale in Q. ilex . In forest trees, only one study, which was conducted on Pinus halepensis (Santos-del-Blanco & Climent, 2014), explores the influence of fruit removal on flowering the following year and shows its positive effect, Such effect is however better documented in some fruit crop trees such as olive, peach and apple tree, but is not systematically observed (e.g. no effect in almond tree nor in grapevine (Reig et al. , 2006; Haouari et al. , 2013; Vaillant-Gaveau et al. , 2014; Fernández et al. , 2015; Haberman et al. , 2016; Fernandez et al. , 2018). Koenig et al. (2016) found that current and subsequent female reproduction were in concurrence at the tree scale in Q. ilex , which is coherent with our results, contrarily to the absence of negative autocorrelation for the reproduction effort in Q. ilex when studied at the population scale ((Koenig et al. , 2016; Bogdziewicz et al. , 2017), Chapitre 3).
This study is one of the firsts to manipulate fruiting intensity at whole tree scale in a forest tree species (but see also (Santos-del-Blanco & Climent, 2014)). However, we measured resource level and organs production along a single reproductive season, and this is insufficient to characterize differences among tree individuals. Indeed, each individual might present a different sensibility to nutrient availability, as showed by the group of trees that had not set fruits at all while presenting similar resource concentrations in spring than the two other groups. One of the assumptions of resource budget models is that resource storage determines organ production. The only way to test this hypothesis is to measure resource concentration and organs production within each individual during multiple years. However, considering that variations in foliar nitrogen among years has been shown to be correlated with initiated fruits production in Q. ilex (Chapitre 3) and to be linked to flower production in Fagus crenata (Miyazaki et al. , 2014; Satake et al. , 2019), the absence of decrease in N and Zn concentrations between spring and winter in the RF group is consistent with increased flowering the following year. Further experiments manipulating fruiting intensity are needed to determine whether there is a causal link between variation in resource storages and/or uptake and flowering in oaks (Allen et al. , 2017), and which level of resource decline after reproduction prevents flower induction the following spring (Crone & Rapp, 2014). Indeed, inhibition of future flowering by current fruit development might not be mediated by depletion of stored resources, but by other processes such as secretion of hormones inhibiting floral induction, 62 Chapitre 2 which is the case of gibberellins in apples (Bangerth, 2009; Haberman et al. , 2016; Pearse et al. , 2016; Sharma et al. , 2019).
On the need to consider the architecture of shoots and the impact of fruiting on leaves production and sex allocation
In shoots borne by shoots that had not set a fruit in 2018 (usually less vigorous than shoot that did (Alla et al. , 2012), the upregulation of female flowers production was accompanied by an upregulation of leaves production to a level similar to shoots that had set a fruit in 2018. Female flower production was strongly correlated to leaf production in all shoot types, suggesting a developmentally constrained positive relationship between leaves and female function at the shoot scale. As the upregulation of female flowers production was correlated to a decrease in catkins production, the upregulation of leaves and female flowers production might be at the expense of male flowers, especially in less vigorous shoots. Finally, fruit removal upregulated female flower production, but this did translate into a higher fruit production in June only in shoots that had not set a fruit in 2018. We can thus hypothesise that the factors upregulating female flowering, whether related to resources, hormones or to another process, was not involved in fruit set.
Fruit removal increased the shoots secondary growth the following year, which suggests that some of the resources that could not be allocated to fruit growth were allocated to shoot growth the following season. Fruit production and shoots basal area are positively correlated in Q. ilex , probably because large shoots can provide more water and nutrients to fruits (Alla et al. , 2012). In contrast to 2018 shoot secondary growth, we observed no effect of treatment on the number of 2019 spring shoots. Q. ilex has preformed buds (Montserrat-Martí et al. , 2009), which were already formed at the time of fruit removal. Thus, treatment could not affect the number of buds and did not affec t bud flush. However, fruit removal affected organs’ architecture during bud development from summer to spring.
In order to explore the physiological bases of masting and in particular of temporal negative autocorrelation, one needs to understand how the development of reproductive organs is regulated. The extension of a shoot is followed in many species by the formation of a resting terminal bud consisting of a set of partially developed organs and of one to several groups of meristems (Guédon et al. , 2006). The primordial organs, called preformed organs, usually complete their development later, during the growth period following that of their inception. A shoot may also grow in length by developing neoformed organs, i.e. a shoot portion or set of organs, each of which differentiates at the time of shoot growth (Guédon et al. , 2006). A recent study in Q. suber showed that its male flowers are preformed and be induced in previous year early summer, whereas its female flowers are most likely induced during the vegetative flush occurring in spring (Sobral et al. , 2020). Similarly,
63 Chapitre 2 the basal part of the shoots is preformed in Q. ilex while their distal part is very likely neoformed (Y. Caraglio, personal communication). Since we observed that fruit removal in late June 2018 modified the frequency of occurrence of (preformed) male flowers and leaves at the basal part of the shoots 2019, we could assume that the preformation of catkins and of first leaves happens later in the summer in Q. ilex .
Trees are modular organisms in which branches might behave as semi-autonomous units (Watson & Casper, 1984; Sprugel et al. , 1991) and in which costs of reproduction may vary depending on the modular level examined (Obeso, 1997, 2002; Sánchez-Humanes et al. , 2011; Sala et al. , 2012; Le Roncé et al. , 2020). In this study, we showed that preventing fruit development in reproductive shoots affected their future allocation to reproduction, but also shoots’ architecture and resource allocation even in shoots of distant parts of the tree crown. These results point to the need to examining reproductive costs at multiple hierarchical levels along the annual reproductive cycle and the development of individuals.
Conclusion
This study showed that even in a year of moderate reproduction, fruit development impacts seasonal dynamics in N and Zn concentrations and inhibits female flower induction in Q. ilex at both the shoot and the crown scale. Fruit removal shortly after fruit set increased productions of female flowers the following year on both shoots that had undergone treatment and shoots that were more distant in the tree crown. Fruit removal also increased partially productions of initiated fruits within the tree crown. We also show that fruit removal modifies the architecture of new shoots, but not their number. Although changes in allocation to organs (female and male flowers and leaves), both in quantity and nature, and changes in nutrient dynamics could not be compared over several years, our results suggest that resource dynamics might constrain flower and fruit production in Q. ilex more strongly than supposed in recent modelling studies using resource budget models. Our results thus strongly encourage further experiments manipulating fruiting intensity over multiple years to confirm the causal link between variations in resource storages and/or uptake and flowering in masting forest tree species, one of the main hypothesis of resource budget models.
Acknowledgments
We thank Anne-Sophie Bonnet-Lebrun for field help, Christoph Bachofen and Yves Caraglio for protocol advices as well as Alexandre Defossez, Patrick Leger and Raphaelle Leclerc who helped with the chemical analyses. This work was supported by LabEx CeMEB, and Agence Nationale de
64 Chapitre 2 la Recherche (ANR) "Investissements d'avenir" program [ANR-10-LABX-04-01]; by the ANR FOREPRO [ANR-19-CE32-0008] and Agroparistech and the French ministry of environment to ILR.
Author Contribution
ILR, IC and JML conceived and designed the study; ILR, ED and IC collected the data; ED, ILR, LS and AG performed the chemical analyses; ILR analysed the data; ILR, IC, JML interpreted the results; ILR led the writing of the manuscript and ILR, IC, JLM worked on the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
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Schermer É, Bel -Venner M, Fouchet D, Siberchicot A, Boulanger V, Caignard T, Thibaudon M, Oliver G, Nicolas M, Gaillard J, et al. 2019 . Pollen limitation as a main driver of fruiting dynamics in oak populations (J Chave, Ed.). Ecology Letters 22 : 98 –107. Sharma N, Singh SK, Mahato AK, Ravishankar H, Dubey AK, Singh NK . 2019 . Physiological and molecular basis of alternate bearing in perennial fruit crops. Scientia Horticulturae 243 : 214 –225. Silvertown JW . 1980 . The evolutionary ecology of mast seeding in trees. Biological Journal of the Linnean Society 14 : 235 – 250. Sobral R, Silva HG, Laranjeira S, Magalhães J, Andrade L, Alhinho AT, Costa MMR . 2020 . Unisexual flower initiation in the monoecious Quercus suber L.: a molecular approach. Tree Physiology .
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Sork VL, Bramble J, Sexton O . 1993 . Ecology of Mast-Fruiting in Three Species of North American Deciduous Oaks. Ecology 74 : 528 –541. Sprugel DG, Hinckley TM, Schaap W . 1991 . The Theory and Practice of Branch Autonomy. Annual Review of Ecology and Systematics 22 : 309 –334. Vaillant-Gaveau N, Wojnarowiez G, Petit A-N, Jacquens L, Panigai L, Clement C, Fontaine F . 2014 . Relationships between carbohydrates and reproductive development in chardonnay grapevine: impact of defoliation and fruit removal treatments during four successive growing seasons. OENO One 48 : 219. Vemmos SN . 1999 . Carbohydrate content of inflorescent buds of defruited and fruiting pistachio (Pistacia vera L) branches in relation to biennial bearing. The Journal of Horticultural Science and Biotechnology 74 : 94 –100. Venner S, Siberchicot A, Pélisson P-F, Schermer E, Bel-Venner M-C, Nicolas M, Débias F, Miele V, Sauzet S, Boulanger V, et al. 2016 . Fruiting Strategies of Perennial Plants: A Resource Budget Model to Couple Mast Seeding to Pollination Efficiency and Resource Allocation Strategies. The American Naturalist 188 : 66 –75. Verreynne JS, Lovatt CJ . 2009 . The Effect of Crop Load on Budbreak Influences Return Bloom in Alternate Bearing ‘Pixie’ Mandarin. Journal of the American Society for Horticultural Science 134 : 299 –307. Watson MA, Casper BB . 1984 . Morphogenetic Constraints on Patterns of Carbon Distribution in Plants. Annual Review of Ecology and Systematics 15 : 233 –258. Wickham H . 2016 . ggplot2: Elegant Graphics for Data Analysis . Springer-Verlag New York. Yacine A, Bouras F . 1997 . Self- and cross-pollination effects on pollen tube growth and seed set in holm oak Quercus ilex L (Fagaceae). Annales des Sciences Forestières 54 : 447 –462. Zhang Y, Hu C-X, Tan Q-L, Zheng C-S, Gui H-P, Zeng W-N, Sun X-C, Zhao X-H. 2014 . Plant nutrition status, yield and quality of satsuma mandarin (Citrus unshiu Marc.) under soil application of Fe-EDDHA and combination with zinc and manganese in calcareous soil. Scientia Horticulturae 174 : 46 –53.
69 Chapitre 3 : La réduction des précipitations à long terme induit une diminution du nombre de fruits qui s’ajoute à l’effet des autres déterminants de la fécondité du chêne vert
Crédit : Elia Dardevet & Marie Devauchelle.
Chapitre 3
Chapeau Le Chapitre 3 de cette thèse est basé sur un article en révision pour le journal New Phytologist . L’objectif de ce Chapitre était de déterminer comment une augmentation de l’aridité simulée par une diminution des précipitations, une des facettes potentielles du changement climatique en milieu Méditerranéen, pouvait affecter la fécondité des arbres. Pour cela, nous avons analysé les données de suivi à long terme du site expérimental de Puéchabon qui comprend un dispositif d’exclusion des précipitations depuis 2003 afin de déterminer si la réduction des précipitations affectait la reproduction des arbres et si elle modifiait l’effet des déterminants de la fécondité sur la variabilité interannuelle de fécondité. Enfin, cette étude nous a permis de tester si les arbres soumis à une réduction des précipitations sur le long terme étaient plus résistants aux épisodes de sécheresse en contexte Méditerranéen.
Dans le Chapitre 3, nous avons identifié différents déterminants liés aux conditions météorologiques et à la p hysiologie de l’arbre qui avaient des effets soit positifs (teneur en azote des feuilles avant la floraison, accroissement en biomasse, température de juin et production primaire brute en été-automne) soit négatifs (pluie pendant la pollinisation, stress h ydrique d’été et d’automne) sur la réalisation du cycle reproducteur. Les arbres qui ont été soumis à une réduction de 27 % des précipitations depuis 2003 (traitement expérimental sec), initient moins de fleurs mâles et de fruits, et leurs fruits sont moin s susceptibles d’atteindre la maturité dans des conditions de stress hydrique intense. Les arbres du traitement sec ne montrent donc pas d’acclimatation de la reproduction au stress hydrique d’été -automne. Au contraire, le traitement sec nous a permis de montrer que la réduction des précipitations à long terme avait un effet sur la fécondité qui était additionnel à celui d’une augmentation du stress hydrique pendant les périodes sèches. En revanche, le traitement sec ne modifie pas la variabilité interannuelle des fructifications.
Ce travail est basé sur l’analyse de données acquises avant mon arrivée par l’équipe du site expérimental de Puéchabon, et sur les sorties du modèle de bilan hydrique SIERRA conçu par Florent Mouillot. Les questions qui sous-tendent ce travail ont été coconçues avec Jean-Marc Limousin et Isabelle Chuine, et j’ai réalisé l’analyse des données avec l’aide de Jordane Gavinet. La rédaction a été une collaboration avec tous les co-auteurs.
Les « Supplementary materials » de cet article sont en Annexe 4 de cette thèse (page 179).
71 Chapitre 3
Holm oak fecundity does not acclimate to a drier world
Iris Le Roncé, Jordane Gavinet, Jean-Marc Ourcival, Florent Mouillot, Isabelle Chuine, Jean-Marc Limousin
Summary
(1) Climate change might impact tree fecundity by altering the relative influences of meteorological and physiological drivers, and by modifying resource investment in reproduction.
(2) Using a 13-year monitoring of Quercus ilex reproduction in a rainfall exclusion experiment, we analysed the interactions between the consequences of increased aridity and the drivers of the inter- annual variability of the number of initiated and mature fruits, fruit abortion rate, and male flower biomass.
(3) Fruit abortion during fruit growth was strongly increased by summer-autumn water stress. Long-term increased aridity did not change the relative contribution of the meteorological and physiological drivers, did not change the inter-annual variability of fruit production and did not increase fruit resistance to severe drought. Contrarily, it strongly reduced the number of initiated and mature fruits even in masting years.
(4) Rather than inducing an acclimation of tree fecundity to water limitation, increased aridity impacted it negatively through more severe water and resource limitations, but also through additive effects on fruit initiation due to changes in resource allocation. Long-term increased aridity affected tree reproduction beyond what is expected from the current response to inter-annual drought variations, suggesting that holm oak forest natural regeneration could be jeopardized in the future.
Key words: climate change impact, fecundity, mast seeding, Mediterranean, rainfall exclusion, reproductive cycle, resource allocation
Introduction Reproduction and recruitment are critical steps of population demography and should therefore be taken into account when modelling the impact of climate change on plant species distribution (Morin et al. , 2008; Bykova et al. , 2012). A global synthesis of plant reproductive patterns showed that seed production has become more variable over the last 115 years and suggested that mean seed production had declined (Pearse et al. , 2017). However, other studies reported the opposite with increased mean seed production and lower inter-annual variability in beech and oak trees over the last decades (Shibata et al. , 2020; Bogdziewicz et al. , 2020c). Long-term data analyses and 72 Chapitre 3 modelling studies have produced contrasting results and shown that the mean seed production of trees might be either positively (Allen et al. , 2014; Buechling et al. , 2016; Caignard et al. , 2017; Shibata et al. , 2020; Bogdziewicz et al. , 2020c), negatively (Mutke et al. , 2005), or not affected (Kelly et al. , 2013; Bisi et al. , 2016), by the on-going climate change. Besides, climate change might also affect the temporal patterns of tree reproduction (Hedhly et al. , 2009; Pearse et al. , 2017; Shibata et al. , 2020; Bogdziewicz et al. , 2020c), with possible consequences for seed predation (Mckone et al. , 1998; Richardson et al. , 2005; Koenig et al. , 2015). The benefits of an increased seed production might indeed be lost due to increased predation if seed production also becomes less variable (Bogdziewicz et al. , 2020c). In such an uncertain context, understanding the drivers of tree reproductive success appears essential to grasp and predict how forest natural regeneration may be affected by future climatic conditions.
Ecosystem manipulation experiments are powerful tools to anticipate how climate change might impact tree fecundity (Pérez-Ramos et al. , 2010, 2013; Bogdziewicz et al. , 2020a). Notably, multi-year ecosystem manipulations can disentangle the effect of the inter-annual variability of climate from the effect of long-term climate change simulated by experimental treatments, thereby enabling us to investigate whether trees may acclimate to climate change or not. In the Mediterranean region where temperatures are predicted to raise (Molina et al. , 2020) and rainfall events to become more stochastic into extremes during the twenty-first century (IPCC, 2013; Lionello & Scarascia, 2018; Vogel et al. , 2020), drought episodes are expected to become more frequent and severe (Ruffault et al. , 2014; Cook et al. , 2020). As drought is already identified as a major meteorological veto driving the inter-annual variability of fruit production in Mediterranean oaks (Pérez-Ramos et al. , 2015), we investigated how long-term increased aridity would affect tree allocation to reproduction using a rainfall exclusion experiment. Although the Mediterranean vegetation is adapted to summer droughts and tolerates repeated periods of water deficit, it will nevertheless be impacted by future increases in drought intensity, duration, and/or frequency (Barbeta & Peñuelas, 2016). Previous studies using long-term rainfall exclusion experiments in Quercus ilex forests, with varying percentages of excluded rainfall, have reported negative effects of increased water deficit on female flowers and total fruit biomass (Ogaya & Peñuelas, 2007; Sánchez- Humanes & Espelta, 2011; Rodríguez-Calcerrada et al. , 2011; Liu et al. , 2015; Gavinet et al. , 2019), although not all of them (Bogdziewicz et al. , 2020b). Some studies also reported negative effects on the mean seed biomass during masting years (Pérez-Ramos et al. , 2010), while they reported an increased number of aborted fruits (Pérez-Ramos et al. , 2010). At last, little effect was observed on male flower biomass (Pérez-Ramos et al. , 2010; Gavinet et al. , 2019) in spite of a reduction of the number of viable pollen grains (Bykova et al. , 2018).
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Oak trees show extremely irregular fruit production but synchronized among individuals of a population. This behaviour, called “masting” or “mast -seeding” (Kelly & Sork, 2002), is widespread in perennial plants, particularly in woody and wind-pollinated species (Herrera, 1998). The proximate drivers of seed production by trees, and of masting in particular, are however still debated. Meteorological conditions, trees internal resource dynamics, mineral nutrient uptake, as well as their interactions, are all probably involved in inter-annual variations and synchrony in seed production (Tanentzap et al. , 2012; Crone & Rapp, 2014; Pearse et al. , 2016). In oaks, no environmental factor has yet been clearly identified as a reliable predictor of massive fruit production, both across and within species (Koenig & Knops, 2014; Pérez-Ramos et al. , 2015). Moreover, some controversies exist because correlations between environmental factors and fruit production are not necessarily indicative of a causal relationship (Kelly et al. , 2013; Pearse et al. , 2014; Bogdziewicz et al. , 2019). Detrimental weather events, such as drought or frost, can prevent flower production, fruit initiation or fruit maturation and have been called meteorological vetoes (Pearse et al. , 2016; Bogdziewicz et al. , 2018, 2019). In Mediterranean oaks, the water deficit during spring, summer and autumn might result in seed abortion and is thus a major meteorological veto (Koenig & Knops, 2014; Pérez-Ramos et al. , 2015; Bogdziewicz et al. , 2017b, 2019). Whether increasing aridity might affect this seasonal drought effect on tree fecundity remains however to be determined.
The dynamics of trees internal resource, and the allocation strategies between reproduction and other tree functions are also commonly identified as potential drivers of reproduction and are themselves affected by the meteorological conditions (Obeso, 2002; Barringer et al. , 2013). Different mechanisms of allocation to reproduction have been proposed to explain masting (Pearse et al. , 2016), among which three have found some support in oaks. First, the resource depletion (or storage) hypothesis proposes that tree’s reserves are depleted during mast years so the tree needs to store new resources for several years before it can invest strongly in reproduction again (Sork et al. , 1993; Sánchez-Humanes et al. , 2011; Pearse et al. , 2016). Second, the resource switching hypothesis proposes that resources are shifted from vegetative growth to reproduction in masting years (Norton & Kelly, 1988; Hirayama et al. , 2008; Sánchez-Humanes et al. , 2011) . Third, the resource matching hypothesis proposes that resources are invested in both reproduction and growth during favourable years (Norton & Kelly, 1988; Monks & Kelly, 2006; Pérez-Ramos et al. , 2010). Finally, a key question to understand masting, and more generally the inter-annual variation in tree fecundity, is which resource limits reproduction the most. Phosphorus, nitrogen and non- structural carbohydrates are all identified as potentially involved in the proximate mechanisms driving masting (Sala et al. , 2012; Miyazaki et al. , 2014; Allen et al. , 2017; Han & Kabeya, 2017;
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Fernández-Martínez et al. , 2019; Satake et al. , 2019). Climate change effects on tree resource acquisition and allocation might thus also affect tree fecundity (Lauder et al. , 2019).
Three stages of reproduction seem particularly key for determining the number of mature fruits produced at the end of the season in Quercus ilex : number of female flowers, initial fruit set (number of fruits initiated after pollination among female flowers), and fruit development (Sork & Bramble, 1993; Tsuruta et al. , 2011). Studies investigating the effects of both meteorological conditions and tree’s internal resource dynamics on reproduction are scarce, and only a few have tried to disentangle their relative effects (Fernández-Martínez et al. , 2017; Bogdziewicz et al. , 2017b; Nussbaumer et al. , 2018; Moreira et al. , 2019). Our aim with this study was thus to identify the main drivers of fruit production in Q. ilex , and to determine the relative contribution of meteorological conditions and physiological variables in explaining fecundity variability and reproduction failure at the different stages of the reproductive cycle in the context of increasing aridity.
Using a 13-year monitoring of Q. ilex reproduction in a long-term rainfall exclusion experiment set in a Mediterranean holm oak coppice, we aimed to answer the following questions:
1) How does long-term rainfall reduction affect tree reproduction?
2) Are the drivers of the inter-annual variability of fruit production modified by long-term rainfall reduction?
3) Can tree reproduction acclimate to increasing aridity through changes in resource acquisition and allocation?
Materials and methods Experimental site and study species
The experiment was conducted in southern France (35 km northwest of Montpellier), on a flat area in the Puéchabon State Forest (43°44’29’’ N; 3°35’46’’ E, 270 m a.s.l.). This forest has been coppiced until the last clear cut in 1942. The evergreen Quercus ilex L. forms a dense canopy with a height of c. 5.5 m, a mean basal area of 26.5 m 2.ha -1, a density of c. 4900 stems.ha -1 and a leaf area index (LAI) of 2.2. The evergreen species Buxus sempervirens, Phyllirea latifolia, Pistacia terebinthus and Juniperus oxycedrus compose a sparse understory shrubby layer with c. 25% cover. The bedrock is a hard Jurassic limestone and the soil is extremely rocky with c. 75% of stones and rocks in the top 0–50 cm and 90% below. The stone-free fraction of the soil within the 0 –50 cm layer is a homogeneous silty clay loam (38.8% clay, 35.2% silt and 26% sand), leading to a field capacity of 210 mm. The area has a Mediterranean-type climate with a mean annual temperature of 13.5°C (on-site meteorological station, 1990-2019), the coldest month being January (6.0°C mean daily
75 Chapitre 3 temperature) and the hottest month July (22.4°C mean daily temperature). The mean annual precipitation is 953 mm with a range of 578 - 1549 mm (1990-2019). Rainfall mainly occurs during autumn and winter with about 80% between September and April.
The dominant species, Quercus ilex L. or holm oak, is an evergreen wind-pollinated monoecious tree that usually flowers in May in the study area. The male inflorescences, called catkins, bear around 20-25 staminate flowers (Yacine & Bouras, 1997; Gómez-Casero et al ., 2004). Female pistillate flowers mature a few days after male flowers. Fertilization occurs in late June early July, leading to the initiation of fruit (acorn) growth. Fruits achieve their maturation in November- December (Yacine & Bouras, 1997). In Q. ilex , two to three cohorts of leaves produced in different years usually coexist on the branches (Limousin et al ., 2012). Floral initiation period remains unknown in Q. ilex , but sporadic observations of second flowering during summer and autumn lead us to suspect that staminate flowers might be initiated in early summer as observed in Q. alba (Merkle et al ., 1980).
Experimental design of the rainfall exclusion
In March 2003, a throughfall exclusion experiment was set up on three replicated blocks located 50-100 m apart one from the other and situated on a flat forested area with no lateral flow. Each block comprised two contiguous 10 × 10 m plots subjected to either control precipitation or throughfall exclusion (further named “dry treatment”). For the throughfall exclusion treatment, the experimental plot was equipped with parallel 14 m long and 0.19 m wide PVC gutters hung below the canopy with a slope, between 1 m and 2 m height, so as to cover 33% of the ground area under the tree canopy within the 10 x 10 m plot and a 2 m buffer zone. Taking into account rainfall interception and stemflow, the net input of precipitation was reduced by 27% compared with the control plots (Limousin et al ., 2008). The experimental design reduces significantly the surface soil water content (García de Jalón et al ., 2020), and induced significantly more negative tree water potentials in most summers since treatment installation in 2003 (Bykova et al ., 2018). In the control plots, identical gutters were installed upside down to homogenize albedo and understorey micro-climate without reducing precipitation inputs. Stand density and mean DBH did not differ significantly between throughfall exclusion and control plots at the start of the experiment, with respectively 5930 and 6430 stems ha -1, and 6.5 and 7.6 cm DBH (Gavinet et al ., 2019). In total, 178 and 193 Q. ilex trees, were respectively included in the three replicated blocks with control and dry treatments.
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Reproduction, growth and phenology data
Stem DBH was inventoried every year in winter from 2003 to 2019 for all the stems with a DBH > 2 cm and converted into aboveground wood biomass (stem and living branches) using an allometric equation calibrated on the study site (Rambal et al ., 2004):
Biomass (g) = 191.6 × DBH 2.171 Eq (1)
Wood biomass increment at the plot scale was calculated by summing annual biomass increment of the trees. Relative plot biomass increment of the year was calculated as the plot aboveground biomass increment divided by the plot biomass.
Litterfall was collected monthly from 2003 to 2019 in 12 litter traps per plot placed regularly on a grid over the gutters at a height of 2 m and representing a total collecting area of 1.1 m 2 per plot. Litterfall was oven-dried at 60°C for 3 days, separated into leaves, wood, flowers and fruits and weighted. Fallen fruits were further categorized into three different categories and counted from 2007 to 2019: (1) aborted (not completely or mal-developed seeds, with length < 13 mm and/or diameter < 7 mm), (2) insect infested (having signs of larvae predation, such as gnaw marks or holes), and (3) mature (attaining mature seed size). The number of initiated fruits was calculated as the sum of the three categories. Annual litterfall was calculated as the sum of monthly litterfall from the 1 st of May of the year until the 30 th of April the following year in order to consider all the flowers and fruits produced during one reproductive season. Fruit number (initiated and mature), fruit biomass and male flower biomass produced per year and per plot were divided by the plot estimated aboveground biomass so that investment in reproduction was comparable among blocks. These variables are thus expressed per kg of aboveground biomass in the following and mentioned as initiated fruit number, mature fruit number, total fruit biomass and male flower biomass.
The phenology of leaves and catkins was monitored weekly during the season on six trees and three ramets per trees in each treatment of one of the blocks where scaffolds allow access to branches from the top of the canopy. A median date of catkin anthesis was calculated for each year and the mean length of the period between anthesis and flower senescence was 9 ± 4 days. We therefore considered a 25-day pollination period comprising 8 days before median anthesis (2 SD), and 17 days after median anthesis (mean flowering period + 2 SD).
Meteorological variables, water stress modelling and carbon fluxes data
Continuous meteorological data were collected by a weather station located in a clearing less than 100 m away from the experimental plots. For each year, precipitation during pollination was calculated as the sum of daily precipitation during the 25 days of the pollination period and was
77 Chapitre 3 considered similar for the two treatments because it impacts airborne pollen above the gutters. The number of days of torrential rain, previously identified as a potential determinant of mature acorn production in Q. ilex (Pérez-Ramos et al ., 2010), was determined as the number of days between July and November (period between fruit initiation and fruit maturity) during which daily rainfall exceeded 76 mm (threshold determined as the 0.99 percentile value of daily precipitations between July and November from 1984 to 2019).
Tree water stress, assessed by the predawn leaf water potential ( Ψlpd ), was simulated at a daily time scale using the water balance module of the SIERRA vegetation model (Mouillot et al. , 2001) calibrated and validated on our experimental site by Cabon et al . (2018). Soil parameters were kept constant for the two treatments, the LAI was reduced by 18% in the dry treatment relatively to the 2.2 value in control to reflect the lower leaf production in this treatment (Gavinet et al. , 2019), and the net precipitation input to the dry treatment was reduced by 27% compared to the control (Limousin et al ., 2008). Model performance was evaluated against 11 years of discrete predawn leaf water potential measurements (described in (Bykova et al. , 2018)) and was similarly good in the two treatments (control treatment: R² = 0.85, RMSE = 0.58 MPa; dry treatment: R² =
0.89, RMSE = 0.57 MPa). Daily absolute values of simulated Ψlpd were summed over the period from April to June to calculate the water stress integral (WSI, defined by (Myers, 1988)) during the period of flower development and fertilization, and from July to November to calculate the WSI during the period of fruit development and maturation. WSI was also calculated for the summer (July-September) of the previous year, which corresponds to the suspected period of initiation of male flowers.
Eddy covariance fluxes of CO 2 between the ecosystem and the atmosphere have been measured continuously during the experiment using a three-dimensional sonic anemometer and a closed path infrared gas analyser set up at the top of a 12-m-high tower located near the experimental plots (see (Allard et al ., 2008) for details). Processing schemes of FLUXNET have been used for filling data gaps and partitioning net ecosystem productivity (NEP) into gross primary productivity (GPP) and ecosystem respiration R eco (Reichstein et al ., 2005; Papale et al ., 2006). Half- hourly values of GPP were summed over the period from July to November as an estimate of ecosystem-scale carbon assimilation during fruit development and maturation.
Leaf chemical analyses
From 2007 to 2018 (except 2013), leaves from the top canopy of the 6 to 8 trees per treatment in the scaffold-equipped block, where we also monitored phenology and fruit growth, were collected in winter (mid-November to mid-February, depending on years) oven-dried at 60°C for 3 days and
78 Chapitre 3 grinded for chemical analyses. Leaves were previously sorted between leaves produced during the current year (less than 1 year old) and leaves produced the year before. Nitrogen (N) mass-based concentration was determined by thermal combustion, using a Flash Smart NC Elemental Analyzer (ThermoFisher Scientific, USA) and phosphorus (P) mass-based concentration was measured colorimetrically using the molybdenum blue method. A mean N and P winter leaf concentration for each leaf cohort was calculated from 2007 to 2018 (except for missing P data in 2013 and 2017, and N data in 2013 and 2018).
Variables selection and statistical analyses
The four main reproduction variables that we studied are the biomass of male flowers, the number of initiated fruits, the fruit abortion rate and the number of mature fruits.
First, we tested for a treatment effect on each reproductive variable, by running generalized linear mixed models (GLMMs) using the R package lme4 (Bates et al. , 2015) with treatment as a fixed effect, and block and year as random effects. We used negative binomial distribution with a log link for count variables to account for over-dispersion issues.
Second, we did an extensive review of the literature to identify all the factors potentially driving the inter-annual variability of Q. ilex fruit production (summarised in Table 1). Due to the large number of potential explanatory variables and of time periods on which they can be considered, and to the high collinearity among some weather variables, we narrowed our analyses based on existing knowledge about the main drivers of fruit production in Q. ilex . We only considered the factors that we could associate to a physiological mechanism, and therefore excluded variables such as the difference of annual temperature between current and previous year (Koenig et al ., 2016) or January minimum temperature (García-Mozo et al ., 2007). We excluded the factors which had a very large temporal coverage, such as annual rainfall (Alejano et al ., 2008), evapotranspiration between January and August (Bogdziewicz et al ., 2017b) or precipitations during the 10 months before fruit maturation (Ogaya & Peñuelas, 2007), or a very large spatial coverage, such as the remote sensing enhanced vegetation index (Camarero et al ., 2010; Fernández-Martínez et al ., 2015). We then attributed the explanatory variables to each period of the reproductive cycle according to the time of the year they cover. When some variables were too strongly correlated to one another (such as temperature, rainfall and water stress in autumn), we chose to include in the model selection only the water stress index (WSI) which is the variable that integrates the best the physiological state of the tree.
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Table 1: Summary of the main meteorological and physiological explanatory variables mentioned in the literature affecting Q. ilex reproductive success. Variables are assigned to a stage of the reproductive cycle. The periods corresponding to each stage of the reproductive cycle are specified based on our knowledge of Q. ilex reproductive biology and our observations. n.s. = non-significant, n.a. = non-applicable, WSI = Water stress index, GPP = Gross primary production.
Stage of the Explanatory variable Effect +/ - Explanatory variable included for reproductive proposed in the [Reference] model selection cycle literature July-September WSI of the previous Water stress - ? Male flower year initiation Male flower biomass of the previous [Summer of Negative - ? year previous year?] autocorrelation Total fruit biomass of the previous year Previous acorn crop - (mitigated by (negative Total fruit biomass of the previous year Female flower weather) [1] initiation autocorrelation) [unknown] July-September WSI of the previous Water stress - ? year Male flower Warm spring + [1] April mean temperature development Secondary growth + ? Annual plot relative biomass increment [April] April air humidity - [2] Precipitations during pollination + [2, 3] Female flower Pollen quantity Male flower biomass development n.s. [1, 4] and pollination Warm spring + [1] [April-May] Mean temperatures of April and of May Min temperature in May - [2] Delayed phenology + [4] n.a. (but related to spring temperature) Spring drought - [2, 4, 5, 6] April-June WSI Fertilisation Spring rainfall + [3, 7] and fruit Mean temperature in initiation + [3] June mean temperature June [June] Spring secondary n.s. [8] Annual plot relative biomass increment growth Drought in summer and - [5, 7, 9, 10, 11] / or early autumn Fruit growth and maturation Temperature in summer July-November WSI - [12] or early autumn [July- November] Autumn rainfall + [3, 7] Torrential rains - [5] Number of days of torrential rain
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Autumn secondary - [8] Annual plot relative biomass increment growth Carbon assimilation + ? July-November GPP [1] (Bogdziewicz et al. , 2017b); [2] (García-Mozo et al. , 2012); [3] (García-Mozo et al. , 2007); [4] (Fernández- Martínez et al. , 2012); [5] (Pérez-Ramos et al. , 2010); [6] (Fernández-Martínez et al. , 2015); [7] (Alejano et al. , 2008); [8] (Martín et al. , 2015); [9] (Carevic et al. , 2010); [10] (Espelta et al. , 2008); [11] (Liu et al. , 2015); [12] (Montserrat-Martí et al. , 2009).
Third, in order to determine the set of variables that best explain the inter-annual variability of the four reproductive response variables, we applied a model selection procedure on the full models including all the variables that we had previously selected, and in which variance inflation factors never exceeded 3 as recommended by Zuur et al . (2010). The four models are described in Table 2, indicating both the variables retained after model selection and the variables excluded by model selection. The model with the best empirical support was selected by minimizing the corrected AIC for small sample size (AICc) using the R package MuMIn (Bartoń, 2019) . We proceeded in two steps. First, we run the full models (GLMMs with block as random factor) on the control treatment only to identify the main drivers of inter-annual variability in tree fecundity. Second, we run the selected model, with the same characteristics (response variables, model distribution and random effect), on both the control and dry treatments in order to determine the impact of long term increased aridity on the trees’ response to seasonal drivers (Table 2). As fixed effects, we included the variables retained by model selection for each response variable (except for GPP, which was not measured in the dry treatment), treatment and the interactions between treatment and all the other variables. We then simplified the model by sequentially removing the insignificant terms, starting with the weakest and least significant interaction.
In order to determine whether the production of initiated fruits and of male flowers was impacted by nitrogen and phosphorus concentration in leaves during the winter before bud break, we used data from the two cohorts of leaves and the two treatments. We fitted GLMMs with a negative binomial distribution for the number of initiated fruits produced per kg of aboveground biomass and with a Gaussian distribution for male flower biomass per kg of aboveground biomass. Treatment, the standardised nutrient concentration and their interaction were included as fixed effects.
All statistical analyses and visual representations were conducted using the software R version 3.6.1 (2019) and the ggplot2 package (Wickham, 2009). Marginal (R²m) and conditional (R²c) R² were calculated with the package MuMIn according to Nakagawa ’s method (Nakagawa &
81 Chapitre 3
Schielzeth, 2013) to estimate the variance explained by fixed effects and fixed plus random effects, respectively.
Table 2: Detail of the structure of GLMMs on which model selection was applied to retain the set of variables that best explained the reproductive responses. For each reproductive stage the distribution of the model, the variables retained and excluded by model selection, and the random effect are presented.
Response Model Variables of the full model retained and excluded Random variable distribution after model selection effect Male flower Gaussian Retained: Total male flower biomass of the previous year, Block biomass per kg July-September WSI of the previous year of Excluded: Total fruit biomass of the previous year, aboveground Relative biomass increment, Mean temperature of April biomass Number of Negative Retained: Relative biomass increment, June mean Block initiated fruits binomial temperature, Precipitations during pollination per kg of Excluded: Total fruit biomass of the previous year, Male aboveground flower biomass, April mean temperature, May mean biomass temperature, April-June WSI, July-September WSI of the previous year Abortion rate Binomial Retained: Number of initiated fruits, Relative biomass Block between increment, July-November WSI, July-November GPP initiation and Excluded: Number of days of torrential rain maturity Number of Negative Retained: Number of initiated fruits, July-November Block mature fruits binomial WSI, July-November GPP per kg of Excluded: Relative biomass increment, Number of days aboveground of torrential rain biomass Results Treatment effect on tree water stress and reproductive variables
Water stress (measured by WSI) of spring (April-June), summer-autumn (July-November) and summer (July-September) of the previous year were significantly increased by 20.6%, 16.8% and 13.4%, respectively, in the rainfall exclusion treatment compared to the control (Supporting Information Fig. S1 ). Rainfall exclusion significantly reduced the male flower biomass, the number of initiated fruits and the number of mature fruits, while it significantly increased the fruit abortion rate (Fig. 1a,b,c,d ). Rainfall exclusion had, however, no impact on leaves nutrient concentrations in winter (Supporting Information Table S1 ).
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Figure 1: Effect of the rainfall exclusion treatments on ( a) male flower biomass, ( b) the number of initiated fruits, (c) fruit abortion rate, and ( d) the number of mature fruits. P-values correspond to GLMMs with year as a random effect (**: p < 0.01, ***: p < 0.001). Each point represents the value for one plot in one year.
Inter-annual variability of reproduction
The number of initiated fruits and the biomass of male flowers were less variable among years (mean CVp 42% and 57%, respectively, in control treatment), than the number of mature fruits (CVp 126 % in control treatment) and the rate of fruit abortion between initiation and maturity (Fig. 2a,b,c,d , Supporting Information Table S2 ). Rainfall exclusion treatment did not affect any of these coefficients of variation (Fig. 3).
The mean mass of mature fruits did not differ between treatments (Supporting Information Table S2 , Fig. S2a ). There was no trade-off between mean mature fruit mass and mature fruit number. On the contrary, there was a positive relationship between the two variables: years of high fruit production were also years during which mature fruits were heavier (Supporting Information Fig. S2b ).
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Figure 2: Mean inter-annual variability (± SD) of the production of ( a) male flower biomass, ( b) number of initiated fruits, ( c) fruit abortion rate and ( d) number of mature fruits in the control (blue) and dry (orange) treatments. Points indicate the mean value of the 12 traps for each plot and each year.
Figure 3: Treatment effect on the inter-annual variability (population coefficient of variation) of mean male flower biomass, mean number of initiated fruits and mean number of mature fruits among the three plots per treatment.
84 Chapitre 3
Male flower biomass
Male flower biomass was standardised by plot biomass, as were fruit number variables. According to model selection, male flower biomass was best explained by (in order of importance) the male flower biomass of the previous year and the summer WSI of the previous year which had both a negative effect and explained collectively 22% of the variance (Table 2, Fig. 4a ). The effect of the rainfall exclusion on male flower biomass remained negative when combined with other variables (Fig. 4b ).
Figure 4: GLMM standardised estimates ± SE and associated p-values (*: p < 0.05, **: p < 0.01) of the variables explaining male flower biomass per kg of aboveground biomass ( a) after model selection in the control plots for 2007-2019 and ( b) with the variables retained by model selection in ( a) plus the dry treatment effect in control and dry plots. Interactions between treatment and all covariables were first included, and non-significant interactions were sequentially removed from the model starting with the least significant ones. We report marginal (R²m) and conditional (R²c) R² for the reduced final model.
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Number of initiated fruits
According to model selection, the number of initiated fruits in the control plots was best explained by (in order of importance) cumulative rainfall during pollination, which had a negative effect, wood biomass increment and mean June temperature, which had both positive effects (Table 2, Fig. 5a ). The variance explained by the fixed effects was around 39% and larger than for male flowers. There was no trade-off between wood biomass increment and reproduction standardised by the plot biomass, on the contrary, years during which large amounts of wood biomass were produced were also years during which a large number of fruits were initiated. Interestingly, neither the fruit crop of the previous year, the male flower biomass nor the spring WSI were retained by model selection as strong predictors of the number of initiated fruits (Table 2). The long-term rainfall exclusion treatment had an additional strong negative effect on fruit initiation (Fig. 5b ). There was no interaction between wood biomass increment and the rainfall exclusion effect.
Figure 5: GLMM standardised estimates ± SE and associated p-values (*: p < 0.05, **: p < 0.01, ***: p < 0.001) of the variables explaining the number of initiated fruits per kg of aboveground biomass ( a) after model selection in control plots for 2007-2019, and (b) with the variables retained by model selection in ( a) plus dry treatment effect in control and dry plots for 2007-2019. Interactions between treatment and all covariables were first included, and non-significant interactions were sequentially removed from the model starting with the least significant ones. We report marginal (R²m) and conditional (R²c) R² for the reduced, final model.
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Abortion rate between fruit initiation and maturity
According to model selection, fruit abortion rate between initiation and maturity was best explained by (in order of importance) July-November WSI, which had a positive effect, and by July- November GPP, the number of initiated fruits and wood biomass increment which had a negative effect, with all the fixed effects explaining 21% of the variance (Table 2, Fig. 6a ). Years during which trees initiated a large number of fruits tended to be years during which fruits survived better. As WSI and the number of initiated fruits were already strongly affected by the dry treatment (Fig. 1b and Supporting Information Fig. S1f ), this later did not have a significant effect on abortion when tested in addition to these two variables (Fig. 6b ). However, treatment and July-November WSI interacted significantly (Fig. 6b ), indicating that trees from the dry treatment experienced less fruit abortion than control trees during years with low water stress in summer and autumn (Supporting Information Fig. S3a,b ).
Figure 6: GLMM standardised estimates ± SE and associated p-values (***: p < 0.001) of the variables explaining fruit abortion rate between initiation and maturity ( a) after model selection in control plots for 2007- 2019 and ( b) with the variables retained by model selection in ( a) plus the dry treatment effect in control and dry plots. GPP was not included in this last model, as it was not measured in the dry treatment. Interactions between treatment and all covariables were first included, and non-significant interactions were sequentially removed from the model starting with the least significant ones. We report marginal (R²m) and conditional (R²c) R² for the reduced, final model.
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Number of mature fruits
According to model selection, the number of mature fruits was best explained by (in order of importance) the number of initiated fruits which had a positive effect, July-November WSI which had a negative effect, and July-November GPP which had a positive effect (Table 2, Fig. 7a ). Our model explained as much as 65% of the variance in mature fruits, probably as a result of the inclusion of the number of initiated fruits as a predictor of the number of mature fruits. However, the final number of mature fruits was more largely driven by the rate of abortion than by the number of initiated fruits (Supporting Information Fig. S4a,b ). The rainfall exclusion treatment did not affect the number of mature fruits when tested in addition to the number of initiated fruits and summer-autumn WSI (Fig. 7b ).
Figure 7: GLMM standardised estimates ± SE and associated p-values (**: p < 0.01, ***: p < 0.001) of the variables explaining the number of mature fruits per kg of aboveground biomass ( a) after model selection in control plots for 2007-2019 and ( b) with the variables retained by model selection in ( a) plus the dry treatment effect in control and dry plots. GPP was not included in this model as it was not measured in the dry treatment. Interactions between treatment and all covariables were first included, and non-significant interactions were sequentially removed from the model starting with the least significant ones. We report marginal (R²m) and conditional (R²c) R² for the reduced, final model.
Effect of leaf nutrients content
Male flower production and the number of initiated fruits were both positively related to winter nitrogen concentration in old leaves (cohort produced two years before) (Table 3). They were not correlated to nitrogen concentration in young leaves, nor with phosphorus concentrations. There were no interactions between treatment and nutrient concentrations. Conversely, the total biomass of fruits produced during summer and autumn (i.e. aborted and mature fruits) and the treatment
88 Chapitre 3 had no effect on the nitrogen and phosphorus concentration of the leaves the following winter in either treatment (Supporting Information Table S1 ).
Table 3: Standardised estimates ± SE and associated statistical significance (*: p < 0.05, **: p < 0.01, ***: p < 0.001) of the GLM relating fruit initiation and male flower production to treatment and leaf nutrient concentration during the preceding winter (from 2007 to 2018). Nutrient concentration was measured in young leaves produced in the preceding spring (n-1 leaves) and old leaves produced 2 years before (n-2 leaves). Interactions between treatment and all covariables were first included, and non-significant interactions were sequentially removed from the model starting with the least significant ones (none of them were significant).
Response variable Fixed effects
Treatment [N] n-1 leaves [P] n-1 leaves
Number of fruits produced a year n -0.93 ± 0.13 *** -0.03 ± 0.08 0.11 ± 0.08
Flower biomass produced a year n 0.06 ± 8.1 0.68 ± 0.5 -0.53 ± 0.5
Treatment [N] n-2 leaves [P] n-2 leaves
Number of fruits produced a year n -0.94 ± 0.13 *** 0.18 ± 0.07 ** -0.03 ± 0.07
Flower biomass produced a year n 0.12 ± 0.7 1.0 ± 0.4 * -0.19 ± 0.4
Discussion Increased aridity has detrimental effects on flower production, fruit set and fruit growth
The long-term experimental reduction of precipitations by 27% increased the water stress during flower development and fruit initiation in spring, fruit growth in summer and autumn, and male flower initiation in summer. Increased aridity decreased significantly the production of male flowers, the number of initiated and mature fruits, and increased fruit abortion during summer and autumn. Consequently, the final fruit crop under rainfall exclusion was reduced due to two cumulative effects, i.e. a lower number of initiated fruits in spring and a higher rate of fruit abortion in summer and autumn. Our results confirm those from other rainfall exclusion experiments in Q. ilex forests, in which a 15 % rainfall exclusion reduced both the number of initiated fruits and of mature acorns (Sánchez-Humanes & Espelta, 2011), as well as the biomass of male flowers and fruits (Ogaya & Peñuelas, 2007; Liu et al ., 2015).
Conversely, the long-term precipitation reduction did not modify the inter-annual variability in seed production, which can result from variation in either flower production, fruit
89 Chapitre 3 initiation rate or fruit abortion rate (Pearse et al ., 2016). In our study, the number of mature fruits was more largely driven by fruit abortion than by fruit initiation and our results confirm that Q. ilex is a 'fruit maturation masting' species (Sork & Bramble, 1993; Espelta et al ., 2008; Bogdziewicz et al ., 2019), i.e. that fruit crop is more largely determined by what happens after fruit set than before it. With a 3 folds higher CVp for the number of mature fruits than for initiated fruits, the difference of inter-annual variability among reproduction stages was larger here than in previous studies on Mediterranean oaks (Espelta et al ., 2008; Pérez-Ramos et al ., 2014). In any case, our experimental reduction of precipitation did not modify the relative variability of either flower production or fruit abortion, and it did not increase the inter-annual variability of seed production. This result thus does not support the hypothesis proposed by some authors that inter-annual variability of fruit production should increase under more frequent adverse weather (Espelta et al ., 2008; Bogdziewicz et al ., 2018; Wion et al ., 2020).
Several studies have shown that fruit abortion rate was strongly influenced by water stress during summer and autumn (Espelta et al ., 2008; Alejano et al., 2008; Carevic et al ., 2010; Pérez- Ramos et al ., 2010; Liu et al ., 2015). Alejano et al . (2008) suggested that water stress may act as a climatic veto when leaf water potential is lower than –3.5 MPa, which is close to the turgor loss point of Q. ilex (Tognetti et al ., 1996). In our study, the two years during which fruit production was the highest (2007 and 2015) were years during which the simulated water potential never reached this threshold in any treatment (in 2015) or only for a few days (in 2007). However, the same condition was also met during other years and did not result in a high fruit crop. A low summer water stress therefore appears as a necessary but not sufficient condition for reproductive success. The fruit abortion rate was the only reproductive variable to exhibit an interactive effect between the dry treatment and summer-autumn water stress (Fig. 6). Fruit abortion had a greater sensitivity to water stress in the dry treatment than in the control, suggesting that increased aridity worsen the effect of summer drought on fruit growth. Although this greater sensitivity to water stress in the dry treatment translated into a lower abortion than in the control in favourable years, it did not allow for a better fruit development in dry years and fruit production remained lower in the dry treatment irrespectively of the drought conditions.
Fruit set has also been shown to be limited by pollination success, which depends on meteorological conditions during pollination because rainfall during flowering washes pollen out of the atmosphere and alters pollination success (García-Mozo et al. , 2007). As our experiment was not designed to manipulate rainfall over the tree canopy, we cannot estimate how fewer precipitation in spring may affect fruit initiation due to pollen washing. Nevertheless, in a previous study, we found that the rainfall exclusion decreased viable pollen production by the trees (Bykova
90 Chapitre 3 et al ., 2018), which may reduce pollen availability in the future. Previous studies on Q. ilex have observed a pollen limitation effect on fruit production, but only in low tree density savannah-like landscapes (García-Mozo et al ., 2007, 2012; Pesendorfer et al ., 2016), and not in dense forests (Fernández-Martínez et al ., 2012; Bogdziewicz et al ., 2017b). Finally, pollination may be positively affected by warmer spring temperatures in Mediterranean oaks because they favour flowering synchrony and thereby the efficiency of pollination (Koenig et al ., 2015; Bogdziewicz et al ., 2017b), or the fertilization (Sork et al ., 1993; Cecich & Sullivan, 1999) through a possible positive influence on pollen tube growth (Hedhly et al ., 2009). Our results validate the positive effect of June temperature, during the fertilization period, on the number of initiated fruits, as previously observed by García-Mozo et al . (2007), but not an effect of the temperatures during flowering (April-May).
Increased aridity has additive carry-over effects on male flower production and fruit set
Experimentally increased aridity had long-term effects on tree allocation to reproductive organs in our experiment. This was revealed by the strong treatment effects on the initial fruit set and male flower production that could not be explained merely by an increased water stress during critical periods of the reproductive cycle. First, because treatment effect on male flower biomass remained significant in addition to the previous year WSI effect, and second, because seasonal water stress had no significant effect on fruit initiation (Fig. 4, Fig. 5). This means that the effect of experimentally increased aridity was additive with the other drivers, so that trees in the dry treatment always produced less male flowers and initiated fruits than the control trees, independently of the meteorological conditions. Conversely, the long-term increased aridity did not interact with the meteorological drivers of fruit production and thus did not modify their hierarchy of importance in explaining the inter-annual variation in fecundity. Increased aridity thus lowered the intercept of the relationship between reproduction and climatic conditions but without affecting the slope (no interaction). Therefore, the sensitivity of the reproductive organs to temperature and seasonal drought remained similar across treatments while the overall capacity to allocate to reproduction was diminished by increased aridity. In other words, the dry treatment reduced fruit production more strongly than what we would predict from a 27% decrease in precipitation with the model adjusted on the control treatment data. Consequently, we argue that Q. ilex acorn production may be more affected by increasing aridity in the future than what current inter-annual variations would lead us to forecast.
We suggest that this additive effect of increased aridity might be induced partly by the carry- over effects of previous summer water stress on bud development. In oak species, primary growth is largely determined by preformed organs inside the buds (Fontaine et al ., 2000; Alla et al ., 2013). 91 Chapitre 3
Bud enlargement (Montserrat-Martí et al ., 2009), leaf primordia initiation (Alla et al ., 2013) and male floral primordia initiation (Merkle et al ., 1980) have all been shown to occur during the previous growing season and to be potentially affected by summer drought. Our results indeed show a significant effect of previous year WSI on male flower production, and previous studies in the same experiment found a 1-year lagged effect of drought on leaf production (decreased leaf number per growth unit) (Limousin et al ., 2012; Gavinet et al ., 2019), and a lower number of viable pollen grains (Bykova et al ., 2018). Female flowers, contrary to other organs, were not affected by water stress of the previous summer. However, because they are generally initiated on the larger shoots bearing numerous leaves (Alla et al. , 2012), their lower number in the dry treatment might be caused indirectly by the reduced number of ramifications and leaves per shoot in this treatment (Limousin et al ., 2012).
Resources are less allocated to reproduction under increased aridity
In our study, years during which conditions were favourable for growth were also favourable for fruit production. Fruit crop and fruit abortion rate in the control treatment were, respectively, positively and negatively correlated to cumulated GPP from July to November. This result is consistent with previous studies showing that the carbon used for fruit development is provided mainly by recent uptake of photo-assimilates (Hoch et al ., 2013; Ichie et al ., 2013; Han et al ., 2016), and it suggests that Q. ilex acorn production might be limited by the availability of carbon resources. Other studies conducted in temperate forest ecosystems on Q. petraea or F. sylvatica have, however, failed to observe such a relationship between GPP and seed production (Delpierre et al. , 2016; Mund et al ., 2020). In the Mediterranean context of our study, where the ecosystem carbon uptake is largely driven by water availability in summer (Rambal et al ., 2014), GPP and acorn production are both largely influenced by water stress. In spite of this, the WSI and GPP were both retained by model selection as significant drivers of the annual fruit crop and fruit abortion rate (Fig. 6; Fig. 7), thereby suggesting an effect of carbon resources on acorn production, in addition to the effect of water availability. The greater sensitivity of the fruit abortion rate to summer-autumn WSI in the dry treatment could be partly explained by a reduced availability of recently assimilated carbon given that leaf carbon assimilation and ecosystem GPP are reduced in this treatment compared to the control (Misson et al ., 2009; Limousin et al ., 2010).
Our results also show that biomass increment was positively related to the number of initiated fruits per kilogram of aboveground biomass and negatively related to the fruit abortion rate, thereby indicating an absence of causal trade-off between growth and reproduction. In masting Fagaceae, massive fruit crops have been either associated to reduced growth (Pearse et al ., 2016), although the relationship might not be causal (Knops et al ., 2007; Knops & Koenig, 2012), 92 Chapitre 3 increased growth (Pérez-Ramos et al ., 2010; Fernández-Martínez et al. , 2015; Bogdziewicz et al. , 2020b), or not associated to growth at all (Yasumura et al ., 2006; Pulido et al ., 2014). The absence of trade-off between growth and reproduction in Q. ilex might be explained by a temporal lag between stem and fruit growth. Q. ilex stem growth takes place mostly in spring and early summer, before the drought and concomitantly to flower development and fruit initiation, but also in autumn when water availability is restored after the drought (Supporting Information Fig. S5 ). Stems and fruits are thus in competition for resources mainly in autumn, consistently with Martín et al. (2015) who observed a negative correlation between acorn production during mast years and autumn stem growth, but not spring growth. Here, we observed no interactive effect between stem growth and the treatment, but the biomass increment and treatment were both retained by model selection as significant drivers of fruit initiation and abortion (Fig. 5; Fig. 6). This means that for a given allocation to wood growth the allocation to fruits was lower in the rainfall exclusion treatment. Our results thus confirm an earlier conclusion by Bogdziewicz et al . (2020b) that increased aridity modifies the relative allocation to growth versus reproduction in Q. ilex , although in our case the allocation to fruits was more impacted by aridity than the allocation to wood. In our experiment, stand aboveground net primary productivity was reduced by 11% in the dry treatment compared to the control, but while wood production was not significantly impacted by the dry treatment, acorn mean biomass production was reduced by as much as 34% over the course of our experiment (Gavinet et al ., 2019).
Male flower biomass and the number of initiated fruits were both positively correlated to mean nitrogen concentration in 2 yr-old leaves sampled during the winter before. These old leaves usually fall in early summer, shortly after new leaves, flowers and initiated fruits have been produced. Our result thus suggests that nitrogen used for flower production might be remobilized from old leaves before they fall (Cherbuy et al ., 2001) and that male flowers development might be limited in part by nitrogen availability. There is increasing evidence that stored nitrogen or phosphorus contribute to masting events (e.g. (Sala et al ., 2012; Ichie & Nakagawa, 2013; Miyazaki et al ., 2014; Han & Kabeya, 2017; Satake et al ., 2019), and fertilisation experiments in oak forests have sometimes increased acorn production (Bogdziewicz et al ., 2017a), although not always (Brooke et al ., 2019). Conversely, the absence of an interactive effect between rainfall exclusion and leaf nutrient content on flower and fruit production suggests that the long-term increased drought effect was not mediated by a nutrient limitation.
Conclusion
Taken together our results have important implications for forecasting the response of tree reproduction to increasing aridity because they suggest that it may affect tree reproduction beyond 93 Chapitre 3 what is expected from the current response to seasonal drought. Under the drier conditions simulated by our experiment, trees initiate less flowers and fruits, and these fruits are less likely to withstand summer drought and reach maturity. We interpret the strong negative effect of increased aridity on fruit production as caused by more severe water stress during fruit development, stronger limitations by resources and changes in resources allocation, all suggesting that holm oak forest natural regeneration could be jeopardized in a drier future.
Acknowledgments
We thank Manon Vaudois and Matthias Grenié for field help. This work was supported by LabEx CeMEB, an Agence Nationale de la Recherche (ANR) "Investissements d'avenir" program [ANR- 10-LABX-04-01]; by the ANR FOREPRO [ANR-19-CE32-0008] and Agroparistech and the French ministry of environment to ILR. The Puéchabon experimental site belongs to the OSU OREME (UMS 3282) and the French national research infrastructure ANAEE-France (ANR-11- INBS-0001).
Author Contribution
ILR, IC and JML conceived and designed the study; JMO and JML collected the data; FM developed the water stress model; ILR and JG analysed the data; ILR led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
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100 Chapitre 4 : Modélisation de la production de fleurs et de fruits grâce au modèle PHENOFIT, un modèle basé sur les processus
Source : https://xkcd.com/2323/
Chapitre 4
1. Introduction Les changements climatiques sont susceptibles de modifier le succès reproducteur des arbres forestiers (Mckone et al. , 1998; Caignard et al. , 2017; Gavinet et al. , 2019; Bogdziewicz et al. , 2020c) ainsi que l’allocation de ressources par les individus à la reproduction (Monks et al ., 2016; Gavinet et al ., 2019; Bogdziewicz et al ., 2020b). Or, le taux de migration de nombreuses espèces d’arbres reste bien inférieur à la vitesse des changements climatiques actuels et projetés (Kremer et al. , 2012; Meier et al. , 2012; Saltré et al. , 2015) , et la capacité d’adaptation des espèces grâce à la plastic ité phénotypique est limitée (Duputié et al. , 2015). Le seul moyen pour les arbres de faire face aux changements climatiques est donc l ’adaptation par le biais de la microévolution (Aitken et al. , 2008; Alberto et al. , 2013; Lefèvre et al. , 2014), même si ce processus est lent chez les arbres (Savolainen et al. , 2004) . La capacité de régénération des populations d’arbres est donc es sentielle au succès de cette adaptation. Par ailleurs, même en cas de démarches de plantation et de migration assistée par les gestionnaires forestiers pour les essences d’intérêt, la disponibilité en graines peut parfois s’avérer limitante. Il devient don c aujourd’hui crucial de mieux comprendre les stratégies d’allocation des arbres à la reproduction relativement aux autres fonctions ainsi que les déterminants environnementaux du succès reproducteur pour pouvoir prédire l’impact des conditions climatiques futures sur la fécondité des arbres. Cela a notamment été l’objet des trois chapitres précédents pour le cas du chêne vert, en particulier le Chapitre 3.
La compréhension des déterminants de la fécondité est par ailleurs complexifiée par le fait que de nombreuses espèces se reproduisent suivant un patron dit de masting caractérisé par des fructifications massives, intermittentes et synchronisées à l’échelle d’une population (Silvertown, 1980; Kelly, 1994; Koenig & Knops, 2000). Bien que ce patron soit répandu chez les espèces pérennes, en particulier chez les espèces ligneuses pollinisées par le vent comme les chênes (Herrera, 1998), le masting et la fécondité de façon plus générale ont encore peu été intégrés aux modèles forestiers (Vacchiano et al. , 2018).
Les modèles qui parviennent à modéliser le succès reproducteur des plantes de façon mécaniste sont essentiellement centrés sur les espèces de grandes cultures comme le modèle STICS (Brisson et al. , 2003) ou sur les arbres fruitiers comme les modèles MAppleT et QualiTree (Pallas et al. , 2015), PEACH et L-PEACH (Grossman & DeJong, 1994; Allen et al. , 2005), PINEA2 (Pereira et al. , 2015), Cashoo (Lechaudel et al. , 2005) ou encore MaluSim (Lordan et al. , 2020). Ces derniers sont principalement centrés sur l’effet de l’architecture et de l’allocation de carbone sur la croissance des fruits (DeJong, 2019). Les conditions de croissance de ces espèces sont souvent peu comparables à celles de milieux naturels (irrigation, amendement des sols, taille, greffe, etc.), ce qui rend l’extrapolation de leurs résultats aux espèces sauvages délicates. Sur les arbres forestiers, des
102 Chapitre 4 travaux sont en cours avec les modèles PDG (Oddou-Muratorio & Davi, 2014) et CASTANEA (Dufrêne et al. , 2005).
En milieu forestier, l’essentie l des modèles (Abe et al. , 2016) visant à expliquer les dynamiques de succès reproducteur des arbres appartient à la famille des « Resource Budget Models » (RBM). Ces modèles simulent des dynamiques de fructifications individuelles à l’échelle d’une population d’arbres (Isagi et al. , 1997; Satake & Iwasa, 2000) , et sont basés sur l’hypothèse qu’une fructification massive ind uirait un épuisement des réserves de l’individu -arbre l’empêchant de produire à nouveau une grande quantité de fleurs et donc de fruits l’année suivante (Pearse et al. , 2014; Crone & Rapp, 2014) . Si ces modèles, initialement de type théorique, ont fait l’objet de nombreuses améliorations depuis une vingtaine d’année s pour prendre davantage en compte différents types de ressources (Abe et al 2016) et les conditions météorologiques (Abe et al. , 2016; Schermer et al. , 2019, 2020), ils restent encore peu aptes à réellement simuler comment les dynamiques de fructification seraient affectées par différents scénarios de changement climatique (Schermer, 2019).
L’objectif de ce travail était donc dans un premier temps d’implémenter une quantification du succès reproducteur dans un modèle basé sur les processus à même de modéliser son évolution en réponse à des changements climatiques, le modèle PHENOFIT (Chuine & Beaubien, 2001). Dans un second temps, nous avions pour objectif de simuler la réponse de la fécondité à différents scénarios climatiques (RCP 8.5 et RCP 4.5) et l’impact de cette réponse sur la distribution des espèces . Jusqu’ici, PHENOFIT permettait d’estimer une valeur annuelle de survie et de succès de reproduction relative à un optimum, mais ne permettait en revanche pas de modéliser des quantités de fleurs et de fruits (nombre et biomasse) ni l’investissement que la reproduction représente en termes de ressources.
Faute de temps, nous n’avons pu mettre en œuvre que la première partie prévue dans le cad re de ce travail, à savoir l’intégration de relations conditionnant le nombre de fleurs et de fruits mis en place par un arbre à différentes étapes du cycle de développement, qui seront présentées dans ce chapitre. Par ailleurs, nous avons cherché dans ce travail à reproduire l’effet additionnel de l’exclusion de pluie à long terme décrit dans le Chapitre 3.
L’élaboration de ces relations a fait l’objet d’un travail joint avec Isabelle Chuine et Marie Devauchelle (stage de Master 1). L’implémentation dans P HENOFIT a été faite par Isabelle Chuine et François de Coligny (AMAP, INRAE). À noter qu’un travail à l’objectif assez similaire a été amorcé par Valentin Journé sur le modèle CASTANEA dans le cadre de son doctorat (Journé, 2020), mais encore non publié.
103 Chapitre 4
Au terme de ce travail, l e modèle parvient actuellement à reproduire l’ordre de grandeur de la quantité de fleurs mâles et de fruits noués produits et de l’effet de l’exclusion d e précipitations (voir Chapitre 3), mais ne parvient pas encore à reproduire l’ordre de grandeur du nombre de fruits à maturité ni les variations interannuelles observées dans les données empiriques.
2. Modèle PHENOFIT et concepts généraux
2.1. Le modèle PHENOFIT
Le modèle PHENOFIT, développé par lsabelle Chuine, est un modèle basé sur les processus qui modélise la réponse quotidienne des processus physiologiques des arbres (phénologie, résistance au stress hydrique et thermique, croissance) aux facteurs environnementaux tels que les conditions météorologiques (température, précipitation, rayonnement, etc.) et les conditions de sol (Fig. 1, (Chuine & Beaubien, 2001) ). L’intégration de ces différents processus permet d’estimer une valeur annuelle de survie et de succès de reproduction qui était jusqu’ici exprimée relativement à un optimum. Le modèle PHENOFIT a été validé pour une quinzaine d’espèces tempérées nord - américaines et européennes (Morin et al. , 2007; Gritti et al. , 2013). La version utilisée dans cette thèse, est une version non publiée à ce jour qui présente des évolutions importantes par rapport aux versions antérieures. Ces évolutions reposent essentiellement sur l’incorporation dans le modèle PHENOFIT de certaines composantes du modèle SIERRA (Mouillot et al. , 2001) qui a permis le passage d’un modèle simulant un arbre adulte moyen d’une espèce, à un modèle pouvant simuler plusieurs individus d’âges différents (3 classes d’âge) appartenant à des espèces différentes, permettant d’intégrer les interactions interspécifiques. La nouvelle version permet également d’inclure un modèle de croissance et d’allocation annuelle de la productivité primaire ainsi qu’une modélisation plus fine du stress hydrique. Cette version ne permettait en revanche pas de modéliser les quantités de fleurs et de fruits (nombre et biomass e) et l’investissement carboné que la reproduction représente, l’allocation se faisant uniquement entre bois, feuilles et racines. Le modèle PHENOFIT est hébergé par la plate-forme CAPSIS (http://www7.inra.fr/capsis/).
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Figure 1 : Représentation schématique de la version initiale du modèle PHENOFIT utilisée dans ce travail (photos correspondant à Populus tremuloides) adaptée de (Chuine & Beaubien, 2001) .
Les modifications apportées au modèle PHENOFIT dans le cadre de ce travail et développées plus bas sont principalement en lien avec quatre composantes existantes du modèle PHENOFIT : la phénologie, la résistance au gel des fleurs, feuilles et fruits, la résistance au stress hydrique, l’allocation d u carbone. Plus précisément, le nombre de fleurs (mâle et femelle) potentiel et leur devenir font appel à la bibliothèque qui modélise la phénologie et l’état de développement des organes. Les dates des différentes étapes du cycle reproducteur (floraison, nouaison, maturité des fruits) étaient déjà calculées par la librairie Phénologie de PHENOFIT en fonction des conditions météorologiques. À noter que la version actuelle du modèle utilise le même modèle phénologique pour les fleurs mâles et femelles, qui est basé sur la date de floraison des fleurs mâles. La biomasse en carbone consommée par les fleurs et les fruits est comptabilisée comme un nouveau compartiment dans l’allocation globale de carbone qui est faite annuellement, en plus des feuilles, du bois et des racines et réserves. Le développement des organes reproducteurs est impacté par deux fonctions de réponse au gel d’une part, et au stress hydrique d’autre part.
2.2. Conception du modèle de reproduction
Le raisonnement général permettant d’estimer les no mbres et biomasses de fleurs mâles et femelles est le même pour les deux types d’organes : un nombre potentiel (ou maximum) de fleurs est calculé au début du cycle reproducteur et, à partir de l’initiation florale, un nombre journalier est recalculé quotid iennement en appliquant des vétos, c’est -à-dire un effet négatif d’une variable
105 Chapitre 4 environnementale sur le processus, et ne peut donc que décroître au cours du cycle reproducteur (Fig. 2, Table 1). Ces vétos déterminent le taux d’avortement des fleurs et des fruits et varient dans leur nature en fonction de la phase du cycle reproducteur et d’une fonction de survie des tissus au gel déjà présente dans PHENOFIT (Fig. 2, Table 1).
Figure 2 : Représentation schématique du modèle de reproduction mâle et femelle implémenté dans PHENOFIT. Entre parenthèses, les noms des variables utilisées dans le code du modèle et les équations explicitées ci-après. Photos correspondant au chêne vert.
Par ailleurs, chaque jour la biomasse investie dans les fonctions mâle et femelle est comptabilisée (Fig. 2, Table 1). En fin de saison de reproduction, le nombre de fleurs mâles parvenues à la fin de la floraison et le nombre de fruits arrivés à maturité peuvent être extraits, ainsi que l’investissement en biomasse fait dans les fonctions mâles et femelles (que les organes soient arrivés à la fin du cycle reproducteur ou qu’ils aient avorté avant) (Table 1). Le choix des variables retenues et implémentées s’est fait en priorité autour de la biologie du chêne vert à partir de r ésultats de la littérature présentés en Table 1 du Chapitre 3 et à partir des grands déterminants identifiés dans ce même chapitre. Certaines variables n’ont pas encore pu être intégrées dans le modèle, leur rôle est décrit en discussion de ce chapitre.
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No us avons choisi d’avoir des noms de variables et de paramètres pour explicites pour faciliter la lecture, la liste des nouvelles variables est en Table 1, la liste des nouveaux paramètres et les valeurs correspondantes spécifiques au chêne vert en Table 2.
Table 1 : Nouvelles variables principales liées à la reproduction implémentées dans PHENOFIT.
Nom Échelle Signification Unité Équations
année Nombre de fleurs mâles Nb / (1) potentialMaleFlowerNumber !"#$ maximum m²
année Nombre de fleurs femelles Nb / (7) potentialFruitNumber !"#$ (et donc fruits) maximum m²
jour Nombre de fleurs mâles Nb / (4), (5) MaleFlowersNumber % m²
jour Nombre de fleurs femelles Nb / (10), (11) FruitsNumber % ou fruits m²
jour Taux d’avortement [0;1] (12), (13), AbortionRate % (14)
jour Biomasse investie dans les g/m² (6) MaleFlowerBiomass % fleurs mâles
jour Biomasse investie dans les g/m² (18) FruitsBiomass % fleurs femelles et les fruits
3. Variables liées à la fonction mâle
3.1. Calcul du nombre potentiel de fleurs mâles
Le nombre potentiel de fleurs mâles qui peut être produit par m² de couvert arboré est déterminé chaque année à chaque début de cycle de reproduction en fonction de la biomasse de feuilles, de l’âge de l’arbre et de la production de fleurs mâles de l’année précédente (autocorrélation négative) : potentialMaleFlowerNumber !"#$ = biomassRelatedMaleFlowerNumber & ageMaleFlowerIndex'– maleFlowerAutocorrelationCoef & !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! (1)! potentialMaleFlowerNumber !"#$() Avec :
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(2)! biomassRelatedMaleFlowerNumber = max *0, +) & leafBiomass . /)1 Avec et les coefficients de la fonction affine reliant le nombre potentiel de fleurs mâles et la +) /) biomasse en feuille et leafBiomass la biomasse en feuilles des deux cohortes de feuilles présentes
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est au moment de la mise en place des fleurs mâles. ageMaleFlowerIndex la fonction qui relie l’âge de l’arbre et sa probabilité de floraison mâle (entre 0 et 1) selon la relation suivante :
0'34'567 < flowerMaturityAge' ageMaleFlowerIndex = 2 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(3) 8'34'567 9 flowerMaturityAge
Avec 567 la variable de l’âge des arbres présents dans le couvert végétal et flowerMaturityAge le seuil d’âge au -delà duquel les individus atteignent la maturité sexuelle (Gea-Izquierdo et al. , 2006).
Enfin, maleFlowerAutocorrelationCoef est le coefficient d’autocorrélation négative entre deux années consécutives de production de fleurs mâles. L’intégration de cette réalité biologique dans le modèle n’est pas encore idéale car elle se fait par rapport au nombr e de fleurs potentielles de l’année précédente et non par rapport au nombre de fleurs mâles effectivement produites, ce point devra être corrigé par la suite.
3.2. Calcul du nombre journalier de fleurs mâles
Nous supposons, d’après plusieurs sources d’information, que l’initiation des fleurs mâles du chêne vert a lieu dans le courant de l’été précédent la floraison, probablement au début de l’été (Chapitres 2 & 3 ; (Sobral et al. , 2020)). Actuellement , la date d’initiation des fleurs mâles est arbitrairement fixée au 31 août, date à laquelle le nombre de fleurs mâles initiées peut être modulé par les conditions environnementales. Cette date devrait à terme être définie par la librairie Phénologie lorsque des observations permettront de faire des hypothèses sur le déterminisme environnemental de l’initiation florale mâle. À cette date :
MaleFlowersNumber :;:%:#%:>; = potentialMaleFlowerNumber !"#$ & MaleFlowerInitiationRate (4)! Avec le nombre potentiel de fleurs mâles défini en section potentialMaleFlowerNumber !"#$ 3.1. et MaleFlowerInitiationRate une fonction du t aux d’initiation des fleurs mâles (entre 0 et 1), dépendant des conditions météorologiques. Bien que quelques informations sur cette fonction soient di sponibles chez d’autres espèces (Kon & Noda, 2007), il est ici provisoirement fixé à 1 pour le chêne vert faute d’information. Cependant, le Chapitre 3 montre un effet du stress hydrique de l’été précédent la floraison sur la quantité de fleurs mâles, cela pourra être ajouté par la suite.
Entre l’initiation des fleurs mâles et leur date de floraison, le nombre journalier de fleurs mâles est défini en fonction du nombre de fleurs de la veille et de la survie au gel (Fig. 2) :!
!!!!!!!!!!!!!!!!!!!!!!!!!(5)! MaleFlowersNumber % = max'*0?'FlowerIndex % & MaleFlowersNumber %()'1
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Avec !la proportion de fleurs (m âles comme femelles) n’ayant pas été détruites FlowerIndex % chaque jour par le gel (entre 0 et 1), variable déjà présente dans la version initiale de PHENOFIT.
3.3. Calcul de la biomasse de fleurs mâles
La biomasse investie dans les fleurs mâles un jour donné est une fonction du nombre de fleurs et de la biomasse par fleur. La quantité totale de biomasse investie depuis le début du cycle reproducteur dans les fleurs mâles un jour donné est donc calculée de la façon suivante :
MaleFlowerBiomass % = ' MaleFlowerBiomass %() . MaleFlowersNumber % &
*FlowerDevelopmentState % @ FlowerDevelopmentState %()'1 & maleFlowerMeanWeight (6)
Avec une fonction de la librairie Phénologie de PHENOFIT FlowerDevelopmentState % calculant l’état de développement des organes reproducteurs entre 0 et 1 , le nombre de fleurs défini dans la section précédente et MaleFlowersNumber % maleFlowerMeanWeight un paramètre correspondant à la masse moyenne d’une fleur mâle. 4. Variables liées à la fonction femelle
4.1. Calcul du nombre potentiel de fleurs femelles / fruits
Suivant la même logique que pour les fleurs mâles, le nombre potentiel de fleurs femelles par m² qu’un couvert arboré peut produire chaque année est calculé à partir de la biomasse de feuilles et corrigé pour l’âge de l’arbre et pour l’autocorrélation négative : potentialFruitNumber !"#$ = biomassRelatedFemaleFlowerNumber & ageFemaleFlowerIndex – 'femaleFlowerAutocorrelationCoef & (7) potentialFemaleFlowerNumber !"#$() Avec :
!(8) biomassRelatedFemaleFlowerNumber = max *0, +E & leafBiomass . /E1 Avec et les coefficients de la fonction affine reliant le nombre potentiel de fleurs femelles et +E /E la biomasse en feuille et leafBiomass la biomasse en feuilles des deux cohortes de feuilles présentes est au moment de la mise en place des fleurs mâles. ageFemaleFlowerIndex la fonction qui relie l’âge de l’arbre et sa probabilité de floraison femelle (entre 0 et 1) selon la même relation que pour les fleurs mâles (équation 3) :
0'34'567 < 'flowerMaturityAge' ageFemaleFlowerIndex = ' 2 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(9) 8'34'567 9 'flowerMaturityAge
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Enfin, femaleFlowerAutocorrelationCoef est le coefficient d’autocorrélation négative entre deux années consécutives de production de fleurs femelles, et qui vaut actuellement vaut 0. Ainsi, nous ne corrigeons pas le nombre potentiel de fleurs femelles pour l’autocorr élation négative, car nous ne l’avons pas observé dans le Chapitre 3 à l’échelle de la parcelle. Bogdziewicz et al. (2019) ont par ailleurs montré avec des RBM que même avec des coûts de reproduction trop faibles pour imposer une période de re-remplissage des réserves avant de pouvoir re-fructifier, il était possible de reproduire les fluctuations de fructification de Quercus ilex avec des vétos environnementaux. En revanche, une autocorrélation négative a été décrite à l’échelle de l’arbre chez Quercus ilex par Koenig et al. (2016) et n’était a priori pas négligeable dans notre étude du Chapitre 2. Cela sera donc un des enjeux majeurs de la suite de ce travail de déterminer l’importance des réserves dans la modélisation de la fécondité du chêne vert et des autres essences de façon plus générale.
4.2. Calcul du nombre journalier de fleurs femelles (avant fécondation) et de fruits (après fécondation)
Le nombre de fleurs femelles et, passé la fécondation qui se matérialise par la nouaison, le nombre de frui ts est calculé quotidiennement. D’après certaines sources d’information, nous supposons que l’initiation des fleurs femelles a lieu juste avant le débourrement ou pendant (Chapitres 2 & 3 ; (Sobral et al. , 2020)). La date d’initiation des fleurs femelles est actuellement arbitrairement fixée au 31 mars et devrait à terme être définie par la librairie Phénologie. À cette date :
(10)! FruitsNumber :;:%#%:>; '' = ' potentialFruitNumber !"#$ & FemaleFlowerInitiationRate Avec le nombre potentiel de fleurs femelles défini en section 4.1. et potentialFruitNumber !"#$ FemaleFlowerInitiationRate une fonction du t aux d’initiation des fleurs femelles (entre 0 et 1) qui dépend probablement des conditions météorologiques, notamment la température (Koenig & Knops, 2014), mais qui vaut actuellement 1. Pour le moment, le nombre de fleurs femelles n’est pas directement dépendant des conditions climatiques car nous ne disposons pas de suffisamment d’élément s sur la question chez le chêne vert. Chez l’olivier, il semble y avoir un optimum vers 10 - 13 °C (Hackett & Hartmann, 1967)).
À partir de l’initiation florale femelle, et jusqu’à la maturité des fruits, le nombre journalier de fleurs femelles, puis de fruits, est défini en fonction du nombre de fleurs ou fruits de la veille, de la survie au gel et du taux d’a vortement dont la nature varie en fonction du stade du cycle reproducteur :
!!(11)! FruitsNumber % = Max'*0?'FlowerIndex % & *8 @ AbortionRate %1 & FruitsNumber %()1
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Avec la proportion journalière de fleurs (mâles comme femelles) n’ayant pas été FlowerIndex % détruites par le gel (entre 0 et 1), cette variable était déjà calculée par PHENOFIT. AbortionRate % est la fonction journalière du taux d’avortement entre 0 et 1 des fleurs femelles et des fruits selon l’étape du cycle reproducteur (Fig. 2) définie comme suit.
Entre l’initiation florale et la veille de la floraison, la fonction vaut 0. AbortionRate %' Entre la floraison et la date de fin de la pollinisation (actuellement définie comme 30 jours après le début de la floraison pour le chêne vert en approximation large des 25 jours estimés dans le Chapitre 3, et qui devrait à terme être définie par la librairie Phénologie), la fonction estime la proportion de fleurs non pollinisées à cause du lessivage du pollen par la AbortionRate %' pluie (García-Mozo et al. , 2007) pendant la pollinisation avec la relation suivante :
0'34'GH7J4K4L+L4OP3 < 'precipQollenWash' ! AbortionRate % = ' 2 pollenWashAbortionRate'34'GH7J4K4L+L4OP3 T 'precipQollenWash (12)
Avec GH7J4K4L+L4OP3 la quantité journalière de précipitations, precipQollenWash le seuil de précipitations au-delà duquel le lessivage du pollen par les précipitations affecte les chances de pollinisation et 'pollenWashAbortionRate le taux d’avortement qui est appliqué lorsque ce seuil est dépassé.
À noter que pour le moment, le nombr e de fleurs mâles produites par les voisins n’est pas pris en compte dans l’estimation du succès de pollinisation des fleurs femelles. En effet, nous n’avons pas observé de corrélation entre production de fleurs mâles et nombre de fruits noués dans les parcelles (Chapitre 3), ce qui est en cohérence avec le fait que la pollinisation semble peu limitée par le pollen en forêt dense de chêne vert (Fernández-Martínez et al. , 2012; Bogdziewicz et al. , 2017b; Schermer et al. , 2020) ; Chapitre 3). D’autre part, le chêne vert étant une essence principalement allogame, la production de fleurs mâles à proximité a peu de chances d’augmenter le succès de pollinisation des fleurs femelles. De même, nous n’avons pas inclus pour l’instant dans le modèle l’effet des températures élevées au moment de la floraison qui peuvent favoriser la synchronie entre les arbres et une meilleure pollinisation (Koenig et al. , 2015; Bogdziewicz et al. , 2017b), car ces variables n’ont pas été retenues comme impactant la production de fruits noués dans le Chapitre 3. À noter également que dans le Chapitre 3, nous avons identifié la température de juin comme un facteur pouvant favoriser la fécondation (Hedhly et al. , 2009) , mais ne l’avons pas inclus dans le modèle faute d’avoir pu identifier le processus en cause.
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Pendant la phase progamique, qui a lieu entre la fin de la pollinisation et la veille de la fécondation, la fonction vaut 0 . Pour le moment, nous n’avons pas inclus d’effet AbortionRate %' du stress hydrique de printemps qui pourrait affecter la formation des fruits comme cela a été observé chez le chêne vert (Alejano et al. , 2008; Pérez-Ramos et al. , 2010; García-Mozo et al. , 2012; Fernández-Martínez et al. , 2015) , car son effet n’a pas été observé dans le Chapitre 3.
Le jour de la fécondation (qui correspond approximativement à la nouaison, qui est fixé par un nombre de jours ap rès la date de pollinisation), on applique un taux d’avortement qui suit la relation suivante :
! AbortionRate U"$%:V:X#%:>; ' = fertilizationAbortionRate & incompatibilityAbortionRate (13)!
Avec fertilizationAbortionRate le taux d’avortement des fleurs femelles dû à un échec de fécondation pour des raisons de qualité du pollen et avec incompatibilityAbortionRate le taux d’avortement des fleurs femelles dû à l’incompatibilité génétique mâle/ femelle (Yacine & Bouras, 1997).
Du lendemain de la fécondat ion jusqu’à la maturité (période qui couvre les phases de multiplication et d’élongation cellulaire à l’intérieur du fruit, puis le remplissage et la maturation du fruit), le taux d’avortement suit la relation suivante :
AbortionRate % = YmbolismAbortionRate % & ZorrentialAbortionRate % & !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(14)! CompetitionAbortionRate % Avec le taux d’avortement journalier lié au stress hydrique de l’arbre YmbolismAbortionRate % par la relation suivante :
(\] (15) YmbolismAbortionRate % = +[ & 7 Avec le potentiel hydrique journalier de l’arbre en fonction des conditions météorologiques. ^% L’effet positif du stress hydrique sur l’avortement des glands a été montré dans le Chapitre 3 à Puéchabon, ce qui est cohérent avec la relation négative entre stress hydrique d’été et productions de g lands matures déjà mis en avant par d’autres auteurs chez le chêne vert Alejano et al 2008 ; Carevic et al 2010 ; Pérez-Ramos et al 2010).
est le taux d’avortement journalier lié aux pluies torrentielles qui ont ZorrentialAbortionRate % lieu en automne et peuvent avoir un impact négatif sur la production de fruits, comme montré par Pérez-Ramos et al. (2010), en décrochant les fruits. Il est défini de la façon suivante :
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0'34'GH7J4K4L+L4OP3 < 'torrRain' !!!!!!!!!!!!!!!!!(16) ZorrentialAbortionRate % = ' 2 torrAbortionRate'34'GH7J4K4L+L4OP3 T 'torrRain
Avec GH7J4K4L+L4OP3 la quantité journalière de précipitations déjà définie dans PHENOFIT, 'torrRain le seuil au-delà duquel les précipitations affectent la survie des fruits et torrAbortionRate le taux d’avortement qui est appliqué lorsque ce s euil est dépassé. Enfin, est le taux d’avortement journalier lié à la compétition CompetitionAbortionRate % entre fruits, en particulier au moment de la reprise de croissance lors des premières pluies d’automne, par la relation suivante :
(17)! CompetitionAbortionRate % = min *maxAbortion, +_ & Relative`rowthRate % ./_1 Avec le taux de croissance journalier des fruits calculé par la librairie Relative`rowthRate % Phénologie :
(18) Relative`rowthRate % = FlowerDevelopmentState % @ FlowerDevelopmentState %() Avec l’état journalier de développement des organes reproducteurs FlowerDevelopmentState % calculé par la librairie Phénologie de PHENOFIT.
Dans le Chapitre 3, nous avons identifié la production primaire brute comme favorisant la production de fruits. Pour le moment, l’allocation du carbone se faisant annuellement à la fin de chaque saison, cette relation est simplement prise en compte en donnant priorité à l’allocation aux fleurs et fruits par rapport aux feuilles, bois racines. L orsqu’une allocation journalière sera possible, nous pourrons affiner la prise en compte de cette relation.
4.3. Calcul de la biomasse produite de fleurs femelles et fruits
La biomasse investie dans les fleurs femelles puis dans les fruits un jour donné est une fonction du nombre de fleurs (ou de fruits, selon l’état de développement) et de la biomasse par fleur (ou fruit). La quantité totale de biomasse investie depuis le début du cycle reproducteur dans les fleurs femelles et les fruits un jour donné est donc calculée de la façon suivante :
FruitsBiomass % = ' FruitsBiomass '%() '.'FruitsNumber % & *FlowerDevelopmentState % '@ (19)! 'FlowerDevelopmentState %()'1 & femaleMeanWeight Avec ! ! l’état de développement journalier des fruits comme FlowerDevelopmentState %', précédemment décrit ; le nombre de fleurs/fruits défini dans la section 'FruitsNumber % précédente et femaleMeanWeight un paramètre de l’espèce indiquant , selon l’état de développement des organes, la masse moyenne d’une fleur femelle, d’un fruit noué ou d’un fruit mature.
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5. Paramétrage du modèle pour le chêne vert
5.1. Les données utilisées pour concevoir, paramétrer et valider le modèle de reproduction
Le modèle de reproduction a été conçu à partir de la revue de la littérature présentée dans le Chapitre 3 et des résultats expérimentaux sur le chêne vert sur le site de Puéchabon (Chapitre 3). Le modèle a été paramétré et validé essentiellement à partir des données disponibles sur le chêne vert sur le site expérimental de Puéchabon décrit dans le Matériel et méthodes général de la thèse.
La masse sèche moyenne d’une fleur mâle juste avant anthèse a fait l’objet de mesures au printemps 2019, et a également servi à calculer le nombre de fleurs mâles par m² contenues dans la biomasse de fleurs mâles sèches des bacs à litière. Les chatons collectés dans les bacs à litière ayant relâché une partie de leur pollen et ayant été lessivés par les précipitations entre leur chute et leur collecte, le nombre de fleurs mâles estimé à partir des biomasses des bacs à litières est probablement sous-estimé, mais permet déjà une première approximation.
En 2015, des mesures du taux d’avortement des fleurs femelles dans les traitements contrôle et sec ont été réalisées par I. Chuine su r les branches hautes d’arbres du dispositif d’exclusion de pluie à Puéchabon. Ces mesures ont permis une estimation de la production du nombre de fleurs femelles par m² en 2015 à partir des nombres de fruits noués collectés dans les bacs à litière (en app liquant le taux d’avortement du traitement sec dans le traitement sec, et celui du traitement contrôle pour les traitements contrôle, éclaircie et éclaircie sec).
Par ailleurs, nous avons utilisé les données de concentration hebdomadaire de pollen du genre Quercus présent dans l’air provenant du Réseau National de Surveillance Aérobiologiques (RNSA, https://www.pollens.fr/) pour la station de Montpellier de 1992 à 2002 et fournies par Isabelle Farrera (Montpellier SupAgro). Ces données ont servi à regarder la relation entre conditions météorologiques et concentration pollinique dans l’air. Elles ont été analysées avec des données météorologiques hebdomadaires issues du terrain d’expérience du laboratoire (voir Matériel et Méthode général).
Les données issues des collectes de litières (biomasse de feuilles, biomasse de fleurs mâles, biomasse et nombre de glands et leur statut vivant, avorté ou piqué) ont servi à la conception du modèle, notamment grâce au travail réalisé dans le cadre du Chapitre 3, ainsi qu’a u paramétrage et à la validation du modèle. La dynamique de croissance des glands au cours de la saison a servi à la fois à la conception et au paramétrage du modèle, notamment pour établir les taux d’avortement lié au stress hydrique et à la compétition entre fruits.
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Enfin, pour les simulations, nous avons utilisé les données météorologiques journalières de la station météorologique du site de Puéchabon.
5.2. Méthodologie d’obtention des paramètres
5.2.1. Autocorrélation négative des fleurs mâles (équation 1) L’existence d’une relation d’autocorrélation négative pour les fleurs mâles est mise en évidence pour le chêne vert dans le Chapitre 3 . Le coefficient d’autocorrélation négative pour la production potentielle de fleurs mâles d’une année sur l’autre a été ajusté à la main à la valeur 0.6 car nous ne disposons pas de données sur le nombre de fleurs potentiel. Une correction importante du code doit encore être faite de manière à relier l’alternance au nombre de fleurs réellement produite l’année précédente. À ce moment-là l’ajustement de ce paramètre pourra être revu.
5.2.2. Relation entre biomasse de feuilles et nombre de fleurs (équations 2 et 8) La relation entre biomasse de feuilles et nombre de fleurs mâles est la conséquence du fait que les bourgeons sont localisés à l’aisselle des feuilles d’une part, et que les bourgeons reproducteurs sont issus d’une conversion des bourgeons végétatifs en bo urgeons reproducteurs d’autre part (Allen et al. , 2017). La relation entre biomasse de feuilles et nombre de fleurs femelles a déjà été mise en évidence chez les chênes pour les fruits (Johnson et al. , 2009).
Figure 3 : Relation entre la biomasse sèche estimée de feuilles des deux cohortes présentes au moment de la floraison et A) le nombre estimé de fleurs mâles et B) le nombre estimé de fleurs femelles en 2015. Chaque point représente une parcelle et les lignes représentent les régressions significatives associées à chacune des relations, avec pour
A) a1 = 112 et b1 = 19148 (p < 0,05) et pour B) a2 = 4,3 et b2 = - 780 (p < 0,001).
Pour obtenir le nombre potentiel de fleurs mâles et femelles liés à la biomasse de feuilles, nous avons choisi l’année de production maximale de fleurs mâles et de fruits noués (2015 dans les
115 Chapitre 4 deux cas, Chapitre 3) au sein de la série temporelle dont nous disposions (2003-2019) pour les deux types d’organes. Dans les 12 parcelles du dispositif MIND de Puéchabon (3 blocs x 4 traitements), nous avons ensuite corrélé le nombre de fleurs mâles estimé et la biomasse de feuilles (Fig. 3A, Table 2) ; et le nombre de fleurs femelles estimé et la biomasse de feuilles (Fig. 3B, Table 2).
5.2.3. Avortement des fleurs femelles dû au lessivage du pollen par la pluie (équation 12) Qualitativement, on constate une chute de la concentration pollinique de l’air hebdomadaire vers 25 mm de précipitations hebdomadaires (Fig. 4A). Nous avons estimé le seuil journalier de précipitations induisant un lessivage du pollen ( precipQollenWash ) à 3,6 mm (25 mm / 7 jours). Ce seuil est relativement cohérent avec le seuil qualitatif de la Fig. 4B (réalisée avec les données du Chapitre 3), soit 5,2 mm (130 mm / 25 jours).
Par ailleurs, nous avons fixé le taux d’avortement journalier dû au lessivage de pollen
(pollenWashAbortionRate ) à 5 %, soit environ 1/30, la durée de la polli nisation étant d’environ 30 jours.
Figure 4 : Relation entre A) la quantité de précipitations hebdomadaires et la concentration hebdomadaire relative en pollen pour chaque année de 1999 à 2002 à Montpellier, et B) la quantité de précipitations cumulées pendant toute la période de pollinisation (25 jours) et la production annuelle de fruits noués à Puéchabon entre 2007 et 2019. Les lignes verticales représentent les seuils au-delà duquel nous avons considéré que les précipitations lessivaient le pollen.
5.2.4. Avortement des fruits dû au stress hydrique (équation 16) À l’aide des suivis de croissance des glands réalisés à Puéchabon en 2015, 2016, 2017, 2018, 2019, nous avons cherché à estimer la relation entre le taux d’avortement journalier des glands et le potentiel hydrique des arbres. Les données dont nous disposons sont des nombres de glands vivants, avortés ou tombés chaque mois entre juillet et décembre pour chaque année de suivi, et le potentiel hydrique de base moyen des arbres tel que simulé par le modèle de bilan hydrique de 116 Chapitre 4
Puéchabon. Nous avons calculé le taux d’avortement journalier moyen entre deux dates pour le traitement témoin et le traitement sec que nous avons relié au potentiel hydrique maximum sur la période correspondante (Fig. 5). La relation montre une forte augmentation du taux d’avortement en dessous de -4 MPa. Si on ne considère pas les avortements dus à autre chose que le stress hydrique, matérialisés par les points pour ^ <-1 on obtient la relation suivante :
(\] YmbolismAbortionRate % = 0j000kq & 7
Figure 5 : Relation entre le taux d’avortement journalier moyen et le potentiel hydrique de ba se simulé par le modèle SIERRA.
5.2.5. Ajustement du modèle de croissance des fruits La relation entre le taux d’avortement journalier lié à la compétition entre fruits ( ) et le taux de croissance relatif des fruits a été paramétrée de la façon CompetitionAbortionRate % suivante. Les données de croissance des glands pour chaque traitement du dispositif MIND et chaque année de 2015 à 2019 ont tout d ’abord été ajustées dans Mathematica® à des fonctions de type logit (Fig. 6) :
# (20) {*L1 = ' {|:; . )}"~•*€~•1
Avec t, le jour, G la taille du gland au jour t, Gmin , la taille du gland à la nouaison, a l’incrément de taille entre la nouaison et la maturité, b la pente au point d’inflexion de la courbe de croissance, c’est -à-dire la croissance journalière maximale, c le temps nécessaire pour atteindre la moitié de la taille finale. Les adjusted-R2 de ces ajustements étaient tous supérieurs à 0,997.
117 Chapitre 4
Figure 6 : Exemple d’ajustement des fonctions de croissance des glands sous Mathematica ® en A) 2016, une année avec un été sec, et B) 2018, une année avec un été plus humide. Une courbe correspond à un traitement : témoin (rouge), sec (bleu), éclaircie (vert) et éclaircie sèche (orange).
À l’aide de ces fonctions, nous avons calculé pour chaque jour la taille des g lands puis estimé le taux de croissance relatif des glands ainsi :
‚€(‚€~ƒ (21) Relative`rowthRate % = ' ‚€~ƒ Ce taux de croissance relatif fut ensuite ajusté aux fonctions suivantes sous Mathematica®.
) ) (22) „{„ *…%, ^%1 = ~• '*† ~'• × ~• '*‡ ~'• )}" † € †1 )}" ‡ € ‡1
Avec T la température moyenne journalière et ^ le potentiel hydrique de base. Ces ajustements ont permis d’obtenir le modèle de croissance des glands en fonction de la température et du stress hydrique de façon inédite. En effet, jusqu’ici les modèles de croissa nce des fruits étaient ajustés sur des séries de dates de maturation des fruits en supposant uniquement un effet de la température (car trop peu de données étaient disponibles pour ajuster des modèles plus complexes) et l’effet du stress hydrique était sur imposé par une relation linéaire qui n’avait jamais été démontrée.
118 Chapitre 4
5.2.6. Avortement des fruits dû à la compétition entre fruits (équation 17)
Figure 7. Taux d’avortement journalier moyen des glands dans les quatre traitements du dispositif d’exclusion de pluie à Puéchabon de juillet à décembre (1 valeur chaque mois par traitement) pour A) les années 2015 à 2019 (à noter qu’en 2019 l’échec de reproduction a été total) en fonction du taux de croissance relatif moyen (RGR) des glands chaque mois et B) l’année 2016 sans les trois points exclus. La ligne représente la régression significative associée à la relation (p < 0.001).
Le taux de croissance relatif des glands, modélisé en 5.2.5, fut mis en relation avec le taux d’avortement obtenu par nos suivis. La Fig. 7A montre que l’effet de la compétition entre glands sur le taux d’avortement ne s’exprime pas chaque année, soit parce que la croissance des glands reste tout le temps très faible et alors d’autres causes d’avortement que la compé tition pour la ressource agissent (2017, 2019), soit parce que le taux d’avortement reste très faible (2018). Si l’on écarte l’année 2015 où seuls deux (témoin et sec) des quatre traitements étaient suivis et pour laquelle nous avons peu de points donnant une relation que nous n’arrivons pas à interpréter, seule l’année 2016 semble avoir rassemblé les conditions pour mettre en évidence l’effet de la compétition entre glands, c’est la seule année pour laquelle à la fois le taux de croissance et le taux d’avortement ont varié sur une large gamme. Pour l’année 2016, les trois données correspondant à un fort taux d’avortement qui n’est pas dû à la compétition entre gland (taux de croissance quasi nul) sont exclues, on observe une relation linéaire positive entre le taux d’avortement et la croissance des glands qui sature ensuite pour des taux de croissance relatifs supérieurs à 0.025 (Fig. 7B). La relation linéaire positive entre taux d’avortement et taux de croissance des glands illustrerait le phénomène de compétition pour la ressource entre glands lorsque leur croissance reprend à l’automne après une longue période de sécheresse suite aux premières pluies. La force du puit généré par l’ensemble des glands survivants à leur reprise de croissance ne pourrait pas être comblée ce qui engendrerait l’avortement des glands exerçant la force de puit la plus faible. La
119 Chapitre 4 relation obtenue est (en utilisant les noms de variables définies dans le modèle présentées dans la section précédente) (Fig. 7A) :
CompetitionAbortionRate% = 8jˆ8‰8 & Relative`rowthRate % . 0j000ˆ
Relation à laquelle nous avons rajouté un maxAbortion de 0.035, correspondant au taux journalier d’avortement maximum possible afin que les valeurs simulées restent bien dans la gamme de validité des données :
! CompetitionAbortionRate % = min *0j0ˆq, 8jˆ8‰8 & Relative`rowthRate % . 0j000ˆ 1 5.3. Paramètres du modèle pour le cas du chêne vert
La liste des nouveaux paramètres du modèle, leurs valeurs et leur méthode d’obtention sont présentées dans la Table 2.
120 Chapitre 4
Table 2 : Liste des paramètres du modèle de reproduction dans le cas du chêne vert.
Éq. Nom du paramètre Valeur Unité Référence (1) maleFlowerAutocorrelationCoef! 0,6 Section 5.2.1
(2) a1! et! b1! 112 et 19148 Section 5.2.2 (3) et (9) flowerMaturityAge! 15 an (Gea-Izquierdo et al. , 2006) (6) maleFlowerMeanWeight! 0,0008 (avec la g Mesure structure du chaton)
(8) a2!et! b2! 4,3 et -780 Section 5.2.2 (12) precipPollenWash! 3,6 mm Section 5.2.3 (12) pollenWashAbortionRate! 0.05 [0;1] Section 5.2.3 (13) fertilizationAbortionRate! 0,103 (Parc National [0;1] (Yacine & de Chréa, Algérie) Bouras, 1997) (13) incompatibilityAbortionRate! 0,1989 (Parc National [0;1] (Yacine & de Chréa, Algérie) Bouras, 1997)
(15) a3! 0.00025 Section 5.2.4 (16) torrRain! 20 mm (Pérez-Ramos et al. , 2010)
(16) torrAbortionRate! 0,06 [0;1] (Pérez-Ramos et al. , 2010) (17) maxAbortion ! 0,035 [0;1] Section 5.2.6
(17) a4!et! b4! 1.3141 et 0.0003 Section 5.2.6 (19) femaleMeanWeight! Fleur femelle : 0,0008 g Chapitre 2 Fruit noué : 0,2 Fruit mature : 0,75
121 Chapitre 4
6. Résultats
Figure 8 : Exemple d’évolution au cours d’une saison de reproduction (2008) du A) taux d’avortement journalier des fleurs femelles o u des fruits (selon l’étape du cycle reproducteur), B) du nombre j ournalier et C) de la biomasse cumulée. Les flèches pointent des exemples de sauts dus à l’application de taux d’avortements journaliers non nuls.
Au cours d’une saison de reproduction, le nombre journalier de fleurs femelles puis de fruits diminue par sau t sous l’effet des taux d’avortement appliqués (Fig. 8). En interannuel, les gammes de valeurs prédites par le modèle paramétré pour le chêne vert sont satisfaisantes pour les variables de biomasses de feuilles et de fleurs mâles ainsi que le nombre de fruits noués (Fig. 9A-F), mais nous n’arrivons pas encore à reproduire les variations interannuelles de ces variables . Les variables de nombre de fruits arrivés à maturité et de biomasse totale de fruits simulés n’arrivent pour le moment pas encore à reproduire le bon ordre de grandeur et sont très surestimées (Fig. 9G-J). Pour toutes les variables, les valeurs simulées pour le traitement sec sont inférieures à celles du traitement témoin, en cohérence avec les données du Bloc S (Fig. 9A-J), à l’exception des fleurs mâles pour lesquelles la différence entre traitement est faible.
Les coefficients de variations des données simulées sont globalement en cohérence avec les coefficients de variations des données observées sur le terrain pour le nombre de fruits noués et pour la biomasse de fruits totale, mais sont un peu sous-estimés pour le nombre de fruits arrivés à maturité et sont en revanche très sous-estimés pour la biomasse de fleurs mâles (Fig. 10).
122 Chapitre 4
Figure 9 : Variation interannuelle et effet du traitement sec sur les simulations de production (colonne gauche) et sur les données de production à Puéchabon dans le Bloc S (colonne droite) de : A) et B) la biomasse de feuilles présente au moment de la floraison, C) et D) de la biomasse de fleurs mâles, E) et F) du nombre de fruits noués, G) et H) du nombre de fruits à la maturité et I) et J) de la biomasse totale investie dans les fruits.
123 Chapitre 4
Figure 10 : Variabilité interannuelle des quatre principales variables reproductives dans les deux traitements et dans les données de terrain et les données simulées. Les coefficients de variation sont présentés sur une échelle logarithmique.
7. Discussion et perspectives Le modèle présenté dans ce chapitre pose les bases d’un des premiers modèles de fécondité des arbres forestiers qui soit à la fois basé sur les processus et peut-être apte à terme à simuler comment les dynamiques de fructification seraient affectées par différents scénarios de changement climatique . Cependant, le modèle s’appuie encore sur différentes hypothè ses qui gagneront à être affinées et produit des résultats encore préliminaires qui nécessitent d’être améliorés. L’objectif de ce chapitre était donc de présenter comment nous avions intégré les différentes hypothèses issues de nos observations et expérimentations ainsi que celles issues de la littérature sur le sujet dans un modèle qui pouvait être intégré au modèle PHENOFIT. Bien que le modèle ne soit pas complétement achevé (certaines hypothèses doivent encore être rajoutées et certains paramètres ajustés) au terme de ce travail de thèse, il pose des bases solides pour le parachever à moyen terme et donner lieu à publication. Il est également un outil qui permettra d’explorer les effets relatifs des différents facteurs et processus déterminant la product ion de fruits matures et d’en identifier peut - être de nouveaux.
Actuellement, la biomasse de fleurs mâles simulée par le modèle est trop régulière, et le nombre de fruits arrivés à maturité (dont est très dépendante la biomasse de fruits totale) est largement surestimé. Le nombre de fruits noués simulé étant dans un ordre de grandeur acceptable, la principale étape à améliorer concernant les organes femelles est donc celle entre la nouaison et la maturité, à savoir celle du développement et de la maturation des fruits durant laquelle le taux d’avortement est trop faible. Certains vétos ne sont donc actuellement pas assez forts, ou pas pris en compte. Les années 2016 et 2017 ont par exemple été des années de très fort avortement durant
124 Chapitre 4 le développement des fruits du fait de fortes sécheresses (Chapitre 3), et ce point ne semble pas se retranscrire dans les données simulées.
Le modèle peut être amélioré sur deux principaux plans : sur sa conception et sur son paramétrage. Différentes améliorations potentielles ponctuelles liées à la conception du modèle ont été présentées au fur et à mesure de la description des variables (taux d’initiation florale mâle et femelle, autocorrélation négative mâle et femelle, probabilité de pollinisation en fonction des voisins et production primaire brute notamment). Plus généralement, le modèle PHENOFIT modélise depuis sa dernière version une allocation du carbone à différents pools, mais ne modélise pas encore d’allocation d’autres nutriments, qui semblent être des déterminants n on négligeables de la production de fleurs (Chapitre 2) et de fruits noués (Chapitre 3) chez le chêne vert. Or, le lien explicite entre la fructification, les réserves et la floribondité l’année suivante a encore très peu été montré empiriquement chez les arbres forestiers ((Allen et al. , 2017) ; Chapitre 2). Même si nous n’avons pas observé d’autocorrélation négative entre la production de fruits sains une année et la production de fruits noués l’année suivante à l’échelle parcelle (Chapitre 3), affiner notre compréhension d’un potentiel lien e ntre réserves de nutriments et régulation de la reproduction à l’échelle des individus, en particulier en interaction avec les conditions environnementales (Bogdziewicz et al. , 2017b; Moreira et al. , 2019), serait un des principaux enjeux de la suite de ce travail. Peu de modèles intègrent actuellement l’allocation de l’azote (CASTANEA, (Dufrêne et al. , 2005)) et encore moins celui du phosphore et aucun n’intègrent d’autres éléments nutritifs en raison de connaissances et de données trop limitées. Une stratégie à développer pourrait consister à trouver des proxys de l’effet de ces nutriments afin de pouvoir les prendre en compte sans avoir à simuler leur bilan à l’échelle de l’arbre.
Par ailleurs, le modèle SIERRA sur lequel s’appuie PHENOFIT pour simuler la production de feuilles fait une allocation du carbone à l’année et non quotidiennement. Un autre des objectifs de la suite du travail pourrait être de passer à une allocation journalière en fonction des conditions environnementales et des puits en activité pour permettre un calcul du nombre de fleurs potentielles plus précis.
Le paramétrage des fonctions devra également être affiné, certaines ayant été paramétrées de façon assez qualitative (comme le taux d’avortement lié au lessivage du pollen), d’autres n’étant pas encore assez finement paramétrées (comme le taux d’avortement lié au stress hydrique). Plus généralement, la suite du travail impliquera de travailler également sur une hiérarchie de l’effet des facteurs et processus impliqués, par le biais d’une analyse de sensibilité par exemple (e.g. (Gauzere et al. , 2019)).
125 Chapitre 4
En conclusion, il est encore trop tôt pour déterminer si les hypothèses sur lesquelles repose ce modèle sont réalistes et suffisantes pour expliquer la production de fruits et de graines au sein d’une population d’arbres et sa variabilité interannuelle. Cependant, malgré ses limites, ce modèle est un des premiers à proposer une modélisation journalière basée sur les processus de la fécondité des arbres forestiers. Un tel travail n’est possible que grâce à de nombreux travaux accu mulés sur la biologie de l’espèce dont on cherche à modéliser le comportement reproducteur. Le chêne vert est une espèce qui a déjà fait l’objet de nombreuses études. Pourtant certaines données biologiques de l’espèce nous ont manqué pour l’établissement d’équations ou pour leur paramétrage. L a difficulté de la démarche de modélisation réside notamment dans l’équilibre entre le réalisme du modèle et le risque d’introduire des incertitudes. Afin de limiter les incertitudes, nous avons choisi de limiter le nombre de processus biologiques à implémenter, mais qui est déjà élevé par rapport aux travaux antérieurs, et de se baser sur des équations simples qui pourront être complexifiées par la suite. Affiner le modèle sur le chêne vert et adapter ce t ravail à d’autres essences nécessitera donc encore un volume important de données de terrain et d’expérimentations.
126 Discussion générale
Crédit : Jean Kempf.
Discussion générale
L’objectif général de cette thèse était dans un premier temps d’approfondir notre connaissance des déterminants de la fécondité des arbres forestiers et de leurs interactions, afin de mieux comprendre, dans un second temps, comment la fécondité pourrait être impactée par les changements c limatiques. Pour atteindre cet objectif, j’ai combiné différentes approches expérimentales, deux manipulations des ressources à l’échelle de l’arbre et une manipulation du climat à l’échelle de l’écosystème, et une approche de modélisation basée sur les pr ocessus. Pouvoir disposer de trois angles de vue sur le fonctionnement de la fécondité des arbres est très enrichissant. Les expérimentations que j’ai réalisées sur un groupe d’arbre pendant deux saisons de reproduction offrent une vision assez précise des processus mais peu généralisable dans le temps et dans l’espace. L’analyse de données d’une expérimentation à long terme offre la puissance d’un suivi sur plusieurs années et d’une expérimentation ambitieuse nécessitant une force de travail qu’une seule p ersonne ne peut fournir, mais a également son lot de contraintes, le dispositif n’ayant pas été spécifiquement prévu pour les questions auxquelles je souhaitais répondre. Enfin, l’approche de modélisation a consisté à implémenter dans un modèle mécaniste les processus identifiés par les approches expérimentales comme essentiels à la production de fruits et de graines. Une approche de modélisation force à une simplification de la réalité qui permet de hiérarchiser les processus à mettre en avant.
Par nos approches expérimentales, nous avons mis en évidence la complexité des relations d’allocation de ressources entre les différentes fonctions de l’arbre et commencé à mettre en évidence un possible contrôle de la reproduction par certains nutriments. Le Chapitre 1 était centré sur la compréhension des règles d’allocation à la reproduction chez le chêne vert. Nous avons montré que la régulation de l’allocation des ressources à la reproduction semblait pouvoir se faire à une plus large échelle que celle du rameau ou de la branche, et qu’en cas de défoliation, même intense, au cours du développement du fruit, la survie et la croissance des fruits étaient très peu affectées, au contraire de l’allocation au printemps suivant , où une production accrue de feuilles par les branches se faisait au détriment de la floraison. Dans le Chapitre 2, nous avons montré que le développement des fruits avait un impact négatif sur les dynamiques des réserves en azote et en zinc dans les branches du chêne vert, ainsi que sur la product ion de fleurs femelles l’année suivante. Ce changement d’allocation aux fleurs femelles induit par les fruits de la saison précédente était par ailleurs lié à un changement d’architecture des ramifications.
Dans le Chapitre 3, nous avons identifié différents déterminants liés aux conditions météorologiques et à la physiologie de l’arbre qui avaient des effets soit positifs (teneur en azote des feuilles avant la floraison, accroissement en biomasse, température de juin et production primaire brute en été-aut omne), soit négatifs (pluie pendant la pollinisation, stress hydrique d’été
128 Discussion générale et d’automne) sur la réalisation du cycle reproducteur. Les arbres qui ont été soumis à une réduction de 27 % des précipitations depuis 2003 (traitement expérimental sec), initient moins de fleurs mâles et de fruits, et ces fruits sont moins susceptibles d’atteindre la maturité dans des conditions de stress hydrique intense que ceux produits par les arbres des parcelles témoin. Les arbres du traitement sec ne montrent donc pas d’acclimatation de la reproduction au stress hydrique d’été - automne. Au contraire, le traitement sec nous a permis de montrer que la réduction des précipitations à long terme avait un effet sur la fécondité qui était additionnel à celui d’une augmentation du stress hydrique en période estivale. En revanche, le traitement sec ne modifie pas la variabilité interannuelle des fructifications.
Figure 1 : Résumé schématique des principaux résultats de cette thèse. GPP = production primaire brute (gross primary production) et CV = coefficient de variation.
Le Chapitre 4, bien que décrivant un travail encore incomplètement abouti, nous a permis d’essayer de reproduire les patrons de fructification du chêne vert décrits dans le Chapitre 3. À défaut d’arriver à reproduire les variations interannuelles de façon fiable, les relations implémentées dans notre modèle nous ont permis de simuler un ordre de grandeur correct pour la production de
129 Discussion générale fleurs mâles et de fruits noués dans les traitements témoin et sec. La modélisation du développement du fruit entre nouaison et maturité reste, quant à elle, encore à améliorer.
Les résultats des différents chapitres, pris dans leur ensemble, montrent que différents déterminants liés aux conditions météorologi ques ainsi qu’à la disponibilité des ressources et à leur allocation sont impliqués dans la régulation de la fécondité du chêne vert (Fig. 1).
1. De la nécessité de clarifier la notion de déterminant Avant de discuter en détail des déterminants spécifiques de la fécondité du chêne vert, je souhaite revenir sur la notion de déterminant qui est au centre de ce travail. Dans notre cas d’étude, un déterminant pourrait être défini comme toute variable, biotique ou abiotique, qui conditionne positivement ou négative ment et plus ou moins fortement un processus biologique d’intérêt, ici la formation des fleurs et des fruits. Le fonctionnement (de la reproduction dans notre cas) est alors le produit de l’interaction entre ces déterminants avec les mécanismes physiologiq ues en place.
Curieusement, cette notion de déterminants, driver en anglais, est souvent utilisée sans être définie dan s les publications que j’ai lues pour ce travail de thèse, alors qu’elle peut recouvrir des choses assez différentes. Je l’ai utilisée da ns ma thèse de façon assez large, en particulier dans le cadre du Chapitre 3, où il a été délicat de trouver une dénomination aux différentes catégories de variables que je souhaitais étudier pour déterminer leurs impacts respectifs. En effet, le découpage entre variables liées à la disponibilité en ressources et variables liées aux conditions météorologiques ne semblait guère pertinent. Dans ce cas, où placer des variables comme la production primaire brute (GPP) ou l’état hydrique de l’arbre (WSI) qui sont à l’intersection entre ces deux catégories ? Des variables comme l’accroissement en biomasse qui permettaient de regarder les relations d’allocation entre croissance et reproduction n’étaient pas strictement rattachables aux questions de disponibilité en réserves non plus, étant le résultat de stratégies d’allocation de la part de l’arbre mais aussi de l’existence de conditions environnementales favorables à la mise en place de ces stratégies. Même des variables directement liées aux ressources comme les concentrations foliaires en nutriments sont issues de conditions météorologiques favorables au moment de l’acquisition et de stratégies d’allocations passées permettant de disposer d’un certain niveau de ressources à un instant donné. De façon générale, mes difficultés à classifier les variables se heurtaient aux notions de stratégie concernant les allocations (que cela signifie-t-il chez les plantes ?) et du fait que la disponibilité en ressources un instant donné est le produit d’interactions passées entre les conditions environnementales et le patrimoine génétique et épigénétique des individus.
Nous avons finalement choisi de séparer les variables du Chapitre 3 entre conditions météorologiques et conditions liées à la physiologie de l’arbre, un terme suff isamment large pour
130 Discussion générale englober différents phénomènes. Cependant, cette difficulté à trouver une terminologie satisfaisante m’a permis de prendre conscience que ce qui peut être mis derrière la notion de déterminant n’était pas si simple à définir. Les relations d’allocation, par exemple, contraignent potentiellement fortement la fructification des arbres, en particulier dans le cas de stratégies assez complexes comme celles qui sous-tendent les Resource Budget Models (RBM) avec des réserves dévolues à la reproduction et des effets seuil. Peut-on pour autant considérer les règles d’allocation et les niveaux de ressource comme des déterminants à proprement parler ? Par ailleurs, la distinction, parfois difficile, entre corrélation et causalité complexifie la compréhension de quelle variable est un déterminant de la fécondité et quelle variable n’en est pas. Par exemple, une relation négative entre la fructification et une autre fonction de la plante n’implique pas forcément de trade- off (Knops et al. , 2007) , de même qu’une relation négative avec une condition météorologique n’implique pas qu’il s’agisse d’un véto (déterm inant ayant un effet négatif assez fortement limitant sur les processus permettant la reproduction). Or, l’analyse des déterminants de la fécondité des arbres nécessitant des jeux de donnée de long terme sur des arbres qui ont souvent besoin de plusieurs années de croissance avant de fructifier, la plupart des approches cherchant à identifier ces déterminants sont corrélatives, comme l’a été notre travail du Chapitre 3. Un de s enjeux de la recherche autour du déterminisme de la fécondité des arbres forestie rs est donc d’arriver à trouver des méthodes expérimentales conciliables avec la longévité des espèces (Bogdziewicz et al. , 2020a).
2. Une fécondité dessinée par les vétos Dans le cadre de ce travail, nous avons pu mettre en évidence que la production de fruits du chêne vert était fortement régulée par les conditions météorologiques au cours du cycle reproducteur, et en particulier par l’état hydrique de l’arbre en été -automne. La production de fruits est aussi influencée, dans une moindre mesure, par la disponibilité en carbone et en nutriments et par une compétition pour les ressources entre les cohortes de fleurs/fruits d’une année à l’autre. En milieu méditerranéen, où la limitation en eau est courante en été et peut se prolonger plus ou moins tard dans l’automne, une telle contrainte hydrique sur le développement des fruits n’est guère surprenante. De façon générale, l’allocation de ressources à la reproduction chez le chêne vert en contexte méditerranéen semble donc se rapprocher du modèle dit de « véto » proposé par Pearse et al. (2016) (Fig. 2d). Dans ce modèle, un véto empêche la production de fruits certaines années et induit un stockage des réserves qui n’ont pu être allouées à la reproduction, disponible pour la reproduction de l’année suivante (en rose/bordeaux sur la Fig. 2d). Une année de masting est alors possible les années où le véto est faible et où une large quantité de ressources est accumulée (Fig. 2d).
131 Discussion générale
Figure 2 : Principales hypothèses d’allocation des ressources (en unité arbitraire) au masting. La production de graines (en bleu) est la même dans les 4 cas. (a) Hypothèse du resource matching, selon laquelle une fraction constante des ressources de l’année est allouée à la reproduction. (b) Hypothèse du resource switching, selon laquelle une part variable de ressources annuelles relativement constantes est allouée à la reproduction. (c) Hypothèse du stockage selon laquelle les réserves sont stockées activement pendant plusieurs années (partie rose sous la ligne) et allouées à la reproduction les années de masting. (d) Hypothèse du véto, décrite dans le corps de texte. Les fortes productions de graines sont issues de ressources de l’année en cours dans les cas (a) et (b), de ressources stockées activement dans le cas (c) et de ressources stockées passivement dans le cas (d). Figure adaptée de (Pearse et al., 2016).
Cette interprétation est cohérente avec les résultats de simulation de la fructification du chêne vert par Bogdziewicz et al. (2019) qui ont montré, en utilisant des RBM, qu’il était possible de simuler le masting chez le chêne vert au moyen de vétos environnementaux avec une faible implication d’une limitation par les ressourc es. Les auteurs proposent alors que la phénologie des plantes aurait été sélectionnée de manière à induire des échecs de reproduction dus au gel ou à la sécheresse comme une façon de générer du masting (Bogdziewicz et al. , 2019). Chez les chênes tempérés, nous faisons une hypothèse assez similaire ((Schermer et al. , 2020) en Annexe 1) : la pollinisation des chênes tempérés, qui est plus précoce que celle des chênes méditerranéens, a souvent lieu dans des conditions relativement peu favorables à la pollinisation, ce qui induit de
132 Discussion générale fréquents échecs de nouaison et donc un patron de masting . Une floraison précoce, et donc généralement peu favorable à la pollinisation, pourrait ainsi avoir une dimension adaptative face à la pression imposée par les consommateurs de graines (Schermer et al. , 2019). Peu de travaux permettent toutefois d’affirmer s’il y a réellement une sélection pour la précocité de la flora ison des chênes tempérés (mais voir (Gauzere et al. , 2020) ) afin d’augmenter la stochasticité de la fécondité. Si Gaüzere et al. (2020) ont montré qu’il y avait effectivement actuellement une sélection pour une plus grande précocité du débourrement (et donc de la floraison) grâce au modèle PHENOFIT, cette pression de sélection était exercée par la longueur de la saison de croissance qui semble contraindre davantage le succès reproducteur que les risques de gel printaniers.
Revenons sur l’idée d’une variabilité de la féco ndité générée par des vétos. Dans la terminologie d’espèces à flowering masting et à fruit maturation masting proposée par Bogdziewicz et al. (2019) , c’est -à-dire dont la variabilité interannuelle de la fécondité est liée principalement soit à la production de fleurs soit au taux d’avortement des fruits, nos travaux ont montré que le chêne vert appartenait plutôt à la deuxième catégorie. Les résultats de cette thèse nous permettent même de proposer plutôt trois catégories en fonction de l’étape du cycle reproducteur qui génère la majeure partie de la variabilité interannuelle de fécondité :
- Les espèces dont la variabilité de fécondité est essentiellement générée par la floraison, comme le hêtre (Overgaard et al. , 2007; Lebourgeois et al. , 2019), qui correspondent à la catégorie flowering masting de Bogdziewicz et al. (2019) ;
- Les espèces dont la variation de fécondité semble essentiellement générée par le passage de la fleur au fruit, les échecs po uvant être dus à des échecs de pollinisation ou à d’autres facteurs, comme les chênes tempérés (Masaka & Sato, 2002; Pearse et al. , 2015b; Schermer et al. , 2019) ;
- Les espèces dont la variabilité de fécondité semble essentiellement générée par le passage du fruit noué au fruit mature comme le chêne vert ((Espelta et al. , 2008; Pérez-Ramos et al. , 2010), Chapitre 3).
Bien évidemment, une telle classification n’exclu t pas qu’une partie de la variabil ité de fécondité soit liée aux autres étapes de développement. La fécondité des chênes en général peut aussi être limitée par leur floraison (Sork & Bramble, 1993; Varela et al. , 2008), et par le nombre de fruits noués chez le chêne vert (Chapitre 3). On pourra donc noter que pour les espèces du 3 ème groupe, les vétos interviennent potentiellement à une période où davantage de ressources ont été investies dans la reproduction que pour le 2 ème groupe, et encore davantage que le premier.
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3. De l’importance d’étudier l’allocation entre puits avec une chronologie fine et à différentes échelles L’allocation à la reproduction du chêne vert semble ainsi davantage suivre l’hypothèse du véto que les trois autres hypothèses proposées par Pearse et al. (2016) en Fig. 2. Nos travaux ont montré que la reproduction est une fonction relativement prioritaire pour l’all ocation des réserves lorsque le cycle reproducteur est entamé (Chapitre 1). La reproduction ne semble toutefois pas être en compétition avec la croissance, ni à l’intérieur de la couronne où les rameaux portant des fruits sont en général plus larges que ce ux qui n’en portent pas (Chapitre 1), ni entre les années, les années favorables à la production de bois et de feuilles étant en général aussi des années favorables à la fructification (Chapitres 3 et 4). L’allocation entre reproduction et croissance chez le chêne vert ne semble donc pas suivre l’hypothèse du resource switching (Fig. 2b), et la corrélation positive entre reproduction et croissance n’est pas une condition suffisante pour appuyer l’hypothèse de resource matching (Fig. 2a).
Plutôt qu’une fract ion constante des réserves allouées à la reproduction ( resource matching ), je fais l’hypothèse que la relation positive entre reproduction et croissance est due à deux facteurs dans le cas du chêne vert : d’une part la contrainte hydrique qui influence tou tes les fonctions de l’arbre en milieu méditerranéen, et d’autre part la séparation temporelle entre la croissance du bois, celle des feuilles et la maturation des glands. En effet, les années très sèches en automne entraînent l’avortement des fruits mais limitent aussi la croissance du bois d’automne, alors que les années avec un automne humide permettent les deux, ce qui génère, de fait, une relation interannuelle positive entre croissance et reproduction. D’autre part, il est essentiel de regarder la « phénologie » des relations source et puits au sein de l’année pour comprendre les relations d’allocation (Chapitre 3). En effet, le chêne vert réalise sa croissance lors de deux périodes distinctes chaque année (Gutiérrez et al. , 2011; Martín et al. , 2014; Campelo et al. , 2018) : de la fin de l’hiver jusqu’au début de l’été (70 % de la croissance en moyenne (Lempereur et al. , 2015)), et après la sécheresse estivale pendant l’automne. La seconde période a lieu alors que le remplissage des glands n’est pas terminé (Fig. 3 ; (Siscart et al. , 1999)), qui est celle qui requiert le plus gros investissement en ressource dans les glands (Knops et al. , 2007) . Les relations d’allocation entre croissance du bois et croissance des glands varient donc au cours de l’ année chez le chêne vert (Martín et al. , 2015), et le remplissage des fruits et la croissance du bois sont relativement peu en compétition pour le carbone, voire pour les autres nutriments, pendant la phase de croissance du bois la plus importante (Fig. 3).
134 Discussion générale
Figure 3 : (a) Phases phénologiques de la croissance végétative et du cycle reproducteur chez le chêne vert en 2018 dans le traitement témoin du site expérimental de Puéchabon, et variation intra-annuelle de (b) la croissance moyenne normalisée des fruits ± SD en jaune et de la croissance secondaire normalisée des troncs ± SD en noir, (c) du potentiel hydrique journalier en rouge et des précipitations journalières en bleu, et (d) de la production primaire brute (GPP) cumulée en noir et de la température journalière en rouge. Adaptée de la Figure S5 dans le Chapitre 3.
Une autocorrélation temporelle négative entre la reproduction d’une année et celle de l’année précédente est l’un des postulats des Resources Budget Models , qui s’appuient sur l’hypothèse de stockage (Fig. 2c). Dan s les travaux de cette thèse je n’ai pas observé d’autocorrélation négative entre années pour la production de fruits à l’échelle de la parcelle (Chapitre 3), en revanche j’ai observé un effet négatif du développement des fruits sur la production de fleurs l’année suivante (Chapitre 2). Cette différence s’explique par la faible fréquence des évènements de masting observés en 13 ans de données, et est cohérente avec les résultats de Koenig et al . (2016), qui ont observé chez le chêne vert une autocorrélation négative à l’échelle de l’individu mais pas de la population, sans doute du fait d’une faible synchronie entre individus. L’autocorrélation négative ne semble donc pas être un déterminant majeur de la production de fruits chez le chêne vert, dont la fécondité est davantage affectée par les vétos, notamment pendant l’été, mais elle affecte sans doute les
135 Discussion générale dynamiques de fécondité d’autres chênes. Le Chapitre 2 ne nous a pas permis de déterminer si cette compétition entre cohortes était le résultat d’une conso mmation des réserves par le développement des fruits. Au vu de nos résultats, il serait probable que l’azote et le zinc fassent partie des nutriments impliqués dans la régulation de l’induction florale femelle (Chapitre 2 et 3). Cependant, notre compréhens ion des déterminants de l’initiation florale chez le chêne vert, et chez les autres essences forestières, reste encore très parcellaire.
Enfin, au sein de l’arbre, l’allocation des ressources à la reproduction semble pouvoir être régulée à un niveau d’orga nisation plus large que celui du rameau, voire de la branche (Chapitre 1). En revanche, le retrait des fruits n’ayant pas impacté les dynamiques des différents éléments dans le tronc, il n’est pas certain que l’ensemble des sources de réserves des individus participe à l’effort reproducteur (Chapitre 2). L’étude de l’allocation des ressources doit donc prendre en compte l’échelle à laquelle se fait la compétition entre les différents puits (Sánchez-Humanes et al. , 2011; Sala et al. , 2012; Martín et al. , 2015).
4. Des déterminants dont la connaissance reste à affiner Dans le cadre de cette thèse, je me suis moins intéressée aux déterminants de la floraison qu’à ceux de la nouaison et du développement des fruits. Or, cette étape du cycle reproducteur est une des plus méconnue chez les arbres forestiers (Allen et al. , 2017). Des expériences de chauffage des bourgeons comme les travaux de Kon & Noda (2007) sur Fagus crenata pourraient déterminer si la température affecte l’initiation florale femelle, ce qui pourrait être pertinent pour comprendre l’impact des hausses des températures à l’échelle globale. Il y a également un gros enjeu à comprendre si la relation de dominance que les fruits en développement semblent avoir sur l’induction des fleurs femelles du printemps suivant (soit au moins 2 mois après la ch ute des fruits d’après nos hypothèses) se fait par une voie hormonale, par consommation de nutriments nécessaires à l’induction, ou par encore une autre voie (Pearse et al. , 2016; Bogdziewicz et al. , 2020a). Sur ce point, la littérature portant sur les arbres fruitiers (Samach & Smith, 2013; Smith & Samach, 2013; Pallas et al. , 2018; Sharma et al. , 2019) peut être riche en enseignements conceptuels et méthodologiques pour la communauté scientifique travaillant sur les espèces sauvages. Les premiers travaux utilisant les marqueurs d’expression génétiques liés à l’induction florale dans différentes conditions expérimentales chez des essences forestières semblent très prometteurs pour en comprendre les déterminants (Miyazaki et al. , 2014; Haberman et al. , 2016; Guitton et al. , 2016; Nishikawa et al. , 2017; Satake et al. , 2019) et commencent à êtr e mis en œuvre chez les chênes (Rocheta et al. , 2014; Sobral et al. , 2020).
136 Discussion générale
Bien que cela ait été un des enjeux identifiés de cette thèse, je n’ai pas vraiment réussi à étudier l’effet sur la fécondité de l’interaction entre déterminants, notamment l’impact des conditions climatiques sur les variables liées à l’acquisition et à l’allocation des ressources (Fig. 1) (Smaill et al. , 2011; Tanentzap et al. , 2012; Allen et al. , 2017). Dans une des rares études portant sur cette problématique pour des espèces sauvages, Tanentzap et al. (2012) ont trouvé que la probabilité de floraison de leur modèle herbacé était positivement corrélée aux températures estivales, mais que les individus poussant sur des sols riches en azotes toléraient des conditions moins favorables que les individus poussant sur des sols plus pauvres, qui devaient proportionnellement investir plus d’azote dans leurs graines. Pouvoir étudier cette interaction nécessiterait idéalem ent un dispositif manipulant à la fois les conditions météorologiques (comme le système d’exclusion de pluie du site expérimental de Puéchabon par exemple) et un système de manipulation de l’acquisition des ressources, avec des expériences de fertilisation par exemple.
Finalement, nos résultats sur le chêne vert amènent à s’interroger sur leur possible généralisation à d’autres espèces. Que peuvent -ils apporter à l’étude d’autres chênes méditerranéens ou tempérés, à d’autres chênes sempervirents ou décidus, dont on sait que les déterminants peuvent différer (Partie 2 de la discussion générale ; (Pérez-Ramos et al. , 2015) ), et à l’étude d’essences d’autres genres ? J’ai manqué de temps pour mener cette réflexion, mais elle me semble cruciale, et ce d’autant plus que la littérature des déterminants de la fécondité des arbres forestiers est abondante et délicate à synthétiser. La grande diversité des espèces étudiées dans la littérature d’une part et la grande diversité des déterminants identifiés pour chaque espèce d’autre part ont en effet constitué une des premières difficultés de mon travail de thèse.
5. Impact des changements globaux sur la fécondité des arbres forestiers Nos résultats du Chapitre 3 ont montré qu’une réduction à long t erme des précipitations affectait la quantité de fleurs mâles et de fruits produits, sans toutefois affecter la variabilité de leur production. Par ailleurs, les arbres soumis à ce traitement ne semblent pas s’acclimater à une sécheresse accrue. Au contraire, la réduction des précipitations à long terme a un effet additif à celui de l’augmentation du stress hydrique dans les périodes sèches, ce qu’une simple étude corrélative sur un sui vi temporel n’aurait pas décelé. Nous n’avons malheureusement pas pu confronter ces résultats à une modélisation de la fécondité sous différents scénarios climatiques. De tels résultats interrogent sur ce qui génère une telle diminution de la fécondité dans le traitement sec. La floraison femelle étant vraisemblablement liée à la production de feuilles (Chapitre 2), et la surface foliaire étant réduite dans le traitement sec pour limiter la transpiration (Limousin et al. , 2009), il reste à déterminer si la baisse de fécondité mâle et femelle découle de contraintes architecturales liées à la diminution de surface folia ire, ou si elle marque un changement d’allocation
137 Discussion générale défavorable à la reproduction. Dans ce cas, dans le cadre proposé par Lauder et al. (2019) de « fight or flight » en cas de trade-off potentiel entre résistance à la sécheresse et la reproduction, le chêne vert serait davantage dans le cas « fight », privilégiant la survie à la reproduction, au contraire de la stratégie « flight » qui privilégie la reproduction au risque d’augmenter le risq ue de mortalité pour l’adulte. Une réduction à long terme des régimes de précipitations pourrait donc sérieusement altérer la « niche de reproduction » (Bykova et al. , 2012) du chêne vert en milieu méditerranéen. Plusieurs études ont d’ailleurs montré une remontée sur la façad e atlantique du chêne vert en France (Delzon et al. , 2013; Urli et al. , 2015), bien que les facteurs abiotiques ne soient pas les seuls facteurs pouvant expliquer ce phénomène, il serait intéressant de comparer comment se comportent ces individus en termes de reproduction par rapport aux individus du pourtour méditerranéen.
Les données du site expérimental de Puéchabon nous ont donc amenés à réfléchir sur l’impact d’une augmentation de l’aridité (qui est représentée par la différence entre les précipitations et l’évapotranspiration) sur la fécondité. Cette augmentation, simulée par une réduction de 27% des précipitations toute l’année, est en effet l’un des principaux aspects du changement climatique attendus pour la région méditerranéenne. D’autres aspects pourraient également affecter la fécondité des arbres, tels qu’une hausse des températures (Hedhly et al. , 2009; Bykova et al. , 2012), une augmentation de la concentration atmosphérique en CO 2 (Millard et al. , 2007; Journé, 2020), une hausse des dépôts atmosphériques azotés (Overgaard et al. , 2007) et l’i nteraction de ces différents phénomènes. Des dispositifs expérimentaux existent pour simuler ces différents aspects comme les dispositifs d’enrichissement en CO 2, les FACE (Calfapietra et al. , 2010), de chauffage de branches (Nakamura et al. , 2010) ou de fertilisation (Pulido et al. , 2014; Bogdziewicz et al. , 2017a). Ces dispositifs lourds sont, comme les dispositifs d’exclusion de pluie, indispensables pour faire avancer la connaissance fondamentale des processus, et anticiper d’éventuels effets additifs des changements climatiques avec les déterminants environnementaux de la reproduction.
Enfin, pour considérer l’impact des changements globaux sur la fécondité des arbres forestiers, rappelons que la production de graines n’est que la première étape de la régénération (Johnson et al. , 2009) , et que la production d’une grande quantité de graines n’est en rien la garantie d’un recrutement conséquent (Clark et al. , 1999; Moran & Clark, 2012; Terborgh et al. , 2014; Szwagrzyk et al. , 2015). Plusieurs travaux sur le site expérimental de Puéchabon ont déjà montré un effet positif ou négatif de la réduction de précipitations sur la capacité de germination des graines et de survie des jeunes plants (Pérez-Ramos et al. , 2013; Jalón et al. , 2020). Plus généralement, il semble que les échecs de recrutements, notamment chez les chênes, soient souvent le fait de facteurs multiples (herbivorie, sécheresse, etc.) (MacDougall et al. , 2010). De nombreux travaux
138 Discussion générale seront donc encore nécessaires pour comprendre comment les changements globaux risquent d’affecter la capacité des arbres à se reproduire et à recruter de nouveaux individus.
6. Enseignements de cette thèse Plus personnellement, cette thèse a souligné pour moi l’énor me enjeu de déterminer comment les politiques publiques peuvent arriver à mieux se saisir des connaissances issues du monde scientifique, sur la problématique de l’adaptation des forêts au changement climatique comme sur de nombreuses autres. Sur la questi on des forêts en particulier, cette articulation est d’autant plus délicate que de nombreuses incertitudes demeurent : il faudrait prendre des décisions aujourd’hui pour gérer ou planter les arbres qui formeront les forêts de demain de façon pertinente en termes d’adaptation, alors que la science est loin de pouvoir apporter des réponses solides aux problèmes actuels et ne le sera probablement pas avant plusieurs années.
Concernant les interactions entre scientifiques et gestionnaires, des temporalités très différentes, des vocabulaires différents, des valeurs différentes aussi, peuvent nuire à l’intérêt et à la compréhension réciproque entre deux mondes qui me semblent trop peu interagir ensemble. Selon moi, la recherche ne doit en aucun ca s n’exister que pour être au service de l’action publique, et tous les chercheurs et chercheuses n’ont pas vocation à être en contact régulier avec le grand public ou les gestionnaires. Cependant, pendant ces trois années de thèse, j’ai croisé de nombreuse s initiatives de scientifiques s’engageant, parfois un peu, parfois beaucoup, afin de faire sortir la recherche et ses résultats de l’espace académique. Ces initiatives riches gagneraient à être encouragées par le système académique, tout comme la volonté de nombreux gestionnaires d’essayer d’inclure tant que possible l’expertise de la recherche dans leurs démarches.
139 Références
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Annexes
Annexe 1 : Schermer et al. 2020 Cet article auquel j’ai participé en tant que collaboratrice est disponible sur le li en suivant : https://doi.org/10.1111/nph.16224
151
4nnexe 1 Research
Flower phenology as a disruptor of the fruiting dynamics in temperate oak species
Eliane Schermer1 , Marie-Claude Bel-Venner 1 , Jean-Michel Gaillard 1 , St ephane Dray 1 , Vincent Boulanger2, Iris Le Ronc e3 , Gilles Oliver 4, Isabelle Chuine 3 , Sylvain Delzon 5,6 and Samuel Venner1 1Laboratoire de Biom etrie et Biologie Evolutive, UMR 5558, Universite de Lyon, Universit e Lyon 1, CNRS, F-69622, Villeurbanne, France; 2Departement recherche, developpement et innovation, Office National des For^ets, F-77300, Fontainebleau, France; 3Centre d’ Ecologie Fonctionnelle et Evolutive, UMR 5175, CNRS, F-34293, Montpellier, France; 4Reseau National de Surveillance A erobiologique, F-69690, Brussieu, France; 5UMR 1202, BIOGECO, Institut National de la Recherche Agronomique, F-33612, Cestas, France; 6UMR 1202, Biodiversite, des genes aux communaut es, Universite de Bordeaux, F-33615, Pessac, France
Summary Author for correspondence: Many perennial plants display masting, that is, fruiting with strong interannual variations, Samuel Venner irregular and synchronized between trees within the population. Here, we tested the hypothe- Tel: +33 4 72 43 29 02 sis that the early flower phenology in temperate oak species promotes stochasticity into their Email: [email protected] fruiting dynamics, which could play a major role in tree reproductive success. Received: 30 July 2019 From a large field monitoring network, we compared the pollen phenology between tem- Accepted: 19 September 2019 perate and Mediterranean oak species. Then, focusing on temperate oak species, we explored the influence of the weather around the time of budburst and flowering on seed production, New Phytologist (2020) 225: 1181–1192 and simulated with a mechanistic model the consequences that an evolutionary shifting of doi: 10.1111/nph.16224 flower phenology would have on fruiting dynamics. Temperate oak species release pollen earlier in the season than do Mediterranean oak Key words: masting, oak species, pollen species. Such early flowering in temperate oak species results in pollen often being released phenology, resource budget model, during unfavorable weather conditions and frequently results in reproductive failure. If pollen stochastic mast seeding. release were delayed as a result of natural selection, fruiting dynamics would exhibit much reduced stochastic variation. We propose that early flower phenology might be adaptive by making mast-seeding years rare and unpredictable, which would greatly help in controlling the dynamics of seed con- sumers.
fruiting dynamics (Crone & Rapp, 2014; Pearse et al. , 2016; Introduction Vacchiano et al. , 2018). Reproduction in many perennial and wind-pollinated plant Fruiting of mast-seeding species, besides fluctuating strongly species is characterized by masting, that is, synchronized and and synchronously over the years, is characterized by negative highly variable amounts of seed production over the years within temporal autocorrelation (Sork et al. , 1993; Herrera et al. , 1998; a population (Janzen, 1976; Kelly & Sork, 2002). Masting is Koenig & Knops, 2000; Koenig et al. , 2003). Such autocorrela- known to impact the demography and evolution of seed con- tion is classically interpreted as resulting from the resource deple- sumers strongly (Yang et al. , 2010; Venner et al. , 2011; Gamelon tion of the trees following mast-seeding years, which prevents et al. , 2013; P elisson et al. , 2013; Rey et al. , 2015; Bogdziewicz them from producing flowers and seeds the following year (i.e. et al. , 2016), with cascading effects on forest biodiversity dynam- resource depletion hypothesis; Monks & Kelly, 2006; Barringer ics together with major economical and societal issues (e.g. forest et al. , 2013; Crone et al. , 2009, but see Kelly et al. , 2013). Conse- regeneration, disease propagation) (Crawley, 2000; Ostfeld & quently, the fruiting dynamics are potentially extremely asym- Keesing, 2000; Frey et al. , 2007; Bogdziewicz & Szymkowiak, metrical, with lean-seeding years consistently occurring after a 2016). Despite the increasing number of studies addressing the mast-seeding year (a deterministic component of masting as a issue of masting and its consequences for ecosystem functioning result of reserve depletion of trees) while mast-seeding years may and service provisioning, its proximate causes remain difficult to not systematically follow one lean-seeding year. This irregularity disentangle, mainly because of the diversity of candidate mecha- in the occurrence of mast-seeding years (hereafter called the nisms possibly interacting and the strong stochasticity (in the stochastic component of masting corresponding to the fluctua- sense of unpredictability for observers or seed consumers) in the tions not explained by the negative temporal autocorrelation) 123 Ó 2019 The Authors New Phytologist (2020) 225: 1181–1192 1181 New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com 4nnexe 1 New 1182 Research Phytologist
would depend on weather conditions possibly affecting resource and then we built a mechanistic model (resource budget model acquisition (Smail et al. , 2011), flower development, pollination (RBM); Isagi et al. , 1997; Satake & Iwasa, 2000, 2002) and we and fertilization of female flowers (Cecich & Sullivan, 1999; simulated fruiting dynamics according to several evolutionary Koenig et al. , 2015; Pearse et al. , 2015; Sabit et al. , 2016; flower phenological strategies (i.e. earlier or later phenology than Bogdziewicz et al. , 2017a; Schermer et al. , 2019) or even fruit currently observed). ripening (Richardson et al. , 2005; P erez-Ramos et al. , 2015; Chang-Yang et al. , 2016; Buechling et al. , 2016). Furthermore, Materials and Methods such a weather effect can act as an ‘environmental veto’ by nearly completely preventing flower or seed development at the tree Study species population scale (Feret et al. , 1982; Cecich & Sullivan 1999; Bogdziewicz et al. , 2018a, 2019). We focused on the four most abundant oak monoecious A promising avenue to understanding both the proximate and species in France: Quercus robur L., Q. petraea Liebl . L., evolutionary causes of masting is to identify the key tree life-his- Q. ilex L. and Q. pubescens Willd. Q. robur and Q. petraea are tory traits governing the stochastic component of masting. present from southern Scandinavia to Spain and western Rus- Flower phenology would be a serious candidate as its evolution- sia in Europe. Q. ilex is the most dominant tree species in the ary change (i.e. the timing of flowering at the population level central and western parts of the Mediterranean basin. Q. which may shift independently of any climate change) could the- pubescens has an intermediate distribution, co-occurring with oretically act on masting in two complementary ways: by modify- Q. robur and petraea in central Europe and with Q. ilex in ing the likelihood of late frost events at the vulnerable flowering southern Europe. Q. robur and Q. petraea co-occur all over stage (Garcıa-Mozo et al. , 2001; Augspurger, 2009), which can France except along the Mediterranean basin where they are act as environmental veto and may strongly impede fruit set replaced by Q. pubescens up to 1200 m, and Q. ilex , especially (Feret et al. , 1982; Cecich & Sullivan 1999; Bogdziewicz et al. , at lower elevations (Badeau et al. , 2017). The phenologies of 2018a); and by partly setting the weather conditions influencing the four species show some differences. Budburst occurs pollen maturation and release which would play a key role in pol- between late April and early May for Q. robur, Q. petraea and len limitation and then in fruiting success. In this sense, high Q. pubescens (Badeau et al. , 2017), and between April and spring temperature has recently been shown to favor phenological May for Q. ilex , depending on the latitude (Garcia-Mozo synchronization between trees, by reducing the flowering period, et al. , 2007; Ogaya & Penuelas 2004; Misson et al. , 2011; which in turn would increase pollination success and promote Fernandez-Martinez et al. , 2012). The four species can have mast-seeding years (Koenig et al. , 2008, 2012, 2015; Bogdziewicz either vegetative buds with leaves only, mixed buds with male et al. , 2017a). High spring temperature also increases the annual flowers, female flowers and leaves or reproductive buds with amount of airborne pollen that can be mobilized for reproduc- male flowers only. Male flowers are mature 2 wk after bud tion. Schermer et al. (2019), after analyzing the interannual varia- flush and 2 wk before female flowers. Leaves have reached tion of both airborne pollen amount and its temporal c. 75% of their final size when female flowers become recep- distribution, suggested that pollen limitation in European tem- tive (Badeau et al. , 2017). In all four species, fertilization perate oaks would rely more on the annual amount of airborne occurs at the end of June or early July (Pesson & Louveaux, pollen than on tree synchrony. 1984). Based on these findings, the aim of our study was to test the hypothesis that flower phenology is a key trait driving the Phenology, pollen and fruiting data stochastic component of masting in two temperate oak species (Quercus petraea and Quercus robur) by keeping mast-seeding The airborne amount of oak pollen was recorded daily using years rare and unpredictable. We thus examined the conse- Hirst traps (Hirst, 1952) at 43 sites in France during a 22 yr sur- – quences of an evolutionary shift in the flower phenology on their vey (1994 2015; Reseau National de Surveillance A erobi- masting. For this purpose, we combined empirical and theoreti- ologique; see Fig. 1a for a map; see Supporting Information cal approaches. First, we tested the hypothesis that pollen phenol- Table S1 for the pollen-sampling site characteristics). As the oak ogy is early in these two temperate oak species in comparison to species was not recorded in the pollen dataset, we relied on the Mediterranean oak species ( Quercus ilex and Quercus pubescens) national forest inventory (Institut G eographique National, and we examined the consequences of phenological differences France; see the forest stand dataset providing the forest cover rate between the two groups on the sensitivity of annual amounts of of each species) to determine within a 50 km radius at each pol- airborne pollen to spring weather conditions. Second, focusing len-sampling site the covering surface of each oak species. We exclusively on the two temperate oak species for which we have split the pollen dataset into two sub-datasets, one called ‘temper- > an extensive network of fruiting monitoring, we explored the ate’, including sites where 80% of oaks are temperate oak consequences of an evolutionary change in flower phenology on species ( Q. robur and/or Q. petraea , 35 sites), and the other one > fruiting dynamics. For this last point, we proceeded in two steps: called ‘Mediterranean’, including sites where 80% of oaks are first, we determined the key weather conditions surrounding the Mediterranean oak species (Q. ilex and/or Q. pubescens , eight timing of budburst that should affect fruiting success (late frost sites). At each site and each year, the total amount of airborne and/or weather conditions affecting pollen release and diffusion); pollen was computed and divided by the percentage of the surface 12& New Phytologist (2020) 225: 1181–1192 Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New 4nnexe 1 Phytologist Research 1183
(a) (b)
Fig. 1 Comparison of the pollen phenology between the temperate and Mediterranean oak forests. (a) Spatial distribution of the 43 pollen-sampling sites. Temperate oak forests are defined to include 80% or more Quercus petraea and/or Quercus robur (35 sites; see orange circles) and Mediterranean oak forests include 80% or more Quercus ilex and/or Quercus pubescens (eight sites; green triangles) relative to the whole oak forest area comprised within a 50 km radius around each pollen-sampling site. The GPS coordinates and the forest cover rate of each oak species of all pollen-sampling sites are indicated in Supporting Information Table S1. (b) Cumulative frequency distribution of the median date of oak pollen release for the ‘temperate’ (orange line) and ‘Mediterranean’ (green line) oak forests. The median date was calculated each year (from 1994 to 2015) at each site as the day by which 50% of the annual pollen amount has already been released. Dates are in Julian days, that is, the number of days elapsed since 1 January (day 1) of each year. covered by oak trees so as to account for disparities in forest den- budburst date available at each of the 30 acorn-sampling sites as a sity between the sites; this corrected amount of pollen (hereafter linear function of the mean March temperature recorded every airborne pollen amount) will be used in all subsequent analyses. year at these sites (see Table S3; Fig. S2). Using this negative rela- We used data on the budburst date and the fruiting dynamics tionship, we then inferred the budburst date each year at each of of temperate oak species from the ONF-RENECOFOR network the 35 pollen-sampling temperate oak sites. This allowed us to (Ulrich, 1995) covering 30 sites for 14 yr (1994 –2007) (see test if the weather conditions around the budburst date (e.g. the Fig. S1 for a map and Table S2 for the GPS coordinates). Among occurrence of late frost within 30 d before, or the mean tempera- the 30 sites, 19 are dominated by Q. petraea , nine by Q. robur ture 1 month afterwards) were linked to both amount of airborne and two of them are mixed oak forests (see Table S2). These sites pollen and fruit production. Focusing on these identified key are all different from the pollen-sampling sites. Acorn production weather conditions around budburst date and using the meteoro- was estimated yearly at each site on a fixed 1 acre (0.405 ha) sur- logical data retrieved at each site since 1959, we carried out fur- face where 10 nonneighboring mature trees were each equipped ther simulations using the RBM (see RBM modeling section) to with one 0.5 m2 raised litter-fall trap; the mature acorns collected explore the effect of a shift in flower phenology in temperate oak were counted exhaustively and summed for the 10 trees. The species on fruiting dynamics. budburst date was estimated at each site and year as the earliest date at which the first 10% of trees had 20 –50% of their buds Data analysis open (phenological stage BBCH 9; Meier et al. , 2009). We compared the ‘temperate’ and ‘Mediterranean’ oak popula- tions for their pollen phenology. We analyzed the differences in Meteorological data and their use the median date of pollen release (i.e. the day by which 50% of On the basis of the daily weather data extracted from the the annual airborne pollen has already been released) using Stu- SAFRAN spatially explicit database (8 9 8 km mesh size grid) dent’s t-test. (Durand et al. , 1993), we calculated for each of the 43 pollen and We analyzed the sensitivity of airborne pollen amount to vari- 30 acorn sampling sites the mean daily temperature ( °C) and the ous spring weather variables separately for the ‘temperate’ and cumulative rainfall (mm) during different periods in spring to ‘Mediterranean’ sites as follows. First, for various spring periods, test the effect of weather conditions on the amount of airborne we performed a principal component analysis (PCA) on mean pollen in both temperate and Mediterranean oak species and on temperature and cumulative rainfall, and used the first principal fruit production in temperate oak species. component (PC1) as a synthetic weather variable reflecting both At each of the 30 acorn-sampling sites of the ONF- temperature and rainfall of each spring periods (see Table S4). RENECOFOR network, we also computed the minimum daily Second we performed generalized linear mixed models (GLMMs temperature (to check for the possibility of late frost acting as an with Gaussian family and identity link) with log-transformed air- environmental veto; see later for a discussion of threshold detec- borne pollen amount as the dependent variable and the PC1 vari- tion). Following Lebourgeois et al. (2008), we first modeled the able – depending on the spring period considered (see Table S4) 12' Ó 2019 The Authors New Phytologist (2020) 225: 1181–1192 New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com 4nnexe 1 New 1184 Research Phytologist
– and the lag-1 yr autocorrelation of airborne pollen amount as evolutionarily shifted pollen phenology (i.e. budburst date) on covariates, considering the factors ‘site’ and ‘year’ as random the fruiting dynamics of temperate oaks. Notably we explored effects. We selected the most parsimonious GLMM separately the impact on fruiting dynamics of a fixed 15 d shift in the bud- for the two forest types using the Akaike information criterion burst date, either advanced or delayed, depending on the model, (see results in Table S5). and a 15 d delayed budburst date corresponding to the actual To assess the importance of pollen phenology on masting in pollen phenology of Mediterranean oaks (this study) and to that temperate oak species, we tested the sensitivity of fruit produc- of ash trees ( Fraxinus spp.) in the temperate region (Vitasse et al. , tion to weather at different time periods around the budburst 2009). We further examined a 30 d delayed pollen phenology, as date, from the date on which male flowers become particularly observed for beech ( Fagus spp.) in the temperate region (see sensitive to frost right up to pollen release. To ensure the robust- Vitasse et al. , 2009). ness of the results reported, we split the whole acorn dataset into The four classical mathematical descriptors for masting are: two mirror sub-datasets, each one comprising full time series of the individual coefficient of variation of fruiting intensity (CVi) 15 acorn-sampling sites evenly distributed over similar altitude, describing the individual between-year variability in seed pro- longitude and latitude gradients (see Fig. S1). We conducted an duction; the degree of synchrony among trees within the popu- exploratory approach on a first sub-dataset to identify candidate lation in their fruiting interannual dynamics (classically the periods and their weather conditions (the minimum temperature mean of pairwise correlation between crop size of individuals threshold below which late frost may act as an environmental within the population); the population coefficient of variation veto on fruiting). We fitted negative binomial GLMMs with log (CVp) describing the fruiting temporal variation at the popula- link to estimate fruit production with the lag-1 yr autocorrelation tion level; and the negative temporal autocorrelation (often at of fruit production, the mean temperature over 30 d after bud- 1 yr time lag) of seed production (classically ACF1; Koenig burst date and the occurrence of frost during several periods et al. , 2003; Herrera, 1998; Kelly & Sork, 2002; Buonaccorsi around budburst date as binary factor (i.e. considering frost et al. , 2003). None of these descriptors, however, is able to whenever minimum daily temperature falls below a threshold describe the asymmetry in the fruiting dynamics (see the Intro- value tested) as covariates, considering the factors ‘site’ and ‘year’ duction). as random effects to increase the probability of identifying candi- Here, to analyze the results of the simulations, we character- date periods and minimum temperature threshold (see ized the intensity of the fluctuations with the CVp parameter. Table S6). On the second sub-dataset, we tested whether the Complementarily, to quantify the deterministic and stochastic weather variables previously identified were still detected by fit- components of fruiting dynamics and the impact of flower phe- ting a negative binomial generalized linear model (GLM) with nology on these components, we analyzed temporal autocorrela- the factors ‘site’ and ‘year’ as fixed effects (see Table S7). tion at the population scale using the standard statistic: All statistical analyses were performed with the R free software environment (v.3.4.3, http://cran.r-project.org). We performed T À1 the PCA using the ‘dudi.pca’ function in the ADE 4 package (Dray S ¼ X ðxt xtþ1Þ with xt ¼ zt À z & Dufour, 2007), and fitted the multiple additive GLMMs using t¼1 the ‘lmer’ function in the LME4 package (Bates et al. , 2015). where T is the length of the fruiting series and z, zt and xt corre- spond, respectively, to the average annual crop size of the popula- tion, the crop size at year t and the centered crop size at year t. RBM modeling This statistic corresponds to the numerator of several standard We built an RBM, that is, an individually based, spatially explicit measures of autocorrelation (Wald & Wolfowitz, 1943; Dray model accounting for the individual strategies for allocating et al. , 2010). resources into reproduction, and allowing us to simulate individ- For each flower phenology, we evaluated the significance of ual flowering and fruiting dynamics within a population. We the observed statistic ( S) by comparing its value to the distribu- modified a previously published RBM (Venner et al. , 2016; tion under the null hypothesis obtained using 999 permutations Schermer et al. , 2019) to incorporate the effect of pollen phenol- of the fruiting series. To compare the different phenology sce- ogy on masting in temperate oaks. According to the former nario for their degree of stochasticity in fruiting dynamics, we RBM, interannual variations of fruit production may partly result computed the ‘standardized effect size’ (SES, Gotelli & McCabe, from interannual variations of airborne pollen available for repro- 2002) by standardizing the observed statistics ( S) by the means duction that depend on both the amount of pollen produced by and SDs estimated under the null hypothesis. Under the null trees and spring temperature at the time of pollen release (see hypothesis that there is no autocorrelation in the fruiting series, Schermer et al. , 2019). Whereas in the former RBM (Schermer the distribution of SES should be centered on 0 with SD = 1, et al. , 2019), the timing of pollen release was set to April, irre- while SES will be all the more negative when the negative tempo- spective of the year and the site, the biological realism of the ral autocorrelation is strong (or when the stochastic component RBM presented here was improved by integrating the identified of the masting is weak). Under the assumption that phenology key weather conditions around the actual timing of budburst (i.e. has no effect on the stochastic component of masting, the distri- pollen phenology; see Methods S1 for further details) and we bution of SES should be similar between the different phenologi- used this enhanced RBM to study the impact of a theoretical cal scenarios. 122 New Phytologist (2020) 225: 1181–1192 Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New 4nnexe 1 Phytologist Research 1185
In addition, we used a dual metric reflecting the degree of sample Student’s t-test: t = 17.42, df = 676, P < 0.001, 95% CI: asymmetry in the fruiting dynamics as follows: the probability 12.96–16.25). PL/M for a lean-seeding year (L) to follow a mast-seeding year Depending on the region (temperate or Mediterranean), pol- (M) at the population scale; and the probability PM/L of having a len release thus occurs under contrasting weather conditions mast-seeding event (M) the year following a lean-seeding year owing to phenological differences between oak species (Fig. 2a). (L). In the RBM outputs, mast- and lean-seeding years are In the temperate region, the annual airborne pollen amount was defined for fruiting allocation > 0.7 and < 0.3, respectively (the positively related to April temperature, following a logistic rela- value 1 being, on average, the mean amount of resources acquired tionship (see Table S8 for results of the GLMM selection; see annually by trees that can be allocated to current reproduction or Table S9 for results of the model selection between the logistic stored for future reproduction; see Methods S1). A sensitivity and linear models) in line with a recent study (Table S5; Scher- analysis was performed by testing various threshold values to mer et al. , 2019). Conditions for pollen release seem optimal for define these two categories of fruiting level and the results remain mean April temperature > 13 °C (value determined by a threshold qualitatively similar (see Fig. S3). model; Huber, 1964; see Fig. S5), which occurred in 11% and For each set of parameters (or sites), 100 repeated simulations 100% of the sites and years for temperate and Mediterranean oak of fruiting dynamics were run over 2000 yr; we then computed, species, respectively (Fig. 2b). over the last 100 years, the CVp to describe masting intensity, the S and SES for analysis of temporal autocorrelation of masting Impact of a shift in the pollen phenology on fruiting (i.e. its deterministic and stochastic components), and the two dynamics in temperate oaks frequencies PL/M and PM/L for describing masting asymmetry. We were then able to compare various scenarios differing in their In the temperate region, the early timing of pollen release makes flower phenology for the degree of asymmetry in their associated reproduction sensitive to late frost ( À5°C or less) whenever it fruiting dynamics. occurs within 30 d before the budburst date (Fig. 3a; Tables S6, We carried out additional simulations to analyze the relative S7), and to the mean temperature > 30 d after budburst date (i.e. contribution of the two modeled meteorological effects (by inte- spring temperature impacting airborne pollen amount; see grating only one of the two mechanisms at a time, i.e. either the Fig. S6) (see also Fig. 3b; Tables S6, S7, S9). ‘environmental veto’ effect related to late frosts or the effect of Our RBM simulations suggest that fruiting dynamics of tem- weather conditions on pollen aerial diffusion) on the fruiting perate oak tree populations would be sensitive to evolutionary dynamics under the different phenological scenarios. Finally, to shifting pollen phenology (Figs 4, 5). Although fruiting dynamics address the issue of decoupled investment made by trees into fluctuate greatly under all phenological scenarios (Fig. 4), the male and female flowers in pollen limitation and fruiting dynam- CVp would decrease under later phenology (Fig. 5a) and the neg- ics (see Crone & Rapp, 2014), we carried out sensitivity analyses ative temporal autocorrelation would become more pronounced considering that the relative allocation of male and female flower- (Fig. 5b) and, hence, variation in the fruiting dynamics more ing resources could deviate from a strict equilibrium of 0.5. We deterministic. A shift towards earlier pollen phenology would be ran analyses in two complementary ways, considering: that trees accompanied by more pronounced asymmetry of fruiting may have their own, consistent allocation ratio into male flower- dynamics (Fig. 5c,d): the probability that a lean-seeding year ing (defined for each tree by randomly sampling in a Gaussian would follow a mast-seeding year remained unchanged and high distribution with 0.5 (0.1), mean (SD)); or each tree may vary (i.e. mainly between 0.8 and 1) irrespective of the phenology sim- from one year to the next in its relative allocation into male and ulated (Fig. 5c). By contrast, the probability of having a mast- female flowers (defined for each tree and each year by sampling seeding year after a lean-seeding one was very variable according the ratio in a Gaussian distribution with 0.5 (0.1), mean (SD)). to the different phenological scenarios and was lowest for the ear- liest phenologies (Fig. 5d). By considering weather factors in isolation in the modeling, Data availability we revealed that the veto-like effect of late frost would play only a Data supporting the results are available from the Dryad Digital minor role in fruiting dynamics under current phenology, Repository (doi: 10.5061/dryad.p8cz8w9k3). whereas the weather conditions that influence pollen spread, as they stand, would retain a key role (Fig. S7b). If oak phenology was 15 d earlier, the negative effect of late frost would then Results emerge (Fig. S7a). Later flower phenologies would almost sys- tematically meet optimal conditions for reproduction, without Pollen phenology in temperate and Mediterranean oaks severe weather conditions unfavorable to flower survival or pollen In oak species growing in the temperate region, pollen is released diffusion (Fig. S7c,d). mainly from the second half of April to early May, occurring ear- Overall, our results suggest that if temperate oak evolved lier in the season as latitude decreases (see Fig. S4). Mediter- towards delayed phenology (independently of any climate ranean oak species, despite being located south of the temperate change), their fruiting dynamics would still fluctuate, but in a oak forests (Fig. 1a), release their pollen mainly in May (Fig. 1b), much less stochastic way, and mast-seeding years should then that is, about 2 wk later on average than temperate oaks (two- become more predictable (i.e. mainly driven by negative 12( Ó 2019 The Authors New Phytologist (2020) 225: 1181–1192 New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com 4nnexe 1 New 1186 Research Phytologist
(a) 700 Discussion Mediterranean Whereas the timing of pollen release is delayed with increasing 600 Temperate latitude within temperate oak species, our results revealed that 500 these species have much earlier pollen phenology (15 d differ- ence) than Mediterranean oaks. In the four species studied, fertil- 400 ization consistently takes place during the same period (late June, of air) of
−3 early July) (Pesson & Louveaux, 1984) and at a much later date 300
(m than pollination, suggesting that the phenology of temperate oak 200 species could theoretically be later than it actually is. In temperate oak forests, we show that the early pollen phenology observed in 100 the field is often associated with weather conditions that are unfa- vorable to pollen maturation and/or aerial diffusion, which could
Mean annual airborne Meanannual pollen amount 0 explain why reproductive failure is common. Our results suggest 6 7 8 9 101112131415161718 that such advanced pollen phenology would give trees a selective (b) advantage by generating a strong stochastic component in fruit- 1 ing dynamics, which is possibly decisive for effective control of seed consumer demography. The early spring pollen maturation of temperate oak species 0.75 could be seen as maladaptive owing to the suboptimal weather conditions encountered at the time of pollen release ( c. 10% of years have mean temperature > 13 °C over 30 d after budburst 0.5 date (Figs 3b, S6)) and to the probability of suffering frost dam- age at flowering (i.e. 5% of years with minimum temperature < À5°C occurring within 30 d before the budburst date). Such 0.25 early pollen phenology might lead to frequent, massive fruiting failure and explain why the fruiting dynamics of some oak species
Cumulative frequencies of sites Cumulative are very sensitive to spring weather conditions (Pearse et al. , 0 2014; Koenig et al. , 2015; Bogdziewicz et al. , 2017a; Caignard 6 7 8 9 101112131415161718 et al. , 2017; Nussbaumer et al. , 2018; Schermer et al. , 2019). By Mean temperatures (°C) contrast, Mediterranean oak pollen, because of the warmer cli- at time of pollen release (1 month) matic conditions encountered and their delayed pollen phenol- Fig. 2 Comparison of the sensitivity of airborne pollen and mean ogy, experience weather conditions that are usually favorable to temperature at time of pollen release between temperate and pollen maturation and release (Fig. 2), with very rare exposure to Mediterranean oak forests. (a) Mean annual airborne pollen amount as a late, intense frost (Fig. S9b). The evolutionary divergence in pol- function of mean temperature at time of pollen release for both len phenology between these oak species would then sustain the Mediterranean (Quercus ilex and/or Quercus pubescens; green circles) and temperate oaks (Quercus petraea and/or Quercus robur; orange diversity of their responses to spring weather conditions and circles). Mean temperatures were computed in April for temperate oaks partly explain why finding common determinants of masting is and between mid-April and mid-May for Mediterranean oaks to account so difficult in the genus Quercus (Sork et al. , 1993). for the 15 d delayed pollen phenology (see Fig. 1). Data shown are means From an evolutionary perspective, pollen phenology could be Æ SE of annual airborne pollen amount ranked according to increasing seen as a key life-history trait that partly controls the degree of temperature and grouped by sets of 10 consecutive values to compute mean Æ SE (see Supporting Information Fig. S10a,b, which shows the stochasticity in fruiting dynamics in temperate oak species. Based same relationship with ungrouped data). Shaded areas show the 95% on our RBM, we show that contrasting yet realistic variations in confidence interval of the model estimates. (b) Cumulative frequencies of oak flower phenology (i.e. within the range of other wind-polli- April temperatures (orange line) and temperatures between mid-April and nated forest species) would all still generate large fluctuations in mid-May (green line) at each site and year for temperate and fruiting (Fig. 4). However, the stochastic component of masting Mediterranean oaks, respectively. The orange vertical dotted line shown in (a) and (b) is the 13 °C threshold value above which the pollen amount of was increased only when simulating earlier pollen phenology temperate oaks reaches high values, independent of mean April (Fig. 5b,d), which generated conditions that are often unfavor- temperature (see Fig. S5 for the deviance profile from the ‘threshold able to reproduction. As proposed from theoretical work (Rees model’ (Huber, 1964)). et al. , 2002), disturbance in fruiting dynamics is probably essen- tial to efficiently control the dynamics of seed consumer popula- tions and maximize tree fitness. Oak acorns are a pulsed resource temporal autocorrelation). These results were obtained consider- for various consumers that affect their population dynamics (in- ing that trees invested equal amounts of energy into male and sects, Venner et al. , 2011; birds, McShea, 2000; rodents, Wolff, female flowers; our findings remain robust to departures from 1996; ungulates, Gamelon et al. , 2017). Among consumers, that assumption (see Fig. S8). insects specialized in this resource are probably the most 12) New Phytologist (2020) 225: 1181–1192 Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New 4nnexe 1 Phytologist Research 1187
(a) 400 (b) 120 Veto No veto ) −2
) 350 –2 100 300 80 250
200 60
150 40 100
Annual acorn Annual production (m 20 50 Mean annual acorn Meanannual production (m
0 0
−10 −8 −6 −4 −2 0 2 4 6 7 8 9 10 11 12 13 14 15 16 Minimum temperatures (°C) occurring Mean temperatures (°C) at within 30 d before budburst date > 30 d after budburst date Fig. 3 Sensitivity of fruiting intensity to temperature around budburst date in temperate oak forests ( Quercus petraea and/or Quercus robur). (a) Annual acorn production along the minimum temperatures occurring within 30 d before budburst. The vertical line corresponds to the À5°C threshold value under which frost is detected causing fruiting failure (see Supporting Information Tables S6 and S7 for results). (b) Mean annual acorn production as a function of mean temperatures at > 30 d after budburst (i.e. at the time of pollen release; see Fig. S6 for a similar relationship between airborne pollen amount and mean temperature at > 30 d after budburst date) (Table S9). The mean ( Æ SE) acorn amounts shown were computed within groups of 10 consecutive site 9 year values once being ranked according to their mean temperature (see Fig. S11, which shows the same relationship with ungrouped data). Shaded area shows the 95% confidence interval of the model estimates.
(a) Early pollen phenology (b) Current pollen phenology 1.5 1.5 CVp = 1.39 CVp = 1.23 PL M = 1 SES = −2.46 SES = −4.23 PM L = 0.25 PL M = 1 PM L = 0.034 1 1
0.5 0.5 Fig. 4 Examples of population fruiting dynamics simulated by our resource budget crop size Population model over 100 yr depending of pollen 0 0 phenology: (a) earlier; (b) current; (c) later; 0 20 40 60 80 100 0 20 40 60 80 100 and (d) very late. The ‘population crop size’ axis corresponds to the index of the mean (c) Late pollen phenology (d) Very late pollen phenology amount of resource allocated to fruiting at 1.5 CVp = 1.04 PL M = 1 1.5 CVp = 0.99 PL M = 1 SES = −6.60 PM L = 0.55 the population level. For each pollen SES = −8.98 PM L = 0.81 phenology scenario, the values of the population coefficient of variation (CVp), 1 1 autocorrelation standardized effect size (SES) and the probabilities PL/M of having a lean- seeding year (L) the year following a mast- 0.5 0.5
seeding event (M) and PM/L of having a Populationcrop size mast-seeding event (M) the year following a lean-seeding year (L) are indicated (see 0 0 legend of Fig. 5 for details on phenology or 0 20 40 60 80 100 0 20 40 60 80 100 masting parameters). Time (yr) Time (yr) problematic for the following reasons: insects are able to respond results suggest that early phenology would play this pivotal role demographically very quickly to the fluctuations of the resource in inducing weather conditions most often detrimental to yearly (Bogdziewicz et al. , 2018b); acorn consumption by insects reproduction, thus making mast-seeding years unpredictable for severely reduces seed germination success and seedling survival seed consumers, and hence maximizing tree fitness. Our results (Andersson, 1992; Mu noz~ et al. , 2014; Leiva et al. , 2018; Yi are therefore in line with the recent proposal by Bogdziewicz et al. , 2019); and several weevil species commonly coexist on the et al. (2019) that the weather conditions causing frequent fruiting same individual trees and display widely diverse life-history traits failure are traditionally perceived as negative for plants, but (Venner et al. , 2011; P elisson et al. , 2012, 2013; Rey et al. , would help to maximize their lifetime reproductive success. 2015), making it difficult for the trees to control the dynamics of Under the current phenology of temperate oak species, fruiting the whole insect community. Efficient control of such insect failure is explained much more widely by climatic conditions that diversity is probably tightly linked to strong stochastic compo- are unfavorable to pollen maturation or diffusion (i.e. the month nent in the fruiting dynamics. In temperate oak species, our following the budburst date) than by the occurrence of late frost 12* Ó 2019 The Authors New Phytologist (2020) 225: 1181–1192 New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com 4nnexe 1 New 1188 Research Phytologist
(a) (b) 1 1 Phenology Early 0.8 Current 0.8 Late Very late 0.6 0.6 Fig. 5 Impact of an evolutionary shift in flower phenology on the fruiting dynamics of temperate oak species. Four flower 0.4 0.4 phenologies were tested for their impact on fruiting dynamics through simulation with 0.2 0.2 the resource budget model. The current phenology corresponds to the phenology
Cumulativefrequencies sites of recorded in the field at the 30 fruit sites. The 0 0 early flower phenology corresponds to a 15 d 0.6 0.8 1 1.2 1.4 1.6 1.8 2 −10 −9 −8 −7 −6 −5 −4 −3 −2 −1 0 advance in the budburst date, and the late CVp Autocorrelation SES and very late pollen phenologies correspond to a 15 d (as observed for Mediterranean oak (c) (d) species; see Fig. 1b) and a 30 d lag (as 1 1 observed for beeches in some temperate forest communities) in the budburst date, 0.8 0.8 respectively. (a –d) Cumulative frequency distribution of sites for: (a) the population coefficient of variation of fruiting (CVp); (b) 0.6 0.6 the autocorrelation ‘standardized effect size’ (SES), which reflects both the deterministic 0.4 0.4 and stochastic components of masting; (c)
the probability PL/M of having a lean-seeding year (L) after a mast-seeding year (M); and 0.2 0.2 (d) the probability PM/L of having a mast- seeding event (M) the year following a lean- Cumulative frequencies of sites Cumulative 0 0 seeding year (L). Together these describe the
0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 degree of asymmetry of the masting. The polygons display the 95% credible interval P P L M M L (i.e. including 95% of the simulations).
(the month preceding the budburst date), which is relatively rare flower maturation and pollen diffusion) would probably depend and which would only have minor effects on fruiting dynamics on the species, local ecological conditions and/or local adaptation (see Figs 3a, S9b). Although very early phenology would make (Koenig et al. , 2016). Considering this last point, the evolution fruiting dynamics even more stochastic (e.g. by reinforcing the of flower phenology could be rapid – as it is tightly linked to leaf ‘environmental veto’ effect of late frost), it would probably be too phenology, which is itself quickly evolving (Franji c et al. , 2011) – costly in terms of fitness, either by producing too rare mast-seed- and could thus be responsible for the short-term change in the ing years or by impairing leaf growth (see later). weight of late frost and weather conditions at the time of pollen Mediterranean oak species are also exposed to greatly diverse release in masting. seed consumers whose control is also expected to require stochas- Our study, in line with previous work (Koenig et al. , 2015; tic fruiting dynamics. Although not studied here, the fruiting Bogdziewicz et al. , 2017b), underlines the need to elucidate the dynamics of Mediterranean oak species are probably as variable interdependency between fruiting strategies (i.e. the interannual and stochastic as those of temperate oak species (Bogdziewicz dynamics of fruiting, possibly masting) and the phenology of et al. , 2017b). The late flower phenology of Mediterranean oaks perennial plants. For example, in oak species, flower maturation seems to promote weather conditions mainly favorable to pollina- is organically linked to leaf maturation because most buds are tion (Fig. 2b). In consequence, the stochastic component of compound buds (i.e. containing leaves and flowers); leaf and masting is likely to be independent of weather-driven pollination flower phenologies are thus tightly related (Koenig et al. , 2012). failure, instead being a result of severe drought in spring or sum- The evolution of flower phenology might thus be a by-product mer, resulting in frequent and very high fruit abortion rate (Fer- of, and driven by, leaf phenology that would be predominantly nandez-Martinez et al. , 2012; P erez-Ramos et al. , 2015 (for a selected to maximize carbon gain through photosynthesis; in this review); Pearse et al. , 2015; Bogdziewicz et al. , 2017b). In sense, the early flower phenology of temperate oak (and the Mediterranean oak species, the late flower phenology could be a stochasticity induced in masting) would be an exaptation. Most way of not adding noise to the already very stochastic fruiting likely, the phenology of temperate oaks would result from a dynamics and, consequently, keeping the frequency of mast-seed- tradeoff between the advantage of being early to trigger stochastic ing years at a minimum threshold. Overall, the proximate mecha- fruiting dynamics and to lengthen the canopy duration, and the nisms of oak masting (i.e. including environmental veto as a advantage of being late to avoid exposing the nascent leaves to result of late frost or water stress, weather conditions impacting late frost. 12+ New Phytologist (2020) 225: 1181–1192 Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust New 4nnexe 1 Phytologist Research 1189
More generally, it would be useful to develop integrative work Isabelle Chuine https://orcid.org/0000-0003-3308-8785 on phenology and masting through comparative approaches of Sylvain Delzon https://orcid.org/0000-0003-3442-1711 the dynamics of fruiting and phenology of flowers and leaves in St ephane Dray https://orcid.org/0000-0003-0153-1105 wind-pollinated perennial plant species. For example, in species Jean-Michel Gaillard https://orcid.org/0000-0003-0174- with separate flower and leaf buds, pollen phenology could be 8451 even earlier, and fruiting dynamics more stochastic, than in other, Iris Le Ronc e https://orcid.org/0000-0002-7484-8819 more constrained species. From a more theoretical perspective, it Eliane Schermer https://orcid.org/0000-0001-7302-2241 might be worth combining several models, including those con- Samuel Venner https://orcid.org/0000-0001-7127-3733 sidering ecophysiological traits for their impact on plant phenol- ogy (Chuine & Beaubien, 2001), those looking at the mechanistic traits of fruiting dynamics (Isagi et al. , 1997; Abe et al. , 2016; this References work) – for example, the evolution of traits affecting resource allo- cation in reproduction – and those dedicated to simulating seed Abe T, Tachiki Y, Kon H, Nagasaka A, Onodera K, Minamino K, Han Q, Satake A. 2016. Parameterisation and validation of a resource budget model for consumer dynamics (Rees et al. , 2002; Tachiki & Iwasa, 2013), to masting using spatiotemporal flowering data of individual trees. Ecology letters link explicitly the proximate causes of masting to fitness conse- 19 : 1129 –1139. quences and plant regeneration success. 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Annals of Mathematics and Statistics 14 : 378 – Table S1 Characteristics of the pollen-sampling sites. 388. Wolff JO. 1996. Coexistence of white-footed mice and deer mice may be Table S2 Characteristics of the acorn-sampling sites. mediated by fluctuating environmental conditions. Oecologia 108: 529 –533. Yang LH, Edwards KF, Byrnes JE, Bastow JL, Wright AN, Spence KO. 2010. A meta-analysis of resource pulse-consumer interactions. Ecological Monographs Table S3 Response of budburst date to March mean daily tem- 80 : 125 –151. perature. 1(3 Ó 2019 The Authors New Phytologist (2020) 225: 1181–1192 New Phytologist Ó 2019 New Phytologist Trust www.newphytologist.com 4nnexe 1 New 1192 Research Phytologist
Table S4 Proportion of variance explained by the various Table S8 Model selection between the various models explaining synthetic weather variables reflecting both temperature and the airborne pollen amount in the temperate region. rainfall. Table S9 Model selection between the logistic model and poly- Table S5 Response of airborne pollen amount to spring nomial regression models for predicting airborne pollen amount weather variables depending on temperate and Mediterranean or fruit production from spring weather conditions in the tem- regions. perate region.
Table S6 Exploring the sensitivity of fruiting intensity to weather conditions around the budburst date. Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the Table S7 Testing the sensitivity of fruiting intensity to weather authors. Any queries (other than missing material) should be conditions around the budburst date. directed to the New Phytologist Central Office.
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1(& New Phytologist (2020) 225: 1181–1192 Ó 2019 The Authors www.newphytologist.com New Phytologist Ó 2019 New Phytologist Trust Annexe 2 : Supp. Mat. du Chapitre 1
Annexe 2 : Matériels supplémentaires du Chapitre 1 Table S1: Mean values (±SD), percentages of different 2018 shoot and fruit characteristics across the treatments. Difference between treatments was tested with (generalised) linear mixed effect models, with Tree / Branch as random effect. Different letters refer to significant differences between defoliation treatments (p < 0.05, Tukey post hoc tests) and characters in bold font refer to significant global effects.
Variable Control Intermediate High Type II Wald Anova defoliation defoliation statistics
2018 shoot basal area (mm²) 3.7 (1.6)a 3.9 (1.7)a 4.3 (2.8)a χ² = 3.6, p = 0.2
Gaussian distribution
Number of fruits per 2018 1.9 (0.8)a 1.9 (0.9)a 2.0 (0.8)a χ² = 0.2, p = 0.9 shoot before defoliation Poisson distribution
Number of leaves per 2018 7.8 (3.5)a 4.6 (3.4)b 1.2 (0.8)c χ² = 187.6, p < 0.001 shoot after defoliation and Poisson distribution before summer flush
Number of leaves per 2018 8.5 (4.5)a 6.9 (6.6)a 1.5 (1.2)b χ² = 61.6, p < 0.001 shoot after summer flush and Poisson distribution second defoliation
Mean leaf area per fruit in 2018 15.4 (8.9)a 12.4 (10.5)b 2.6 (1.8)c χ² = 79.7, p < 0.001 shoots after summer flush and Gaussian distribution second defoliation (mm² / fruit)
Proportion of 2018 shoots that 10 % (of 80 21 % (of 80 15 % (of 81 χ ² = 2.7, p = 0.3 flushed in summer shoots)a shoots)a shoots)a Binomial distribution
Proportion of fruits that are 40 % (of 48 % (of 282 33 % (of 332 χ² = 10.1, p = 0.007 single at the axil of leaves 304 fruits)a fruits)a fruits)b Binomial distribution
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Figure S1: Relationship between (A) number of leaves per shoot and shoot basal area and (B) mature fruit mass and mature fruit length in the control treatment for each tree (named from I to VIII). Global relationship between the number of leaves and shoot basal area was significant (Type II Wald Anova with Gaussian distribution and Tree / Branch as a random effect: χ² = 29.0, p < 0.001), as well as the relationship between fruit mass at maturity and fruit length (Type II Wald Anova with Gaussian distributio n and Tree / Branch as a random effect: χ² = 303.7, p < 0.001).
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Figure S2: Variation of (A) fruit survival rate and (B) mean fruit mass between branches and trees (named from I to VIII). Dot represents a rate or a mean value per branch.
Figure S3: Effect of the reproductive status of the 2018 spring shoot on its basal area. Full black points indicate group mean. Different letters indicate significant differences between treatments according to the GLMM (fitted with a Gaussian distribution and Tree / Branch as random effect: χ² = 78.2 (Type II Wald Anova), p < 0.001).
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Table S2: Summary of GLMM testing the effect of defoliation and of other biological covariates on fruit survival, growth and germination, as well as photosynthetic rate in 2018. We report statistics, p-values, standardised estimates ± SE for treatment comparisons (control (C), moderate defoliation (MD), intense defoliation (ID)) and covariable effects. Characters in bold font refer to significant effects (p < 0.05) and stars to levels of significance (0.1 < p (.) < 0.05; 0.05 < p* <0.01; 0.01 < p** < 0.001; p*** < 0.0001).
# Response Predictor Effect estimated estimate ± SE variable (1) Fruit survival Defoliation treatment MD-C -0.67* ± 0.29 between initiation (ID, MD, C) ID-C 0.15 ± 0.32 and maturation ID-MD -0.82** ± 0.31
2018 shoot basal area slope -0.064 ± 0.119 Mean leaf area per fruit on slope 0.057 ± 0.133 2018 shoot Summer flush in 2018 yes-no -0.94** ± 0.33 More than one fruit initiated yes-no 0.37* ± 0.19 per leaf Minimum predawn potential slope 0.28 ± 0.35 (2) Photosynthesis Defoliation treatment MD-C -0.80 ± 1.4 rate ID-C -0.49 ± 1.5 ID-MD 0.30 ± 1.5 (3) Mature fruit mass 2018 shoot basal area slope -0.042 ± 0.030 Mean leaf area per fruit on slope 0.002 ± 0.035 2018 shoot Summer flush in 2018 yes-no 0.010 ± 0.078 Minimum predawn potential slope -0.096 ± 0.119 Defoliation treatment (ID, MD-C yes 0.22* ± 0.09 MD, C) x MD-C no -0.085 ± 0.10 More than one fruit per leaf MD-C yes vs. no 0.31** ± 0.10 (yes, no) ID-C yes 0.21* ± 0.09 ID-C no 0.050 ± 0.110 ID-C yes vs. no 0.17 (.) ± 0.09 ID-MD yes -0.002 ± 0.092 ID-MD no 0.14 ± 0.11 ID-MD yes vs. no -0.14 ± 0.10 (4) Fruit germination Defoliation treatment MD-C -0.16 ± 0.58 success ID-C 0.19 ± 0.50 ID-MD 0.35 ± 0.57 Fruit mass slope 0.84*** ± 0.25
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Table S3: Summary of GLMM testing the effect of experimental defoliation and of other biological covariates on growth parameters in 2019. For each model, interactions between treatment and all covariables were first included, and non-significant interactions were sequentially removed from the model starting with the least significant ones. We report statistics, p-values, standardised estimates ± SE for treatment comparisons (control (C), moderate defoliation (MD), intense defoliation (ID)) and covariable effects. Characters in bold font refer to significant effects (p < 0.05) and stars to levels of significance (0.1 < p (.) < 0.05; 0.05 < p* <0.01; 0.01 < p** < 0.001; p*** < 0.0001).
# Response Predictor Effect estimated Standardised variable estimate ± SE (5) Number of 2019 Defoliation (ID, MD, C) x C Slope 0.57*** ± 0.07 spring shoots per 2018 shoot basal area MD Slope 0.39*** ± 0.06 2018 shoot ID Slope 0.19*** ± 0.03 MD-C Slope -0.18 (.) ± 0.09 ID-C Slope -0.39*** ± 0.07 ID-MD Slope -0.21** ± 0.07 Defoliation (ID, MD, C) x MD-C yes 0.42* ± 0.21 Summer flush in 2018 (yes, no) MD-C no -0.16 ± 0.13 MD-C yes vs. no 0.58** ± 0.21 ID-C yes -0.069 ± 0.221 ID-C no -0.37** ± 0.13 ID-C yes vs. no 0.30 ± 0.22 ID-MD yes -0.49** ± 0.20 ID-MD no -0.21 ± 0.13 ID-MD yes vs. no -0.28 ± 0.19 (6) Number of leaves Defoliation (ID, MD, C) x MD-C yes 0.39* ± 0.16 per 2019 spring Summer flush in 2018 (yes, no) MD-C no 0.55** ± 0.14 shoot MD-C yes vs. no -0.17 ± 0.12 ID-C yes 0.23 ± 0.16 ID-C no 0.68*** ± 0.14 ID-C yes vs. no -0.45** ± 0.12 ID-MD yes -0.16 ± 0.15 ID-MD no 0.13 ± 0.14 ID-MD yes vs. no -0.29** ± 0.10 2018 shoot basal area slope 0.082*** ± 0.016
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Table S4: Summary of GLMM testing the effect of defoliation and of other biological covariates on flowering and fruit set parameters in 2019. We report statistics, p-values, standardised estimates ± SE for treatment comparisons (control (C), moderate defoliation (MD), intense defoliation (ID)) and covariable effects. Characters in bold font refer to significant effects (p < 0.05) and stars to levels of significance (0.1 < p (.) < 0.05; 0.05 < p* <0 .01; 0.01 < p** < 0.001; p*** < 0.0001).
# Response Predictor Effect estimated estimate ± SE variable (7) Number of Defoliation treatment MD-C -0.36** ± 0.09 catkins per 2019 (ID, MD, C) ID-C -0.59*** ± 0.10 spring shoot ID-MD -0.23* ± 0.10 2018 shoot basal area slope -0.05* ± 0.02 Summer flush in 2018 yes-no 0.074 ± 0.058 Number of mature acorns on 2018 slope 0.024 ± 0.022 shoot Defoliation x Leaves per 2019 shoot C Slope -0.070 ± 0.050 MD Slope -0.33*** ± 0.05 ID Slope -0.35*** ± 0.07 MD-C Slope -0.26** ± 0.07 ID-C Slope -0.28** ± 0.08 ID-MD Slope -0.023 ± 0.079 (8) Number of Defoliation treatment MD-C -2.77* ± 1.17 staminate (ID, MD, C) ID-C -3.70* ± 1.20 flowers per catkin ID-MD -0.92 ± 1.21 (9) Number of Defoliation treatment MD-C -0.20 ± 0.14 stamens per (ID, MD, C) ID-C -0.01 ± 0.15 staminate flower ID-MD 0.19 ± 0.15 (10) Number of Defoliation treatment MD-C -0.47* ± 0.20 female flowers (ID, MD, C) ID-C - 0.70** ± 0.19 per 2019 spring shoot ID-MD - 0.23 ± 0.20 Summer flush in 2018 yes-no 0.14 ± 0.14 Number of leaves per 2019 shoot slope 0.66*** ± 0.07 Defoliation (ID, MD, C) x C Slope 0.25* ± 0.11 2018 shoot basal area MD Slope -0.10 ± 0.08 ID Slope 0.09* ± 0.04 MD-C Slope -0.35** ± 0.13 ID-C Slope -0.16 ± 0.11 ID-MD Slope 0.19* ± 0.08
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Defoliation (ID, MD, C) x C Slope -0.064 ± 0. 091 Number of mature acorns MD Slope -0.46*** ± 0.11 ID Slope -0.47*** ± 0.12 MD-C Slope -0.40** ± 0.14 ID-C Slope -0.41** ± 0.14 ID-MD Slope -0.01 ± 0.15 (11) Sex ratio 2018 shoot basal area slope 0.28*** ± 0.05 Number of mature acorns on 2018 slope -0.27*** ± 0.07 shoot Defoliation (ID, MD, C) x C Slope 1.00*** ± 0.14 Leaves per 2019 shoot MD Slope 0.32*** ± 0.07 ID Slope 0.60** ± 0.18 MD-C Slope -0.39 ** ± 0.14 ID-C Slope -0.41 ** ± 0.14 ID-MD Slope -0.01 ± 0.15 Defoliation (ID, MD, C) x MD-C yes -0.13 ± 0.38 Summer flush in 2018 (yes, no) MD-C no 0.32 ± 0.23 MD-C yes vs. no -0.45 ± 0.38 ID-C yes -0.60 ± 0.41 ID-C no 0.62* ± 0.24 ID-C yes vs. no -1.22** ± 0.41 ID-MD yes -0.47 ± 0.38 ID-MD no 0.30 ± 0.25 ID-MD yes vs. no -0.77* ± 0.39 (12) Fruit set in 2019 Defoliation treatment MD-C 0.33 ± 0.32 (ID, MD, C) ID-C -0.43 ± 0.35 ID-MD -0.75 (. ) ± 0.33 2018 shoot basal area slope 0.026 ± 0.072 Number of leaves per 2019 shoot slope 0.40* ± 0.18
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Figure S4: Variation of (A) sex ratio (defined here as the proportion of female flowers produced by the shoot out of the total number of inflorescences of the shoot, i.e. female flowers plus catkins) and (B) the proportion of fruits initiated out of the number of female flowers in 2019 between treatments. One small dot represents one 2018 shoot. Full black dots indicate group mean.
Figure S5: Relationship between the number of female flowers per 2019 shoot and the number of male flowers per 2019 shoot within each defoliation treatment. There was no significant relationship between the two variables within the control and the intense defoliation treatments, however the two variables were negatively correlated within the moderate defoliation treatment (GLMM with Poisson distribution and Tree / Branch as a random effect, coefficient parameter estimate ± standard error: - 0.28 *** ± 0.09).
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Figure S6: Effect of defoliation in both bearing and non-bearing shoots in 2018 on the production of (A) 2019 shoots per 2018 shoot, of (B) leaves per 2019 shoot, of (C) catkins per 2019 shoot and of (D) female flowers per 2019 shoot on a pseudo-log scale. Within treatments, “*” indicates a signif icant effect of bearing a fruit or not in 2018, whereas “ns” indicates no significant differences (Tukey post-hoc tests with Bonferroni-Holm correction on the GLMM presented in Table S2, p < 0.05).
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Table S5: Summary of GLMM (Poisson distribution and Tree / Branch as a random effect) testing the global effect of shoot basal area, of defoliation, of being a 2018 shoot bearing fruits or not in 2018 and of the interaction between defoliation and fruit bearing on the production of 2019 shoots, of leaves per 2019 shoot, of catkins per 2019 shoot and of female flowers per 2019 shoot (p* < 0.05; p** < 0.01; p*** < 0.001).
Response variable Wald χ² and p -value associated to the predictors of the model
2018 shoot 2018 shoot Treatment Treatment x basal area bearing a fruit Bearing a fruit in in 2018 2018
(yes / no)
Number of 2019 spring shoot 342.7 *** 23.4 *** 15.8 *** 2.1 per 2018 shoot
Number of leaves per 2019 99.1 *** 75.3 *** 28.0 *** 24.1 *** shoot
Number of catkins per 2019 30.6 *** 2.2 35.6 *** 5.0 shoot
Number of female flowers per 18.4 *** 26.4 *** 6.9 * 6.8 * 2019 shoot
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Annexe 3 : Matériels supplémentaires du Chapitre 2 Table S1: Mean values (± SD) of tree basal area and of nitrogen (N), phosphorus (P), zinc (Zn), potassium (K) and starch concentrations in 1 and 2-yr old leaves, 1 and 2-yr old twigs, and trunk across the three tree groups before fruit removal treatment in spring 2018, and of 2018 shoots basal area in winter 2019. Difference between groups was tested with linear models for tree basal area and nutrient concentrations, and with linear mixed effects models for shoot basal area with Tree as random effect. Different letters refer to significant differences between defoliation treatments (p < 0.05, Tukey post hoc tests).
Variable Control Removed fruit Naturally no fruits N (n = 9 trees) (n = 9 trees) (n = 6 trees) Tree basal area (mm²) 49.9 (20.1)a 52.5 (24.4)a 54.7 (43.0)a 24 Basal area of shoots that did 3.2 (1.2)a 3.6 (1.8)a - 354 set a fruit in 2018 (mm²) Basal area of shoots that did 3.1 (1.6)a 3.8 (1.6)a 4.4 (2.6)a 191 not set a fruit in 2018 (mm²) [N] 1 yr-old leaves (%) 1.39 (0.17)a 1.44 (0.14)a 1.45 (0.17)a 24 [N] 2 yr-old leaves (%) 1.45 (0.26)a 1.27 (0.2)a 1.46 (0.31)a 23 [N] 1 yr-old twigs (%) 0.99 (0.24)a 0.85 (0.34)a 0.91 (0.42)a 24 [N] 2 yr-old twigs (%) 0.96 (0.1)a 0.83 (0.13)a 0.92 (0.16)a 24 [N] trunk (%) 0.25 (0.5)a 0.3 (0.6)a 0 (0)a 24 [Starch] 1 yr-old leaves (%) 4.43 (2.14)a 4.74 (1.47)a 5.02 (1.77)a 24 [Starch] 2 yr-old leaves (%) 4.95 (2.49)a 5.96 (1.83)a 4.47 (1.41)a 23 [Starch] 1 yr-old twigs (%) 5.41 (1.08)a 7.15 (2.04)a 6.88 (3.3)a 24 [Starch] 2 yr-old twigs (%) 8.29 (1.85)a 10.32 (1.94)a 7.94 (3.21)a 24 [Starch] trunk (%) 7.15 (4.16)a 9.39 (1.35)a 9.31 (5.45)a 24 [P] 1 yr-old leaves (mg.g -1) 0.68 (0.09)a 0.65 (0.1)a 0.65 (0.05)a 23 [P] 2 yr-old leaves (mg.g -1) 0.61 (0.2)a 0.65 (0.11)a 0.62 (0.15)a 23 [P] 1 yr-old twigs (mg.g -1) 0.89 (0.18)a 0.83 (0.35)a 0.67 (0.17)a 23 [P] 2 yr-old twigs (mg.g -1) 0.9 (0.36)a 0.84 (0.29)a 0.74 (0.03)a 24 [P] trunk (mg.g -1) 0.47 (0.19)a 0.52 (0.26)a 0.4 (0.2)a 24 [Zn] 1 yr-old leaves (mg.g -1) 0.0094 (0.0047)a 0.0085 (0.0024)a 0.0082 (0.0022)a 23 [Zn] 2 yr-old leaves (mg.g -1) 0.0114 (0.007)a 0.0098 (0.002)a 0.01 (0.0083)a 23 [Zn] 1 yr-old twigs (mg.g -1) 0.0202 (0.0044)a 0.0148 (0.0068)a 0.0182 (0.007)a 23 [Zn] 2 yr-old twigs (mg.g -1) 0.0182 (0.0048)a 0.0144 (0.0028)a 0.0174 (0.008)a 24 [Zn] trunk (mg.g -1) 0.0052 (0.0068)a 0.0035 (0.0062)a 0.003 (0.0037)a 24 [K] 1 yr-old leaves (mg.g -1) 0.66 (0.11)a 0.78 (0.19)a 0.7 (0.17)a 23 [K] 2 yr-old leaves (mg.g -1) 0.69 (0.17)a 0.63 (0.16)a 0.6 (0.12)a 23 [K] 1 yr-old twigs (mg.g -1) 1.06 (0.22)a 0.78 (0.26)a 0.85 (0.39)a 23 [K] 2 yr-old twigs (mg.g -1) 0.9 (0.16)a 0.74 (0.2)a 0.83 (0.18)a 24 [K] trunk (mg.g -1) 0.37 (0.22)a 0.5 (0.31)a 0.38 (0.22)a 24
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Figure S1: Variation of A) mean ± SD dry mass of female organs and B) normalized mean ± SD of carbon (C), potassium (K), nitrogen (N) and zinc (Zn) contents in dry female organs depending on developmental stage in May (flowers at anthesis, n = 5 trees), June (initiated fruits at the time of fruit removal, 3 trees) and (mature fruits and cupules, 3 trees).
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Figure S2: Seasonal dynamics of tissues concentrations (mean ± SD) in nitrogen (N, top panels), starch (upper middle panels), phosphorus (P, middle panels), zinc (Zn, lower middle panels) and potassium (K, bottom panels) in less than 1-yr old (top left panels) and less than 2-yr old (middle left panels) leaves, in less than 1-yr old (middle panels) and less than 2-yr old (middle right panels) twigs and in trunk (top right panels); depending on the treatment applied to the trees (N = 9 in each group).
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Table S2: Summary of GLMMs testing the effect of fruit removal and of 2018 shoot diameter in shoots that had not set a fruit in 2018 (and were only adjacent to shoots that had underwent fruit removal in defruited trees) on: the production of spring shoots in 2019, the numbers female flowers, catkins, leaves and nodes per spring shoot in 2019, on shoot 2018 secondary growth during spring, on fruit set in 2019 and on the mean number of initiated fruits per spring shoot after fruit set in 2019. For each model, interactions between treatment and diameter were first included, and non-significant interactions were removed from the model. We report the number of observations, standardised estimates ± SE and p-values for treatment comparisons (control (C), removed fruit (RF)), diameter and interaction effects, marginal (R² m) and conditional (R² c) R² for the reduced, final model. Characters in bold font refer to significant effects (p < 0.05) and stars to levels of significance (0.05 < p* <0.01; 0.01 < p** < 0.001; p*** < 0.0001).
Response variable N Standardised estimate ± SE of the predictor R² m
(R² c) (Effect estimated) Treatment 2018 shoot Treatment x (RF – C) diameter Shoot diameter (RF – C x slope) Nb of female flowers per 2019 655 1.47 ± 0.66 * 0.60 ± 0.18 *** n.s. 0.22 spring shoot (0.62) Nb of catkins per 2019 spring 655 -0.93 ± 0.47 * -0.10 ± 0.07 n.s. 0.14 shoot (0.78) Nb of leaves per 2019 spring 655 1.13 ± 0.49 * 0.27 ± 0.09 ** n.s. 0.19 shoot (0.74) Nb of nodes per 2019 spring 655 0.24 ± 0.08 ** 0.09 ± 0.03 ** n.s. 0.10 shoot (0.26) Nb of 2019 spring shoots per 131 -0.12 ± 0.13 0.32 ± 0.05 *** n.s. 0.28 2018 shoot (0.41) 2018 shoot spring secondary 134 2.2 ± 0.60 *** 0.08 ± 0.19 1.13 ± 0.36 ** 0.14 growth (mm²) (0.27) Fruit set in 2019 123 0.42 ± 0.38 -0.31 ± 0.14 * n.s. 0.03 (0.05) Mean nb of initiated fruits per 123 1.51 ± 0.60 * 0.50 ± 0.21 * n.s. 0.30 2019 spring shoot (0.37)
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Figure S3: Female inflorescences relative frequency at each node rank of 2019 spring shoots in control and RF trees (detail from Fig. 4). Increment in node rank indicates location of the node from the base toward the end of the shoot. The dotted line represents the node 5, which is approximatively the mode for leaves and female flowers
Figure S4: Relationship between the number of female flowers and the number of leaves per spring shoot in control group (upper panels) and removed fruit group (RF, lower panels) and on shoots borne by a 2018 that had set a fruit (left panels) and by shoots that had not (right panels). “** *” indicates the p-value associated to the relationship for each combination of treatment group and shoot reproductive status in 2018 (GLMMs with negative binomial distribution and 2018 shoot identity nested in tree identity as a random effect, p*** < 0.001)
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Annexe 4 : Matériels supplémentaires du Chapitre 3 Figure S1: Mean inter-annual variability of the Water Stress Integral (WSI) of ( a) spring, ( c) summer of the previous year and ( e) summer-autumn of the current year in the control and dry treatments, and effect of the treatment on ( b) spring WSI, ( d) summer WSI of the previous year and (f) summer-autumn WSI of the current year. P-values correspond to the treatment effect in the LMM (***: p < 0.001). Treatment effect on water stress integrals was tested with a linear mixed model (LMM) with treatment as fixed effect and year as a random effect.
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Table S1: Standardised estimates ± SE and associated p-values (*: p < 0.05, **: p < 0.01, ***: p < 0.001) of the linear model testing the effect of fruit biomass, rainfall exclusion treatment and their interaction on leaf nutrient concentration the following winter between 2007 and 2018.
Response variables Fixed effects
Total fruit biomass Treatment Fruit biomass x produced during year treatment n
[N] in leaves produced during year n 0.02 ± 0.02 0.10 ± 0.10 - 0.007 ± 0.05
[N] in leaves produced during year n-1 - 0.008 ± 0.02 0.02 ± 0.11 - 0.01 ± 0.06
[P] in leaves produced during year n - 0.0003 ± 0.002 0.006 ± 0.008 - 0.002 ± 0.004
[P] in leaves produced during year n-1 -0.0004 ± 0.001 - 0.004 ± 0.007 0.005 ± 0.004
Table S2: Population coefficient of variation (CVp) in % for male flower biomass, number of initiated fruits, number of mature fruits and mean fruit mass at the plot level between 2007 and 2019.
Treatment Control Dry
Block R1 R2 S Mean R1 R2 S Mean
Male flower biomass per kg of 49 52 71 57 52 52 46 50 aboveground biomass
Number of initiated fruits per kg of 50 51 25 42 46 65 37 49 aboveground biomass
Number of mature fruits per kg of 108 162 107 126 135 157 119 137 aboveground biomass
Mean fruit mass 36 23 31 30 35 39 32 35
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Figure S2: (a) Inter-annual variability of mean mature fruit mass (± SD) depending on treatment, and ( b) relationship between mean mature fruit mass and the number of mature fruits depending on treatment. Mature fruit mean mass was significantly and positively correlated to the number of mature fruits (Wald χ² = 11.4, p < 0.001), whereas treatment and the interaction between treatment and fruit number were not significant using a linear mixed model with block as a random effect, mean fruit mass as response variable, number of fruits, treatment and their interaction as fixed effects.
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Figure S3: Relationship between absolute values of summer-autumn WSI and fruit abortion rate depending on treatment observed in data from 2007 to 2019 ( a) and predicted by the GLMM described in Fig. 6b (b).
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Figure S4: (a) Relationship between the number of mature fruits, the number of initiated fruits and the fruit abortion rate between initiation and maturity in control and dry treatments between 2007 and 2019. ( b) GLMM standardised estimates ± SE and associated p-values (**: p < 0.01, ***: p < 0.001) of the number of initiated fruits, the fruit abortion rate and their interaction in explaining the number of mature fruits per kg of aboveground biomass in control plots for 2007- 2019 (negative binomial distribution with Block as a random effect). R²m = marginal R² and R²c = conditional R².
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Figure S5: Phenological phases of vegetative growth and reproductive cycle in Quercus ilex in 2018 (a), and intra-annual variation of mean normalized fruit growth ± SD and stem growth ± SD of trees in the control plot ( b), daily water potential in red and daily precipitations in blue ( c), and cumulated GPP in black and daily temperature in red ( d).
Methods
We tagged 20 to 30 initiated fruits per tree, located in the top upper canopy of 4 to 6 trees in the control treatment of the scaffold-equipped block. From July to November, fruit abortion and fruit size were monitored monthly. Fruit size was measured with an electronic calliper. During the early stage of fruit development, we measured the diameter of the fruit because the seed is invisible inside the acorn cup and the fruit has a spherical shape. As soon as the seed protrudes from the cup, we measured fruit length from the basis of the cup to the top of the seed excluding the remains of the style.
Stem circumference changes were continuously recorded using automatic band- dendrometers installed approximately 1.3 m above the ground (ELPA -98, University of Oulu, Finland). The daily maximum circumference value was used to calculate the tree daily basal area,
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Résumé
Le changement climatique exerce une pression croissante sur les forêts du monde entier, et ses impacts potentiels sur la fécondité des arbres sont encore mal compris. De nombreuses espèces d’arbres forestiers se reproduisent suivant des patrons de fructification intermittente et synchronisée entre les individus, dits de masting , dont les déterminants complexes doivent être identifiés pour comprendre l’effet du climat sur la fécondité des arbres forestiers. Cette thèse a comme objectif général d’approfondir notre connaissance des déterminants environnementaux de la fécondité des arbres forestiers et de leurs interactions, en se focalisant sur le chêne vert, une espèce très répandue en milieu méditerranéen. Elle a aussi pour objectif de déterminer comment la fécondité du chêne vert pourrait être affectée par le changement climatique, et de développer un modèle basé sur les processus capable de prédire cet impact. Ce travail de thèse a donc combiné des analyses de suivis à long terme dans une forêt de chêne vert soumise à une manipulation des précipitations, des expérimentations de manipulation des relations sources-puits pour l’allocation des ressources à la reproduction à l’échelle individuelle, et de la modélisation. J’ai montré que la reproducti on était fortement régulée par les conditions météorologiques durant le cycle reproducteur, et en particulier par la disponibilité en eau en été-automne, et dans une moindre mesure par la disponibilité en carbone et nutriments ainsi que par la compétition entre fruits et nouveaux bourgeons. Mes résultats expérimentaux montrent que l’allocation de ressources à la reproduction peut être modifiée en cas de limitation des ressources par une défoliation ; et que les fruits en développement ont un impact négatif sur les dynamiques des réserves en azote et en zinc dans les branches ainsi que sur la production des fleurs femelles l’année suivante. Les observations en forêt indiquent qu’une réduction continue des précipitations sur plusieurs années induit une diminution du nombre de fruits produits, indépendamment des effets de la variabilité interannuelle et sans que les arbres semblent être capables de s’y acclimater. Enfin, j’ai développé, au sein du modèle de fonctionnement de l’arbre PHENOFIT, un modèle de fécond ité se basant sur les hypothèses et résultats issus de mes travaux et de la littérature. Mots-clés : allocation de ressources, changement climatique, floraison, modélisation, sécheresse, Quercus ilex
Abstract
PhD title: Drivers of holm oak fecundity Climate change is placing increasing pressure on forests around the world, and its potential impacts on the reproductive capacity of individuals are yet poorly understood. Many forest tree species reproduce following so-called masting patterns, in which seed production is intermittent and synchronized among individuals, whose drivers need to be identified in order to better understand the impact of climate on the fecundity of forest trees. The general objective of this thesis is to deepen our knowledge of the environmental drivers of the fecundity of forest trees and their interactions, by focusing on the holm oak, a widespread species in the Mediterranean basin. It also aimed to determine how fecundity could be impacted by climate change, and to develop a process-based model capable of predicting this impact. This thesis work combines analyses of long-term monitoring in a holm oak forest exposed to a rainfall exclusion experiment, experimental manipulations of source-sink interactions involved in the resource allocation to reproduction, and modelling. I have shown that reproduction was strongly regulated by weather conditions during the reproductive cycle and in particular by the water availability in summer-autumn, and to a lesser extent by the availability of carbon and nutrients, as well as by competition between fruits and new buds. My experimental results show that the allocation of resources to reproduction could be modified in case of resource limitation due to defoliation; and that developing fruits have a negative impact on the dynamics of nitrogen and zinc reserves in branches, as well as on the production of female flowers the following year. Long-term observations show that multi-year rainfall reduction induce a decrease in the number of fruits produced which is additional to the interannual variability and that tree reproduction does not acclimate to drought. Finally, within the PHENOFIT model, I have developed a fecundity model based on the assumptions and results from my work and from the literature. Keywords: resource allocation, climate change, flowering, modelling, drought, Quercus ilex
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