relations entre la diversitÉ et la productivitÉ … · de la faculté de foresterie de...

ALEXANDRE LENIÈRE

RELATIONS ENTRE LA DIVERSITÉ ET LA PRODUCTIVITÉ VÉGÉTALE VIS-À-VIS DES CARACTÉRISTIQUES STRUCTURALES ET

FONCTIONNELLES DES ÉRABLIÈRES DU CENTRE DU QUÉBEC

Thèse présentée à la Faculté des études supérieures de l’Université Laval

dans le cadre du programme de doctorat en Biologie pour l’obtention du grade de Philosophiae Doctor (Ph.D.)

DÉPARTEMENT DE BIOLOGIE FACULTÉ DES SCIENCES ET GÉNIE

UNIVERSITÉ LAVAL QUÉBEC

2007 © Alexandre Lenière, 2007

Résumé La flore du sous-bois représente près des deux tiers de la richesse spécifique des végétaux

supérieurs dans les forêts tempérées décidues. Ce pool d’espèces, composé presque

essentiellement de plantes pérennes, agit sur la régénération, la structure et la composition

du couvert forestier, et sur les fonctions de l’écosystème (par exemple : productivité et

cycles biogéochimiques). La présente thèse montre les effets de plusieurs facteurs qui

modulent la richesse et la productivité des espèces de sous-bois en forêt décidue. Les deux

premiers objectifs de la thèse portent sur l’influence de l’hétérogénéité structurale et de

l’hétérogénéité de la répartition spatiale des nutriments sur les espèces de sous-bois. Le

dernier objectif porte sur la complémentarité entre les groupes fonctionnels, une propriété

essentielle pour le maintien de tout écosystème forestier.

À mesure que l’hétérogénéité structurale de la forêt augmente, la complexité

environnementale et la diversité végétale augmentent également. Toutefois, une

perturbation (e.g. aménagement acéricole) peut réduire la richesse et la variabilité de la

taille des arbres (simplification de la stratification), altérer les multiples interactions entre

les facteurs environnementaux et entraîner une diminution de la richesse en sous-bois. Nos

résultats indiquent que l’hétérogénéité structurale, qui tient compte de la richesse et de

l’abondance relative des espèces ligneuses, agit positivement sur la diversité de la flore

herbacée du sous-bois. En présumant que l’hétérogénéité structurale est inversement reliée

à l’intensité de l’aménagement, l’aménagement acéricole semble avoir des effets délétères

sur la diversité des plantes herbacées. Pour ce qui est des facteurs environnementaux, le pH

et l’humidité du sol contribuent au maintien de la diversité des espèces herbacées de sous-

bois. Par contre, l’absence de relation entre la lumière et la diversité des espèces herbacées

nous porte à croire que la plupart de ces espèces sont bien adaptées ou tolérantes à de

faibles niveaux d’irradiance.

La richesse spécifique et la richesse fonctionnelle contribuent à maintenir et à pérenniser

les fonctions écosystèmiques dans un environnement variable. Pour simplifier cette

complexité écologique, la flore du sous-bois peut être subdivisée en groupes fonctionnels

(e.g. espèces printanières éphémères, espèces printanières persistantes, espèces estivales,

fougères, et plantules et juvéniles des espèces ligneuses). Nous proposons que la perte d’un

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groupe peut avoir des conséquences variées sur le maintien des interactions entre les

groupes fonctionnels pour une fonction donnée. Nos résultats révèlent que la contribution

directe de chacun des cinq groupes fonctionnels à la richesse spécifique, au couvert et à

l’équitabilité est positive. Les plantules et juvéniles des espèces ligneuses et les espèces

printanières persistantes ont un effet positif sur les autres groupes fonctionnels, alors que

les espèces estivales semblent avoir un effet négatif sur les autres groupes. Ces interactions

correspondent à des boucles de rétroactions négatives qui pourraient contribuer au maintien

et la pérennité de la communauté. Ainsi, ces différents groupes ne sont pas strictement

indépendants les uns des autres, mais sont plutôt interconnectés. De plus, les espèces de

sous-bois semblent peu sensibles à l’hétérogénéité spatiale des nutriments. Une croissance

lente ou une faible plasticité peut expliquer l’absence de réponse de la richesse et du

couvert des plantes de sous-bois à l’hétérogénéité spatiale des nutriments. Par contre, la

variété des espèces estivales en ce qui a trait au système racinaire permet de maximiser leur

couvert à une très fine échelle spatiale d’hétérogénéité des nutriments. Ces différences dans

les stratégies racinaires représentent une spécialisation de niche, favorisant la coexistence

des espèces vis-à-vis de l’hétérogénéité spatiale. Toutefois, ces résultats montrent que, de

façon générale, ce pool d’espèces est peu sensible à la disponibilité et à l’hétérogénéité

spatiale des nutriments.

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Avant-Propos Cette thèse comporte cinq chapitres. Les chapitres 1 et 5 correspondent respectivement à

l’introduction générale et à la conclusion générale. Les chapitres 2, 3 et 4 (corps de la thèse)

font état de mes travaux de recherche dans les érablières du Centre du Québec et ont été

rédigés en anglais, sous forme d’articles scientifiques.

J’ai conçu et réalisé toutes les expériences et observations de terrain et de laboratoire,

effectué la totalité des analyses statistiques, puis rédigé les différents chapitres avec l’aide

de mon directeur de thèse, Gilles Houle.

Les résultats du chapitre 2 ont été présentés lors d’un congrès (Lenière, A. & Houle, G.

2005. Response of understorey plant diversity to reduced forest structural diversity.

Ecological Society of America, 90th Annual Meeting, Montréal, Canada). De plus, ce

chapitre est publié : Lenière, A. and Houle, G. 2006. Response of herbaceous plant

diversity to reduced structural diversity in maple-dominated (Acer saccharum Marsh.)

forests managed for sap extraction. Forest Ecology and Management 231: 94-104.

Les résultats du chapitre 3 seront bientôt soumis pour publication, alors que les résultats du

chapitre 4 ont été soumis à la revue Oikos, en 2007.

Remerciements La réalisation de ces quatre années et demie de recherche (2002-2007) a été rendue possible

grâce au dévouement et à la disponibilité de plusieurs personnes.

Je tiens à remercier fortement et chaleureusement mon directeur de thèse, Gilles Houle,

pour son encadrement et la confiance qu’il m’a témoignée tout au long de mes recherches.

Sa disponibilité exemplaire, son support moral et financier et ses nombreux conseils

scientifiques pendant la rédaction de ma thèse ont été particulièrement appréciés. C’est une

personne très humaine et ce fut très plaisant de travailler à ses côtés. Cette recherche a été

financée par le Conseil de recherche en sciences naturelles et en génie du Canada

(CRSNG).

J’exprime ma gratitude et ma reconnaissance envers mon jury de thèse, à savoir Stéphane

Boudreau et Line Lapointe de l’Université Laval, et Daniel Gagnon, mon examinateur

externe de l’Université du Québec à Montréal. Un merci particulier aussi à Ladd E.

Johnson, mon président de jury.

Un grand merci à mes précieuses assistantes de recherche pour l’ensemble des heures

passées sur le terrain et au laboratoire : Geneviève Descôteaux, Anabelle Goupil, Virginie

Bolduc. Merci à mes collègues du laboratoire de G. Houle avec qui j’ai partagé des

moments de plaisir et de joie, aussi bien sur le terrain qu’en laboratoire : Ines Ben Moktar,

Patricia Désilets, Pascal Marchand, Guillaume de Lafontaine et Fatima Sahim.

Un gros merci au personnel du Département de biologie : Michelle Carignan, Raymonde

Gosselin, France Lépine, Nicole Tanguay, Silvia Cleary, Carole Martel, Martine Boucher,

Jacinthe Goulet, Louise Lapointe, Gaétan Rochette, Josée Pelletier, Léo Gaudreau.

J’adresse un merci particulier aux professeurs que j’ai eu la chance de rencontrer en cours

(Serge Payette, Ladd E. Johnson, Nicole Benhamou) ou dans les couloirs (Warwick

Vincent, Julian J. Dodson, Line Lapointe, Cyrille Barrette et Helga Guderley).

Je remercie tous les amis rencontrés (1) au département : Sylvain Gutjahr, Sylvain Lerat,

Christophe Gouraud, Anthony Gandin, Amélie Collard, Mireille Bellemare, Julie Naud,

Julie Bussières, Jin Zhou, Patrick Gagnon, Ian Boucher, Alexis Deshaies, Guillaume

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Théroux-Rancourt, ainsi que tous les étudiants du baccalauréat de biologie de 2002 à 2006

(2) à l’Université Laval : Ha et tous les vietnamiens, Hamid, et (3) à l’extérieur de

l’Université : Stéfanie, Frédéric, Élisa, Antoine, Patrick, Billy, Yann, Denis, …

Je dédie cette thèse de doctorat à toute ma famille, à savoir, mes parents, mon grand-père,

Bernard. Toutes ces personnes ont toujours été derrière moi pour m’encourager, me

propulser sur le devant de la réussite. Leur sensibilité et leur support à plusieurs niveaux

m’ont fortement permis de réaliser ce projet de doctorat avec enthousiasme et ténacité. Un

grand merci à la famille de ma compagne : Patricia, Pierre, Louise et Pierre Bernard

Lafond. Un merci particulier au grand-père de Marie-Françoise, André Lafond, ex-doyen

de la Faculté de foresterie de l’Université Laval, qui est une personne unique et généreuse,

avec qui discuter s’apparente à un véritable voyage de passion et de découverte …

Enfin, un Énorme merci à Marie-Françoise, ma meilleure amie, ma confidente, ma

conjointe de vie depuis le début de cette aventure. C’est une personne sociable, motivante

et attentionnée. Elle a toujours été derrière moi, elle m’a soutenu dans toutes mes épreuves.

Pour tout cela et bien d’autres choses, je tiens à la remercier pour sa simplicité, son sourire,

sa bonne humeur. Notre amour ne fait que commencer et cela va en rendre jaloux plus d’un

…

Merci à toutes et à tous!

À mon père ……

Table des matières Résumé.....................................................................................................................................i Avant-Propos ........................................................................................................................ iii Remerciements.......................................................................................................................iv Table des matières .................................................................................................................vi Liste des tableaux...................................................................................................................ix Liste des figures .....................................................................................................................xi CHAPITRE 1 – Introduction Générale...................................................................................1

1.1. Perturbations naturelles...........................................................................................4 1.1.1 Les tempêtes de verglas ..................................................................................4 1.1.2 Les épidémies d’insectes et autres agents pathogènes....................................5 1.1.3 Les grands herbivores .....................................................................................6

1.2 Perturbations anthropiques .....................................................................................7 1.3 Problématiques, buts et objectifs de la thèse ..........................................................9

1.4 Références citées...................................................................................................13 CHAPITRE 2 – Response of herbaceous plant diversity to reduced structural diversity in maple-dominated (Acer saccharum Marsh.) forests managed for sap extraction ................23

2.1 Avant-propos ........................................................................................................23 2.2 Résumé..................................................................................................................23 2.3 Abstract.................................................................................................................24 2.4 Introduction...........................................................................................................25 2.5 Material and Methods ...........................................................................................26

2.5.1 Study site description....................................................................................26 2.5.2 Sampling protocol and variables measured ..................................................29 2.5.3 Biological variables ......................................................................................29 2.5.4 Environmental variables ...............................................................................29 2.5.5 Structural variables .......................................................................................31 2.5.6 Statistical analyses ........................................................................................32

2.6 Results...................................................................................................................33 2.6.1 Species richness ............................................................................................33 2.6.2 Forest structure .............................................................................................33 2.6.3 Environmental factors...................................................................................34 2.6.4 Path analyses.................................................................................................34

2.7 Discussion.............................................................................................................39 2.7.1 Plant diversity and soil pH............................................................................40 2.7.2 Plant diversity and the other environmental variables..................................41 2.7.3 Local diversity and the regional species pool...............................................43

2.8 Conclusion ............................................................................................................43 2.9 Acknowledgements...............................................................................................43

2.10 References cited....................................................................................................44

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CHAPITRE 3 – Short-Term understory plant community response to nutrient spatial heterogeneity in the cold-temperate deciduous forest of south-eastern Canada...................50

3.1 Avant-Propos ........................................................................................................50 3.2 Résumé..................................................................................................................50 3.3 Abstract.................................................................................................................51 3.4 Introduction...........................................................................................................52 3.5 Material and Methods ...........................................................................................54

3.5.1 Study site description....................................................................................54 3.5.2 Sampling protocol and variables measured ..................................................55 3.5.3 Biological variables ......................................................................................58 3.5.4 Environmental variables ...............................................................................60 3.5.5 Data analysis .................................................................................................60

3.6 Results...................................................................................................................61 3.6.1 Pre-experimental data (2003)........................................................................61 3.6.2 Nutrient addition experiment (2005) ............................................................62 3.6.3 Environmental factors...................................................................................65 3.6.4 Inter-site differences .....................................................................................66

3.7 Discussion.............................................................................................................66 3.7.1 Cover.............................................................................................................67 3.7.2 Richness ........................................................................................................68 3.7.3 Evenness .......................................................................................................69 3.7.4 Environmental variables ...............................................................................70 3.7.5 Caveats..........................................................................................................70

3.8 Acknowledgements...............................................................................................71 3.9 References cited....................................................................................................72 CHAPITRE 4 – Plant community response to species losses in the understory of cold-temperate deciduous forests in south-eastern Canada. .........................................................79

4.1 Avant-Propos ........................................................................................................79 4.2 Résumé..................................................................................................................79 4.3 Abstract.................................................................................................................80 4.4 Introduction...........................................................................................................81 4.5 Material and Methods ...........................................................................................83

4.5.1 Study site description....................................................................................83 4.5.2 Sampling protocol and variables measured ..................................................84 4.5.3 Biological variables ......................................................................................85 4.5.4 Environmental variables ...............................................................................86 4.5.5 Data analysis .................................................................................................87

4.6. Results...................................................................................................................88 4.6.1. Pre-experimental data (2003)........................................................................88 4.6.2. Experimental data (2005)..............................................................................88

4.6.2.1. Richness, cover, and evenness..............................................................88 4.6.2.2. Abiotic variables ...................................................................................96

viii 4.6.2.3. Inter-site differences .............................................................................96

4.7. Discussion..................................................................................................................97 4.7.1. Richness ........................................................................................................97 4.7.2 Cover.............................................................................................................98 4.7.3 Evenness .......................................................................................................99 4.7.4 Environmental variables .............................................................................101

4.8 Acknowledgements.............................................................................................101 4.9 References cited..................................................................................................102 CHAPITRE 5 – Conclusion générale .................................................................................107

5.1 Réponses des plantes herbacées à la réduction de la diversité structurale..........109 5.2 Réponses de la flore du sous-bois à l’hétérogénéité spatiale des nutriments .....110 5.3 Réponse des plantes de sous-bois à la perte d’un groupe fonctionnel................111 5.4 Synthèse ..............................................................................................................113 5.5 Perspectives de recherche ...................................................................................113

5.6 Références citées dans la conclusion ..................................................................115

Liste des tableaux Table 2.1 General characteristics, number of species (in parentheses, herbaceous plant

species), proportion of herbaceous species (vs. total species number), and parameter and statistics of the species-area curve for the 30 study sites (Bois-Francs region, Québec, Canada). ..........................................................................................................28

Table 2.2 Structural characteristics of the 30 study sites (Bois-Francs region, Québec,

Canada). . ......................................................................................................................30 Table 2.3 Mean value (± SD) and coefficient of variation (CV, in %) of the environmental

variables at the 30 study sites (Bois-Francs region, Québec, Canada). ........................35 Table 2.4 Pearson coefficient of correlation between the variables studied (*: P < 0.05; **:

P < 0.01; ***: P < 0.001), n = 30 (Bois-Francs region, Québec, Canada)...................38 Table 3.1 Functional groups, symbols, and examples of species in the different groups.....56 Table 3.2 Experimental protocol for the different nutrient addition treatments. .................57 Table 3.3 F-values (P-values in parentheses) from the ANOVAs for the effects of site (df =

2), treatment (df = 4), and their interaction (df = 8) on total cover (Ctot), total richness (Rtot), spatial autocorrelation of total richness (SA), total evenness (Htot), functional group evenness (HSE, HSP, HSM, HFE, and HTS), and three environmental factors (soil pH, soil organic matter (SOM), and irradiance).. .................................................62

Table 3.4 Species richness for the three sites combined and for each site according to

functional group . ..........................................................................................................63 Table 3.5 Total cover (Ctot), functional group cover (CSE, CSP, CSM, CFE, and CTS),

total richness (Rtot), functional group richness (RSE, RSP, RSM, RFE, and RTS), total evenness (Htot), and functional group evenness (HSE, HSP, HSM, HFE, and HTS) for each treatment. .............................................................................................64

Table 3.6 Mean (± SD; n = 9) of three environmental variables (soil pH, soil organic matter

(SOM), and irradiance) as a function of year and treatment. .......................................66 Table 4.1 Functional groups, symbols, and examples of species in the different groups.....84 Table 4.2 Species richness for the three sites combined and for each site (data from 2005)

according to functional group .......................................................................................89 Table 4.3 F-values (in parentheses, P-values) from the ANOVAs for the effects of site (df =

2) and treatment (df = 5 for Rtot, SA, Ctot, Htot, soil ph, soil organic matter content, and irradiance; df = 4 for the other variables) and for their interaction (df = 10 for

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Rtot, SA, Ctot, Htot, pH, SOM, and irradiance; df = 8 for the other variables) on the variables studied. ..........................................................................................................90

Table 4.4 Observed and expected total richness (Rtot), total cover (Ctot), and total evenness

(Htot) as a function of treatment (LSE: removal of spring-flowering ephemeral species; LSP: removal of spring-flowering persistent species; LSM: removal of summer-flowering species; LFE: removal of ferns; LTS: removal of seedlings and juveniles of woody species). .........................................................................................91

Table 4.5 Mean (± SD) of the environmental variables as a function of treatment (n = 9)..96

Liste des figures Figure 1.1 Répartition des régions forestières du Canada pour la province de Québec (Tirée

de Ressources Naturelles Canada – Service canadien des forêts). .................................3 Figure 1.2 Répartition des différents domaines bioclimatiques de l’érablière dans la

province du Québec ........................................................................................................4 Figure 2.1 Localization of the forest fragments studied in the Bois-Francs region, Québec,

Canada. .........................................................................................................................27 Figure 2.2 Path diagram for the relationships between the mean of the environmental

variables, the structural diversity index (H), and herbaceous plant diversity (zherb) at the 30 study sites (Bois-Francs region, Québec, Canada). ...........................................36

Figure 2.3 Path diagram for the relationships between the CV of the environmental

variables, the structural diversity index (H), and herbaceous plant diversity (zherb) at the 30 study sites (Bois-Francs region, Québec, Canada). ...........................................37

Figure 2.4 Positive relationship between soil pH and herbaceous plant diversity (zherb) over

the 30 study sites, Bois-Francs region, Québec, Canada. .............................................41 Figure 3.1 Experimental design: (a) control, i.e., no nutrient application (N0); (b)

homogeneous nutrient application (N1); (c, d, and e) three increasing heterogeneity levels of nutrient application (N4, N16, and N64). ......................................................57

Figure 3.2 Experimental set up to record plant cover and richness in each 4 m2 plot..........59 Figure 4.1 Experimental set up to record plant cover and diversity in each 4 m2 plot. ........86 Figure 4.2 Total richness (Rtot) and functional group richness (RSE: spring-flowering

ephemeral species; RSP: spring-flowering persistent species; RSM: summer-flowering species; RFE: fern species; and RTS: seedlings and juveniles of woody species) for each treatment. ..............................................................................................................92

Figure 4.3 Total cover (Ctot) and functional group cover (CSE: spring-flowering ephemeral

species; CSP: spring-flowering persistent species; CSM: summer-flowering species; CFE: fern species; and CTS: seedlings and juveniles of woody species) for each treatment. ......................................................................................................................94

Figure 4.4 Total species evenness (Htot) and functional group species evenness (HSE:

spring-flowering ephemeral species; HSP: spring-flowering persistent species; HSM: summer-flowering species; HFE: fern species; and HTS: seedlings and juveniles of woody species) for each treatment................................................................................95

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Figure 4.5 Positive (continuous lines) and negative (dotted lines) interactions between the different functional groups for species richness. ..........................................................97

Figure 4.6 Positive (continuous lines) and negative (dotted lines) interactions between the

different functional groups for species cover. ..............................................................99

CHAPITRE 1 – Introduction générale

La diversité végétale est responsable de la structure et des fonctions de l’écosystème, ce qui

lui confère le rôle de répondre aux phénomènes de perturbations (Johnson et al. 1996;

Loreau et Behera 1999). Par sa capacité à maintenir une fonction écosystèmique, une

communauté résiste aux changements (résistance) ou retrouve des niveaux fonctionnels

normaux après une perturbation (résilience) (Upadhyay et al. 2000). Ces deux propriétés

sont responsables de la persistance des écosystèmes dans un environnement variable et sont

fortement influencées par les traits physiologiques, morphologiques et phénologiques des

espèces. En somme, les espèces végétales, de par leurs interactions spécifiques, sont

responsables de l’hétérogénéité spatiale verticale et horizontale (Johnson et al. 1996). Cette

hétérogénéité crée une variabilité dans les facteurs environnementaux et dans la

disponibilité des ressources. Cette variation entraîne une différenciation de niches et une

coexistence entre les différentes espèces (Dupré et al. 2002; Tews et al. 2004). Toutefois,

bien que certains écosystèmes puissent se maintenir pendant plusieurs centaines d’années

(pessières à lichens dans le nord du Québec, Morneau et Payette 1989), la végétation est

rarement stable localement et les principaux phénomènes de la variabilité de

l’environnement sont les perturbations et la succession écologique (Bengtsson et al. 2000).

Le phénomène de succession représente des changements temporels (à court-terme ou à

long-terme) et directionnels dans la composition et la structure des communautés (Connell

et Slatyer 1977; Sousa 1979). Autrement dit, c’est une série d’événements qui résulte

d’interactions entre les individus des espèces présentes et les variables environnementales.

Selon leur sévérité et leur intensité, les perturbations peuvent modifier en partie ou en

totalité une communauté, ce qui peu aboutir à des pertes d’espèces ou de groupes

d’espèces. Dans ce sens, plusieurs travaux ont montré que sous des conditions stables, un

faible nombre d’espèces permet un bon fonctionnement de l’écosystème. Toutefois, les

écosystèmes naturels sont constamment soumis aux perturbations, ce qui sous-entend

qu’une large diversité d’espèces est nécessaire pour maintenir et pérenniser les fonctions de

l’écosystème (Naeem et al. 1999; Loreau et al. 2001). Pour simplifier la complexité d’un

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écosystème naturel, la diversité peut être divisée en groupes fonctionnels, c’est-à-dire des

jeux d’espèces qui montrent des effets similaires sur les processus de l’écosystème (Hooper

et al. 2002). Dès lors, la perte d’un de ces groupes fonctionnels peut entraîner des

diminutions, voire des effondrements dans les principaux processus écosystèmiques

(productivité primaire, cycles des nutriments et flux d’énergie).

En milieu forestier, les perturbations agissent sur la structure verticale et horizontale du

couvert forestier et du sous-bois, et sur les facteurs environnementaux (lumière et

ressources du sol; Bengtsson et al. 2000). Ces phénomènes homogénéisent la structure

verticale et environnementale et agissent directement sur la diversité végétale et sur le rôle

fonctionnel (Gilliam et Turrill 1993; Small et McCarthy 2002; de Ruiter et al. 2002; Tilman

et al. 2002; Roberts 2004). Dans ce sens, plusieurs travaux ont montré les effets des

perturbations sur la strate arborée (Leak et Smith 1996; Hubbell et al. 1999; Frelich 2002;

Haeussler et al. 2002; Williams-Linera 2002), mais peu d’études se sont intéressées aux

conséquences de ces perturbations sur la strate du sous-bois (≤ 1 m de hauteur), dominée

par les plantes herbacées et les plantules et les juvéniles des espèces ligneuses (Meier et al.

1995; Ellison et al. 2005).

Pour tenter d’évaluer l’importance du rôle des espèces herbacées dans le fonctionnement

d’un écosystème forestier, nous nous sommes intéressés aux forêts tempérées de l’est du

Canada, dans la région forestière des Grands Lacs et du Saint-Laurent (Figure 1.1), et, plus

particulièrement, aux érablières du centre-sud du Québec (Figure 1.2). Cette région est

soumise à plusieurs perturbations naturelles et anthropiques qui engendrent des

modifications dans la structure verticale, la composition et l’abondance végétale, ainsi que

dans le fonctionnement des écosystèmes.

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Figure 1.1 Répartition des régions forestières du Canada pour la province de Québec (Tirée de Ressources Naturelles Canada – Service canadien des forêts). http://192.75.17.6/OG/OldGrowthPosters/OldGrowthPoster-f.htm

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Figure 1.2 Répartition des différents domaines bioclimatiques de l’érablière dans la province du Québec (Extrait de Auger et al. (2004)).

1.1. Perturbations naturelles Les perturbations écologiques résultant de processus naturels altèrent les paysages et

affectent fortement la composition des communautés, la structure verticale et les fonctions

des écosystèmes à différentes échelles spatiales et temporelles. Les perturbations les plus

fréquemment rencontrées dans les forêts tempérées décidues sont les tempêtes de verglas

associées ou non au vent, les épidémies d’insectes et d’agents pathogènes, et l’activité des

grands herbivores.

1.1.1 Les tempêtes de verglas Les tempêtes de verglas sont parmi les perturbations les plus fréquentes dans le Nord-Est

de l’Amérique du Nord. Elles influencent l’histoire et la dynamique des écosystèmes

forestiers, ainsi que les phases de la succession écologique (Hooper et al. 2001; Proulx et

Greene 2001). Les conséquences directes se situent surtout au niveau de la strate arborée :

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changement de la stratification, augmentation de débris au sol (arbres morts, branches,

brindilles) et réduction du couvert forestier (Bruederle et Stearns 1985; Rebertus et al.

1997). La topographie et l’altitude seraient responsables de l’intensité des tempêtes

verglaçantes (Proulx et Greene 2001). Ces perturbations façonnent la structure et la

composition des forêts en créant des ouvertures dans la voûte forestière. Les trouées

accroissent la fréquence et l’intensité de l’action du vent et entraînent des modifications de

la stratification du couvert forestier, une redistribution des ressources du sol, une

augmentation de la lumière en sous-bois, et un accroissement de la diversité végétale

composée à la fois d’espèces des milieux avoisinants et des espèces des milieux forestiers

(Schumann et al. 2003). Toutefois, les trouées entraînent une hétérogénéité spatiale de la

structure verticale maintenant la diversité forestière (Lautenschlager et al. 2005). La

tempête de verglas de 1998 a été la plus dévastatrice parmi celles enregistrées dans l’est

canadien. Elle a touché l’est de l’Ontario, le sud-ouest du Québec, et le sud du Nouveau-

Brunswick et de la Nouvelle-Ecosse entre les 4 et 10 janvier 1998. La quantité de débris

ligneux retrouvés au sol a été estimée à 33,6 m3 ha-1, soit 10 à 20 fois la production

annuelle moyenne de litière habituellement retrouvée sur le Mont Saint-Hilaire, au sud-est

du Québec (Hooper et al. 2001). Parmi les essences forestières dominantes, le hêtre à

grandes feuilles (Fagus grandifolia Ehrh.) a subi le plus de dommages, alors que l’érable à

sucre (Acer saccharum Marsh.) aurait le mieux résisté à cette perturbation (Rhoads et al.

2002).

1.1.2 Les épidémies d’insectes et autres agents pathogènes La vigueur des espèces ligneuses est fonction de plusieurs facteurs : l’état du peuplement,

les stress naturels et anthropiques, et la qualité du site. Dans les érablières, l’érable à sucre a

la capacité de développer de nouvelles feuilles au cours d’une même saison suite à une

défoliation, bien que celle-ci provoque une chute de croissance (Boutin 1995). Si la

défoliation se situe vers la fin de l’été, les nouveaux bourgeons développés n’ont pas le

temps d'atteindre la maturité pour résister à l’hiver. Ainsi, une défoliation résulte en une

perte significative de croissance, de production de sève élaborée ainsi que de son contenu

en sucre, entraînant parfois la mort de l’individu. Dépendamment de leur action, les

6

insectes phytophages n’entraînent pas systématiquement la mort des individus infectés. Les

insectes primaires (thrips du poirier, Taeniothrips inconsequens; arpenteuse d’automne,

Alsophila pometaria) attaquent les arbres peu importe leur vigueur, ce qui incite l’arbre à

puiser dans ses réserves énergétiques pour les combattre. Lorsque la défoliation est sévère

(50 à 60%), l’arbre est affaibli et se retrouve confronté aux insectes secondaires (Arbour

1991; Teulon et al. 1998).

La présence de certains pathogènes peut parfois nuire à la régénération de l’érable à sucre.

Par exemple, Forrester et Runkle (2000), Hane (2003), et Lovett et Mitchell (2004) ont

montré que la maladie corticale du hêtre à grandes feuilles avait un effet négatif indirect sur

la survie des jeunes érables à sucre. Cette maladie, provoquée par une cochenille,

Cryptococcus fagisuga, en combinaison avec un champignon pathogène, Nectria coccinea

var. faginata, entraînerait un accroissement du nombre de drageons de hêtre ce qui

diminuerait la quantité de ressources disponibles pour le développement des plantules

d’érable à sucre.

1.1.3 Les grands herbivores À fortes densités, les grands herbivores (par exemple, le cerf de Virginie, Odocoileus

virginianus) altèrent fortement la reproduction, la croissance et la survie des espèces

végétales de toutes les strates et modifient, de façon indirecte, les patrons d’abondance

relative et la dynamique végétale (Leland Russell et al. 2001; McGraw et Furedi 2005). Par

exemple, des études ont montré que, par leur broutement, les cerfs réduisent le nombre de

fleurs et de fruits, la hauteur et la surface foliaire, ainsi que la survie de certaines plantes

printanières (Erythronium americanum et Trillium spp.) de la strate herbacée (Anderson

1994; Balgooyen et Waller 1995). D’autres études ont montré que les ongulés avaient des

effets plus prononcés sur les plantes herbacées sensibles au broutement (fortement

attaquées, jusqu’à parfois éradication totale), ce qui favorise les espèces délaissées par les

cerfs telles que les fougères, les graminées, les joncs et les carex (Rooney et Waller 2003).

Ce changement dans la composition de la végétation a des impacts indirects sur la diversité

des insectes herbivores monophages et les pollinisateurs spécifiques de certaines espèces

broutées. De plus, cette nouvelle composition de sous-bois nuit à la régénération de la forêt,

7

en affectant la structure et la composition du futur couvert forestier (Rooney et Waller

2003). Les facteurs environnementaux sont également modifiés par les cerfs lorsque les

densités sont élevées. De plus, la perte d’espèces végétales causée par les grands herbivores

engendre des modifications dans les cycles des nutriments et du carbone, ainsi que dans les

phases de la succession écologique.

1.2 Perturbations anthropiques La plupart des érablières ont une structure inéquienne, et sont naturellement riches et

diversifiés en espèces ligneuses et herbacées. Cette diversité permet à l’érablière de se

maintenir et d’être moins vulnérable aux perturbations (Auger et al. 2004). Les érablières

du sud du Québec (Figure 1.2) sont soumises à deux types d’activités humaines :

l’aménagement forestier et l’aménagement acéricole (Allen et al. 1992; Coons 1992).

Comparativement aux phénomènes naturels, les perturbations anthropiques altèrent

davantage la structure et la composition des espèces ligneuses et de la strate du sous-bois

(Leak et Smith 1996; Bengtsson et al. 2000; Battles et al. 2001; Crow et al. 2002). Sachant

que les arbres offrent une mosaïque d’habitats pour les communautés animales et la flore

du sous-bois (Bobiec 1998), ils créent une différenciation de niches et une variabilité dans

les facteurs environnementaux. Toutefois, toute modification dans la structure verticale

implique des ouvertures dans le couvert forestier. De telles pratiques forestières intensives

associées à une courte révolution interdisent ou retardent le développement des forêts

matures, ce qui aboutit à long terme à une perte de diversité. L’aménagement de parcelles

ayant de longues révolutions (150-300 ans) maintiendrait la diversité en sous-bois (Halpern

et Spies 1995).

L’aménagement acéricole a pour objectif de conserver les érables à sucre les plus gros et

les plus vigoureux, et d’éliminer les arbres malades ou morts, ce qui transforme

progressivement la structure inéquienne en structure équienne (Lautenschlager et al. 2005).

Par ailleurs, plusieurs espèces ligneuses considérées comme accompagnatrices de l’érable à

sucre sont présentes : l’érable rouge (Acer rubrum L.), le bouleau jaune, le tilleul

d’Amérique (Tilia americana L.), le chêne rouge (Quercus rubra L.), le hêtre à grandes

feuilles, l’ostryer de Virginie (Ostrya virginiana (Mill.) K. Koch), et parfois le sapin

8

baumier (Abies balsaema (L.) Mill.). Leur retrait provoque des ouvertures dans le couvert

forestier, ainsi que des changements environnementaux (Duffy et Meier 1992). De plus, ces

trouées entraînent l’introduction d’espèces pionnières intolérantes à l’ombre et non désirées

(érable à épis, Acer spicatum Lam.; bouleau à papier, Betula papyrifera Marsh.) qui entrent

en compétition avec les jeunes érables à sucre pour les ressources (Vetaas 1997; Wirth et al.

2001; Haeussler et al. 2002). La diversité augmente alors avec l’ajout des espèces

intolérantes à l’ombre, mais la fermeture du couvert forestier entraîne leur disparition au

profit des espèces présentes avant l’ouverture du couvert forestier (Brunet et al. 1996). Les

trouées créent ainsi une hétérogénéité spatiale en termes de ressources et de composition et

de richesse végétales du sous-bois (Scheller et Mladenoff 2002).

De même, les arbres influencent les propriétés du sol (e.g. pH du sol), la disponibilité de

l’eau, l’acidité du sol et les éléments nutritifs (van Breemen et al. 1997). Les précipitations

ruissellent à la fois sur le feuillage et le long du tronc des arbres et sur les strates

inférieures. L’eau de ruissellement irrigue le sol et peut modifier le pH et la disponibilité

des nutriments du sol (Finzi et al. 1998; Muller 2003). Le passage d’une forêt naturelle à

une monoculture entraîne de graves conséquences pour l’érablière : augmentation de

l’activité des insectes défoliateurs; baisse de la disponibilité de certains nutriments et

diminution de la richesse végétale (Rosenzweig 1995). Par ailleurs, l’aménagement

acéricole inflige des blessures aux troncs et aux racines par la machinerie légère servant au

débroussaillage et au débardage, facilitant l’introduction d’agents pathogènes. La

machinerie affecte le sol et contribue au compactage du sol, ce qui nuit à la santé des

arbres, et à la production de sève et de racines. Le compactage élimine les espaces d’air

(macropores), réduisant fortement la perméabilité du sol et les échanges gazeux (Brosofske

et al. 2001). La création d’ornières perturbe le drainage naturel et réduit la vigueur des

arbres à proximité des érables retenus pour la récolte de la sève. Ainsi, plus la proportion de

racines blessées est grande, plus la croissance de l’arbre, et la qualité et la quantité de sève

sont affectées négativement. Les tubulures ont progressivement remplacé les chaudières,

mais leur installation à la fin du printemps (mi-mars à mi-avril) occasionne des blessures au

système racinaire du fait que le sol est humide (Houston et al. 1990).

9

Les pratiques forestières réduisent la diversité des espèces ligneuses, mais également celle

des plantes du sous-bois (plantes herbacées, et plantules et juvéniles des espèces ligneuses).

À part les activités anthropiques, d’autres facteurs affectent la distribution et l’abondance

des herbacées : topographie, qualité du sol (Bratton 1976), composition du couvert

forestier, attributs structuraux comme la surface terrière, herbivorie (Rooney et Waller

2003) et taille des trouées (Collins et Pickett 1982). Cette perte d’espèces est préjudiciable

au niveau de la complexité fonctionnelle et structurale, de par l’importance des espèces

végétales dans les processus des écosystèmes : productivité primaire, cycle des éléments

nutritifs et du carbone (Hooper et Vitousek 1997; Tilman 1997; Loreau et al. 2001;

Symstad et Tilman 2001; Crow et al. 2002).

1.3 Problématiques, buts et objectifs de la thèse La plupart des études présentes dans la littérature se sont focalisées sur l’impact des

perturbations naturelles et anthropiques sur la composition et la richesse spécifique des

espèces ligneuses matures (Tews et al. 2004), mais peu sur les espèces du sous-bois, en

l’occurrence les plantes herbacées et les plantules et les juvéniles des espèces ligneuses

(Reader 1987; Gilliam et Roberts 1995; Scheller et Mladenoff 2002). La proportion de la

strate du sous-bois est relativement faible en termes de la biomasse totale d’un écosystème

forestier. Toutefois, elle joue un rôle majeur dans les fonctions de l’écosystème :

productivité végétale et cycle des nutriments (Gilliam et al. 1995). Ce rôle semble être

directement relié avec le patron de distribution des plantes, qui est lui-même influencé par

la disponibilité des nutriments et par l’hétérogénéité de l’habitat (Miller et al. 2002;

Whigham 2004; Small et McCarthy 2005). Les espèces herbacées jouent un rôle de filtre

dans la régénération des forêts en agissant sur la structure et la distribution spatiale des

plantules des espèces ligneuses (George et Bazzaz 1999). Les plantes printanières

éphémères (Erythronium americanum, Claytonia caroliniana) et les plantes printanières

persistantes (Trillium spp.) jouent un rôle fondamental dans la rétention des nutriments tôt

au printemps: «vernal herbs» (Muller 1978; Eickmeier et Schussler 1993; Meier et al. 1995;

Anderson et Eickmeier 1998). Selon cette hypothèse, les nutriments sont capturés et

stockés temporairement dans des réserves souterraines (Tessier et Raynal 2003). De plus,

10

les espèces printanières éphémères et quelques espèces alpines stockent même des quantités

supplémentaires de nutriments sans pour autant les transformer rapidement en biomasse. Ce

trait physiologique (luxury consumption) permettrait d’utiliser, à retardement, les

nutriments stockés lorsque ceux-ci deviennent limitants dans le milieu (Blank et al. 1980;

Chapin 1980; Eickmeier et Schussler 1993; Körner 1999; Heer et Körner 2002). Le

métabolisme des espèces vernales est très actif pendant ca. 6 semaines, soit de la fonte de la

neige jusqu’à la fermeture du couvert forestier (Muller et Bormann 1976; Anderson et

Eickmeier 1998; Tessier et Raynal 2003). En somme, ces plantes sont des réservoirs

temporaires retardant la perte de nutriments, ce qui favorise la rétention des nutriments.

Cependant, elles sont en concurrence avec les microorganismes qui sont considérés comme

plus efficaces dans la rétention de l’azote (Rothstein 2000; Tessier et Raynal 2003). Par

ailleurs, plus le couvert forestier se referme, moins les nutriments et la lumière deviennent

accessibles aux plantes herbacées (estivales).

Dans une perspective régionale, le Chapitre 2 a pour objectif de déterminer les effets de

l’aménagement acéricole sur la richesse spécifique des espèces herbacées et sur les facteurs

environnementaux. Il a été montré qu’à l’échelle du peuplement, un traitement d’éclaircie

permet de conserver un couvert forestier tout en éliminant des espèces ligneuses gênant le

développement et la croissance des érables à sucre. Les ouvertures dans le couvert forestier

favorisent cette espèce et les espèces semi-tolérantes à l’ombre (Huot 1995). Ainsi, il n’est

pas rare de rencontrer, entre autres, le bouleau jaune, le tilleul d’Amérique et le frêne blanc

(Fraxinus americana L.) qui sont des espèces adaptées aux conditions d’une forêt de

structure inéquienne. Leur retrait entraîne une structure plus homogène et transforme

l’érablière en une monoculture d’érable à sucre et, donc, plus sensible aux attaques des

agents pathogènes (Paquet 1980; Gerlach et al. 1997; Majcen 2003). Au regard des

érablières du centre-sud du Québec, la diversité structurale est très variable, allant de

structure inéquienne à structure équienne. Notre étude repose sur la prémisse que plus

l’intensité de l’aménagement acéricole s’accroît, plus la structure de la forêt devient

homogène et simplifiée, et plus l’hétérogénéité environnementale diminue ce qui aurait un

effet négatif sur la diversité des plantes de sous-bois. Dans ce sens, ce second chapitre

11

porte sur les conséquences de l’aménagement acéricole sur la diversité des plantes

herbacées en sous-bois.

De plus, les perturbations agissent à des échelles plus locales (i.e. au niveau des

communautés végétales) et sur la répartition spatiale des ressources. Cette dernière est

directement reliée aux patrons de distribution des espèces végétales, ce qui conditionne les

interactions de compétition et de coexistence responsables de l’hétérogénéité spatiale des

nutriments (Collins et Wein 1998; Bliss et al. 2002; Chipman et Johnson 2002; Anderson et

al. 2004). Les arbres agissent directement sur l’eau de ruissellement (issue des

précipitations) qui représente une entrée minérale majeure dans la zone située

immédiatement près du tronc et les zones associées avec les racines des arbres

(Døckersmith et al. 1999). Cette eau ruisselle le long du tronc et modifie entre autres le pH

du sol; la disponibilité des nutriments du sol diminue à mesure de l’éloignement du tronc

(Burghouts et al. 1998; Finzi et al. 1998). Une telle hétérogénéité spatiale des nutriments

favoriserait la coexistence locale des différentes espèces, en leur permettant d’occuper des

niches distinctes correspondant à des microsites de qualité différente (Roberts et Zhu 2002).

Les effets sur les propriétés chimiques du sol et l’accès aux nutriments par les végétaux

sont significatifs (Zinke 1962; Gersper et Holowaychuk 1971). C’est dans cette optique que

le Chapitre 3 traite de l’effet de la répartition spatiale des ressources du sol sur les espèces

du sous-bois. Le choix des ressources du sol vient du fait de leur facilité à les manipuler, à

l’inverse de la lumière (création de trouées) et de l’eau (Grace 1999; Fridley 2002;

Vanderschaaf et al. 2002). Les objectifs de ce troisième chapitre sont donc de déterminer si

l’échelle spatiale des nutriments, à une échelle fine, influence la richesse spécifique et

fonctionnelle des plantes de sous-bois et leur productivité.

Partant de l’idée que l’hétérogénéité spatiale de l’environnement favorise la stabilité des

écosystèmes et la coexistence des espèces végétales, cela peut mener à une

complémentarité spatiale entre les espèces végétales pour l’utilisation des ressources dans

les habitats (Burghouts et al. 1998; Bengtsson et al. 2000). Dans ces conditions, la

productivité se retrouve répartie entre toutes les espèces végétales (Cardinale et al. 2000).

De même, la diversité influence la productivité, la pérennité et la stabilité des écosystèmes

12

(Tilman et al. 1996). Si un écosystème possède plusieurs espèces ayant le même rôle

fonctionnel (Naeem et al. 1999; Symstad et al. 2003), la disparition d’une espèce sera

compensée par les autres espèces, démontrant leur rôle répétitif ou redondant (Wilsey et

Potvin 2000; Lepš 2004). Les perturbations réduisent fortement l’abondance des espèces

tout en limitant parfois la dominance de certaines (Stevens et Carson 2001) et peuvent

entraîner la perte de plusieurs espèces d’un même groupe et, par conséquent, la perte de

fonctions écosystémiques à certains niveaux. Les substitutions de fonctions altèrent la

stabilité et les propriétés des écosystèmes telles que la productivité, les taux de

décomposition, les cycles des nutriments, la résistance et la résilience (Tilman et al. 1996;

Wardle et al. 1999; Loreau et al. 2001). Lorsqu’un groupe fonctionnel est éliminé dans sa

totalité, les conséquences peuvent être soit néfastes, bienfaitrices ou neutres pour les autres

groupes à cause de leurs multiples inter-connections. Cette problématique a été longuement

traitée dans des écosystèmes assemblés ou en milieu prairial (Symstad et al. 1998; Hector et

al. 1999; Tilman et al. 2002; Petchey et al. 2004), mais peu en milieu naturel et forestier

(Díaz et Cabido 2001). Étant donné son rôle important, la strate du sous-bois (plantes

herbacées et plantules et juvéniles des espèces ligneuses) représente un pool d’espèces

limité à partir duquel des groupes fonctionnels peuvent être définis. C’est dans cette

optique que le dernier chapitre (Chapitre 4) porte sur l’effet de la perte d’un groupe

d’espèces sur la diversité et la productivité des autres groupes fonctionnels du sous-bois à

une échelle locale.

13

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CHAPITRE 2 – Response of herbaceous plant diversity to reduced structural diversity in maple-dominated (Acer saccharum Marsh.) forests managed for sap extraction

2.1 Avant-propos Le texte de ce chapitre a été publié dans la revue Forest Ecology and Management (Lenière,

A. and Houle, G. 2006. Response of herbaceous plant diversity to reduced structural

diversity in maple-dominated (Acer saccharum Marsh.) forests managed for sap extraction.

Forest Ecology and Management 231: 94-104). De plus, ce chapitre a fait l’objet d’une

affiche au 90e Colloque Annuel de l’Ecological Society of America qui s’est déroulé au

Palais des Congrès de Montréal du 8 au 12 août 2005 (Canada). Enfin, les résultats de ce

chapitre ont été présentés au 75e congrès de l’Association Francophone pour le Savoir, qui

s’est tenu du 7 au 11 mai 2007, à l’Université du Québec à Trois-Rivières (Canada).

2.2 Résumé En milieu forestier, une haute diversité structurale engendre une grande complexité

environnementale et est souvent associée à une diversité végétale en sous-bois élevée.

Cependant, l’aménagement forestier peut diminuer la diversité structurale. En réduisant la

richesse et la variabilité de la taille des arbres, l’aménagement acéricole (Acer saccharum

Marsh.) pour l’extraction de la sève peut diminuer la complexité environnementale et la

diversité de la flore herbacée du sous-bois. Nous avons testé cette hypothèse dans 30

érablières du sud du Québec (Canada), qui représentaient différents niveaux

d’aménagement. La moyenne et la variabilité spatiale de plusieurs paramètres

environnementaux (irradiance et ressources du sol) ont été mesurées, ainsi que la diversité

structurale et la richesse du sous-bois (plantes herbacées). Par des analyses de pistes, nous

avons déterminé qui de la moyenne (modèle 1) ou de la variabilité spatiale (modèle 2) des

paramètres environnementaux expliquait le mieux la richesse du sous-bois. Le modèle 1 a

expliqué 58% la variance de la richesse végétale contre 23% pour le modèle 2. Les

structures des covariances observées (basées sur les données) concordaient parfaitement

23

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(chi-carré = 5.152, df = 4, P = 0.272, GFI = 0.946 pour le modèle 1; chi-carré = 5.784, df =

4, P = 0.216, GFI = 0.940 pour le modèle 2) avec la structure des covariances prédites

(basées sur le modèle). La diversité structurale n’avait pas d’effet significatif sur les

paramètres environnementaux mais avait une influence significative et positive sur la

richesse du sous-bois. Le pH du sol avait un effet significatif et positif sur la richesse

spécifique dans le modèle 1; cependant, aucun autre paramètre n’affectait significativement

cette richesse. Dans le modèle 2, aucun paramètre environnemental n’avait d’effet

significatif sur la richesse du sous-bois. Une augmentation du pool d’espèces avec une

augmentation du pH du sol démontrerait un effet positif du pH du sol sur la richesse

spécifique. En présumant que la diversité structurale est inversement reliée à l’intensité de

l’aménagement, nos résultats suggèrent que l’aménagement acéricole traditionnel pour

l’extraction de la sève aurait des effets délétères sur la diversité spécifique du sous-bois.

2.3 Abstract High forest structural diversity is thought to be associated with high understory plant

diversity, through a positive effect on environmental complexity. However, forest

management may decrease structural diversity. For instance, by reducing tree species

richness and variability in tree size, management of maple-dominated (Acer saccharum

Marsh.) forests for sap extraction may decrease environmental complexity and, therefore,

understory plant diversity. We tested this hypothesis by studying 30 maple-dominated

forest fragments representing various levels of management, in southern Québec (Canada).

The mean and spatial variability of several environmental variables (light and soil

resources) were assessed, along with forest structural diversity and plant diversity. Using

path analyses, we determined whether the mean of environmental variables (model 1) or the

spatial variability of environmental variables (model 2) was most important for understory

plant diversity. Model 1 explained 58% and model 2 explained 23% of the variance in plant

diversity. The observed covariance structure (based on the data) fitted the predicted

covariance structure (based on the model) perfectly (minimum fit function chi-square =

5.152, df = 4, P = 0.272, GFI = 0.946 for model 1; minimum fit function chi-square =

5.784, df = 4, P = 0.216, GFI = 0.940 for model 2). Forest structural diversity had no

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significant effect on the environmental variables (irradiance and soil resources), although it

had a significant and positive influence on understory plant diversity. Soil pH also had a

significant and positive effect on plant diversity in model 1; however, no other variable

significantly affected species diversity in this model. In model 2, none of the environmental

variables had a significant effect on understory plant diversity. An increase of the species

pool with an increase of soil pH most likely accounts for the positive effect of soil pH on

understory plant diversity. Assuming forest structural diversity is inversely related to

management intensity, our results suggest that the traditional management of maple-

dominated forests for sap extraction may have deleterious effects on understory plant

diversity.

2.4 Introduction Natural disturbances can alter the landscape and affect ecosystem structure and functions at

different spatial and temporal scales (Sousa 1979; Tang et al. 1997; Roberts 2004). While

natural fires and insect outbreaks are frequent in the boreal forests of North America, wind,

ice storms, and epidemics caused by pathogens are relatively more important for the

dynamics of deciduous forests (Houston et al. 1990; Hooper et al. 2001; Frelich 2002).

However, natural disturbances are not the only factors that can alter the structure and

functions of forests. Indeed, management practices have inevitable effects on ecosystem

properties, including plant diversity (Bengtsson et al. 2000). Although the effects of tree

harvesting practices on canopy tree species have been well studied, comparatively little

work has considered understory plant species (Reader 1987; Gilliam et al. 1995; Decocq et

al. 2004). Yet, understory species contribute significantly to plant diversity and are

important for a wide range of functions, including forest productivity and nutrient cycling

(Muller and Bormann 1976; Reader 1987; Small and McCarthy 2005). Moreover,

understanding understory response to disturbances is critical to promoting species

conservation (Roberts 2004).

Maple-dominated forests are among the most managed of deciduous forests in northeastern

North America. They are typically composed of sugar maple (Acer saccharum Marsh.), in

association with red maple (Acer rubrum L.), American beech (Fagus grandifolia Ehrh.),

26

and yellow birch (Betula alleghaniensis Britton). However, during the course of past

management practices, the proportion of the associate species has been reduced in favor of

sugar maple (Coons 1992). Selective-cutting practices applied in maple-dominated forest

fragments have consisted of felling older, sick, infected, and less productive maples, and all

trees other than maples (Majcen 2003). The objectives of such practices have been to

increase the diameter of maple trees, encourage the expansion of maple crowns, and

eliminate most of the understory trees and shrubs. After a few decades of intensive

management, several maple-dominated forest fragments have been reduced to uniform,

even-aged stands (Paquet 1980). Such practices have simplified the age and vertical

structure of the forests and reduced tree species diversity, sometimes to the point of a

monoculture of sugar maple.

The objective of the present study is to determine the effects of maple-dominated forest

management practices on understory plant species diversity and environmental factors. We

propose to determine what variables most strongly influence plant species diversity in

conditions altered by management in maple-dominated forest fragments, using multivariate

analyses. The research hypothesis is as follows: an increase in the intensity of management

results in a more homogeneous forest structure and, therefore, in reduced environmental

complexity. In turn, a reduction in environmental complexity causes a decrease in

understory plant diversity (Bérard and Côté 1996; Whitney and Upmeyer 2004). We use

the Shannon diversity index, applied to tree pseudo-species (basal area per species and

diameter class), as an indirect measure of management intensity (Magurran 1988;

Staudhammer and LeMay 2001; Tews et al. 2004).

2.5 Material and Methods

2.5.1 Study site description This research was conducted in 30 forest fragments of the Bois-Francs region, in central

Québec, between 46°04’- 46°32’ N and 71°56’- 72°43’ W (Table 2.1, Figure 2.1). The

study area is part of the Great Lakes – St. Lawrence forest region of Rowe (1972), sub-

sections Mid (L-2) and High (L-3) St-Lawrence. The dominant tree species are sugar

27

maple, American beech, American linden (Tilia americana L.), and yellow birch. The

regional climate is wet and continental, with an annual precipitation of ca. 1,100 mm (24%

falls as snow) and an annual daily mean temperature of 4.9°C (at the nearby Trois-Rivières

weather station; URL: www.climate.weatheroffice.ec.gc.ca). The parent soil is generally

poorly drained, and clayey and sandy deposits are dominant. Humo-ferric podzols and

dystric brunisols are major soil types in the region (Choinière and Laplante, 1948).

Figure 2.1 Localization of the forest fragments studied in the Bois-Francs region, Québec, Canada. The number of sites at each locality is given in parentheses.

Among the 30 study sites, 27 were on private land and had been selected from a list

provided by the Fédération des Producteurs Acéricoles du Québec (December 2001). The

Léon Provancher Ecological Reserve site LP1 (Table 2.1) has been protected by the Québec

government since 1995 and has not been managed for sap since at least 1975. Two other

forest fragments (LP2 and LP3) are located near LP1, one in which maple sap is still

currently extracted (LP3). The sites were selected with respect to the following

characteristics: sugar maple was the dominant tree species or one of the dominant tree

species; each site had easy road access; plant species diversity was representative of the

region; topography was relatively flat; and the sites represented an array of structural

diversity. None of the sites had been fertilized or limed.

1: Réserve Léon Provancher (3) 2: Ste-Angèle de Laval (1)3: St-Grégoire (3) 4: St-Célestin (1) 5: La Visitation (1) 6: Baie-du-Febvre (3) 7: St-Zéphirin (1) 8: St-Léonard d’Aston (4) 9: St-Wenceslas (2)

10: St-Sylvère (2) 11: Ste-Gertrude (2) 12: Ste-Sophie-de-Lévrard (2) 13: Ste-Françoise (2) 14: Parisville (3)

1: Réserve Léon Provancher (3) 2: Ste-Angèle de Laval (1)3: St-Grégoire (3) 4: St-Célestin (1) 5: La Visitation (1) 6: Baie-du-Febvre (3) 7: St-Zéphirin (1) 8: St-Léonard d’Aston (4) 9: St-Wenceslas (2)

10: St-Sylvère (2) 11: Ste-Gertrude (2) 12: Ste-Sophie-de-Lévrard (2) 13: Ste-Françoise (2) 14: Parisville (3)

28

Table 2.1 General characteristics, number of species (in parentheses, herbaceous plant species), proportion of herbaceous species (vs. total species number), and parameter and statistics of the species-area curve for the 30 study sites (Bois-Francs region, Québec, Canada).

a zherb represents the slope of the species-area relationship calculated for herbaceous species in a serial nested system.

Study sites Symbols Latitude (N)

Longitude (W)

Number of species

Proportion of herbaceous species zherb

a r2 P

Baie-du-Febvre 1 BF1 46°18.005' 72°32.011' 59 (41) 69.5 0.599 0.995 0.0001

Baie-du-Febvre 2 BF2 46°08.668' 72°35.230' 56 (44) 78.6 0.662 0.963 0.0091

Baie-du-Febvre 3 BF3 46°06.468' 72°43.990' 42 (29) 69.0 0.591 0.965 0.0029

La Visitation LaVi 46°06.615' 72°36.626' 68 (53) 77.9 0.707 0.947 0.0063

Léon Provancher Reserve 1 LP1 46°17.820' 72°30.740' 62 (44) 71.0 0.655 0.957 0.0038



Parisville 1 P1 46°06.616' 72°36.630' 43 (24) 55.8 0.553 0.973 0.0019

Parisville 2 P2 46°32.161' 72°02.520' 43 (27) 62.8 0.544 0.982 0.0011

Parisville 3 P3 46°31.460' 72°01.460' 55 (35) 63.6 0.562 0.983 0.0009

Saint-Célestin StCe 46°08.385' 72°24.670' 36 (22) 61.1 0.538 0.974 0.0018

Saint-Grégoire 1 StGr1 46°16.760' 72°26.586' 76 (56) 73.7 0.691 0.970 0.0022



Saint-Léonard d'Aston 1 StL1 46°08.798' 72°22.105' 43 (29) 67.4 0.563 0.986 0.0007




Saint-Sylvère 1 StSy1 46°16.036' 72°11.370' 51 (34) 66.7 0.593 0.978 0.0014

Saint-Sylvère 2 StSy2 46°12.123' 72°17.076' 34 (19) 55.9 0.529 0.954 0.0042

Saint-Wenceslas 1 StW1 46°09.270' 72°16.971' 28 (16) 57.1 0.495 0.967 0.0026

Saint-Wenceslas 2 StW2 46°10.206' 72°17.450' 33 (22) 66.7 0.495 0.994 0.0002

Saint-Zéphirin StZe 46°05.888' 72°37.001' 75 (59) 78.7 0.689 0.966 0.0027

Sainte-Angèle-de-Laval StAn 46°18.135' 72°32.063' 57 (36) 63.2 0.629 0.964 0.0029

Sainte-Gertrude 1 StGe1 46°16.050' 72°19.078' 20 (12) 60.0 0.401 0.995 0.0002

Sainte-Gertrude 2 StGe2 46°16.053' 72°18.905' 42 (28) 66.7 0.519 0.988 0.0006

Sainte-Sophie-de-Lévrard 1 StS1 46°27.198' 72°04.805' 47 (30) 63.8 0.562 0.990 0.0004

Sainte-Sophie-de-Lévrard 2 StS2 46°27.125' 72°04.691' 39 (20) 51.3 0.498 0.987 0.0006

Sainte-Françoise 1 StF1 46°29.341' 71°56.156' 61 (43) 70.5 0.670 0.959 0.0036

Sainte-Françoise 2 StF2 46°29.658' 71°56.343' 39 (22) 56.4 0.501 0.991 0.0004

29

2.5.2 Sampling protocol and variables measured At each site, a plot of 40 m x 20 m (800 m2) was delimited, with a serial nested-quadrat

system of one 20 m x 10 m (200 m2), one 10 m x 5 m (50 m2), and one 5 m x 2.5 m (12.5

m2). Biological, environmental, and structural variables were measured in 2002 or in 2003.

2.5.3 Biological variables All vascular plant species (trees, shrubs, and herbs) present within each quadrat (from 12.5

m2 to 800 m2) at each site were identified and recorded. Floristic surveys were made in

mid-April, in July, and at the end of August (Ristau et al. 2001). Nomenclature was based

on Marie-Victorin (2002). Because the relationship between plant diversity and many

environmental variables often varies with spatial scale (Heikkinen 1996; Heikkinen and

Neuvonen 1997), a scale-independent estimate of diversity was desirable. Thus, we

calculated the slope of the species-area curve for each site (for herbaceous plants only, all

three sampling periods combined) from the following equation: log S = log C + z log A;

where S is herbaceous species richness, A is the sampling area, C is a constant, and z is the

slope of the species-area-curve (Arrhenius 1921). Z represents the accumulation rate of

species with respect to sampling area (Whittaker 1972) and can be used as an index of plant

diversity (Harner and Harper 1976; Dzwonko and Loster 1989; Murakami et al. 2004;

Désilets and Houle 2005). The slope (z) and the coefficient of determination (r2) were

determined for each site (Table 2.1), using linear regression techniques. Regressions were

forced through the origin so that log C = 0.

2.5.4 Environmental variables Several environmental variables associated with the availability of resources important to

plants (irradiance, soil moisture, soil organic matter content, and soil pH) were measured at

1 m intervals along a 45-m long transect oriented diagonally on each 800 m2 plot (46

measurements). This design provided an easy, yet intensive sampling scheme to estimate

both the mean and coefficient of variation (CV) of each variable.

30

Table 2.2 Structural characteristics of the 30 study sites (Bois-Francs region, Québec, Canada). Symbols as in Table 2.1.

Sites

Living stem

number (ha-1)

Dead stem

number (ha-1)

Basal area (m2

ha-1)

Major tree

speciesa

Canopy cover (%)

Stump number (ha-1)

Mean stump

diameter (cm)

Coefficient of variation

of basal areab

H

BF1 2,375 325 31.0 Ar-As 88.3 87.5 16.9 313.1 2.92 BF2 662.5 25 36.7 Ar-As 90.0 25.0 29.7 99.3 2.44 BF3 1,962.5 112.5 17.1 As 91.8 175.0 26.1 217.6 2.39 LaVi 1,387.5 187.5 35.0 Ar-As 92.0 337.5 26.4 192.3 2.72 LP1 3,200 412.5 32.0 As 96.8 275.0 22.9 301.4 2.69 LP2 1,875 100 54.4 As-Ov 99.8 37.5 32.8 492.2 1.86 LP3 1,212.5 100 38.3 As-Ta 89.0 87.5 20.9 228.4 1.89 P1 862.5 125 23.6 Ab-As 94.5 112.5 30.4 225.9 2.19 P2 1,325 12.5 31.8 As 93.0 12.5 15.2 214.8 2.50 P3 1,837.5 87.5 35.8 Ar-As 97.3 75.0 20.5 176.7 2.25

StCe 1,312.5 0 46.3 Ar-As 85.0 187.5 10.8 174.6 2.36 StGr1 1,962.5 275 32.3 As 95.0 37.5 44.3 191.9 2.67 StGr2 1,325 62.5 36.1 As 89.3 37.5 21.6 261.9 1.80 StGr3 1,612.5 475 28.5 As 92.0 75.0 33.3 181.3 3.31 StL1 1,350 125 34.1 Ar 85.8 37.5 33.3 199.0 2.25 StL2 2,000 62.5 30.7 Ar-As 93.8 75.0 60.8 388.3 2.00 StL3 1,187.5 50 22.1 Ar-As 92.8 75.0 38.4 179.8 2.86 StL4 1,562.5 175 31.1 As 94.8 200.0 25.7 234.3 2.49 StSy1 1,975 137.5 28.7 Ar 95.0 150.0 37.8 249.4 2.36 StSy2 1,700 87.5 25.0 As 95.5 162.5 25.9 244.3 2.64 StW1 712.5 0 28.2 As 97.5 125.0 24.6 149.7 1.89 StW2 1,225 475 31.6 Ar 95.3 25.0 40.1 212.6 1.75 StZe 475 37.5 21.6 As 96.5 87.5 32.8 149.9 1.69 StAn 612.5 25 60.2 As-Fg 95.3 87.5 26.9 101.8 2.83 StGe1 800 12.5 31.2 As 96.5 125.0 42.8 233.2 1.49 StGe2 1,275 75 23.7 As 95.8 37.5 75.1 213.1 2.07 StS1 1,650 200 22.3 Ar-As 96.3 375.0 24.8 153.4 2.21 StS2 2,100 162.5 28.6 Ar-As 94.5 500.0 18.8 147.5 1.70 StF1 737.5 25 29.6 As 86.0 137.5 29.1 113.5 2.26 StF2 1,200 62.5 38.0 Ar-As 92.5 25.0 37.8 181.7 2.55

a: Ab: Abies balsamea; Ar: Acer rubrum; As: Acer saccharum; Fg: Fagus grandifolia; Ov: Ostrya virginiana; Ta: Tilia americana. Species listed alphabetically, for each site, when more than one dominant species. b: in %; for an 800 m2 plot.

Irradiance (photosynthetic photon flux density; µmol m-2 s-1) was measured between 10h

and 14h, on sunny, cloudless days using a portable radiometer (Licor Inc., Lincoln,

Nebraska, USA). Measurements were made at ~ 1 m from the ground. Two measurements

31

were also made in a nearby opening or outside the forest, before and after each series of

readings for standardization.

Soil samples were collected (528 cm3 to a depth of 10-15 cm) after a period of at least 2-3

days without rain. In the laboratory, samples were passed through a 2-mm-mesh sieve to

remove roots, twigs, and stones; they were weighed, dried at 80°C for 24 hours, and

weighed again to determine percent soil moisture (SM). Soil organic matter content (SOM)

was estimated as percent mass loss on ignition (at 450°C for 5 hours). Soil pH was

measured in a 1:1 soil:water solution.

Four photographs were taken at a height of 1.5 m from the ground in each 800 m2 plot to

determine canopy cover (50 mm lens, Minolta, Tokyo, Japan). These photographs covered

65-115 m2 of canopy (depending on tree height). A grid comprised of 100 evenly spaced

points was superposed over each photograph to calculate percent cover (Cov).

2.5.5 Structural variables The intensity of forest management was estimated using the Shannon diversity index

applied to ‘pseudo-species’ (basal area per species and diameter class). Ten diameter

classes were used (tree diameter measured at breast height): ≤ 5 cm; 5-10 cm; 10-15 cm;

15-20 cm; 20-25 cm; 25-30 cm; 30-35 cm; 35-40 cm; 40-45 cm; > 45 cm. The index was

calculated using the formula H = -Σ pi ln pi, with pi representing the proportion of basal

area per pseudo-species. Thus, H is a structural diversity index (Magurran 1988;

Staudhammer and LeMay 2001; Tews et al. 2004) and we expect it to decrease with

increased management intensity (an increase in management intensity should result in a

more homogeneous and uniform forest structure, in terms of both tree species composition

and size distribution; Paquet 1980; Coons 1992; Majcen 2003). Dead stump and dead tree

diameters were also measured, but they were not included in the calculation of the

structural diversity index.

32

2.5.6 Statistical analyses Path analyses (Sokal and Rohlf 1995) were used to analyze the dependence of the slope of

the species-area curve (z) with the measured variables. Path analysis, an extension of

multiple regression techniques, is a statistical tool used to explore and understand “causal

relationships” among multiple variables (Grace and Pugesek 1998). The relationships

among the variables are shown in a path diagram containing boxes and arrows. Each

variable is assigned to a box and is linked to other variables with arrows. The thickness of

the arrows depicts the strength of the relationship between variables, which is determined

by the partial coefficient of determination (the path coefficient). In this study, two a priori

defined models (confirmatory approach to path analysis) were tested to determine which

environmental and structural variables had an effect on understory species diversity, zherb.

The first path model examined structural diversity (H) and the means of the measured

variables: soil moisture*, soil organic matter content*, soil pH, and irradiance* (*: these

variables were not normally distributed according to a Shapiro-Wilk test and, thus, were

log-transformed). The second path model examined structural diversity (H) and the

coefficients of variation (in fact, the residuals of the regression between the CV and their

means, to remove any covariance effect) of soil moisture, soil organic matter content, soil

pH, and irradiance. By doing so, we wanted to determine which of resource availability

(model 1) or resource heterogeneity (model 2) contributed most to explain plant diversity.

Path analyses were carried out with Lisrel 8.71 (SSI Inc., Lincolnwood, Illinois, USA).

Three indices were used to verify how well the covariance structure generated by the data

fitted the covariance structure associated with each model (a P > 0.05 means that the data

are consistent with the causal processes suggested by the model): the minimum fit function

chi-square, which tests for an “exact” or “perfect” fit between the model and the data (the

“best” fit); the root mean square error of approximation, RMSEA, which tests for a “close”

fit between the model and the data (the “second best” fit); and the goodness of fit index,

GFI, which tests for an “acceptable” fit between the model and the data (the “third best”

fit).

33

Pearson coefficients of correlation were calculated to estimate the intensity of the

association between the variables (e.g., to determine how our structural diversity index

relates to tree species diversity, basal area, and CV of basal area).

2.6 Results

2.6.1 Species richness Total species richness amounted to 201 and included 29 tree, 18 shrub, and 154 herb

species. The mean (± SD) number of species per site was 48.8 ± 13.3 (min.-max.: 20-76;

Table 2.1) and the average proportion of herbaceous species was 65.0 ± 7.0%. Understory

species diversity (zherb) averaged 0.575 ± 0.074 and varied between 0.401 and 0.707 (for all

regressions, r2 > 0.947; Table 2.1).

2.6.2 Forest structure The mean abundance of trees and shrubs (diameter at breast height ≥ 2 cm; Table 2.2) was

1,449.2 ± 597.1 stems ha-1 (min.-max.: 475-3,200 stems ha-1). Tree diameter (DBH) was

measured to calculate the basal area for each species and the total basal area (BA) for each

site. Mean BA was 32.2 ± 9.1 m2 ha-1 (min.-max.: 17.1-60.2 m2 ha-1; Table 2.2). Basal area

was dominated by Acer saccharum on 20 sites, by Acer rubrum on nine sites, and by

Ostrya virginiana on one site (due to one extremely large individual). The coefficient of

variation of BA (within site) averaged 214.1 ± 81.0% (min.-max.: 99.3-494.2%; Table 2.2).

Another element of forest structure is the presence of stumps. The number (min.-max.:

12.5-500 ha-1) and mean diameter (min.-max.: 10.8-75.1 cm) of stumps varied greatly

among sites and were dependent upon the intensity of management and upon other natural

or anthropogenic disturbances (Table 2.2). However, these variables were not used as an

index of management intensity as tree death might have resulted from causes unrelated to

management practices.

Overstory cover (mean ± SD: 93.2 ± 3.7%) varied between 85.0 and 99.8%, which

indicated that the canopy was dense and was comprised of several layers (Table 2.2).

34

Structural diversity (mean ± SD: 2.30 ± 0.43) ranged from 1.49 to 3.31 and presented a

variability of 18.7% (CV) among sites.

2.6.3 Environmental factors For all sites taken together, soil organic matter content averaged 15.28 ± 9.12% (min.-max.:

5.27-36.38%), soil pH 4.29 ± 0.38 (min.-max.: 3.55-5.02), and soil moisture 27.18 ± 9.34%

(min.-max.: 15.54-46.36%; Table 2.3).

Mean irradiance for all sites combined (± SD) was 66.97 ± 71.94 µmol m-2 s-1 (min.-max.:

14.74-334.26 µmol m-2 s-1; Table 2.3). Standard deviations were often high, indicating high

variability within sites.

2.6.4 Path analyses In this study, environmental and structural variables were defined as predictor variables

(Tables 2.2 and 2.3) and herbaceous plant diversity (zherb, the slope of the species-area

curve; Table 2.1) as criterion variable. Two path models were constructed: model 1 (Figure

2.2) was based on the means of the environmental variables, while model 2 (Figure 2.3)

was based on the coefficients of variation of the same variables. H, our index of forest

management intensity, was significantly correlated with tree species richness (r = 0.382; P

< 0.05), but not with the CV of basal area (r = -0.144; P > 0.05; Table 2.4).

Model 1 (Figure 2.2), based on the mean values, explained 58.1% (P < 0.001) of the

variance of zherb (χ2 = 5.152; P = 0.272; RMSEA = 0.0947; P (RMSEA<0.05) = 0.322; GFI =

0.946). There was a significant and positive association between soil pH and zherb (ρ =

0.543, Figure 2.2). This result was confirmed by the Pearson correlation matrix (r = 0.496,

P < 0.01; Table 2.4): as soil pH increased, herbaceous species diversity increased (Figure

2.4). Likewise, there was a significant and positive association between the structural

diversity index H and zherb (ρ = 0.388; Figure 2.2). This result was also confirmed by the

Pearson correlation matrix (r = 0.373, P < 0.05; Table 2.4): as H increased so did zherb.

Tabl

e 2.

3 M

ean

valu

e (±

SD

) and

coe

ffic

ient

of v

aria

tion

(CV

, in

%) o

f the

env

ironm

enta

l var

iabl

es a

t the

30

stud

y si

tes

(Boi

s-Fr

ancs

re

gion

, Qué

bec,

Can

ada)

. Sym

bols

as i

n Ta

ble

2.1.

Mea

sure

s (n

= 46

) wer

e ta

ken

on th

e di

agon

al o

f an

800

m2 p

lot a

t eac

h si

te.

So

il or

gani

c m

atte

r (%

)

Soil

pH

So

il m

oist

ure

(%)

Ir

radi

ance

(µm

ol m

-2 s-1

) Si

tes

Mea

n C

V

Mea

n C

V

Mea

n C

V

Mea

n C

V

BF1

11

.96

± 12

.83

107.

25

4.34

± 0

.24

5.56

23

.27

± 10

.70

45.9

7 26

.45

± 16

.48

62.3

2 B

F2

11.0

4 ±

5.97

54

.06

4.63

± 0

.20

4.40

31

.97

± 17

.92

56.0

5 27

3.14

± 2

07.2

3 75

.87

BF3

12

.10

± 17

.40

143.

75

4.13

± 0

.14

3.27

25

.52

± 17

.44

68.3

4 17

.32

± 17

.67

102.

03

LaV

i 9.

96 ±

8.0

2 80

.59

4.19

± 0

.37

8.78

23

.21

± 7.

67

33.0

4 32

.43

± 17

.51

53.9

9 LP

1 12

.77

± 2.

51

19.6

4 4.

79 ±

0.6

0 12

.45

19.8

9 ±

3.09

15

.51

68.4

4 ±

147.

07

214.

88

LP2

10.4

3 ±

2.11

20

.18

4.59

± 0

.24

5.16

26

.59

± 3.

04

11.4

1 31

.59

± 62

.59

198.

13

LP3

8.76

± 2

.27

25.8

7 4.

54 ±

0.2

8 6.

18

30.9

1 ±

3.74

12

.10

52.7

2 ±

90.4

6 17

1.59

P1

11

.18

± 6.

77

60.5

5 4.

03 ±

0.2

4 6.

02

27.8

5 ±

9.71

34

.88

34.6

1 ±

34.7

7 10

0.45

P2

30

.50

± 21

.96

72.0

0 3.

62 ±

0.2

2 6.

14

44.1

9 ±

17.3

6 39

.30

86.2

8 ±

149.

33

173.

06

P3

25.1

0 ±

24.4

2 97

.28

3.97

± 0

.30

7.64

26

.57

± 12

.51

47.0

8 33

4.26

± 3

86.5

2 11

5.63

St

Ce

7.19

± 4

.59

63.8

2 3.

98 ±

0.2

6 6.

60

19.7

2 ±

7.18

36

.43

68.7

9 ±

91.7

4 13

3.36

St

Gr1

9.

84 ±

6.9

3 70

.45

5.02

± 0

.48

9.61

24

.01

± 6.

65

27.6

9 48

.53

± 64

.33

132.

54

StG

r2

36.3

8 ±

20.0

5 55

.11

4.83

± 0

.70

14.4

8 46

.36

± 10

.10

21.8

0 15

.47

± 21

.68

140.

15

StG

r3

16.3

2 ±

7.88

48

.31

4.33

± 0

.24

5.57

26

.38

± 7.

75

29.3

9 50

.30

± 86

.87

172.

72

StL1

33

.01

± 26

.73

80.9

9 3.

55 ±

0.2

4 6.

77

39.9

6 ±

16.1

0 40

.28

138.

31 ±

275

.50

199.

19

StL2

14

.32

± 7.

25

50.6

0 3.

77 ±

0.1

7 4.

39

15.8

8 ±

5.22

32

.86

42.2

9 ±

83.9

6 19

8.52

St

L3

18.0

6 ±

14.7

2 81

.49

4.86

± 0

.63

12.9

8 20

.42

± 10

.80

52.9

0 11

6.54

± 2

03.7

7 17

4.85

St

L4

34.8

4 ±

14.5

2 41

.68

4.62

± 0

.27

5.91

43

.46

± 15

.28

35.1

7 96

.71

± 17

4.69

18

0.63

St

Sy1

28.9

9 ±

26.7

2 92

.19

3.89

± 0

.25

6.33

45

.26

± 18

.33

40.5

0 43

.79

± 48

.27

110.

24

StSy

2 6.

05 ±

2.7

5 45

.53

4.18

± 0

.20

4.79

19

.64

± 4.

49

22.8

6 33

.57

± 38

.04

113.

31

StW

1 5.

32 ±

1.7

8 33

.49

4.23

± 0

.26

6.11

18

.88

± 4.

11

21.7

7 75

.34

± 12

0.62

16

0.10

St

W2

17.0

2 ±

12.5

1 73

.49

3.87

± 0

.27

6.96

20

.58

± 11

.61

56.3

9 18

.85

± 17

.62

93.4

3 St

Ze

16.1

2 ±

5.84

36

.26

4.67

± 0

.20

4.20

36

.57

± 6.

27

17.1

4 42

.54

± 74

.96

176.

19

StA

n 13

.60

± 5.

26

38.7

1 4.

01 ±

0.1

7 4.

31

17.3

9 ±

6.00

34

.48

26.0

5 ±

7.08

27

.20

StG

e1

5.27

± 1

.22

23.1

2 4.

49 ±

0.2

7 6.

01

15.5

4 ±

2.90

18

.63

14.7

4 ±

15.7

6 10

6.91

St

Ge2

7.

47 ±

13.

48

180.

48

4.19

± 0

.30

7.24

17

.25

± 10

.05

58.2

5 16

.33

± 21

.15

129.

51

StS1

17

.82

± 10

.89

61.1

0 4.

35 ±

0.4

1 9.

33

36.9

7 ±

9.53

25

.78

72.8

2 ±

111.

25

152.

78

StS2

11

.85

± 8.

99

75.8

9 4.

23 ±

0.2

7 6.

40

26.5

2 ±

11.5

7 43

.64

22.1

4 ±

17.6

6 79

.78

StF1

7.

86 ±

2.1

8 27

.72

4.70

± 0

.30

6.34

25

.54

± 3.

85

15.0

9 83

.20

± 65

.25

78.4

3 St

F2

7.29

± 3

.64

49.9

9

3.95

± 0

.30

7.47

19.0

6 ±

4.24

22

.26

25

.49

± 36

.92

144.

85

35

Soil organic matter content (SOM) and soil moisture (SM) also had positive effects on

zherb; however, these were not statistically significant (P > 0.05).

Figure 2.2 Path diagram for the relationships between the mean of the environmental variables, the structural diversity index (H), and herbaceous plant diversity (zherb) at the 30 study sites (Bois-Francs region, Québec, Canada). R2 represents the total explained variance and ρ the direct causal covariance (path coefficient). Irrad: irradiance; SOM: soil organic matter; SM: soil moisture.

H

IRRAD R2 = 0.068 P > 0.05

zherb

R2 = 0.581 P < 0.001

Soil pH R2 = 0.024 P > 0.05

SOM R2 = 0.011 P > 0.05

SM R2 = 0.560 P < 0.001

ρ = 0.260 ρ = -0.007

ρ = 0.388

ρ = 0.543

ρ = 0.212

ρ = 0.260

ρ = 0.749

ρ = -0.155

ρ = 0.035

ρ = 0.106

ρ = -0.145

P > 0.05

P < 0.05

P < 0.01

P < 0.001

36

Figure 2.3 Path diagram for the relationships between the CV of the environmental variables, the structural diversity index (H), and herbaceous plant diversity (zherb) at the 30 study sites (Bois-Francs region, Québec, Canada). R2 represents the total explained variance and ρ the direct causal covariance (path coefficient). Irrad: irradiance; SOM: soil organic matter; SM: soil moisture.

H

IRRAD R2 = 0.012 P > 0.05

zherb

R2 = 0.233 P > 0.05

Soil pH R2 = 0.016 P > 0.05

SOM R2 = 0.017 P > 0.05

SM R2 = 0.628 P < 0.001

ρ = -0.111 ρ = -0.202

ρ = 0.388

ρ = 0.102

ρ = -0.080

ρ = -0.124

ρ = 0.778

ρ = 0.090

ρ = 0.080

ρ = 0.132

ρ = 0.079

P > 0.05

P < 0.05

P < 0.01

P < 0.001

37

Tabl

e 2.

4 Pe

arso

n co

effic

ient

of c

orre

latio

n be

twee

n th

e va

riabl

es s

tudi

eda (*

: P <

0.0

5; *

*: P

< 0

.01;

***

: P <

0.0

01),

n =

30 (B

ois-

Fran

cs re

gion

, Qué

bec,

Can

ada)

.

z h

erb

H

SM

CV

SM

SOM

C

VSO

M

pH

CV

pH

Irra

d C

VIr

rad

Cov

C

VC

ov

BA

C

VB

A

z her

b -

H

0.37

3*

-

SM

0.37

5*

-0.0

89

-

CV

SM

-0.0

56

0.18

5 -0

.029

-

SOM

0.

247

0.03

3 0.

809*

**

0.17

4 -

CV

SOM

-0

.028

0.

136

-0.0

11

0.80

6***

0.

107

-

pH

0.49

6**

0.01

8 0.

006

-0.3

52

-0.1

67

-0.3

15

-

CV

pH

0.28

7 0.

090

0.11

9 -0

.134

0.

281

-0.0

07

0.42

7*

-

Irra

d 0.

169

0.12

6 0.

187

0.25

6 0.

267

0.02

5 -0

.018

0.

024

-

CV

Irra

d -0

.143

-0

.106

0.

230

-0.3

52

0.29

0 -0

.265

0.

099

0.23

2 0.

046

-

Cov

-0

.210

-0

.220

-0

.171

-0

.130

-0

.101

-0

.101

0.

090

-0.0

35

-0.0

77

0.14

9 -

CV

Cov

0.

053

-0.0

73

0.13

6 -0

.014

0.

117

0.15

0 -0

.080

0.

087

-0.1

36

-0.1

13

-0.6

53**

* -

BA

0.

059

0.05

1 -0

.116

-0

.271

-0

.032

-0

.379

* -0

.081

-0

.077

0.

066

-0.1

29

-0.0

71

-0.1

28

-

CV

BA

-0.1

98

-0.1

44

-0.0

33

-0.2

18

0.06

3 -0

.079

0.

002

0.01

4 -0

.297

0.

431*

0.

248

-0.1

00

0.15

7 -

38

A significant and positive relationship was detected between soil organic matter content

and soil moisture (r = 0.809, Table 2.4; ρ = 0.749, Figure 2.2). The path analysis based on

the coefficients of variation of the environmental variables and the structural diversity

index, H, explained only 23.3% (P > 0.05) of herbaceous plant species diversity (χ2 =

5.784; P = 0.216; RMSEA = 0.118; P (RMSEA < 0.05) = 0.268; GFI = 0.940) and none of the

environmental variables (irradiance, soil pH, soil organic matter, or soil moisture) had a

significant effect on zherb (Figure 2.3). However, there was a significant and positive

relationship between CVSOM and CVSM (r = 0.806, Table 2.4; ρ = 0.778, Figure 2.3).

Again, the structural diversity index H had a significant and positive effect on zherb (ρ =

0.388; Figure 2.3).

In summary, between the two models considered here, only model 1, which examined the

mean values of the environmental variables, explained a significant proportion of the

criterion variable.

2.7 Discussion Our structural diversity index H, calculated from the basal area of the different diameter

classes and the different species, was significantly correlated with tree species richness (r =

0.382, P < 0.05), but not with the CV of basal area (Table 2.4). These results suggest that H

was more sensitive to tree alpha diversity than to variability in tree size (we had postulated

that management practices for sap extraction tended to decrease tree species richness and

variability in tree size). Our analyses detected a significant and positive association

between H and understory plant diversity, but not between H and light availability or any of

the soil variables (Figures 2.2 and 2.3). Assuming H adequately represents the management

intensity of maple-dominated forests (a low value of H represents high management

intensity), our results suggest that traditional management practices for sap extraction in

maple-dominated forests may have detrimental effects on understory plant diversity (see

also Reader 1987; Bengtsson et al. 2000), although such practices do not appear to affect

environmental complexity (represented by the CV of the different variables).

Both of our path models were significant. The analysis using the means of the

environmental variables and the structural diversity index (H) explained 58% of the

39

40

variance in herbaceous plant diversity, while that based on the CV of the environmental

variables explained only 23% of the variance of diversity (Figures 2.2 and 2.3). These

values are in the range of those reported by Grace (1999), who estimated that an average of

57% (23-89%) of the variance in plant species diversity could be explained by structural

and environmental variables (Grace and Pugesek 1997: 45%; Chipman and Johnson 2002:

52%; Le Brocque and Buckney 2003: 62%; Lundholm and Larson 2003: 74%; Weiher

2003: 27%; Désilets and Houle 2005: 73%; Schuster and Diekmann 2005: 51-60%).

2.7.1 Plant diversity and soil pH In our first model (Figure 2.2), soil pH was the variable that explained most of the variance

of z (see also: Palmer 1991; Vetaas 1997; Stevens and Carson 2002; Schuster and

Diekmann 2003; Graae et al. 2004). This positive relationship between soil pH and species

diversity supports the conclusions reached by Weiher et al. (2004), who underlined the

importance of soil factors for species richness, and by Chytrý et al. (2003), who

demonstrated that soil pH could explain a significant portion of the variance in local

species diversity in deciduous forests of eastern Europe. Soil pH (associated with soil

fertility) is thus a good index of species diversity, as attested to by numerous studies of

temperate and boreal forests in the Netherlands (Roem and Berendse 2000), the Vosges and

the Carpathians (Gégout and Krizova 2003), Germany (Härdtle et al. 2003, 2004), northern

Europe (Brunet et al. 1996; Dupré et al. 2002; Pärtel et al. 2004), and North America (Peet

et al. 2003). Indeed, the relationship between soil pH and species diversity is typically

unimodal for pH values between 2 and 8, where the greatest richness falls between pH of 4

to 6 (Pausas 1994; Dupré et al. 2002; Schuster and Diekmann 2003, 2005): at low values

(from 3 to 5), the relationship is linear and positive (Figure 2.4) and at higher pH values,

the relationship tends to become negative (Pärtel 2002). Furthermore, the relationship

between soil pH and species richness is typically positive in floristic regions where the

evolutionary centre is situated on soils with high pH (e.g. at mid-high latitudes). On the

other hand, the relation tends to be negative where the evolutionary centre is situated on

soils of low pH, such as near the equator (Pärtel 2002; Pärtel et al. 2004).

41

Figure 2.4 Positive relationship between soil pH and herbaceous plant diversity (zherb) over

the 30 study sites, Bois-Francs region, Québec, Canada.

2.7.2 Plant diversity and the other environmental variables Other than for the significant, positive relationship between soil pH and understory plant

diversity, the path analyses did not indicate any other significant relationships between the

environmental variables and plant diversity (Figures 2.2 and 2.3). Approximately 42% of

the variance in diversity remained unexplained in the model based on the means of the

environmental variables and 77% in the model based on the CV; this indicates that plant

diversity was mostly influenced by variables other than those considered in the present

study. These other important variables might be historical; they might be associated with

temporal, instead of spatial, environmental variability; they might result from the effects of

micro-organisms or herbivores, from productivity, from variations in the local plant

community, such as density, or from habitat configuration related to forest fragment size

5.25.04.84.64.44.24.03.83.63.4

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

Soil pH

z he

rb

r = 0.496 P < 0.01

42

and isolation, etc. (Oksanen 1996; Grace 1999; Grace 2001; Le Brocque and Buckney

2003; Schuster and Diekmann 2003, 2005).

However, a marginally significant relation was present between plant diversity and one of

the other environmental variables: soil moisture. Indeed, this variable presented a positive

association with zherb (ρ = 0.260; P < 0.20; Figure 2.2). Interestingly, Brosofske et al.

(2001) found that soil characteristics, such as moisture, positively influenced species

richness (vascular plants, bryophytes, and lichens) in managed Wisconsin forests.

Similarly, Härdtle et al. (2003) showed that herbaceous plant species richness was

positively correlated with soil moisture and organic matter content. Fu et al. (2004) found

that soil organic matter content was a good index of soil fertility and nutrient availability,

two factors that often positively influence plant diversity (Grime 1979; Decocq et al. 2004;

Small and McCarthy 2005; but see: Tilman 1982). The results of the present study show

that soil organic matter content and soil moisture were indeed positively associated in terms

of both mean values and heterogeneity indices (Table 2.4; Figures 2.2 and 2.3).

Average light conditions or spatial variations in light conditions had no significant effects

on plant species diversity (Table 2.4; Figures 2.2 and 2.3), most likely because the majority

of the species in our 30 forest fragments are adapted or tolerant to low light intensities and

because the pool of species tolerant to low light levels is fairly limited. Our findings

support those of Bauhus et al. (2001) and of Härdtle et al. (2003), who reported that

understory light level was not an important factor influencing plant species richness, and

those of Stevens and Carson (2002) who could not find any correlation between

heterogeneity in light and diversity (mostly herbaceous species diversity). However, in

intensively managed forests, it is possible that species diversity increases with light

availability mostly because of the sudden appearance of open-condition species (Brosofske

et al. 2001; Nash Suding 2001; Decocq et al. 2004); however, the objectives of forest

management for sap extraction are to conserve the tree canopy, thus restricting the species

pool to shade tolerant species.

43

2.7.3 Local diversity and the regional species pool In a nested-quadrat design such as ours, species richness in the smaller quadrat (x) and

species richness in the larger quadrat (y) are not independent. We calculated, for each site,

the difference (y’) between the species richness of each quadrat pair (i.e., the number of

new species as we went from a smaller to a larger quadrat) and correlated x and y’ over the

30 study sites. The threshold of significance of such correlations has been used in other

studies to evaluate the effect of the regional species pool on local diversity (Dupré et al.

2002): indeed, significant positive correlations indicate a significant species pool effect.

Our nested-quadrat system included four different sizes, which allowed the calculation of

six possible correlations: four of these were positive and significant. Our results thus

support recent evidence, which shows that species richness on a local scale is often

determined by the number of species on a regional scale (Butaye et al. 2001; Pärtel 2002).

2.8 Conclusion Our study suggested that intense management of maple-dominated forests (the reduced

forest structural diversity that results from it) can significantly affect herbaceous plant

diversity. Although both of our analyses showed an “exact” fit between the model and the

data, the analysis based on the means of the environmental variables explained more of the

variance of plant diversity than that based on the spatial heterogeneity of the environmental

variables (see also Désilets and Houle 2005). Among the environmental variables

measured, only soil pH had a significant (positive) effect on diversity, most likely through

its influence on the species pool. However, forest structural diversity (i.e., management

intensity) was not associated with any of the environmental variables or their spatial

variability.

2.9 Acknowledgements The authors thank V. Bolduc-Tremblay, G. de Lafontaine, P. Désilets, G. Descôteaux, P.

Marchand, and F. Sahim for field assistance, and L. Lapointe, M.F. McKenna, and S.

Payette for comments on an early version of the manuscript. This study was financed by the

Natural Sciences and Engineering Council of Canada through a grant to G. Houle.

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Chytrý, M., Tichý, L. and Rolecek, J. 2003. Local and regional patterns of species richness

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Coons, C.F. 1992. Sugar bush management for maple syrup producers. Ontario Ministry of

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Désilets, P. and Houle, G. 2005. Effects of resource availability and heterogeneity on the

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49

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CHAPITRE 3 – Short-term understory plant community response to nutrient spatial heterogeneity in the cold-temperate deciduous forest of south-eastern Canada

3.1 Avant-Propos Le texte de ce chapitre sera bientôt soumis pour publication.

3.2 Résumé Dans les communautés végétales, la distribution spatiale des plantes est souvent reliée à la

distribution des ressources (e.g. nutriments du sol). Du fait que la plupart des espèces de

sous-bois ont de petits systèmes racinaires, la production de sous-bois, la richesse et

l’équitabilité devraient augmenter à mesure que les nutriments deviennent de plus en plus

finement hétérogènes. Nous avons testé cette hypothèse en manipulant l’échelle

d’hétérogénéité des nutriments dans trois fragments forestiers de la région des Bois-Francs,

dans le centre du Québec (Est du Canada). Les traitements comprenaient quatre échelles

spatiales avec ajout de nutriments (homogène à hétérogène) et un témoin (aucun ajout).

Cinq groupes fonctionnels ont été définis : plantes printanières éphémères, plantes

printanières persistantes, plantes estivales, fougères, et plantules et juvéniles des espèces

ligneuses. Le couvert, la richesse, l’autocorrélation spatiale (SA) de la richesse,

l’équitabilité et certaines variables environnementales (irradiance, pH et teneur en matière

organique du sol) ont été suivies pendant deux ans. Nos analyses n’ont révélé aucune

différence significative entre les traitements pour la plupart des variables étudiées : couvert,

richesse, SA de la richesse, et variables environnementales. Cependant, une équitabilité

plus élevée a été détectée chez les espèces estivales pour la plus fine échelle

d’hétérogénéité spatiale. De par la capacité de croissance limitée de leur système racinaire,

certaines espèces estivales ne peuvent exploiter les nutriments que dans un petit volume de

sol. Cependant, d’autres espèces estivales capturent des nutriments dans des parcelles plus

éloignées grâce à des systèmes racinaires plus étendus. Ainsi, des différences dans les

stratégies racinaires représentent une spécialisation de niche, favorisant la coexistence des

50

51

espèces vis-à-vis de l’hétérogénéité spatiale. Malgré tout, la flore du sous-bois est

typiquement plus sensible à de fines variations spatiales que les espèces de bordures, qui

présentent une croissance rapide de l’élongation de leurs rhizomes. Néanmoins, nos

résultats suggèrent que la disponibilité en nutriments ne semble pas limitante pour les

espèces de sous-bois, caractérisées par une croissance lente et par une forte tolérance à

l’ombre. Les filtres environnementaux sont hautement sélectifs dans la végétation du sous-

bois (tolérance à l’ombre), si bien que la richesse apparaît saturée et que les espèces

semblent peu sensibles à l’hétérogénéité spatiale des nutriments.

3.3 Abstract Within plant communities, species spatial distribution is often related to the distribution of

resources (e.g. soil nutrients). Because most understory plants have small root systems,

understory production, richness, and evenness should increase as nutrients become spatially

more heterogeneous. We tested this hypothesis by manipulating the scale of heterogeneity

of nutrient availability in three forest fragments of the Bois-Francs region, in central

Québec (eastern Canada). The treatments included four spatial scales of nutrient addition

(from homogeneous to finely heterogeneous) and a control (no nutrient addition).

Understory species were classified into five functional groups: spring-flowering ephemeral

species; spring-flowering persistent species; summer-flowering species; ferns; and

seedlings and juveniles of woody species. Species cover, richness, spatial autocorrelation

(SA) of species richness, evenness, and some environmental variables (irradiance, soil pH,

and soil organic matter content) were followed over two years. Our analyses revealed no

significant differences among treatments for most of the variables studied: cover, richness,

SA of richness, and environmental variables. However, a higher evenness (more uniform

production) among summer-flowering species was detected at the finest scale of

heterogeneity. Because of their limited root growth ability, some summer-flowering species

can exploit nutrients only within a small volume of soil. However, other summer-flowering

species can take advantage of more distant nutrient-rich patches with their extensive root

system. Such differences in rooting strategy represent niche specialization and may favour

species coexistence. Nevertheless, understory species are typically more sensitive to small-

52

scale variations in habitat conditions than forest edge species, which can grow through

rapid rhizome elongation. Our results suggest that nutrient availability may not be limiting

in the forest understory, characterized by slow-growing, shade-tolerant species. Because

environmental filters are highly selective in the forest understory (shade tolerance), richness

appears to be saturated and species to be irresponsive to nutrient heterogeneity.

3.4 Introduction In forest ecosystems, the soil represents a three-dimensional mosaic, within which nutrients

are non-randomly distributed at different spatial scales (Bliss et al. 2002; Day et al. 2003;

Lundholm and Larson 2003. This contributes to increase environmental heterogeneity and

influence plant production and diversity (Collins and Wein 1998; Day et al. 2003). At the

level of the community, soil resources are influenced by several factors, including micro-

topography and local disturbances. On the other hand, at the level of the plant

neighbourhood, the distribution of soil micro-organisms and the presence of competitors

are largely responsible for the spatial heterogeneity of soil nutrients (Bliss et al. 2002;

Anderson et al. 2004). Consequently, the distribution of nutrients in the soil is directly

related to the dispersion patterns of plants, influencing competitive interactions,

coexistence, and the structure of plant communities (Huston 1980; Stevens and Carson

2002). Under homogeneous fertile conditions, interspecific competition for nutrients is

often intense and, as a result, dominant species may represent a larger proportion of the

production (Roem and Berendse 2000). In moist tropical forests and, to some degree, in

temperate forests, soil resources are distributed heterogeneously (Hammer et al. 1987),

often at a relatively fine spatial scale, generally < 1 m (Sollins 1998; Farley and Fitter

1999b; Lister et al. 2000; Scheller et Mladenoff 2002). If different species are able to share

nutrients because of their specific root system (rooting depth, lateral extension), then

production increases, specific and functional richness increase, and competitive exclusion is

reduced (Gough et al. 2000a).

There is a direct relationship between the spatial heterogeneity of soil resources and plant

richness in several natural ecosystems, such as in wetlands (Fitter 1982), in arid and semi-

arid deserts (Collins and Wein 1998; Titus et al. 2002), and in forests (Small and McCarthy

53

2005). For instance, Lundholm and Larson (2003) showed a strong positive association

between plant species richness and spatial heterogeneity in microsite quality in limestone

alvars. In addition, several non-experimental studies have reported a positive correlation

between the degree of environmental heterogeneity and species richness in grasslands

(Reynolds et al. 1997). Fine-scale spatial heterogeneity in nutrient availability should thus

facilitate local coexistence, by allowing species to occupy distinct microsites (Roberts and

Zhu 2002). On the other hand, at a more regional scale, species coexistence should be

related more to habitat heterogeneity and environmental gradients (Gough et al. 2000b;

Anderson et al. 2004; Tews et al. 2004).

Most of the studies reported in the literature have focussed on how plants affect the

distribution of nutrients in the soil, and few have examined the way in which the

distribution of resources influences species distribution in natural communities (Casper and

Cahill 1998). An experimental study by Wijesinghe et al. (2005) has shown that spatial

heterogeneity in soil resources can influence the structural properties of communities, such

as aboveground biomass and species composition. In forests, nutrients are often more

concentrated and soil pH can be more acid near the base of the trees because of stem flow

(Huston 1980; Kleb and Wilson 1997; Burghouts et al. 1998; Gómez et al. 2004).

Highly associated with the spatial dynamics of nutrients, the understory represents

approximately two-thirds of the plant species richness in forest ecosystems. Furthermore,

understory species can have significant effects on the patterns of tree recruitment (Small

and McCarthy 2005); it is thus important that the understory be considered even though it

contributes only modestly to the total biomass of forests. In addition, because understory

plants are highly sensitive to abiotic conditions, as demonstrated by Gilliam and Turrill

(1993) in deciduous Appalachian forests, they can serve as indicators of soil fertility.

This paper reports the results of an experimental study on the effects of spatial

heterogeneity in nutrient availability on the production and richness of the understory in

three forest patches in southern Québec (eastern Canada). The main objective of this study

was to determine the effects of the spatial scale of nutrient availability on production, i.e.,

plant cover, and on understory plant richness in the absence of major disturbances. Our

54

main hypothesis is that, because the root system of most understory plants is spatially

confined, production, richness, and evenness should be higher when nutrients are more

finely heterogeneous (Hutchings et al. 2003; Wijesinghe et al. 2005). In addition, we could

expect the spatial scale of richness (i.e. richness patch size) to respond to the spatial scale of

nutrient heterogeneity.


3.5.1 Study site description This experiment was conducted in three distinct forest patches (at least 1 km apart) in the

Bois-Francs region, more precisely, at Saint-Léonard d’Aston (StL1: 46°08.798' N,

72°22.105' W; StL2: 46°05.090' N, 72°22.161' W; StL3: 46°04.600' N, 72°22.340' W), on

the south shore of the St. Lawrence River, near Trois-Rivières, Québec, Canada. The three

forest fragments (between 5 and 10 ha) are on private land and have been lightly managed

for sap over several years. The dominant overstory species is sugar maple (Acer

saccharum), with red maple (Acer rubrum), American linden (Tilia americana), and yellow

birch (Betula alleghaniensis) as associate species. The area is part of the Great Lakes–St.

Lawrence forest region of Rowe (1972), subsection Mid St. Lawrence (L-2). The annual

precipitation totals ~ 1160 mm, of which 23.5% falls as snow. The annual daily mean

temperature is 5.0 °C at the nearby Arthabaska weather station (URL:

www.climate.weatheroffice.ec.gc.ca). The soils of the region are mostly brunisols except in

low areas, which are characterised by gleysols (Choinière and Laplante 1948).

Site selection was based on the following criteria: relatively uniform topographical

features, easy road access, plant species richness representative of the region, and

permission from the owners to carry out the experiment. StL1 is characterised by an

abundance of Canada yew (Taxus canadensis) and a topographic relief composed of small

mounds and pits. The topography of StL2 and StL3 is uniform and mostly level.

55

3.5.2 Sampling protocol and variables measured In the summer of 2003, we established randomly 30 2 m x 2 m plots at each of the three

study sites (similar species composition and absence of woody plants > 1 m in height).

Each species present on the sites was assigned to one of five functional groups (Table 3.1),

based principally on its flowering period: spring-flowering ephemeral species (SE); spring-

flowering persistent species (SP); summer-flowering species (SM); ferns (FE); and

seedlings and juveniles of woody species (TS). We considered SE separately from SP

because of the important role they play in the nutrient dynamics of deciduous forests.

Indeed, SE are believed to store in their tissues the nutrients flushed into the system during

snowmelt and to release them slowly as they senesce (vernal dam hypothesis; Eickmeier

and Schussler 1993; Rothstein 2000). The interest in considering FE and TS comes from

the significant role that they play in ecosystem productivity and in competition with other

understory species.

For the experiment, we chose 15 plots per site, the most similar in terms of species

composition and topography, among the 30 initially marked. Within site, plots were

grouped in three blocks on the basis of their spatial proximity, and treatments were

randomly assigned to the plots within each block. The treatments were as follows: control

(N0, no nutrient application); homogeneous nutrient application (N1); and three scales of

nutrient application, i.e., from finer to coarser grain: N64, N16, and N4. A checkerboard

design was used to create the three heterogeneous patterns (Fig. 3.1). Plots were subdivided

into 64 (25 cm x 25 cm, N64), 16 (50 cm x 50 cm, N16), or 4 (1 m x 1 m, N4) cells (Table

3.2).

56

Table 3.1 List of all vascular plant species recorded (all treatments and all sites combined). Nomenclature follows Marie-Victorin (1995). SE: spring-flowering ephemeral species; SP: spring-flowering persistent species, SM: summer-flowering species; FE: ferns; and TS: seedlings and juveniles of woody species.

SP Aralia nudicaulis SM Aster sp. TS Abies balsamea Aralia racemosa Carex bromoides Acer pensylvanicum Clintonia borealis Carex gracillima Acer rubrum Maianthemum racemosum Carex grisea Acer saccharum Maianthenum canadensis Carex intumescens Acer spicatum Medeola virginiana Carex pedunculata Amelanchier sp. Polygonatum pubescens Carex sp. Betula alleghaniensis Ribes sp. Carex tribuloides Betula papyrifera Streptopus roseus Carex trisperma Carya condiformis Tiarella cordifolia Circaea lutetiana Fagus grandifolia Trientalis borealis Coptis groenlandica Fraxinus sp. Trillium erectum Galium sp. Ostrya virginiana Trillium undulatum Huperzia lucidula Prunus sp. Uvularia sessilifolia Lycopodium obscurum Prunus virginiana Mitchella repens Quercus rubra

FE Athyrium filix-femina Oxalis acetosella Sambucus racemosa Dennstaedtia punctilobula Oxalis stricta Taxus canadensis Dryopteris spinulosa Polygonum convolvulus Tilia americana Matteuccia struthiopteris Prenanthes sp. Ulmus americana Rubus idaeus Viburnum cassinoides

SE Claytonia caroliniana Rubus pubescens Viburnum lantanoides Erythronium americanum Rubus sp. Viburnum sp. Solidago sp. Viburnum trilobum Viola sp.

57

Figure 3.1 Experimental design: (a) control, i.e., no nutrient application (N0); (b) homogeneous nutrient application (N1); (c, d, and e) three increasing scales of nutrient application (N64, N16, and N4).

Table 3.2 Experimental protocol for the different nutrient addition treatments. No fertiliser was added to the control plots (N0).

Symbols Number of cells per plot

Cell surface (m2)

Number of fertilised cells

per plot

Quantity of fertiliser per

fertilised cell (g)

N0 1 4 0 0 N1 1 4 1 200 N64 64 0.0625 32 6.25 N16 16 0.25 8 25 N4 4 1 2 100

2 m

2 m

0.5 m(a) (b)

(d)

2 m

2 m

0.5 m(a) (b)

1 m

1 m

(e)

1 m

1 m

(d)

0.25 m

0.25 m

(c)

0.25 m

0.25 m

2 m

2 m

0.5 m(a) (b)

(d)

2 m

2 m

0.5 m(a) (b)

1 m

1 m

(e)

1 m

1 m

1 m

1 m

(e)

1 m

1 m

(d)

0.25 m

0.25 m

(c)

0.25 m

0.25 m

0.25 m

0.25 m

(c)

0.25 m

0.25 m

58

The fertilizer we used (18:6:8 N:P:K, plus micronutrients; Nutricote total, Fertiliser Co.

LTD, Chisso-Assati, Tokyo, Japan) was in the form of time-release pellets, insensitive to

soil moisture and pH. These pellets are coated with a polyolefin type resin to provide the

slow release of all nutrients (80% of N is released over a period of ~ 100 days, at a

temperature of 25° C). For each fertilizer addition treatment, in October 2003, and again in

October 2004 before leaf fall, the fertilizer was applied manually at a rate of 50 g m-2 (9 g

N, 3 g P, and 4 g K m-2 year-1; Table 3.2). Our choice of a 50 g m-2 application follows what

is commonly reported in the literature (Elemans 2004).

3.5.3 Biological variables Surveys of vascular plants ≤ 1 m in height were carried out three times in 2003 and 2005

(nomenclature follows Marie-Victorin 1995): in early May, in mid-July, and at the end of

August. In 2003, before the nutrient-addition experiment began, the entire 4 m2 surface of

each plot was surveyed (30 plots per site). When the nutrient-addition experiment began (in

the fall of 2003), a buffer zone was established at the periphery of each selected plot to

avoid trampling artefacts (Fig. 3.2). Although this buffer zone received the appropriate

treatment in the fall of 2003 and in the fall of 2004, no data were collected from that section

in 2005. When the plots were surveyed in 2005, the central section of each plot (1.60 x 1.60

m; 2.56 m2) was divided into 256 cells of 100 cm2 each (Fig. 3.2) on which all species

present were recorded. The recorded species are cited in Table 3.1.

59

Figure 3.2 Experimental set up to record plant cover and richness in each 4 m2 plot.

Species-specific cover values for each plot were estimated as the frequency of 100 cm2

cells (over 256) in which a given species was present. Percent cover (C) per functional

group (CSE, CSP, CSM, CFE, and CTS) and total percent cover (Ctot) were calculated as

the sum of species-specific-cover values for each plot to estimate production. As three

surveys were done in 2005, the maximum species-specific-cover value was used for each

species to estimate maximum annual production.

Consequently, cover values often exceeded 100% for a plot (in addition, two or more

species were often present in a given 100 cm2 cell). An evenness index (H) was calculated

for each functional group (HSE, HSP, HSM, HFE, and HTS), and for all species (Htot)

from the cover data using the formula H = - Σ pi ln pi, where pi represents the cover

proportion of species i in relation to total cover. Species richness (R) per functional group

(RSE, RSP, RSM, RFE, and RTS) and total species richness (Rtot) were also determined

for each plot.

60

3.5.4 Environmental variables A single soil sample (90.75 cm3, to a depth of 10 cm) was collected at the centre of each 4

m2 plot in May 2003. In July 2005, a soil sample was collected at each corner of each plot.

All samples were collected after a period of 2–3 days without rain. In the laboratory, each

sample was passed through a 2-mm-mesh sieve to remove roots, twigs, and stones, and then

dried for 24 hours at 75 °C. Soil pH was measured in a 1:1 soil:water solution using a pH

meter (Barrow and Jenkinson 1962; Kieft 1994). Soil organic matter (SOM) was estimated

as percent mass loss on ignition (at 450 °C for 5 hours; Garnier et al. 2003).

Irradiance (µmol m-2 s-1) was measured at 0.5-1 m above ground level (20 sec. scan)

between 10h and 14h, on sunny, cloudless days using a portable radiometer (LI-189, Licor,

Inc., Lincoln, Nebraska, USA). Readings were taken at the end of June 2003 (one per plot)

and at the end of July 2005 (four per plot, one at each corner).

3.5.5 Data analysis Total cover, cover of each functional group, total richness, and richness of each functional

group (for each plot) were analyzed with randomized complete blocks design ANOVAs . A

MANOVA was used to determine the overall statistical significance of richness, cover, and

evenness of the different functional groups (Wilk’s λ). For the three environmental

variables, i.e. irradiance, soil organic matter content, and soil pH, a mean value was

calculated from the four measurements taken on each plot in 2005. These abiotic variables

were also analyzed using randomized complete blocks design ANOVAs.

Spatial autocorrelation for total species richness was calculated for each plot using Moran’s

I. The centroid of each 100 cm2 cell served as spatial coordinates. Twenty-one distance

classes of 10 cm each were used in the analyses. A correlogram, which is a graphical

representation of the autocorrelation index as a function of distance classes, was globally

significant when at least one value of I was significant at P ≤ 0.002, i.e., 0.05/k, where k

represents the number of distance classes (k = 21; Bonferroni adjustement, Legendre and

Fortin 1989). The value of maximum distance of positive autocorrelation was used to

represent the patch size; these values were then used in an ANOVA to evaluate the effect of

61

the treatments on the patch size of total species richness, i.e., the spatial scale at which

species richness is contagiously dispersed. The statistical program R Version 4.0 (Casgrain

et al. 2004) was used for the spatial autocorrelation analyses.

Thus, the experimental design was made up of five treatments per block, three blocks, i.e.,

repetitions, per site, and three sites. The sources of variation were sites (random), blocks

within site (random), treatments (fixed), and the interaction between treatments and sites

(fixed), as well as an experimental error term. Differences between treatments for species

richness, cover, evenness, and the environmental variables were identified using protected

LSD (least significant differences) when significant differences among treatments were

detected in the ANOVA at α < 0.05. The statistical software package SAS version 6.12

(SAS Institute, Inc., Cary, North Carolina, USA) was used for the analyses. The following

variables were transformed to satisfy the assumption of normality: total richness and spatial

autocorrelation of total richness (log10 variable); richness of each functional group (log10

variable + 1). Means are presented with their standard deviation (SD).

3.6 Results

3.6.1 Pre-experimental data (2003) The analyses performed on the 2003 data, those prior to nutrient addition, indicated that

there were no significant differences among treatments in total richness (P = 0.410) or in

the richness of the different functional groups (MANOVA: F = 0.90, P = 0.571). Likewise,

the environmental factors, i.e., soil pH, soil organic matter content, and irradiance did not

differ significantly among treatments at the beginning of the experiment (P = 0.951, P =

0.560, and P = 0.979, respectively; Table 3.3).

62

Table 3.3 F-values (P-values in parentheses) from the ANOVAs for the effects of site (df = 2), treatment (df = 4), and their interaction (df = 8) on total cover (Ctot), total richness (Rtot), spatial autocorrelation of total richness (SA), total evenness (Htot), functional group evenness (HSE, HSP, HSM, HFE, and HTS), and three environmental factors (soil pH, soil organic matter (SOM), and irradiance). SE: spring-flowering ephemeral species; SP: spring-flowering persistent species, SM: summer-flowering species; FE: ferns; and TS: seedlings and juveniles of woody species. Significant values are in boldface characters.

2003 (pre-experimental)

2005 (2 years of experimentation)

Variables Site Treatment Site x treatment Site Treatment Site x

treatment Ctot - - - 28.26 (0.001) 1.32 (0.292) 0.79 (0.612) Rtot 4.83 (0.056) 1.04 (0.410) 0.57 (0.793) 7.20 (0.025) 2.06 (0.118) 1.52 (0.204) SA - - - 0.52 (0.617) 0.25 (0.909) 1.02 (0.451)

Htot - - - 2.59 (0.154) 1.60 (0.206) 0.98 (0.473) HSE - - - 3.85 (0.084) 1.46 (0.244) 2.08 (0.080) HSP - - - 0.11 (0.901) 0.15 (0.962) 0.44 (0.886) HSM - - - 3.83 (0.085) 4.18 (0.010) 1.08 (0.411) HFE - - - 10.17 (0.012) 0.71 (0.591) 1.13 (0.379) HTS - - - 232.42 (<0.001) 2.54 (0.066) 1.89 (0.110) pH 9.92 (0.013) 0.17 (0.951) 0.31 (0.956) 14.11 (0.005) 0.33 (0.853) 0.39 (0.918)

SOM 7.14 (0.026) 0.76 (0.560) 0.79 (0.615) 12.07 (0.008) 1.05 (0.402) 1.21 (0.334) Irradiance 6.96 (0.027) 0.11 (0.979) 0.16 (0.995) 10.49 (0.011) 1.12 (0.372) 0.08 (0.999)

- : These variables were not recorded in 2003.

3.6.2 Nutrient addition experiment (2005) The species richness of all the sites combined comprised 67 vascular plant species, 16 of

which were trees, seven of which were shrubs, and 44 of which were herbaceous plants

(Table 3.4). Thus, herbaceous plant species made up approximately two-thirds (65.7%) of

the overall species richness. There were 41.3 ± 7.0 (mean ± SD) understory species per site.

Close to 55% of the understory flora were summer-flowering species (SM), i.e. 24 species,

whereas there were only two spring-flowering ephemeral (SE) species.

63

Table 3.4 Species richness for the three sites combined and for each site according to functional group (SE: spring-flowering ephemeral species; SP: spring-flowering persistent species; SM: summer-flowering species; FE: ferns; TS: seedlings and juveniles of woody species).

Functional

groups All 3 sites StL1 StL2 StL3

SE 2 2 1 1 SP 14 12 8 10 SM 24 15 5 13 FE 4 3 2 3 TS 23 16 18 15

Total 67 48 34 42

Our results showed that there were no significant differences among treatments in total

cover (P = 0.292) or in the cover of the different functional groups (MANOVA: F = 1.26, P

= 0.234; Table 3.3). Total cover varied from 264.0 ± 154.8% to 340.2 ± 117.7% (mean ±

SD), according to treatment, with seedling and juveniles of woody species making the

largest contribution among the five functional groups (Table 3.5).

Total species richness did not differ significantly among treatments (P = 0.118) and neither

did the species richness of the different functional groups (MANOVA: F = 0.79, P = 0.719;

Table 3.3).

64

Table 3.5 Total cover (Ctot), functional group cover (CSE, CSP, CSM, CFE, and CTS), total richness (Rtot), functional group richness (RSE, RSP, RSM, RFE, and RTS), total evenness (Htot), and functional group evenness (HSE, HSP, HSM, HFE, and HTS) for each treatment. SE: spring-flowering ephemeral species; SP: spring-flowering persistent species; SM: summer-flowering species; FE: ferns; and TS: seedlings and juveniles of woody species. Different letters indicate significant differences among treatments for each variable (protected LSD with P ≤ 0.05). Mean ± SD (n = 9). See Fig. 3.1 and Table 3.2 for details on the nutrient application treatments.

COVER (%)

Treatments

Variables N0 N1 N64 N16 N4

Ctot 292.40 ± 138.60 300.99 ± 118.32 363.29 ± 116.22 271.66 ± 136.57 264.44 ± 120.87

CSE 48.22 ± 39.02 44.76 ± 37.67 39.80 ± 32.13 45.13 ± 36.38 34.33 ± 33.18

CSP 64.89 ± 51.11 38.84 ± 39.20 124.52 ± 119.43 19.77 ± 27.39 44.93 ± 61.05

CSM 38.58 ± 38.38 43.52 ± 54.59 27.42 ± 27.80 43.79 ± 72.56 27.70 ± 45.08

CFE 14.37 ± 20.03 41.79 ± 51.59 67.01 ± 76.98 39.28 ± 69.83 25.62 ± 40.95

CTS 126.36 ± 89.32 132.08 ± 71.06 104.51 ± 21.78 123.69 ± 88.17 131.91 ± 117.85

RICHNESS Treatments


Rtot 10.11 ± 2.26 7.77 ± 3.03 8.56 ± 2.92 8.78 ± 4.35 8.56 ± 2.92

RSE 0.67 ± 0.50 0.78 ± 0.44 0.67 ± 0.50 0.67 ± 0.50 0.78 ± 0.67

RSP 2.89 ± 2.03 2.11 ± 2.09 2.89 ± 2.26 2.00 ± 1.94 2.44 ± 1.33

RSM 1.89 ± 1.69 1.22 ± 1.48 1.11 ± 1.27 1.67 ± 2.40 1.00 ± 1.00

RFE 0.89 ± 0.60 0.78 ± 0.83 0.89 ± 0.60 1.00 ± 1.00 0.89 ± 0.60

RTS 3.78 ± 1.09 2.88 ± 0.60 3.00 ± 0.71 3.44 ± 0.88 3.44 ± 1.59

EVENNESS Treatments


Htot 1.42 ± 0.15 1.37 ± 0.36 1.57 ± 0.35 1.29 ± 0.38 1.40 ± 0.31

HSE 0.09 ± 0.12 0.21 ± 0.22 0.23 ± 0.18 0.16 ± 0.19 0.17 ± 0.14

HSP 0.55 ± 0.14 0.54 ± 0.10 0.50 ± 0.08 0.54 ± 0.13 0.56 ± 0.30

HSM 0.30 ± 0.18ab 0.20 ± 0.16b 0.43 ± 0.34a 0.17 ± 0.15b 0.28 ± 0.29b

HFE 0.25 ± 0.20 0.18 ± 0.19 0.19 ± 0.14 0.23 ± 0.29 0.17 ± 0.21

HTS 0.22 ± 0.17 0.25 ± 0.14 0.22 ± 0.17 0.19 ± 0.15 0.23 ± 0.18

65

Total richness, averaged over all treatments, was 8.70 ± 3.10 species per 2.56 m2 plot

(mean ± SD), with the richness of seedlings and juveniles of woody species making the

largest contribution among the five functional groups (Table 3.5). Likewise, the ANOVA

did not detect significant differences among treatments in the patch size of species richness,

SA: 36.7 ± 15.8 cm for N0; 42.2 ± 15.6 cm for N1; 38.9 ± 17.6 cm for N64; 41.1 ± 17.6 cm

for N16; 36.7 ± 15.8 cm for N4; P = 0.909, Table 3.3).

There were no significant differences among treatments for total species evenness (P =

0.206), although it was slightly higher in the N64 treatment than in the others (Table 3.5).

Evenness for the different functional groups differed significantly among treatments as

shown by the MANOVA (F = 1.74; P = 0.048). However, among the five functional groups

considered, the differences in evenness were significant only for summer-flowering species

(HSM; P = 0.010, Table 3.3). Indeed, HSM was the highest in the N64 treatment, but the

lowest in the N1, N16, and N4 treatments (Table 3.5).

3.6.3 Environmental factors The ANOVAs performed on the three environmental variables showed that there were no

significant differences among treatments, even after two years of experimentation (Tables

3.3 and 3.6). While the variability associated with the mean was low for soil pH, it was

quite high for irradiance. These results are not surprising considering the canopy gap

dynamics characteristic of the type of forests studied.

66

Table 3.6 Mean (± SD; n = 9) of three environmental variables (soil pH, soil organic matter (SOM), and irradiance) as a function of year and treatment. There are no significant differences among treatments for each variable. See Fig. 3.1 and Table 3.2 for details on the nutrient application treatments.

2003 (pre-experimental) Treatments

Variables N0 N1 N64 N16 N4 pH 3.88 ± 0.33 3.98 ± 0.36 3.95 ± 0.37 3.87 ± 0.26 3.96 ± 0.44

SOM (%) 11.95 ± 7.45 15.44 ± 10.82 25.47 ± 33.59 22.28 ± 21.90 22.02 ± 24.54 Irradiance

(µmol m-2 s-1) 173.20 ± 158.14 147.81 ± 358.73 195.15 ± 271.50 206.01 ± 321.89 148.81 ± 236.22

2005 (after 2 years of experimentation)

Treatments Variables N0 N1 N64 N16 N4

pH 3.65 ± 0.09 3.74 ± 0.20 3.68 ± 0.23 3.68 ± 0.19 3.66 ± 0.18 SOM (%) 14.51 ± 8.87 18.14 ± 7.79 23.69 ± 23.67 19.82 ± 17.40 23.98 ± 23.67 Irradiance

(µmol m-2 s-1) 54.40 ± 117.09 138.42 ± 138.19 59.91 ± 123.93 92.48 ± 130.24 56.65 ± 75.11

3.6.4 Inter-site differences There were significant differences among sites for most of the variables studied: Ctot and

Rtot; C, R, and H of most of the functional groups (MANOVAs: F = 17.28, F = 34.20, and

F = 39.17, respectively, for the effect of site; all Ps < 0.05); soil pH, soil organic matter

content, and irradiance (Table 3.3). On the other hand, the similarity of the sites with

respect to the spatial autocorrelation of total species richness (SA) and to total evenness

(Htot) was remarkable.

3.7 Discussion Among the variables studied, only summer-flowering species evenness responded to our

treatments.

67

3.7.1 Cover No significant differences were found among treatments for total cover or for the cover of

the different functional groups. These results can be explained by the fact that most of the

understory plants that we recorded on our field sites are slow-growing, shade-tolerant

perennials (Whigham 2004). Indeed, understory perennials seem to have a low ability to

adjust to rapid changes in resource availability (Collins and Pickett 1987; Farley and Fitter

1999a). Yet, understory annuals such as the shade tolerant Impatiens parviflora can

respond quickly when additional nutrients are provided: plants of this species, grown in a

greenhouse for 37 days with ample nutrients, reach a biomass two-fold that of control

plants, even under low light conditions (the response is, however, more important under

increased light availability; Peace and Grubb 1982). Some perennials typical of forest

edges, such as Aegopodium podagraria, can also respond to nutrient addition, but only

under relatively high light conditions (Elemans 2004). These latter results suggest that light

often modulates the effect of nutrient addition on plant growth (Peace and Grubb 1982).

We cannot exclude that the absence of response to our nutrient addition treatments may be

related to the low irradiances typical of the deciduous forest understory (as low as 2-4% of

open-field conditions; Meekins and McCarthy 2000).

The lack of response to our treatments may be explained by still other mechanisms. For

instance, the species studied might have absorbed and stored in their tissues the extra

nutrients, but without producing more biomass. This physiological trait, luxury

consumption, occurs in some alpine and arctic plants, as well as in certain spring-flowering

ephemeral species (Chapin 1980; Körner 1999; Heer and Körner 2002); this characteristic

allows the plants to draw upon stored nutrients when they become more restricted in the

environment. However, nutrient addition often increases the activity of soil micro-

organisms. It has been shown that micro-organisms can absorb as much nutrients as (or

even more than) plants, and thus, contribute to immobilize nutrients significantly (Vitousek

and Matson 1985; Zak et al. 1992; Jonasson et al. 1996; Ostertag and Verville 2002). Such

nutrient immobilization may have been a factor constraining the response of plants to our

nutrient additions.

68

3.7.2 Richness At fine spatial scales of nutrient heterogeneity, species with fast growth rates tend to

monopolize resources, eliminating slower-growing species, thereby reducing local species

richness (Poorter 1999, in tropical forests; Hutchings et al. 2003, in grassland ecosystems).

In our study, there were no significant differences among treatments for total richness or for

the richness of the different functional groups: this result is consistent with that reported in

the previous section for cover.

Understory species tend to respond more slowly to habitat modification than open habitat

and forest edge species (Elemans 2004). These differing responses can be explained by

different clonal growth habits and contrasting seed dispersal mechanisms. Indeed, nutrient

foraging by the underground structures of the species that have a rapid growth rate (open

habitat and forest edge species) is characterised by a high morphological plasticity (Slade

and Hutchings 1987c; Wijesinghe and Hutchings 1997). Understory perennials typically

show much lower levels of plasticity. In addition, plants of open habitats and those of forest

edges produce numerous seeds and can efficiently colonize new habitats because of their

efficient modes of dispersal (Ostertag and Verville 2002). Many understory plants have one

major mechanism of seed dispersal, myrmecochory (Bierzychudek 1982), which does not

contribute to move seeds on long distances in contrast to wind (Thompson 1980; Duffy and

Meier 1992; Meier et al. 1995). Such limited dispersal ability might be an important factor

influencing the spatial distribution and recruitment of plants in the understory of temperate

forests (Ehrlen and Eriksson 2000; see also Hubbell et al. 1999, recruitment limitation in

tropical forests). Nevertheless, canopy gaps generally allow more light to reach the forest

floor (Lautenschlager et al. 2005). Several herbaceous pioneer species, which maintain a

persistent seed bank in the soil, rapidly colonise these “open” microhabitats (particularly

when they are associated with soil disturbances) and compete with understory perennials

for light and soil resources (Ehrenfeld 1980; Collins and Pickett 1987) with potential

effects on understory species richness. However, on our plots, light availability did not vary

significantly with the nutrient treatments and soil disturbances were absent: as a

consequence, there was no pioneer species in the understory.

69

In the present study, the spatial autocorrelation of total richness was significant and

indicated a mean patch size of ~ 40 cm, regardless of the nutrient addition treatment. Such

contagion can be explained by the low seed dispersal ability and the phalanx-type clonal

growth habit of the species, two factors that shape the composition of the understory

community of deciduous forests (Lovett Doust 1981; Loreau 2000; Jacquemyn et al. 2001).

3.7.3 Evenness At fine spatial scales of nutrient heterogeneity, species with fast growth rates tend to

monopolize resources, eliminating slower-growing species, thereby reducing local species

richness (Hutchings et al. 2003). Under such conditions, the community is more uniform

(i.e. characterized by a higher dominance) than one in a habitat divided into larger patches,

where low-nutrient patches represent refuges for slower-growing species. Yet, our results

contradict this prediction. Although there were no significant responses to our treatments,

in terms of species cover or richness, a higher evenness (i.e. lower dominance) was

detected for summer-flowering species at the finest scale of nutrient heterogeneity. This

result can be explained by a concordance between the spatial scale of the nutrient-rich

patches and the spatial scale of the root system of the species (Poorter 1999; Hutchings et

al. 2003), as well as by the species-specific root and rhizome morphology (Antos 1988;

Farley and Fitter 1999a; Wijesinghe and Whigham 2001).

Root system architecture, as well as the physiological and morphological plasticity of the

plants, varies with the quantity of nutrients in the environment (Hodge 2004). In fertile

patches, root systems search for nutrients locally; rhizomes are short and ramified to

capture the maximum amount of nutrients (Slade and Hutchings 1987b; Dong et al. 1997).

The influx of nutrients, through both the experimental nutrient additions and the senescence

of spring-flowering ephemeral species (Muller 1978; Lapointe 2001), might have caused a

spatial reorganization in the summer-flowering species. Indeed, while the root system of

some summer-flowering species is spatially restricted (e.g. Oxalis sp.) and can respond

only to fine-scale nutrient enrichment, that of species such as Rubus sp. and Stachys sp. is

more spread out, allowing for a more opportunistic response to spatially heterogeneous

nutrient availability (Farley and Fitter 1999a). Such an opportunistic root system enables

70

the plants to forage continuously for nutrient-rich patches to maintain growth (Antos 1988).

In the forest understory, complementary root growth strategies can explain the more even

distribution of production among species in a habitat in which nutrients are spatially

heterogeneous at a fine spatial scale (Slade and Hutchings 1987a; de Kroon and Hutchings

1995; Fransen et al. 1999; Tessier et al. 2001; Hodge 2006). Such a diversity in root growth

habit among summer-flowering species suggests finer niche differentiation and greater

ability of coexistence. However, in nature, nutrient-rich patches are short lived, lasting only

2 to 4 weeks, forcing plants to adjust rapidly through ramet, root, and rhizome proliferation

(Farley and Fitter 1999a).

3.7.4 Environmental variables Fertilizer treatments had no significant effects on soil pH or on soil organic matter content

in our experiment. This lack of response with respect to soil pH might be explained by the

type of fertilizer used, i.e. a slow-release fertilizer, and/or by the short duration of the study.

However, in southern Finland, Saarsalmi et al. (2001) reported an increase from 0.6 to 1.0

unit in soil pH, after 16 years of fertilization with wood ash. The lack of response with

respect to organic matter content might be explained by the high activity of soil micro-

organisms during the summer, when the soil samples were taken (Zak et al. 1992).

3.7.5 Caveats The lack of response in species richness, spatial autocorrelation of species richness, and

cover to our treatments might be explained by the relatively short period over which the

treatments were applied and/or by inadequate nutrient applications. However, Peace and

Grubb (1982) and Elemans (2004) recorded significant responses to short-term (only a few

months) fertilization treatments on herbaceous understory and forest edge species. On the

other hand, some studies have shown that species richness did not change significantly

following mid-term (4 years: Misson et al. 2001; 6 years Bauhus et al. 2001) or longer-term

(27 years: He and Barclay 2000) nutrient addition experiments in forest ecosystems.

Elemans (2004) found that the growth of perennial edge species responded significantly to

levels of fertilization (30 and 300 kg N ha-1 year-1) similar to those used in our experiment

71

(90 kg N ha-1 year-1). However, Bauhus et al. (2001) observed no response in the

understory species richness of a forest in the New South Wales, Australia, following

nutrient enrichment of 100 kg N ha-1. Our results are thus consistent with those reported

elsewhere and suggest that nutrient availability is not limiting for the understory flora of the

forests studied. The slow-growing understory species typical of the deciduous forests of

northeastern North America may have a very restricted ability to respond to fine-scale

heterogeneity in nutrient availability.


Marchand, and F. Sahim for field assistance, and S. Boudreau, D. Gagnon, L. Lapointe, and

M.F. McKenna, for comments on an early version of the manuscript. This study was

financed by the Natural Sciences and Engineering Research Council of Canada through a

grant to G. Houle.

72

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CHAPITRE 4 – Plant community response to species losses in the understory of cold-temperate deciduous forests in south-eastern Canada.

4.1 Avant-Propos Le texte de ce chapitre a été soumis à la revue Oikos.

4.2 Résumé La richesse taxonomique et la richesse fonctionnelle contribuent à la stabilité et à la

pérennité des écosystèmes, particulièrement dans des environnements variables. La perte

d’un groupe fonctionnel peut modifier le rôle des autres groupes d’espèces dans une

fonction donnée, par exemple la productivité. Nous avons testé cette hypothèse en

éradiquant, à tour de rôle, différents groupes fonctionnels de la flore du sous-bois dans trois

fragments forestiers de la région des Bois-Francs, dans le centre du Québec (est du

Canada). Le protocole expérimental incluait un témoin (aucune éradication) et cinq

traitements d’éradication d’un groupe fonctionnel. Cinq groupes fonctionnels ont été

définis, principalement en fonction de leur phénologie : espèces printanières éphémères,

espèces printanières persistantes, espèces estivales, fougères, et plantules et juvéniles des

espèces ligneuses. La richesse, le couvert, l’équitabilité et certains facteurs

environnementaux (irradiance, pH et contenu en matière organique du sol) ont été suivis

durant deux ans. Comme il fallait s’y attendre, les résultats de notre expérience révèlent que

la contribution directe de chacun des cinq groupes fonctionnels à la richesse spécifique, au

couvert et à l’équitabilité est positive. Par contre, la richesse spécifique observée est

significativement plus faible que celle à laquelle on s’attendait suite à l’éradication des

espèces printanières persistantes ou des plantules et des juvéniles des espèces ligneuses,

suggérant non seulement un effet positif direct mais également un effet positif indirect de

l’un et l’autre de ces deux groupes sur la richesse totale (par leurs effets sur certains des

autres groupes fonctionnels). L’éradication des plantules et des juvéniles des espèces

ligneuses résulte en une diminution du couvert des espèces estivales, mettant en évidence le

79

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rôle positif du premier groupe sur le second. L’éradication des espèces estivales mène à une

équitabilité plus élevée que celle à laquelle on se serait attendue, soulignant l’influence

négative indirecte de ce groupe sur l’équitabilité totale (par leurs effets sur certains des

autres groupes fonctionnels). De plus, l’éradication des espèces printanières éphémères ou

des espèces printanières persistantes contribue à réduire l’équitabilité des fougères, révélant

le rôle positif des deux groupes d’espèces printanières sur la répartition du couvert entre les

différentes espèces de fougères. Toutefois, durant la période estivale, les fougères ne

semblent pas être capables de compétitionner avec les espèces estivales pour l’acquisition

de ressources du sol. Ces résultats suggèrent que les différents groupes fonctionnels ne sont

pas strictement indépendants les uns des autres mais, plutôt, qu’ils sont interconnectés.

Bien que nous n’ayons pas étudié les mécanismes spécifiques responsables des réponses

décrites ici, nous suggérons que de multiples interactions interspécifiques peuvent conduire

à une plus grande différenciation des niches entre les différents groupes fonctionnels et que

ceci peut favoriser la coexistence des espèces.

4.3 Abstract Both taxonomic species richness and functional species richness contribute to the stability

and persistence of ecosystems, particularly in changing environments. The loss of a given

functional group may alter the role that other groups play in a specific function, e.g.

productivity. We tested this hypothesis by removing different functional groups from the

understory of three maple-dominated forest fragments in southern Québec (Canada). The

experimental design included a control (no removal) and five removal treatments. Five

functional groups were defined, mostly with respect to their phenology: spring-flowering

ephemeral species, spring-flowering persistent species, summer-flowering species, ferns,

and seedlings and juveniles of woody species. Species richness, cover, and evenness, and

major environmental variables (irradiance, soil pH, and soil organic matter content) were

followed over two years. The results of our experiment revealed that, as expected, the direct

contribution of each one of the five functional groups to species richness, cover, and

evenness was positive. However, observed species richness was significantly lower than we

expected when spring-flowering persistent species or seedlings and juveniles of woody

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species were removed, suggesting not only direct, but also indirect positive effects of both

of these groups on total richness (through effects on some of the other functional groups).

Removal of the seedlings and juveniles of woody species lead to a decrease in the cover of

summer-flowering species, implying a significant positive effect of the former group on the

latter. Observed evenness was significantly higher than expected when summer-flowering

species were removed, a result that reveals indirect negative effects of this group on total

evenness (through effects on some of the other functional groups). In addition, when

spring-flowering ephemeral or spring-flowering persistent species were removed, evenness

among fern species decreased, revealing the positive effect of both groups of spring-

flowering species on fern evenness. However, in summer, a competition between ferns and

summer-flowering species seems to exist, and summer-flowering species seem to have an

advantage in the capture of nutrients. These results suggest that the different functional

groups are not strictly independent from one another but, rather, that they are strongly

interconnected. Although we have not investigated the specific mechanisms responsible for

the responses reported here, we suggest that complex interspecific interactions may lead to

increased niche differentiation among the different functional groups of the forest

understory, and this, in turn, may favour species coexistence.

4.4 Introduction Taxonomic plant richness (number of taxa) and functional plant richness (number of

functional groups) are two of the determinants of primary production in terrestrial

ecosystems, along with e.g. disturbance regime, climate, and soil fertility (Naeem et al.

1999; Díaz and Cabido 2001; Hooper et al. 2002; Symstad et al. 2003; Wardle and

Zackrisson 2005). These components of diversity allow ecosystems to resist change and/or

to re-establish essential functions following disturbances (Johnson et al. 1996; Tilman et al.

1996).

Species that have similar physiological, morphological and/or phenological characteristics

can be placed into groups (Naeem et al. 1999) and can be said to be functionally redundant

within these groups. Thus, the loss of one or a few species within a group can be

compensated for by the presence of other functionally similar species (Wardle et al. 1999;

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Loreau et al. 2001). Although the loss of a given species can embrittle an ecosystem

function, ecosystem persistence can still be maintained because of such functional

redundancy.

Because of their distinct physiological, morphological, and/or phenological characteristics,

functional groups can partition resources among themselves (Fitter 1982; Hooper et al.

2002); such resource partitioning may contribute to reduce competitive exclusion

(Cardinale et al. 2000) and to increase overall ecosystem production (Tilman 1997).

However, the loss of a given functional group can have negative effects on an ecosystem

and may even lead to a slowing-down or a collapse in its functioning. While trying to

determine the consequences of the loss of groups of species on the functioning of

ecosystems, several studies have shown two opposing properties of functional groups,

namely their complementarity and their dominance (Naeem et al. 1999; Wardle et al. 1999;

Cardinale et al. 2000; Díaz and Cabido 2001). In fact, these two properties are believed to

be largely responsible for the specific form of the relationship between diversity and

productivity (Mittelbach et al. 2001; Tilman et al. 2002; Symstad et al. 2003).

Many studies have shown that a decrease of either taxonomic or functional richness can

alter ecosystem properties and/or functions (Naeem et al. 1995; Wardle et al. 1999; Loreau

et al. 2001; Symstad and Tilman 2001). However, several of these studies have been

performed under controlled conditions, with synthetic, simplified assemblages (Tilman et

al. 1997; Symstad et al. 1998; Hector et al. 1999; Naeem 2001; Hooper et al. 2002). In

these, richness was manipulated (independent variable) and ecosystem response was

measured through changes in primary production (Lepš 2004). From their review of the

literature on the subject, Loreau et al. (2001) concluded that the relationship between

richness and ecosystem functions could be shown under natural conditions using two

approaches: (1) an experimental approach, e.g., one in which species or groups of species

are removed, and (2) a comparative approach, e.g. one in which factors other than richness

(biogeochemical processes, niche complementarity for better nutrients use, or interspecific

interactions) are controlled (Huston 1997; Wardle et al. 1999; Troumbis and Memtsas

2000; Buonopane et al. 2005).

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In the present study, we follow the recommendations of Loreau et al. (2001) and use an

experimental approach, based on the removal of functionally-similar species, to elucidate

the relationship between richness and ecosystem functions and properties. Our objective is

to determine how the loss of a group of species can affect the structural and functional

properties of the understory, in three forest patches in Québec (southern-eastern Canada).

Our study is based on the premise that functional groups are strictly independent and on the

null hypothesis that the loss of a functional group will not have significant deleterious

effects on the other groups and on ecosystem functions and properties.


4.5.1 Study site description Our experiment was conducted in three forest fragments of the Bois-Francs region, two at

Saint-Grégoire (46°17.820' N, 72°30.740'; 46°17.525' N, 72°31.076' W) and one at Sainte-

Françoise (46°29.341' N, 71°56.156' W), on the south shore of the St. Lawrence River,

between Trois-Rivières and Québec, Québec, Canada. The dominant overstory species is

sugar maple (Acer saccharum Marsh.), with American linden (Tilia americana L.), yellow

birch (Betula alleghaniensis Britton), eastern hemlock (Tsuga canadensis L.), and balsam

fir (Abies balsamea L). The region is part of the Great Lakes–St. Lawrence forest region of

Rowe (1972), sub-section Mid (L-2) and High St. Lawrence (L-3).

At the nearby Trois-Rivières and Québec weather stations, average annual precipitation

totals 1099.8 mm (24% as snow) and 1230.3 mm (38% as snow), respectively, and annual

daily mean temperature is 4.9°C and 4.0°C, respectively (URL:

www.climate.weatheroffice.ec.gc.ca). The soils of the region are mostly brunisols except in

low areas, which are characterized by gleysols (Choinière and Laplante 1948).

All three forest fragments have been lightly managed for sap over several years, although

one of the sites at Saint-Grégoire is protected by the Québec government and has not been

exploited since at least 1975. Site selection was based on the following criteria: relatively

uniform topographical features; easy road access; plant species richness representative of

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the region; different functional groups present; permission (from the owners or the Québec

government) to carry out the experiment.

4.5.2 Sampling protocol and variables measured In the summer of 2003, we established 30 2 x 2-m plots at each of the three study sites

(similar species composition and absence of woody plants > 1 m in height). Each species

present on the sites was assigned to one of five functional groups (Table 4.1), based

principally on its flowering period: spring-flowering ephemeral species (SE); spring-

flowering persistent species (SP); summer-flowering species (SM); ferns (FE); and

seedlings and juveniles of woody species (TS). We considered SE separately from SP

because of the important role they play in the nutrient dynamics of deciduous forests.

Indeed, SE are believed to store in their tissues the nutrients flushed into the system during

snowmelt and to release them slowly as they senesce (vernal dam hypothesis; Eickmeier

and Schussler 1993; Lapointe 2001).

Table 4.1 Functional groups, symbols, and examples of species in the different groups.

Functional groups Symbols Examples of species

Spring-flowering ephemeral species SE Claytonia caroliniana, Erythronium americanum

Spring-flowering persistent species SP Aralia nudicaulis, Arisaema atrorubens, Trillium spp.

Summer-flowering species SM Carex spp., Circaea sp., Poa spp., Solidago spp.

Fern species FE Dryopteris spp., Onoclea sensibilis, Osmunda spp.

Seedlings/juveniles of woody species TS Acer spp., Fagus grandifolia, Tilia americana

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The interest in considering FE and TS comes from the significant role that they play in

ecosystem productivity and in competition with herbaceous species.

For the experiment, we chose 18 plots per site, the most similar in terms of species

composition and topography, among the 30 initially marked. Within site, plots were

grouped in three blocks on the basis of their spatial proximity, and treatments were

randomly assigned to the plots within each block. The treatments were as follows: control

(L0, no removal, L to Loss); removal of spring-flowering ephemeral species (LSE);

removal of spring-flowering persistent species (LSP); removal of summer-flowering

species (LSM); removal of ferns (LFE); removal of seedlings and juveniles of woody

species (LTS). Only aboveground parts were removed to avoid soil disturbance. Removals

took place twice monthly, between May and August, in 2004 and in 2005.

We are aware that the persistence of underground structures (bulbs, rhizomes, or corms)

and an increase in the decomposition of roots following the removal of aboveground parts

may influence the responses to our removal treatments. However, the method we used is

common in the literature and offers the advantage of avoiding the confounding effects of

soil disturbances to species removal (Wardle et al. 1999; Buonopane et al. 2005; Wardle

and Zackrisson 2005).

4.5.3 Biological variables Floristic surveys (vascular plants ≤ 1 m in height) were done three times in 2005: in early

May, in mid-July, and at the end of August. Each plot (4 m2) was divided into two sections.

A buffer zone (20 cm; 1.44 m2) was established at the periphery of each plot (Fig. 4.1) to

avoid trampling effects and no data were collected from this zone. The central section of

each plot (1.60 x 1.60; 2.56 m2) was divided into 256 cells of 100 cm2 each (Fig. 4.1). In

each cell, all understory species present were recorded.

Species-specific cover values for each plot were estimated as the frequency of 100-cm2

cells (over 256) in which a given species was present. Percent cover (C) per functional

group (CSE, CSP, CSM, CFE, and CTS) and total percent cover (Ctot) were calculated as

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the sum of the species-specific cover values for each plot to estimate production. As three

surveys were done in 2005, the maximum species-specific cover value was used for each

species to estimate maximum annual production. An evenness index, H, was calculated for

each functional group (HSE, HSP, HSM, HFE, and HTS) and for all species (Htot) from

the cover data using the formula H = - Σ pi ln pi, where pi represents the cover proportion of

species i in relation to total cover. Species richness (R) per functional group (RSE, RSP,

RSM, RFE, and RTS) and total species richness (Rtot) were also determined for each plot.

Figure 4.1 Experimental set up to record plant cover and diversity in each 4 m2 plot.

4.5.4 Environmental variables In each plot, four soil samples (each of 90.75 cm3, to a soil depth of 10 cm) were collected

in July 2005. All samples were collected after a period of 2-3 days without rain. In the

laboratory, each sample was passed through a 2-mm-mesh sieve to remove roots, twigs,

87

and stones, and then dried for 24 hours at 75°C. Soil pH was measured in a 1:1 soil:water

solution using a pH-meter. Soil organic matter (SOM) was estimated as percent mass loss

on ignition (10 ml of soil at 450 °C for 5 hours).

Four irradiance readings per plot were taken at 0.5-1 m from the ground between 10h and

14h, on sunny, cloudless days at the end of July 2005 using a portable radiometer (Licor,

Inc., Lincoln, Nebraska, USA).

4.5.5 Data analysis Total species richness, for the pre-experimental data set of 2003, was analysed with a

randomized complete block design ANOVA.

Total species richness, the species richness of each functional group, total cover, and the

cover of each functional group, for each 4 m2 plot, were analysed with randomized

complete block design ANOVAs. For the three environmental variables, i.e. light, soil

organic matter content, and soil pH, a mean value (which integrated the spatial variability

of each variable) was calculated from the four measurements taken on each plot. These

abiotic variables were also analyzed with randomized complete block design ANOVAs.

Total species richness and total cover in any given removal treatment (LSE, LSP, LSM,

LFE, LTS) may be expected to be lower than in the control (L0), simply because species

are removed. Thus, we calculated an expected value of total species richness and total

cover, for each removal treatment, by subtracting the richness or cover of a given species

group from the total species richness or total cover of each control plot. For example, for

the expected total species richness of an LSE quadrat: RtotLSE = RtotL0 – RSEL0. A paired t-

test (n = 9, since we had a total of 9 control plots) was used to determine the significance of

the differences between the observed and the expected values, i.e. to determine if a removal

treatment had a significant positive or negative effect on the variable of interest. We

similarly calculated expected values for total species evenness. However, the procedure for

evenness was a bit more complex: indeed, evenness represents at the same time a structural

component (richness) and a functional component (cover) of the understory.

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The experimental design was made up of six treatments per block, three blocks per site, and

three sites. The sources of variation were sites (random), blocks within site (random),

treatments (fixed), and the interaction between treatments and sites (fixed), as well as an

experiment-wise error term. Differences between treatments for the environmental

variables, species richness, cover, and evenness were located using protected LSD (least

significant difference) when the ANOVAs indicated significant differences among

treatments at α < 0.05. The statistical package SAS version 6.12 (SAS Institute, Inc., Cary,

NC, USA) was used for all ANOVAs.

4.6. Results

4.6.1. Pre-experimental data (2003) In 2003, before the experiment began, total species richness differed significantly among

sites (F = 257.71; P < 0.0001), but not among treatments (F = 0.95, P = 0.4635). This latter

result indicates that plots were initially similar within sites and validates our randomization

procedure within blocks.

4.6.2. Experimental data (2005)

4.6.2.1. Richness, cover, and evenness Species richness for all sites combined was dominated by herbaceous species (56 species,

representing > 75% of the total pool), whereas only 14 tree and 4 shrub species were

recorded (Table 4.2). The mean number of species per site was 47.0 ± 11.3 (mean ± SD).

The most important difference in species composition among sites was observed at Sainte-

Françoise, which had 26 summer-flowering (SM) species (in comparison to 13 and 16 for

Saint-Grégoire 1 and Saint-Grégoire 2, respectively; Table 4.2b).

The ANOVAs indicated that total species richness differed significantly among treatments

(P < 0.001; Table 4.3): the control (L0) had the highest, but the LTS treatment (removal of

seedlings/juveniles of woody) the lowest total species richness (Fig. 4.2). Observed total

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species richness was significantly lower than expected total species richness for the LSP

and LTS treatments (P = 0.0304 and P = 0.0232, respectively; Table 4.4), but it was

marginally higher for the LSM treatment (P = 0.0907).

Table 4.2 Species richness for the three sites combined and for each site (data from 2005) according to functional group (SE: spring-flowering ephemeral species; SP: spring-flowering persistent species; SM: summer-flowering species; FE: ferns; TS: seedlings and juveniles of woody species; Total: all species combined).

Sites Functional groups All three sites

combined Sainte-

Françoise Saint-

Grégoire 1 Saint-

Grégoire 2 SE 3 3 3 3 SP 14 11 8 8 SM 30 26 13 16 FE 9 7 3 6 TS 18 13 13 8

Total 74 60 40 41

There was a notable effect of our treatments on SM richness (marginally significant at P =

0.058; Table 4.3): indeed, SM richness was low in the plots in which spring-flowering

persistent species (SP), ferns (FE), or seedlings and juveniles of woody species (TS) had

been removed (Fig. 4.2). There were no significant differences among treatments in the

richness of spring-flowering ephemeral species (SE), SP, FE, or TS (all P values > 0.05;

Table 4.3, Fig. 4.2).

Table 4.3 F-values (in parentheses, P-values) from the ANOVAs for the effects of site (df = 2) and treatment (df = 5 for Rtot, SA, Ctot, Htot, soil ph, soil organic matter content, and irradiance; df = 4 for the other variables) and for their interaction (df = 10 for Rtot, SA, Ctot, Htot, pH, SOM, and irradiance; df = 8 for the other variables) on the variables studied. Rtot: total richness; RSE, RSP, RSM, RFE, RTS: richness of spring-flowering ephemeral species, spring-flowering persistent species, summer-flowering species, ferns, and seedlings and juveniles of woody species, respectively; Ctot: total cover; CSE, CSP, CSM, CFE, and CTS: cover of spring-flowering ephemeral species, spring-flowering persistent species, summer-flowering species, ferns, and seedlings and juveniles of woody species, respectively; Htot: evenness of all species; HSE, HSP, HSM, HFE, and HTS: evenness of spring-flowering ephemeral species, spring-flowering persistent species, summer-flowering species, ferns, and seedlings and juveniles of woody species, respectively. Significant values are in boldface characters.

Effects Variables

Site Treatment Site x treatment Rtot 189.16 (<0.001) 5.92 (<0.001) 1.52 (0.181) RSE 4.27 (0.070) 0.28 (0.884) 1.35 (0.264) RSP 62.60 (<0.001) 0.77 (0.556) 3.20 (0.013) RSM 30.05 (<0.001) 2.64 (0.058) 2.26 (0.058) RFE 25.55 (0.001) 1.34 (0.286) 0.58 (0.779) RTS 10.55 (0.011) 0.53 (0.713) 0.99 (0.468) Ctot 50.08 (<0.001) 2.89 (0.030) 1.20 (0.327) CSE 6.15 (0.035) 0.31 (0.864) 0.53 (0.823) CSP 47.53 (<0.001) 0.48 (0.746) 0.40 (0.909) CSM 4.05 (0.071) 2.81 (0.048) 2.42 (0.045) CFE 24.33 (0.001) 1.37 (0.273) 2.02 (0.087) CTS 29.17 (<0.001) 0.82 (0.519) 0.74 (0.655) Htot 33.63 (<0.001) 2.33 (0.067) 1.43 (0.212) HSE 9.73 (0.013) 2.05 (0.118) 1.29 (0.291) HSP 34.74 (<0.001) 1.73 (0.177) 0.33 (0.942) HSM 6.53 (0.031) 0.57 (0.683) 0.79 (0.609) HFE 26.28 (0.001) 3.14 (0.033) 1.85 (0.115) HTS 13.23 (0.006) 0.70 (0.595) 0.37 (0.924)

Soil pH 5.92 (0.038) 1.53 (0.210) 1.00 (0.460) Soil organic

matter content

5.11 (0.051) 1.30 (0.289) 0.44 (0.910)

Irradiance 0.59 (0.581) 0.83 (0.534) 1.10 (0.393)

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Table 4.4 Observed and expected1 total richness (Rtot), total cover (Ctot), and total evenness (Htot) as a function of treatment (LSE: removal of spring-flowering ephemeral species; LSP: removal of spring-flowering persistent species; LSM: removal of summer-flowering species; LFE: removal of ferns; LTS: removal of seedlings and juveniles of woody species). Mean ± SD (n = 9). Different letters indicate significant differences between the observed and expected values for each treatment (P < 0.05; paired t-test).

Treatments Rtot LSE LSP LSM LFE LTS Observed 12.9 ± 6.8 10.3 ± 4.1a 12.2 ± 5.2 11.8 ± 4.6 9.7 ± 4.6a Expected 13.6 ± 5.1 12.6 ± 4.1b 10.4 ± 3.0 13.2 ± 4.9 12.0 ± 4.7b

Treatments Ctot LSE LSP LSM LFE LTS Observed 329.2 ± 148.9 291.6 ± 143.2 292.9 ± 153.7 250.9 ± 102.5 267.8 ± 128.1 Expected 308.8 ± 147.2 340.3 ± 128.7 246.3 ± 127.3 262.5 ± 124.6 298.6 ± 125.0

Treatments Htot LSE LSP LSM LFE LTS Observed 1.4 ± 0.7 1.6 ± 0.4 1.7 ± 0.3b 1.4 ± 0.4 1.6 ± 0.3 Expected 1.6 ± 0.4 1.7 ± 0.4 1.6 ± 0.5a 1.6 ± 0.4 1.6 ± 0.4

1 The expected value for the richness of the LSE treatment was calculated as follows: expected Rtot(LSE) = Rtot(L0) - RSE(L0), with Rtot(L0) and RSE(L0) representing total richness and richness of spring ephemeral species (SE) on control quadrat (L0), respectively. Expected values of richness, cover, and evenness for the other treatments were similarly calculated from the data of SP (spring-flowering persistent species), SM (summer-flowering species), FE (fern), and TS (seedlings and juveniles of woody species).

Figure 4.2 Total richness (Rtot) and functional group richness (RSE: spring-flowering ephemeral species; RSP: spring-flowering persistent species; RSM: summer-flowering species; RFE: fern species; and RTS: seedlings and juveniles of woody species) for each treatment. Different letters indicate significant differences among treatments for each variable (P < 0.05, protected LSD). Mean + SD.

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There were significant differences among treatments for the variable ‘total cover’ (P =

0.030, Table 4.3): indeed, total cover was higher in the control (L0), but lower in the LSP,

LSM, LFE, and LTS treatments (Fig. 4.3). There were, however, no significant differences

between the expected and the observed values of total cover for any of the removal

treatments, although observed cover was much lower than expected in the LSP treatment

(marginally significant at P = 0.1015; Table 4.4). The cover of SM differed significantly

among treatments (P = 0.048; Table 4.3): there was a marked decrease of SM cover when

SP, but particularly when TS were removed (Fig. 4.3). However, the treatment x site

interaction indicated that the intensity of the treatment effect on SM cover varied with site

(P = 0.045; Table 4.3). No significant differences were detected among treatments in the

cover of the other functional groups (all P values > 0.05; Table 4.3, Fig. 4.3).

Total evenness (marginally significant at P = 0.067) varied among treatments (Table 4.3): it

was somewhat higher in the control (L0) than in the five removal treatments, particularly

than in the treatments in which SE or FE had been removed (Fig. 4.4). Observed total

evenness was significantly higher than expected total evenness in the LSM treatment (P =

0.0036, Table 4.4), indicating that production (i.e. cover) was more evenly distributed

among species when SM were removed. FE evenness (P = 0.033) differed among

treatments (Table 4.3): it was the highest in LSM, but the lowest in LSE and LSP (Fig. 4.4).

However, there was no significant difference among treatments for the evenness of the

other functional groups (Table 4.3, Fig. 4.3).

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Figure 4.3 Total cover (Ctot) and functional group cover (CSE: spring-flowering ephemeral species; CSP: spring-flowering persistent species; CSM: summer-flowering species; CFE: fern species; and CTS: seedlings and juveniles of woody species) for each treatment. Different letters indicate significant differences among treatments for each variable (P < 0.05, protected LSD). Mean + SD.

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Figure 4.4 Total species evenness (Htot) and functional group species evenness (HSE: spring-flowering ephemeral species; HSP: spring-flowering persistent species; HSM: summer-flowering species; HFE: fern species; and HTS: seedlings and juveniles of woody species) for each treatment. Different letters indicate significant differences among treatments for each variable (P < 0.05, protected LSD). Mean + SD.

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4.6.2.2. Abiotic variables The ANOVAs for the soil variables (pH and organic matter content) and for irradiance did

not reveal any significant differences among treatments (Tables 4.3 and 4.5). Soil pH

values varied only from 4.48 ± 0.22 to 4.68 ± 0.22 (mean ± SD). Soil organic matter

content was relatively similar among treatments with means from 11.11 ± 1.14 % to 14.24

± 5.75 %. However, irradiance was much more variable among treatments, with means

from14.02 ± 14.87 to 123.10 ± 324.33 µmol m-2 s-1.

Table 4.5 Mean (± SD) of the environmental variables as a function of treatment (n = 9). There are no significant differences among treatments (L0: control; LSE: removal of spring-flowering ephemeral species; LSP: removal of spring-flowering persistent species; LSM: removal of summer-flowering species; LFE: removal of ferns; LTS: removal of seedlings and juveniles of woody species), for each variable.

Treatments Variables L0 LSE LSP LSM LFE LTS

Soil pH 4.68 ± 0.22

4.60 ± 0.27

4.48 ± 0.22

4.59 ± 0.27

4.67 ± 0.21

4.56 ± 0.16

Soil organic matter content

(%)

11.11 ± 1.14

12.20 ± 1.36

11.93 ± 1.53

14.24 ± 5.75

12.81 ± 2.48

12.64 ± 2.00

Irradiance (µmol m-2 s-1)

123.10 ± 324.33

23.56 ± 17.47

49.45 ± 64.75

23.58 ± 23.58

17.59 ± 18.01

14.02 ± 14.87

4.6.2.3. Inter-site differences There were significant differences among sites for most of the variables studied: Rtot, Ctot,

and Htot; the richness, cover, and evenness of almost all of the functional groups; and soil

pH and organic matter content (Table 4.3).

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4.7. Discussion

4.7.1. Richness As one would expect, total species richness decreased significantly regardless of which

functional group was removed (Fig. 4.2, Fig. 4.5). Nevertheless, it decreased markedly

when seedlings and juveniles of woody species (TS) or spring-flowering persistent species

(SP) were eradicated (Fig. 4.2, Table 4.4). The richness of the summer-flowering species

group (SM) was particularly low in the LSP, LFE, and LTS treatments, suggesting that SP,

ferns (FE), and TS all had a positive effect on SM richness. In contrast, the richness of SP,

FE, and TS increased somewhat when SM were removed (Fig. 4.2). As a consequence, the

observed total species richness was somewhat higher than expected when SM were

eliminated (Table 4.4). These latter results suggest that although SM had a direct positive

effect on total species richness, their total effect (direct plus indirect, through SP, FE, and

TS richness) was negative.

Figure 4.5 Interactions between the different functional groups for species richness (based on Fig. 4.2 and Table 4.4).

Spring-flowering persistent species (SP), ferns (FE), and seedlings and juveniles of woody

species (TS) begin their aboveground growth early in the spring, before tree canopy

closure, but persist through the entire summer even under a dense overstory. They often

form a compact understory that protects the soil against erosion and that contributes to

97

Total richness

SM

FESPTS SE

Total richness

SM

FESPTS SE

Total richness

SM

FESPTS SE

Total richness

SM

FESPTS SE

98

reduce nutrient losses (Tessier and Raynal 2003). In some way, these species can be

considered to ameliorate the conditions for those that come later during the season, like

SM, and they are thus comparable to the pioneer species of successional environments,

which facilitate the growth and survival of later successional species (Connell et Slatyer

1977). Yet, summer-flowering species seem to restrict the growth of the species from the

other functional groups without, however, reducing overall species richness. Indeed, the

underground system of SM shows a greater developmental flexibility and is more efficient

in capturing soil resources than that of the species from the other functional groups (Farley

and Fitter 1999).

The positive and negative interactions outlined above thus correspond to feedback loops

and may increase the stability of the forest understory community (MacArthur 1955;

Tilman and Downing 1994; Johnson et al. 1996). These interactions may (1) lead to a

higher collective capacity of the species to resist minor disturbances and/or (2) allow the

system to re-establish essential functions following disturbances (Walker et al. 1999;

Upadhyay et al. 2000; Díaz and Cabido 2001). Each one of the five functional groups

contributes positively to total species richness (Tilman et al. 1997), most likely because of

the complementary morphological, physiological, and phenological traits of their respective

species (Loreau and Behera 1999; Reich et al. 2003).

Our initial null hypothesis stipulated that the different functional groups had little or no

interactions among one another, and that the loss of a group would not have serious

consequences on the functional and structural properties of the system. Our results on

richness do not support this hypothesis: functional groups interact with one another (Fig.

4.5) and, thus, are not strictly independent.

4.7.2 Cover Like total species richness, total cover decreased significantly regardless of which

functional group was removed (except for the removal of SE, Fig. 4.3 and Fig. 4.6).

Nevertheless, total cover decreased proportionately more when SP were eliminated (Table

4.4). In contrast, total cover was somewhat higher than expected (17% higher) when SM

99

were eradicated: this was mostly through an increase in the cover of SP and FE relative to

the control (Fig. 4.3).

Our results for total cover support those presented previously with respect to the effect of

the removal of a functional group on species richness: strong interactions exist among

functional groups (Fig. 4.6) and, thus, strict among-group independence is refuted.

Figure 4.6 Interactions between the different functional groups for species cover (based on Fig. 4.3 and Table 4.4).

The removal of TS led to a significant decrease in the cover of SM (Fig. 4.3). This result

refutes our initial hypothesis of no interactions among functional groups. Under the cover

of seedlings and juveniles of woody species, temperature, evapo-transpiration and water

stress are reduced and soil moisture is higher (Scholes and Archer 1997), conditions that

may favor the growth of summer-flowering species. In addition, TS cover may negatively

affect the activity of herbivores and/or pathogens on SM.

4.7.3 Evenness Like total species richness and total cover, total evenness was lower (marginally significant

at P = 0.067) in the removal treatments than in the control. In particular, evenness

decreased when SE or FE were removed. However, total evenness was higher than

expected when SM were eradicated. Of the five functional groups in this study, only FE

showed a significant response in terms of evenness to our removal treatments: indeed, the

Cover

TS

FE

SE

SM

SP

Cover

TS

FE

SE

SM

SP

100

presence of SE or of SP seemed to allow for a more even distribution of the cover among

FE.

Evenness integrates how production (a functional property) is ‘shared’ among different

species (a structural property) and, thus, it represents a measure of the structural

heterogeneity of a community (Staudhammer and Lemay 2001). We expected the response

of the variables ‘richness’ and ‘cover’ to our removal treatments to be integrated into the

variable ‘evenness’. This was mostly confirmed, demonstrating that the removal of a given

functional group resulted in a compensatory reorganization among the remaining groups

(Folke et al. 2004).

As the growing season progresses, the spatial distribution of resources becomes more

heterogeneous in the understory. Summer-flowering species show greater variability in

their strategies of resource capture than the other groups and this may allow them to be

better competitors (McCormick and Bowersox 1997; Farley and Fitter 1999); however, it

may also contribute to reduce evennes, particularly that of ferns. Yet, these negative effects

do not appear to be compensated for, if we consider the results on total richness and total

cover.

Nutrients stored in the old fronds of Dryopteris intermedia are not sufficient to maintain the

current growth and reproduction of the plant (Tessier 2001). Spring-flowering and spring-

persistent flowering species reduce nutrient leaching, thereby, increasing the growth of and

allowing for a more uniform distribution of cover among fern species (Eickmeier et

Schussler 1993; Rothstein 2000). Yet, SP and SE do not compete for light with ferns:

indeed, the mean height of SP and SE does not exceed 15-20 cm (Marie-Victorin 2002),

while that of ferns often exceeds 30 cm (Hill and Silander 2001; Cobb et al. 2005).

By reducing nutrient leaching from the system, SP and SE favor the growth of the species

of the other functional groups. However, FE do not appear to be able to compete with SM

for soil resources. In short, SM seem to be better at capturing and using soil nutrients than

FE.

101

4.7.4 Environmental variables Remarkably, our removal treatments had no significant effects on soil pH and organic

matter content. These results may suggest that understory species have only weak effects

on the characteristics of the forest floor compared to those of mature woody species (van

Oijen et al. 2005) or that the duration of the study was too limited to affect soil properties.


Marchand, and F. Sahim for field assistance, and S. Boudreau, L. Lapointe, M.F. McKenna,

and S. Payette for comments on an early version of the manuscript. This study was financed

by the Natural Sciences and Engineering Research Council of Canada through a grant to G.

Houle.

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CHAPITRE 5 – Conclusion générale

La richesse végétale influence le fonctionnement et la structure des écosystèmes (Díaz et

Cabido 2001; Hughes et Petchey 2001; Symstad et al. 2003). Elle affecte par conséquent

les réponses de l’écosystème aux perturbations via deux mécanismes principaux: la

résistance et la résilience (Folke et al. 2004). En maintenant une fonction écosystèmique,

une communauté résiste aux changements (résistance) ou retourne à des niveaux

fonctionnels et structuraux similaires à ceux qui étaient en place avant la perturbation

(résilience; Upadhyay et al. 2000; Folke et al. 2004; Walker et al. 2004). Malgré ses

capacités de résilience et de résistance, un écosystème n’est jamais strictement stable. Les

perturbations locales altèrent, voire bouleversent la diversité et la composition de la

communauté végétale, les conditions micro-environnementales, et la disponibilité et

l’hétérogénéité spatiale des ressources. Ces différents changements peuvent avoir des

conséquences sur la pérennité et la stabilité des écosystèmes forestiers. Plusieurs travaux se

sont intéressés aux conséquences des perturbations sur le fonctionnement et la structure de

la strate ligneuse, particulièrement les conséquences de l’aménagement forestier (Leak et

Smith 1996; Tang et al. 1997; Hubbell et al. 1999; Bengtsson et al. 2000).

Par contre, l’intérêt pour la strate de sous-bois (plantes vasculaires ≤ 1 m) est relativement

récent. Bien que sa proportion en termes de biomasse totale (dominée par la strate

supérieure) soit faible, cette strate ne représente pas moins des deux tiers de la richesse

spécifique des plantes vasculaires en milieu forestier. Ce pool d’espèces est composé

presque essentiellement de plantes pérennes à croissance lente et tolérantes à l’ombre

(Whigham 2004). Cette flore de sous-bois agit directement sur la régénération, la structure

et la composition du futur couvert forestier (Kolb et Robberecht 1996; Kleb et Wilson

1997). De plus, la répartition des nutriments semble fortement reliée aux patrons de

distribution des plantes, ce qui conditionne les interactions de compétition et de

coexistence, et la structure des communautés (Huston 1980; Stevens et Carson 2002).

Les objectifs de la présente thèse étaient de déterminer la réponse des plantes de sous-bois

vis-à-vis d’un aménagement acéricole, de leur réponse à l’hétérogénéité de la répartition

107

108

spatiale des nutriments ainsi que de leur complémentarité au sein d’un l’écosystème

forestier. Pour répondre à ces objectifs, nous avons défini deux échelles spatiales. Dans un

premier temps, une échelle d’ordre régional nous a permis de déterminer la réponse des

plantes herbacées à une réduction de la diversité structurale dans des forêts aménagées

(Chapitre 2). Dans un second temps, une échelle plus locale nous a permis de déterminer

les tendances fonctionnelles et structurales des plantes de sous-bois au sein des

communautés (Chapitres 3 et 4). Pour ces deux derniers chapitres, nous nous sommes

servis des recommandations de Loreau et al. (2001), c’est-à-dire l’utilisation de deux types

d’expériences complémentaires. Dans la première, la répartition spatiale des nutriments

(fonction) variait et les changements dans l’abondance des groupes fonctionnels ont été

mesurés (chapitre 3). Dans la seconde expérience, un groupe fonctionnel était retiré

(diminution de la diversité) et les conséquences de ce retrait ont été mesurées en termes

fonctionnels (chapitre 4). Les forêts du sud-est du Québec nous ont offert l’opportunité

d’apporter des réponses concernant les relations entre la diversité et la productivité

végétales et l’hétérogénéité structurale.

Dans le second chapitre de la présente thèse, l’intensité de l’aménagement acéricole a des

effets délétères sur la richesse des plantes herbacées. Par contre, les facteurs du sol (pH et

humidité du sol) expliquent en partie la variance du pool d’espèces. Dans le troisième

chapitre, une fine répartition hétérogène des nutriments dans le sol entraîne des réponses

variées selon les plantes, ce qui pourrait s’expliquer particulièrement par une diversité des

stratégies racinaires (e.g. architecture, longueur des rhizomes, étendue latérale). Ces

différents attributs morphologiques permettent aux plantes de coexister et de mieux occuper

l’espace (Hodge 2006). Le quatrième chapitre montre que tous les groupes fonctionnels

interagissent entre eux (e.g. compétition, facilitation) et qu’ils ne sont pas strictement

indépendants au niveau du couvert, de la richesse et de l’équitabilité.

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5.1 Réponses des plantes herbacées à la réduction de la diversité structurale

L’indice d’hétérogénéité structurale (H) que nous avons utilisé représente l’intensité de

l’aménagement acéricole dans les érablières, considérant qu’une faible valeur coïncide avec

un aménagement de forte intensité. Tel que démontré dans la présente thèse,

l’aménagement acéricole provoque des effets délétères sur la flore du sous-bois mais ne

semble pas agir sur les variables abiotiques. Parmi les deux modèles d’analyses de pistes,

celui basé sur les moyennes des variables environnementales explique jusqu’à 58% de la

variance de la diversité de la flore de sous-bois. Le reste de la variance peut être expliqué

par les évènements historiques, la variabilité spatiale, temporelle et environnementale, les

effets des micro-organismes et des herbivores, la productivité, et la configuration de

l’habitat. Parmi les variables environnementales, une association positive et significative

entre le pH du sol et la diversité spécifique a été décelée. Elle supporte l’importance des

facteurs édaphiques pour la diversité végétale locale (Chytrý et al. 2003; Weiher et al.

2004). Dans ce sens, Pärtel (2002) et Pärtel et al. (2004) suggèrent que la relation est

typiquement positive dans les régions floristiques où le centre évolutif est situé sur des sols

à pH élevé; cette relation est négative si le centre évolutif se situe sur des sols à faible pH.

Selon l’auteur, un centre évolutif correspond à un milieu où les changements climatiques et

géologiques ont créé des zones appropriées au développement de plusieurs espèces. Ces

régions sont des réservoirs d’espèces pour les régions avoisinantes. En somme, la richesse

spécifique à une échelle locale semble être déterminée par le nombre d’espèces à une

échelle plus régionale dans le temps et l’espace (Butaye et al. 2001; Dupré et al. 2002;

Pärtel 2002). Par ailleurs, l’humidité du sol a un effet marginal positif sur la richesse

végétale, ce qui supporte les travaux de Brosofske et al. (2001) et de Härdtle et al. (2003).

Enfin, notre pool d’espèces est composé de plantes adaptées ou tolérantes à de faibles

intensités de lumière. En absence de perturbation majeure, la lumière ne semble pas être un

facteur influençant la richesse spécifique (Bauhus et al. 2001; Härdtle et al. 2003). Nos

résultats montrent qu’un aménagement acéricole intensif affecte significativement la

110

richesse spécifique de la flore de sous-bois, et que les facteurs édaphiques (pH et humidité

du sol) ont des effets positifs sur la diversité.

5.2 Réponses de la flore du sous-bois à l’hétérogénéité spatiale des nutriments

La plupart des plantes de sous-bois sont des espèces pérennes tolérantes à l’ombre et à

croissance lente, qui semblent peu ou ne pas réagir à l’ajout de nutriments, et ce,

indépendamment de la concentration de ces derniers (Bauhus et al. 2001; Stevens et Carson

2002; Härdtle et al. 2003). Cette absence de réponse peut provenir de la faible capacité de

ces espèces à répondre aux changements rapides de la disponibilité en ressources limitantes

(Collins et Pickett 1987; Farley et Fitter 1999a; Whigham 2004). Toutefois, certaines

espèces ont pu absorber et stocker dans leurs tissus les surplus de nutriments sans pour

autant les transformer en biomasse (luxury consumption). Cette stratégie se rencontre chez

les espèces arctiques-alpines et chez les espèces printanières éphémères (Chapin 1980;

Heer et Körner 2002). L’ajout de nutriments a pu aussi entraîner une compétition entre les

plantes du sous-bois et les micro-organismes (Zak et al. 1992) ou les arbres et arbustes

(Kleb et Wilson 1997).

Par ailleurs, l’absence de réponse significative de la richesse peut provenir d’un potentiel de

dissémination limité (principalement la myrmécochorie) des plantes pérennes de sous-bois.

L’absence de changement dans la composition de la diversité montre une absence de

compétition entre les espèces puisque quelque soit le traitement, aucun groupe fonctionnel

n’a pris le dessus sur les autres. Par ailleurs, la croissance clonale de type phalange des

plantes de sous-bois explique le patron de distribution contagieux. Celle-ci provoque des

impacts sur la composition des communautés de sous-bois des forêts décidues (Jacquemyn

et al. 2001).

Nos résultats montrent qu’une fine hétérogénéité spatiale des nutriments dans de petites

parcelles (échelle locale) favorise une répartition plus uniforme de la production entre les

espèces estivales. Ces résultats pourraient s’expliquer par une concordance entre l’échelle

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spatiale du système racinaire des plantes et l’échelle spatiale des traitements (Hutchings et

al. 2003), et par la longueur et la densité des racines des espèces de sous-bois (Antos 1988;

Wijesinghe and Whigham 2001). Dans ce sens, les systèmes racinaires varient fortement

entre les espèces du sous-bois. Par exemple, quelques espèces estivales (Oxalis acetosella)

ont des systèmes racinaires restreints (e.g. étendue latérale limitée), concentrés à proximité

de la plante mère (Farley et Fitter 1999b). Par contre, la plupart des espèces estivales

(Rubus pubescens et Rubus idaeus) ont des systèmes racinaires extensibles et diffus (e.g.

étendue latérale extensible), capables de modifier rapidement la longueur des rhizomes

selon la richesse en nutriments, et ce, loin de la plante mère (Zobel et Antos 1987; Farley et

Fitter 1999b). Ces différentes stratégies racinaires représentent une spécialisation de niches

et favorisent la coexistence des espèces de sous-bois vis-à-vis de l’hétérogénéité spatiale.

De plus, cette variabilité de niches est reliée à la disponibilité des nutriments qui devient de

plus en plus limitante, à mesure que le couvert forestier se ferme. Outre leur tolérance à de

faibles niveaux d’irradiance, nos résultats suggèrent que les espèces de sous-bois semblent

peu sensibles à l’hétérogénéité spatiale des nutriments).

5.3 Réponse des plantes de sous-bois à la perte d’un groupe fonctionnel

Dans cette étude, nous avons défini des groupes fonctionnels en fonction principalement de

critères phénologiques. En somme, chaque groupe fonctionnel correspondait à un ensemble

d’espèces (e.g. différenciées par un ou plusieurs traits) utilisant une ressource à des niveaux

différents. Dès lors, notre hypothèse de départ pour ce quatrième chapitre stipulait une

absence d’interactions (c’est-à-dire une forte indépendance) entre des groupes fonctionnels,

et donc, une absence de conséquences sérieuses suite à la perte d’un groupe sur les

fonctions de l’écosystème. Nos résultats permettent de montrer que la richesse, le couvert et

l’équitabilité ne supportent pas, sans équivoque, cette hypothèse. Les groupes fonctionnels

ont des relations entre eux : ils ne sont pas strictement indépendants, mais plutôt

complémentaires.

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Les espèces printanières et les plantules et juvéniles des espèces ligneuses agissent

favorablement aussi bien sur la richesse, le couvert et l’équitabilité totale, et également sur

la richesse et le couvert des espèces estivales. Ces deux groupes semblent réduire le

lessivage des nutriments (Rothstein 2000; Lapointe 2001) qui représentent une ressource

limitante au cours de la période de croissance. Ces espèces concentrent les nutriments à

proximité de leur base, ils pourraient représenter des réservoirs pour les autres espèces.

Leur présence faciliterait donc le développement, la survie et la croissance des espèces

estivales. De plus, le maintien et la disponibilité des nutriments peuvent aussi permettre aux

fougères de poursuivre leur développement et d’avoir une meilleure répartition du couvert

entre les espèces.

Par contre, en retour, les espèces estivales semblent agir négativement sur la richesse totale

et sur la répartition de la productivité totale entre les espèces. Ces espèces ont une

variabilité souterraine qui leur permet d’acquérir des nutriments de façon plus efficace que

la plupart des autres espèces. Dès lors, la compétition semble être la principale interaction

entre les différents groupes (McCormick et Bowersox 1997) pouvant nuire à une répartition

équitable de la production entre les fougères. Au cours de l’été, les SM réduisent les pertes

d’eau maintenant un taux d’humidité néfaste au développement de certaines espèces

(plantules de Quercus rubra).

Ces interactions positives et négatives correspondent à des boucles de rétroactions

négatives qui pourraient contribuer au maintien et la pérennité de la communauté

(MacArthur 1955; Tilman et Downing 1994). Finalement, les différents groupes

fonctionnels sont étroitements reliés les uns aux autres, ce qui contribuerait à leur

complémentarité. Ces résultats montrent aussi l’absence d’effet compensatoire suite au

retrait d’un groupe puisque ni la richesse et ni le couvert des différents groupes ne varient

selon les traitements.

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5.4 Synthèse

La richesse spécifique du sous-bois est composée d’espèces essentiellement pérennes, à

croissance lente, adaptées ou tolérantes à de faibles niveaux d’irradiance et peu sensibles à

l’hétérogénéité spatiale des nutriments (NPK). Par contre, ces espèces sont sensibles à la

réduction de l’hétérogénéité structurale (i.e. effets délétères de l’aménagement acéricole).

De plus, les facteurs édaphiques (pH et humidité du sol) semblent maximiser la diversité

spécifique. Les différentes associations entre les espèces témoignent d’une complémentarité

fonctionnelle et d’une coexistence entre les différents groupes d’espèces. Ces différentes

réponses sont elles-mêmes interconnectées ce qui assure le maintien des fonctions de

l’écosystème.

5.5 Perspectives de recherche

À la vue des résultats de cette thèse de doctorat, la prise en considération de la strate de

sous-bois est donc nécessaire pour améliorer la compréhension du fonctionnement de

l’écosystème forestier. L’emploi de plusieurs échelles spatiales permet de mieux cerner la

complexité structurale et fonctionnelle de l’écosystème, dans son ensemble. Toutefois, de

nombreuses questions peuvent être posées pour améliorer notre compréhension sur le rôle

des espèces de sous-bois dans un écosystème forestier.

Cette étude a montré que les groupes fonctionnels étaient interconnectés entre eux et

complémentaires pour une fonction donnée. Ainsi, chaque groupe joue un rôle particulier

dans le fonctionnement de l’écosystème. Toutefois, chaque groupe fonctionnel est composé

d’espèces redondantes entre elles. Si la redondance fonctionnelle contribue à la régulation

de l’écosystème, la présence de ces espèces améliore également la résilience de

l’écosystème. Dépendamment de leurs caractéristiques physiologiques, morphologiques ou

phénologiques, les plantes réagissent différemment selon les changements de l’habitat.

L’objectif serait de (1) déterminer les espèces dominantes (e.g. en terme de couvert) au sein

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d’un groupe, (2) retirer ces espèces dominantes et (3) déterminer les réponses des autres

espèces du groupe pour vérifier le rôle compensatoire (aspect de redondance).

Relativement homogène au début du printemps, la répartition des nutriments devient

hétérogène au cours de l’été. La flore du sous-bois semble, selon nos conclusions, peu

sensible à l’hétérogénéité de la répartition spatiale des nutriments. Mais qu’en est-il de leur

sensibilité à l’hétérogénéité de la répartition temporelle des nutriments ? La teneur en

nutriments dans un écosystème forestier varie au cours de la saison de croissance, et les

parcelles riches en nutriments sont éphémères dans le temps.

Étant donné que le pH du sol soit relié à la richesse spécifique, il serait judicieux de

déterminer dans plusieurs écosystèmes forestiers (décidues, tropicales, boréales) cette

relation.

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