MARIE-LOU COULOMBE

EFFETS DE LA DENSITÉ DE POPULATION SUR LE COMPORTEMENT

D’APPROVISIONNEMENT ET LE BUDGET D’ACTIVITÉ DU CERF DE VIRGINIE

(ODOCOILEUS VIRGINIANUS) À L’ÎLE D’ANTICOSTI

Mémoire présenté

à la Faculté des études supérieures de l’Université Laval

dans le cadre du programme de maîtrise en biologie

pour l’obtention du grade de maître ès sciences (M.Sc.)

Département de biologie

FACULTÉ DES SCIENCES ET GÉNIE

UNIVERSITÉ LAVAL

QUÉBEC

2006

© Marie-Lou Coulombe, 2006

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Résumé

Nous avons étudié l’influence de la densité de population sur les déplacements, le budget

d’activité et l’utilisation de l’espace chez le cerf de Virginie en densités contrôlées

expérimentalement. Les déplacements et le budget d’activité étaient peu influencés par la

densité. Dans les densités contrôlées, les cerfs réduisaient leur activité avec l’augmentation

de biomasse de la végétation durant la saison ou selon le nombre d’années après coupe. En

densité naturelle, les cerfs passaient moins de temps en activité au début de l’été lorsque la

végétation était moins abondante. À haute densité, les cerfs ne recherchaient pas les zones

de couvert plus dense contrairement à ce qui se passait pour les cerfs à faible densité. Si la

quantité de végétation diminue avec l’augmentation de la densité de cerfs, nous prédisons

que les cerfs s’adapteront en augmentant leur temps d’alimentation ou, lorsque la

végétation sera fortement réduite, ils augmenteront leur temps de rumination et délaisseront

les milieux sous couvert.

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Abstract

We investigated the influence of population density on movements, activity budgets and

space utilization of white-tailed deer in a controlled-density experiment. Movements and

activity budgets were generally not greatly affected by density. Seasonal and annual

increases in vegetation abundance resulted in a reduction in the length of activity bouts

because the time required to gather food decreases when vegetation becomes more

abundant. In unenclosed areas, deer spent less time active at the beginning of the summer

and more time resting, likely to process less digestible forage. Deer at high density,

contrarily to deer at low density, did not select areas with dense cover. If population density

reduces forage availability, we predict that deer will adapt by feeding for longer periods,

particularly at the beginning of the summer when forage is more limited. Space utilization

in relation to food and cover is affected by population density.

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Avant-propos

Ce mémoire comprend trois articles écrits en anglais pour être publiés dans des revues

scientifiques ainsi qu’une introduction et une conclusion générale en français. Le premier

chapitre décrit une étude de validation d’une méthode que nous avons utilisée pour mesurer

les budgets d’activité décrits dans le chapitre 2. Steeve D. Côté a participé à l’élaboration

de l’étude ainsi qu’à la correction du manuscrit. Ariane Massé a participé au processus

complet, allant de la mise en place, en passant par la prise de données jusqu’à l’analyse et

la rédaction du manuscrit. Ce manuscrit a été soumis à la revue « Wildlife Society

Bulletin » et a été accepté en avril 2005. Le deuxième chapitre présente une étude qui visait

à quantifier les effets de la densité de population sur le comportement de déplacement et le

budget d’activité du cerf de Virginie (Odocoileus virginianus). Les coauteurs Jean Huot et

Steeve D. Côté ont participé à l’élaboration de l’étude ainsi qu’à la correction du manuscrit.

Le troisième chapitre expose la deuxième partie de l’étude qui s’intéressait aux effets de la

densité de population sur l’utilisation des sites en relation avec l’abondance de couvert et de

végétation. Les coauteurs Jean Huot et Steeve D. Côté ont participé à l’élaboration de

l’étude ainsi qu’à la correction du manuscrit.

Je tiens d’abord à remercier Jean Huot puisque c’est grâce à lui que j’ai pu participer au

merveilleux projet de la chaire de recherche industrielle CRSNG-Produits forestiers

Anticosti. Jean a su me faire confiance et ce même dans les moments les plus difficiles. Il a

aussi su démontrer une patiente incomparable et m’apporter des conseils judicieux dans la

prise et l’analyse des données ainsi que la correction des manuscrits. Ensuite, il m’est

essentiel de remercier Steeve Côté puisque c’est grâce à son soutien et à ses conseils si j’ai

pu terminer cette maîtrise avec autant de succès. Je dois le remercier aussi pour les

nombreuses fois qu’il est venu me visiter à Anticosti et pour tous les conseils qu’il a pu

apporter dans la mise en place, la prise de données, l’analyse et la rédaction.

Le laboratoire de Jean et Steeve est aussi reconnu pour son dynamisme incontestable! Je

remercie chacun de vous, membres du « love labo » pour l’aide que vous m’avez apporté à

travers ces années. Il m’est impossible de passer à côté de Jean-Pierre Tremblay puisque

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sans lui, ce projet n’aurait pu aussi bien fonctionner. Merci pour ton aide, tes conseils et ta

bonne humeur. Un merci particulier à Sonia DeBellefeuille pour toute son aide autant au

niveau moral qu’au niveau de la correction ou de son aide sur le terrain. Tu fais vraiment un

travail exceptionnel! Aussi un merci spécial à Christian Dussault pour les conseils qu’il m’a

donné à partir du complexe G. Et puis, à François Fournier pour son aide dans l’analyse des

données et dans la correction du chapitre 1. Je voudrais remercier Sébastien Lefort et

Vanessa Viera puisque c’est grâce à eux si j’ai pu faire mon premier été de terrain à

Anticosti. Merci à Ariane Massé et Anouk Simard pour m’avoir donné tant de conseils,

d’aide sur le terrain et puis pour tant de discussions importantes en moment de détresse!

Merci, Joëlle pour tout le soleil que tu as su apporter dans mes journées et puis pour ton

aide intarissable sur le terrain. Merci aussi à Sandra Hamel pour sa gentillesse

incomparable et pour m’avoir accepté dans sa cabane sur la montagne. Aussi merci à toi

Daniel Sauvé pour toute l’aide que tu as donné sur le terrain et au bureau. Merci à vous mes

chères colocataires, Catherine Bajzak et Vanessa Viera qui ont su garder mon moral haut, et

puis aussi parce que « la journée la plus perdue est celle où on n’a pas ri ». Merci aussi à

Robert Weladji pour tous les commentaires sur les chapitres 2 et 3. Merci aussi à Martin

Barrette, Valérie Harvey, Julien Mainguy, Stéphanie Pellerin, Antoine St-Louis et Suzy

Tremblay pour toutes les discussions et votre soutien.

Ce projet détenait un terrain laborieux qui n’aurait pu être réalisé sans l’aide de dizaines de

personnes. Merci d’abord à Jean-Pierre Tremblay pour avoir établi le dispositif de densités

contrôlées. Les captures de cerfs ont nécessité l’aide d’une grande équipe. Alors, merci à

Laurier Breton et Bruno Rochette du Ministère des Ressources naturelles et de la Faune du

Québec ainsi qu’à Denis Duteau, François Fournier, Ariane Massé, Gérald Picard, Daniel

Sauvé, Anouk Simard, Jean-François Therrien et Jean-Pierre Tremblay. Merci à tous ceux

qui ont participé aux battues qui n’auraient certainement pas pu avoir lieu sans l’aide

incontournable de Gaétan Laprise et Danièle Morin. Merci aussi à Rémi Pouliot, Martin

Renière, Jean-François Therrien, Jescika Lavergne et Vanessa Viera pour l’aide

inépuisable qu’ils ont su m’apporter lors des suivis télémétriques. Merci aussi à Sonia

DeBellefeuille, Christian Dussault, Michel Duteau, Marie-Andrée Giroux, Léon L’Italien,

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Ariane Massé, Joëlle Taillon, Jean-Pierre Tremblay, Andréanne Tousignant et Vanessa

Viera pour leur contribution dans les inventaires de végétation.

Finalement, je dois souligner l’importance de l’aide des partenaires de la Chaire à l’île :

Produits forestiers Anticosti, la SÉPAQ, le MRNFQ, les résidents de l’île d’Anticosti. Ce

sont des partenaires incomparables pour la réussite d’un tel programme de recherche. Merci

aussi à Sophie Baillargeon pour sa contribution dans les analyses statistiques. Merci à

Christian Dussault, Daniel Fortin et Kim Lowell pour les conseils judicieux qu’ils ont pu

m’apporter dans les analyses du chapitre 2. Et merci à ma famille et amis qui m’ont soutenu

durant toutes ces années.

Un tel projet n’aurait pu avoir lieu sans l’appui financier et logistique de Produits forestiers

Anticosti inc., du conseil de recherches en sciences naturelles et en génie du Canada, du

Fonds québécois de la recherche sur la nature et les technologies, du Ministère des

Ressources naturelles et de la Faune du Québec ainsi que du Centre d’études nordiques.

vii

Table des matières

Table des matières ................................................................................................................ vii

Liste des tableaux .................................................................................................................. xi

Liste des figures .................................................................................................................... xii

Liste des annexes ................................................................................................................. xiv

Introduction générale .............................................................................................................. 1

Les populations de cervidés ................................................................................................ 1

L’île d’Anticosti ................................................................................................................. 3

Les effets de la densité de population sur le comportement ............................................... 4

Le comportement d’alimentation........................................................................................ 5

Le budget d’activité ........................................................................................................ 5

Effets des caractéristiques individuelles ......................................................................... 6

Influence des variables environnementales .................................................................... 7

La densité de population ................................................................................................. 8

La sélection d’un site d’alimentation ................................................................................. 8

Objectifs de l’étude ........................................................................................................... 10

Méthodologie .................................................................................................................... 11

Chapitre 1. Quantification and accuracy of activity data measured with VHF and GPS

telemetry ............................................................................................................................... 17

Résumé ............................................................................................................................. 18

Abstract ............................................................................................................................. 19

Introduction ...................................................................................................................... 20

Study area ......................................................................................................................... 23

Methods ............................................................................................................................ 24

Calibration of VHF and GPS motion sensors on captive white-tailed deer fawns ...... 24

Validation of GPS motion sensors on free-ranging deer .............................................. 25

Data analysis ................................................................................................................. 26

Validation of activity counts of GPS collars on free-ranging deer .............................. 29

Results .............................................................................................................................. 29

viii

Determination of specific behaviors ............................................................................. 31

Calibration of VHF and GPS motion sensors ............................................................... 31

Calculation of activity bouts ......................................................................................... 33

Activity of free-ranging deer ........................................................................................ 33

Discussion ......................................................................................................................... 37

Determination of specific behaviors ............................................................................. 37

VHF collars .................................................................................................................. 37

GPS collars ................................................................................................................... 39

Research and management implications........................................................................... 41

Acknowledgements .......................................................................................................... 41

Literature cited .................................................................................................................. 42

Chapitre 2. Influence of population density on white-tailed deer movements and activity

budgets .................................................................................................................................. 44

Résumé ............................................................................................................................. 45

Abstract ............................................................................................................................. 46

Introduction ...................................................................................................................... 47

Study area ......................................................................................................................... 49

Methods ............................................................................................................................ 49

Experimental design ..................................................................................................... 49

Deer captures ................................................................................................................ 50

Forage abundance ......................................................................................................... 52

Movements ................................................................................................................... 52

Activity budgets ............................................................................................................ 53

Analyses ........................................................................................................................... 55

Results .............................................................................................................................. 57

Forage abundance ......................................................................................................... 57

Movements ................................................................................................................... 57

Proportion of time spent active ..................................................................................... 57

Number of activity bouts .............................................................................................. 61

Length of active and inactive bouts .............................................................................. 66

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Discussion ......................................................................................................................... 67

Influence of population density .................................................................................... 67

Annual differences ........................................................................................................ 69

Seasonal differences ..................................................................................................... 70

Diel activity pattern ...................................................................................................... 72

Conclusion ........................................................................................................................ 72

Acknowledgements .......................................................................................................... 73

Literature cited .................................................................................................................. 73

Chapitre 3. Influence of forage abundance, cover and population density on white-tailed

deer space use ....................................................................................................................... 78

Résumé ............................................................................................................................. 79

Abstract ............................................................................................................................. 80

Introduction ...................................................................................................................... 81

Study area ......................................................................................................................... 83

Methods ............................................................................................................................ 83

Experimental design ..................................................................................................... 83

Deer captures ................................................................................................................ 84

Telemetry ...................................................................................................................... 84

Biomass and cover sampling ........................................................................................ 86

Analyses ....................................................................................................................... 87

Results .............................................................................................................................. 90

Spatial analysis ............................................................................................................. 90

Descriptive statistics ..................................................................................................... 97

Deer space use .............................................................................................................. 97

Discussion ....................................................................................................................... 101

Deer space use in relation to plant biomass and cover ............................................... 101

Limitations and strengths of the study ........................................................................ 103

Acknowledgments .......................................................................................................... 105

Literature cited ................................................................................................................ 106

Conclusion générale ........................................................................................................... 110

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Validation des capteurs d’activité .................................................................................. 110

Les déplacements et le budget d’activité ........................................................................ 111

Le compromis couvert/nourriture ................................................................................... 114

Conclusions et recommandations ................................................................................... 117

Bibliographie générale ........................................................................................................ 119

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Liste des tableaux

Table 1–1. Individual and combined relative mean pulse rates (BPM) from variable-pulse

activity sensors that correctly classifieda the observed behaviors of captive white-tailed

deer fawns fitted with VHF collars during 21 November-23 December on Anticosti

Island, Québec. ............................................................................................................. 32

Table 1–2. Activity counts recorded during 4-minute intervals that correspondeda to

observed behaviors from captive white-tailed deer fawns fitted with GPS collars on

Anticosti Island, Québec............................................................................................... 34

Table 2–1. Characteristics of white-tailed deer used in an experiment on the effects of

population density on deer activity budgets on Anticosti Island, Québec.................... 51

Table 2–2. Comparisons of white-tailed deer summer movement rates in two controlled

densities according to age class, week and period of the day (Anticosti Island,

Québec). ........................................................................................................................ 59

Table 2–3. Proportion of time that white-tailed deer spent active in summer at two

controlled densities on Anticosti Island, Québec. ........................................................ 60

Table 2–4. Number of daily activity bouts (a) and length (min.) of active (b) and inactive

bouts (c) during summer of yearling and adult white-tailed deer at two controlled

densities on Anticosti Island, Québec. ......................................................................... 65

Table 3–1. Number of locations recorded in each diel period for radiocollared white-tailed

deer tracked in controlled-density enclosures on Anticosti Island, Québec. ................ 85

Table 3–2. Mean biomass (g/m²), lateral covera (/20 points) and canopy coverb (/4; ± SD)

according to deer density and stratum (forest stands and clear-cuts). Deer were kept in

3 sets of enclosures with 2 densities each on Anticosti Island, Québec. ...................... 99

Table 3–3. White-tailed deer relative space usea according to biomass, canopy cover and

lateral cover at 2 different densities (7.5 and 15 deer/km²) in a controlled-density

experiment on Anticosti Island, Québec for each diel period.b .................................. 100

xii

Liste des figures

Figure 1. Le bloc A et la disposition des 2 densités contrôlées dans les enclos construits sur

l’île d’Anticosti (Québec, Canada). Nous avons introduits 3 cerfs de Virginie dans 2

enclos de différentes grandeurs pour obtenir 2 densités différentes

……………………………………….....................................……….........................13

Figure 2. Localisation des sites où nous avons mesuré le budget d’activité, les déplacements

et l’utilisation de l’espace dans deux densités contrôlées dans des enclos situés sur

l’île d’Anticosti (Québec, Canada). La carte présente aussi les grands groupes

forestiers présents sur l’île en 1999…………………………………………………..14

Figure 1–1. Box plot representations of relative mean pulse rates (BPM) for VHF variable-

pulse sensors (a) and of activity counts for vertical (b), and horizontal (c) GPS activity

sensors for the 4 different behaviors observed. ............................................................ 27

Figure 1–2. Determination of a criterion to separate relative mean pulse rates (BPM) for

VHF collars equipped with variable-pulse motion sensors (a), and activity counts for

GPS collars equipped with double-axis motion sensors (b) into active and inactive

behaviors of white-tailed deer on Anticosti Island, Québec. ........................................ 28

Figure 1–3. Observed behavior and relative mean pulse rate (BPM) obtained during one

day for a white-tailed deer fawn fitted with a VHF collar equipped with a variable-

pulse sensor on Anticosti Island, Québec. .................................................................... 30

Figure 1–4. Relationship between observed and estimated proportion of daily active time

obtained with variable-pulse activity sensors of VHF collars fitted to 4 white-tailed

deer fawns each observed for 6 days ( x = 4 hr of observation per day) on Anticosti

Island, Québec. ............................................................................................................. 35

Figure 1–5. Mean activity counts (± 1 SE) recorded by horizontal and vertical sensors of

GPS collars fitted on free-ranging deer on Anticosti Island in summer and autumn

2001 (n = 8 deer) and 2002 (n = 8 deer). ...................................................................... 36

Figure 2–1. Mean plant biomass available to white-tailed deer in a controlled-density

experiment on Anticosti Island, Québec containing known densities (7.5 deer/km²:

black bars, 15 deer/km²: grey bars) of deer. ................................................................. 58

xiii

Figure 2–2. Activity budgets of white-tailed deer from Anticosti Island (Québec) according

to the number of years since the onset of a controlled-density experiment. ................ 62

Figure 2–3. Proportion of daily time spent active (a), number of daily activity bouts (b),

length of active (c), and inactive bouts (d) during summer for adult white-tailed deer

on Anticosti Island (Québec), pooled across years....................................................... 63

Figure 2–4. Proportion of daily time spent active (a), number of daily activity bouts (b),

length of active (c), and inactive bouts (d) during summer for yearling white-tailed

deer on Anticosti Island (Québec), pooled across years. .............................................. 64

Figure 3–1. Plant biomass (g/m²) available to white-tailed deer and interpolated by kriging

in 2003 for Block A on Anticosti Island, Québec. ................................................. 91−92

Figure 3–2. Lateral cover, or mean concealment (attributed to 4 classes 1: 0-25; 2: 26-50; 3:

51-75; 4: 76-100%) of the first 2 sections of a concealment board (2.5 m×0.3 m

divided in 0.5 m sections) in 2 opposite directions, available to white-tailed deer and

interpolated by kriging in 2003 for Block A on Anticosti Island, Québec. ............ 93−94

Figure 3–3. Canopy cover, or proportion of 20 points set at every 3 m from the center of

each sampling unit in 4 directions (east, southeast, southwest and west) where foliage

of >4 m trees was present, available to white-tailed deer and interpolated by kriging in

2003 for Block A on Anticosti Island, Québec. ..................................................... 95−96

Figure 3–4. Relationships between white-tailed deer relative space use (number of

overlapping buffers for a deer divided by the total number of positions for that deer in

every diel-period and at each random point) and plant biomass, lateral (mean

concealment value attributed by 4 classes of 25%) and canopy cover (proportion of 20

points where foliage of >4 m trees was present).. ........................................................ 98

xiv

Liste des annexes

Appendix 3−1. Spatial statistical data of biomass (a) lateral cover (b) and canopy cover (c)

in cuts and forests of enclosures containing different densities of white-tailed deer on

Anticosti Island, Québec............................................................................................. 126

1

Introduction générale

Les populations de cervidés

Depuis les dernières décennies, les populations de cervidés ont augmenté considérablement

dans plusieurs régions de l’Amérique du Nord et de l’Europe (Garrott et al. 1993). Dans

plusieurs régions, le déclin des populations de cerf de Virginie (Odocoileus virginianus)

dans les années 1950 avait pourtant incité les gestionnaires à diminuer la récolte par la

chasse (Rooney et Waller 2003, Côté et al. 2004). La diminution des prédateurs tels que le

loup (Canis lupus) a certes profité à l’augmentation des populations de cervidés mais la

diminution des prédateurs et la baisse de la chasse ne sont pas les seules responsables des

augmentations. En effet, la population humaine grandissante a fragmenté le territoire en le

défrichant pour en faire des champs agricoles ou simplement pour en récolter le bois (Côté

et al. 2004). Ces champs procurent un milieu favorable pour le cerf de Virginie en

augmentant la quantité de végétation disponible (Porter et Underwood 1999). Somme toute,

le cerf de Virginie est un animal qui s’adapte rapidement à son environnement et c’est

sûrement un facteur déterminant pour lequel les populations ont augmenté aussi

rapidement. Le cerf a trouvé avantage aux perturbations anthropiques.

Les cerfs à haute densité peuvent à leur tour modifier la structure et la composition des

communautés végétales (Rooney et Waller 2003, Côté et al. 2004). Le cerf de Virginie est

considéré comme une espèce généraliste qui sélectionne les plantes ou parties de plantes

dont il se nourrit afin d’optimiser l’acquisition d’énergie (Hofmann 1989). Les cerfs

peuvent consommer certaines espèces préférées à un point tel qu’à haute densité,

l’abondance de ces espèces diminue et certaines peuvent même disparaître (Healy 1997).

En effet, plusieurs études ont établi une relation directe entre l’intensité du broutement et

l’abondance des espèces préférées par le cerf (Balgooyen et Waller 1995, Rooney et Dress

1997).

Des études en enclos et des comparaisons insulaires ont démontré que les ongulés peuvent

même modifier la composition de la strate arborescente (Tilghman 1989, Healy 1997). En

effet, le broutement peut contribuer à l’échec de régénération et à la création d’ouvertures

2

qui contribuent indirectement à l’augmentation des graminées, des fougères et des

cypéracées (Anderson et al. 2001, Cooke et Farrell 2001, Kirby 2001, Rooney 2001,

Bradshaw et al. 2003) qui à leur tour, contribuent à l’échec de régénération (Stromayer et

Warren 1997).

Pour se défendre du broutement, certaines plantes produisent des métabolites secondaires

qui diminuent la digestibilité de leurs tissus ou encore développent des structures de

protection comme des épines (Schultz 1988, Hobbs 1996, Augustine et McNaughton 1998)

qui diminuent l’attrait des plantes pour les cerfs (Palo 1985, Bryant et al. 1991, Palo et

Robbins 1991, Bryant et al. 1992). Avec le temps, les espèces tolérantes ou résistantes au

broutement (sensu Boege et Marquis 2005) deviennent plus abondantes que les espèces

vulnérables (Hobbs 1996, Augustine et McNaughton 1998).

Ainsi, il semble qu’à haute densité les cerfs peuvent se retrouver dans des milieux où

l’abondance des plantes préférées est limitée. Les impacts du cerf sur la végétation peuvent

même être irréversibles sans une intervention anthropique (Stromayer et Warren 1997,

Augustine et al. 1998). Pour subsister, les cerfs doivent donc s’acclimater à leur milieu en

modifiant, notamment, leur comportement. En absence d’adaptation physiologique

nouvelle, s’ils ne peuvent combler leurs besoins énergétiques en modifiant leur

comportement, on peut prédire une diminution de la masse, de la reproduction et

éventuellement de la survie en fonction de la densité de la population (Clutton-Brock et al.

1987).

Dans un contexte où plusieurs populations de cervidés sont en croissance en Amérique du

Nord et en Europe et que ces populations ont des impacts importants sur leur

environnement, nous devons nous demander si et comment le comportement des cerfs est

modifié en fonction de l’augmentation de la compétition intra-spécifique et de la

diminution de la disponibilité de la végétation.

3

L’île d’Anticosti

L’île d’Anticosti est un cas particulier, où les fortes densités de cerfs ont causé des impacts

considérables sur la végétation. Depuis l’introduction d’environ 200 cerfs de Virginie sur

l’île d’Anticosti à la fin du 19e siècle, la population a connu une croissance rapide de sorte

que dès le milieu des années 1940, l’île était déjà identifiée à l’échelle nord-américaine

comme un endroit de surpopulation de cerf (Leopold et al. 1947). Le dernier inventaire a

estimé la densité à 16 cerfs/km2, soit un total de 127 000 têtes (Rochette et al. 2003). Cette

forte croissance serait due à la conjugaison de facteurs favorables à l’établissement du cerf

de Virginie tels que l’absence de prédateurs, le climat favorable, et les perturbations

naturelles et anthropiques qui prévalent sur Anticosti qui ont favorisé la création de bons

habitats pour le cerf en offrant une grande abondance de nourriture.

Déjà, les premiers impacts du broutement sur la végétation ont été notés dans les années

1930 (Marie-Victorin et Rolland-Germain 1969). Aujourd’hui, l’ampleur des dommages

causés par les cerfs est inquiétante pour l’avenir des forêts d’Anticosti (Potvin et al. 2003).

En effet, la composition spécifique des strates arbustives et herbacées a largement été

modifiée (Huot 1982, Potvin et al. 2003). Plusieurs espèces arbustives et herbacées sont

maintenant rares ou ont disparues (Potvin et al. 2000). Des plantes aussi résistantes que le

framboisier (Rubus idaeus) et l’épilobe à feuilles étroites (Epilobium angustifolium) ne se

retrouvent plus dans les parterres de coupe de l’île (Potvin et al. 2000). Les cerfs ont même

transformé la composition de la strate arborescente à l’échelle de l’île (Potvin et al. 2003).

Des études ont montré que les semis de sapin (Abies balsamea) sont fortement broutés et ce

même en été et jusqu’au centre de grandes coupes situées à plus de 800 m de la bordure de

la forêt (Potvin et Laprise 2002). Depuis les années 1930, les sapinières qui occupaient

initialement environ 40% de la superficie de l’île sont graduellement remplacées par des

peuplements d’épinette blanche (Picea glauca), une espèce qui est peu broutée (Potvin et

al. 2003). Les cerfs en sont donc arrivés à modifier leur environnement de façon globale. La

situation pourrait cependant changer de façon majeure au cours des prochaines décennies

puisque les sapinières, qui procurent aux cerfs la principale source d’alimentation hivernale

4

(environ 70% du régime alimentaire; Huot 1982, Lefort 2002) disparaissent rapidement

(Potvin et al. 2003).

Plusieurs indicateurs montrent déjà la vulnérabilité des cerfs à Anticosti; ils sont parmi les

plus petits en Amérique du Nord (Boucher et al. 2004) et les femelles ne se reproduisent en

général qu’à l’âge de deux ans et demi, soit une année plus tard qu’ailleurs (Potvin 1985).

De plus, elles ne se reproduisent pas chaque année et ont très peu de jumeaux. Étant donné

l’ampleur des effets du broutement du cerf sur la végétation et la diminution de la qualité de

l’habitat d’hiver, il est important de connaître si et comment la densité de population affecte

le comportement d’alimentation des cerfs afin de pouvoir adopter des méthodes de gestion

convenables au maintien des écosystèmes de l’île.

L’île d’Anticosti représente donc un milieu idéal pour poursuivre une telle étude puisque

les communautés végétales de l’île ont été profondément modifiées par le broutement du

cerf (Potvin et al. 2003). De plus, l’île se trouve à la limite nord de l’aire de répartition du

cerf de Virginie, l’été représente donc une saison critique pour les cerfs puisqu’ils doivent

profiter de la brève saison de croissance de la végétation pour rétablir leur condition

physique et amasser des réserves corporelles qui seront essentielles pendant la période

hivernale (Putman et al. 1996, Lesage et al. 2001). Ils ont donc avantage à optimiser leur

temps d’alimentation et leur sélectivité durant cette période.

Les effets de la densité de population sur le comportement

Quelques études en milieu naturel ont démontré que le comportement d’approvisionnement

et le budget d’activité différaient chez l’orignal (Alces alces) dans deux populations vivant

à différentes densités (Cederlund et al. 1989), chez le cerf Sika (Cervus nippon) au cours de

deux années pendant lesquelles la densité de population avait diminué (Borkowski 2000) ou

chez des cerfs de Virginie vivant dans des habitats et des densités différents (Rouleau et al.

2002). Les effets de la densité de population sur le comportement d’approvisionnement ont

été indirectement mesurés en contrôlant expérimentalement la quantité et/ou la qualité de la

biomasse végétale (Trudell et White 1981, Vivås et Sæther 1987) ou en mesurant le temps

passé à s’alimenter en fonction de la quantité de biomasse disponible (Gillingham et al.

5

1997). Malgré tout, en milieu naturel, les effets de la densité de population sur le

comportement d’alimentation et le budget d’activité des cervidés sont encore mal connus

étant donné la difficulté d’y observer des ongulés comme le cerf de Virginie en milieu

naturel et de contrôler la densité de population (Stenseth 1981, Borkowski 2000). Pourtant,

le comportement d’approvisionnement est déterminant dans l’expression des stratégies

d’histoire de vie adoptées par les animaux et son étude plus approfondie permettrait de

mieux comprendre l’impact des cervidés sur la végétation forestière (Miller 1997, Miller et

Ozoga 1997).

Depuis plusieurs années, les expériences de contrôle de la densité ont été utilisées pour

mieux comprendre les effets de la densité de population sur le comportement

d’alimentation des espèces domestiques (Hester et Kirby 1996). Les recherches manipulant

la densité de population sont maintenant fortement suggérées pour l’étude du

comportement des espèces sauvages puisqu’elles permettent de contrôler et de répliquer

directement différentes densités de cerfs (Hester et al. 2000, Gordon et al. 2004). Pour les

cervidés sauvages, ces études s’intéressent généralement à l’influence de la densité

d’herbivores sur l’abondance et la diversité des espèces végétales (Tilghman 1989, Hester

et al. 2000); cependant ces expériences sont aussi des outils exemplaires pour mesurer

l’influence de la densité sur le comportement d’approvisionnement et le budget d’activité.

Le comportement d’alimentation

Le comportement d’alimentation des herbivores peut être examiné selon deux angles

distincts et complémentaires qui nous permettront d’estimer les effets de la densité de

population sur le comportement : soit le budget d’activité ou la répartition du temps

octroyé à différentes activités et le choix de sites d’alimentation permettant de maximiser le

gain d’énergie par unité de temps.

Le budget d’activité

Pendant l’été, les cerfs consacrent en général 90–95% de leur temps passé en activité à

s’alimenter (Beier et McCullough 1990, Gillingham et al. 1997). Le temps passé en activité

représente donc majoritairement le temps passé en alimentation. Par ailleurs, le temps passé

6

inactif donne un indice du temps passé en rumination. En effet, une diminution de la qualité

de la végétation est reliée à une augmentation du temps nécessaire à la rumination et à une

augmentation du temps passé en inactivité (Mysterud 1998, Pérez-Barbería et Gordon

1999). Le temps passé actif peut être fonction des caractéristiques individuelles mais aussi

des conditions de l’environnement. Ainsi le rôle de la densité de population sur le

comportement des cerfs doit être considéré en fonction de ces deux groupes de facteurs.

Effets des caractéristiques individuelles

Le temps optimal qu’un individu passe à s’alimenter est fonction de son âge, sexe et statut

reproducteur puisque les demandes énergétiques reliées au métabolisme, à la croissance et à

la reproduction peuvent varier selon ces paramètres (Clutton-Brock et al. 1982). Puisque les

grands herbivores ont une demande énergétique absolue plus grande que les petits, il a été

suggéré que le temps passé en alimentation augmente avec la taille corporelle au niveau

inter et intra-spécifique (Bell 1971). Cependant, chez différentes espèces d’ongulés des

zones tempérées, Mysterud (1998) a trouvé que la proportion du temps passé en activité

diminuait de façon allométrique avec la taille corporelle. En effet, le taux métabolique est

lié allométriquement à la masse corporelle alors que la taille du rumen est reliée de façon

isométrique à la masse corporelle (Illius et Gordon 1987) de telle sorte que lorsque la masse

corporelle augmente, la taille du rumen devient proportionnellement plus grande par

rapport aux coûts métaboliques. Puisque le temps de passage de la végétation est

proportionnel à la taille du rumen, il a été suggéré que les plus gros ruminants pouvaient

consommer de la végétation de moins bonne qualité et ainsi, qu’ils passaient moins de

temps actif à la rechercher (Mysterud 1998, Pérez-Barbería et Gordon 1999). Par exemple,

chez les espèces où le dimorphisme sexuel est important, il a été démontré que les mâles

passent moins de temps en activité que les femelles mais ces différences diminuent lorsque

le dimorphisme est réduit (Zhang 2000, Shi et al. 2003) et augmentent avec l’importance du

dimorphisme (Moncorps et al. 1997, Ruckstuhl 1997, Ruckstuhl et Neuhaus 2002).

Des études ont démontré que les juvéniles passaient davantage de temps en activité que les

adultes puisqu’ils ont un taux métabolique plus élevé relativement à la taille de leur

système digestif (Bunnell et Gillingham 1985). L’influence de la densité de population

7

pourrait donc être différente selon l’âge des individus puisque les besoins énergétiques en

termes de croissance et de métabolisme varient entre les juvéniles et les adultes (Bunnell et

Gillingham 1985). Puisqu’ils utilisent les ressources différemment, l’augmentation de la

densité de population pourrait aussi influencer différemment ces deux groupes d’âge. Par

exemple, la survie des juvéniles serait davantage affectée par la densité que la survie des

adultes (Jorgenson et al. 1997).

Influence des variables environnementales

L’abondance et la qualité des plantes peuvent varier au cours de l’année. À Anticosti, la

saison de croissance débute à la fonte des neiges entre le début et la mi-mai (Ressources

naturelles Canada 2005). On observe alors une croissance rapide des plantes dans les

milieux ouverts et puis, graduellement, une croissance des plantes herbacées dans les sous-

bois. Les nouvelles pousses sont riches en protéines mais au cours de l’été, la végétation

augmente en abondance et sa teneur en fibres augmente, ce qui a pour effet de diminuer sa

digestibilité (Tremblay 1981, Robbins 1983, Van Soest 1994). Ces changements dans la

qualité et l’abondance de la végétation pourraient avoir un impact sur le comportement

d’alimentation en modifiant le temps nécessaire à l’acquisition et à l’assimilation de la

végétation et, donc, de l’énergie (Van Soest 1982). Par exemple, étant donné la diminution

de qualité de la végétation pendant l’été, Beier et McCullough (1990) ont trouvé que le

temps passé en activité augmentait pendant l’été et qu’au contraire, pendant l’hiver, pour

conserver leur énergie, les cerfs diminuaient leur activité.

Le budget d’activité est aussi influencé par des variables abiotiques telles que l’heure de la

journée et les conditions météorologiques. En effet, les cerfs ont tendance à être plus actifs

au lever et au coucher du soleil (Zagata et Haugen 1974, Kammermeyer et Marchinton

1977). De plus, plusieurs études ont démontré qu’il existe des relations entre les conditions

météorologiques et l’activité des herbivores. Par exemple, les cerfs sont moins actifs et

utilisent davantage les milieux fermés lorsque les conditions météorologiques sont

défavorables (p. ex. vents forts et précipitations abondantes pendant l’hiver; Miller 1970,

Zagata et Haugen 1974, Drolet 1976, Beier et McCullough 1990).

8

La densité de population

La biomasse de plantes préférées par individu diminue généralement en fonction de la

densité de population (Boucher et al. 2004) mais le taux de consommation ne diminue pas

nécessairement en fonction de la densité d’herbivores (Fortin et al. 2004). Néanmoins, à

forte densité d’herbivores, le temps passé en alimentation pourrait augmenter en réponse à

une diminution de la biomasse (Wickstrom et al. 1984, Renecker et Hudson 1986).

L’augmentation de la population peut donc contraindre les individus à rester actifs plus

longtemps et à se déplacer davantage pour acquérir une même quantité de nourriture

(Herbers 1981, Trudell et White 1981, Clutton-Brock et al. 1982, Gates et Hudson 1983,

Moncorps et al. 1997). Les cervidés peuvent aussi répondre à la diminution de l’abondance

de végétation en se nourrissant de manière moins sélective et en diminuant leurs

déplacements (Gates et Hudson 1983). Cependant, puisque le temps de rumination

augmente avec la quantité de fibres des espèces tolérantes au broutement (Baker et Hobbs

1986, Spalinger et al. 1986), la consommation de plantes de moindre qualité nécessite un

temps de rumination plus long, ce qui contribue à la diminution du temps passé en activité.

La sélection d’un site d’alimentation

En second lieu, les individus doivent sélectionner des endroits pour s’alimenter. Pour les

cervidés, un bon site d’alimentation représente habituellement un compromis entre la

proximité d’un abri (couvert) et l’abondance de nourriture (Tufto et al. 1996).

Le couvert peut être séparé en deux parties : 1) le couvert vertical représente l’abri formé

par la projection des cimes de la canopée jusqu’au sol, il est généralement formé de

végétation, 2) le couvert latéral représente l'obstruction latérale et peut être formé de

végétation ou de topographie (Mysterud et Østbye 1999). Le couvert latéral diminue

habituellement le risque de prédation donc le temps que les animaux doivent consacrer aux

comportements de vigilance (Mysterud et Østbye 1995) et en absence de prédateurs, il est

considéré comme jouant un rôle « psychologique » dans la sélection d’habitat en relation

aux prédateurs fantômes du passé (Byers 1997, Mysterud et Østbye 1999). De plus, les

animaux sont exposés à des conditions météorologiques moins stressantes dans les milieux

9

fermés (couvert latéral ou vertical élevés) que dans les milieux ouverts (Ozoga 1968, Huot

1974, Beier et McCullough 1990, Schimtz 1991, Mysterud et Østbye 1999). Cependant, les

milieux ouverts offrent généralement une plus grande abondance de nourriture estivale pour

les herbivores (Hanley 1984). On peut donc prédire que les cervidés se nourriront

principalement près des bordures puisqu’ils minimisent ainsi le compromis entre trouver un

abri contre les prédateurs et les conditions météorologiques plus difficiles et la disponibilité

de la nourriture qui est plus grande dans les milieux ouverts (Keay et Peek 1980, Tufto et

al. 1996).

Le budget d’activité, les conditions environnementales et le risque de prédation peuvent

varier selon la période du jour. Ainsi, l’importance du couvert peut aussi varier selon ces

facteurs. Dans plusieurs études, on a observé que les cerfs préfèrent utiliser les milieux

ouverts pendant la nuit soit parce que la prédation (Altendorf et al. 2001), le harcèlement

par les insectes (Mysterud et Østbye 1999) ou les activités anthropiques, telles la chasse

(Kilgo et al. 1998) et les activités agricoles (Rouleau et al. 2002) sont réduits pendant les

périodes de noirceur. Beier et McCullough (1990) ont trouvé que les cerfs utilisent aussi

des environnements ouverts pendant les périodes de noirceur, excepté en été où les cerfs

utilisent aussi des milieux ouverts pendant le jour. Ils proposèrent que les cerfs pourraient

utiliser les milieux ouverts pendant le jour puisque les graminées et leur couvert végétal

grand et dense offrent un couvert suffisant pour se cacher des prédateurs et s’abriter des

fortes températures. Le couvert de la canopée et le couvert latéral, couplés à l’abondance de

végétation peuvent donc jouer des rôles importants dans la sélection d’un site

d’alimentation.

Les coupes forestières procurent de tels habitats ouverts, où l’on rencontre une grande

quantité de végétation, entremêlées à des îlots forestiers qui présentent des milieux plus

fermés mais fournissant peu de nourriture (Masters et al. 1993). Les cerfs sélectionnent

habituellement ces coupes par rapport aux îlots forestiers lorsqu’elles offrent davantage de

nourriture et qu’elles ont suffisamment de couvert latéral (Lyon et Jensen 1980). Tierson et

al. (1985) avaient trouvé que les cerfs arrêtaient leur migration aux sites d’hivernage pour

se nourrir dans les coupes.

10

La densité de population peut aussi jouer un rôle important quant au compromis

couvert/nourriture en diminuant la quantité de végétation disponible (Healy et al. 1997) et

en augmentant le nombre d’individus dans un endroit donné. Le compromis

couvert/nourriture pourrait donc aussi être modifié selon la densité. En effet, on a trouvé

que les cerfs évitent de se nourrir dans les milieux ouverts à moins qu’ils ne se trouvent à

haute densité parce qu’ils y consacraient trop de temps aux comportements de vigilance

(Lesage et al. 2000). Rouleau et al. (2002) avaient trouvé que les cerfs vivant à haute

densité dans les milieux agricoles utilisent ces milieux aussi pendant la nuit contrairement

aux cerfs à faible densité. Pendant l’été, dans les milieux agricoles, l’augmentation de

l’utilisation des habitats ouverts pendant le jour et la nuit pourrait donc refléter les impacts

de fortes densités et l’abondance plus faible des plantes. En effet, la sélection des sites

d’alimentation peut être modifiée à haute densité puisque la nourriture est limitée, les cerfs

doivent donc quitter le couvert pour se nourrir dans des endroits où la nourriture est plus

abondante (Mysterud et Østbye 1999). En milieu forestier, il reste encore à savoir si la

densité a un impact sur l’utilisation des coupes pendant l’été.

Objectifs de l’étude

Les effets de la densité de population sur le comportement d’approvisionnement et le

budget d’activité des cerfs sont encore mal connus. Les expériences en densités contrôlées

sont des outils efficaces pour mesurer directement son influence. Au cours de cette étude,

notre objectif était donc de mieux comprendre le rôle de la densité de population sur les

déplacements et le budget d’activité de cerfs de Virginie (Chapitre 2) ainsi que sur le

compromis couvert/nourriture (Chapitre 3) montré par les cerfs lorsqu’ils sélectionnent leur

habitat. Nous avons utilisé une approche expérimentale en manipulant la densité de cerfs

afin de tester les hypothèses suivantes:

Hypothèse 1: Le comportement d’approvisionnement (déplacements et utilisation des sites

d’alimentation ou de couvert) des cerfs est déterminé par l’abondance de la nourriture qui

diminue avec l’augmentation de la densité de population.

11

Hypothèse 2: Les cerfs répondent à la diminution de l’abondance de la nourriture à haute

densité en augmentant la proportion de temps passé en activité et la durée des périodes

d’activité possiblement de façon à sélectionner la végétation de meilleure qualité même si

celle-ci est moins abondante.

Hypothèse 2 alternative: Les cerfs répondent à la diminution de l’abondance de la

nourriture à haute densité en modifiant leur budget d’activité possiblement de façon à

maximiser l’acquisition d’énergie à partir d’une végétation de faible qualité.

Afin d’étudier l’influence de la densité de population sur le comportement d’alimentation

du cerf, nous présentons 3 chapitres qui permettront de tester les hypothèses. Nous

présentons dans le chapitre 1 une étude qui justifie l’utilisation de capteurs d’activité afin

de mesurer le budget d’activité des cerfs. Ensuite, dans le chapitre 2 nous montrons les

effets de la densité de population sur le comportement de déplacement et le budget

d’activité du cerf de Virginie. Finalement, le chapitre 3 considère des effets de la densité de

population sur l’utilisation des sites en relation avec l’abondance de couvert et de

végétation.

Méthodologie

Dans le premier chapitre, nous avons déterminé la précision des capteurs d’activité à

impulsions variables des colliers VHF et des capteurs à deux axes des colliers GPS à

mesurer l’activité des cerfs. À cette fin, 4 cerfs de Virginie ont été munis de colliers

émetteurs VHF et 4 cerfs ont été munis de colliers GPS dans des enclos de 50×80 m. Nous

avons directement observé l’activité des individus dont les signaux étaient simultanément

enregistrés soit, pour les colliers VHF, dans un récepteur automatisé qui mesurait

l’intervalle moyen entre deux impulsions pendant 65 impulsions ou pour les colliers GPS,

dans l’enregistreur de données des colliers GPS à toutes les 5 minutes. Ensuite, nous avons

comparé les données observées et obtenues pour évaluer la précision des capteurs à

discerner différents comportements actifs (p.ex. alimentation vs. déplacements) et inactifs

(p.ex. repos vs. debout) et développé une méthode pour quantifier les périodes d’activité

des individus munis de colliers VHF. La proportion du temps actif, la durée des périodes

12

d’activité, d’inactivité et le nombre de périodes par jour estimés à partir des données des

colliers ont alors été comparés avec les données observées. Nous avons enfin évalué si les

données d’activité des colliers GPS pouvaient décrire des patrons d’activité journaliers de

16 cerfs en liberté sur l’île d’Anticosti (Québec, Canada).

Afin d’étudier le rôle de la densité sur le comportement d’approvisionnement et le budget

d’activité, nous avons mis en place un dispositif de densité contrôlée formé de 3 blocs.

Dans chaque bloc, deux densités contrôlées ont été mises en place en disposant 3 individus

dans un enclos de 40 ha (7.5 cerfs/km²) et 3 individus dans un enclos de 20 ha (15

cerfs/km²; Figure 1). Les enclos ont été disposés dans des coupes forestières effectuées en

2001 pour lesquelles 30% de la surface forestière était maintenue pour servir d’abris. Les

cerfs étaient munis de colliers émetteurs VHF équipés de capteurs d’activité. Nous avons

mesuré le budget d’activité des individus dans un des blocs la première année (A) et la

troisième année d’application du traitement de densité contrôlée. La deuxième année, le

budget d’activité des cerfs a été étudié dans 2 blocs (A, C). Les déplacements et l’utilisation

de l’espace disponible ont été étudiés dans un de ces blocs (A) la première et dans les 3

blocs (A, B, C) la deuxième année après l’application du traitement des densités contrôlées

(Figure 2). Nous avons équipé de colliers émetteurs et quantifié le budget d’activité de 4

femelles adultes dans des coupes non clôturées pendant une année, soit en 2003 ou la

deuxième année après le début des traitements contrôlés (T; Figure 2).

Dans le deuxième chapitre, nous avons mesuré l’influence de la densité de population sur

les déplacements et le budget d’activité des cerfs. D’abord, à chaque année, afin de

connaître la disponibilité de la végétation selon les densités, 20 parcelles ont été placées

dans la coupe et 20 autres parcelles ont été placées dans les îlots forestiers pour chaque

enclos ce qui donnait un total de 80 parcelles par bloc. À toutes les parcelles, la biomasse

herbacée a été estimée pour les espèces les plus importantes pour le cerf en évaluant

visuellement le pourcentage de recouvrement. À l’aide de régressions établies entre le

pourcentage de recouvrement et de la biomasse, nous avons estimé la biomasse disponible

dans chaque parcelle (Bonham 1989). Le nombre d’échantillons requis pour établir les

13

Figure 1. Le bloc A et la disposition des 2 densités contrôlées dans les enclos construits sur

l’île d’Anticosti (Québec, Canada). Nous avons introduit 3 cerfs de Virginie dans 2 enclos

de différentes grandeurs pour obtenir 2 densités différentes.

Coupe

Forêt résiduelle

200 m

3 cerfs dans 20 ha ou

15 cerfs/km²

3 cerfs dans 40 ha ou

7.5 cerfs/km²

14

Figure 2. Localisation des sites où nous avons mesuré le budget d’activité, les déplacements

et l’utilisation de l’espace de cerfs de Virginie munis de colliers VHF dans deux densités

contrôlées dans des enclos situés sur l’île d’Anticosti (Québec, Canada). La carte présente

aussi les grands groupes forestiers présents sur l’île en 1999.

A

BC

T

Épinette blanche

Sapin

Épinette noire

Tourbières

60 km

Pessières blanches

Sapinières

Pessières noires

Tourbières

15

régressions a été établi de façon empirique en reportant graphiquement les coefficients de

régression en fonction du nombre d’échantillons jusqu’à l’obtention d’une asymptote

(Frontier 1983). Le pourcentage de recouvrement a été estimé visuellement à l’intérieur de

deux quadrats de 1 m2 choisis aléatoirement à l’intérieur d’un cadre de 10×10 m centré sur

le milieu de la parcelle. La biomasse totale a été comparée entre les blocs et les années à

l’aide d’une analyse de variance en tenant compte des blocs comme facteurs aléatoires. Les

déplacements ont été estimés par la distance entre deux localisations consécutives séparées

de moins de 3 heures. Les positions étaient obtenues par triangulation à l’aide de stations au

sol localisées avec un GPS. Le budget d’activité a été quantifié à partir d’un récepteur

automatisé. Nous avons mesuré l’influence de la densité sur les déplacements, la proportion

du temps passé en activité, le nombre de périodes d’activité, et la durée des périodes

d’activité et d’inactivité entre les années, les semaines et les périodes du jour pour tous les

adultes et les juvéniles à l’aide d’analyses de variance en tenant compte des blocs et des

années comme facteurs aléatoires.

Dans le troisième chapitre, nous avons mesuré l’influence de la densité de population sur

l’utilisation de l’espace dans les enclos. Nous avons utilisé des parcelles pour estimer la

quantité de biomasse et la quantité de couvert latéral et vertical disponible. La végétation

dans les unités expérimentales a été évaluée selon un plan d’échantillonnage par degrés.

Afin de caractériser uniformément l’enclos, une grille a été générée dans ArcView GIS

avec des carreaux de 2 ha. Chaque carreau comprenait 5 parcelles qui ont été disposées

aléatoirement à l’aide de l’extension « Generate-randomly distributed points » de ArcView

GIS. Les points générés par cette méthode ont été transférés dans un GPS et retrouvés sur le

site. À chaque parcelle, la biomasse herbacée a été estimée par espèce en évaluant le

pourcentage de recouvrement de la même façon que dans le Chapitre 2. La fermeture du

couvert arborescent (arbres >4 m) a été déterminée par la projection verticale des cimes au-

dessus de 20 points équidistants de 3 m distribués sur 4 axes (est, sud-est, sud-ouest et

ouest) couvrant un demi-cercle et partant du centre de la parcelle. Le couvert latéral a été

mesuré à l’aide d’une planche à profil (2.5×0.3 m divisée en sections de 50 cm) située à 15

m du centre de la parcelle dans deux directions différentes (Nudds 1977).

16

Pour tous les enclos, nous avons ensuite caractérisé la quantité de biomasse et de couvert

vertical et latéral disponibles à l’aide d’une méthode géostatistique. Le krigeage est une

méthode qui permet d’interpoler les valeurs entre deux points en mesurant la relation

spatiale qui existe entre cette variable et l’espace (Cressie 1993). L’erreur des localisations

par triangulation des cerfs étant grande (107 m), nous avons placé une pastille d’erreur de

100 m autour de chaque localisation. Puisque les pastilles étaient grandes par rapport à la

taille des enclos, nous ne pouvions pas les considérer indépendantes les unes des autres.

Nous avons donc placé une grille avec des carreaux de 150×150 m sur chaque enclos et tiré

aléatoirement un point dans chaque carreau. À chaque point, le nombre de pastilles qui se

superposaient à chaque période du jour et la quantité de biomasse et de couvert latéral et

vertical ont été obtenus à partir des cartes préalablement établies. Ensuite, pour mesurer la

relation entre l’utilisation d’un certain point par les cerfs et des variables d’habitat, nous

avons simplement utilisé une analyse de régression en tenant compte des blocs et des

années comme des facteurs aléatoires.

17

Chapitre 1. Quantification and accuracy of activity data

measured with VHF and GPS telemetry

Marie-Lou Coulombe,

Ariane Massé et Steeve D. Côté

Ce chapitre a été accepté dans la revue « The Wildlife Society Bulletin » en avril 2005 et il

est maintenant sous presse. « The Wildlife Society » nous a accordé la permission de le

reproduire dans ce mémoire.

18

Résumé

Afin de valider l’utilisation de la télémétrie pour quantifier l’activité des ongulés, nous

avons équipé 8 cerfs (Odocoileus virginianus) en captivité avec des colliers pour

déterminer la précision des capteurs d’activité des colliers VHF et GPS ainsi que la

performance de colliers VHF pour mesurer les budgets d’activité. Chez 16 cerfs en milieu

naturel munis de colliers GPS, nous avons évalué si les capteurs pouvaient mesurer des

patrons d’activité journaliers. Les données VHF correspondaient aux observations dans

74% des cas et en considérant 3 échantillons successifs, nous avons augmenté la précision à

84% et déterminé avec succès 87% des périodes d’activité. Les valeurs obtenues à partir du

capteur vertical des colliers GPS étaient plus précises (92%) que les données obtenues à

partir du capteur horizontal (83%) et décrivaient correctement des pics d’activité à l’aube et

au crépuscule. Nous concluons donc que les colliers GPS et VHF, en utilisant 3

échantillons successifs, peuvent être utilisés pour quantifier l’activité des grands herbivores.

19

Abstract

Quantifying activity budgets and determining the accuracy of behavioral data obtained by

telemetry is essential to understand the behavior of animals that are difficult to observe. We

fitted 8 captive white-tailed deer (Odocoileus virginianus) with VHF or GPS collars to

determine the accuracy of VHF variable-pulse sensors and GPS dual-axis sensors and

validate the performance of VHF telemetry for the measurement of activity budgets. We

also evaluated whether instantaneous activity counts could measure daily activity patterns

of 16 free-ranging deer fitted with GPS collars on Anticosti Island (Québec, Canada).

Comparison of VHF telemetry data and visual observations of active (feeding, moving and

standing) and inactive (resting) deer behaviors were correct in 74% of the scans. By using

the activity values of 3 successive VHF scans, we increased accuracy to 84% of the

observed behaviors and detected 87% of observed activity bouts. The accuracy of GPS

activity data varied with orientation of the sensor: activity counts of vertical sensors (92%

agreement) were better able to predict observed behaviors than activity counts from

horizontal sensors (83% agreement). GPS activity sensors detected peaks of activity after

dawn and at dusk in free-ranging deer. We conclude that dual-axis GPS motion sensors can

be used to reliably record activity data and successive scans from VHF sensors can

precisely detect activity bouts in large herbivores.

20

Introduction

Conventional Very High Frequency (VHF) telemetry and animal tracking with Global

Positioning System (GPS) collars allow animal ecologists to quantify the activity of

wildlife and have been used to measure time budgets of species that are difficult to observe.

Initially, signal strength (Singer et al. 1981, Cederlund et al. 1989, Hölzenbein and

Schwede 1989) and linear distance between relocation points determined by radiotelemetry

(Sparrowe and Springer 1970, Kammermeyer and Marchinton 1977), and later by GPS

collars (Merrill and Mech 2003), were used to measure activity budgets of many species.

The interpretation of signal evenness, however, has been found to be subjective and

influenced by particular animals and the environment between the transmitter and the

antenna (Garshelis et al. 1982, Gillingham and Bunnell 1985, Rouys et al. 2001). The use

of relocation distances has been criticized because estimates of radiolocations have large

errors (for VHF telemetry) and distance traveled may misclassify stationary, but active,

animals as inactive (Craighead et al. 1973, White and Garrott 1990, Rouys et al. 2001).

VHF and GPS activity sensors made it possible for biologists to quantify remotely

continuous or instantaneous activity data.

Three types of VHF activity sensors have been used. Reset sensors are equipped with a

timer and a mercury switch that initiate a pulse rate change when the switch is not triggered

within a specified time lapse. Tip-switch sensors transmit different pulse rates depending

on orientation of the sensor. The number of pulse rate changes in a specific time period

may be used to index activity. Reset sensors and tip-switch sensors were found to be easily

triggered by head and comfort movements made by resting animals (Garshelis et al. 1982,

Gillingham and Bunnell 1985). Nonetheless, a strong correlation (r = 0.9) was found

between distance moved by black bears (Ursus americanus) and activity measured by reset

sensors (Garshelis et al. 1982). The proportion of time spent active measured with

tip-switch sensors could be estimated from telemetry data with 90% accuracy in a study of

black-tailed deer (Odocoileus hemionus columbianus; Gillingham and Bunnell 1985). To

refine the use of tip-switch sensors, Beier and McCullough (1988) increased sampling

interval from 1 to 5.25 minutes and this led to the correct classification of 98% of

21

individual samples. In a validation study of tip-switch sensors used to measure time budget

of Dall’s sheep (Ovis dalli), examination of scan pattern changes rather than fixed time

samples also increased accuracy of activity detection (Hansen et al. 1992). Variable-pulse

sensors were developed because it was thought that by adding extra pulses to every switch

movement, specific behaviors such as moving versus feeding could be identified from

different pulse patterns. As for tip-switch sensors, variable-pulse sensors are triggered by

individual movements and changes in pulse rates are not dependent on a specific time-delay

as for reset sensors. The first versions of variable-pulse sensors assessed movement

periodically (every 0.25 sec), but an increase in animal movement did not necessarily result

in higher pulse rates because instantaneous samples of movements missed concentrations of

rapid pulses (Gillingham and Bunnell 1985). Rather than sampling movement

instantaneously, later versions of variable-pulse sensors, such as the ones we used,

integrated the amount of movements by adding pulses to a base pulse rate. Depending on

scan duration (1–5 min), Relyea et al. (1994) found that 74 to 81% of the scans

discriminated resting deer from non-resting deer, but that variable-pulse sensors could not

discriminate amongst different active or inactive behaviors. Errors with variable-pulse

sensors are still associated with head and comfort movements in resting periods or with

sensors that fail to detect movements while animals are active, but keep their head still for

extended periods.

Methods for effectively gathering continuous VHF sensor data on numerous animals have

also been developed. In the beginning, researchers listened directly to signal changes

(Garshelis et al. 1982). Other systems registered signal variations on strip charts

(Gillingham and Bunnell 1985, Beier and McCullough 1988, Hansen et al. 1992).

Gathering data was still time consuming because every signal change had to be manually

recorded. In the 1990s, new automated systems that recorded time and pulse rates

electronically in an immediately usable form were developed (Relyea et al. 1994). We used

a version of this automated datalogging system to gather and analyze data on deer activity

budgets.

22

In the past decade, GPS collars have been equipped with different motion sensors: tilt-

switch activity sensors that tally head-down occurrence (Rumble et al. 2001) and activity

counters composed of dual-axis motion sensors sensitive to vertical and horizontal head and

neck movements (Moen et al. 1996, Turner et al. 2000, Adrados et al. 2003). The first

validation tests of activity counters were conducted on GPS 1000 collars (Lotek

Engineering, Newmarket, Ontario, Canada; Moen et al. 1996, Turner et al. 2000, Adrados

et al. 2003). Both the vertical and the horizontal sensors of GPS 1000 collars consist of a

cylinder that contains a small sphere. An integrated datalogger registers the number of

times that the sphere hits the extremities of the cylinders in a specific time interval. The

activity counts of GPS 1000 collars are combined values of the vertical and the horizontal

sensors. When the GPS fix interval is longer than the activity observation window, the

activity value recorded is averaged over the GPS fix interval. For example, if the GPS fixes

are taken every 2 hours and the activity counts are recorded at 5-minute intervals, then the

reported activity counts would be the average of 24 observations for every 2-hour period.

Moen et al. (1996), Turner et al. (2000) and Adrados et al. (2003) validated these activity

counters with captive animals and were able to classify correctly 91%, 95% and 69% of the

active samples and 75%, 91% and 89% of the inactive samples, respectively. Moen et al.

(1996) also validated activity sensors on free-ranging moose (Alces alces) and found that

the amount of time that moose were active as estimated from activity sensors was

comparable to daily time active reported in other studies of moose for the same region.

Moen et al. (1996) suggested that activity counts should be recorded during a time interval

≤10 minutes and that they should not be averaged over the entire GPS fix interval.

Recently, new models of GPS collars have allowed us to record 2 activity counts, one for

the vertical and one for the horizontal sensor. Furthermore, each value is the actual activity

count recorded during the observation window directly preceding the reported GPS

position, and not an average like for the GPS 1000 collars. These new sensors, however,

have never been validated in the field but may provide a considerable improvement over

older models for fine-scale analysis of foraging behavior and habitat use because they allow

the correlation of actual activity counts to reported GPS positions and corresponding

habitat. A comparison of activity counts measured on captive deer fitted with GPS collars

23

and the corresponding observed behavior would allow the verification of sensor accuracy.

Another approach to validate activity sensor data would be to look at circadian activity

patterns of free-ranging animals fitted with GPS collars. Circadian activity peaks

synchronized with dawn and dusk are widely observed in white-tailed deer (Odocoileus

virginianus; Montgomery 1963, Kammermeyer and Marchinton 1977, Beier and

McCullough 1990, Rouleau et al. 2002). If the sensors can track activity peaks as daylight

changes throughout seasons, then this would indicate that the sensors are reliable.

Our main objective was to validate the use of motion sensors to estimate the activity of

free-ranging large herbivores. We wanted to 1) determine the accuracy of VHF variable-

pulse sensors and GPS dual-axis sensors by comparing sensor data with observed behavior

2) determine the performance of VHF variable-pulse sensors to estimate activity budgets

and 3) verify the ability of instantaneous activity counts generated by GPS motion sensors

to estimate daily activity patterns of free-ranging deer.

Study area

We conducted this study on Anticosti, a 7,943-km2 island located in the Gulf of St.

Lawrence, Québec, Canada (49° 28’ N, 63° 00’ W). The climate was maritime and

characterized by cool summers and by mild and long winters. Mean daily temperature is

15°C in July and –14°C in January (Environment Canada 1993). The boreal forest that

prevailed on Anticosti was dominated by balsam fir (Abies balsamea), white spruce (Picea

glauca), and black spruce (Picea mariana) (Rowe 1972). White-tailed deer were introduced

on Anticosti in 1896 and in the absence of predators, their numbers increased rapidly to

>100,000. Currently, deer density is about 20/km2 and severe impacts of browsing on the

vegetation have occurred across the whole island (Potvin et al. 2003).

24

Methods

Calibration of VHF and GPS motion sensors on captive white-tailed deer

fawns

Deer captures

We captured 8 white-tailed deer fawns between 21 November and 12 December 2003 with

dartguns, Stephenson box-traps or cannon nets. Deer were released in 2 50×80 m semi-

natural enclosures that contained cover, forage and daily supplemental food placed in a

feeder. Low tree branches and shrubs were absent in the enclosures. The Animal Care and

Use Committee of Université Laval, Québec, Canada approved all capture methods

(reference number 2003-014).

VHF collars

LMRT-3 VHF collars equipped with STO-2a variable-pulse sensors (Lotek Engineering,

Newmarket, Ontario, Canada) were fitted on 4 deer. Each collar had a board fitted parallel

to the ground on the bottom of the transmitter case to which a tilt-switch oriented

perpendicular to the spine of the animal (horizontal sensor) was attached. Pulses were

automatically added each time the switch was triggered. Transmitter signals from the

collars were received and recorded in a SRX-400 Version W9 receiver-datalogger (Lotek

Engineering, Newmarket, Ontario, Canada) connected to a multidirectional antenna and a

12 V battery to ensure a constant electrical input.

The receiver was programmed to measure duration between 2 pulses for 65 consecutive

pulses, record mean pulse rate and then automatically switch to scan another transmitter.

The time needed to record 65 pulses was thus dependent on pulse rate. As the SRX receiver

scanned one transmitter at a time and because 4 individuals were followed each day, a

measure of pulse rate for each deer was obtained approximately every 4 minutes. At the end

of the day, data were downloaded on a laptop computer with Winhost software 1.0.0.1

(Lotek Engineering, Newmarket, Ontario, Canada).

25

GPS collars

The GPS 2200R collars (Lotek Engineering, Newmarket, Ontario, Canada) that we used

were equipped with dual-axis motion sensors (vertical and horizontal) that recorded the

number of times a switch was triggered during the 4 minutes immediately preceding a GPS

fix. Both sensors are fixed on a board parallel to the ground in the transmitter case. The

vertical sensor is oriented parallel to the spine of the animal and the horizontal sensor is

oriented perpendicular to the spine of the animal. We obtained GPS locations every 5

minutes. The maximum number of events that could be recorded during each 4-minute

interval was 255.

Behavioral observations

We waited 48 hours after deer had been introduced into the enclosures before beginning

visual observations so that deer could habituate to wearing a VHF or a GPS collar. Deer

were observed during mornings and afternoons from a 4-m high observation tower between

21 November and 23 December 2003. When needed, we used 8×42 binoculars or 20–

25×60 spotting scopes. The time and type of each behavior were recorded on tape

recorders. Watches were set to match the time on the receiver and the GPS collars. We

recorded 4 behaviors during observations: feeding, moving, standing, or resting.

Validation of GPS motion sensors on free-ranging deer

We monitored 16 free-ranging white-tailed deer does equipped with GPS 2200R collars

between July and November 2001 (n = 8) and 2002 (n = 8). Does were captured in late June

or early July in peat bogs with a net-gun fired from a helicopter. Handling time was less

than 5 minutes and deer were released at the capture site. We set GPS fix interval to 2 hours

and recorded activity counts during the 4 minutes immediately preceding every GPS

location. We predicted that if the motion sensors record activity accurately, then we could

track activity peaks at dawn and dusk as daylight changes throughout summer and fall.

26

Data analysis

Calibration of VHF and GPS motion sensors

All VHF mean pulse rates (BPM) were divided by the transmitter’s respective base pulse

rate (from 59–64 pulses per minute). A sensor that had not moved would thus give a

relative BPM of 1. Due to receiver or collar variations, pulse rate could be smaller but close

to base pulse rate (i.e. >0.95 of the base pulse rate). We rounded these data to 1 because we

considered them as equivalent to base pulse rate. When movements occurred, scans should

then give a relative BPM higher than 1 (up to about 2.5). The dual-axis motion sensors of

GPS 2200R collars provided 2 distinct activity counts, one for vertical and the other for

lateral head and neck movements. Number of events ranged from 0–255.

To test whether we could relate ranges of relative BPM and GPS activity counts to different

behaviors, we selected the periods when deer were observed performing only 1 of the 4

behaviors identified for the whole sampling period. We then compared means and ranges of

relative BPM and GPS activity counts (Figure 1-1). We determined the percentage of

correctly classified scans for VHF and GPS sensors when deer were active (i.e. feeding,

moving or standing) or inactive (resting) for the entire sampling period using different

mean relative BPM for VHF collars and different activity counts for GPS collars as

separation thresholds. Samples with relative BPM and activity counts under the threshold

value were considered inactive and samples over the threshold value were considered

active. The value of the best threshold was determined by plotting the percentage of

correctly classified samples when deer were active and inactive against all possible relative

BPM or activity count thresholds (Figure 1-2).

Calculation of activity bouts with VHF collars

To compare the results obtained with VHF collars to observed activity budgets and

calculate the accuracy of scans we combined the information from 1 scan with that of the

following 2 successive scans, i.e. a time interval of 10–15 minutes. We depicted activity

bouts using the following criterions: an inactive bout began when at least 3 consecutive

scans considered as inactive were recorded, and conversely an animal was classified as

27

Figure 1–1. Box plot representations of relative mean pulse rates (BPM) for VHF variable-

pulse sensors (a) and of activity counts for vertical (b), and horizontal (c) GPS activity

sensors for the 4 different behaviors observed. Collars were fitted to white-tailed deer

fawns that were simultaneously observed on Anticosti Island, Québec during 21 November-

23 December 2003. Solid middle bars correspond to the median for each behavior observed

and dashed bars symbolize the mean value. Error bars represent 90% quantiles and dots are

outlier values (<10% of data). Numbers in parentheses represent the number of telemetry

samples for each behavioral category. Distribution of VHF relative BPM and GPS activity

counts show clearly how values overlap between active (feeding, moving and standing) and

inactive (resting) behaviors.

Behaviors

Feeding Moving Standing Resting

Ho

rizo

nta

l acti

vit

y c

ou

nts

(G

PS

co

llars

)

0

25

50

75

100

125

150

175

200

225

250

Mean

rela

tiv

e B

PM

(V

HF

co

llars

)1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6(128) (44) (63) (662)

Vert

ical

acti

vit

y c

ou

nts

(G

PS

co

llars

)

0

25

50

75

100

125

150

175

200

225

250

(234) (10) (18) (472)

a)

b)

c) (234) (10) (18) (472)

Figure 1. Box plot representations of relative mean pulse rates (BPM) for VHF variable-

pulse sensors (a) and of activity counts for vertical (b), and horizontal (c) GPS activity

sensors for the 4 different behaviors observed. Collars were fitted to white-tailed deer

fawns that were simultaneously observed on Anticosti Island, Québec. Solid middle bars

correspond to the median for each behavior observed and dashed bars symbolize the mean

value. Error bars represent 90% quantiles and dots are outlier values (<10% of data).

Numbers in parentheses represent the number of telemetry samples for each behavioral

category. Distribution of VHF relative BPM and GPS activity counts show clearly how

values overlap between active (feeding and moving) and inactive (standing and lying)

behaviors.

28

Figure 1–2. Determination of a criterion to separate relative mean pulse rates (BPM) for

VHF collars equipped with variable-pulse motion sensors (a), and activity counts for GPS

collars equipped with double-axis motion sensors (b) into active and inactive behaviors of

white-tailed deer on Anticosti Island, Québec. The percentages of correctly classified

samples in relation to all possible separation thresholds of relative BPM or activity counts

are illustrated. The best separation threshold is found where the 2 curves intersect or where

the classification error of active and inactive behaviors is the smallest.

Mean relative BPM threshold

1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0

Pro

po

rtio

n o

f co

rrectl

y c

lass

ifie

d b

eh

av

iors

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Inactive

Active

Activity count threshold

0 25 50 75 100 125 150 175 200 225 250

Pro

po

rtio

n o

f co

rrectl

y c

lass

ifie

d b

eh

av

iors

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

a)

b)

VHF collars

GPS collarsObserved accuracy

Selected separation threshold

29

active when at least 3 consecutive scans considered as active were recorded (Figure 1-3).

To compare our method of estimating active and inactive bout durations from telemetry-

derived data to observed data, we simply correlated the observed and estimated proportions

of time active and quantified the number of correctly classified activity bouts for each day

of observation. We also calculated the mean daily duration of active and inactive bouts and

compared the estimated and observed results with Student's t-tests. When needed, we either

square-root or log transformed bout length to achieve data normality (Zar 1999).

Validation of activity counts of GPS collars on free-ranging deer

GPS collars recorded 12 4-minute activity counts per day (every 2 hr) for each free-ranging

deer for both vertical and horizontal motion sensors. Data were classified into 4 periods of

the day and the numbers of 4-minute activity counts varied for each period: dawn (from

half an hour before sunrise to an hour after, n = 1), dusk (from an hour before sunset to half

an hour after, n = 1), day (n = 4–7), and night (n = 3–6). Mean activity counts were thus

used for day and night. For each sensor, we used repeated-measures ANOVAs (proc GLM;

SAS 1989) to evaluate the influence of the period of the day on activity counts from July to

November and we compared the results with the LSMEANS statements of SAS (1989). We

ensured that the residuals were normally distributed (Shapiro-Wilk test) and were

homogeneous by visual examination of the plots. Horizontal activity counts were square-

root transformed to normalize the residuals (Zar 1999). Unless specified, all results are

presented as x ± 1 SE.

Results

We collected 102 hours of observation on 4 captive deer wearing 4 different VHF collars

and 105 hours on 4 other captive deer wearing 3 different GPS collars. We observed each

deer with a VHF collar for a total of 6 days and each deer with a GPS collar for 3 to 6 days

(approx 4 hr of observation per day). Deer spent 30 ± 2% of their time feeding, 13 ± 1%

standing, 13 ± 2% moving, and 43 ± 4% resting (n = 42 deer-days) during the daily

observations. We obtained simultaneous data of relative BPM and behavioral observations

30

Figure 1–3. Observed behavior and relative mean pulse rate (BPM) obtained during one

day for a white-tailed deer fawn fitted with a VHF collar equipped with a variable-pulse

sensor on Anticosti Island, Québec. An inactive bout (white rectangles) began when at least

3 inactive scans (relative BPM of 1) were observed. To return to an active bout (black

rectangles), at least 3 active scans (relative BPM higher than 1) had to be observed. Note

the presence of single inactive scans surrounded by active scans (a), and single active scans

(b) surrounded by inactive scans that did not change the calculation of the duration of the

bout.

Time

08:00 10:00 12:00 14:00 16:00

Mean

rela

tiv

e B

PM

0.5

1.0

1.5

2.0

2.5

3.0

3.5

a b

Estimated activity bouts

Observed activity bouts

31

for 1,357 relative BPM samples from VHF collars and 1,126 4-minute samples from GPS

collars.

Determination of specific behaviors

For VHF collars, the ranges of 90% quantiles for relative BPM of resting (1–1.02), standing

(1–1.16), feeding (1–2.02) and moving (1–1.69) overlapped considerably (Figure 1-1a)

because they all included 1. Ranges of active behaviors, however, were much larger than

for inactive behaviors (Figure 1-1a). For GPS collars, 90% quantiles for vertical activity

counts ranged from 0 to 4 for resting, from 2 to 148 for standing, from 4 to 166 for feeding

and from 2 to 237 for moving and thus also overlapped considerably (Figure 1-1b). Overlap

ranges were even greater for horizontal activity counts (Figure 1-1c). Even if relative BPM

and activity counts of standing deer resembled those of resting deer, standing deer were

considered active because they were observed during active bouts and were standing only

for short periods (VHF: x = 33 ± 1 seconds; GPS: x = 19 ± 2 seconds).

Calibration of VHF and GPS motion sensors

Deer were either 100% active (n = 662) or 100% inactive (n = 662) for 1,324 scans of the

VHF collars. We identified the best separation threshold of relative BPM as 1 (i.e. the

smallest classification error, Figure 1-2a). The best separation criterion was found where

the 2 curves intersected or where the classification error of active and inactive behaviors

was the smallest (Relyea et al. 1994). For example, if we had used a separation threshold of

1.3 mean relative BPM for VHF collars, we would have correctly classified 97% of the

inactive samples (Figure 1-2a, top curve), but only 20% of the active samples (Figure 1-2a,

bottom curve). We correctly discriminated 87% and 61% of inactive and active samples,

respectively, for a total of 74% of correctly classified samples (Table 1-1). Most errors

occurred when deer were observed feeding or moving, but VHF telemetry signals indicated

that they were inactive. Using all 4-minute intervals when deer were completely inactive

(n = 472) or active (n = 614) (Figure 1-2b), we found that for GPS collars the best cut-off

activity count to separate active from inactive samples was a value of 10 for both vertical

and horizontal sensors. This separation criterion allowed us to correctly classify 92% and

32

Table 1–1. Individual and combined relative mean pulse rates (BPM) from variable-pulse

activity sensors that correctly classifieda the observed behaviors of captive white-tailed deer

fawns fitted with VHF collars during 21 November-23 December on Anticosti Island,

Québec.

Percentage of relative mean BPM correctly classified

Individual scans Combination of 3 scansc

Deer Id Nb Inactive Active Total Inactive Active Total

30 297 90 61 74 93 86 89

35 341 88 50 70 95 72 84

39 310 84 99 92 82 99 91

40 376 86 32 60 95 53 75

Total 1,324 87 61 74 92 77 84

a The threshold value for relative mean BPM was determined graphically (Figure 1-2a).

b N refers to the number of scans when deer were observed either 100% active or 100%

inactive.

c To determine whether each individual scan should be considered active or not, we used

the information of the following 2 scans. Samples were considered active until at least 3

successive scans changed to inactive relative mean BPM values, and vice-versa.

33

83% of the samples for the vertical and the horizontal activity sensor, respectively

(Table 1-2).

Calculation of activity bouts

Using the values of 3 successive VHF scans, we correctly estimated 92% (n = 662) of the

scans related to inactive behaviors and 77% (n = 662) of the scans related to active

behaviors and thus correctly classified 84% of the scans (Table 1-1). A strong correlation

existed between the proportion of time active observed and estimated by VHF activity

sensors (r = 0.81; n = 24 deer-days; P ≤ 0.001; Figure 1-4). Mean time observed active per

day was slightly higher ( x = 56 ± 5%, n = 24 deer-days), but not significantly different,

from estimated time spent active ( x = 50 ± 6%, n = 24 deer-days; t23 = 1.86; P = 0.08). In

addition, we correctly estimated 89% of inactive bouts and 82% of active bouts. We found

no difference between the mean duration of active bouts estimated by variable-pulse

sensors ( x = 62 ± 9 minutes, n = 23 deer-days) and those observed ( x = 64 ± 8 minutes,

n = 23 deer-days; t22 = -0.45; P = 0.65). The duration of inactive bouts estimated

( x = 75 ± 9 minutes, n = 23 deer-days) and observed ( x = 71 ± 8 minutes, n = 23

deer-days; t22 = -0.90; P = 0.44) were also comparable. We also did the comparisons for

each deer separately to account for individual deer-collar effects, and found no differences.

Activity of free-ranging deer

During the summer and fall of 2001 and 2002, we found an influence of the period of the

day (vertical: F3,54 = 7.69, P ≤ 0.001; horizontal: F3,54 = 6.89, P ≤ 0.001) and month

(vertical: F4,216 = 46.93, P ≤ 0.001; horizontal: F4,216 = 4.67, P ≤ 0.001) on mean activity

counts of free-ranging does. Mean activity counts decreased from July to November

(Figure 1-5). The vertical and horizontal activity sensors revealed 2 daily activity peaks that

were synchronized just after dawn and during dusk from July to November (Figure 1-5).

The activity peaks closely tracked the changes of daytime duration. The interaction

between period of the day and month was highly significant for horizontal sensors

(F12,216 = 6.80, P ≤ 0.001) but, although the direction of the results was similar, the

interaction was not significant for vertical sensors (F12,216 = 1.43, P = 0.15).

34

Table 1–2. Activity counts recorded during 4-minute intervals that correspondeda to

observed behaviors from captive white-tailed deer fawns fitted with GPS collars on

Anticosti Island, Québec.

Percentage of 4-minute intervals correctly classified

Vertical sensor Horizontal sensor

Deer Id Nb Inactive Active Inactive Active

29 451 90 96 87 63

32 219 95 96 64 100

33 296 95 79 94 97

41 120 88 90 59 99

Total 1,086 92 83

a The threshold value for activity counts was determined graphically for each sensor

(Figure 1-2b).

b N refers to the number of periods observed when each deer was either 100% active or

100% inactive.

35

Figure 1–4. Relationship between observed and estimated proportion of daily active time

obtained with variable-pulse activity sensors of VHF collars fitted to 4 white-tailed deer

fawns each observed for 6 days ( x = 4 hr of observation per day) on Anticosti Island,

Québec.

Observed proportion of daily time active

0.0 0.2 0.4 0.6 0.8 1.0

Est

imate

d p

rop

ort

ion

of

dail

y t

ime a

cti

ve

0.0

0.2

0.4

0.6

0.8

1.0

001.0,81.0 Pr

36

Figure 1–5. Mean activity counts (± 1 SE) recorded by horizontal and vertical sensors of

GPS collars fitted on free-ranging deer on Anticosti Island in summer and autumn 2001

(n = 8 deer) and 2002 (n = 8 deer). Grey sections indicate dawn and dusk, white sections

daytime and black sections nighttime. Identical letters identify mean activity counts that did

not differ statistically between periods of the day: the top row show the results for the

horizontal sensor and the bottom row for the vertical sensor. Analyses were performed

separately every month for each sensor.

Time

July

0 2 4 6 8 10 12 14 16 18 20 22

Act

ivit

y co

un

ts

0

25

50

75

100

125

150

175

200

225

September

Time

0 2 4 6 8 10 12 14 16 18 20 22

Act

ivit

y co

un

ts

0

25

50

75

100

125

150

175

200

225

October

0 2 4 6 8 10 12 14 16 18 20 22

Act

ivit

y co

un

ts

0

25

50

75

100

125

150

175

200

225

November

Time

0 2 4 6 8 10 12 14 16 18 20 22

Act

ivit

y co

un

ts0

25

50

75

100

125

150

175

200

225

0 2 4 6 8 10 12 14 16 18 20 22

Act

ivit

y co

un

ts

0

25

50

75

100

125

150

175

200

225

August

a ab b

a aab b

a aa b

a ab b

a abab b

a ab b

a acbc b

a ab c

a abb

a aa b

Time

Time

Vertical sensor

Horizontal sensor

37

Discussion

Our study revealed that activity sensors of both VHF and GPS collars can provide reliable

information on activity budgets and patterns of large herbivores. Data from VHF and GPS

activity sensors allowed us to determine accurately 74% and 88% of observed behaviors,

respectively, by using a unique separation criterion for each type of collar. In addition,

activity sensors of VHF collars accurately detected 87% of activity bouts.

Determination of specific behaviors

Particular behaviors could not be identified from mean relative BPM for VHF collars or

activity counts for GPS collars. Validation studies have never been successful in assigning

distinct patterns or ranges of activity sensor data to more specific behaviors than active and

inactive behaviors (Gillingham and Bunnell 1985, Beier and McCullough 1988, Hansen et

al. 1992, Relyea et al. 1994). A deer may engage in several different activities during a 1-

minute interval. Feeding and walking deer often move their heads similarly and thus trigger

switches equally. Standing deer do not trigger the switch often and pulse patterns resemble

those of resting deer. This resulted in strong overlap of relative BPM and activity counts

between behaviors, and observations were thus classified by the proportion of time that

deer were observed active or inactive. Deer feeding, moving or standing were considered

active and resting deer were considered inactive. Distinguishing between feeding and

walking is not essential in most studies interested in foraging behavior because deer spend

most (90–95%) of their active time foraging during the plant-growing season (Beier and

McCullough 1988, Gillingham et al. 1997).

VHF collars

We developed a criterion that correctly distinguished 74% of relative BPM samples. The

accuracy of our collars was comparable to other validation studies of variable-pulse sensors

(73–81%; Gillingham and Bunnell 1985, Relyea et al. 1994), but lower than for tip-switch

activity sensors (>90%; Gillingham and Bunnell 1985, Beier and McCullough 1988). Tip-

switch sensors have possibly given more accurate results than variable-pulse sensors

38

because switches are oriented parallel to the spine of the animal (vertical sensors) and are

thus more sensitive to foraging and walking movements.

Inactive samples were correctly classified in 87% of the cases and accuracy was high and

constant among deer (84–90%). Sensors transmitted an active signal only 13% of the time

when deer were resting compared to 27% for mule deer (Odocoileus hemionus; Relyea et

al. 1994) and 14% for black-tailed deer (Gillingham and Bunnell 1985). A large proportion

of active behaviors (39%), however, were misclassified into inactive behaviors. By

analyzing the information of 3 successive scans, we reduced this percentage to 23% and

correctly classified 87% of active bouts. Misclassification of active behaviors was also

rather high (10–63% of inactive pulses depending on the type of active behavior) for

variable-pulse sensors fitted on captive black-tailed deer (Gillingham and Bunnell 1985).

Relyea et al. (1994) found a higher and more constant accuracy (74–82%) for active

samples of free-ranging deer that had greater movement rates than captive deer. Differences

in study results might be due to the observation of captive deer that move slowly

(Gillingham and Bunnell 1985, Beier and McCullough 1988; this study). Many active

sequences did not induce a change in pulse rates because deer walked slowly with their

head in a horizontal position or kept their head down to feed for several minutes, with

imperceptible sensor movement.

Inaccuracy also varied between individuals (1–68%). Variability could be attributed to

individual differences in movement such as more head tipping while walking. The fit of the

collar around the neck could also influence how easily the switch is triggered by

movements. For example, a tighter fit could help to trigger the sensor more easily and

provide higher rate values. On the contrary, a looser fit could allow the collar to slide on the

neck rather than tilt the mercury switch during certain behaviors. We consider this

possibility as a minor problem in our study however, because all collars were adjusted the

same way. Head movements and tightening of the collar can vary depending on the age-sex

class of an individual and on the season. Researchers should, therefore, be careful in

analyzing activity data collected through long periods of time over many animals.

39

Taking into account 3 successive scans in our quantification of the length of activity bouts

allowed us to accurately determine 87% of active and inactive bouts. Active periods were

characterized by fast pulses interspersed by slow pulses. Using 3 successive scans

considerably decreased the effect of misclassified individual scans. Inactive bouts were

depicted by lasting and constant signals equal or very similar to base pulse rates and could

also be differentiated from active bouts, even if some individual scans were misclassified.

We conclude that variable-pulse sensors are a reliable method to quantify activity budgets

for behavioral studies when the information of successive scans is used, but individual

samples are less precise.

GPS collars The horizontal sensor of GPS collars was more sensitive than the vertical one and detected

head movements even when captive deer were resting or standing. These results are

consistent with the study of Relyea et al. (1994) who also found that horizontal sensors of

VHF collars were activated when deer were resting or standing. Due to its greater

sensitivity, the horizontal sensor lost some information because it reached the maximum

activity count (255) more often than the vertical sensor. Over-sensitivity to slight

movements is not necessarily recommended for activity sensors because it then becomes

more difficult to discriminate active from inactive behaviors and greater variations of

activity counts are recorded between individuals. Better classifications of active and

inactive samples with VHF motion sensitive tip-switches were also found using vertical

sensors (Beier and McCullough 1988, Hansen et al. 1992).

Our method provided a valid classification of active and inactive samples. Using a cut-off

value of 10 for both sensors allowed us to classify correctly 92% and 83% of the samples

for vertical and horizontal sensor, respectively. Other studies (Moen et al. 1996, Turner et

al. 2000) on GPS activity sensors used higher cut-off values to discriminate active from

inactive behaviors, but these sensors recorded a combined value of vertical and horizontal

sensor instead of two separated values. Adrados et al. (2003) developed an individually-

based method to discriminate active from inactive behaviors using the mean daily activity

count of each collar as a reference. This method is useful because it avoids bias due to

40

potential variations in collar tightening among animals and seasons. The horizontal sensor

of our GPS collars was more sensitive and variable than the vertical sensor and thus may

need to be calibrated for each individual or used with an individually-based method. An

individually-based method, however, is not necessary when using a vertical sensor that

records actual activity counts. The vertical sensor of our collars was very accurate and

using a cut-off value of 0 instead of 10, would still have correctly identified 86% and 97%

of active and inactive samples, respectively. The calibration of GPS motion sensors on

captive deer allowed us to validate the use of these sensors to quantify activity of free-

ranging animals. We also compared the magnitude and distribution of activity counts from

captive deer to those from free-ranging deer and found that they were similar. Therefore,

we are confident that the magnitude of the activity of free-ranging animals can be reliably

captured with the sensors.

GPS collars are widely used to study habitat use of large herbivores across seasons or years.

GPS positions are usually taken at intervals varying from 1 to 4 hours. Moen et al. (1996)

suggested that activity counts should be recorded during a time interval ≤10 minutes and

that activity counts should not be averaged over the whole GPS fix interval. The GPS

2200R collars that we used recorded actual activity counts over 4-minute intervals

immediately preceding every GPS fix and thus allowed the correlation of actual activity

counts to specific periods of time. The use of GPS activity sensors on free-ranging deer on

Anticosti Island allowed us to detect 2 activity peaks that were synchronized just after dawn

and during dusk from July to November, as well as a daytime decrease in activity for July

and August. Mean activity counts during daytime possibly increased from September to

November because of the approaching rut. The horizontal activity counts are higher and

more variable than the vertical activity counts and this may explain why the interaction

between period of the day and month was significant for the horizontal sensors, but not for

the vertical sensors. Circadian activity peaks are widely observed in white-tailed deer

(Montgomery 1963, Ozoga and Verme 1970, Kammermeyer and Marchinton 1977,

Rouleau et al. 2002). Similarly to our study, Beier and McCullough (1990) observed

morning activity peaks after sunrise and evening activity peaks during dusk. Even if we

41

used a coarse measure of activity taken every 2 hours, activity sensors could reliably track

the daily activity peaks of deer as daylight changed from summer to fall. Our results

indicate that GPS activity sensors can be used to estimate activity and are therefore an

advantageous tool to monitor the daily activity patterns of large herbivores.

Research and management implications

Activity sensor technology allows the possibility to quantify activity of animals, especially

for species difficult to observe in nature. We found that vertical sensors were more accurate

than horizontal sensors for both GPS and VHF collars (Gillingham and Bunnell 1985, Beier

and McCullough 1988). We encourage scientists and managers to use activity sensors to

record activity of their study animals, but we suggest using VHF vertical variable-pulse

sensors because they will possibly give more reliable results than horizontal sensors.

VHF and GPS activity sensors allow the quantification of continuous activity information

(active vs. inactive behaviors) to study the foraging behavior and assess fine-scale habitat

use and temporal activity patterns of wild mammals. For example, activity sensors can be

used to examine the influence of population density and resource abundance on activity

budgets. In addition, each GPS fix obtained from free-ranging individuals is accompanied

by an activity value that can be related to the environmental characteristics of each position.

This information can be used, for example, to analyze the effects of habitat quality on the

time budget of free-ranging deer at a fine scale. Activity data are necessary to improve our

understanding of foraging behavior and, more generally, of plant-herbivore relationships. In

the context of high deer density on Anticosti Island and elsewhere, activity data can

contribute to generate predictive models that will help wildlife managers and land use

planners to integrate plant-herbivore relationships into forest and wildlife management.

Acknowledgements

We thank L. Breton and B. Rochette from the Ministère des Ressources Naturelles, de la

Faune et des Parcs du Québec, as well as D. Duteau, G. Picard, F. Fournier, D. Sauvé, A.

Simard and J. Taillon, for capturing deer. J. Taillon and A. Tousignant helped with the

behavioral observations and R. Pouliot during the preliminary steps of the study. S.

42

DeBellefeuille, F. Fournier, J. Huot and many graduate colleagues reviewed an earlier draft

of the manuscript. We are also thankful to G. C.White, R. Moen and M. P. Gillingham for

their helpful comments on the manuscript. This project was funded by Produits forestiers

Anticosti inc., the Natural Sciences and Engineering Research Council of Canada and the

Fonds québécois de la recherche sur la nature et les technologies.

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44

Chapitre 2. Influence of population density on

white-tailed deer movements and activity budgets

45

Résumé

Nous avons mesuré l’influence de la densité de population sur les déplacements et le budget

d’activité de cerfs de Virginie se trouvant à différentes densités en milieux contrôlés et

naturels. Le budget d’activité, les déplacements et la biomasse de plantes disponibles ne

variaient généralement pas selon les densités contrôlées (7.5 et 15 cerfs/km²). Cependant,

nous avons trouvé des différences interannuelles reliées à l’augmentation de l’abondance de

végétation après coupe. En effet, suite à l’augmentation de l’abondance de végétation dans

les enclos à densités contrôlées, la durée des périodes passées en activité diminuait et les

cerfs augmentaient le nombre de périodes passées en activité par jour. Au cours de l’été,

lorsque la végétation a augmenté en abondance, les cerfs adultes à 7.5 cerfs/km²

diminuaient la proportion du temps passé en activité par jour mais pas à 15 cerfs/km². Au

début de l’été en milieu naturel (>20 cerfs/km²), la végétation est peu abondante et les cerfs

ont diminué le temps passé en activité possiblement afin d’augmenter le temps de

rumination d’une végétation de moins bonne qualité.

46

Abstract

Foraging decisions in herbivores may be affected by population density as this factor is

frequently related to changing availability of preferred plant species, net abundance of

biomass and variation in intra-specific competition. We studied the effect of population

density on white-tailed deer movements and activity budgets using a controlled-density

experiment. The controlled densities (7.5 deer/km² and 15 deer/km²) were obtained by

placing 3 deer in 2 enclosures of different sizes (20 ha and 40 ha) where forest was partially

harvested in 2001. We repeated the experiment with the same two densities in 3 different

locations. Summer activity budgets and movements of yearlings and adults were quantified

by VHF telemetry the first, second and third year after the onset of the controlled-density

experiment. During one year, we also measured the activity budget of 4 adults in an

unfenced area at a density of >20 deer/km². In each enclosure, biomass of 13 of the most

abundant and preferred plant species was measured in 40 plots every year.

Adults were less active than yearlings at 7.5 but not at 15 deer/km². Otherwise, movements,

activity budgets and available biomass were similar between 7.5 deer/km² and 15 deer/km².

However, the available biomass increased through years and activity budgets varied

accordingly. As biomass increased, deer increased the number of daily activity bouts,

which also became shorter. With increasing biomass, it seemingly took less time for deer to

obtain sufficient forage to enter a rumination bout. Adult deer decreased time spent active

throughout summer, but only at 7.5 deer/km². However, adults in unfenced cutblocks were

less active at the beginning of the summer than deer at 7.5 deer/km² and increased time

spent active through summer. When vegetation was less abundant, such as in early summer,

deer at 7.5 deer/km² seemed to spend more time active gathering vegetation. In unfenced

areas at high density, forage was even less available, free-ranging deer in early summer had

to increase processing time to extract available energy, and thus decrease the proportion of

time spent active. This study demonstrates that relationships between density and foraging

behavior are complex and that controlled-density experiments may help to understand the

behavior of herbivores in relation to available resources.

47

Introduction

Summer is a critical season for herbivores in temperate and boreal regions to restore body

condition and build up body reserves for over-winter survival (Putman et al. 1996, Lesage

et al. 2001). Maximising energy intake requires that herbivores utilize the most profitable

plants in terms of energy content and foraging time necessary to crop them (Bunnell and

Gillingham 1985). Herbivores have access to less forage as population density increases

(Healy et al. 1997, Côté et al. 2004), hence, foraging strategies may become even more

critical at high density. Deer have become the dominant herbivores in most ecosystems of

North America and Europe and they have recently reached historic high densities over large

areas (Côté et al. 2004). The impacts of deer on ecosystem functioning are far reaching

(Côté et al. 2004) and the influence of population density on deer foraging behavior

therefore needs to be assessed.

Deer may change their behavior in response to changing availability of preferred plant

species, net abundance of forage biomass or social competition in relation to population

density. Many studies have obtained conflicting results regarding the influence of forage

quality and availability on herbivore behavior (Henriksen et al. 2003). Usually, herbivore

density is negatively related to forage availability and ungulates at high density may be

constrained to remain active for longer periods in order to ingest enough forage (Trudell

and White 1981, Moncorps et al. 1997). Deer may also increase search movements and

foraging time with increasing density to consume the most rewarding plants and plant parts

(Wickstrom et al. 1984, Bartmann et al. 1992). However, as forage is distributed in

spatially separated patches, movements and increased foraging time may also entail higher

time and energy costs (Murray 1991). Alternatively, deer may also respond to forage

depletion by foraging less selectively to reduce movement costs (Gates and Hudson 1983),

and their diel active time would then remain unaffected (Kohlmann and Risenhoover 1994).

By feeding on lower quality vegetation, i.e. plants with a high content of structural

compounds that reduce plant digestibility (Bryant and Kuropat 1980, Palo 1985, Côté

1998), the rate of passage of forage from the rumen to the lower digestive tract should slow

down and rumination time may then increase (Van Soest 1982, Spalinger et al. 1986).

48

Studies have shown that yearlings spend more time active than adults do as is expected

given their smaller mass and relatively smaller digestive system, higher metabolic rate and

growth energetic demands (Bunnell and Gillingham 1985, Côté et al. 1997, Shi et al. 2003).

As energetic demands increase allometrically (W0.75) with body size, larger individuals

need less energy per unit of mass than smaller individuals (Illius and Gordon 1987), and

because gut size increases linearly with body size and turnover time declines, this allows

larger individuals to extract more energy from lower quality forage than smaller individuals

(Demment and Van Soest 1985). Active time thus tends to decrease with increasing body

size (Moncorps et al. 1997, Ruckstuhl 1997, Mysterud 1998, Pérez-Barbería and Gordon

1999, Jeschke and Tollrian 2005). Given their different use of resources, an increase in

population density should thus affect differently juveniles and adults. For example, fawn

survival is more affected by density than adult survival (Jorgenson et al. 1997).

As vegetation increases in abundance during the growing season, it also becomes more

lignified and its protein content decreases (Hanley 1984). Time spent active could thus

differ between periods of plant growth and periods of plant senescence (Gates and Hudson

1983). Diel-patterns of activities have also been largely documented and studies have

shown that deer usually synchronize activity bouts with dawn and dusk (Kammermeyer and

Marchinton 1977, Beier and McCullough 1990) and shift their activity periods to times

when weather conditions are most favourable for thermoregulation (Beier and McCullough

1990).

Controlled-density experiments have been used as a research tool for many years to study

the foraging behavior of domestic animals and its use is now strongly encouraged for wild

ungulates (Hester et al. 2000, Gordon et al. 2004). Most studies have investigated the

effects of browser densities on vegetation abundance and diversity (Tilghman 1989, Hester

et al. 2000); however, controlled-density experiments may also be very useful to understand

how browsers modify their behavior at different population densities. Our general objective

was to assess how the daily and summer activity patterns of yearling and adult white-tailed

deer vary in relation to population density. We predicted that yearling and adult deer at high

density would be more active and have higher movement rates than those at lower densities

49

because of the increased time necessary to gather forage at high density. Alternatively,

yearling and adult deer could increase time spent inactive at high density to process

vegetation that is more fibrous. Total active and inactive times would then remain similar in

all densities but the length of inactive bouts would increase.

Study area

Anticosti Island (49° 28’ N, 63° 00’ W) is located in the Gulf of St. Lawrence, Québec,

Canada and covers 7,943 km2. Forests are naturally dominated by balsam fir (Abies

balsamea), white spruce (Picea glauca) and black spruce (P. mariana). White birch (Betula

papyrifera) and trembling aspen (Populus tremoloides) are irregularly found on the island.

Around 200 deer were introduced on the island at the turn of the 19th century. The

population spread and grew rapidly because of the absence of predators and the presence of

natural and human disturbances that created openings favourable to deer. Today, deer

densities of >20 deer/km² are found in many areas on the island (Potvin and Breton 2005).

Forest composition has been strongly modified by selective browsing, deciduous browse

species have almost disappeared and balsam fir stands are now being replaced by white

spruce stands (Potvin et al. 2003). The climate on Anticosti is typically maritime and

characterized by long and milder winters than on the continent (Huot 1982). Mean

temperatures are -12°C in January and 15°C in July and an average of 406 cm of snow and

630 mm of rain falls every year on the island (Environment Canada 1993).

Methods

Experimental design

Our experimental design is made of 4 blocks, three of which (A, B, C) consist in two

enclosures of different sizes (20 ha and 40 ha) in which we introduced deer. The last block

(T) is an unfenced area where density was estimated at >20 deer/km². We located all blocks

in balsam fir-dominated forests that were partially cut in the summer of 2001. Two of the

blocks (B, C) were localised in the center of the island (Jupiter River area); the other two

were located 130 km away in the western part of the island (A, T). Between 30 and 40% of

residual forest patches of different sizes (0.19–21.6 ha) were left uncut in the enclosures.

50

Water was easily accessible to deer in streams or artificial water holes in every enclosure.

To assess the effects of population density on activity and movement of deer, 2 controlled

densities were established in blocks A, B and C. Controlled densities were 7.5 deer/km2

(LDE; 40 ha enclosures with 3 deer) and 15 deer/km2 (HDE; 20 ha enclosures with 3 deer).

We chose these deer densities to cover a gradient that would include white-tailed deer

density levels proposed for sustainable tree regeneration (7 deer/km²; Tilghman 1989,

deCalesta and Stout 1997), the estimated density on Anticosti Island at the beginning of the

experiment (15.6 deer/km²; Rochette et al. 2003) and the local estimated in situ density

level in management areas adjacent to experimental blocks (>20 deer/km²). Our set up is

part of a larger study trying to determine which deer densities are compatible with forest

regeneration (Tremblay et al. in prep.).

Deer captures

We used different methods to capture deer: dart guns (Pneu-dart Inc, Williamsport,

Pennsylvania, USA), netguns (Coda Enterprises Inc., Mesa, Arizona, USA) shot from a

helicopter, Stephenson box traps and cannon nets baited with cattle feed and balsam fir

twigs. In June or early July of the first, second and third year after the onset of the

controlled-density experiment, deer were released in the study enclosures. On the second

year after the onset of the controlled-density experiment, we captured and released 4 adult

females fitted with VHF collars in the unfenced cutblocks (Table 2-1). The Animal Care

and Use Committee of Université Laval, Québec, Canada (Reference number 2005–008)

approved all capture methods.

All deer were fitted with VHF collars equipped with sto-2a variable-pulse activity sensors

(LMRT series, Lotek Engineering, Newmarket, Ontario, Canada). One adult male and 1

yearling male lost their collars and 1 yearling male had a malfunctioning collar. We

verified reproductive status of adult females by direct observation at capture and at the end

of summer. As only 2 reproductive females were monitored, we did not include

reproductive status as a variable in the analyses but verified if activity and movements were

comparable to the other females.

51

Table 2–1. Characteristics of white-tailed deer used in an experiment on the effects of

population density on deer activity budgets on Anticosti Island, Québec.

a Number of years since the onset of the controlled-density experiment.

b The number of days for which activity budgets of radio-collared deer were monitored

during each month and each year of the study.

c These females were observed with a fawn.

Deer no. Yeara Block Density (deer/km²) Age class Sex Number of days monitoredb

July August September

1 1 A 7.5 Adult Male 21 19

2 Yearling Male 21 19

3 Yearling Female 21 9

4 15 Yearling Female 21 19

5 Yearling Female 21 19

6 Yearling Female 20 19

7 2 A 7.5 Adult Femalec 15 22 13

8 Yearling Female 14 22 13

9 Adult Female 15 22 13

10 15 Adult Female 14 22 13

11 Adult Male 15 22 13

12 Yearling Male

13 2 B 7.5 Yearling Female

14 Yearling Male

15 Adult Female

16 15 Adult Female

17 Yearling Female

18 Adult Femalec

19 2 C 7.5 Yearling Female 15 7 5

20 Adult Female 14 8 5

21 Yearling Male 14 7 5

22 15 Adult Male 15 8 5

23 Yearling Female 13 8 5

24 Adult Male

25 2 T >20 Adult Female 0 2 5

26 Adult Female 9 8 8

27 Adult Female 9 6 4

28 Adult Female 9 9 8

29 3 A 7.5 Yearling Male

30 Adult Male

31 Yearling Male

32 15 Yearling Female

33 Yearling Male

34 Adult Male

35 3 C 7.5 Yearling Female 8 13 1

36 Yearling Female 0 12 1

37 Adult Male 7 10

38 15 Adult Male 13 29 1

39 Yearling Male 13 31 2

40 Yearling Male 13 30 2

52

Forage abundance

To assess plant biomass available in the enclosures, we randomly placed 20 sampling

points in cuts and 20 points under forest cover in each experimental unit. At each sampling

point, percent of plant cover was estimated in 2 1-m2 plots randomly located in a 10×10 m

quadrat centered on the sampling point. We quantified biomass of the following species:

Abies balsamea, Betula papyrifera, Cirsium spp., Coptis groenlandica, Cornus canadensis,

Epilobium angustifolium, Grass sp., Hieracium sp., Maianthemum canadense, Picea

glauca, Rubus idaeus, Rubus pubescens and Trientalis borealis. Plant biomass was assessed

using regressions between percent plant cover and mass of dried plants (Bonham 1989).

Number of samples needed for regressions was estimated empirically by plotting regression

coefficients with number of samples until an asymptote was reached (Frontier 1983). We

did not assess forage abundance in the experimental unit located outside the enclosures but

considered it comparable to the forage abundance of the experimental units during the first

year of the experiment.

Movements

In July and August 2002, the first year of the controlled-density experiment, we

radiotracked 6 deer in block A and in the second year after the onset of the experiment, 18

deer were tracked in 3 blocks (A, B, C). Deer were located with receivers (SRX-400

version W9, Lotek Engineering, Newmarket, Ontario, Canada and TR-2 scanner/receiver,

Telonics, Meza, Arizona, USA), a unidirectional yagi antenna and a compass. Telemetry

stations were positioned with a GPS Garmin (Garmin international, Olathe, Kansas, USA;

precision of <5 m) on forest roads adjacent to the enclosures. To limit human disturbance,

stations were generally located more than 100 m away from the enclosures. At least 3

azimuths differing by a minimum of 30º were obtained by moving between stations with a

vehicle (White and Garrott 1990). To reduce location error, positioning had to be

completed within 15 minutes (White and Garrott 1990). Periods of the day were evenly

sampled by separating them into 3 periods of 8 hours (8h00–16h00, 16h00–0h00 and

0h00–8h00). These 8-hour periods were rotated between 2 observers and between groups of

53

enclosures every 3 days to evenly sample the complete 24 hours of a day and all the

enclosures.

LOAS software (Location of a Signal; Version 2.07, Ecological Software Solutions,

Schwägalpstrasse, Urnäsch, Switzerland) was used to estimate positions and error

polygons. Error polygons were calculated with “Andrews” estimator. All locations were

plotted with LOAS on maps and were assigned Universal Transverse Mercator (UTM)

coordinates. The average error from plotted to actual locations was determined by placing

VHF collars at known locations throughout the enclosures and was estimated at 107 m

(SE = 88 m; n = 88 trials). We deleted telemetry locations with error polygons greater than

0.1 ha. After processing, 2,916 usable locations amongst the 3,251 recorded were obtained.

The minimum movement rate was estimated as the linear distance between two successive

deer-locations separated by less than 3 hours divided by the time elapsed between these 2

locations. One deer (deer #3 in Table 2-1) was also fitted with a GPS collar to verify the

influence of positioning error on movement rate estimation. The distance moved per hour

was equivalent between VHF and GPS collars (VHF: x = 241.1 ± 12.6 m/hour;

n = 196 movements; GPS: x = 247.4 ± 7.8 m/hour; n = 944 movements; F1,1138 = 0.12;

P = 0.73). The distance moved was slightly related to the time interval between two

locations (r = 0.11; P < 0.01; n = 2,219 movements) and the mean time interval between

positions was 1h38 (SE = 27 min., n = 2,219 movements).

Activity budgets

Variable-pulse activity sensors of VHF collars use mercury switches that add pulses to the

base pulse rate of the collar each time the switch is triggered. The number of pulses above

the base pulse rate indicates the degree of animal activity during the period when the pulses

were counted (Type STO-2A, Lotek Engineering, Newmarket, Ontario, Canada).

Transmitter signals of activity sensors were received and recorded in a SRX-400 version

W9 receiver-datalogger (Lotek Engineering, Newmarket, Ontario, Canada) connected to a

multidirectional antenna, a 12 V battery, and a solar panel.

54

The receiver was programmed to measure the duration between 2 pulses for 65 consecutive

pulses, record mean pulse rate and then automatically switch to scan another transmitter.

The time needed to record 65 pulses was thus dependent on pulse rate. As the SRX receiver

scanned one transmitter at a time and because 6 individuals were monitored each day, a

measure of pulse rate for each deer was obtained approximately every 6 minutes. Data were

downloaded in a portable computer with the help of Winhost software (version 1.0.0.1,

Lotek Engineering, Newmarket, Ontario, Canada).

Validation studies of activity sensors have obtained mixed results in overall reliability (74

to 98% accuracy) and have demonstrated that it is necessary to validate methods used to

measure activity budgets with direct animal observations (Gillingham and Bunnell 1985,

Beier and McCullough 1988, Relyea et al. 1994). We conducted our own validation study

with direct observations of deer in small enclosures (Coulombe et al. 2006). By combining

the information of 3 successive scans, we correctly assessed 87% of all activity bouts

(Coulombe et al. 2006). An inactive bout began when at least 3 inactive scans were

observed. To return to an active bout, at least 3 active scans had to be observed. Activity

data did not allow us to differentiate amongst different active or inactive behaviors, e.g.

resting could not be differentiated from ruminating or eating from moving. However, as it

was shown for the Odocoileus genus, activity periods not corresponding to feeding

activities (e.g. vigilance, social interactions) represent only 5 to 15% of time spent active

(Beier and McCullough 1990, Gillingham et al. 1997) and thus are not an important part of

the activity budget. Additionally, a decrease in plant quality is generally related to an

increase in rumination time, and simultaneously to an increase in time spent inactive

(Mysterud 1998, Pérez-Barbería and Gordon 1999). Time spent inactive is thus an

indication of rumination time.

At the onset of the controlled-density experiment, in July and August 2002, one block (A)

with 2 densities was available for the study of activity budgets (Table 2-1). The second

year, in July, August and September 2003, 2 blocks (A, C) were studied. The third year, in

July, August and September 2004, we monitored activity budgets of deer in 1 block (C).

Another unit (T) was located in an unfenced area where density was estimated by an aerial

55

survey to >20 deer/km2. We monitored the activity budget of these free-ranging deer from

July to September 2003. To analyse time budgets, we used the proportion of time spent

active, the length of active and inactive bouts and the number of activity bouts per day.

Analyses

We first tested if plant biomass available to deer differed between densities (7.5 deer/km²,

15 deer/km²), strata (clear-cuts, forests) and years since the onset of the controlled-density

experiment, using an analysis of variance with block as a random factor. We then

contrasted the mean distance moved and the proportion of time spent active between

densities, periods of the day (dawn: 1h30 before sunrise to 1h30 after sunrise, day: 1h30

after sunrise to 1h30 before sunset, dusk: 1h30 before sunset to 1h30 after sunset and night:

1h30 after sunset to 1h30 before sunrise) and week with block and year as random factors.

We also used week2 in certain analyses (because plant quality first increases but then

decreases throughout the summer) but if the quadratic term did not significantly explain

extra variability, it was removed. Periods of the day and weeks were repeated for each deer,

we thus used a repeated measures analysis with density as the treatment and block as

replicates (Proc Mixed, SAS Version 9.1, SAS Institute Inc., Cary, North Carolina, USA).

We considered the enclosure as the experimental unit to take into account the potential

problem of autocorrelation between individuals in a same enclosure. Mean length of active

and inactive bouts and number of daily activity bouts were compared between densities and

week (or week’s quadratic term) for yearlings and adults with block and year as random

factors and with weeks (or week’s quadratic term) as repeated measures. We did not have

enough individuals for each year and density to account for sex, age and density in a full

model. We therefore chose to compare the effects of density between different age groups

(yearlings and adults) of both sexes with an analysis of variance because the effects of

density may differ between growing individuals (i.e. yearlings) and adults as their activity

budgets vary (Bunnell and Gillingham 1985, Shi et al. 2003). To account for differences

between years in movements and time budgets, we used simple contrast comparisons of the

random factor “block×year” (McLean et al. 1991). After models were developed, we

compared with a Z-test the means for adults and yearlings.

56

Because weather conditions vary daily and weekly and can influence deer activity patterns

(Beier and McCullough 1990), we verified the influence of weather on deer movements and

on the proportion of time spent active. We obtained mean hourly temperature data from the

Environment Canada meteorological station located in the western part of the island.

Although mean hourly temperature is not a complete weather descriptor because weather

conditions may also depend on the combination of solar radiation, wind velocity, air

temperature, and other factors, it provides a practical approximation of weather conditions

(Beier and McCullough 1990). For every period of the day, we thus related the proportion

of time spent active for adults and yearlings to the mean temperature for the period and

tested if slopes were significantly different from zero and if they differed between densities.

As normality was violated because of extreme movement or proportion of time spent active

values, we used a robust regression (proc Robustreg, SAS Version 9.1, SAS Institute Inc.,

Cary, North Carolina, USA).

We could not integrate deer in the unfenced area in the same models as above because we

had only one unfenced block (that contained only adults). To test for differences between

the adult activity budget in controlled densities and in the unfenced area, we subtracted

each value of activity from controlled densities to the corresponding value from the

unfenced area, for each week and period of the day. Differences were tested in a model

containing density, period of the day and week (or its quadratic term) with block and year

as random factors. If the estimated parameter confidence intervals contained zero, we

concluded that there was no difference between activity budgets in controlled densities and

in natural densities.

After each analysis, residuals were examined for normality and variance homogeneity.

Significance was set at 0.05 and all results, unless specified, are presented as

means ± standard error. To simplify the presentation of results, we do not show interaction

terms, as they, unless specified, were not significant.

57

Results

Forage abundance

Total biomass of forage did not vary with deer density (F1,2 = 0.03; P = 0.87), but was

greater 2 and 3 years after the onset of the controlled-density experiment then during the

first year (F2,20 = 18.53; P < 0.01; Figure 2-1). Total biomass was greater in cutblocks than

under forest cover 2 and 3 years after the onset of the experiment but not during the first

year (F2,20 = 3.73; P = 0.04; Figure 2-1).

Movements

Mean temperature did not affect deer movements or activity budgets during any period of

the day (all P-values > 0.05), we therefore did not include temperature in any further

analyses. Movement rates were similar between controlled densities for both yearlings and

adults (Table 2-2). In addition, movement rates did not differ between adults and yearlings

(Z = 0.70, P = 0.50). Movement rates did not change according to the number of years after

onset of the controlled-density experiment for both yearlings (1 year: x = 109 ± 7 m/hour;

n = 38 daily periods; 2 years: x = 111 ± 6 m/hour; n = 92; F1,106 = 0.00, P = 1) and adults

(1 year: x = 107 ± 11 m/hour; n = 19; 2 years: x = 116 ± 6 m/hour; n = 114; F1,113 = 1.09,

P = 0.3) or according to periods of the day or number of weeks since the beginning of the

summer (Table 2-2).

Proportion of time spent active

Yearlings were active about 73-75% of the time, both in low (LDE) and in high (HDE)

density enclosures (Table 2-3). Adults in LDE were about 14% more active than those in

HDE (Table 2-3). Adults in unfenced areas were as active ( x = 0.72 ± 0.01; n = 21 daily

periods) as those in controlled density enclosures (Table 2-3; LDE: t2 = 1.23, P = 0.34;

HDE: t2 = -2.34, P = 0.14). Yearlings were more active than adults in HDE (Z = -1.98,

P = 0.05), but not in LDE (Z = -0.44, P = 0.66). For adults, the proportion of time spent

active

58

Year(s) after onset of the controlled-density experiment

1 2 3

Bio

mass

(g

/m²)

0

25

50

75

100

125

150

175

200

1 2 3

Bio

mass

(g

/m²)

0

5

10

15

20

25

30

35

a

b

c

b

c

a) Forest

b) Cutblock

a

Figure 2–1. Mean plant biomass available to white-tailed deer in a controlled-density

experiment on Anticosti Island, Québec containing known densities (7.5 deer/km²: black

bars, 15 deer/km²: grey bars) of deer. Biomass was estimated 1, 2, and 3 years after the

onset of a controlled-density experiment in residual forest stands (a) and cutblocks (b, note

the different scales on the Y axes). Biomass was compared between densities, years and

stratum (forest vs. cutblock) with block (3 sites) as a random factor. Bars with different

letters are statistically different.

59

Table 2–2. Comparisons of white-tailed deer summer movement rates in two controlled densities according to age class,

week and period of the day (Anticosti Island, Québec).

a Movements were compared between densities and weeks for yearlings (a) and adults (b) with block (3 sites) and year as

random factors and period of the day and weeks as repeated measures.

b Numbers in parentheses are the number of values used in the analysis, each corresponding to a specific period of the day,

week, density and block.

Factor Movement (m/hour)a DF F-value P-value

a) Yearlings Density 7.5 deer/km²: 120.0 ± 5.9 (77)b 15 deer/km²: 97.1 ± 6.9 (53) 1,5 2.84 0.15

n = 12 deer Week slope: -0.36 ± 0.41 (130) 1, 108 1.39 0.36

Period of the day dawn

118.4 ± 9.0 (31)

day

105.8 ± 6.8 (38)

dusk

120.6 ± 11.0 (25)

night

102.1 ± 10.3 (36)

3,15 1.42 0.28

b) Adults Density 7.5 deer/km²: 124.1 ± 7.6 (75) 15 deer/km²:102.6 ± 6.7 (58) 1,5 0.29 0.61

n = 9 deer Week slope: 0.28 ± 0.39 (133) 1,101 0.02 0.89

Period of the day dawn

128.6 ± 13.7 (34)

day

116.6 ± 7.9 (37)

dusk

120.6 ± 11.0 (25)

night

102.1 ± 10.3 (37)

3,15 1.46 0.26

60

Table 2–3. Proportion of time that white-tailed deer spent active in summer at two controlled densities on Anticosti

Island, Québec.

Factor Proportion of time spent active† DF F-value P-value

a) Yearlings Density 7.5 deer/km²: 0.75 ± 0.03 (102) †† 15 deer/km²: 0.73 ± 0.03 (72) 1,5 0.98 0.34

n = 13 deer Week slope: 0.0027 ± 0.014 (174) 1,153 0.24 0.62

Week × density difference between slopes at 7.5 and 15 deer/lm²: -0.0018 ± 0.018 (174) 1,153 2.37 0.13

Period of the day dawn

0.70a††† ± 0.03 (47)

day

0.75a ± 0.03 (44)

dusk

0.90b ± 0.03 (44)

night

0.62c ± 0.04 (39)

3,15 3.22 0.05

b) Adults Density 7.5 deer/km²: 0.73 ± 0.03 (98) 15 deer/km²: 0.63 ± 0.04 (60) 1,4 24.9 0.03

n = 9 deer Week slope: 0.0045 ± 0.015 (147) 1,138 0.51 0.48

Week × density difference between slopes at 7.5 and 15 deer/lm²: -0.0306 ± 0.019 (147) 1,138 11.12 0.03

Period of the day dawn

0.63 ± 0.04 (42)

day

0.69 ± 0.04 (41)

dusk

0.82 ± 0.04 (39)

night

0.59 ± 0.04 (36)

3,12 0.78 0.53

† The proportion of time spent active was compared between densities, weeks since the beginning of summer and periods

of the day for yearlings (a) and adults (b) with block (3 sites) and year as random factors and periods of the day and week

as repeated measures.

† † Numbers in parentheses are number of values used in the analysis, each corresponding to a specific period of the day,

week, density and block.

††† Values of proportion of time spent active between periods of the day with different letters are statistically different

(P < 0.05).

61

decreased from the first to the third year after the onset of the controlled-density experiment

(1 vs. 2 years: F1,142 = 1.85, P = 0.18; 1 vs. 3 years: F1,142 = 8.52, P < 0.01; 2 vs. 3 years:

F1,142 = 4.47, P = 0.04). For yearlings, time spent active was similar through years (1 vs. 2

years: F1,158 = 0.93, P = 0.33; 1 vs. 3 years: F1,158 = 0.12, P = 0.73; 2 vs. 3 years:

F1,158 = 1.54, P = 0.22; Figure 2-2a). For adults, the proportion of time spent active

decreased throughout summer in LDE (t138 = -2.14; P = 0.03) but not in HDE (t138 = 0.96;

P = 0.34; Table 2-3, Figure 2-3a). For yearlings, time spent active did not change through

summer in both densities (Table 2-3, Figure 2-4a). Adults in the unfenced area spent less

time active during July than adults in LDE (F1,36 = 4.14, P = 0.05), but no differences

occurred in other months or with deer in HDE. Yearlings were more active at dusk than at

any other period of the day and less active at night than during the day or at dawn

(Table 2-3). Although adults were also 19% more active at dusk than at night, the

difference was not significant (Table 2-3).

Number of activity bouts

The number of daily activity bouts did not differ with density or weeks since the beginning

of the summer for both yearlings and adults (Table 2-4). The number of daily activity bouts

did not differ between deer in the unfenced area ( x = 8.8 ± 2.2; n = 15 weeks) and those in

controlled-density enclosures (Table 2-4; LDE: t4 = -0.04; P = 0.97; HDE: t4 = 1.99;

P = 0.12). Yearlings and adults had a similar number of activity bouts (LDE: Z = 0.62,

P = 0.54; HDE: Z = -0.16, P = 0.88). Yearlings had more activity bouts 2 and 3 years after

the onset of the controlled-density experiment than after 1 year (1 vs. 2 years: F1,37 = 75.32,

P < 0.01; 1 vs. 3 years: F1,34 = 83.62, P < 0.01; Figure 2-2b) and adults had more activity

bouts 3 years after the onset of the controlled-density experiment than 2 years after it (1 vs.

3 years: F1,34 = 1.34, P = 0.26, 2 vs. 3 years: F1,34 = 6.28, P = 0.02; Figure 2-2b). Despite

small sample size, we detected a gradual decrease in the number of activity bouts for deer

in unfenced areas during the summer. Deer in unfenced areas had more activity bouts than

deer in LDE until the end of July, but less bouts at the end of August (week×week:

F1,8 = 12.95, P < 0.01; Table 2-4; Figure 2-3b).

62

Figure 2–2. Activity budgets of white-tailed deer from Anticosti Island (Québec) according

to the number of years since the onset of a controlled-density experiment. Data were pooled

between two controlled deer densities (7.5 and 15 deer/km²). Comparisons were made by

simple contrasts in a model accounting for density, period of the day and weeks since the

beginning of the summer with block and year as random factors. Bars with different letters

are statistically different.

Adults Yearlings

Nu

mb

er

of

dail

y a

cti

vit

y b

ou

ts

0

2

4

6

8

10

12

14

16

Adults Yearlings

Pro

po

rtio

n o

f ti

me a

cti

ve

0.5

0.6

0.7

0.8

0.9a) b)

Adults Yearlings

Len

gth

of

inacti

ve b

ou

ts (

min

.)

20

30

40

50

60

70

80

1

2

3

d)

Adults Yearlings

Len

gth

of

acti

ve b

ou

ts (

min

.)

20

40

60

80

100

120

140c)

a

a b

a a

a

aab

b

a a

b

a

b

c

a

ab

b

a

b

c

a a

b

year(s) after onset

of the controlled-

density experiment

63

Figure 2–3. Proportion of daily time spent active (a), number of daily activity bouts (b),

length of active (c), and inactive bouts (d) during summer for adult white-tailed deer on

Anticosti Island (Québec), pooled across years. Each circle corresponds to the mean for one

block-density during one week. Regression lines were drawn from model predicted values

(Tables 2–3, 2–4).

7.5 deer/km²

Date

07/02 07/16 07/30 08/13 08/27 09/10

Pro

po

rtio

n o

f ti

me s

pen

t acti

ve

0.2

0.4

0.6

0.8

1.0

Date

07/02 07/16 07/30 08/13 08/27 09/10

Len

gth

of

acti

ve b

ou

ts (

min

.)

0

20

40

60

80

100

120

140

160

Date

07/02 07/16 07/30 08/13 08/27 09/10

Len

gth

of

inacti

ve b

ou

ts (

min

.)

0

20

40

60

80

Date

07/02 07/16 07/30 08/13 08/27 09/10

Nu

mb

er

of

dail

y a

cti

vit

y b

ou

ts

4

6

8

10

12

14

15 deer/km²

>20 deer/km²

a)

c) d)

b)

64

Figure 2–4. Proportion of daily time spent active (a), number of daily activity bouts (b),

length of active (c), and inactive bouts (d) during summer for yearling white-tailed deer on

Anticosti Island (Québec), pooled across years. Each circle corresponds to the mean for one

block-density during one week. Regression lines were drawn from model predicted values

(Tables 2–3, 2–4).

Date

07/02 07/16 07/30 08/13 08/27 09/10

Nu

mb

er

of

dail

y a

cti

vit

y b

ou

ts

4

6

8

10

12

14

16

18

Date

07/02 07/16 07/30 08/13 08/27 09/10

Len

gth

of

inacti

ve b

ou

ts (

min

.)

0

20

40

60

80

Date

07/02 07/16 07/30 08/13 08/27 09/10

Len

gth

of

acti

ve b

ou

ts (

min

.)

0

20

40

60

80

100

120

140

160

180

200

Date

07/02 07/16 07/30 08/13 08/27 09/10

Pro

po

rtio

n o

f ti

me s

pen

t acti

ve

0.5

0.6

0.7

0.8

0.9

1.0a)

c) d)

b)

7.5 deer/km²

15 deer/km²

Table 2–4. Number of daily activity bouts (a) and length (min.) of active (b) and inactive bouts (c) during summer of yearling

and adult white-tailed deer at two controlled densities on Anticosti Island, Québec.

a The number of activity bouts and length of active and inactive bouts were compared between densities and weeks since the

beginning of summer for yearlings and adults with block (3 sites) and year as random factors and weeks as repeated measures.

b Numbers in parentheses are sample sizes or number of values used in the analysis, each corresponding to a specific week,

density and block.

Factor Parameter estimatesa DF F-value P-value

a) Yearlings Density 7.5 deer/km²: 10.1 ± 1.5 (20)b 15 deer/km²: 9.9 ± 1.5 (18) 1,4 0.60 0.48

n = 13 deer Week slope: 0.06 ± 0.13 (38) 1,3 0.02 0.89

Adults Density 7.5 deer/km²: 8.9 ± 1.1 (8) 15 deer/km²: 10.2 ± 1.1 (8) 1,4 2.04 0.23

n = 9 deer Week slope: -0.05 ± 0.20 (16) 1,3 0.35 0.56

b) Yearlings Density 7.5 deer/km²: 77.3 ± 16.0 (29) 15 deer/km²: 104.6 ± 17.3 (24) 1,7 3.07 0.12

n = 13 deer Week slope:-4.52 ± 3.00 (53) 1,42 0.88 0.35

Adults Density 7.5 deer/km²: 104.6 ± 18.1 (29) 15 deer/km²: 68.3 ± 20.0 (23) 1,7 0.85 0.39

n = 9 deer Week slope:-0.60 ± 2.40 (52) 1,41 0.06 0.81

c) Yearlings Density 7.5 deer/km²: 43.7 ± 6.1 (29) 15 deer/km²: 48.9 ± 6.7 (20) 1,5 2.72 0.16

n = 13 deer Week slope: 1.93 ± 0.90 (49) 1,4 0.46 0.50

Week × density difference between slopes: -3.01 ± 1.14 (49) 1,4 7.24 0.01

Adults Density 7.5 deer/km²: 50.5 ± 6.1 (28) 15 deer/km²: 58.2 ± 6.3 (22) 1,5 3.51 0.12

n = 9 deer Week slope: -0.60 ± 0.99 (50) 1,41 0.08 0.78

65

66

Length of active and inactive bouts

The length of active bouts did not differ with density for yearlings (Table 2-4). For adults, it

was also similar for all densities, including the unfenced area ( x = 93.0 ± 3.8 min.; n = 15

weeks; LDE vs. HDE: Table 2-4; LDE vs. unfenced area: t5 = -0.12, P = 0.91; HDE vs.

unfenced area: t5 = -0.16, P = 0.30). Adults and yearlings also had active bouts of similar

lengths (Table 2-4; LDE: Z = 0.80, P = 0.42; HDE: Z = 1.03, P = 0.30). Active bouts were

longer 1 year compared to 2 (F1,39 = 21.79, P < 0.01) and 3 (F1,39 = 27.22, P < 0.01) years

after the onset of the controlled-density experiment for adults and greater the first year than

the third year after the onset of the controlled density experiment for yearlings (F1,49 = 4.03,

P = 0.05; Figure 2-2c). The length of active bouts did not change through summer in any

age-class or density (Table 2-4; Figures 2–3c and 2–4c).

The length of inactive bouts was similar for yearlings and adults in all controlled densities

(Table 2-4). In the unfenced area, the length of inactive bouts ( x = 50.3 ± 5.9 min.; n = 10

weeks) was similar to LDE (Table 2-4; t3 = 1.38, P = 0.26), but deer in HDE had slightly

longer inactive bouts than deer in the unfenced area (Table 2-4; t3 = 3.20, P = 0.05). The

length of inactive bouts was also similar for adults and yearlings (Table 2-4; LDE:

Z = 1.13, P = 0.26; HDE: Z = -1.37, P = 0.17). The length of inactive bouts, however,

gradually increased from the first to the third year after the onset of the controlled-density

experiment for adults (1 vs. 2 years: F1,46 = 9.72, P < 0.01; 2 vs. 3 years: F1,46 = 11.72,

P < 0.01; Figure 2-2d). For yearlings, inactive bouts were also longer the third year than

after the second (F1,45 = 3.85, P = 0.05) and the first year (F1,45 = 3.87, P = 0.05) after the

onset of the controlled-density experiment (Figure 2-2d). For adults, the length of inactive

bouts did not vary with time in LDE and HDE (Table 2-4; Figure 2-3d), but deer from the

unfenced area had longer inactive bouts compared to deer in LDE until the end of July but

shorter inactive bouts after (week×week: F1,19 = 6.10, P = 0.02; Figure 2-3d). The length of

inactive bouts tended to decrease throughout the summer for yearlings in LDE (t40 = -1.60;

P = 0.12), and increase in HDE (t40 = 2.15; P = 0.04; Figure 2-4d).

67

Discussion

Influence of population density

Cervids may adjust their behavior to lower quantity and quality of vegetation by either

increasing movement rates (Wickstrom et al. 1984, Bartmann 1992), spending more time

active (Trudell and White 1981, Moncorps et al. 1997) or by increasing the length of

inactive bouts to process vegetation of lower quality (VanSoest 1982, Spalinger et al.

1986). We predicted that population density would influence movement rates, proportion of

time spent active or length of inactive and active bouts because population density is

inversely related to the quantity and quality of available vegetation. To our knowledge, our

study is the first to control for cervid population density using experimental enclosures to

test these predictions.

Movement rates of deer did not differ between controlled densities. In agricultural and

forested landscapes differing in population density, Rouleau et al. (2002) also monitored

movements of white-tailed deer and found that variations in movement rates were related to

ecological differences between landscapes and not to population density. In our study, we

used adjacent and ecologically comparable enclosures and found that there was no

influence of population density on deer movement rates. Despite the error associated with

each telemetry position, movement rates for a deer fitted with both GPS (high precision)

and VHF (low precision) collars were comparable. We applied the same protocol to

measure movement rates at both densities and we are therefore confident that movements

between densities are comparable. Fences could have limited deer movements and biased

movement rates. In addition, deer fitted with GPS collars on Anticosti Island in unfenced

clear-cuts moved at a much slower rate ( x = 78.9 ± 32.8 m/hour; n = 4 deer; unpublished

data) than deer in both enclosures (Table 2-2). Consequently, it appears that the fence did

not refrain deer movements and that population density did not influence movement rates at

controlled densities. For mule deer (Odocoileus hemionus), it was found that movement

rate was lower when forage was more abundant (Bartmann 1992, Wickstrom et al. 1984).

We did not detect differences, however, in plant biomass between the two experimental

densities because the treatment was not applied for long enough or because the difference

68

in density was not sufficiently large. Observations from a companion study suggest that

differences in biomass between treatments arise when density differences are greater and

when the treatment is applied for one year longer (Tremblay 2005). Additionally, both

controlled densities (7.5 and 15 deer/km²) were lower than the average deer density found

under natural conditions on the island (i.e., >20 deer/km²) and this might have prevented us

from observing a difference in movement rates. However, although plant biomass increased

from the first to the second year of the treatment application, we did not observe any

additional difference between movement rates of deer in controlled densities.

Flexible time-activity budgets may allow animals to circumvent the effects of declining

food abundance at high population density (Cederlund et al. 1989, Beier and McCullough

1990, Borkowski 2000). In winter, for example, cervids usually reduce activity in response

to food scarcity (Moen 1978, Georgii 1981, Risenhoover 1986). Observational studies have

found that concentrate feeders such as white-tailed deer spend 20 to 68% of their time

active in summer (Bunnell and Herstad 1989). Depending on density and age, we found

that deer spent 63 to 75% of the time active in summer. Compared to observational studies,

these activity values are thus rather high. Extra time needed for foraging, because of the

high density prevailing on Anticosti Island, might cause such high activity rates. However,

high activity values were found even in low-density enclosures two and three years after the

onset of the experience, when biomass availability had significantly increased. For

yearlings, we found no difference in the proportion of time spent active, number of daily

activity bouts or length of active and inactive bouts between controlled densities. Adults,

however, were more active at low density than at high density. During the growing season,

high plant biomass and availability, especially at low density, may allow adult deer to be

more selective in their search of plant species or plant parts (Owen-Smith and Novellie

1982, Belovsky 1984). At low density, adult deer would increase time spent active because

high quality food is more abundant and is then more cost-effective to search for (Trudell

and White 1981, Moncorps et al. 1997), although this did not occur for yearling deer.

Yearlings spent a larger part of their diel time active in high-density enclosures than adult

deer and this may have prevented them from increasing time spent active in low-density

69

enclosures. Because of their smaller mass and digestive system, higher metabolic rate and

growth energetic demands, yearlings generally spend more time active than adults (Bunnell

and Gillingham 1985, Shi et al. 2003). Clutton-Brock et al. (1987) proposed that

differential use of resources among ungulates should increase at high density when

resources are low since larger individuals, who have higher energy requirements are

excluded from areas of low plant biomass used by smaller individuals. At high density,

yearlings spent more time active than adults probably because they continued to feed on

less abundant high quality vegetation that could not be used by adult deer that had to gather

more abundant vegetation given their higher mass.

Annual differences

Plant biomass was higher the second and third year after the initiation of the controlled-

density experiment than during the first year (Figure 2-1). Similarly to a study comparing

activity budgets in two moose (Alces alces) populations at different plant biomass densities

(Cederlund et al. 1989), we found that the repartition of time between active and inactive

bouts varied through years. Indeed, the length of active bouts decreased through years as

the length of inactive bouts and the number of daily activity periods increased (Figure 2-2).

The decrease in the length of active bouts was probably due to an increase in food

availability rather than to an improvement of diet quality. As biomass increased, deer could

fill their rumen faster before entering a rumination bout, which could also explain the

higher number of daily activity bouts 3 years after the initiation of the controlled-density

experiment compared to the first year. Longer inactive bouts may be due to the extra

available time for processing vegetation (Moncorps et al. 1997) or the possibility to reduce

exposition to adverse environmental conditions. Indeed, when forage quantity increases,

time spent inactive decreases as deer may select higher quality forage and spend less time

ruminating (Mysterud 1998, Pérez-Barbería and Gordon 1999), but this relationship does

not appear to hold when high quality vegetation is available and abundant. For example, in

mountain goats (Oreamnos americanus), the proportion of time spent ruminating relative to

the time spent inactive was negatively related to availability of high quality vegetation (S.

Hamel, unpublished data). As the abundance of high quality vegetation increased, goats

70

spent relatively less time ruminating and more time resting in protected areas because it

was faster for them to fill their digestive tract and ruminate vegetation. Correspondingly, in

our study under abundant forage conditions, deer increased the length of inactive bouts.

Alternatively, if the abundance and diversity of available plant species increase when deer

density is reduced, herbivores may select more digestible and profitable forage and thus the

extra time spent searching for higher quality food may be compensated by the higher net

energy gain (Cederlund et al. 1989). On a daily basis, as deer select more profitable forage,

they require less time to process vegetation and can spend more time searching for highly

digestible plants or plant parts (Bartmann et al. 1992, WallisDeVries 1996). Under this

scenario, we should have expected inactive bouts to decrease in length as the active bouts

increase in length, the opposite of what we found. Additionally, if deer increase selectivity

as biomass increases in adjacent enclosures, we should have expected movement rates to

increase (Gates and Hudson 1983, Kohlmann and Risenhoover 1994). However, movement

rate did not change between the first and the second year of the experiment. We therefore

conclude that deer responded to an increase in plant biomass the first and second year of the

experiment by reducing the time necessary to fill their rumen and increasing the number of

foraging bouts per day. This change in their foraging behavior allowed them to gain about

25% more mass during summer than deer under natural conditions on the island, i.e. at high

density (Simard et al., in prep.).

Seasonal differences

Forage quantity and quality normally vary during and between seasons. On Anticosti, the

vegetation-growing season begins when snow melts in early May (Natural resources

Canada 2005). New shoots are rich in protein and easily digestible (Van der Wall et al.

2000). Through the growing season, vegetation on the Island increases in structural

compounds and decreases in protein content (Tremblay 1981) and thus digestibility

decreases (Robbins 1983, Van Soest 1994). Constraints due to body size and energy

requirements suggest that adults and yearlings may have to allocate their activity budgets

differently (Bunnell and Gillingham 1985). We found that through summer, juveniles and

adults responded differently to an increase in forage abundance and to a decline in plant

71

digestibility. An increase in time spent feeding through the plant growing season is normal

for young individuals and has been reported for the mouflon (Ovis musimon; Moncorps et

al. 1997) and feral goats (Capra hircus; Shi et al. 2003). Yearlings grow during summer,

their energy requirements also increase and they need more forage and thus increase

feeding time (Bunnell and Gillingham 1985). For yearlings in our study, however, the

proportion of time spent active did not increase in either density but the length of inactive

bouts increased at high density during summer. This may have been caused by a decrease in

vegetation quality and thus an increase in processing time. The proportion of time spent

active was constantly high at both densities and thus yearlings might not have had time

available to increase time spent active through summer. In addition, controlled-densities

might not have been sufficiently different to lead to a different response between densities.

Activity gradually decreased during summer in adults at low density. Abundance of

vegetation gradually increases through summer and adult deer may need to spend less time

active, searching and eating vegetation as the season progresses. Forage biomass as of mid-

July may exceed deer metabolic demands, and they could thus meet their energy

requirements by foraging fewer hours per day (Beier and McCullough 1990). This is

consistent with our interpretation above that as biomass increases deer may be as selective,

but they decrease the amount of time required to fill their rumen.

Until the end of July, deer under natural density (>20 deer/km2) had access to less

digestible forage than those under controlled densities (biomass in unfenced areas was

similar or lower than year 1 in Figure 2-1), leading to less time spent active and longer

inactive bouts necessary to digest forage. As the abundance of forage increased through

summer, deer in natural areas increased the proportion of time spent active, but still had

fewer daily activity bouts than in controlled densities. An increase in foraging time could

be expected if deer increase selectivity as vegetation becomes more abundant (Wickstrom

et al. 1984). This is also supported by the observation that active bouts became gradually

longer and inactive bouts decreased in length as summer progressed.

72

Diel activity pattern

For yearlings at both densities, the proportion of time spent active was higher at dawn but

especially at dusk, and lower during the night than during daytime. Peaks of activity at

dawn but especially at dusk have been largely documented in deer and are part of their

diurnal foraging-ruminating cycle (Kammermeyer and Marchinton 1977, Beier and

McCullough 1990). Foraging bouts usually occur following and before periods of darkness,

so deer may gather food in periods of daylight, when foraging is more profitable. As in

many other studies, we found that peaks of activity at dawn were not as constant as those at

dusk (Skogland 1983, Beier and McCullough 1990), and they probably occurred just after

dawn (Beier and McCullough 1990). The interval of 1h30 before and after sunrise may also

be too large to detect differences between dawn and daytime. We did not detect statistically

higher activity rates at dawn and dusk for adults, but activity was nonetheless 16 to 28%

higher at dusk than during other periods of the day (Table 2-3).

Conclusion

We expected that population density would influence deer foraging behavior as increased

density is often related to a decrease in forage abundance. Forage abundance did not differ

between 7.5 and 15 deer/km2 during the course of our study, and deer density had a limited

impact on deer movements and time-budgets. However, as demonstrated by inter-annual

differences in activity and activity budgets of free-ranging deer, when forage abundance is

reduced at high population density, deer activity budgets may change to compensate for a

diminution in available energy. Considering the behavioral plasticity of white-tailed deer, it

is not surprising that relationships between population density, plant quantity and foraging

behavior are complex. On one end, when deer density is high, plant biomass is low and

individual energy acquisition is limited by low forage abundance (Fowler 1981, Sæther et

al. 1996). As for deer under natural conditions on Anticosti Island, the length of inactivity

bouts are also longer because deer are limited in their digestive capabilities (Van Soest

1982). Evidences that energy acquisition is low for deer on Anticosti Island exist since deer

on Anticosti are 40% smaller (Boucher et al. 2004) and age at primiparity occurs one year

later than deer from the source population and other populations on the continent

73

(Goudreault 1980). We found that when forage increased in abundance deer increased the

length of activity bouts probably to select more digestible forage. On the other end, when

deer population density was low and forage abundant, deer increased net energy acquisition

by decreasing the length of activity bouts and increasing the number of daily activity bouts

per day. Controlled-density experiments could help to better understand the behavior of

herbivores in relation to available resources as they directly manipulate population density.

Acknowledgements

We thank L. Breton and B. Rochette from the Ministère des Ressources naturelles, et de la

Faune du Québec, as well as D. Duteau, F. Fournier, G. Picard, D. Sauvé, A. Simard, J.

Taillon, and J.-P. Tremblay for capturing deer. R. Pouliot, M. Renière, V. Viera and

especially J.-F. Therrien thankfully assisted to radiotrack deer. S. DeBellefeuille, C.

Dussault, M. Duteau, M.-A. Giroux, L. L’Italien, A. Massé, J. Taillon, J.-P. Tremblay, A.

Tousignant, and V. Viera helped with vegetation sampling. We are also most grateful to J.-

P. Tremblay for the establishment of the enclosures and biomass data. R. Weladji, S.

DeBellefeuille and many graduate colleagues reviewed an earlier draft of the manuscript.

We are also thankful to S. Baillargeon for help with the statistical analyses and to the

Centre d’études nordiques for computer support. This project was funded by Produits

forestiers Anticosti inc., the Natural Sciences and Engineering Research Council of Canada

and the Fonds québécois de la recherche sur la nature et les technologies.

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78

Chapitre 3. Influence of forage abundance, cover and

population density on white-tailed deer space use

79

Résumé

L’influence de la densité de population sur la répartition du cerf de Virginie dans l’espace

en relation avec la biomasse et le couvert disponibles a été mesurée dans 3 blocs formés de

2 enclos contenant respectivement 7.5 (faible densité) et 15 cerfs/km2 (haute densité). La

biomasse et le couvert latéral et vertical ont été interpolés par krigeage et les localisations

télémétriques des cerfs ont été divisées en 3 périodes quotidiennes. Pendant l’aube et le

crépuscule, aux deux densités, l’utilisation de l’espace était positivement reliée à la

biomasse de la végétation. À faible densité, la répartition des cerfs était aussi positivement

reliée au couvert vertical à l’aube et au crépuscule. Pendant le jour, la distribution des cerfs

était positivement reliée à la biomasse mais seulement à haute densité où les cerfs

réduisaient aussi l’utilisation d’endroits avec un couvert latéral dense. Cette étude souligne

l’influence de la densité de population et de la période du jour sur la répartition des cervidés

dans l’espace ainsi que la valeur des expériences en densités contrôlées et de la

géostatistique pour mieux comprendre les facteurs qui influencent le comportement

d’approvisionnement des grands herbivores.

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Abstract

Deer population densities are increasing in many areas of North America and Europe and

are strongly affecting species composition and structure of plant communities. The effects

of increased density on deer foraging behavior, however, have received little attention. We

assessed the influence of population density on deer space use in relation to vegetation

abundance and cover. We quantified space use in 3 blocks with 2 enclosures each

containing 3 radio-collared deer at densities of 7.5 (low density) and 15 deer/km2 (high

density), respectively. Vegetation biomass, canopy and lateral cover were interpolated by

kriging and deer observations were divided into 3 periods: dawn/dusk, day, and night. At

dawn and dusk, in both densities, space use was positively related to forage abundance.

During the day, deer space use was also positively related to forage abundance but only at

high density. At low density, habitat use was positively related to canopy cover during

dawn/dusk. Deer decreased the use of areas with dense lateral cover during the day at high

density, but no relationship was found at low density. Contrarily to our prediction, deer did

not use open habitats more frequently at night than during the day. This study underlines

the effect of population density and diel periods on the utilization of available resources and

the value of controlled density experiments and geostatistics to disentangle the factors

affecting foraging behavior of large herbivores.

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Introduction

In summer, northern ungulates typically devote most of their time to finding and consuming

food (Beier and McCullough 1990). Foraging decisions are thus important to an herbivore’s

energy and time budget. When food acquisition is the primary determinant of patch

selection, use of habitat patches should be positively related to forage availability

(MacArthur and Pianka 1966, McNamara et al. 1993). There are, however, several other

constraints such as heat or wind exposure (Belovsky 1981) and predation risk (Berger

1991) that can affect foraging behavior independently of forage characteristics. For cervids,

a good foraging site is usually represented by a trade-off between proximity to protective

cover and abundance of vegetation (Kotler et al. 1994).

Cover may be divided into two components: (1) the canopy cover corresponds to the

projection of the tree crowns to the ground and (2) the lateral cover which is made up of

concealing understory vegetation or topography. Lateral cover reduces predation risk and

thus time devoted to vigilance (Altendorf et al. 2001) and, in the absence of predators, has

been considered to play a “psychological” role in habitat selection related to the ghosts of

predators past (Byers 1997, Mysterud and Østbye 1999). Animals are also typically

exposed to milder weather conditions (e.g. temperature, wind, precipitations) in closed

habitats than in open habitats (Mysterud and Østbye 1999). Open sites generally offer more

abundant forage in summer (Hanley 1984), but present a higher risk of predation (Tufto et

al. 1996) and higher thermoregulatory costs due to an increased heat load (Beier and

McCullough 1990). Cervids thus often prefer feeding near edges of forests and open

habitats because edges minimize the trade-off between exposition to predators and/or harsh

weather, and forage abundance (Keay and Peek 1980, Tufto et al. 1996).

Clear-cuts provide open habitats that offer abundant food resources to deer and they are

usually interspersed with forest stands that present dense canopy cover but low forage

availability (Masters et al. 1993). Deer select cutblocks when clearings produce higher food

resources than forest stands and if they provide sufficient hiding cover (Lyon and Jensen

1980). Clearings have been used many times to enhance forage production and thus

improve deer habitat conditions (Masters et al. 1993). Tierson et al. (1985) found that deer

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stopped migrating to traditional winter ranges to feed in recently logged areas but summer

home ranges were not modified by the presence of cutblocks.

It has been proposed that population density modulates the trade-off between using habitats

rich in forage and habitats rich in cover (Mysterud and Østbye 1999) because population

density is generally negatively related to forage abundance (Healy et al. 1997) and

increases intraspecific competition (Clutton-Brock et al. 1982). Indeed, Lesage et al. (2000)

found that white-tailed deer generally increased the use of forests over agricultural fields

when competition for forage in forest stands was low but, as deer density increased, the use

of open areas increased, likely because forage was more abundant in open habitats. Another

study showed that because of the low availability of forage in the forest due to high

population density, deer adapted to feeding in agricultural crops at night (Rouleau et al.

2002). In agricultural landscapes, population density and landscape composition may thus

affect the degree to which deer feed on crops. An increase in the use of open habitats, such

as agricultural fields, in summer may indicate the low abundance of forage in areas with

adequate cover and the impacts of high density on space use (Mysterud and Østbye 1999).

Population densities of many cervid populations in North America are rapidly increasing

(Côté et al. 2004). Although of high interest, it is unknown whether increasing density is

modifying deer behavior, especially in relation to trade-offs between selection for forage or

cover in the context of landscapes composed of forests and clear-cuts.

Controlled-browsing experiments have been used for many years to study the foraging

behavior of domestic animals and their use is now advocated for wild ungulates (Hester et

al. 2000). Controlled-browsing studies generally investigate the effects of different

herbivore densities on vegetation abundance and diversity (Tilghman 1989, Hester et al.

2000); however, they can also be very useful tools to understand the effects of population

density on the foraging behavior of wild ungulates.

Deer use open habitats more often during the night than during the day (Beier and

McCullough 1990, Rouleau et al. 2002) and since their activity peaks at dawn and dusk

(Beier and McCullough 1990), the trade-off between using forage or cover patches may

83

also depend on diel periods (Mysterud et al. 1999). Therefore, our objective was to address

the influence of population density on deer space use in relation to vegetation abundance

and cover by experimentally controlling population density in large enclosures in which we

obtained 2 replicated densities. We also examined how daylight and activity peaks

influence the forage/cover trade-off by separating our radiolocations into 3 daily periods

(dawn/dusk, day, night). We predicted that deer at high density would use open areas of the

enclosures where forage is more abundant independently of cover characteristics. However,

deer at low density should use areas of the enclosures in relation to available cover because

competition for forage is less influential at low density.

Study area

Anticosti Island (Québec, Canada, 49° 28’ N, 63° 00’ W) is located at the northern fringe of

the white-tailed deer range in North America and covers 7,943 km2. Forests are naturally

dominated by balsam fir (Abies balsamea), white spruce (Picea glauca) and black spruce

(P. mariana). White birch (Betula papyrifera) and trembling aspen (Populus tremoloides)

are irregularly found on the island. About 220 deer were introduced on the island at the turn

of the 19th century. In the absence of predation, the population spread and grew rapidly.

Today, deer densities of >20 deer/km² are found in most areas on the island (Potvin and

Breton 2005). Deer have modified the original forest and greatly reduced the abundance of

deciduous woody vegetation on the island (Potvin et al. 2003, Tremblay et al. 2005). The

climate of Anticosti is maritime and characterized by longer and milder winters compared

to the white-tailed deer range on the continent (Huot 1982). Mean temperatures are -12°C

in January and 15°C in July, snow precipitation averages 406 cm annually and rainfall 630

mm (Environment Canada 1993).

Methods

Experimental design

Our experimental design consists in 3 sets of enclosures (A, B, C) in which we introduced

24 deer during 2 different years. Enclosures were located in balsam fir dominated forests

that were partially cut in the early summer of 2001. Water was easily accessible to deer at

84

many streams and artificial water holes in every enclosure. One block (A) was erected in

the western part of the island near Simonne Lake and two blocks (B and C) were erected

130 km to the east in the central part of the island near the Jupiter River. Between 30 and

40% of residual forest stands of different sizes (0.19 ha - 21.6 ha) were left in the

enclosures. In 2002, one site (A) was studied and in 2003, the 3 sites were studied. To test

the influence of deer density, the blocks were divided in two enclosures to obtain densities

of 7.5 deer/km2 (40 ha enclosure with 3 deer; LDE) and 15 deer/km2 (20 ha enclosure with

3 deer; HDE). We used different animals in 2002 and 2003 (Table 3-1).

Deer captures

In late June, we fitted 6 deer in 2002 and 18 deer in 2003 with VHF collars (LMRT series)

equipped with sto-2a variable pulse activity sensors (Table 3-1). We used different methods

to capture deer: dart guns, Stephenson box traps and cannon nets baited with cattle feed and

balsam fir twigs. Deer were released in the study enclosures shortly after capture. The

Animal Care and Use Committee of Université Laval, Québec, Canada (2005-008)

approved all capture methods. We verified reproductive status of adult females by direct

observation at capture and at the end of summer. Since only 2 monitored females had a

fawn, we did not include reproductive status in the analyses.

Telemetry

In July and August 2002, we radiotracked 6 deer in block A and in July and August 2003,

we radiotracked 16 deer in the 3 blocks (Table 3-1). One adult male lost its collar and 1

yearling male had a malfunctioning collar. Deer were located with telemetric receivers

(SRX-400 version W9, Lotek Engineering, Newmarket, Ontario, Canada and a TR-2

scanner/receiver, Telonics, Meza, Arizona, USA), unidirectional antennas and compasses.

Telemetry stations were positioned with a GPS Garmin (Garmin international, Olathe,

Kansas, USA; precision of <5 m) on trails adjacent to the enclosures. To limit human

disturbance, trails were generally located more than 100 m away from the enclosures. At

least 3 azimuths differing by a minimum of 30º were obtained by moving between stations

with a vehicle (White and Garrott 1990). To reduce location error, positioning had to be

completed within 15 minutes (White and Garrott 1990). 24-h days were evenly divided

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Table 3–1. Number of locations recorded in each diel period for radiocollared white-tailed

deer tracked in controlled-density enclosures on Anticosti Island, Québec.

Number of locations

ID Year Block Density Age Sex Dawn/dusk Day Night

(deer/km²)

1 2002 A 7.5 Adult Male 71 124 55

2 Yearling Male 68 125 56

3 Yearling Female 52 108 37

4 15 Yearling Female 66 117 62

5 Yearling Female 69 123 58

6 Yearling Female 73 125 56

7 2003 A 7.5 Adult Female 24 46 15

8 Yearling Female 20 43 21

9 Adult Female 22 50 18

10 15 Adult Female 27 47 19

11 Adult Male 25 38 18

12 Yearling Male 0 0 0

13 2003 B 7.5 Yearling Female 21 44 16

14 Yearling Male 19 48 17

15 Adult Female 20 42 17

16 15 Adult Female 19 46 21

17 Yearling Female 17 48 16

18 Adult Female 22 46 16

19 2003 C 7.5 Yearling Female 24 41 22

20 Adult Female 28 43 21

21 Yearling Male 22 34 26

22 15 Adult Male 24 43 21

23 Yearling Female 21 46 21

24 Adult Male 0 0 0

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into 3 periods of 8 hours (8h00–16h00, 16h00-0h00 and 0h00–8h00). These 8-hour periods

were evenly sampled and rotated between 2 observers and between groups of enclosures

every 3 days. During sampling periods of 8 hours, deer were positioned about every 2

hours.

LOAS software (Location Of A Signal Version 2.07, Ecological Software Solutions,

Schwägalpstrasse, Urnäsch, Switzerland) was used to estimate positions and error

polygons. Error polygons were calculated with “Andrews” estimator. All locations were

plotted with LOAS software on maps and were assigned Universal Transverse Mercator

(UTM) coordinates. The average error from plotted to actual locations was determined by

using control transmitters set at known locations throughout the enclosures and was

estimated at 107 m (SE = 88 m; n = 88 trials). We removed locations with error polygons

greater than 0.01 ha. After processing, we kept 2,916 locations from the 3,251 original

locations. Positions were assigned to 3 diel-periods: dawn and dusk (1h30 before sunrise to

1h30 after sunrise, and 1h30 before sunset to 1h30 after sunset), day (1h30 after sunrise to

1h30 before sunset), and night (1h30 after sunset to 1h30 before sunrise).

Biomass and cover sampling

To characterize uniformly the vegetation of the enclosures, 5 sampling points were drawn

with the « Generate-randomly distributed points » extension of ArcView GIS (ArcView

GIS Version 3.1, Environmental systems research institute, Redlands, California, USA) in

every 2 ha squares of a grid superposed to each enclosure. We found the sampling stations

in the field with a GPS Garmin. At each sampling point, percent of plant cover was

estimated in two 1-m2 quadrats randomly chosen in a 10×10 m quadrat centered at the

sample point. In block A, the same sampling points were used in 2002 and 2003.

Plant biomass was estimated for every major plant component of deer diet and the most

abundant species on Anticosti (Huot 1982) using regressions between percent of plant

cover and mass of the corresponding dried plant biomass (Bonham 1989). We analysed the

following species: Abies balsamea, Betula papyrifera, Cirsium spp., Coptis groenlandica,

Cornus canadensis, Epilobium angustifolium, Grass sp., Hieracium sp., Maianthemum

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canadense, Picea glauca, Rubus idaeus, Rubus pubescens and, Trientalis borealis. The

number of samples needed for regressions was estimated empirically by plotting regression

coefficients with number of samples until an asymptote was reached (Frontier 1983). We

summed the biomass values of all plants for each quadrat and used the mean value of the

two quadrats for each sample point in the analyses.

At each sampling point, canopy cover was estimated by vertically projecting foliage (>4 m

trees) to 20 points distributed equally on the ground every 3 m in four directions (east,

southeast, southwest and west) from the center of the sampling unit. Each point was judged

as with cover or not and canopy cover corresponded to the sum of all sampled directions

(value of 1 for each point with cover). Lateral cover was measured with a cover board (2.5

m×0.3 m divided in 0.5 m sections) in 2 opposite directions by attributing board

concealment to 4 classes (1: 0-25; 2: 26-50; 3: 51-75; 4: 76-100%; Nudds 1977). We used

the mean value from the first two sections of the board (0-1 m) and values from both

directions were averaged.

Analyses

Data points could not account for spatial relationships and spatial correlation between

biomass abundance and cover. We thus estimated biomass and cover abundances with a

geostastistical software (Geostatistical analyst; ArcMap 9.0, Environmental systems

research institute, Redlands, California, USA). Geostatistics is a branch of applied statistics

that focuses on the detection, modeling, and estimation of spatial patterns in spatially

correlated data (Rossi et al. 1992). Spatial autocorrelation occurs because samples collected

closer to each other are more similar than samples collected farther apart. This particularly

occurs when the variable sampled is spatially structured (e.g. in patches). In their simplest

form, geostatistics involve 2 steps: 1) characterizing the spatial structure of the variable

with a variogram, thus defining the degree of autocorrelation between the data points; and

2) predicting values between measured points based on the estimated degree of

autocorrelation (Robertson 1987). Semivariance is the average measure of the variance

associated to any two sampled points in a given distance class. For example, with a lag size

of 50 m, a mean variance value would be obtained for each distance class (0-50 m, 51-100

88

m, 101-150 m, etc.). In spatially structured data, changes of semivariance represented in a

variogram usually augment with increasingly separated points (Cressie 1993). Variograms

may also include a directional value to weight for directional changes in the factor (e.g.

slope direction; Cressie 1993). The semivariogram models provide the following

parameters: 1) the nugget effect which indicates the residual spatial variability below the

lag size that cannot be modeled with the current sampling resolution, 2) the sill, which

defines the asymptotic value of semivariance; and 3) the range, defined as the distance over

which autocorrelation is present (Cressie 1993).

Semivariograms were prepared individually for forest stands and cuts of each enclosure

because vegetation and cover drastically change between these two habitats (Masters et al.

1993). Biomass values were log-transformed to normalize data. By trial and error, we

determined the best fit of spherical variograms until a maximum lag distance of 125 m (i.e.,

half of the minimum enclosure dimension) in 5-m increments and in all directions (Jurado-

Expòsito et al. 2004). The best-fitted values, determined by cross-validation results, of

nugget, sill and range were calculated and recorded for further analysis in ordinary kriging

(Cressie 1993). Ordinary kriging is an interpolation technique that uses observed values

associated with X and Y coordinates and estimates values for all locations within the

sampled coordinates with the help of a variogram describing how values change through

space (Cressie 1993). We used kriging values to validate the fitted variogram through cross-

validation. This procedure is based on the systematic removal of observations, one by one,

from the raw data set, which is then estimated by kriging (Isaaks and Svriastava 1989).

Kriging provides an error term for each estimated value, thus giving a measure of reliability

for the interpolations. Biases in estimation errors were evaluated using the standardised root

mean squared error (Appendix 3−1; RMSE; Isaaks and Svriastava 1989). The nugget value

divided by the total variance (sill) gives an estimation of the spatial dependence (Appendix

3−1; Jurado-Expòsito et al. 2004).

Rettie and McLoughlin (1999) recommended the use of buffers to account for telemetry

error in habitat selection studies. In our study, the error of locations obtained by telemetry

was 107 m on average and thus larger than most forest stands present in the enclosures. In

89

70% of the trials with hidden collars at known locations, collars were located less than 100

m away from the real position. Furthermore, up to 100 m, the probability of obtaining a

position closer to the real position was not a function of distance (F1,9 = 0.11; P = 0.75).

We thus took into account the mean location error by placing a 100 m buffer around each

location. Since the buffers were quite large compared to the size of the enclosures, they

overlapped considerably and were thus dependent on one another. To take this into account,

we randomly placed 1 point in every 150×150-m square of a grid placed over each

enclosure with the extension “Simple random sample” (ArcView GIS, Version 3.1,

Environmental systems research institute, Redlands, California, USA). We used 150×150-

m squares because they were sufficiently large compared to the buffer size and allowed for

a reasonable number of sampling points for the regression (20-30 points). For each of the 3

diel periods, we counted the total number of overlapping buffers at each point for each deer

with the extension “dissect overlaps” (ArcView GIS, Version 3.1, Environmental systems

research institute, Redlands, California, USA). We then divided the total number of

overlapping buffers for each random point by the total number of locations for this deer

during each diel period to describe a relative use of the enclosures that was independent of

the number of positions taken on an individual.

Resources and relative use (mean relative number of overlapping buffers per diel-period)

were evaluated at random points distributed across the total surface of the enclosures. We

estimated relationships between relative use and resource abundance by means of

regression analyses. We did not consider resource availability in the models because a

random and independent sample of points were already considered in the analysis. For each

diel period, the relationship between relative use for all deer in a particular enclosure and

biomass, lateral and canopy cover was quantified using a linear mixed model with block

and year as random factors (Proc Mixed, SAS Version 9.1, SAS Institute Inc., Cary, North

Carolina, USA). Correlation coefficients between factors and absolute values were all <0.4,

we thus included all variables in the model. We performed regressions and then compared

slopes between densities and determined if slopes differed from 0 for each density. As deer

may often use sites with intermediate biomass because plants of the early phenological

90

stages have more nutrients and offer a high rate of energy intake (Stewart et al. 2000), we

also tested if the relationship between biomass abundance and mean relative use was

parabolic and if it varied between densities. We did not have enough animals in every

block, density and year to include sex in the models, but we contend that sex may be taken

into consideration in further studies because space use in relation to food or cover may vary

between sexes (Beier and McCullough 1990, DePerno et al. 2003).

To compare resource abundance between densities and stratum (residual forest stands or

clear-cuts), we compared the mean values of total biomass, vertical cover and lateral cover

available at the random points located on the sampling grid with an analysis of variance

with block and year as random factors. As only one block was studied for two years, year

could not be treated as a fixed effect. We verified the normality of the residuals and the

homogeneity of variance by visual examinations of the residual plots. Significance levels

were 0.05.

Results

Spatial analysis

The relationships between space and biomass, lateral cover and canopy cover varied

between forests and cuts, and between enclosures (Appendix 3−1). RMSE values ranged

from 0.27 to 2.38 but most values were close to 1 ( x = 1.01; SE = 0.30; n = 48 models;

Appendix 3−1), indicating that variability was generally not biased towards higher or lower

values than those measured. The nugget effect was greater than zero in many cases,

indicating that observations separated by small distances were highly variable (Isaaks and

Srivastava 1989; Appendix 3−1). A general feature of the variograms was the relatively

close nugget and C values (i.e. the variance explained by the spatial variation in the data;

Appendix 3−1), indicating that spatial autocorrelation was imperceptible in some cases.

Spatial relationships, however, provided a good representation of how biomass

(Figure 3-1), lateral (Figure 3-2) and canopy cover (Figure 3-3) varied within clear-cuts and

forest stands and allowed us to examine how

91

Forage abundance (g/m²)

0 – 20

20 – 60

60 – 140

140 – 230

230 – 380

a) Dawn/dusk relative use

0.00 – 0.02

0.02 – 0.06

0.06 – 0.10

0.10 – 0.18

0.18 – 0.30

0 100 20050 Meters

b) Daylight relative use

0.01 – 0.03

0.03 – 0.05

0.05 – 0.08

0.08 – 0.13

0.13 – 0.18

92

Figure 3–1. Plant biomass (g/m²) available to white-tailed deer and interpolated by kriging

in 2003 for Block A on Anticosti Island, Québec. Semivariogram values are in

Appendix 3−1. Relative use was measured in every 150×150 m square of a grid superposed

on the Block by drawing a random sampling point and calculating its relative use (i.e. the

number of overlapping buffers for a deer divided by the total number of positions for that

deer in every diel-period) at this location. Use was averaged for the deer at 7.5 deer/km²

and at 15 deer/km² during dawn and dusk (a), during the day (b) and at night (c).

c) Nighttime relative use

0.00 – 0.02

0.02 – 0.04

0.04 – 0.07

0.07 – 0.11

0.11 – 0.18

93

b) Daylight relative use

0.01 – 0.03

0.03 – 0.05

0.05 – 0.08

0.08 – 0.13

0.13 – 0.18

a) Dawn/dusk relative use

0.00 – 0.02

0.02 – 0.06

0.06 – 0.10

0.10 – 0.18

0.18 – 0.30

Lateral cover (/4)

0.40

0.4 – 0.8

0.8 – 1.2

1.2 – 1.6

1.6 – 2.0

2.0 – 2.4

0 100 20050 Meters

94

Figure 3–2. Lateral cover, or mean concealment (attributed to 4 classes 1: 0-25; 2: 26-50; 3:

51-75; 4: 76-100%) of the first 2 sections of a concealment board (2.5 m×0.3 m divided in

0.5 m sections) in 2 opposite directions, available to white-tailed deer and interpolated by

kriging in 2003 for Block A on Anticosti Island, Québec. Semivariogram values are in

Annex 3−1. Relative use was measured in every 150×150 m square of a grid superposed on

the block by drawing a random sampling point and calculating its relative use (i.e. the

number of overlapping buffers for a deer divided by the total number of positions for that

deer in every diel-period) at this location. Use was averaged for the deer at 7.5 deer/km²

and at 15 deer/km² during dawn and dusk (a), during the day (b) and at night (c).

c) Nighttime relative use

0.00 – 0.02

0.02 – 0.04

0.04 – 0.07

0.07 – 0.11

0.11 – 0.18

95

b) Daylight relative use

0.01 – 0.03

0.03 – 0.05

0.05 – 0.08

0.08 – 0.13

0.13 – 0.18

a) Dawn/dusk relative use

0.00 – 0.02

0.02 – 0.06

0.06 – 0.10

0.10 – 0.18

0.18 – 0.30

0 100 20050 Meters

Canopy cover (/20)

0.0 – 0.7

0.7 – 2.4

2.4 – 4.9

4.9 – 8.5

8.5 – 13.7

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Figure 3–3. Canopy cover, or proportion of 20 points set at every 3 m from the center of

each sampling unit in 4 directions (east, southeast, southwest and west) where foliage of

>4 m trees was present, available to white-tailed deer and interpolated by kriging in 2003

for Block A on Anticosti Island, Québec. Semivariogram values are in Annex 3−1. Relative

use was measured in every 150×150 m square of a grid superposed on the block by drawing

a random sampling point and calculating its relative use (i.e. the number of overlapping

buffers for a deer divided by the total number of positions for that deer in every diel-period)

at this location. Use was averaged for the deer at 7.5 deer/km² and at 15 deer/km² during

dawn and dusk (a), during the day (b) and at night (c).

c) Nighttime relative use

0.00 – 0.02

0.02 – 0.04

0.04 – 0.07

0.07 – 0.11

0.11 – 0.18

97

these variables could be related to the utilization of the enclosures by deer.

Descriptive statistics

Vegetation abundance was negatively related to canopy cover at both densities (LDE:

r = -36, P < 0.01, n = 98 random points; HDE: r = -0.21, P = 0.02, n = 60 random points).

Lateral cover, however, was not significantly related to biomass abundance at both

densities (LDE: r = 0.16, P = 0.11, n = 98 random points; HDE: r = 0.15, P = 0.24, n = 60

random points). Biomass in the enclosures did not vary with density but, as expected, was

greater in clear-cuts than under forest cover (Table 3-2). Lateral and canopy cover were

positively correlated in the low-density enclosures (r = 0.36, P < 0.01, n = 98 random

points) but not in the high-density enclosures (r = 0.14, P = 0.27, n = 60 random points).

Lateral and canopy cover did not differ among the enclosures but obviously, canopy cover

was more important in forest stands than in cuts. Relative use was higher in the high-

density enclosures than in the low-density enclosures (Figure 3-4), likely because the

enclosures at high density were twice smaller than at low density. Relative use between all

3 diel periods were correlated at both densities (LDE; r’s > 0.47, P’s < 0.01; HDE;

r’s > 0.46, P’s < 0.01).

Deer space use

At dawn and dusk, relative use increased with biomass at both densities and the relationship

between relative use and canopy cover differed between densities (Table 3-3). The slope

parameter was positive for deer in low-density enclosures (t144 = 2.66; P < 0.01) but was

not different from 0 for deer in high-density enclosures (t144 = -1.25; P = 0.21; Figure 3-4c),

indicating that deer at low density used sites with dense canopy cover more often than deer

at high density. In no diel periods or densities did the quadratic term of biomass was related

to mean relative use (F’s1, 142 < 2.89; P’s > 0.09). At dawn and dusk, deer did not select

areas with denser lateral cover at any density (Table 3-3). During the day, the slope

parameter relating deer space use to biomass abundance for deer in high-density enclosures

was positive (t144 = -1.70; P = 0.09), but the slope for deer in low-density enclosures did not

differ from zero (t144 = -1.13; P = 0.25; Figure 3-4d), indicating that deer at high density

used more often sites with high biomass during the day than sites with low

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Figure 3–4. Relationships between white-tailed deer relative space use (number of

overlapping buffers for a deer divided by the total number of positions for that deer in every

diel-period and at each random point) and plant biomass, lateral (mean concealment value

attributed by 4 classes of 25%) and canopy cover (proportion of 20 points where foliage of

>4 m trees was present). Results are shown for dawn and dusk (a to c), day (d to f) and

night (g to i). Data for biomass and cover were obtained by kriging and taken at one

random location in each 150×150 m square of a grid superposed on enclosures containing

7.5 deer/km² (solid circles and line) or 15 deer/km² (empty circles and dashed line) on

Anticosti Island, Québec. For each diel period, regression lines were obtained from mixed

models relating relative use to biomass, canopy cover and lateral cover for each density,

with block and year as random factors (statistics are in Table 3-3).

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Table 3–2. Mean biomass (g/m²), lateral covera (/20 points) and canopy coverb (/4; ± SD) according to deer density and stratum

(forest stands and clear-cuts). Deer were kept in 3 sets of enclosures with 2 densities each on Anticosti Island, Québec.

ANOVAs

Density (deer /km²) Clear-cut Forest Factor DF F Value P-value

a) Biomass 7.5 81.3 ± 38.6 29.6 ± 12.1 Density 1,3 0.17 0.71

15 68.9 ± 20.2 34.1 ± 4.4 Stratum 1,6 20.10 <0.01

Density × Stratum 1,6 0.76 0.42

b) Lateral cover 7.5 2.7 ± 0.9 3.0 ± 0.8 Density 1,3 7.30 0.07

15 3.1 ± 0.8 3.3 ± 0.4 Stratum 1,6 2.13 0.19

Density × Stratum 1,6 0.04 0.84

c) Canopy cover 7.5 1.0 ± 0.4 9.8 ± 3 Density 1,3 1.00 0.39

15 1.8 ± 3.0 6.8 ± 2 Stratum 1,6 38.06 <0.01

Density × Stratum 1,6 2.97 0.14

a Mean concealment value (attributed to 4 classes 1: 0-25; 2: 26-50; 3: 51-75; 4: 76-100%) of the first 2 sections of a

concealment board (2.5 m×0.3 m divided in 0.5 m sections) in 2 opposite directions.

b Proportion of 20 points set at every 3 m from the center of each sampling unit in 4 directions (east, southeast, southwest and

west) where foliage of >4 m trees was present.

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Table 3–3. White-tailed deer relative space usea according to biomass, canopy cover and

lateral cover at 2 different densities (7.5 and 15 deer/km²) in a controlled-density

experiment on Anticosti Island, Québec for each diel period.b

Source of variation Parameter Std DF F-value P-value

a) Dawn/dusk Density -0.1350 0.0827 1,3 2.66 0.201

Biomass 0.0006 0.0004 1,14 3.82 0.053 *

Biomass × densityc -0.0002 0.0045 1,14 0.16 0.691

Lateral cover -0.0207 0.0222 1,14 2.36 0.127

Lateral cover × density 0.0007 0.0265 1,14 0.000 0.980

Canopy cover -0.0005 0.0039 1,14 0.31 0.580

Canopy cover × density 0.0124 0.0039 1,14 6.66 0.011 **

b) Day Density -0.1727 0.0685 1,3 6.36 0.086

Biomass 0.0006 0.0004 1,14 0.97 0.326

Biomass × density -0.0008 0.0004 1,14 4.10 0.045 **

Lateral cover -0.0355 0.0184 1,14 0.77 0.382

Lateral cover × density 0.05171 0.0219 1,14 5.56 0.020 **

Canopy cover -0.0017 0.0032 1,14 0.37 0.544

Canopy cover × density 0.0009 0.0040 1,14 0.06 0.810

c) Night Density -0.0466 0.1044 1,3 0.20 0.699

Biomass 0.0003 0.0006 1,14 0.81 0.370

Biomass × density -0.0001 0.0006 1,14 0.03 0.853

Lateral cover -0.0049 0.0279 1,14 1.03 0.311

Lateral cover × density -0.0234 0.0334 1,14 0.52 0.471

Canopy cover -0.0050 0.0049 1,14 0.02 0.902

Canopy cover × density 0.0092 0.0061 1,14 2.29 0.132

* P < 0.10, ** P < 0.05

aRelative space use represents the total number of overlapping buffers divided by the total

number of positions taken on a particular deer for each of the 3 diel periods at one point in

every 150×150 m square of a grid superposed on each enclosure.

bThe relationships between use and biomass, lateral and canopy cover were quantified

using linear mixed models with block and year as random factors.

cInteraction parameters represent the differences between the slopes at 7.5 deer/km² and

15 deer/km².

101

but not deer at low density. Space use was not related to canopy cover at any density during

the day. The slope for deer space use in relation to lateral cover during the day did not

differ from 0 at low density (t144 = 1.35; P = 0.18), but was negative for deer at high density

(t144 = -1.93; P = 0.05; Table 3-3, Figure 3-4e). This revealed that deer at high density,

compared to deer at low density, decreased the use of sites with dense lateral cover during

the day. At night, relative space use did not vary with deer density, biomass, canopy or

lateral cover (Table 3-3, Figure 3-4g to i).

Discussion

An increase in population density generally leads to a decrease in the abundance of plant

species preferred by deer (Healy et al. 1997) and an increase in intraspecific competition

(Clutton-Brock et al. 1982). To our knowledge, our study is the first to manipulate

population density to test its influence on deer use of forage and cover resources. As

observed in agricultural landscapes (Lesage et al. 2000, Rouleau et al. 2002), we predicted

that deer would use more frequently open habitats than closed habitats at high density

because more forage was available in open areas and thus competition was likely lower

than in closed habitats. At dawn and dusk, deer used space in relation to forage abundance

at high density. Deer at low density used space in function of biomass, but also used sites

with high canopy cover during dawn and dusk. During the day, deer space use was also

positively related to forage abundance at high density but decreased as a function of lateral

cover. It thus seems that the use of cover by deer differed with density. We predicted that

deer would use open habitats more often at night than during the rest of the day because

foraging in open habitats is then less costly in terms of thermoregulation costs (Parker and

Gillingham 1990) and because deer are more secure in covered areas during daylight

(Kufeld et al. 1988, Naugle et al. 1997). Contrarily to our prediction, however, deer did not

use areas of higher biomass and lower canopy cover at night.

Deer space use in relation to plant biomass and cover

Resource distribution, such as forage and cover, is known to affect the spacing patterns of

ungulates (e.g. Bowyer et al. 1998, DePerno et al. 2003, Palmer et al. 2003). The

102

abundance of available biomass may determine the time a herbivore spends in a particular

patch (MacArthur and Pianka 1966, McNamara et al. 1993) and when energy acquisition is

the predominant activity, more time should be devoted to patches where biomass is high

(Wickstrom et al. 1984). In cutblocks, other studies reported that white-tailed deer space

use was related to forage abundance (Stewart et al. 2000, Rothley 2002). Similarly, Cimino

and Lovari (2003) found that removing food in clearings incited roe deer to use woodlots

instead of open habitats.

In most studies, deer spent proportionally more time feeding during the hours surrounding

sunrise and sunset than during the day or at night (Beier and McCullough 1990).

Accordingly, at dawn and dusk, we found that deer space use was positively associated to

forage abundance at both densities. In a given environment, foraging in areas of higher

biomass benefits deer by allowing a higher rate of energy intake and, because these patches

are usually more profitable, deer can stay longer in areas where forage is more abundant

(Wickstrom et al. 1984). When forage is limited, cervids increase the use of habitats that

have a greater abundance of forage to increase the rate of energy acquisition (Van der Wall

2000, Dussault et al. 2005).

Compared to open habitats, forested habitats are cooler in summer and daily variations in

temperature are lower than in open habitats, providing adequate thermal and radiation cover

that allow animals to minimize thermoregulatory costs (Parker and Gillingham 1990). At

dawn and dusk, deer space use was therefore also positively related to canopy cover, at

least at low density. In our study, forage biomass was negatively related to the amount of

canopy cover at both densities (Table 3-2). Differences among slopes of relative use at low

and high densities suggest that deer used space according to forage abundance and canopy

cover at low density, but deer at high density did not use areas of dense canopy cover.

Studies have shown that white-tailed deer are capable of simultaneously considering

multiple habitat factors and circumstantially adjusting their behavior (Schmitz 1992,

Naugle et al. 1997, Rothley 2002). Possibly, this compromise could not be achieved at high

density because increased competition forced deer to stay in open areas where forage was

readily available. In agreement with findings in agricultural-forest landscapes (Lesage et al.

103

2000, Rouleau et al. 2002), it seems that population density affects the use of resources in

forested areas.

During daytime, deer space use was positively related to forage biomass at high density but

not at low density. Using areas with high biomass of forage thus seemed as important

during the day as during dawn and dusk for deer at high density, but deer at low density

decreased their use of open areas with high forage biomass during daytime, possibly to

reduce the costs of exposition to high radiation. Beier and McCullough (1990) found that

deer often used open areas with low canopy cover in summer because of tall grasses and

lateral cover that provided deer with a protection from harsh weather or, in their study,

predation. Deer at high density in our study, however, used areas in relation to forage

abundance and reduced utilization with increasing lateral cover, confirming our prediction

that deer habitat use is based more strongly on forage biomass than on cover as population

density increases.

Many authors reported that deer use of open habitats is higher at night than during daytime

because deer are safer in darkness (Kufeld et al. 1988, Naugle et al. 1997). Deer in our

study, however, did not use open areas more often at night at any density. Consequently,

lateral and canopy cover on Anticosti Island seem to be more important for

thermoregulation in daytime than at night. Cover removal had a strong effect on diurnal

locations, but not on the night locations of female roe deer because of the lower impact of

an increase in visibility during the night than during the day (Cimino and Lovari 2003).

Rothley (2002) also observed that interspersion of cover and forage was important for

habitat utilization of white-tailed deer in presence of hunters, but that in the absence of

disturbance, only forage abundance was an important factor. Surprisingly, however, space

use in our study was not related to forage abundance at night at any density.

Limitations and strengths of the study

Sampling of habitat characteristics in deer habitat use studies is generally restricted by time

and labour and, consequently, only a small portion of the study areas can be described in

details. For that reason, researchers often estimate mean biomass and available cover as one

104

value each for all different habitat types (Mysterud et al. 1999) and assume that vegetation

and cover are distributed homogeneously over each habitat (Sweeney et al. 1984). In fine-

scale habitat studies, however, means cannot account for heterogeneity present in the

environment. A better representation of the environment can be achieved by taking

surrounding sampling points into account (Turner and Gardner 1995). For example, as in

our study, patches of high forage abundance may occur in small forest openings or patches

of denser canopy (Figure 3-1) or lateral cover may remain in clear-cuts (Figure 3-2). The

high variability in the distribution of biomass and cover data (Figure 3-4) reveals the

heterogeneous distribution of resources and how a mean value for clear-cuts and for forest

stands would bias the results. The use of geospatial GIS software to account for spatial

relationships is of major interest for fine-scale habitat studies (Turner and Gardner 1995),

but still has received little attention. Even if a drawback of such a method in large study

areas is that the estimated interpolation errors increase as the distance between sampled

points augments, for sample points adequately distributed spatially, this method may

provide additional insights into ungulate foraging behavior and fine-scale habitat use

studies.

There is also an inherent error associated with radiotelemetry and if radiolocations are

taken as exact locations, this may introduce a bias in the data (White and Garrott 1990,

Rettie and McLoughlin 1999). It has been recommended to use visual observations to study

fine-scale space use (Rettie and McLoughlin 1999); however, for white-tailed deer and

other hard to observe species, this is generally not possible without disturbing their natural

behavior. As suggested by Rettie and McLoughlin (1999), the use of buffers allowed us to

consider telemetry error and to develop a spatial representation of how deer utilized the

enclosures. Our approach gave conservative results as the relationships were based on

spatial relationships and not on individual data points distributed through space.

The use of controlled density experiments has proven useful in describing how

communities respond to known herbivore densities (Tilghman 1989, deCalesta 1994,

Hester et al. 2000, McShea and Rappole 2000, Horsley et al. 2003) and has lead to a better

understanding of critical herbivore densities thresholds allowing vegetation regeneration

105

(Hester et al. 2000). No study has used, however, these controlled browsing experiments to

investigate the role of population density on the behavior of large herbivores. Although

based on a small number of deer, the use and replication of enclosures with controlled

densities allowed us to test directly the influence of known population density on deer

foraging behavior.

Considering diel-periods was also essential to assess space use in relation to forage

abundance and cover because space use varied during the 24-h period. During the day, deer

space use was positively related to forage abundance but only at high density. Deer at high

density restrained their use of areas with greater lateral cover. It thus appears that

population density influences the use of available habitat constraining deer at higher

densities to feed in areas with lower canopy or lateral cover but higher food resources than

deer at lower densities.

In the long-term, forest exploitation likely benefits most cervid populations by increasing

forage availability and quality (Ford et al. 1994). Deer at high-densities may use clear-cuts

as crop fields for foraging, irrespectively of available cover. This, however, may be costly

for the forest industry, especially if deer eat seedlings of commercial species (Côté et al.

2004). The use of large clear-cuts as a management strategy to dissuade deer from

browsing in open areas may not be a sufficient method to limit browsing (Potvin and

Laprise 2002), as lateral cover may also be used for protection.

Acknowledgments

We thank L. Breton and B. Rochette from the Ministère des Ressources naturelles et de la

Faune du Québec, as well as D. Duteau, F. Fournier, G. Picard, D. Sauvé, A. Simard, J.

Taillon, J.-F. Therrien and J.-P. Tremblay for help capturing deer. R. Pouliot, M. Renière,

J.-F. Therrien and V. Viera thankfully assisted to radiotrack deer and S. Debellefeuille, C.

Dussault, M.-A. Giroux, A. Massé, J. Taillon, J.-P. Tremblay, A. Tousignant, and V. Viera

helped for vegetation sampling. We are also indebted to J.-P. Tremblay for the

establishment of the enclosures. R.B. Weladji, S. DeBellefeuille and many graduate

colleagues reviewed an earlier draft of the manuscript. We are also thankful to S.

106

Baillargeon, D. Fortin and K. Lowell for help with statistical analyses. This project was

funded by Produits forestiers Anticosti inc. and the Natural Sciences and Engineering

Research Council of Canada.

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Conclusion générale

Cette étude a permis d’évaluer les effets de la densité de population sur le comportement

d’approvisionnement et le budget d’activité d’un grand herbivore, le cerf de Virginie.

D’abord, nous avons validé l’utilisation des capteurs d’activité pour mesurer le budget

d’activité des cerfs munis de colliers VHF (Coulombe et al. 2006). Nous nous sommes

ensuite intéressés aux déplacements ainsi qu’au budget d’activité des cerfs adultes et

juvéniles et nous avons étudié leur variabilité selon la densité, le nombre d’années après le

début de l’expérience de densité contrôlée, ainsi qu’au cours de l’été et de la journée. Enfin,

nous avons mesuré l’utilisation des enclos par les cerfs afin de mieux comprendre comment

la densité de population influence l’utilisation de l’espace en fonction du couvert et de la

nourriture disponibles.

Validation des capteurs d’activité

Afin d’étudier l’influence de la densité de population sur le budget d’activité, nous devions

d’abord valider l’utilisation des capteurs d’activité horizontaux à impulsions variables des

colliers VHF à l’aide d’observations directes. Au début, nous avons tenté de discriminer

différents comportements actifs (p.ex. cerfs en alimentation vs. en déplacement) et inactifs

(p.ex. cerfs en comportement de repos vs. en vigilance) mais tel que démontré dans d’autres

études (Gillingham et Bunnell 1985, Beier et McCullough 1988, Hansen et al. 1992, Relyea

et al. 1994), nous n’avons pu différencier ces comportements en se basant sur les données

obtenues par télémétrie. Les comportements ont donc simplement été séparés en

comportements actifs ou inactifs. Les données télémétriques individuelles étaient

correctement classifiées en comportements actifs et inactifs dans 74% des cas et

introduisaient donc des erreurs dans la quantification des périodes d’activité. En intégrant

l’information de 3 données d’activité successives, nous avons correctement identifié 84%

des comportements actifs et inactifs et 87% des périodes d’activité. Nous avons donc

conclu que les capteurs d’activité horizontaux utilisés pourraient décrire avec suffisamment

de précision les budgets d’activité des cerfs à l’étude. Cependant, à l’aide des capteurs

d’activité à deux axes des colliers GPS qui ont été validés simultanément, il nous a aussi été

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possible de déterminer que des capteurs d’activité orientés verticalement seraient plus

précis dans l’évaluation des budgets d’activité.

Les déplacements et le budget d’activité

Dans un milieu donné, en absence de prédateurs, l’abondance des ressources contrôle

généralement le comportement d’approvisionnement. La flexibilité des comportements

permet aux individus de faire face à une diminution de l’abondance des ressources causée

par une augmentation de la densité de population (Cederlund et al. 1989, Beier et

McCullough 1990, Borkowski 2000).

Tel que mentionné dans l’introduction générale, nous voulions vérifier les hypothèses

suivantes : 1) le comportement d’approvisionnement (déplacements et budget d’activité)

des cerfs est déterminé par l’abondance de la nourriture qui diminue avec l’augmentation

de la population. Les cerfs répondent à la diminution de l’abondance de la nourriture à

haute densité en modifiant leur budget d’activité possiblement de façon (a) à sélectionner la

végétation de meilleure qualité même si celle-ci est moins abondante en augmentant le

temps de recherche de la végétation et le taux de déplacement ou (b) les cerfs répondent à

la diminution de l’abondance de la nourriture à haute densité en augmentant le temps passé

à la rumination de la végétation de moindre qualité, ce qui accroit la durée des périodes

d’inactivité. Cette étude est la première à tester ces hypothèses en contrôlant pour la densité

de population dans des enclos de façon expérimentale.

Les taux de déplacement des cerfs ne différaient pas entre les densités contrôlées. Rouleau

et al. (2002) avaient trouvé que les taux de déplacement différaient entre des individus se

trouvant en milieu forestier et ceux en milieu agricole à différentes densités mais les

différences étaient reliées à des différences écologiques entre les milieux. Dans notre étude,

les cerfs étaient placés dans des milieux adjacents et écologiquement comparables et malgré

les densités différentes, nous n’avons pas trouvé de différences dans le taux de

déplacement. Cependant, notre étude s’est déroulée dans un milieu récemment perturbé par

une coupe forestière. Les différences de biomasse entre les années étaient donc plus

grandes qu’entre les densités. Nous n’avons pu détecter de différences en ce qui concerne la

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biomasse disponible entre les densités contrôlées puisque les densités n’étaient pas

suffisamment différentes et parce que l’expérience n’avait pas été appliquée pendant assez

longtemps. Les conclusions d’une étude connexe suggèrent que des différences de

biomasse entre les densités se manifestent lorsque l’écart entre les densités est plus grand

(Tremblay 2005). Néanmoins, le taux de déplacement n’était pas différent entre la première

et la deuxième année d’application du traitement de densité contrôlée même si l’abondance

de végétation avait augmentée. De plus, les changements dans la quantité de biomasse entre

les densités n’étaient pas assez grands dans notre étude pour affecter directement la plupart

des caractéristiques du budget d’activité des cerfs. Cependant, les densités contrôlées (7.5

et 15 cerfs/km²) étaient plus faibles que la densité naturelle habituellement présente sur l’île

(>20 cerfs/km²).

En effet, pour les juvéniles, aucune différence n’a été trouvée dans la proportion du temps

passé en activité, la durée des périodes d’activité ou d’inactivité et le nombre de périodes

d’activité par jour entre les densités. Par ailleurs, nous avons trouvé que les adultes dans les

enclos à 7.5 cerfs/km² passaient une plus grande proportion de temps en activité par jour

que ceux à 15 cerfs/km². Nous avons également observé que les juvéniles passaient plus de

temps en activité que les adultes, mais seulement à haute densité. Cependant, nous

n’excluons pas la possibilité que nos conclusions soient influencées par un rapport

mâle/femelle différent entre les traitements.

La végétation disponible dans les coupes était plus abondante la deuxième et la troisième

année que la première année après le début de l’expérience de densité contrôlée. Bien que

les cerfs passaient une proportion totale de temps en activité équivalente durant les trois

années, nous avons trouvé qu’avec une augmentation de l’abondance de la végétation, la

répartition du temps actif a changé. En effet, la durée des périodes d’activité a diminué au

cours des années tandis que la durée des périodes d’inactivité et le nombre de périodes

d’activité par jour ont augmenté. Lorsque la biomasse a augmenté, les cerfs pouvaient donc

remplir leur rumen plus rapidement avant de débuter une période de rumination, ce qui

pourrait expliquer l’augmentation du nombre de périodes d’activité 3 ans après le début de

l’application du traitement (Moncorps et al. 1997). Lorsque la biomasse a augmenté au

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cours des années, les cerfs pouvaient remplir leur rumen en moins de temps et donc

commencer plus rapidement une période de rumination. Ce changement dans le budget

d’activité avec l’augmentation de la biomasse permettrait aux cerfs de gagner 25% de plus

en masse corporelle pendant l’été par rapport aux cerfs en densité naturelle (Simard et al.,

en prép). Nous avons aussi trouvé que la durée des périodes d’inactivité a diminué au cours

des années. Bien que le temps passé à ruminer augmente généralement en fonction du

temps passé inactif lorsque la qualité de la végétation diminue (Mysterud 1998, Pérez-

Barbería and Gordon 1999), ceci ne s’applique peut-être pas lorsque la végétation de bonne

qualité est abondante (S. Hamel, comm. pers.). Les herbivores augmenteraient alors la

durée des périodes inactives en restant au repos sous couvert sans nécessairement

augmenter le temps de rumination parce qu’ils auraient ainsi davantage de temps disponible

grâce à l’abondance de végétation pour diminuer l’exposition aux conditions

environnementales plus stressantes des milieux ouverts.

À Anticosti, la saison de croissance de la végétation débute à la fonte des neiges entre le

début et la mi-mai (Ressources naturelles Canada 2005). Les nouvelles pousses sont riches

en protéines et facilement digestibles (Van der Wall et al. 2000). Au cours de l’été, la

végétation augmente en lignine et sa concentration en protéines décroît (Tremblay 1981) et,

en conséquence, la digestibilité des plantes diminue (Robbins 1983, Van Soest 1994). Pour

tous les cerfs, les taux de déplacements étaient semblables au cours de l’été. Cependant, les

adultes et les juvéniles ont répondu différemment aux changements saisonniers dans la

qualité et l’abondance de la végétation. En effet, la durée des périodes d’inactivité a

augmenté chez les juvéniles à haute densité. Les adultes à faible densité ont

progressivement diminué la proportion du temps passé en activité au cours de l’été. Puisque

l’abondance de la végétation augmente durant l’été, les cerfs ont eu besoin de moins de

temps pour chercher et ingérer la végétation. De plus, il est possible que l’abondance de la

nourriture à partir du mois de juillet excédait la biomasse nécessaire pour les besoins

énergétiques des cerfs et ceux-ci auraient donc pu rencontrer leurs besoins énergétiques en

moins de temps (Beier et McCullough 1990).

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Jusqu’au début du mois d’août, les cerfs en densité naturelle ont passé davantage de temps

à ruminer et en conséquence, ils ont passé moins de temps en activité par jour et avaient des

périodes d’inactivité plus longues que les cerfs à faible densité. En densité naturelle, les

cerfs sont nombreux et les jeunes pousses disparaissent rapidement. Les cerfs en densité

naturelle auraient donc eu accès à de la nourriture moins abondante et probablement de

moins bonne qualité que ceux à des densités contrôlées. Lorsque la végétation est devenue

plus abondante au cours de l’été, les cerfs en densité naturelle ont augmenté la proportion

du temps passé en activité. L’augmentation de la proportion du temps passé en activité

pourrait être reliée à une augmentation de leur sélectivité. Ceci est aussi appuyé par le fait

qu’au cours de l’été, la durée des périodes d’activité a augmenté et la durée des périodes

d’inactivité a diminué.

Pour les juvéniles dans les deux densités contrôlées, la proportion du temps passé en

activité était plus élevée à l’aube et au crépuscule et moins élevée durant la nuit que

pendant le jour et l’aube. Des pics d’activité au cours de la journée ont été rapportés dans

plusieurs études et font partie du cycle d’activité des cerfs (Kammermeyer et Marchinton

1977, Beier et McCullough 1990). Ils représentent des périodes d’alimentation intenses en

prévision ou suivant une période de noirceur pendant laquelle la recherche de nourriture est

probablement moins efficace. Comme dans plusieurs études, les pics au crépuscule étaient

plus constants que ceux à l’aube (Skogland 1983, Beier et McCullough 1990). Comme

nous avons considéré un intervalle équivalent de 1h30 après et avant le lever du soleil,

n’avons pas détecté de pic d’activité après l’aube comme d’autres études (Beier et

McCullough 1990). Aucune différence statistique de l’activité en fonction de la période de

la journée n’a été trouvée pour les adultes. Cependant, comme pour les juvéniles, la

proportion du temps d’activité était de 16 à 28% plus élevée au crépuscule que pendant les

autres périodes de la journée.

Le compromis couvert/nourriture

L’augmentation de la densité de population dans un endroit est généralement reliée à une

diminution de l’abondance des espèces préférées par le cerf (Healy et al. 1997) et à une

augmentation de la compétition intraspécifique (Clutton-Brock et al. 1982). Nous pensions

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que les cerfs utiliseraient davantage les milieux ouverts à haute densité puisque, bien que

les coûts reliés à la thermorégulation y soient plus élevés, la végétation y est plus abondante

et donc la compétition moins intense qu’en milieu fermé. De plus, puisque l’utilisation de

l’espace peut varier selon la période du jour (Cimino et Lovari 2003), nous avons pris en

compte ce facteur.

En général, les cerfs passent proportionnellement plus de temps à s’alimenter à l’aube et au

crépuscule (Beier et McCullough 1990). Il est donc énergétiquement plus avantageux pour

eux de passer davantage de temps à ces moments dans des endroits où la nourriture est

abondante (Wickstrom et al. 1984). À l’aube et au crépuscule, dans les deux densités, les

cerfs utilisaient l’espace en fonction de l’abondance de la végétation. Les cerfs à faible

densité utilisaient aussi, pendant ces périodes, des endroits où le couvert arborescent était

abondant. Les habitats fermés, pendant l’été, sont plus frais que les milieux ouverts et

présentent un couvert qui minimise les coûts de thermorégulation (Parker et Gillingham

1990). Dans notre étude, la quantité de biomasse était négativement reliée à la densité du

couvert vertical dans les deux densités. Ainsi, il semble que les cerfs à faible densité

conciliaient l’utilisation de nourriture et de couvert mais les cerfs à haute densité utilisaient

l’espace uniquement en fonction de la quantité de nourriture. Le compromis n’était

probablement pas possible à haute densité parce que la compétition pour la nourriture était

plus élevée et poussait les individus à utiliser davantage des milieux ouverts pour se nourrir

car la quantité de nourriture y était plus grande. De plus, contrairement aux cerfs à faible

densité, les cerfs à haute densité utilisaient l’espace pendant le jour en fonction de

l’abondance de nourriture. Aussi, les cerfs à haute densité pendant le jour évitaient les sites

où le couvert latéral était dense pour se nourrir dans des endroits où l’abondance de

nourriture était plus élevée.

Nous supposions que les cerfs utiliseraient davantage les endroits découverts la nuit

puisque le comportement d’approvisionnement y est moins coûteux en termes de

thermorégulation (Parker et Gillingham 1990) et parce que le risque d’être repéré par un

prédateur est plus faible (Kufeld et al. 1988, Naugle et al. 1997). Cependant, pendant la

nuit, les cerfs n’utilisaient pas l’espace en fonction de l’abondance de nourriture ou de

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couvert. Le couvert latéral et vertical semblaient donc plus importants pendant le jour,

l’aube et le crépuscule lorsque les coûts de thermorégulation sont plus importants. Rothley

(2002) a aussi trouvé que l’abondance de nourriture entremêlée de milieux fermés était

importante lorsque les coûts reliés à l’utilisation des milieux ouverts étaient élevés, mais

qu’en absence de dérangements seule l’abondance de nourriture était importante.

Cependant, nous n’avons pas trouvé que l’abondance de nourriture influençait l’utilisation

de l’espace pendant la nuit.

L’inventaire des habitats utilisés par le cerf est souvent limité par des contraintes de temps

et de main d’œuvre. Pour cette raison, les chercheurs estiment souvent une valeur moyenne

par catégorie d’habitat (Mysterud et al. 1999) et supposent que la biomasse et le couvert

disponibles sont homogènes à travers cet habitat (Sweeney et al. 1984). Cependant, pour les

études fines de sélection d’habitat, les moyennes ne peuvent représenter l’hétérogénéité

spatiale présente dans l’environnement et une meilleure représentation peut être obtenue en

tenant compte des relations spatiales existant entre plusieurs points d’échantillonnage

(Turner et Gardner 1995). Par exemple, nous avons trouvé une grande variabilité dans les

données à cause de la présence d’arbres laissés dans la coupe ou d’ouvertures dans les

forêts qui laissent entrer la lumière et favorisent la croissance des végétaux. Vu leurs petites

tailles, il est possible d’estimer les ressources disponibles dans l’ensemble des enclos à des

intervalles réguliers et ainsi de décrire des zones représentatives de la biomasse et du

couvert latéral et vertical disponible à l’aide des méthodes géostatistiques. Il existe aussi

une erreur associée aux localisations télémétriques et si l’on considère les localisations sans

en tenir compte, cela peut donner des résultats erronés (White et Garrott 1990, Rettie et

McLoughlin 1999). Il a été recommandé d’utiliser des observations directes pour faire des

études fines d’utilisation de l’espace (Rettie et McLoughlin 1999); cependant, pour le cerf

de Virginie et plusieurs espèces de cervidés, ceci n’est généralement pas possible sans

déranger leur comportement naturel. Tel que recommandé par Rettie et McLoughlin

(1999), nous avons donc utilisé des pastilles d’erreurs de taille correspondante à l’erreur de

télémétrie. L’utilisation de ces nouvelles techniques nous a permis d’obtenir des résultats

novateurs, mais conservateurs quant à l’utilisation des enclos à différentes densités.

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Conclusions et recommandations

Nous avions prédit que la densité de population influencerait le comportement

d’approvisionnement puisqu’elle est reliée à une diminution de l’abondance de la

végétation. Cependant, les déplacements et le budget d’activité étaient semblables entre les

densités. À une densité de 15 cerfs/km², les cerfs avaient un budget d’activité et un taux de

déplacement semblable à ceux à 7.5 cerfs/km². Cependant, les adultes à 15 cerfs/km² n’ont

pas diminué leur activité pendant l’été comme ceux à 7.5 cerfs/km².

Selon les différences interannuelles et les budgets d’activité observés des cerfs en densité

naturelle, on peut supposer que si la densité de population affecte suffisamment

l’abondance et la qualité de la végétation, alors les cerfs modifieront leur comportement

afin de compenser pour la diminution ou l’augmentation de la quantité d’énergie

disponible. En effet, selon nos résultats, il existe une série de changements

comportementaux reliés à une diminution de la quantité de végétation due à une

augmentation de la densité de population. Dans un milieu où la végétation est peu

abondante, les cerfs réduisent d’abord le temps passé en activité pour ruminer plus

longtemps la végétation plus fibreuse. Ensuite, lorsque la biomasse augmente en réponse à

une diminution de la densité, les cerfs deviennent plus sélectifs et augmentent le temps

passé en activité. Finalement, la durée des périodes d’activité diminue à nouveau lorsque la

biomasse devient si abondante que le temps nécessaire pour remplir le rumen diminue. Les

cerfs à haute densité, contrairement aux cerfs à faible densité, n’utilisaient pas pendant

l’aube, le crépuscule et la journée des milieux où le couvert était plus abondant.

L’utilisation de l’espace serait donc aussi modifiée par la densité de population. En effet,

l’utilisation des milieux ouverts augmenterait à haute densité lorsque la compétition pour la

nourriture est plus élevée.

Maintenant, on peut se demander si, et à combien de cerfs/km2, la densité affectera les traits

d’histoire de vie du cerf de Virginie car malgré sa grande capacité d’adaptation, il est limité

par ses capacités de digestion puisqu’il doit ruminer (Van Soest 1982). Des études

s’intéressant au taux d’acquisition des réserves corporelles à différentes densités

permettraient de connaître si le taux d’acquisition des réserves est réduit à ≥15 cerfs/km².

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Certains indicateurs démontrent que les densités élevées de cerfs retrouvées à l’île

d’Anticosti ont entraîné des impacts sur les traits d’histoire de vie de celui-ci. En effet, en

comparaison aux cerfs de la population source du continent, les cerfs d’Anticosti sont 40%

plus petits (Boucher et al. 2004) et les femelles se reproduisent pour la première fois un an

plus tard (Goudreault 1980).

Généralement, les coupes forestières de petite superficie avantagent les populations de

cervidés en leur procurant davantage de nourriture à proximité du couvert (Ford et al.

1994). Cependant, l’utilisation de grandes coupes pour réduire le broutement du cerf dans

des zones où le couvert vertical est éloigné ne semble pas être une solution satisfaisante

pour limiter l’utilisation des coupes par le cerf à Anticosti (Potvin et Laprise 2002). La nuit

et le couvert latéral fourni par les grandes graminées sont des moyens alternatifs au couvert

vertical que peuvent utiliser les cerfs pour s’alimenter en milieu ouvert. À Anticosti, cela

semble être le cas puisque des relevés ont montré que les semis de sapin (Abies balsamea)

sont fortement broutés et ce même en été et jusqu’au centre de grandes coupes situées à

plus de 800 m de la bordure de la forêt (Potvin et Laprise 2002).

Le cerf de Virginie est vraisemblablement une espèce plastique qui peut rapidement

s’adapter aux changements d’abondance et de qualité de la végétation. Il a trouvé le moyen

de subsister à l’île d’Anticosti, dans des conditions où beaucoup d’autres grands herbivores

ne pourraient subsister à de telles densités. En effet, malgré la faible végétation disponible,

il accumule suffisamment de réserves pour survivre aux hivers rigoureux qui sévissent à

l’île en modifiant son comportement d’approvisionnement.

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Appendix 3−1. Spatial statistical data of biomass (a) lateral cover (b) and canopy cover (c)

in cuts and forests of enclosures containing different densities of white-tailed deer on

Anticosti Island, Québec.

Year Block Density Stratum Na Rangeb C0c Cd RMSEe C-Ratiof

(deer/km²) (m)

a) biomass 2002 A 7.5 Cutg 87 245 1.07 1.08 0.96 0.50

Forest 22 240 1.19 1.18 0.85 0.50

15 Cut 30 280 0.00 1.46 1.92 0.00

Forest 21 250 1.07 1.13 0.82 0.49

2003 A 7.5 Cut 87 280 0.91 0.16 0.51 0.85

Forest 21 180 0.27 0.07 0.78 0.80

15 Cut 30 250 0.87 0.00 0.58 1.00

Forest 21 150 0.78 0.51 0.27 0.60

2003 B 7.5 Cut 53 120 1.01 1.16 0.97 0.46

Forest 46 420 0.07 0.06 1.10 0.57

15 Cut 26 300 0.76 0.21 0.77 0.79

Forest 34 300 0.38 0.16 1.00 0.71

2003 C 7.5 Cut 61 315 0.38 0.51 0.71 0.43

Forest 33 180 0.56 0.00 1.22 1.00

15 Cut 27 350 0.89 0.00 0.73 1.00

Forest 27 315 0.23 0.14 0.88 0.62

b) lateral cover 2002 A 7.5 Cut 87 420 1.06 0.71 1.07 0.60

Forest 22 280 10.1 2.66 1.12 0.79

15 Cut 30 250 0.45 0.01 1.03 0.98

Forest 21 180 6.34 17.8 0.92 0.26

2003 A 7.5 Cut 87 240 0.00 6.63 2.38 0.00

Forest 21 180 13.5 3.42 1.01 0.80

15 Cut 30 420 0.16 0.14 1.19 0.54

Forest 21 180 6.04 17.9 0.92 0.25

2003 B 7.5 Cut 53 350 1.64 3.45 1.21 0.32

Forest 46 500 19.2 7.42 0.97 0.72

15 Cut 26 420 0.20 0.14 1.12 0.59

Forest 34 300 11.5 11.8 1.03 0.49

2003 C 7.5 Cut 61 350 2.04 1.41 1.07 0.59

Forest 33 600 12.6 4.37 1.00 0.74

15 Cut 27 500 0.32 0.76 1.26 0.29

Forest 27 660 5.89 28.7 1.10 0.17

c) canopy cover 2002 A 7.5 Cut 86 360 1.03 0.40 0.99 0.72

Forest 22 180 0.47 0.66 1.02 0.41

15 Cut 30 200 0.64 0.00 1.01 1.00

Forest 21 276 0.82 0.77 0.92 0.52

127

Year Block Density Stratum Na Rangeb C0c Cd RMSEe C-Ratiof

(deer/km²) (m)

2003 A 7.5 Cut 87 350 0.61 0.28 1.02 0.68

Forest 21 150 0.00 1.50 0.86 0.00

15 Cut 30 150 0.85 0.00 0.99 1.00

Forest 21 120 1.00 0.52 0.90 0.66

2003 B 7.5 Cut 53 450 0.22 0.49 0.98 0.31

Forestg 46 300 0.28 0.32 0.98 0.47

15 Cut 26 300 0.43 0.00 1.07 1.00

Forest 34 300 0.27 0.30 1.00 0.47

2003 C 7.5 Cut 61 420 0.31 0.27 1.01 0.54

Forest 33 350 0.32 0.35 1.09 0.48

15 Cut 27 300 0.10 0.09 0.93 0.53

Forest 27 420 0.08 0.25 1.08 0.25 a Number of sample points where biomass, canopy and lateral cover were measured and

used to quantify the semivariogram and cross-validations.

b Range (m) is the distance over which spatial autocorrelation was detected.

c The nugget effect (C0) or the inherent data variability below which the minimum lag

distance cannot be modelled with the current sampling resolution.

d The variance (C) associated to the spatial variability in the data.

e Biases in estimation errors evaluated by the standardised root mean squared error (RMSE)

from the cross-validation analysis. The RSME should be close to 1 if the predicted standard

errors are valid. If the RSME is greater than 1, the variability is underestimated in the

predictions. If the RSME is less than 1 the variability is overestimated.

f The C-ratio, given by the nugget value divided by the total variance (sill or (C0+C)),

defines the asymptotic value of semivariance and gives an estimation of the dependence

between estimated values of biomass or cover and their position in space. A ratio lower

than 0.25 usually represents values that are highly spatially correlated, a ratio between 0.25

and 0.75 usually corresponds to values that are moderately correlated and a ratio near 1

represents values that are not spatially correlated (Jurado-Expòsito et al. 2004).

g These models were developed with directional semivariograms (direction of 350º) as they

increased the fit of the estimated values.