PREDICTION DES DOMMAGES FOLIAIRES CAUSES PAR LES HERBIVORES INVERTEBRES DANS UNE PRAIRIE EXPERIMENTALE A PARTIR DES TRAITS DES PLANTES
par
Jessy Loranger
memoire presente au Departement de biologie en vue de l'obtention du grade de maitre es sciences (M.Sc.)
FACULTE DES SCIENCES UNIVERSITE DE SHERBROOKE
Sherbrooke, Quebec, Canada, juillet 2012 Library and Archives Bibliotheque et Canada Archives Canada
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le jury a accepte le memoire de Monsieur Jessy Loranger
dans sa version finale.
Membres dujury
Professeur John-William Shipley Directeur de recherche Departement de biologie
Professetire Fanie Pelletier Membre Departement de biologie
Professeur Robert L. Bradley President rapporteur Departement de biologie SOMMAIRE
Les herbivores invertebres sont presents dans presque tous les habitats de la planete et peuvent grandement affecter la performance des plantes en nature. Que ce soit en communautes naturelles ou artificielles, une grande variation des taux d'herbivorie entre differentes especes de plante peut etre observee. Ceci s'explique par le fait que les caracteristiques fonctionnelles des plantes, aussi appelees «traits», affectent les preferences des herbivores. Une espece de plante, de par ces traits physiologiques, morphologiques ou phenologiques, peut done decourager et/ou eviter l'herbivorie. La comprehension des relations entre les herbivores invertebres et les traits des plantes est done indispensable a la comprehension de 1'assemblage des communautes herbivores- plantes. Cependant, les connaissances sur les liens entre les differents traits des plantes et la preference des herbivores sont diffuses et incompletes. Ce memoire vise done a quantifier et caracteriser les effets de plusieurs traits fonctionnels de plantes herbacees sur la quantite de dommage foliaire faite par des herbivores invertebres et a comparer ces effets entre monocultures et polycultures. Pour ce faire, j'ai participe au projet du Jena Experiment, une prairie experimentale situee en Allemagne visant a etudier la biodiversite. Cette prairie est exposee aux herbivores invertebres naturels se trouvant sur le site de l'etude. Pour realiser mon projet, les degres d'herbivorie causes par les invertebres ainsi que les valeurs de plusieurs traits fonctionnels des plantes ont ete mesures pour chacune de 51 especes de plante se trouvant sur le site d'etude.
Travaillant d'abord avec des monocultures, sept traits sur 42 mesures ont ete selectionnes dans une regression multiple en tant que predicteurs importants de l'herbivorie. Le modele est robuste et explique 63% de la variation en dommage subi par les especes a l'etude. Parmi ces sept traits, deux sont physiologiques (concentration de lignine et d'azote dans les feuilles), deux sont morphologiques (architecture des racines
ii et erection de la tige), un est phenologique (duree de vie du feuillage) et deux sont relics aux herbivores (nombre d'especes de coleoptere et d'hemiptere pouvant potentiellement predater les plantes). Dans une seconde etape, a partir de l'herbivorie mesuree en monoculture et des sept traits selectionnes dans le premier volet, trois modeles ont ete developpes pour predire l'herbivorie dans des communautes formant un gradient de biodiversite de une a 60 especes de plantes. Pour les trois differents modeles, le pouvoir de prediction etait faible; de 6 a 32% de variance en dommage foliaire expliquee. De plus, la deviation entre valeurs observees et valeurs predites de l'herbivorie augmentait significativement avec 1'augmentation du niveau de biodiversite dans les communautes.
Les resultats de ce memoire suggerent que des patrons complexes de correlation entre les differents traits des plantes existent et qu'il est done necessaire de travailler avec le plus de traits possibles pour identifier ceux qui controlent vraiment l'herbivorie. De plus, des interactions entre les differentes especes de plante d'une communaute semblent affecter de fagon importante, directement ou pas, les dommages foliaires infliges par les herbivores invertebres. Les conclusions de ce memoire remettent en question 1'importance relative des traits agreges pour predire des processus ecologiques a facteur biotique tel que l'herbivorie et soulignent la complexite des relations entre deux niveaux trophiques.
Mots-cles: herbivorie, trait fonctionnel, plante, insecte, monoculture, polyculture, modelisation, Jena Experiment
iii REMERCIEMENTS
Je tiens d'abord a remercier mon directeur de projet, Bill Shipley, pour l'aide, les sages conseils et le soutien qu'il m'a donnes tout au long de ma maitrise. H m'a toujours appuye et aide dans mes demarches de cooperation avec FUniversite de Jena en Allemagne et sans sa grande flexibility je n'aurais pas pu realiser le projet que j'ai fait. La meme chose est vraie pour mon superviseur a Jena, Wolfgang Weisser, qui a rendu possible cette collaboration.
Merci h Sebastian Meyer qui m'a accueilli a Jena, guide et aussi supervise tout au long de mon travail en Allemagne. J'ose esperer que mes talents en tant que scientifique ce sont beaucoup ameliores grace a lui.
Merci a tous ceux qui m'ont aide a Jena pour mon experience et pour faire mes analyses: Hannah Kern, Sylvia Creutzburg, Gerlinde Kratsch, Winfried Voigt, Christiane Roscher, Michael Rzanny, Enrica de Luca, Anne Ebeling et d'innombrables travailleurs qui se sont occupes du site experimental.
Je tiens aussi a remercier mes parents, Robert et Francine, pour leur soutien moral et les encouragements qu'ils m'ont apportes durant toute la duree de ma maitrise. Un grand merci bien particulier a ma fiancee, Hannah Kern, pour ses conseils, son soutien inepuisable et son amour omnipresent qui m'ont aide a passer au travers des moments les plus difficiles.
Merci au CRSNG, au FQRNT et a la Deutsche Forschungsgemeinschaft pour leur soutien financier.
iv TABLE DES MATIERES
SOMMAIRE ii
REMERCIEMENTS iv
TABLE DES MATIERES v
LISTE DES ABBREVIATIONS viii
LISTE DES TABLEAUX ix
LISTE DES FIGURES xii
CHAPITRE I - Introduction 1 1.1- Herbivores vertebres versus invertebres 2 1.2- Impacts de l'herbivorie sur les plantes 3 1.3- Influence des plantes sur les herbivores invertebres. 4 1.4- Traits des plantes et herbivorie 7 1.5- Le Jena Expermient 10
CHAPITRE II - Predicting invertebrate herbivory from plant traits: evidence from 51 grassland species in experimental monocultures 12 2.1- Description de l'article et contribution 13 2.2- Abstract 14 2.3- Introduction 15 2.4- Methods 17 2.4.1-Study site 17
v 2.4.2- Herbivory and biomass measurements 18 2.4.3- Plant traits 19 2.4.3.1- Trait data extracted from published literature and database 19 2.4.3.2- Trait measurements in the Jena Experiment 19 2.4.4- Statistical analyses 21 2.4.4.1- Data preparation 21 2.4.4.2- Data analyses 22 2.5-Results 23 2.5.1- Correlation and exclusion of traits 24 2.5.2- Predicting herbivore damage 25 2.6- Discussion 29 2.6.1-Physiological traits 30 2.6.2-Morphological traits 31 2.6.3- Phenological traits 32 2.6.4- Herbivore-related traits 33 2.6.5- Secondary metabolites 34 2.6.6- Patterns of correlation 35 2.7- Conclusion 35 2.8- Acknowledgements 36
CHAPITRE HI - Predicting invertebrate herbivory from plant traits: polycultures show strong non-additive effects 38 3.1- Description de l'article et contribution 39 3.2- Abstract 40 3.3- Introduction 41 3.4- Methods 44 3.4.1-Study site 44 3.4.2- Biomass and herbivory measurements 45
vi 3.4.3- Predicting herbivory in monocultures 46 3.4.4- Additive vs. non-additive scaling 47 3.4.5- Testing the additive scaling hypothesis 48 3.5- Results 50 3.6- Discussion 56 3.7- Conclusion 60 3.8- Acknowledgements 60
CHAPITRE IV - Conclusion 62 4.1- Necessite de travailler avec un grand nombre de traits 63 4.2- Les interactions entre differentes especes de plantes affectent l'herbivorie 65
ANNEXES 69 Annexe A 69 Annexe B 71 Annexe C 89 Annexe D 91 Annexe E 92 Annexe F 96 Annexe G..... '. 97 Annexed 98 Annexe G2 99
BDBLIOGRAPHIE 100
vii LISTE DES ABBREVIATIONS
Abreviation Definition Page*
SLA Specific leaf area 19 NDF Neutral detergent fiber 20 ADF Acid detergent fiber 20 ADL Acid detergent lignin 20 RSE Residual standard error 23 RF Random forest 26 HD Herbivore damage 28
* : Page de premiere apparition dans le texte
viii LISTE DES TABLEAUX
Tableau tire du chapitre II
Tableau 2.1 Plant functional traits selected by a random forest (RF) approach to be of importance to predict leaf standing herbivore damage of 51 different species monocultures at the field site of the Jena Experiment (Germany). Traits that remained in the final model after the stepwise backward selection are given in bold 26
Tableaux tires du chapitre III
Tableau 3.1 Statistics for the models presented in Figure 3.1 on the relationship between (1) herbivory predicted from three different additive models and herbivory measured in plant communities of differing diversity and (2) the deviance of these models and diversity of the plant communities 51
Tableau 3.2 Multiple regression model of the loge-transformed herbivory measured in 70 plant communities at the field site of the Jena Experiment (Germany) as determined by seven community- weighted traits that are important to predict species specific herbivory in monocultures 54
Tableaux tires des annexes
Annexe A Species pool of the Jena Experiment with an indication whether the particular species was included in the analysis.
ix Species in bold were excluded from the analysis. For details about exclusion refer to the manuscript 69
Annexe B List of all measured and collected plant traits from published literature, the database of the Jena Experiment and international databases to predict leaf standing herbivore damage observed in monocultures at the field site of the Jena Experiment (Germany). The traits are divided into four types of trait (the functional groups not being considered as traits) and the specific sources of each trait are stipulated as well as a detailed description of the determination of each trait 71
Annexe C List of the traits for which some values necessitated imputation before those variables could be used to predict leaf standing herbivore damage observed in monocultures at the field site of the Jena Experiment (Germany) 89
Annexe E Description of sources and methods of collection or measurement for the seven traits selected in Loranger et al. (in press) that were used to calculate community-weighted trait values. Those community-weighted traits were used to predict the leaf standing herbivore damage measured in communities at the field site of the Jena Experiment (Germany). The traits are divided into four trait groups 92
Annexe F Correlation matrix of different community properties of the communities in the Jena Experiment 96
Annexe G2 Statistics for the models presented in Annex G1 on the
x relationship between community species richness (log(sowndiv)) and the log ratio of non-additive and additive effects contributing to measured community herbivory (logratio) as predicted from three different additive models
xi LISTE DES FIGURES
Figure tiree du chapitre I
Figure 1.1 Photo aerienne du Jena Experiment 11
Figure tiree du chapitre II
Figure 2.1 Correlation (r = 0.83, p-value = <0.001) between leaf herbivore damage in monocultures (HD) measured as proportion of the damaged leaf area at the field site of the Jena Experiment (Germany) and damage predicted by a model based on seven traits. For the model see Equation 2.3 and
Table 2.2. Values of herbivore damage are loge-transformed.... 28
Figure tiree du chapitre m
Figure 3.1 Herbivory (standing herbivore damage for whole plant communities in percent) as predicted from different additive models regressed against measured herbivory at the field site of the Jena Experiment (Germany) and the relationship between deviance of the models (difference between predicted and measured herbivory) and species richness of the plant communities; note the logarithmic axis for herbivory and species richness. Dashed lines give expected relationships (a slope of one between predicted and observed herbivory in the panels on the left and a constant average deviance of zero in the panels on the right). The solid lines are best fit lines of
xii significant linear models given in detail in table 3.1. In the upper row of panels (A, B), predictions are based on herbivory levels of the species in monocultures (Equation 3.2). In the middle row (C, D), predictions are from a model based on seven plant-functional traits that was developed to predict species specific herbivory in plant monocultures and which was used with community-weighted traits for the different plant communities (Equations 3.1 and 3.4). In the lower row (E, F), predictions are based on a trait model with relaxed assumptions in which new partial slopes were estimated for the same seven community-weighted traits directly for the herbivory measured in communities (the resulting model is given in table 3.2). For details refer to the text 53
Figure tiree des annexes
Annexe D Graph of the results from the Random Forest (RF) analyses to select which plant traits are of importance to predict the invertebrate herbivore damage in the monocultures of the Jena Experiment (Germany). Each point is a trait and the x-axis represents the order of the traits according to their importance score value which is represented by the y-axis. The higher a trait's score is the more important is the trait to predict herbivore damage. All traits (13) to the left of the line were selected as being of importance; all traits to the right were discarded. The selection was based on the curve illustrated here, looking at the decrease in importance between traits. The last relatively large drop in importance was after the 12th trait,
xiii thus the first 12 traits should have been selected. However, the five following traits were also tested with a multiple regression to confirm that traits could be excluded from this point. Only the 13th trait proved to be of importance and thus was also kept for the next step of the analyses 91
Annexe G1 The log ratio of the absolute contribution of non-additive and additive effects to measured herbivory in plant communities of differing diversity. Estimates are based on species specific herbivory measured in monocultures (A), a trait based model previously employed in monocultures (B) and a newly estimated trait based model (C). For details on these three models see the main manuscript. Plots with a positive non- aditive effects are given with closed circles, plots with negative non-adive effects in open circles. Bars are the mean within a diversity level. Everything below the dashed line at zero means that the non-additive effects are smaller than the additive effects. The solid line shows the trend with diversity.. 98
xiv CHAPITRE I
INTRODUCTION
i INTRODUCTION
Un facteur biotique cle definissant un ecosysteme est le reseau trophique dudit ecosysteme. Ainsi, qualifier, ou encore mieux quantifier, les interactions entre les differents niveaux trophiques permets de mieux comprendre et de definir les variables biotiques d'un ecosysteme et done de mieux comprendre le fonctionnement de celui-ci. Comme les plantes forment la base de presque toutes les chaines alimentaires de la planete, l'herbivorie est probablement la forme la plus commune et repandue d'interaction inter-trophique. L'etude des partis impliques dans cet important processus est done essentielle pour notre comprehension des ecosystemes en general.
1.1- Herbivores vertebres versus invertebres
L'herbivorie causee par les herbivores vertebres (en particulier par les grands mammiferes) est souvent plus evidente et visible que celle causee par les invertebres. Ceci a pour effet que les impacts des vertebres sur les plantes se voient souvent accorder une haute importance et ce, au detriment des impacts de la part des invertebres (Weisser et Siemann, 2004). De fait, les effets de l'herbivorie causee par des mammiferes sur les communautes vegetales ont ete bien Studies (Whigham et Chapa, 1999 ; Oba et al., 2001 ; Adler et al., 2004 ; Diaz et al., 2007 ; Mason et al., 2010) et souvent h plus grande echelle que les etudes sur les effets de l'herbivorie causee par des invertebres. Cependant, les impacts des herbivores invertebres sur les communautes vegetales peuvent etre d'aussi grande envergure que ceux causes par les vertebres (Gibson et al., 1987 ; Weisser et Siemann, 2004 ; Allan et Crawley, 2011). De plus, il semble que les facteurs influengant l'herbivorie different largement entre vertebres et invertebres
2 (Tanentzap et al., 2011). Dans un tel contexte, des etudes plus poussees sur l'herbivorie causae par des invertebres sont essentielles a la comprehension plus generate du processus inter-trophique de l'herbivorie.
1.2- Impacts de l'herbivorie sur les plantes
L'herbivorie causae par les invertebres (ci-apres simplement « herbivorie ») est une des principales pressions selectives auxquelles les plantes doivent faire face en nature. Plus specifiquement, l'herbivorie peut affecter la physiologie et le succes reproducteur des plantes (Karban et Strauss, 1993 ; Hulme, 1996a ; Bigger et Marvier, 1998 ; del-Val et Crawley, 2004), influengant ainsi leur performance (Bigger et Marvier, 1998) et leur evolution (Holeski et al., 2010 et references a l'interieur). L'herbivorie peut done aussi affecter la composition et la succession des communautes vegetales (Brown, 1985 ; Gibson et al., 1987 ; Brown and Gange, 1992) notamment en diminuant la richesse specifique de la communaute (Stein et al., 2010 ; Allan et Crawley, 2011).
Les effets de l'herbivorie, transmis par divers mecanismes, peuvent affecter non seulement les niveaux populationnel et communautaire, mais aussi le niveau ecosystemique (Huntly, 1991), d'ou l'importance de comprendre les relations entre les herbivores invertebres et les plantes. Par exemple, la presence d'herbivorie peut influencer le cyclage des nutriments (Seastedt et Crossley, 1984 ; Belovsky et Slade, 2000 ; Schadler et al., 2003) et la productivity vegetale (McNaughton, 1983 ; Belovsky et Slade, 2000). II est cependant a noter que la direction des impacts de l'herbivorie, par exemple 1'augmentation versus diminution de la diversity d'une communaute, depend grandement du contexte donne (Huntly, 1991).
3 Effectivement, les impacts de I'herbivorie sur les communautes vegetales varient en fonction de la composition (Brenes-Arguedas et al., 2009) et de la richesse specifique des communautes (Scherber et al., 2010b ; Stein et al., 2010). Ces quelques exemples represented des evidences qu'il semble bel et bien y avoir des interactions entre differentes especes de plante d'une communaute et que ces interactions peuvent modifier les effets de I'herbivorie dans cette communaute. Les relations herbivores-plantes sont done tres complexes et comprennent de nombreuses interactions, autant intra qu'inter- trophiques, qui semblent y jouer un role important. Ainsi, etant donne les impacts que peuvent avoir les herbivores invertebres a l'echelle communautaire et meme ecosystemique, il serait important de savoir ce qui influence les herbivores invertebres et leur choix. En d'autres termes, quels facteurs determineront les preferences des herbivores invertebres et, eventuellement, leurs impacts? Cette question est d'autant plus complexe - mais aussi d'autant plus interessante - qu'il a ete ddmontre que differentes communautes sont affectees differemment par les herbivores invertebres.
1.3- Influence des plantes sur les herbivores invertebres
Au cours des quatre dernieres decennies, plusieurs hypotheses ont ete formulees mettant a l'avant la biodiversite vegetale comme principal facteur influen?ant les taux d'herbivorie. Ceci inclut premierement les travaux de Root (1973), encore trfes influents aujourd'hui et qui stipulent que les communautes faibles en diversity (monocultures) subiront plus de dommages dus aux herbivores invertebres specialistes que les polycultures. Deux hypotheses sont fournies pour expliquer cette affirmation. Premierement, il est suppose qu'une communaute formee d'une haute densite d'une espece de plante particuliere attirera necessairement un plus grand nombre d'herbivores specialistes pour cette plante. Deuxiemement, il semble qu'une communaute plus diverse puisse offrir un habitat plus propice aux ennemis naturels des herbivores,
4 diminuant ainsi l'impact que ceux-ci peuvent produire. Ces hypotheses ont d'ailleurs ete confirmees par plusieurs etudes (Andow, 1991 ; Coll et Bottrell, 1994 ; Schellhorn et Sork, 1997). Toutefois, ces etudes utilisent des niveaux de diversite tres faibles, allant meme a comparer des monocultures avec des cultures a deux especes (Coll et Bottrell, 1994) et tous se concentrent uniquement sur les insectes specialistes se nourrissant uniquement d'un h6te specifique. Cet effet de protection contre les herbivores lorsqu'une plante « non hote » est intercalee avec la plante hote est bien connu dans le domaine de 1'agriculture, ou les cultures sont souvent menacees par un herbivore se nourrissant seulement de l'espece cultivee. De fait, selon les travaux de Finch et Collier (2012), semer une autre espece de plante dans un champ cultive reduit significativement l'intensite de l'herbivorie sur l'espece dite « de valeur ». De plus, cet effet atteint son maximum lorsque l'espece « non economique » est semee en pourtour d'une parcelle de champ cultive. Leur conclusion est que la reduction de l'herbivorie est causee par un phenomene purement physique et que n'importe quelle espece de plante serait en mesure de produire cet effet. Leur explication est que les insectes generalistes se distribueront entre les deux esp&ces de plantes, reduisant ainsi l'intensite d'herbivorie sur l'espece qui etait auparavant en monoculture. Pour les insectes specialistes, il semble qu'a force de rencontrer plus souvent un hote non convenable, ils quitteront le champ plus rapidement que s'ils se trouvaient dans une monoculture. Ces hypotheses s'accordent bien aux observations de Coll et Bottrell (1994), qui ont trouve que seul la colonisation par les insectes etait affectee a la baisse lorsqu'une plante non hote etait semee, pas la performance des herbivores.
Les resultats qui decoulent de travaux effectues a des niveaux bas de biodiversite sont done difficiles a extrapoler a des communautes possedant de plus hauts niveaux de biodiversite, d'autant plus qu'il existe d'autres hypotheses predisant une augmentation du taux d'herbivorie de la part de specialistes avec 1'augmentation de la biodiversite d'une communaute. Le raisonnement derriere cette seconde supposition est que dans une
5 communaute ou la densite d'une espece favorable est faible, l'insecte specialise pour manger cette espece n'aura pas d'autres choix que de devorer les quelques individus de sa plante hote qui se trouvent dans la communaute (Otway et al., 2005). Aussi, la plupart des hypotheses concernant les herbivores generalistes predisent un plus haut taux d'herbivorie avec une plus grande biodiversite (Bernays et Lee, 1988 ; Bernays et Bright, 1993). L'idee est que les herbivores generalistes seraient favorises par une diete diverse qui leur permettrait d'avoir une alimentation equilibree et complete. En realite, des relations positives et negatives entre taux d'herbivorie et biodiversity ont toutes deux ete souvent observees, y compris dans la literature plus recente (Unsicker et al., 2006 ; Schuldt et al., 2010). Des travaux etudiant specifiquement plusieurs especes ou groupes d'herbivores invertebres ont meme demontre que les deux directions sont possibles et dependent du contexte (Kunin, 1999 ; Koricheva et al., 2000). De fait, plusieurs mecanismes jouent un role sur comment la biodiversity affectera les herbivores invertebres et ces mecanismes semblent changer avec la specificite des differents herbivores, mais aussi avec plusieurs autres de leurs caracteristiques (Kunin, 1999). Ceci a pour resultat qu'aucun des mecanismes avec lesquels la biodiversite semble affecter les herbivores n'est general, ce qui indique que la richesse specifique d'une communaute ne semble pas etre un facteur d'influence constant pour les herbivores invertebres.
De nombreuses etudes travaillant avec un gradient de biodiversite ont conclu que le facteur principal influen^ant la performance des herbivores invertebres et les taux d'herbivorie n'etait pas la richesse specifique des communautes, mais plutot leur composition, c'est-a-dire la presence/absence d'especes de plante appartenant a des groupes fonctionnels particuliers (Koricheva et al., 2000 ; Scherber et al., 2006b ; Unsicker et al., 2006 ; Specht et al., 2008). Un groupe fonctionnel de plantes est un ensemble arbitraire d'especes qui, possedant des caracteristiques fonctionnelles similaires, remplissent des fonctions similaires pour la communaute et/ou l'ecosysteme. La masse des graines et la concentration des feuilles en azote sont deux exemples de ces
6 caracteristiques fonctionnelles, que l'on appelle aussi des traits. Ainsi, c'est principalement la presence ou l'absence de certaines plantes, possedant un certain ensemble de traits, qui influencera l'intensite d'herbivorie infligee a une communaute vegetale ainsi que la resistance a 1'herbivorie de cette communaute (Scherber et al., 2010b). Ces conclusions s'expliquent par le fait que la capacite des plantes de resister ou eviter 1'herbivorie est transmise par leurs traits. Les differences de susceptibilite des plantes a 1'herbivorie et de preference des herbivores invertebres sont done attribuables, au moins en partie, aux differentes valeurs de trait possedees par les differentes especes de plante. Dans un tel contexte, identifier les traits et leur importance relative pour predire le degre d'herbivorie subi par les plantes releve de la plus haute importance si nous voulons comprendre les relations entre plantes et herbivores invertebres.
1.4- Traits des plantes et herbivorie
Pourquoi les plantes peuvent-elles influencer l'intensite de 1'herbivorie qui leur est infligee a partir de leurs traits? La raison est que, de par la valeur de certains de leurs traits, les plantes peuvent affecter la capacite des herbivores invertebres de les trouver, les selectionner et finalement de les consommer. Ces traits incluent plusieurs caracteristiques phenologiques qui determineront si la presence des parties de la plante qui sont consommees et des herbivores invertebres qui pourraient potentiellement les consommer sera synchrone ou asynchrone (Murali et Sukumar, 1993). De plus, plusieurs plantes emettent un cocktail volatile de composes secondaires dont les insectes volants specialistes peuvent se servir pour detecter la presence d'un hote potentiel (Finch et Collier, 2012). Une fois l'insecte pose sur une plante, des composes secondaires non- volatiles peuvent le stimuler ou le dissuader a se nourrir (Finch et Collier, 2012). En fait, de nombreux traits de resistance a 1'herbivorie sont connus, autant morphologiques (epines, trichomes, pubescence) que physiologiques (principalement des composes
7 secondaires, mais aussi des composes primaires tels que la lignine). Ces traits de resistance peuvent done en principe dissuader certains herbivores invertebres de se nourrir sur une plante qui les possede. II faut cependant noter que les relations entre traits et susceptibilite a l'herbivorie sont susceptibles de changer pour differentes especes d'herbivore (Kunin, 1999 ; Tanentzap et al., 2011). Ceci est surtout vrai pour les composes secondaires des plantes, ou un meme compose peut a la fois etre dissuasif ou stimulant, dependamment de l'espece d'herbivore concernee (Carroll et Hoffman, 1980).
Depuis le debut des annees 1980, une tr&s grande quantite de travaux se sont consacres a l'etude des correlations entre les traits de certaines especes de plante et l'herbivorie causee par certaines especes d'herbivore invertebre (voir chapitre II). L'avantage de travailler d'un point de vue fonctionnel (traits) plutot que d'un point de vue taxonomique (especes de plante et composition d'une communaute), est le plus haut potentiel de generalisation. Effectivement, pour developper un modele a partir d'especes, il faut proceder de maniere analytique, e'est-a-dire analyser independamment chaque espece pour ensuite pouvoir les comparer entre elles, souvent qualitativement. Toutefois, ceci devient tres difficile au niveau pratique lorsque le nombre d'especes etudiees est grand. Travailler avec des traits permet done de simplifier un module en diminuant le nombre de ses composants et d'effectuer des analyses statistiques plus poussees et plus precises, permettant ensuite de comparer quantitativement differentes especes a partir de leurs valeurs des traits inclus dans le modele (Keddy, 1999).
La notion de «traits agreges » permet meme d'aller encore un peu plus loin dans cette direction. Un trait agrege est la valeur moyenne d'un trait donne pour une communaute composee d'une ou plusieurs especes de plantes et il est calcule a partir des valeurs de ce trait pour toutes les especes d'une communaute et de leurs abondances relatives (plus de details sont donnes dans le chapitre III). Cette notion permet done d'etudier directement
8 les relations entre les traits et les processus ecologiques au niveau communautaire et ce, meme si une communaute contient un tres grand nombre d'especes. Par exemple, il serait possible de correler le degre total d'herbivorie au niveau d'une communaute vegetale avec ses valeurs de trait moyens, ce qui permettrait de voir si des relations trait- herbivorie decouvertes au niveau d'especes individuelles sont toujours aussi fortes au niveau communautaire. II serait done extremement interessant de tester si cette notion pourrait nous aider a mieux comprendre l'herbivorie au niveau de communautes vegetales entieres, ce qui n'a jamais ete tente auparavant.
Ainsi, l'approche fonctionnelle (traits) confere des avantages certains comparee a l'approche taxonomique. En lien avec cette constatation, plusieurs traits de differents types (physiologique, morphologique ou phenologique) ont ete proposes au cours des 30 dernieres annees comme etant des predicteurs importants du degre d'herbivorie subi par les plantes. Certains traits, tels que le ratio carbone : phosphore et la concentration en phenols des feuilles, ont meme ete suggeres comme etant des facteurs «cles» influengant l'herbivorie en general (Kurokawa et al., 2010). Cependant, la grande majorite des travaux traitant des relations trait-herbivorie n'implique que peu d'especes de plante, peu d'especes d'herbivore et/ou peu de traits, alors qu'il est necessaire de travailler avec le plus d'especes et de traits possibles pour identifier des patrons de relation g6neraux entre herbivorie et traits. De plus, la plupart des etudes qui incluent plusieurs traits ou types de trait les traitent de fagon independantes, ne considerant done pas les correlations complexes qui peuvent survenir entre differents traits. Etant donne ce reseau de correlations complexes entre les traits, il est important d'appliquer des analyses multivariees lorsque nous travaillons avec plusieurs traits a la fois. Dans ce memoire, je tenterai d'identifier quels traits des plantes affectent le plus les degres d'herbivorie subis par celles-ci. Afin d'aller plus loin que les etudes precedentes a ce sujet, j'ai participe au Jena Experiment, un projet qui m'a permis de travailler avec un tres grand nombre 1) d'especes de plante, 2) d'especes d'herbivore et 3) de traits.
9 1.5- Le Jena Expermient
Le Jena Experiment (Figure 1.1) est une prairie experimentale con?ue pour etudier 1'importance fonctionnelle de la biodiversite. Ce projet fut lance en 2002 a Jena, en Allemagne centrale. L'experience comprend 80 parcelles de 20 metres par 20 metres et chacune a ete semee avec une combinaison particuliere d'especes de plante herbacee. L'ensemble total d'especes consiste en 60 especes divisees en quatre groupes fonctionnels : graminees, legumineuses, petites et grandes herbes. Ainsi, le long des 80 parcelles, il y a un gradient de biodiversite de une a 60 esp&ces incluant de un a quatre groupes fonctionnels. De plus, il y a une petite monoculture pour chacune des 60 espkces de l'experience (1 m2). Toutes les parcelles sont exposees aux herbivores invertebres naturellement presents a ce site. Le Jena Experiment est la plus grande experience au monde portant sur la biodiversite vegetale et sa disposition experimentale particuliere permet de differencier et quantifier les effets de differents aspects de la biodiversite tels que le nombre d'especes, le nombre de groupes fonctionnels ou la presence d'especes « cles » (Roscher et al., 2004). Le nombre eleve d'especes, la longueur du gradient de biodiversity et la distribution equilibree des especes dans les differents groupes fonctionnels sont autant d'ameliorations importantes face aux autres experiences existantes sur la biodiversite (Roscher et ai, 2004). Au fil des ans, de nombreuses equipes de recherche provenant de plusieurs pays ont etudie les effets de la biodiversite dans divers contextes, tels que la productivity des communautes (Marquard et ai, 2009a ; Marquard et al., 2009b ; Allan et al., 2011), la resistance k l'invasion des communautes (Mwangi et al., 2007), l'etablissement des populations (Lorentzen et al., 2008), divers processus souterrains (Milcu et al., 2008 ; Milcu et al., 2010 ; Eisenhauer et al., 2010 ; Butenschoen et al., 2011), le stockage de carbone (Steinbeiss et al., 2008), le cyclage des nutriments (Oelmann et al., 2011), la performance individuelle des especes de plante en communaute (Scherling et al., 2010 ; Schmidtke et al., 2010),
10 Fherbivorie (Scherber et al., 2006a ; Scherber et al., 2006b ; Scherber et al., 2010b ; Specht et al., 2008) et plusieurs autres. Ainsi, l'envergure et le potentiel de recherche du Jena Experiment sont enormes et uniques au monde.
Figure 1.1. Photo aerienne du Jena Experiment.
Dans ce memoire, je tenterai d'abord dans le chapitre II d'identifier quels traits sont vraiment d'importance pour predire les degres d'herbivorie causes par des invertebres dans les monocultures du Jena Experiment. Ensuite, dans le chapitre III, je tenterai de decouvrir si les relations traits-herbivorie trouvees en monocultures peuvent s'appliquer dans les diff6rentes mixtures du Jena Experiment et a quel point les interactions entre les differentes especes de plante de ces mixtures affectent les degres d'herbivorie.
11 CHAPITREII
PREDICTING INVERTEBRATE HERBIVORY FROM PLANT TRAITS: EVIDENCE FROM 51 GRASSLAND SPECIES IN EXPERIMENTAL MONOCULTURES
12 PREDICTING INVERTEBRATE HERBIVORY FROM PLANT TRAITS: EVIDENCE FROM 51 GRASSLAND SPECIES IN EXPERIMENTAL MONOCULTURES
par Jessy Loranger, Sebastian T. Meyer, Bill Shipley, Jens Kattge, Hannah Kern, Christiane Roscher et Wolfgang W. Weisser Ecology (sous presse)
2.1- Description de Particle et contribution
Les plantes semblent pouvoir influencer leurs taux d'herbivorie causes par des invertebres d'apres leurs valeurs de traits fonctionnels. Par contre, pour des raisons surtout methodologiques, les etudes qui se consacrent aux relations entre les traits des plantes et l'herbivorie qu'elles subissent ne permettent pas des predictions precises et/ou generalisables des taux d'herbivorie a partir des traits. Ainsi, la notion selon laquelle certains traits seraient generalement responsables de la susceptibilite des plantes a l'herbivorie est encore incertaine et tres peu etudiee. Identifier de tels traits est encore plus difficile avec le niveau de nos connaissances actuelles sur le sujet. Dans cet article, nous avons done rassemble un grand nombre de traits de plusieurs types (morphologique, physiologique, phenologique et relie aux herbivores) pour plusieurs espfeces de plantes differentes. En utilisant des methodes d'analyses multivariees {random forest, regression multiple), nous avons tente d'identifier les quelques traits parmi ceux que nous avons rassembles qui sont d'importance particuliere pour predire
13 les dommages foliaires subis par les plantes en monocultures et causes par les herbivores invertebres environnants.
Ma contribution pour cet article fut tres importante. J'ai recolte les donnees sur le terrain avec l'aide de Hannah Kern et Sebastian Meyer. J'ai collecte les donnees provenant de la literature publiee, mais plusieurs traits m'ont ete fournis par Christiane Roscher, qui a aussi fourni des commentaires precieux a la redaction, et par plusieurs participants du TRY initiative, une base de donnees internationale sur les traits des plantes et dont Jens Kattge est le responsable. J'ai fait les analyses de laboratoire ainsi que les analyses statistiques. J'ai ecrit plusieurs versions preliminaires de l'article qui ont ete grandement ameliorees par Sebastian Meyer, Bill Shipley et Wolfgang Weisser.
2.2- Abstract
Invertebrate herbivores can impact plant performance and plant communities. Conversely, plants can affect the ability of herbivores to find, choose and consume them through their functional traits. While single plant traits have been related to rates of herbivory, most often involving single herbivore - plant pairs, much less is known about which suite of plant traits is important for determining herbivory for a pool of plant species interacting with a natural herbivore community. In this study we measured aboveground herbivore damage on 51 herbaceous species growing in monocultures of a grassland biodiversity experiment and collected 42 different plant traits representing four trait groups: physiological, morphological, phenological and herbivore-related. Using the method of random forests and multiple regression, we identified seven traits that are important predictors of herbivore damage (leaf nitrogen and lignin concentration, number of coleopteran and hemipteran herbivores potentially feeding on
14 the plants, leaf lifespan, stem growth form and root architecture); leaf nitrogen and lignin concentraton were the two most important predictors. The final model accounted for 63% of the variation in herbivore damage. Traits from all four trait groups were selected showing that a variety of plant characteristics are statistically important when assessing folivory, including root traits. Our results emphasize that it is necessary to use a multivariate approach for identifying traits affecting complex ecological processes such as herbivory.
Key words: Jena Experiment, physiology, morphology, phenology, herbivore-related traits, modeling, prediction, invertebrate herbivory, plant trait, monoculture
2.3- Introduction
Herbivory is a major selective pressure affecting plant physiology and plant fitness (Karban and Strauss 1993, Hulme 1996a, Bigger and Marvier 1998), plant community composition and succession (Brown 1985, Gibson et al. 1987, Brown and Gange 1992, Hulme 1996b, del-Val and Crawley 2004) and plant evolution (Holeski et al. 2010 and references therein). Since the capacity of plants to resist and/or tolerate herbivory is mediated by their functional characteristics (i.e. traits sensu Violle et al. 2007), plant species differing in their traits can show large differences in rates of herbivory.
Plants may alter herbivory through traits that affect an herbivore's ability to find, choose or consume a plant. The traits determining the susceptibility of a particular plant to a particular herbivore, and hence to rates of herbivory, are likely to differ among different plant - herbivore interactions (Tanentzap et al. 2011). On the other hand, there is a long
15 history of research aimed at identifying plant traits that generally structure patterns of insect herbivory observed in the field (Perez-Harguindeguy et al. 2003 and references therein). However, most of this research has focused on only one or few plant traits or, when several traits were included in the analysis, the focus was on pairwise correlations between herbivory and single traits. This is potentially problematic because plant traits often display complicated patterns of multivariate correlation that potentially mask the functional links underlying observed correlations. Studies using a multivariate approach to quantitatively analyze variation in herbivory rates have identified three different groups of trait affecting levels of herbivory: physiological (Coley 1983, Johnson et al. 2009, Kurokawa et al. 2010), morphological (Coley 1983, Kurokawa et al. 2010) and phenological (Johnson et al. 2009). For example physiological traits, such as concentrations of nitrogen and secondary compounds, impact the nutritional quality of plants. Morphological traits such as height and growth form influence the accessibility of plants and the ease with which herbivores can locate them. Phenological traits such as the growth period determine the availability of plants in a seasonal context. In addition to these classical traits, other traits can be defined. For example the number of species from a particular herbivore group that can potentially feed on a plant species, which is an indirect measure of herbivore pressure on the plant, may include information not contained in the three other groups of traits. Several studies have identified single traits such as leaf toughness, foliar C:N ratios, or nitrogen concentration that correlate with levels of herbivory (Perez-Harguindeguy et al. 2003, Boege 2005, Peeters et al. 2007, Karley et al. 2008, Kurokawa et al. 2010). Multivariate studies linking herbivory to plant traits have so far only used subsets of the groups of traits in their analysis but it is likely that they all act simultaneously to affect herbivory in a plant community (Johnson et al. 2009, de Bello et al. 2010). Studies that combine several groups of plant traits and assess their predictive power of explaining herbivory for a large number of species are missing. Thus, it remains unclear which combination of plant traits is of importance in modulating plant susceptibility to herbivory, especially when comparing several plant species (Agrawal 2011).
16 The goals of the current study were, (1) to determine the correlation structure between a large number of traits for a large pool of co-occurring herbaceous species and (2) to identify the suite of traits that best predicts invertebrate herbivory across monocultures of different grassland plant species. Working with monocultures of a range of species excludes the effects of complex interactions that can arise when several plant species co- occur (the subject of another study), but keeps the generality of a multi-species approach by identifying how variation in trait values across plant species affects susceptibility to herbivores.
2.4- Methods
2.4.1- Study site
This study was conducted as part of the Jena Experiment, a grassland biodiversity experiment (Roscher et al. 2004). The field site has a Eutric Fluvisol (FAO-Unesco 1997) and is located on the floodplain of the Saale River (50°55' N, 11°35' E, altitude 130 m) at the northern edge of Jena (Thuringia, Germany). Established in 2002, the experiment has a 60-species pool (Annex A) consisting of herbaceous plants commonly occurring in semi-natural, mesophilic grasslands of the region (Molinio-Arrhenatheretea meadows, Arrhenatherion community) (Ellenberg 1996). In addition to 80 main plots along a gradient in plant species richness, there is one monoculture plot of 1 m2 for each plant species; only the monoculture plots are used in this study. All plots are mown twice a year and weeded regularly (twice or thrice a year), keeping only the target species of each plot.
17 2.4.2- Herbivorv and biomass measurements
In May and August 2010, plant material was sampled from the species' monocultures for herbivory measurements. Within the lm2 area of each monoculture plot a single whole individual, or all the rosette-/ stolon-born leaves on one spot, was cut 3 cm aboveground every 10 cm along a side-to-side transect. Additional transects were added if fewer than 30 leaves were sampled in the first transect. The plant material was stored in a cooler in plastic bags with humid tissue and directly brought to the laboratory after sampling, where 30 leaves per sample were randomly selected (except for Ranunculus repens in May: 22 leaves). Invertebrate standing leaf herbivore damage was then measured on each selected leaf as the proportion of consumed leaf area (damaged leaf area/original undamaged leaf area). Damage area was estimated by visual comparison to a template card with a range of shapes of known area. Four types of herbivory (i.e.: chewing, rasping, sucking and mining) were separately estimated for each leaf. The leaf was then measured using a LI-3000C Area Meter (LI-COR Biosciences, Lincoln (NE), USA). To calculate the original undamaged leaf areas the estimated chewed area was added to the measured remaining area of each leaf.
Due to lack of plant material, herbivory measurements were not possible for eight species. Also, herbivore damage of Bromus hordeaceus was exceptionally high in August 2010 (at least 10 times higher than in May 2010 and during a repeated measurement in May and August 2011). As we could not explain this extremely high value, this species was excluded from the analyses. Thus, the final dataset includes 51 of the complete 60 plant species pool of the Jena Experiment (see Annex A for species names and inclusion status). The four separately estimated herbivore damage types were
18 summed for each leaf and averaged for each species over the two seasons to give 51 values of species-specific standing herbivore damage.
Prior to sampling for herbivory measurements, the biomass inside a 20 x 50cm frame was cut 3cm aboveground in each monoculture, in both seasons. Weeds were removed and the target species biomass was oven-dried (70°C/48h) and weighted. This estimate of biomass was used as a covariate in the analyses.
2.4.3- Plant traits
2.4.3.1- Trait data extracted from published literature and databases
Information on 84 traits was extracted from the literature (a total of 148 sources, see Annex B), including published (Roscher et al. 2004, 2011a, 2011b, Gubsch et al. 2011) and unpublished trait data collected in the Jena Experiment and international databases on plant functional traits, i.e. TRY (Kattge et al. 2011), LEDA (Kleyer et al. 2008), and Biolflor (Klotz et al. 2002). The data extracted from the TRY database include several different sources listed in Annex B with their associated traits.
2.4.3.2- Trait measurements in the Jena Experiment
Data on specific leaf area (SLA), carbon and nitrogen tissue concentration and plant height were measured in the monocultures of the Jena Experiment as follows: bulk
19 samples of fully expanded sun leaves (5-20 leaves dependent on leaf size and number) were collected at estimated peak biomass shortly before mowing in late May and August. Samples from two replicate monoculture plots per species were taken from 2003-2005, and one monoculture plot per species was sampled in May 2007. After measuring leaf area (including rachis and petioles of compound leaves) with a leaf area meter (LI-3100 Area Meter, LI-COR, Lincoln, USA) samples were dried to a constant weight at 70°C (48 h). SLA was calculated as the ratio of total projected leaf area divided
2 by total leaf dry mass per sample (mm ieaf mg"\eaf). Dry leaf material was ground to a fine powder with a ball mill and approximately 10-20 mg were analysed for leaf carbon and nitrogen concentration with an elemental analyzer (Vario EL Element Analyzer, Hanau, Germany). Data of 13 grass species and 12 legume species were collected in 2006 according to the procedures described in Gubsch et al. (2011) and Roscher et al. (2011a). The same sampling protocol was used for measurements on 28 non-legume herb species in 2006 and the remaining species in 2008 or 2009. In spring and summer 2003 and 2004, mean vegetative and total (flowering) plant height were also measured in monocultures. Leaf lignin, cellulose, hemicellulose and water-soluble matter concentration were measured following a sequential extraction analysis (Vansoest et al. 1991) of neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL). Leaf samples were collected between May and October 2010. As only 1 gram of dry material was necessary, the number of leaves per species varied (at least ten leaves per species in total, from at least five different individuals; for most species many more leaves). Subsamples were mixed and ground to 1 mm particle size to obtain an average value of fiber concentration per species over the growing season. For the NDF and ADF analyses, an ANKOM 200 fiber analyzer (ANKOM200, 65rpm agitation, ANKOM Technology) was used and the ADL analysis was done in beakers with 72% sulfuric acid. The samples were dried and weighted between each analysis to calculate the different fiber fractions. A complete list of method procedure is given on ANKOM technology website (www.ankom.com/default.aspx).
20 The final dataset, combining the information from the literature and measurements, contained 104 traits representing four main trait groups: physiological, morphological, phenological and herbivore-related. See Annex B for a detailed description of all traits and associated references.
2.4.4- Statistical analyses
2.4.4.1- Data preparation
In spite of best efforts to obtain trait values for each plant species, there were between one and 15 missing values for six of the traits (Annex C). Missing values of the traits stem growth form, seed shedding height, beginning of seed shedding and period of seed shedding were imputed from reference traits or reference species. Missing values of the traits leaf phosphorus concentration and relative growth rate were imputed via a multiple imputations method using the function and package "mi" in R (Su et al. 2011) (this and all subsequent statistical analyses were done using the R statistical software version 2.10.0; R Development Core Team 2009). Annex C gives a detailed explanation of the traits or species used as reference for every imputed value. Finally, several traits and the standing herbivore damage were loge-transformed to better approximate normality (indicated in Annex B).
When a given trait was available from different sources or for different years, the values were averaged in order to be as independent of individual measurements as possible. Highly correlated traits (r > 0.75) were either averaged (when having the same units, e.g. different measures of plant height) to give a more general new trait, or one (or more) of
21 the correlated traits were excluded (see Annex B and the online supplement for more details). The number of aphid herbivores was separated from the total number of hemipteran herbivores, because aphid damage is less likely to be detected than that of larger hemipterans. Some traits were combined even without being correlated, e.g. the numbers of different secondary metabolites in several similar groups were summed to form larger groups (see Annex B for details). Monophagous, oligophagous and polyphagous herbivore classes, even when not correlated, were also combined to give the total number of potential herbivores for each group of herbivores. This is because 1) information on the specificity of herbivores was often unavailable and 2) there was not enough information when all classes were analyzed separately. The resulting final dataset for analysis included 42 plant traits (Annex B).
2.4.4.2- Data analyses
The 42 traits were checked for phylogenetic signals using the "k" statistic of Blomberg et al. (2003) calculated by the "phylosignal" function of the "picante" package in R (Kembel et al. 2010) and based on a phylogeny of the 60 species pool of the Jena Experiment (Tania Jenkins, personal communication). As none of the traits were clustered, a correction of trait values for phylogenetic signals was not considered to be necessary (Carvalho et al. 2006).
The Random Forest method was used employing the "RF' function of the "randomForest" package in R (Liaw and Wiener 2002) to determine traits with which the 51 loge-transformed values of herbivore damage in monocultures were correlated. This method determines the most important factors that predict a response variable among a large number of different factors by calculating importance scores for each factor (Breiman 2001, Prasad et al. 2006). The method will work even for highly
22 correlated factors, assigning similar importance scores to each. The identified traits were then used as predictor variables in a multiple regression on herbivore damage with plant biomass as a covariable. The resulting model was simplified by a backward stepwise procedure until all remaining traits in the regression were significant (p-value < 0.05), resulting in an equation of linear combinations of traits predicting degrees of herbivore damage (Equation 2.1):
Equation 2.1 —*predicted damage = fio + Piti + ^2 + ••• +/?/*/ where fio is the intercept of the model and /?, and /, are the partial slope and the species- specific trait values of the ith predictive trait, respectively. As trait values were not standardized, the relative importance of the partial slopes, or magnitude of effect, was calculated by multiplying each partial slope by the range (maximum - minimum) of its associated trait. The predictive potential of the resulting equation was cross-validated as follows: a thousand subsets of 38 species (75% of the total) were randomly chosen to calculate the intercept and the partial slopes of the final multiple regression. Using those parameters in Equation 2.1, the predicted values of herbivory were calculated for the remaining 13 species (25%) of each subset. The observed damage values for those 13 species were then regressed against their respective predicted values obtained from the cross-validation. The mean value of the residual standard errors of those 1000 regressions (RSE*) was compared to the residual standard error of the regression using the full dataset (RSE):
Equation 2.2 —• Error (%) = (R5g ~RSE) ^ v J RSE'*100
This equation calculates the percentage increase in the error of prediction (Error) between the full model error and the mean error from the 1000 cross-validations (Efron and Tibshirani 1993).
2.5- Results
23 2.5.1- Correlation and exclusion of traits
The original dataset of 104 traits was reduced to 42. Grazing tolerance was highly correlated with trampling tolerance (r = 0.95) and moderately correlated with mowing tolerance (r = 0.61). As grazing tolerance is likely to be the most relevant variable predicting herbivore damage, the two others were excluded. Leaf carbon and nitrogen concentration were respectively correlated with shoot carbon and nitrogen concentration (r = 0.75; r = 0.84). As the leaf herbivore damage was the variable of interest in this study, shoot carbon and nitrogen concentrations were discarded. Leaf hemicellulose and cellulose concentrations were also correlated (r = 0.79), so they were summed to give a new variable: leaf primary fiber concentration. This variable was in turn highly correlated (r = -0.88) with leaf water-soluble matter concentration, which was excluded as it can include both deterrent and attractive compounds for herbivores (see Annex B). The 11 different measures of plant height were combined to give three plant height traits: height spring, height summer and height (Annex B). All original plant height measures were correlated (r > 0.75) to at least one of these three variables. Height spring (summer) is the average of the different heights measured in spring (summer) in the Jena experiment (see Annex B). Height represents a general averaged value of several different height measurements from the TRY database and is highly correlated (r > 0.75) to all other excluded height traits (see Annex B). Interestingly, leaf lignin concentration was moderately positively and negatively correlated to leaf nitrogen and primary fiber concentrations, respectively (r = 0.59; r = -0.62). It was also moderately positively correlated to the number of coleopteran herbivores potentially feeding on the plants (r = 0.66) and negatively correlated to stem growth form (r = -0.59). These correlations and their implications for this study will be further discussed in the following.
24 2.5.2- Predicting herbivore damage
The random forest method selected 13 traits as being important to predict loge- transformed values of herbivore damage (Table 2.1), whereas the decrease in importance score between each of the other "less" important traits was very small (see Annex D for details). In a multiple regression, these 13 traits were further reduced to seven traits significantly predicting herbivore damage using a backward stepwise selection. The resulting highly significant model explained 63% of the variation in loge-transformed damage values (Table 2.1). Figure 2.1 shows the correlation between observed and predicted values of damage from the multiple regression.
Herbivore damage increased with increasing leaf nitrogen concentration, leaf lifespan (1 = deciduous; 2 = partly deciduous; 3 = evergreen), the number of coleopteran herbivores potentially feeding on the plants and with root architecture score (1 = long-living primary root system; 2 = secondary fibrous roots in addition to the primary root system; 3 = short-living primary root system, extensive secondary root system). Root architecture is regarded here as an ordered variable that increases with increasing importance and size of the secondary root system and with decreasing lifespan and size of the primary root system. Herbivore damage decreased with increasing values of stem growth form (% of erection of the stem), leaf lignin concentration and the number of hemipteran herbivores potentially feeding on the plants. Based on their magnitude scores (Table 2.1), the physiological traits (leaf nitrogen and lignin concentration) were the most important predictors for herbivore damage, followed by the traits related to the herbivore community (coleopteran and hemipteran herbivores), the life-history trait (leaf lifespan) and finally the morphological traits (stem growth form and root architecture).
25 TABLE 2.1. Plant functional traits selected by a random forest (RF) approach to be important to predict standing herbivore leaf damage of 51 different species monocultures at the field site of the Jena Experiment (Germany). Traits that remained in the final model after the stepwise backward selection are given in bold.
Variables RF value p-value Regression coefficient Magnitude
- Intercept - <0.001 -10.649 ± 1.520 -
e - Biomass* - 0.246 - - 1 Leaf nitrogen concentration 110.6 <0.001 1.749 ±0.392 1.75 2 Coleopteran herbivores 50.9 <0.001 0.547 ±0.118 1.48 3 Leaflignin concentration 48.2 0.003 -0.073 ±0.023 1.56 4 Stem growth form 43.6 0.039 -0.007 ±0.003 0.70 5 Root architecture 34.9 0.032 0.268 ±0.121 0.54
6 Orthopteran herbivores* 25.1 0.134 - - 7 LL 19.5 <0.001 0.370 ±0.092 0.74 c 8 Leaf distribution 16.6 0.765 - -
1 9 Aphid herbivores' 12.6 0.393 - -
— 10 Leaf phosphorus 11.7 0.819 — concentrationb
11 N-containing compound/ 11.6 0.196 - -
12 LDMC 11.6 0.865 - - 13 Hemipteran herbivores 9.4 0.003 -0.406 ±0.127 0.79
26 TABLE 2.1 continued
Final model p = <0.001 R2 = 0.631 Notes: 1-13: ordered importance of traits as given by the RF; RF value: importance scores given by the RF for each trait; a-g (superscripts): order in which the traits have been removed in the backward stepwise selection; p-value: p-values for the intercept and the partial slopes of the traits in the multiple regression of loge-transformed specific herbivore damage against traits at the time it was removed by backward stepwise selection (p-value > 0.05) or in the final model (p-value < 0.05); Regression coefficient: intercept and partial slopes of the remaining traits in the final model, with standard error. The R- squared value and p-value of the model are indicated in the last row; Magnitude: Magnitude of the maximal effect of an explanatory variable on the response variable calculated as the absolute value of the partial slope multiplied by the range (max-min) of the explanatory variable; f: the biomass measured in the monocultures has been used as covariable in the model. It was never significant together with other variables and was consequently removed from the final model.
27 CMi -
CO -
Q X TJ +-»0) f- U TJ
<0I -
-6 -5 -4 -3 -2 Measured HD FIGURE 2.1. Correlation (r = 0.83, p-value = <0.001) between leaf herbivore damage in monocultures (HD) measured as proportion of the damaged leaf area at the field site of the Jena Experiment (Germany) and damage predicted by a model based on seven traits. For the model see Equation 2.3 and Table 2.2. Values of herbivore damage are loge- transformed.
The cross-validation results showed that the model is robust and consistent (RSE = 0.498, RSE* = 0.515, SD(RSE*) = 0.112). The mean residual standard errors (RSE*, over 1000 regressions) were only 3.43% higher when fitting was restricted to 25% of the species not used to calculate the partial slopes, compared to the residual standard errors when fitting all species in the original regression (RSE). Moreover, the mean correlation coefficient between observed and predicted damage values for those 1000 regressions is
28 only 10% lower than the correlation coefficient for the whole model. Thus the presence of particular species has little impact on the goodness of fit of the model.
2.6- Discussion
Standing invertebrate herbivore leaf damage in monocultures of 51 herbaceous species was successfully predicted from plant traits, explaining 63% of the variation in herbivore damage. The seven traits selected as predictors come from all four trait groups included in the study: physiological, morphological, phenological and herbivore-related. To our knowledge, this study is the first where several trait groups have been combined in a predictive model for general invertebrate herbivory on herbaceous species. The resulting model accounted for a surprisingly high proportion of the total variance in herbivore damage and was strongly supported by cross-validation results that showed that the influence of any single plant species on the model was low and that the selected traits are consistently good predictors of herbivore damage among different subsets of the 51 species.
Our results are similar to those from two other studies that quantitatively linked invertebrate herbivory to plant traits in a large interspecific context using multiple regression despite the fact that both Coley (1983) and Kurokawa et al. (2010) addressed tropical tree species, did not use monocultures, and focused on only two trait groups (physiological and morphological). Although we used more plant and herbivore species, our model explained more variation in herbivore damage than Kurokawa et al.'s (2010), which was based on four traits (specific leaf area, leaf C:P, N:P and total phenolics) and explained 48% of the variation in leaf consumption by one herbivore on 41 woody species. The model of Coley (Coley 1983) explained 70% of the variation in natural invertebrate herbivory on mature leaves of 46 tree species. Note, however, that 16 traits
29 were included in Coley's multiple regression, even though most of them were not significant, which yields a higher r-squared value than if only significant traits had been kept in the model. Thus, with less than a half of the traits used in Coley's study, we could account for a similar fraction of the variation in herbivore damage. Moreover, while their final model included many more traits, we both identified leaf nitrogen and lignin concentrations as being the two most important predictors of herbivore damage, although leaf lignin concentration is an important and significant predictor only in a multivariate context including at least three other variables.
2.6.1- Physiological traits
As hypothesized by Perez-Harguindeguy et al. (2003), and in line with Coley (1983)'s findings, leaf nitrogen and lignin concentrations had the largest effect on the herbivore damage since, leaf consumption should be directly linked to leaf nutritional quality. Invertebrate herbivores have been shown to feed preferentially on plant tissue with higher nitrogen concentrations since they are nitrogen-limited because of the much lower C:N ratio in animal compared to plant tissue (Elser et al. 2000). Correlations between herbivory and leaf nitrogen concentration have also been found in many other invertebrate herbivory-related studies (Karley et al. 2008, Cronin et al. 2010, Kurokawa et al. 2010). While the herbivore damage increased with higher leaf nitrogen concentration, it decreased with leaf lignin concentration, which is known to be a compound decreasing herbivore performance (Wainhouse et al. 1990). While correlations between herbivory and lignin concentration have been documented before (Coley 1983, Wainhouse et al. 1990, Poorter et al. 2004), results have not been consistent (Coley 1983, Kurokawa and Nakashizuka 2008, Kurokawa et al. 2010). Coley (1983) suggested that leaf toughness, which is related to leaf fiber and lignin concentration, could be a better predictor of herbivory, which is in line with findings of
30 Perez-Harguindeguy et al. (2003). While precise information on leaf toughness was unavailable for the species in this study, leaf lignin concentration had a consistent impact on the observed damage. This is of interest as most previous studies documenting the importance of lignin have worked with woody plant species from tropical rainforests (Coley 1983, Poorter et al. 2004, Kurokawa and Nakashizuka 2008, Kurokawa et al. 2010). We found that leaflignin concentration may have an underestimated role for the interactions between invertebrate herbivores and herbaceous vegetation in temperate grasslands.
2.6.2- Morphological traits
The observed degree of herbivore damage was negatively correlated with values of stem growth form (% of erection of the stem) suggesting that more prostrate species are more fed upon. Plant height could represent a physical barrier to some invertebrate herbivores (Perez-Harguindeguy et al. 2003). Because only invertebrate herbivore damage was measured, including damage from molluscs and other flightless herbivores, prostrate or rosette-shaped plant species are likely to be generally more easily grazed on than erect species.
We know much less about plant-herbivore interactions below ground than above ground, and even less about feedbacks between the two compartments. While the current study demonstrates a significant positive correlation between observed aboveground herbivore damage and the plant root architecture, the underlying mechanism is somewhat difficult to interpret. On the one hand, different root systems have different resource acquisition potentials (Fitter et al. 1991, Doussan et al. 2003) which impacts aboveground leaf quality with obvious implications for herbivores. On the other hand, belowground
31 herbivore preferences might depend on root architecture and their feeding can induce physiological effects in the plants which can enhance (Newingham et al. 2007) or reduce (Kaplan et al. 2011 and references therein) aboveground herbivory. In addition, variation in root architecture could correlate with some other aboveground trait that is important for invertebrate herbivores but that was not included in this study. Regardless of the mechanisms, the fact that root architecture was selected in our model highlights the importance of considering characteristics of the whole plant even when assessing only folivory.
2.6.3- Phenological traits
In the model, the degree of herbivore damage was positively related to leaf lifespan (l=deciduous, 2=semi-deciduous, 3=evergreen). The positive partial regression coefficient associated with this variable reflects the effect of these different types of leaf lifespans after controlling for the other variables in the model, including leaf nitrogen and lignin concentrations. Thus, for leaves with the same concentrations of nitrogen and lignin, those whose leaves typically persist longer on the plant were more susceptible to herbivore damage. This is opposite to published results from trees (Coley et al. 1985, Coley 1988). However, in contrast to trees in the above-cited studies, for the herbaceous vegetation in the present study, leaf lifespan is not a measure of the actual lifespans for individual leaves in this study; rather it is a measure of potential leaf availability for herbivores. Since the field site is mown twice a year and all leaves had been produced in the periods between harvests, the effect of leaf lifespan does not simply reflect an accumulation of herbivory over different periods. Rather, herbivores (especially specialists) might prefer host plants which, when controlling for other important variables such as nitrogen and lignin concentrations, provide food (leaves) for the
32 longest period by being available earlier in spring or providing opportunities to overwinter.
2.6.4- Herbivore-related traits
The number of coleopteran herbivores known to feed on a given plant species was selected as an important factor positively correlated to values of herbivore damage. In line with that importance, coleopterans were the most numerous herbivore group found on the field site in 2010, followed by hemipterans (Anne Ebeling, personal communication). This agrees with herbivorous coleopterans playing a major role in grasslands as important invertebrate herbivores.
A surprising result, at first glance, is the negative correlation between herbivore damage and the species richness of potential hemipteran herbivores. Generally, since damage by sap-sucking insects is difficult to quantify on the basis of damaged or removed leaf area (as was done in this study), this causes an underestimate of their direct impacts. If there are indirect effects between sap-sucking herbivores and herbivores that cause more visible damage (e.g. by competition), then these interactions could explain the lower damage with higher potential (and likely realized) hemipteran infestation. Such competitive effects have been shown for two sap-sucking herbivore species (Inbar et al. 1995) and similar competitive effects might extend to other herbivore guilds as Kaplan and Denno (2007) found in their review that the strength of competition between invertebrate herbivores does not change significantly between intra- and inter-guild interactions. We found that plant species with high levels of sap-sucking damage had low levels of other types of damage, which might indicate competitive interactions, although contrasting correlations with other plant traits cannot be excluded.
33 2.6.5- Secondary metabolites
Surprisingly, only leaf lignin concentration was selected in the model as a chemical herbivore deterrent of importance and there is no correlation with the secondary metabolites we investigated. Many previous studies have emphasized the importance of at least one group of secondary metabolites in modulating herbivory (Carroll and Hoffman 1980, Bernays and Chapman 1994, Schoonhoven et al. 2005, Johnson et al. 2009). In contrast to the current study, most of these reports have focused on specific interactions between single plant and herbivore species or specific compounds, whereas interactions involving many herbivore and plant species have rarely been addressed. The minor importance of secondary compounds in our model is confirmed by a recent meta- analysis aiming to determine what traits generally influence plant susceptibility to herbivores. Carmona et al. (2011) and Schuldt et al. (2012) found no secondary metabolites that were correlated to susceptibility; instead life-history, morphological and geographical traits were important. A possible reason is that particular compounds within a group of secondary compounds (e.g. phenolics, terpenoids) can have a range of functions other than anti-herbivory defenses (Bernays and Chapman 1994). Secondary compounds might even have at the same time both stimulating and inhibiting effects depending on the herbivore species (Carroll and Hoffman 1980). Consequently, the effect of a particular secondary compound is often specific to a particular herbivore species or group of species, making such metabolites poor predictors of herbivory by the entire community. Also, because of the "specific" function of several secondary metabolites, using concentrations of particular compounds as predictors rather than the number of different compounds might increase their predictive power for herbivory patterns.
34 2.6.6- Patterns of correlation
It is interesting to note the positive correlations among leaf lignin concentration, leaf nitrogen concentration and coleopteran herbivores, the three most important predictors of our final model. The negative effect of leaf lignin concentration on herbivore damage disappears when both other variables are excluded, and is not significant when only one of them is included in the model. Thus, leaf lignin concentration seems to negatively affect herbivores for leaves with the same nitrogen concentration and it seems to have a particularly important effect on coleopteran herbivores (which are mostly chewers), but a more detailed analysis of this interaction is needed. Thus, pre-selecting a few traits believed to be important runs the risk of drawing false conclusions due to unknown interactive effects between traits, and working with as many traits as possible increases the reliability of the identified relations between plant traits and herbivore damage.
2.7- Conclusion
Beginning with 104 traits a final set of seven traits, belonging to all trait groups addressed in this study, predicted leaf standing herbivore damage in monocultures. This confirmes our expectations that i) herbivore damage can be predicted from a relatively small number of plant traits and ii) one trait group is not sufficient to capture the major variation sources in degree of herbivore damage. Multiple aspects of plants are important in controlling herbivory. It is important to remember that our results are based on monocultures. In natural plant communities, in which different plant species grow together in close proximity, the presence of species with less-desirable traits might either reduce or enhance herbivory on species possessing more desirable traits beyond the levels expected in plant monocultures. If interactions in multi-species communities are
35 not important then it would be possible to extrapolate our results to multispecies communities simply by weighting the expected herbivore damage of each species by its relative abundance in the community. Testing whether the relations expressed in monocultures in this study remain consistent in more complex plant communities is an important next step toward understanding how plants and invertebrate herbivores impact each other.
2.8- Acknowledgements
We thank Anne Ebeling, the gardeners and technical staff who have worked on the Jena Experiment, for maintaining the site. The study has also been supported by the TRY initiative on plant traits (www.trvdb.org) and we thank all the contributors that have provided trait data via the TRY database. TRY is/has been supported by DIVERSITAS, IGBP, the Global Land Project, the UK Natural Environment Research Council (NERC) through its program QUEST (Quantifying and Understanding the Earth System), the French Foundation for Biodiversity Research (FRB), and GIS "Climat, Environnement et Societe" France. We thank Enrica de Luca for the biomass data, Annett Lipowsky and Marlen Gubsch for some of the plant trait data and Tania Jenkins for the phylogeny. This study was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds Quebecois de Recherche sur la Nature et les Technologies (FQRNT) and the Jena Experiment was funded by the Deutsche Forschungsgemeinschaft (FOR 456).
36 SUPPLEMENTAL MATERIAL
Annex A. Species pool of the Jena Experiment with inclusion status in the analyses.
Annex B. Detailed list of all traits considered for this manuscript with detailed references.
Annex C. Details on the imputation analyses used in this manuscript.
Annex D. Graph of the results from the Random Forest (RF) analysis, explaining how the selection of traits was done.
37 CHAPITRE III
PREDICTING INVERTEBRATE HERBIVORY FROM PLANT TRAITS: POLYCULTURES SHOW STRONG NON-ADDITIVE EFFECTS
38 PREDICTING INVERTEBRATE HERBIVORY FROM PLANT TRAITS: POLYCULTURES SHOW STRONG NON-ADDITIVE EFFECTS
par Jessy Loranger, Sebastian T. Meyer, Bill Shipley, Hannah Kern, Christiane Roscher, Christian Wirth et Wolfgang W. Weisser cet article sera soumis a Ecology
3.1- Description de I'article et contribution
Dans le chapitre n, nous avons identifie sept traits d'importance pour predire le degre d'herbivorie subi par des plantes en monoculture. Nous savons done que les traits des plantes peuvent influencer l'intensite avec laquelle une plante se fera consommer par les herbivores invertebres. Cependant, qu'en est-il de l'intensite avec laquelle une communaute se fera consommer? Est-ce que nous pouvons utiliser les traits des plantes d'une communaute pour predire le degre d'herbivorie moyen subi par cette derniere? Sinon, pourquoi? Ces questions sont d'importance particuliere puisqu'en nature, on retrouve principalement des communautes d'especes de plante et tres peu de monocultures naturelles. Dans cet article, nous avons utilise la notion de trait agrege (valeur moyenne d'un trait ponderee par l'abondance relative des especes de la communaute) pour tenter de predire l'herbivorie dans plusieurs mixtures des plantes etudiees au chapitre II a partir des sept traits qui y ont €\€ identifies. C'est la premiere fois que les traits agreges sont utilises pour adresser l'herbivorie des invertebres, malgre le fait que les traits agreges ont ete utilises avec grand succes pour pr6dire d'autres processus ecologiques, comme la decomposition de la litiere. Utiliser le modele developpe en monocultures dans un contexte de polycultures nous permet de voir si des
39 interactions existent entre des especes de plante co-existantes et si ces interactions affectent les relations entre traits et herbivorie. En travaillant le long d'un gradient de biodiversite, nous pouvons verifier si les effets potentiels des especes de plante avoisinantes sur l'herbivorie subie par une espece donn6e changeront avec 1'augmentation de la complexity des communautes.
Les donnees sur le terrain ont ete recoltees par Hannah Kern, Sebastian Meyer et moi- meme. Plusieurs donnees sur les traits ont ete fournies par Christiane Roscher et aussi par le TRY database, une base de donnees internationale geree par Jens Kattge et Christian Wirth. Sebastian Meyer et moi-meme avons effectue les analyses statistiques (a l'aide des precieuses suggestions de Bill Shipley) et j'ai ecrit une premiere version complete de 1'article, celle-ci ayant et6 grandement amelioree par Bill Shipley, Sebastian Meyer et Wolfgang Weisser.
3.2- Abstract
Plants can affect the capacity of herbivores to find, choose and consume them through their functional traits and these relationships might differ between monospecific stands and polyculture plant communities. Here we ask if community herbivory can be predicted either based on species specific herbivory measured in monocultures or based on a set of plant functional traits previously used to model herbivory in monocultures. We applied this model in mixed species plots based on community-weighted traits and used measurements of standing invertebrate herbivore damage along an experimental plant diversity gradient ranging from monocultures to 60 species mixtures and additional measurements from monoculture plots of all species to verify predictions.
40 Species specific herbivory measured in monoculture was a poor predictor of herbivory measured in plant communities. Likewise, the model successfully predicting herbivory in monocultures did not scale up to multispecies communities when predictions were based on community-weighted traits. This model explained only 7% of the variation in measured herbivory and predicted consistently lower values of herbivory than observed. A second model with relaxed assumptions that estimated new partial slopes in mixtures for the same set of traits, thus allowing for changed importance of the traits in mixtures, explained 32% of variation, which was considerably less explained variation compared to the monoculture model. For all three different models deviations between predicted and measured herbivory increased with diversity of the plant communities and non- additive effects contributed more than a quarter to the measured community.
Models based on community-weighted traits that assume no interactions between plant species in mixtures and also mechanisms linking traits and response variables to be unaltered in communities have been used to produce valid predictions for plant physiological processes, e.g. biomass production. The low performance of similar models in our study when predicting community herbivory indicates a high degree of non-additive effects in the interactions between the herbivore and food plant communities.
Keywords: Jena Experiment, community-weighted traits, diversity, insects, consumers, interactions, monocultures, polycultures, grassland
3.3- Introduction
41 Herbivory is a major selective pressure affecting plant physiology and plant fitness (Karban and Strauss 1993, Hulme 1996b, Bigger and Marvier 1998), plant community composition and succession (Brown 1985, Gibson et al. 1987, Brown and Gange 1992, Hulme 1996a, del-Val and Crawley 2004) and plant evolution (Holeski et al. 2010 and references therein). Since the capacity of plants to avoid, resist or tolerate herbivory is mediated by their functional traits, plant species differing in such traits can drastically differ in rates of herbivory (Coley and Barone 1996, Rasmann and Agrawal 2009). For a plant occurring in a multi-species community, does the rate of herbivory on this plant depend on its own traits only or also on the characteristics (species and functional composition, structure, traits) of the surrounding vegetation?
Consider first the null hypothesis Ho that we call "additive" scaling. This hypothesis assumes that the decision of the herbivore to consume tissues of a given plant depends only on the plant's functional traits, irrespective of the surrounding vegetation. If this is true, then herbivory experienced by a species is the same in multispecies vegetation and in monoculture and the total rate of herbivory of the entire vegetation is the sum of the monoculture rates of herbivory of each species weighted by the relative abundance of each species in the vegetation.
Under the alternative hypothesis of "non-additive" scaling (Hi), the probability of a herbivore to consume tissues of a given plant depends not only on that plant's functional traits but also on properties of the surrounding vegetation. For instance, herbivory on a plant might be higher/lower than in monoculture if it is surrounded by plants that are more/less attractive and the focal plant experiences damage by herbivores attracted by the surrounding vegetation or is spared from damage due to preferential feeding on the more attractive surrounding vegetation. Actually, studies have shown that, due to plant interactions such as associational resistance - which is the resistance to herbivory by a
42 plant mediated by the presence of another neighboring species - the amount of damage a given herbivore caused on a particular plant species differs between monoculture and mixed plant communities (Finch & Collier, 2012; Hamback et al. 2000; Huntly, 1991) so that non-additive effects are likely to occur in mixed communities. How strong these are compared to a simpler additive scenario and whether they are consistently positive or negative remains unstudied.
By calculating the deviation between community herbivory predicted from species specific monoculture herbivory and herbivory measured in real communities the size and direction of non-additive effects can be quantified. In addition, comparing communities that differ in the magnitude of this deviation can give insights what community properties cause non-additive effects to become strong.
Under the additive scaling hypothesis, the herbivory experienced by a multispecies plant community can also be predicted based on aggregated functional traits, because herbivory in plant monocultures can be predicted as a linear function of species-specific plant traits (Loranger et al. in press). Therefore, herbivory experienced by a multispecies plant community could be predicted as the sum of the predicted herbivory experienced by each species as determined by its specific traits weighted by the relative abundance of each species in the vegetation. This is the same as treating a multispecies community like a single species with averaged trait values.
In this study we aimed at estimating the importance of non-additive effects in the relation between plant-herbivore interactions and plant diversity. To do so, we determined the degree to which herbivory in experimental herbaceous grassland communities ranging from monocultures to 60 species exposed to a natural community
43 of invertebrate herbivores can be predicted based on additive models using monoculture herbivory or community-weighted plant traits and herbivore - trait relationships previously observed in monocultures. The specific goals were (1) to evaluate the predictive power of different additive scaling models for the herbivory experienced by plant communities, (2) to detect non-additive effects, (3) quantify magnitude and direction of non-additive effects as deviation from additive models, and (4) analyze if deviations are related to community properties.
3.4- Methods
3.4.1- Study site
This study was conducted as part of the Jena Experiment, a field site with a Eutric Fluvisol (FAO-Unesco 1997), located on the floodplain of the Saale River (50°55' N, 11°35' E, altitude 130 m) at the northern edge of Jena (Thuringia, Germany). Established in 2002, this large scale biodiversity experiment consists of 80 large plots (5 x 6 m) in a randomized block design, containing 1, 2, 4, 8, 16 or 60 plant species with 14, 16, 16, 16, 14 and 4 replicates of these levels of species richness, respectively. The pool of 60-species (Annex A) consists of herbaceous plant species commonly occurring in seminatural, mesophilic grasslands in Central Europe: Molinio-Arrhenatheretea meadows, Arrhenatherion community (Ellenberg 1996). Furthermore, there is one small monoculture plot (lxlm) of each of the 60 species on the experimental site. All plots are mown twice a year and weeded regularly (twice or thrice a year), keeping only the target species of each plot. A detailed description of the set-up of the experiment is given in Roscher et al. (2004).
44 3.4.2- Biomass and herbivorv measurements
In May and August 2010, biomass inside a 20 x 50cm frame was cut 3cm above the ground in each large plot. The weeds (= unsown species) were separated and the remaining biomass was sorted to species, oven-dried (78°C/48h) and weighed. Previously to drying, the same samples were used to measure invertebrate leaf standing herbivore damage (simply called herbivory) which was quantified as the proportion of consumed leaf area for each plant species occurring in a plot. For 30 randomly chosen leaves per plot and species, herbivory was estimated by visual comparison to a template card with a range of shapes of known area. In some large plots, less than 30 leaves were available for rare species (minimum = 1 leaf per species, mean = 21 leaves per species). A total damaged area that encompassed four types of herbivory (i.e.: chewing, rasping, sucking and mining) was estimated for each leaf. The remaining surface area of each leaf was measured using a LI-3000C Area Meter (LI-COR Biosciences, Lincoln USA). Following the same protocol, these four types of herbivory were estimated separately for leaf samples from the small-area monocultures (thereafter simply referred as monocultures; see Loranger et al. (in press) for details on sampling in monocultures). The mean proportion of chewing damage relative to total damage was calculated in each monoculture as a species-specific correction factor. Since the remaining leaf surface measured by the area meter is equal to the total leaf surface minus chewing damages, the herbivory value of each leaf in the large plots was multiplied by the respective species- specific correction factor and added to the measured remaining leaf area to give the potential undamaged leaf area. Herbivory was calculated as the total damaged area divided by the potential undamaged leaf area.
From the sampled large plots, a total of 10 were excluded because of missing herbivory measurements (six plots) or because they contained more than 15% biomass of one or
45 more of the 9 species for which reliable herbivory measurements in monoculture were not available (four plots; see Annex A for inclusion status of the species). When these species accounted for less than 15% of the biomass in a plot, the species were excluded (also for the calculation of average traits as detailed below) but the plot was kept in the analyses with the remaining species. For each large plot (thereafter referred as "mixed communities", including the 12 remaining large-area monoculture plots), a community- level estimate of herbivory was calculated by summing up herbivory per species in that plot multiplied with the respective relative biomass of each species. Values for the two harvests were averaged, giving 70 values of measured community herbivory.
3.4.3- Predicting herbivory in monocultures
Using the method of random forests (Breiman 2001) followed by a multiple regression with a model simplification via stepwise selection, seven plant traits out of a dataset of 42 traits were identified as being important predictors of herbivory in monocultures (Loranger et al. in press). In order of importance in the final multiple regression (the trait values were not standardized), the seven traits were: leaf nitrogen concentration (loge- transformed), leaf lignin concentration, number of coleopteran herbivores potentially feeding on the plants (loge-transformed), number of hemipteran herbivores potentially feeding on the plants (excluding aphids; loge-transformed), leaf lifespan, stem growth form (% of erection of the stem) and root architecture (see Annex E for a detailed description of the traits and all associated sources). Equation 3.1 gives the final model, which explained 63% of the variation in herbivory measured in monocultures (see Figure 2.1) and was supported by cross-validation tests (Loranger et al. in press).
Equation 3.1 -> ln(predicted herbivory) = - 10.65 + 1.75*ln(leaf [nitrogen]) - 0.07*leaf [lignin] + 0.55*ln(coleopteran herbivores) - 0.41*ln(hemipteran herbivores) + 0.37*leaf lifespan - 0.007*stem growth form + 0.27*root architecture
46 3.4.4- Additive vs. non-additive scaling
The additive scaling hypothesis (Ho) implies that the predicted herbivory of the vegetation ^7 (i/0)) is the abundance weighted average of the species specific monoculture herbivory rates (Equation 3.2).
"• t \ •«r-i Equation 3.2 -> hj (H0)= 2_, i=i where /i, is the herbivory experienced by an average individual of plant species i when growing in a monoculture and raij is the relative abundance of species i in a community j composed of S species. The non-additive scaling hypothesis {Hj) assumes that the herbivory suffered by species i in monoculture changes by an amount 5, when growing in mixture. This implies that the predicted total herbivory experienced by the entire vegetation j deviates from the sum of the predicted herbivory experienced by each species in monoculture by an amount Ay (Equation 3.3).
s + P t. ,, . hj iHi)=Zraij (hi+^ >)=Zra'A ZratA Equation 3.3 %r £7 " h,(H, ) = A/(»o)+A,
Note that the deviation term 8, does not represent a statistical error term (i.e. a random value from a distribution having a zero mean) but rather the deviation from linearity (from additive scaling) between measured and predicted herbivory which is due to species interactions in the community. If these deviations (5,) vary randomly and independently for each species in the vegetation, and if they are equally likely to be synergistic (positive) or antagonistic (negative) then they will cancel each other and Ay will be close to zero. In this case additive scaling would be a reasonable approximation.
47 If, however, these deviations vary systematically then A; will be a significant deviation from additive scaling. Here, we did not explicitly model 8, but quantified A; as a measure for the strength of non-additive effects in determining herbivory in plant communities.
3.4.5- Testing the additive scaling hypothesis
The additive scaling hypothesis was tested in three ways. First, community herbivory was predicted from community-weighted species specific herbivory measured in monocultures (Equation 3.2). Second, predictions were based on plant functional traits and relationships between traits and herbivory demonstrated to be important in monocultures (Loranger et al. in press). To do so, the species-specific trait values in the monoculture model (Equation 3.1) were replaced by the associated community-weighted trait values (tk), while keeping the intercept (Po) and partial slopes (Pk) at the values estimated in the monoculture model (Equation 3.4):
Equation 3.4 fc, (H0)= £ ra^h, =£ ra, (] = A + +•••+PA i=1 i=l V *=i )
Third, to relax the strict assumption of unchanged relative importance of the functional traits in mixtures while still maintaining the basic assumptions of additive scaling, the measured values of community herbivory were freely regressed against the same seven selected community-weighted traits as in the monoculture model. Rather than using the partial regression coefficients from the monoculture model, we thus obtained new partial slope values {0k) where SK is the change in the value of the regression coefficients (Equation 3.5):
48 ra t hj(HQ)-^d ijhi-(fiQ + S0)+ (fi 1+S1) l + ...+ (/} i:+SK)tK Equation 3.5 /=i
{HQ) ~Pa ^ fi\h +•••+ PK*K
Herbivory predicted by all three different additive models was regressed against measured herbivory for all 70 plant communities from the Jena Experiment that could be included in the study. If the additive scaling hypothesis is correct, the regression slopes will not be significantly different from unity. To verify if, on average, predicted values of community herbivory from the first two models were significantly lower or higher than measured values, the difference between them was tested with a paired t-test. For the relaxed trait model, it is a statistical consequence of freely estimating the regression slopes and intercept that the difference between them is on average zero. With this model, if the only difference between monocultures and polycultures is that the relative importance (i.e. regression slopes) of the different traits vary, an equivalent predictive ability relative to the result in monocultures (i.e. -63% variance explained) is to be expected.
In addition, we aimed at detecting if the presence of the deviation term A, from the non- additive scaling equations (Equation 3.3) depends on community properties. To do so, the deviations (Aj) between measured and predicted herbivory was calculated for all three approaches and regressed against sown species richness to assess if community properties affect the size or direction of deviations. Sown species richness was selected as a surrogate for other community properties: community biomass, realized species richness, Shannon diversity, evenness (diversity divided by species richness), functional diversity (Rao's quadratic entropy) and functional dispersion because of highly significant correlations between all these community characteristics but community biomass and evenness (see Annex F). Because of these correlations, the qualitative
49 results for the correlation with the deviation between measured and predicted herbivory are the same for all these community properties. Even community biomass and evenness showed similar but weaker effects. Given this, and because sown species richness is the directly manipulated variable in the Jena Experiment, we only present the results of sown species richness. In addition sown species richness is a property representing the whole plot area (the scale most likely of importance for the herbivore community), whereas some other properties represent only the sampled area (20 cm x 50 cm).
All statistical analyses were done using the R statistical software version 2.10.0 (R Development Core Team 2009). The values of herbivory, deviance, sown species richness and of the traits leaf nitrogen concentration, coleopteran and hemipteran herbivores were loge-transformed for statistical analyses. R-squared values adjusted according to the ratio (N-l)/(N-k-l) of number of observations (N) and number of predictors (k) of each regression are presented as provided by the "lm" function in R. In the relaxed trait model, the relative importance of the partial slopes, or magnitude of effect, was calculated by multiplying the absolute value of each partial slope by the range (maximum - minimum) of its associated community-weighted trait. To estimate the relative importance of additive and non-additive effects in determining community herbivory, the log ratio of their absolute contributions was calculated. The additive contribution is the value predicted by the three different models; the non-additive contribution is the difference between predicted and measured herbivory. This difference can be positive or negative and the log ratio of the absolute values quantifies their relative importance (Annex G).
3.5- Results
50 Predicted herbivory in communities based on the community-weighted herbivory values measured for each species in monoculture explained 23% of the variation in measured community herbivory (Table 3.1). The slope of this regression was far from unity (Figure 3.1 A, Table 3.1) and on average the deviation between measured and predicted values was significantly greater than zero (t-test: t = 2.39; df = 69; p = 0.020) and increased significantly with sown species richness of the communities (Figure 3.IB, Table 3.1).
TABLE 3.1. Statistics for the models presented in Figure 3.1 on the relationship between (1) herbivory predicted from three different additive models and herbivory measured in plant communities of differing diversity and (2) the deviance of these models and diversity of the plant communities.
model intercept ± SE slope ± SE r2 F statistic with df P herbivory predicted from monocultures OO log(HpM) ~ log(Hm) 0.15 + 0.07 0.43 ± 0.09 0.23 so = 21.7 « 0.001 00 log(DevM+15) ~ log(sowndiv) 2.70 ±0.02 0.02 ±0.01 0.07 vq = 6.06 0.016 herbivory predicted from trait-based model O OO OO \q log(Hpx) ~ log(Hm) 0.16 ±0.08 0.22 ±0.10 0.06 = 5.28 0.025 log(Devx+10) ~ log(sowndiv) 2.29 ± 0.03 0.04 ± 0.02 0.08 vq = 7.10 0.010 herbivory predicted from relaxed trait-based model log(Hpl7) ~ log(Hm) 0.37 ± 0.05 0.33 ± 0.06 0.32 Fl,68 = 33.5 « 0.001 log(Devx2+3) ~ log(sowndiv) 0.98 ± 0.07 0.08 ± 0.03 0.07 Fl,68 = 6.33 0.014
Predicted herbivory based on community-weighted trait values and a model linking species specific herbivory in monoculture to species specific traits (Equation 3.4) explained only 6% (Table 3.1) of the variation in measured community herbivory.
51 Again, the slope of the regression was far from unity (Figure 3.1C, Table 3.1). The deviation between measured and predicted values was on average higher and more significantly differed from zero (t-test: t = 3.31; df = 69; p = 0.001) than with predictions based on measured monoculture herbivory. Deviations likewise increased with increasing sown species richness of the plant community (Figure 3.ID, Table 3.1).
As a relaxed null model, new partial slopes were estimated for the community-weighted traits to predict measured community herbivory. In this multiple regression, the seven traits explained 25% (Table 3.2) of the variation in community herbivory. This predictive power was much weaker than for the model in monocultures (r2 = 0.63; Figure 2.1). While the new model was highly significant, only the slopes associated with coleopteran herbivores and leaf lifespan were (marginally) significant (Table 3.2). Note that the relative importance of the traits changed considerably as compared to the model for monocultures (Table 3.2). Importantly, the partial slopes for the variables leaf nitrogen concentration and leaf lignin concentration even changed direction compared to the monoculture model (Equation 3.1), although neither was significantly different from zero. A new set of predicted herbivory values was calculated from the multiple regression and was regressed against measured herbivory. The r2 value of 0.32 (Table 3.1) indicated that predictions from the relaxed trait based model were much better than with the two first sets of predicted values. Again, the slope was much smaller than one (Figure 3.IE, Table 3.1) and the deviation between measured and predicted herbivory correlated significantly and positively with sown species richness (Figure 3.IF, Table 3.1).
For all three models predictive power of the model decreased as diversity of a community increased as the deviance of all models increased with species richness of
52 a E o s £ 10 Go ° TD CO a> a> • o Csl O 3 O 0 3 01 o° ol^ C _ o & Cx'aJltC o o (0 O I § e go o 0 1 • ~ o o
O
&B •em iS» o J= <0 -
0.5 1.0 2.0 5.0 1 2 5 10 20 50 measured herbivory Sown species richness
FIGURE 3.1. Herbivory (standing herbivore damage for whole plant communities in percent) as predicted from different additive models regressed against measured herbivory at the field site of the Jena Experiment (Germany) and the relationship between deviance of the models (difference between predicted and measured herbivory)
53 and species richness of the plant communities; note the logarithmic axis for herbivory and species richness. Dashed lines give expected relationships (a slope of one between predicted and observed herbivory in the panels on the left and a constant average deviance of zero in the panels on the right). The solid lines are best fit lines of significant linear models given in detail in table 3.1. In the upper row of panels (A, B), predictions are based on herbivory levels of the species in monocultures (Equation 3.2). In the middle row (C, D), predictions are from a model based on seven plant-functional traits that was developed to predict species specific herbivory in plant monocultures and which was used with community-weighted traits for the different plant communities (Equations 3.1 and 3.4). In the lower row (E, F), predictions are based on a trait model with relaxed assumptions in which new partial slopes were estimated for the same seven community-weighted traits directly for the herbivory measured in communities (the resulting model is given in table 3.2). For details refer to the text.
TABLE 3.2. Multiple regression model of the loge-transformed herbivory measured in 70 plant communities at the field site of the Jena Experiment (Germany) as determined by seven community-weighted traits that are important to predict species specific herbivory in monocultures.
Variables Regression P Magnitude coefficient
Intercept -4.202 ± 1.212 « 0.001 - Leaf nitrogen -0.381 +0.327 0.248 0.35 concentration Leaf lignin concentration 0.042 ±0.034 0.216 0.75 Coleopteran herbivores 0.356 ±0.185 0.059 0.85 Hemipteran herbivores -0.037 + 0.130 0.776 0.07 Leaf lifespan 0.222 + 0.124 0.078 0.44
54 TABLE 3.2 continued
Stem growth form -0.005 ±0.004 0.194 0.47 Root architecture 0.227 ±0.153 0.143 0.45 Model Rz = 0.254 p < 0.001 p: p-values for the regression coefficient; Regression coefficient: intercept or partial slope, with standard error. The R-squared and p values of the model are indicated in the last row; Magnitude: Magnitude scores of the maximal effect of a trait on herbivory: absolute value of the partial slope multiplied by the range (max-min) of the trait. The traits in the table are in decreasing order of magnitude score from the monoculture results (Loranger et al. in press).
the communities. Also, all three models overestimated herbivory in communities in which low herbivory had been measured and underestimated herbivory in communities showing high levels of damage (Figure 3.1 A, C, E). To estimate the relative importance of additive and non-additive effects in determining community herbivory the log ratio of their absolute contributions was calculated (Annex G). In the majority of communities, the non-additive effects where positive (as indicated in Annex Gl), thus the measured herbivory was higher than herbivory predicted by the additive models. For none of the three models the relative contributions changed with species richness (Annex G2). On average, the log ratio was about -1 which means that the additive effects are about 2.7 times as big as the non-additive effects. That also means that more than a quarter of the measured herbivory is determined by non-additive effects which emphasizes why purely additive models underestimate herbivory in polyculture communities (Annex G).
55 3.6- Discussion
All three additive models predicting herbivory in communities performed poorly irrespectively whether they were based on monoculture herbivory values or plant functional traits. Especially the model based on plant functional traits, which was a good predictor of herbivory in monocultures (Loranger et al. in press), was a much weaker predictor of herbivory when applied in multispecies mixtures based on community- weighted traits and the model coefficients estimated in monocultures. Even when allowing the partial slopes to change the predictive power of the model was far from the one for monoculture herbivory (Loranger et al. in press). Thus, the assumption of the additive-scaling underlying all three models was violated in all cases indicating a high importance of non-additive effects. On average, more than 25% of the measured herbivory in communities could be attributed to non-additive effects, irrespective of which additive model was used for the calculations. These results suggest that an insect herbivore does not respond in the same way to a plant monoculture characterized by particular values of traits as to a polyculture having the same average values of these traits. That indicates that invertebrate herbivores make different choices about where and how much to feed when in a monoculture compared to when in a mixed communities of different plant species growing in close proximity. That the deviation between measured and predicted community herbivory grew larger with increasing species richness further supports this interpretation by showing that non-additive effects are of higher importance in more complex plant communities. This is in line with other studies showing that community properties such as diversity, evenness or species richness were correlated to levels of invertebrate herbivory (Scherber et al. 2006a, 2010b, Unsicker et al. 2006, 2010, Stein et al. 2010, Allan and Crawley 2011). On the other hand, community-weighted traits have been successfully used to predict ecological processes like net primary productivity, litter decomposition or nitrification (Gamier et al. 2004,
56 Laughlin 2011, Pakeman 2011). Why then would trait-herbivore relationships that were strong and robust predictors when herbivores were confronted with monocultures not apply as well when herbivores are confronted with multispecies vegetation? There are at least five possible explanations for why predictions from additive scaling models deviated from measured herbivory in mixed communities, the first three related more directly to the herbivores' reaction to increasing biodiversity and the last two being more related to effects of plant-plant interactions.
First, the studies successfully employing community average traits in modeling ecosystem processes (Gamier et al. 2004, Laughlin 2011, Pakeman 2011) modeled relations between traits and abiotic factors rather than biotic interactions. Consequently, an average trait value might be of different usefulness. While two species with a low and a high nutrient uptake rates might equal an average uptake rate for the community a herbivore might not perceive a mixture of a high and low quality species as intermediate. For example, a monoculture with intermediate concentrations of nitrogen and a two- species mixture composed equally of one species with high and the other with low nitrogen concentration have the same intermediate community-weighted value. In the intermediate monoculture, levels of herbivory would also be intermediate. However, in the mixture of low and high quality species, herbivores could concentrate feeding on the high quality species because of feeding preferences. The removal of the same amount of biomass from the high quality species in a monoculture and in a mixture would cause the species level herbivory to be twice as high in the mixed community due to the halved relative abundance. Taking into account the rejected low quality plant causes the community level herbivory of the mixture to equal the herbivory suffered by a monoculture of the high quality plant species rather than the expected intermediate level. Given this, strong preferences by the feeding herbivores would cause levels of community herbivory to be close to the levels of the monocultures of the most attractive species. As a consequence, additive models based on community-weighted traits
57 underestimate herbivory in mixed communities. That is supported by the results of the current study.
Second, the community of herbivores can change with plant composition thus changing herbivore loads and levels of herbivory and their relations to plant traits. A general trend of higher plant diversity is to increase the diversity and abundance of herbivores, which was also documented at the field site of the Jena Experiment (Scherber et al. 2010a). A likely explanation is that increasing plant diversity generally leads to increased plant architectural complexity which in turn can provide more protection to more types of herbivores with more hiding and resting places (Southwood et al. 1979, Lawton 1983). This is supported by the fact that our relaxed model indicated architectural traits to be potentially more important than nutritive quality traits. Higher plant diversity also leads to higher plant productivity (Naeem et al. 1994, Tilman et al. 1996), which was also found in the Jena Experiment (Marquard et al. 2009a). The higher availability of resources may correlate with higher herbivore pressure (Haddad et al. 2001).
Third, herbivores can change their selection of food plants based on the composition of available plant species. For example, more diverse communities also provide a more diverse nutritional regime, thus potentially diluting deterrent chemicals or improving the nutrient balance of generalists (Bernays and Bright 1993). Therefore, the absolute nutritional quality of a particular species (or community) is less important than the availability of different resources allowing for dietary mixing. These effects can contribute to the decrease of importance of nutritional characteristics (leaf nitrogen and lignin concentration) in predicting herbivory in communities compared to herbivory in monocultures.
58 Fourth, the effect of trait values of a given plant species can be modulated by traits of other surrounding plants. For example, the level of herbivory in a community could be partly driven by magnet species (or their absence) in a similar way as documented for plant pollinator interactions (Johnson et al. 2003). For instance, highly nutritious species could attract herbivores to the site. Once the preferred plant species has been consumed, it might be advantageous for the herbivore to feed on surrounding less nutritious species rather than leaving the site and using time to search for a new site or accepting risks associated with moving to a new site. This is known as spillover effect (White and Whitham 2000), evidences for which has been found in the Jena Experiment where the presence of legumes increased the rates of invertebrate herbivory on the other species (Scherber et al. 2006b). On the other hand, also associational resistance between plant species can occur, leading to a decrease of herbivore damage on attractive plant species due to impaired host finding because of interference by associated non-host plant species (Tahvanainen and Root 1972, Hamback et al. 2000, Finch and Collier 2012).
Fifth, the trait values expressed by a species can change depending on vegetation context, i.e. the other species that are growing in close proximity. For example, where legume species were present in the Jena Experiment, leaf nitrogen concentration in grasses may increase (Gubsch et al. 2011). It has also been shown for one plant species in the Jena Experiment that the allocation into two different secondary compounds changes with increasing diversity (Mraja et al. 2011). While trait variation certainly is an important mechanism causing non-additive effects, the values of several of the traits that were important in monocultures are unlikely to change much across communities (e.g. root architecture or leaf life span). Consequently, trait-plasticity cannot be the only mechanisms causing non-additive effects and the other above-mentioned mechanisms most likely also play an important role in determining levels of herbivory in plant communities.
59 3.7- Conclusion
Our study has shown that the predictability of herbivory in mixed plant communities decreases considerably compared to trait-based predictions of herbivory in monocultures. It seems that complex plant-insect interactions are of importance in determining the levels of herbivory in multispecies communities. Which of the above- described potential mechanisms causes predictions of community herbivory to deviate when based on additive models remains to be investigated in more detailed studies on (1) the plasticity of relevant traits, (2) changes in the herbivore community along the diversity gradient, and (3) direct investigations on feeding preferences and behavior of important herbivore groups. Advances in the mechanistic understanding of plant herbivore interactions are to be expected by explicitly incorporating non-additive effects in trait-based models. To do this the damage of individual species/community combinations has to be modeled separately incorporating also community properties like e.g. height, biomass, diversity and/or the distinctiveness of the target species (e.g. the difference between the traits of the target species to all other species divided by the differences between the other species).
3.8- Acknowledgements
We would like to thank Anne Ebeling, the gardeners and technical staff who have worked on the Jena Experiment for maintaining the site. We thank Enrica de Luca who provided biomass data and Annett Lipowsky and Marlen Gubsch who provided some plant trait data. The experiment was funded by the Deutsche Forschungsgemeinschaft (FOR 456). This study was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds Quebecois de Recherche sur la Nature et les
60 Technologies (FQRNT) and by the AquaDiva@Jena project financed by the state of Thuringia. The study has also been supported by the TRY initiative on plant traits (www.trydb.org) and we thank all the contributors that have provided trait data via the TRY database. TRY is/has been supported by DIVERSITAS, IGBP, the Global Land Project, the UK Natural Environment Research Council (NERC) through its program QUEST (Quantifying and Understanding the Earth System), the French Foundation for Biodiversity Research (FRB), and GIS "Climat, Environnement et Societe" France.
SUPPLEMENTAL MATERIAL
Annex A: Species pool of the Jena Experiment with inclusion status in the analyses.
Annex E: Detailed list of the seven traits selected as being good predictors of herbivore damage in monocultures and associated graph of predicted against measured values of damage.
Annex F: Correlation matrix of the different community properties of the communities in the Jena Experiment.
Annex G: Log-response ratios for non-additive and additive effects based on the different models.
61 CHAPITRE IV
CONCLUSION
62 CONCLUSION
Dans ce memoire, j'ai d'abord demontre qu'il est possible de predire avec un haut niveau de confiance les dommages foliaires causes par des herbivores invertebres et subis par des plantes poussant en monoculture et ce, a partir seulement de certains traits cles des plantes etudiees. Ensuite, j'ai demontre que des interactions entre differentes esp£ces de plantes poussant ensembles dans une communaute modifient les relations entre traits et herbivorie qui sont observees en monocultures. Dans ce chapitre, je ferai d'abord un court resume des resultats principaux des chapitres II et ID. Ensuite, pour chaque section, je parlerai des implications et des prochaines etapes a suivre pour aller plus loin et avancer nos connaissances dans ce domaine suite a mes resultats.
4.1- Necessite de travailler avec un grand nombre de traits
A partir d'un tres grand nombre de traits, seulement sept traits ont ete identifies comme d'importants predicteurs de l'herbivorie causee par des invertebres sur des plantes poussant en monocultures (chapitre II). Les patrons de correlations entre les traits inclus dans les analyses et la nature des sept traits finalement selectionnes demontrent qu'il est important de travailler avec le plus grand nombre de traits possible pour predire de fagon la plus precise possible l'herbivorie. Ceci est principalement supporte par deux facteurs qui sont illustres par les resultats du chapitre H
Premierement, si deux traits, x et y, sont fortement correles et affectent de la meme maniere le degre d'herbivorie subi par les plantes, leur effet sera potentiellement
63 confondu lorsqu'ils seront mis ensembles dans une analyse multifactorielle. Inversement, si seulement le trait x est disponible pour analyse, il est possible que son effet sur l'herbivorie soit significatif, mais depende surtout de sa correlation avec le trait y. En d'autres mots, il y a un lien de causalite entre y, qui est indisponible, et l'herbivorie, mais pas entre x et l'herbivorie. On peut done croire que JC affecte l'herbivorie alors que e'est en fait y qui l'affecte. Ainsi, en travaillant avec un tres grand nombre de traits, on peut etablir les niveaux de correlation entre les differentes variables. Ceci permet, en se basant sur la variable que l'on veut predire et les connaissances actuelles a son sujet, de combiner certains traits, d'en eliminer ou simplement d'etre alerte aux effets confondants potentiels.
Le deuxieme facteur supportant l'utilisation d'un grand nombre de traits pour tenter d'expliquer un processus tel que l'herbivorie est que les sept traits qui ont ete selectionnes comme predicteurs sont repartis sur un large eventail de types de trait (morphologique, physiologique, phenologique et relie aux herbivores; chapitre II). Aussi, un resultat surprenant fut la selection de 1'architecture des racines comme predicteur important de l'herbivorie sur les feuilles. Le raisonnement ici est que 1) plusieurs differents aspects d'une plante influencent probablement l'herbivorie et 2) on ne connait pas a priori tous ces facteurs influents avant de les tester. 11 faut done travailler avec le plus de facteurs (traits) possibles afin d'identifier ceux qui sont d'importance malgre le fait que l'importance de certains puisse etre contre-intuitive.
Cette proposition se base sur la supposition que, en travaillant avec le plus grand nombre de traits possibles, j'ai vraiment pu selectionner les traits qui sont generalement importants pour le processus d'herbivorie. Pour mettre a l'epreuve cette supposition, il serait interessant de faire deux choses. Premierement, refaire les memes analyses de selection de trait dans les memes monocultures, mais pour differentes annees pour voir
64 si les relations observees entre traits et herbivorie sont constantes au fil du temps. Deuxiemement, il serait extremement interessant de faire ces memes analyses dans d'autres prairies experimentales avec d'autres especes de plantes, ce qui pourrait potentiellement permettre une plus grande generalisation de mes resultats.
Independamment de nouvelles analyses concernant 1'herbivorie, il serait hautement interessant de verifier si mes resultats et mes suppositions s'appliquent aussi a d'autres processus ecologiques. Effectivement, une grande quantity de travaux a ete consacree k tenter de predire, a partir des traits des plantes, certains processus ecologiques tels que 1'assemblage ou la composition des communautes (Sonnier et al., 2010 ; Tanaka et Koike, 2011). Cependant, et principalement pour des raisons de logistique telles que le temps et les moyens necessaires pour mesurer un trait donne sur de nombreuses especes de plante, plusieurs des etudes utilisant des traits pour predire des processus, quoique couronnee de succes, sont basees sur un relativement petit nombre de traits. II faut done se demander si ces traits mis en relation avec divers processus ecologiques sont vraiment les meilleurs predicteurs de ces processus. L'inclusion de nouveaux traits dans les modeles existants (et peut-etre aussi 1'exclusion de certains traits deja inclus dans ces modeles) pourraient potentiellement ameliorer les resultats et, par le fait meme, notre comprehension de certains processus. Des initiatives comme le TRY database (Kattge et al., 2011), une base de donnees regroupant les donnees de dizaines de chercheurs sur des centaines de traits de centaines d'especes de plante, devraient pouvoir nous aider a realiser ces defis et ce, dans plusieurs domaines.
4.2- Les interactions entre differentes especes de plantes affectent 1'herbivorie
65 Des relations fortes observees en monocultures entre traits des plantes et herbivorie subie par ces plantes ne s'appliquent pas bien en polycultures, ou alors de fa§on beaucoup moins convaincante (chapitre HI). Des interactions entre les differentes especes de plantes d'une polyculture sont probablement la cause de cette difference entre une vegetation mixte et une monoculture. Bien que le(s) mecanisme(s) en jeu ne soi(en)t toujours pas clairement identifie(s), ces effets des interactions entre especes de plante augmentent avec l'augmentation d'interactions potentielles, c'est-£-dire avec 1'augmentation du nombre d'especes de plante presentes dans la communaute (chapitre HI)-
D y a deux possibilites generates pouvant expliquer les effets non-additifs que les interactions entre les especes de plante semblent avoir sur le degre d'herbivorie subi par une communaute et qui font que les donriees obtenus en monocultures ne sont d'une utilite que limitee lorsqu'en polycultures. Premi£rement, il se peut que ces interactions affectent les relations entre traits et herbivorie en rendant certains traits moins importants qu'en monocultures et d'autres traits plus importants. Dans ce cas, il se peut que d'autres traits que ceux selectionnes en monocultures aient ete de bons predicteurs de l'herbivorie en polycultures, mais ne se trouvaient pas dans notre modele final. Deuxiemement, il se peut qu'en polycultures, des proprietes de la communaute soient de meilleurs predicteurs de l'herbivorie que les traits agreges des plantes. Dans un tel cas, l'importance accordee aux traits en monocultures serait surpassee en polycultures par l'importance de certaines caracteristiques de la structure de la vegetation des communautes comprenant plusieurs especes de plante, ou encore par la productivity, ou une autre propriete de la communaute. Cependant, la nature d'une telle propridte reste nebuleuse pour 1'instant (chapitre HI). D est aussi important de noter que ces deux possibilites ne s'excluent pas mutuellement et que les resultats de ce memoire indiquent qu'un melange des deux provoque probablement les effets non-additifs observes.
66 La prochaine etape evidente suite a mes resultats et mes nouvelles hypotheses serait d'agreger, pour toutes les communautes etudiees au chapitre HI, tous les traits (42 traits) utilises dans les analyses du chapitre II et peut-etre meme aussi d'autres traits. Ensuite, il faudrait refaire toutes les analyses de selection des traits faites au chapitre n, mais cette fois-ci pour les polycultures du chapitre HI. De cette fagon, il serait possible de verifier si d'autres traits deviennent d'importance particuliere pour predire le degre d'herbivorie en polycultures et ce, au detriment des traits d'importance en monocultures. Suite a ces nouvelles analyses, un continuum de resultats serait possible entre deux extremes : i) aucun autre trait n'est selectionne et le pouvoir de prediction du modele reste faible; ii) seuls d'autres traits que ceux identifies en monocultures sont selectionnes et le pouvoir de prediction du nouveau modele est egal ou superieur au modele developpe en monocultures. Le premier cas demontrerait que les proprietes d'une communaute, quelles qu'elles soient, deviennent plus importantes que les traits des plantes pour predire le degre d'herbivorie d'une communaute comprenant plusieurs especes. Le deuxieme cas demontrerait que les interactions entre les especes de plante d'une communaute affectent grandement les relations entre traits et herbivorie, mais que les traits restent d'importance majeure pour predire 1'herbivorie.
En r6alit6, des resultats se situant entre ces deux extremes sont plus probables. De plus, dans tous les cas il serait important de mesurer le plus de proprietes des communautes possibles afin de pouvoir identifier celle(s) qui affecte(nt) le plus les degres d'herbivorie. Ceci pourrait nous permettre de comprendre a la fois comment les interactions entre especes de plante affectent les degres d'herbivorie et pourquoi elles modifient de fagon non-additive les relations entre traits et herbivorie. Cette differentiation serait un avancement important dans le domaine de l'herbivorie puisque, malgre l'importance que pourrait avoir une telle quantification pour notre comprehension du phenomene, ces deux aspects n'ont jamais 6t€ adresses ensembles dans une meme etude.
67 Identifier les facteurs influen?ant le degre d'herbivorie subi par les plantes (ou n'importe quel autre processus ecologique) est la cle a la comprehension de ce phenomene d'importance populationnel, communautaire et ecosystemique. Pour ce faire, nous devons utiliser des ressources comme le TRY database et, ce qui est peut-etre encore plus important, continuer a travailler dans des experiences comprenant beaucoup de polycultures differentes. Ceci permettra d'avoir les donnees necessaires pour departager plus precisement le role des traits des plantes du r61e de la communaute en tant que telle dans le controle de l'herbivorie.
68 ANNEXES
ANNEX A. Species pool of the Jena Experiment with an indication whether the particular species was included in the analysis. Species in bold were excluded from the analysis. For details about exclusion refer to the manuscript.
Species Functional Species Functional group group Achillea millefolium Tall herb Leucanthemum vulgare agg. Tall herb Ajuga replans Small herb Lotus corniculatus Legume Alopecurus pratensis Grass Luzula campestris Grass Anthoxanthum odoratum Grass Medicago lupulina Legume Anthriscus sylvestris Tall herb Medicago x. varia Legume Arrhenatherum elatius Grass Onobrychis viciifolia Legume Avenula pubescens Grass Pastinaca sativa Tall herb Bellis perennis Small herb Phleum pratense Grass Bromus erectus Grass Pimpinella major Tall herb Bromus hordeaceus Grass Plantago lanceolata Small herb Campanula patula Tall herb Plantago media Small herb Cardamine pratensis Tall herb Poa pratensis Grass Carum carvi Tall herb Poa trivialis Grass Centaurea jacea Tall herb Primula veris Small herb Cirsium oleraceum Tall herb Prunella vulgaris Small herb Crepis biennis Tall herb Ranunculus acris Tall herb Cynosurus cristatus Grass Ranunculus repens Small herb Dactylis glomerata Grass Rumex acetosa Tall herb Daucus carota Tall herb Sanguisorba officinalis Tall herb
69 ANNEX A continued
Festuca pratensis Grass Taraxacum officinale agg. Small herb Festuca rubra Grass Tragopogon pratensis Tall herb Galium mollugo agg. Tall herb TrifoUum campestre Legume Geranium pratense Tall herb Trifolium dubiurn Legume Glechoma hederacea Small herb Trifolium fragiferum Legume Heracleum sphondylium Tall herb Trifolium hybridum Legume Holcus lanatus Grass Trifolium pratense Legume Knautia arvensis Included Trifolium repens Legume Lathyrus pratensis Legume Trisetum flavescens Grass Leontodon autumnalis Small herb Veronica chamaedrys Small herb Leontodon hispidus Small herb Vicia cracca Legume
70 ANNEX B. List of all measured and collected plant traits from published literature, the database of the Jena Experiment and international databases to predict leaf standing herbivore damage observed in monocultures at the field site of the Jena Experiment (Germany). The traits are divided into four types of trait (the functional groups not being considered as traits) and the specific sources of each trait are stipulated as well as a detailed description of the determination of each trait.
Name Method Sources FUNCTIONAL GROUPS Grasses Three groups of ecologically relevant attributes Roscher et al. (2004) Tall herbs were distinguished in order to divide the species Roscher et al. (2004) Small herbs pool into functional groups focusing mainly on Roscher et al. (2004) Legumes attributes describing the spatial and temporal Roscher et al. (2004) complementarity of established plant individuals in the community. See Roscher et al. (2004) for details. HERBIVORE-RELATED TRAITS Polyphagous acarians Information on all types of potential invertebrate Bacon et Rathod (1964) Oligophagous acarians herbivores was extracted through an extensive Monophagous acarians research of published literature and by screening Acarian herbivores various online databases specialized in the Polyphagous aphids documentation of host-insect interactions. Bacon et Rathod (1964); Fitter et Peat Oligophagous aphids Herbivores not found in Europe or feeding only (1994); Holman (2009); Blackman et
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Monophagous aphids on other plant parts than leaves were excluded. Eastop (2006); Borner (1952) Aphid herbivores Information about host-herbivore specificity was Polyphagous coleopterans also recorded (i.e. monophagous, oligophagous Bacon et Rathod (1964); Edmunds (2003); Oligophagous or polyphagous). Since polyphagous, Fitter et Peat (1994); Hemerik et al. coleopterans oligophagous and total herbivores were often (2003); Bohme (2001) Monophagous correlated and that there was a general lack of coleopterans information for the monophagous category, the Coleopteran herbivores three specificity categories were summed up and Polyphagous dipterans only the total numbers of potential herbivores, Bacon et Rathod (1964); Edmunds (2003); Oligophagous dipterans for all groups of herbivore, were used. The total Fitter et Peat (1994); Pitkin et al. (2009);
Monophagous dipterans number of potential herbivores was loge- Stibick (2004) Dipteran herbivores transformed for all herbivore groups to improve Polyphagous hemipterans their distributions. The references listed here are Bacon et Rathod (1964); Fitter et Peat Oligophagous the same for all categories for a given group of (1994); Evans (2007) hemipterans herbivores (i.e. polyphagous, oligophagous, Monophagous monophagous and total). hemipterans Hemipteran herbivores
72 ANNEX B continued
Polyphagous Bacon et Rathod (1964); Edmunds (2003); lepidopterans Fitter et Peat (1994); Eeles et al. (2002); Oligophagous Schon et al. (2002); Reinhardt et al. lepidopterans (2007) Monophagous lepidopterans Lepidopteran herbivores Mollusc herbivores Koztowski et al. (2006); Buschmann et al. (2005); del-Val et Crawley (2004); Wilby et Brown (2001); Keller et al. (1999); Scheidel et Bruelheide (1999); Hill et Silvertown (1997); Rollo (1983); Dirzo (1980); Carter et al. (1979); Beyer et Saari (1978); Davidson (1976); Getz (1959); Fromming (1950a); Fromming (1950b); Fromming (1939a); Fromming (1939b); Fromming (1938); Schmid (1929) ANNEX B continued
Polyphagous orthopterans Bacon et Rathod (1964); Fitter et Peat Oligophagous (1994); Ingrisch et Kohler (1998) orthopterans Orthopteran herbivores Palatability Ordinal variable (1-9), from poisonous to high Kiihn et al. (2004); Briemle et al. (2002) nutritive value. Based on several plant characteristic like preference from the herbivores, protein and mineral content, period of availability for herbivores, toxicity and other defenses (based on Dierschke and Briemle 2002). Mowing tolerance Ordinal variable (1-9), from intolerant to very Biolflor; Briemle et al. (2002) tolerant to mowing. Based on Briemle and Ellenberg (1994) Trampling tolerance Ordinal variable (1-9), from intolerant to very Biolflor; Briemle et al. (2002) tolerant to trampling. Based on G. Briemle's observations.
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Grazing tolerance Ordinal variable (1-9), from intolerant to very Kiihn et al. (2004); Briemle et al. (2002) tolerant to vertebrate grazing. Based on regeneration capacity, capacity of avoiding grazing by being small, a very punctual development in spring or general unpopularity. PHYSIOLOGICAL TRAITS Secondary metabolites Alkaloids Information on all types of secondary metabolites Sources for all secondary metabolites : Amines was extracted through an extensive research of Raal et al. (2011); Tava et al. (2011); Amides published literature. The present list of Veitch et al. (2011); Agerbirk et al. Amino-acids references enclosed all types of secondary (2010a); Agerbirk et al. (2010b); Bostan Cyanogenics compounds. Compounds that are found only in et al. (2010); Reynaud et al. (2010); Glucosinolates other plant parts than leaves were excluded. Saviranta et al. (2010); Hu et al. (2009); N-containing Precise information on several small different Kigathi et al. (2009); Koelzer et al. compounds groups of compounds was collected. Those small (2009); Regos et al. (2009); Carlsen et groups were combined, i.e. summed up, together Fomsgaard (2008); Hunt et al. (2008); Non-phenolic oxygen to create classes (e.g. N-containing compounds) Skladanka et al. (2009); Benedek et al. heterocycles of similar compounds, because there was not (2007); Fraisse et al. (2007); Kiipeli et al. (2007); Okrslar et al. (2007); Nikolova et
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Benzopyranoids (non- enough information to use the. small groups Gevrenova (2006); Budzianowski et al. flavonoid) alone. Some of the smaller groups were totally (2005); Ramazanov (2005); Tostao et al. Flavonoids excluded (e.g. flavonoids) because many (2005); Jiirgens et Dotterl (2004); Tannins compounds of those groups have other functions Reichling et Galati (2004); Boland et al. Polyaromatic compounds than deterrence (e.g. pigmentation). The (2003); Jordon-Thaden et Louda (2003); Simple phenols resulting traits are number of different secondary Wu et al. (2003); Bos et al. (2002); Aromatic compounds compounds of a given class of compounds that Castells et al. (2002); Cornu et al. (2001); can be found in the leaves of a plant. The Silica Hegnauer (2001); Laca et al. (2001); Volz Homoterpenoids class is a binary variable indicating only if silica et Clausen (2001); Zidorn et Stuppner Hemiterpenoids bodies are found in a plant. The N-containing (2001); Zidorn et al. (2000); Komprda et Monoterpenoids compounds group is the sum of all smaller al. (1999); Kunvari et al. (1999); Dohi et Sesquiterpenoids groups containing nitrogen. The Aromatic al. (1998); Fons et al. (1998); Mariaca et Diterpenoids compounds group is the sum of Non-phenolic al. (1997); Saleh et Glombitza (1997); Tri terpenoids oxygen heterocycles, Benzopyranoids, Tannins Avato et Tava (1995); Iason et al. (1995); Tetraterpenoids and Polyaromatic compounds. The Terpenoids Buckingham (1994); Duke (1992); Terpenoids group is the sum of Homoterpenoids, Miyase et al. (1992); Glasby (1991); Polyacetylenes Hemiterpenoids, Diterpenoids and Triterpenoids. Bicchi et al. (1990); Saadi et al. (1990);
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Silica N-containing compounds, Aromatic compunds Koshino et al. (1989); Koshino et al.
and Terpenoids were loge-transformed. (1988); Kisiel et Kohlmiinzer (1987); Dmitruk (1986); Kojima et Ogura (1986); Molgaard (1986); StPyrek (1985); Yoshihara et al. (1985); Jay et al. (1984); Teslov (1979); Rambeck et al. (1979); Ingham (1978); Shelyuto et al. (1978); Teslov (1976); Hultin et Torssell (1965); Davies et Ashton (1964); Ashton et Jones (1959) Primary metabolites Leaf/shoot nitrogen Average from different sources, including different Roscher et al. (2011a); Gubsch et al. concentration sources from the TRY database and (2011); Laughlin et al. (2010); Craine et measurements from different years in the al. (2009); Fortunel et al. (2009); Kattge monocultures of the Jena Experiment. See et al. (2009); Pakeman et al. (2009); Material and Methods section for details on the Willis et al. (2009); He et al. (2008);
measurements in the Jena Experiment. Loge- Pakeman et al. (2008); Reich et al. transformed. (2008); Gamier et al. (2007); He et al. (2006); Kazakou et al. (2006); Wright et
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al. (2006); Craine et al. (2005); Han et al. (2005); Comelissen et al. (2004); Wright et al. (2004); Quested et al. (2003); Meziane et Shipley (1999a); Comelissen (1996); Jena-Experiment database Leaf/shoot carbon Average from different sources, including different Roscher et al. (2011a); Gubsch et al. concentration sources from the TRY database and (2011); Laughlin et al. (2010); Craine et measurements from different years in the al. (2009); Fortunel et al. (2009); monocultures of the Jena Experiment. See Pakeman et al. (2009); Willis et al. Material and Methods section for details on the (2009); He et al. (2008); Pakeman et al. measurements in the Jena Experiment. (2008); Gamier et al. (2007); He et al. (2006); Kazakou et al. (2006); Craine et al. (2005); Han et al. (2005); Comelissen et al. (2004); Quested et al. (2003); Jena- Experiment database Leaf lignin See Material and Methods section for details on Own measurements concentration the measurements. Leaf cellulose See Material and Methods section for details on Own measurements concentration the measurements.
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Leaf hemicellulose See Material and Methods section for details on Own measurements concentration the measurements. Leaf water-soluble matter It includes simple sugars, amino-acids, peptides, Own measurements concentration water-soluble phenolics, but also some cell wall components, such as 6-glucans and pectins, mucilage and some storage polysaccharides (Vansoest et al. 1991). See Material and Methods section for details on the measurements. Leaf primary fiber See Material and Methods section for details on Own measurements
concentration the measurements. Loge-transformed. Relative growth rate Average from several different sources, including Kazakou et al. (2006); Vile (2005); Shipley different sources from the TRY database, as the (2002); McKenna et Shipley (1999); maximum relative growth rate of the seedling (g Meziane et Shipley (1999b); Cornelissen of new biomass/g of total biomass/day). 15 (1996); Fitter et Peat (1994); Grime et values have been imputed (see Annex C for Hunt (1975); Elias et Chadwick (1979) details). Leaf phosphorus Average from different sources, including different Roscher et al. (2011b); Laughlin et al. concentration sources from the TRY database and (2010); Fortunel et al. (2009); Pakeman et measurements in the monocultures of the Jena al. (2009); He et al. (2008); Pakeman et
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Experiment. See Material and Methods section al. (2008); Gamier et al. (2007); He et al. for details on the measurements in the Jena (2006); Kazakou et al. (2006); Han et al. Experiment. Six values have been imputed (see (2005); Cornelissen (1996) Annex C for details). MORPHOLOGICAL TRAITS SLA Specific leaf area. Average from different sources Roscher et al. (2011a); Gubsch et al. including different sources from the TRY (2011); Laughlin et al. (2010); Craine et database and measurements from different years al. (2009); Fortunel et al. (2009); Kattge in the monocultures of the Jena Experiment. See et al. (2009); Pakeman et al. (2009); Material and Methods section for details on the Willis et al. (2009); He et al. (2008); measurements in the Jena Experiment. Kleyer et al. (2008); Pakeman et al. (2008); Reich et al. (2008); Gamier et al. (2007); He et al. (2006); Kazakou et al. (2006); Shipley et al (2006); Wright et al. (2006); Craine et al. (2005); Han et al. (2005); Vile (2005); Cornelissen et al. (2004); Wright et al. (2004); Quested et al. (2003); Shipley (2002); Shipley et Vu (2002); McKenna et Shipley (1999);
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Meziane et Shipley (1999a); Meziane et Shipley (1999b); Cornelissen (1996); Shipley (1995); Jena-Experiment database Pubescence of the stem Ordinal variable (0-4). This index of pubescence Eggenberg et Mohl (2007) Pubescence of the leaves was designed based on Eggenberg and Mohl Pubescence (2007), which describes all anatomical characteristics of the plants for identification without flowers. 0 = hairless, 1 = sparsely hairy to densely hairy, 2 = finely pubescent to hirsute, 3 = densely pubescent to pilosulous and 4 = piliferous to roughly hairy. The pubescences of the leaves and of the stem were summed to give a general value for the whole plant (0-8) and loge-transformed. Leaf dry matter content Average from different sources within the TRY Laughlin et al. (2010); Fortunel et al. database. This is the leaf dry matter content (g of (2009); Pakeman et al. (2009); Kleyer et dry weight/g of fresh weight) al. (2008); Pakeman et al. (2008); Gamier et al. (2007); Kazakou et al. (2006); Shipley et Vu (2002)
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Leaf distribution Ordinal variable (1-3) representing the distribution LEDA; Biolflor of the leaves on the stem, averaged from two sources when conflicting. 1 = Rosette, 2 = Hemirosette, 3 = Erosulate Stem growth form Percentage of erection of the stem from 0 to 100%. LEDA One value has been imputed (see Annex C for details). Leaf sclerophylly Ordinal variable (1-5) representing the physical Ktthn et al. (2004); Biolflor strenght of the leaves, or classes of scleropylly, averaged from two sources when conflicting. 1 = Helomorphic, 2 = Hygromorphic, 3 = Mesomorphic, 4 = Scleromorphic and 5 = Succulent Root architecture Ordinal variable (1-3), representing the type of the Roscher et al. (2004) root system architecture: 1 = long-living primary root system, 2 = secondary fibrous roots in addition to the primary root system, 3 = short- living primary root system with extensive secondary root system.
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Root depth Ordinal variable (1-5), representing the depth of Roscher et al. (2004) the roots in the soil. 1 = up to 20cm, 2 = up to 40cm, 3 = up to 60cm, 4 = up to 100cm, 5 = >100cm Seed mass Average from several sources, including different Laughlin et al. (2010); Fortunel et al. sources from the TRY database and one from the (2009); Green (2009); Pakeman et al. seeds sown in the Jena experiment (see Roscher (2009); Paula et al. (2009); Kleyer et al.
et al. (2004) for details). Loge-transformed. (2008); Pakeman et al. (2008); Paula et Pausas (2008); Royal Botanic Gardens Kew (2008); Gamier et al. (2007); Kiihn et al. (2004); Roscher et al. (2004); Otto (2002) Vegetative height May Average of the mean vegetative plant height at the Jena-Experiment database end of May 2003-4. Vegetative height August Average of the mean vegetative plant height at the Jena-Experiment database end of August 2003-4. Maximum height Theoretical value of the maximum plant height. LEDA; Hegi (1931) From an identification book and the LEDA database.
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Minimum height Theoretical value of the minimum plant height. LEDA; Hegi (1931) From an identification book and the LEDA database. Vegetative height spring Measured several times over the springs 2003-4 Jena-Experiment database and averaged here, as the mean vegetative plant height in spring. More an indication of the general height over spring than a punctual variable. Vegetative height summer Measured several times over the summers 2003-4 Jena-Experiment database and averaged here, as the mean vegetative plant height in summer. More an indication of the general height over summer than a punctual variable. Total height spring Measured several times over the springs 2003-4 Jena-Experiment database and averaged here, as the mean total plant height (includes flower if higher than vegetative parts) in spring. It is more an indication of the general height over spring than a punctual variable.
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Total height summer Measured several times over the summers 2003-4 Jena-Experiment database and averaged here, as the mean total plant height (includes flower if higher than vegetative parts) in summer. It is more an indication of the general height over summer than a punctual variable. Typical height Average of the typical plant height over several Fortunel et al. (2009); Green (2009); sources within the TRY database. Pakeman et al. (2009); Pakeman et al. (2008); Paula et Pausas (2008); Gamier et al. (2007); Shipley et al. (2006); Cornelissen et al. (2004); Quested et al. (2003); Cornelissen (1996) Seed shedding height Mean seed shedding height. Three values have Kleyer et al. (2008) been imputed (see Annex C for details). Vegetative height Average of the mean vegetative plant height over Fortunel et al. (2009); Pakeman et al. several sources within the TRY database. (2009); Pakeman et al. (2008); Gamier et Available only for few species. al. (2007) Height spring Average of "Vegetative height May", "Vegetative height spring" and "Total height spring". Gives a general value of plant height in spring in the Jena
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Experiment. Height summer Average of "Vegetative height August", "Vegetative height summer" and "Total height summer". Gives a general value of plant height in summer in the Jena Experiment. Height Average of "Typical height" and "Seed shedding height" since they were strongly correlated. Gives a general value of plant height from the TRY database. PHENOLOGIC AL TRAITS Reproduction Principal way of reproduction: 1 = principally by Fitter et Peat (1994); Biolfior seeds, 2 = by seeds and vegetatively, 3 = principally vegetatively. Average from two sources when conflicting. Leaf lifespan Leaf lifespan or seasonality of foliage: 1 = Rothmaler (2002) deciduous, 2 = partly deciduous, 3 = evergreen. Beginning of flowering Beginning month of the flowering period (January Biolfior; Senghas et Seybold (1996); Hegi = 1, December = 12). Average from different (1931) identification books and database.
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End of flowering Last month of the flowering period (January = 1, Biolflor; Senghas et Seybold (1996); Hegi December = 12). Average from different (1931) identification books and database. Period of flowering Length in month of the flowering period, calculated from "Beginning of flowering" and
"End of flowering". Loge-transformed. Beginning of seed Beginning month of the seed shedding period LEDA shedding (January = 1, December = 12). Six values have been imputed (see Annex C for details). End of seed shedding Last month of the seed shedding period (January = LEDA 1, December = 12). Period of seed shedding Length in month of the seed shedding period, calculated from "Beginning of seed shedding" and "End of seed shedding". Six values have
been imputed (see Annex C for details). Logc- transformed. Flowering phase Number of flowering phases in one year. Biolflor Longevity Plant longevity: 1 = annual, 2 = biennial, 3 = LEDA; Biolflor; Eggenberg et Mohl perennial. Average from different sources. (2007); Senghas et Seybold (1996)
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Competitor Binomial variable (y/n, 1/0). Forbs with high Biolflor competitive power due to their morphological and/or physiological characters and life history traits. Stress-tolerant Binomial variable (y/n, 1/0). Species with only Biolflor little growth and morphological and/or physiological adaptations to conditions that may be either very rare or overabundant. Ruderal Binomial variable (y/n, 1/0). Usually annual, Biolflor weedy plant species which produce many seeds and can easily colonize pioneer habitats. Notes: "Method" gives a detailed description of how a trait has been measured or collected and how exactly it was determined. The references TRY, LEDA and Biolflor refers respectively to Kattge et al. (2011), Kleyer et al. (2008) and Klotz et al. (2002). The reference "Jena-Experiment database" refers to unpublished data measured by colleagues in the Jena Experiment. In bold are the traits used in the analysis, the other being excluded or combined.
88 ANNEX C. List of the traits for which some values necessitated imputation before those variables could be used to predict leaf standing herbivore damage observed in monocultures at the field site of the Jena Experiment (Germany).
Variables Nb Species missing values Method Reference variables or species Relative growth 15 Cirsium oleraceum; Crepis biennis; Multiple imputation Seed mass; Beginning of seed rate Galium.mollugo; Geranium shedding; LL; Shoot nitrogen pratense; Knautia arvensis; concentration; Trampling Onobrychis viciifolia; Pastinaca tolerance sativa; Pimpinella major; Plantago media; Primula veris; Tragopogon pratensis; Trifolium fragiferum; Trifolium hybridum; Veronica chamaedrys Leaf phosphorus 6 Alopecurus pratensis; Cirsium Multiple imputation LDMC; Leaf hemicellulose concentration oleraceum; Pastinaca sativa; content; Leaf water-soluble Plantago media; Primula veris; content Trifolium fragiferum Stem growth 1 Taraxacum officinale Comparison Leontodon hispidus; L. form autumnalis Seed shedding 3 Phleum pratense; Poa trivialis; Multiple regression 7.51997 + 0.3019*Maximum height Taraxacum officinale height +0.45872*Minimum
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height +0.05004*Typical height +0.52805* Vegetative height R-squared = 0.8525 Beginning of Centaurea jacea; Galium mollugo; Comparison Beginning of flowering + 1 seed shedding Geranium pratense; Medicago x varia; Taraxacum officinale; Tragopogon pratensis Period of seed 6 Centaurea jacea; Galium mollugo; Comparison Period of flowering + 1,5 shedding Geranium pratense; Medicago x varia; Taraxacum officinale; Tragopogon pratensis
Notes: Nb: Number of missing values for a given trait; Method: "Comparison" means that the imputed variable has simply been compared with a similar complete variable or a similar species to deduce the missing values. "Multiple regression" means that the imputed variable has been regressed against several correlated variables to get an equation predicting the missing values. "Multiple imputation" means that the imputation has been done following Su et al. (2011); The fourth column lists the variables (or species) that have been used to impute the missing values of a given trait.
90 ANNEX D. 1 100 to u 09 QJ U c 03 o _ t o oQ. o _ o E CVl ° " CCL o CVl - 0 10 20 30 Trait order in the RF results ANNEX D. Graph of the results from the Random Forest (RF) analyses to select which plant traits are of importance to predict the invertebrate herbivore damage in the monocultures of the Jena Experiment (Germany). Each point is a trait and the x-axis represents the order of the traits according to their importance score value which is represented by the y-axis. The higher a trait's score is the more important is the trait to predict herbivore damage. All traits (13) to the left of the line were selected as being of importance; all traits to the right were discarded. The selection was based on the curve illustrated here, looking at the decrease in importance between traits. The last relatively large drop in importance was after the 12th trait, thus the first 12 traits should have been selected. However, the five following traits were also tested with a multiple regression to confirm that traits could be excluded from this point. Only the 13th trait proved to be of importance and thus was also kept for the next step of the analyses. 91 ANNEX E. Description of sources and methods of collection or measurement for the seven traits selected in Loranger et al. (in press) that were used to calculate community-weighted trait values. Those community-weighted traits were used to predict the leaf standing herbivore damage measured in communities at the field site of the Jena Experiment (Germany). The traits are divided into four trait groups. Name Sources Method PHYSIOLOGICAL TRAITS Leaf nitrogen Roscher et al. (201 la); Gubsch et a/.(2011); Average from different sources, including sources from the concentration Laughlin et al. (2010); Craine et al. TRY database (Kattge et al. 2011) and measurements from (2009); Fortunel et al. (2009); Kattge et al. different years in the monocultures of the Jena Experiment (2009); Pakeman et al. (2009); Willis et following this procedure: bulk samples of fully expanded al. (2009); He et al. (2008); Pakeman et sun leaves (5-20 leaves dependent on leaf size and number) al. (2008); Reich et al. (2008); Gamier et were collected at estimated peak biomass shortly before al. (2007); He et al. (2006); Kazakou et al. mowing in late May and August. Samples from two (2006); Wright et al. (2006); Craine et al. replicated monoculture plots per species were taken from (2005); Han et al. (2005); Cornelissen et 2003-2005, and one monoculture plot per species was al. (2004); Wright et al. (2004); Quested sampled in May 2007. Samples were dried to constant et al. (2003); Meziane et Shipley (1999a); weight at 70°C (48 h) and dried leaf material was ground to Cornelissen (1996); Jena-Experiment a fine powder with a ball mill. Approximately 10-20 mg database were analysed for leaf nitrogen concentration with an 92 ANNEX E continued elemental analyzer (Vario EL Element Analyzer, Hanau, Germany). Leaf nitrogen concentration data of 13 grass species and 12 legume species were collected according to the procedures described in Gubsch et al. (2011) and Roscher et al. (2011a). The same sampling protocol was used for measurements on 28 non-legume herb species in 2006 and the remaining species in 2008 or 2009. Leaflignin Own measurements Leaf lignin concentration was measured following a concentration sequential extraction analysis (Vansoest et al. 1991) of neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL). Leaf samples for each species were collected between May and October 2010. As only 1 gram of dry material was necessary, the number of leaves per species was variable (min = ten leaves per species in total, from at least five different individuals). Subsamples were mixed and ground to 1mm particle size to obtain an average value of fiber concentration per species over the growing season. For the NDF and ADF analyses, ANNEX E continued an ANKOM 200 fiber analyzer (ANKOM™, 65rpm agitation, ANKOM Technology) was used and the ADL analysis was done in beakers with 72% sulfuric acid. The samples were dried and weighted between each analysis to calculate the different fiber fractions. A complete list of material and method procedure is given on ANKOM technology website (www.ankom.com/default.aspx). HERBIVORE-RELATED TRAITS Coleopteran Bacon et Rathod (1964); Edmunds (2003); These two traits represent the number of coleopteran and herbivores Fitter et Peat (1994); Hemerik et al. hemipteran species that could potentially feed on the plants (2003); Bohme (2001) at the field site of the Jena Experiment. Information on both Hemipteran Bacon et Rathod (1964); Fitter et Peat types of potential herbivores was extracted through an herbivores (1994); Evans (2007) extensive research of published literature and by screening various online databases specialized in the documentation of host-insect interactions. Herbivores not documented for Europe or feeding only on other plant parts than leaves were excluded. The total number of potential herbivores was loge-transformed for both groups to improve their distributions. 94 ANNEX E continued PHENOLOGICAL TRAITS Leaf lifespan Rothmaler (2002) Leaf lifespan or seasonality of foliage: 1 = deciduous, 2 = partly deciduous, 3 = evergreen. MORPHOLOGICAL TRAITS Stem growth form Kleyer et al. (2008) Percentage of erection of the stem from 0 to 100%. The species Taraxacum officinale had one missing value for this trait. This value was imputed simply by referring to morphologically similar species (i.e. Leontodon autumnalis and L. hispidus). Root architecture Roscher et al. (2004) Ordinal variable (1-3), representing the type of the root system architecture: 1 = long-living primary root system, 2 = secondary fibrous roots in addition to the primary root system, 3 = short-living primary root system with extensive secondary root system. Notes: "Method" gives a detailed description of how a trait has been" measured or collected and how exactly it was determined. The reference Kleyer et al. (2008) refers to the LEDA database. The reference "Jena-Experiment database" refers to unpublished data measured by colleagues in the Jena Experiment and a detailed description of those measurements is given in "Method". 95 ANNEX F. Correlation matrix of different community properties of the communities in the Jena Experiment. Biomass Realized Sown species Shannon Evenness Rao's functional Funcitonal species richness richness diversity index diversity index dispersion Biomass 1 Realized species richness 0.50 1 Sown species richness 0.56 0.87 1 Shannon diversity index 0.44 0.88 0.87 1 Evenness (0.23) (0.18) 0.38 0.56 1 Rao's functional diversity 0.37 0.68 0.75 0.88 0.62 1 index Functional dispersion 0.38 0.69 0.76 0.90 0.71 0.97 1 Notes: Correlation coefficients in brackets represent non-significant correlations (p-value > 0.05). Functional diversity and functional dispersion were calculated with the "dbFD" function of the "FD" package of R (Laliberte and Shipley 2011). 96 ANNEX G. Log-response ratios for non-additive and additive effects based on the different models. To estimate the relative importance of additive and non-additive effects in determining community herbivory the log ratio of their absolute contributions was calculated. The additive contribution is the value predicted by the three different additive models. The non-additive contribution is the difference between predicted and measured herbivory. This difference can be positive or negative (as indicated in Annex Gl). In the majority of communities the non-additive effects where positive, thus the measured herbivory was higher than herbivory predicted by the additive models. The log ratio of the two contributions quantifies their relative importance. For none of the three models the relative contributions changed with species richness (Annex G2). On average the log ratio was about -1 which means that the additive effects are about 2.7 times as big as the non-additive effects. That also means that more than a quarter of the measured herbivory is determined by non-additive effects which emphasizes why purely additive models underestimate herbivory in polyculture communities. 97 ANNEX Gl ~ E 0 12 3 4 log(sown species richness) Annex Gl. The log ratio of the absolute J. contribution of non-additive and additive effects to measured herbivory in plant communities of differing diversity. Estimates are based on species specific herbivory measured in monocultures (A), a trait based model previously employed in monocultures 0 12 3 4 (B) and a newly estimated trait based model log(sown species richness) (C). For details on these three models see the main manuscript. Plots with a positive non- aditive effects are given with closed circles, plots with negative non-adive effects in open co £5 circles. Bars are the mean within a diversity level. Everything below the dashed line at zero means that the non-additive effects are smaller than the additive effects. The solid line shows the trend with diversity. 0 12 3 4 log(sown species richness) 98 ANNEX G2. Statistics for the models presented in Annex G1 on the relationship between community species richness (log(sowndiv)) and the log ratio of non-additive and additive effects contributing to measured community herbivory (logratio) as predicted from three different additive models. model intercept ±SE slope ±SE r2 F statistic with df p herbivory predicted from monocultures logratio ~ log(sowndiv) -1.08 ±0.23 (0.07 ±0.12) 0.01 FL68 = 0.29 0.589 herbivory predicted from trait-based model logratio ~ log(sowndiv) -0.86 ±0.28 (0.01 ±0.14) 0.00 Fi.68 = 0.005 0.944 herbivory predicted from relaxed trait-based model logratio ~ log(sowndiv) -1.23 ± 0.22 (0.07 ±0.11) 0.01 Fi.6g = 0.37 0.545 99 BIBLIOGRAPHIE Adler, P.B., Milchunas, D.G., Lauenroth, W. K., Sala, O.E., and Burke, I.C. (2004). Functional traits of graminoids in semi-arid steppes: a test of grazing histories. Journal of Applied Ecology 41,653-663. Agerbirk, N., Chew, F.S., Olsen, C.E., and Jorgensen, K. (2010a). 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