Utilisation hivernale des ravages du cerf de Virginie · 2018-04-17 · selection may influence white-tailed deer winter survival. In this study, I showed that the influence of habitat

ÉMILIE ALLARD

UTILISATION HIVERNALE DES RAVAGES DE CERFS DE VIRGINIE

Mémoire présenté à la Faculté des études supérieures de l’Université Laval

dans le cadre du programme de maîtrise en sciences forestières pour l’obtention du grade de maître es sciences (M.Sc.)

DÉPARTEMENT DES SCIENCES DU BOIS ET DE LA FORÊT FACULTÉ DE FORESTERIE ET DE GÉOMATIQUE

UNIVERSITÉ LAVAL QUÉBEC

2009 © Émilie Allard, 2009

Résumé

La sélection d’habitat et les mouvements sont parmi les caractéristiques les plus étudiées en

écologie animale puisqu’ils font partie intégrante de la dynamique et de la distribution des

populations. En effet, la sélection d’habitat peut influencer la survie des cerfs de Virginie

en hiver. Dans cette étude, j’ai montré que cette influence semble être plus importante à

grande échelle, indiquant par le fait même son plus grand potentiel pour influencer la

survie, d’où l’importance d’aménager adéquatement les ravages de cerfs de Virginie à

grande échelle. Les cerfs ont également démontré une sélection pour les chemins

primaires, influence qui s’est répercutée sur les mouvements. Ainsi, les cerfs ont diminué

la distance parcourue lorsqu’ils se trouvaient à proximité des chemins lorsque les

mouvements sont étudiés à fine échelle. De plus, l’utilisation d’intervalles de temps

continus pour les points GPS, une méthode innovatrice, a permis de découvrir que, peu

importe l’intervalle de temps étudié, les cerfs maximisent l’utilisation de l’habitat tout en

minimisant leurs déplacements en revenant fréquemment sur leur pas.

ii

Abstract

Habitat selection and movements are among the most studied parameters in wildlife

ecology since they influence population distribution and its dynamics. Indeed, habitat

selection may influence white-tailed deer winter survival. In this study, I showed that the

influence of habitat on survival appears to be more important at broad scale, thus indicating

its greater potential to influence survival. This demonstrates the importance of adequately

managing white-tailed deer yards at a broader scale. Deer also showed a selection for

primary roads, an influence that was reflected on movements. They lowered their distance

moved when movements were occurring near roads when these were studied at a fine

temporal scale. Also, the use of a continuous-time interval for GPS fixes, a novel sampling

strategy we used here, demonstrated that no matter the time interval used, white-tailed deer

maximize their habitat use while minimizing displacements since they tend to reverse their

direction.

Remerciements

Mener à bien un projet d’une telle envergure, de l’achat des colliers GPS à leur installation

sur les cerfs jusqu’à leur récupération l’année suivante, aurait été une tâche impossible sans

l’aide de nombreuses personnes. Tout d’abord, je tiens à remercier mon directeur Eliot

McIntire qui a toujours été présent pour répondre à mes questions, qu’il soit en Italie ou

dans un chalet inaccessible des Rocheuses. De même, sa rapidité à corriger les premières

versions des chapitres a grandement contribué à la réussite de ce projet dans les délais

prescrits. Je veux aussi remercier mon co-directeur Jean-Pierre Tremblay pour ses

commentaires tout au long du projet ainsi qu’à la toute fin lors des dernières corrections.

Ce projet a grandement été facilité par la collaboration de Georges Laferrière qui a

gentiment mis à ma disposition du personnel et du matériel du MRNF. Ainsi, j’ai pu faire

la connaissance de Jean-Pascal Trudeau et de Marianne Moffat qui m’ont apporté une aide

précieuse lors des captures de cerfs. Ces derniers étaient toujours prêts pour une petite

séance de captures, même un 2 janvier. De bénévoles et d’aides sur le terrain, ils sont

devenus des amis. Plusieurs autres, tels que François Éthier, Antoine Ste-Marie et Éric

Parent ont aussi apporté une aide indispensable.

Je tiens aussi à remercier Josh Nowak, notamment pour la construction des boîtes, les

nombreuses discussions et la révision des chapitres. Je remercie aussi l’ensemble des

étudiants composant le méta-lab qui ont rendu la vie au laboratoire plus facile. Finalement,

la dernière personne mais non la moindre que je tiens à remercier est mon conjoint, David

Poulin. Sans lui et son aide sur le terrain et dans les moments de découragement, ce projet

aurait été autrement plus fastidieux.

Table des matières

Résumé.....................................................................................................................................i

Abstract.................................................................................................................................. ii

Remerciements...................................................................................................................... iii

Table des matières .................................................................................................................iv

Liste des tableaux...................................................................................................................vi

Liste des figures ................................................................................................................... vii

Introduction générale ..............................................................................................................8

Do resource selection functions tell us who lived and who died?.......12

Résumé..................................................................................................................................13

Abstract.................................................................................................................................14

Introduction...........................................................................................................................15

Materials and methods ..........................................................................................................17

Study area .........................................................................................................................17 Deer capture and monitoring ............................................................................................20 Land use and vegetation data............................................................................................20 Resource Selection............................................................................................................21

Scales of analysis ..........................................................................................................22 Landscape scale ........................................................................................................22 Home range...............................................................................................................22 Daily movements ......................................................................................................22

Individual variation.......................................................................................................23 Model selection and analysis ............................................................................................23 Resource selection and survival........................................................................................25

Results...................................................................................................................................25

Resource Selection............................................................................................................27 Landscape scale ............................................................................................................27 Home range...................................................................................................................27 Daily movements ..........................................................................................................28 Resource selection and survival....................................................................................29

Discussion.............................................................................................................................31

Management implications.................................................................................................33

v

Acknowledgements...............................................................................................................33

Bibliography .........................................................................................................................34

Appendix...............................................................................................................................38

The use of irregular time intervals to detect movement bouts and the influence of primary roads ...................................................................40

Résumé..................................................................................................................................41

Abstract.................................................................................................................................42

Introduction...........................................................................................................................43

Materials and methods ..........................................................................................................45

Study area .........................................................................................................................45 Deer capture and monitoring ............................................................................................48 Road data ..........................................................................................................................48 Analysis ............................................................................................................................49

Observations .................................................................................................................49 Parameter estimates ......................................................................................................49

Results...................................................................................................................................50

Distance ............................................................................................................................51 Angles ...............................................................................................................................53

Discussion.............................................................................................................................55

Acknowledgements...............................................................................................................58

Bibliography .........................................................................................................................58

Conclusion générale..............................................................................................................61

Bibliographie ........................................................................................................................63

Liste des tableaux

Table 1 - Description of environmental variables from GIS layers used in the models for

white-tailed deer in the Lake David yard, Quebec, Canada (2008)..............................24

Table 2 - Resource selection functions at three spatial scales for 11 white-tailed deer in the Lake David yard, Quebec, Canada (2008)....................................................................28

Table 3 - T-test summary for winter RSF meta-analysis for 16 white-tailed deer in the Lake David yard, Canada, 2009.............................................................................................29

Table 4 - Distance to primary roads for each white-tailed deer monitored in the Lake David yard, Canada, 2009 .......................................................................................................51

Table 5 - Parameter estimates for a biased correlated random walk indicating the presence of a breakpoint with a time interval of 66 minutes for white-tailed deer in the Lake David yard, Canada (2009). ..........................................................................................54

Table 6 - Parameters of a biased correlated random walk with a time interval of 66 minutes for trios inside the breakpoint distance getting closer to primary roads for white-tailed deer in the Lake David yard, Canada (2009). ...............................................................55

Table 7 - Variation in deviation of turning angles for white-tailed deer in the Lake David yard, Québec, 2008 .......................................................................................................55

Liste des figures

Figure 1 - Study area for white-tailed deer in the Lake David yard, 130km north of Ottawa,

Canada. Light grey lines are the major highways........................................................19

Figure 2 – Example of two different overwintering area use for white-tailed deer in the Lake David yard, Canada (2009). Green circles represent locations from a deer that survived to the winter and black triangles represent locations from a deer that died. Black, red and dotted black lines represent, respectively, primary, secondary and unused roads..................................................................................................................26

Figure 3 - Relative probability of presence for white-tailed deer at the landscape scale in the Lake David yard, Canada (2008). .................................................................................30

Figure 4 - Study area for white-tailed deer in the Lake David yard, 130km north of Ottawa, Canada. Light grey lines are the major highways.........................................................47

Figure 5 – Distribution of move lengths and deviation in turning angle inside and outside the breakpoint value of 116m for a time interval of 66 min (a and b respectively). Lognormal distributions were fit to move length data and Von Mises distributions were fit to deviation in turning angle data. Bars are the observed densities................52

Figure 6 – Mean move lengths, calculated with lognormal distribution for white-tailed deer winter movements with varying time interval sampling. Move length for the 66 minutes time interval was calculated with all trios, without considering the 116m zone. Error bars represent the 95% confidence interval. .............................................53

Introduction générale

Pourquoi les organismes vivants sont-ils là où ils sont? Ceci est une des questions

auxquelles les écologistes n’auront de cesse de répondre, examinant encore plus en détail et

en profondeur les multiples facettes du problème. En effet, cette simple question est

beaucoup plus complexe qu’elle n’y parait. La répartition des organismes mobiles, tels que

les espèces animales, est notamment influencée par la qualité et la quantité des habitats

disponibles. Ceci est reflété par la sélection d’habitat, correspondant à une utilisation plus

ou moins prononcée d’un type d’habitat particulier en comparaison avec une utilisation

aléatoire (Johnson, 1980).

Or, il existe d’innombrables méthodes d’analyse de sélection d’habitat. Au fil des

décennies et avec le développement de nouvelles technologies, les méthodes d’analyse de

sélection d’habitat se sont grandement diversifiées et améliorées. Ainsi, différents types

d’indice de sélectivité ou de table de contingence ont été utilisés au cours des années 1960

et 1970 (Alldredge and Ratti, 1986). Cependant, les conclusions pouvant être tirées de ces

méthodes peuvent être fortement influencées par ce que le chercheur prétend être

disponible pour l’animal en question. Johnson (1980) a proposé une nouvelle méthode où

il ordonne les types de ressources étudiées par ordre de préférence, la préférence étant

établie comme la différence entre le rang de la ressource utilisée et celui de sa disponibilité.

Par la suite, l’apparition des colliers émetteurs a permis d’analyser encore plus en détail la

sélection d’habitat. Toutefois, cette nouvelle technologie a aussi ses problèmes tels que

décrits par Aebischer et al. (1993). Aujourd’hui, une des méthodes les plus utilisées

consiste à effectuer des fonctions de sélection des ressources. Cette méthode, développé

par Manly et al. (2002), utilise notamment la régression logistique pour comparer les

ressources utilisées aux ressources non-utilisées ou jugées disponibles. Diverses variables

environnementales peuvent être incorporées dans ces modèles, de même que d’autres

informations telles que le risque de prédation. Alliant la capacité de quantifier l’importance

relative des ressources à une grande possibilité d’intrants disponibles, cette analyse permet

de mieux comprendre les interactions entre l’espèce étudiée et son habitat.

La dispersion affecte également la distribution des espèces en permettant l’exploration de

nouveaux territoires. Celle-ci peut toutefois être freinée ou améliorée par l’hétérogénéité,

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la fragmentation et la connectivité de l’habitat, chacun pouvant augmenter les coûts reliés à

la dispersion (Bélisle, 2005; Schtickzelle et al., 2007). La dynamique de population,

résultant entres autres des taux de reproduction et de survie, sera également affectée par la

sélection d’habitat (Pulliam and Danielson, 1991). Plusieurs études ont été faites à petites

échelles, autant spatiale que temporelle, telle que celle de Loegering et Fraser (1995) où les

couvées de pluvier siffleur (Charadrius melodus) ont eu un taux de survie plus élevé

lorsqu’elles se trouvaient à l’intérieur de l’île plutôt qu’en bordure de mer. Aussi, Klaver et

al. (2008) ont divisé les cerfs de Virginie (Odocoileus virginianus) suivis en deux groupes,

un groupe avec une survie hebdomadaire élevée et un autre faible, pour ensuite les relier à

des caractéristiques d’habitat différentielles. Par contre, les études établissant la relation

entre la performance individuelle et l’utilisation non aléatoire de l’habitat à plus grande

échelle demeurent encore peu communes (McLoughlin et al., 2007). Ces derniers ont

cependant fait le lien entre le succès reproducteur total des femelles de cerfs européens

(Capreolus capreolus) et les emplacements de leur domaine vital durant la période des

soins maternels tout au long de leur vie. Il serait donc possible d’influencer positivement

ou négativement la dynamique d’une population en manipulant certaines caractéristiques de

son habitat à fine ou à grande échelle.

La distribution d’une population résulte donc d’un ensemble de facteurs concomitants où

l’habitat joue un rôle central. Or, les habitats et les écosystèmes qui en découlent ont

depuis toujours été sujets au changement, que ce soit d’origine naturelle ou anthropique.

Plus récemment, le réchauffement climatique a modifié certains habitats pour permettre

l’établissement de nouvelles espèces autrefois inadaptées aux conditions initiales

(Parmesan and Yohe, 2003). L’être humain a aussi contribué, entres autres, en développant

d’importants réseaux routiers ou en faisant de l’exploitation forestière (Forman and

Alexander, 1998). Ces modifications anthropiques peuvent, selon l’espèce, contribuer à

son expansion ou plutôt la menacer.

Le cerf de Virginie est une des nombreuses espèces ayant su s’adapter à la nature

changeante de son écosystème grâce, en partie, à sa nature généraliste. En effet, de

nombreux endroits en Amérique du Nord ont eu une augmentation de la population de cerf

de Virginie plus forte que prévue au cours des dernières années (Huot et al., 2002).

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Plusieurs attribuent cette hausse à l’augmentation de la quantité de nourriture disponible,

conséquence directe de l’exploitation forestière (Waller and Alverson, 1997). Au Québec,

les conditions climatiques plus clémentes, la modification de l’habitat et l’augmentation

subséquente des populations a fait en sorte de repousser la limite nordique de l’aire de

répartition des cerfs. Cette espèce étant considérée comme un herbivore clé (Miller et al.,

2003; Côté et al., 2004) capable d’altérer les écosystèmes puisqu’il peut sélectionner et

concentrer sa prise de nourriture sur seulement quelques espèces végétales (Schmitz and

Nudds, 1994), la régénération forestière commence à en subir les conséquences. Une

augmentation du risque de collision avec les voitures et du risque de transmission du vers

des méninges (Parelaphostrongylus tenuis) à l’orignal sont autant d’autres effets possibles

(Jenouvrier et al., 2003). Cependant, l’augmentation de la population a permis de

développer l’industrie de la chasse dans de nouvelles régions, faisant du même coup

profiter l’économie locale. La nature changeante des écosystèmes et les conséquences qui

en découlent démontrent toute l’importance qu’il faut accorder à la compréhension de

l’influence de ces changements.

La région des Hautes-Laurentides, située à 130km au nord de la ville d’Ottawa, n’a pas fait

exception à cette augmentation de population suivie de la colonisation de territoire situé

plus au nord. Cependant, la taille de ces populations plus nordiques peut être soumise à

d’importantes variations inhérentes à leur localisation. Ainsi, les populations situées aux

extrémités de leur aire de répartition ou dépendantes de conditions particulières d’habitat

sont caractérisées par une plus grande sensibilité aux conditions climatiques (DelGiudice et

al., 2002). Par exemple, le taux de mortalité observé dans une population de cerfs du

Minnesota peut varier entre 17 et 46%, selon la sévérité de l’hiver (Mech et al., 1987;

DelGiudice et al., 2002). En effet, pour les ongulés tels que le cerf de Virginie, la saison

hivernale est considérée comme une période présentant un taux élevé de mortalité en raison

d’un risque accru de prédation et de famine (Parker et al., 1984; Dumont et al., 1998).

C’est à cette période que les cerfs se regroupent en ravage, ce dernier possédant des

caractéristiques particulières d’habitat pouvant amoindrir l’impact de l’hiver. La

diminution de la qualité et de la quantité de nourriture disponible, l’effet refroidissant de la

température et du vent et l’augmentation des coûts énergétiques reliés au déplacement dans

la neige profonde sont autant de facteurs pouvant modifier les comportements du cerf.

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Connaître et surtout comprendre ce qui influence les cerfs durant la période de l’année où

ils sont les plus vulnérables représente un atout non négligeable et un argument de poids

pour tout gestionnaire de population animale. Ainsi, un des objectifs généraux de cette

étude est de vérifier l’influence de la sélection d’habitat sur la survie hivernale des cerfs et

de comprendre comment cette influence variera en fonction de l’échelle. Ainsi, nous

serons en mesure de définir les interactions entre la survie hivernale et l’habitat et ce, autant

à grande échelle spatiale et temporelle puisque l’étude se déroulera sur une saison hivernale

complète et à l’échelle du paysage. L’hypothèse de départ est que les individus n’ayant pas

survécu à l’hiver auront fait une sélection d’habitat différente de celle des individus ayant

survécu et plus différente que celle de la population moyenne, toujours par rapport à celle

des individus ayant survécu. Nous supposons également que cette différence variera en

fonction de l’échelle spatiale. Le deuxième objectif sera de définir les interactions entre les

routes primaires présentes dans le ravage et le mouvement des cerfs à l’aide d’une marche

aléatoire corrélée. Ces interactions seront étudiées à fine échelle temporelle puisque

l’intervalle de temps définissant les segments de la marche aléatoire corrélée sera de courte

durée. L’hypothèse de départ est que la présence et la proximité des chemins primaires va

induire une modification du comportement des cerfs.

Do resource selection functions tell us who lived and who

died?

13

Résumé Comprendre comment l’habitat et la sélection d’habitat influence la survie au sein d’une

population est un outil puissant pour pouvoir mieux gérer les populations animales. En

utilisant un processus de méta-analyse et des fonctions de sélection des ressources (FSR),

nous avons examiné la sélection hivernale d’habitat du cerf de Virginie (Odocoileus

virginianus) et l’influence de l’échelle spatiale sur cette sélection. Nous avons comparé les

résidus du modèle de la population des individus morts et vivants pour tester l’approche des

FSR et une des suppositions de base, soit que la sélection d’habitat est importante pour la

survie. Les résultats se sont révélés similaires à chacune des échelles avec une sélection de

la proximité des chemins primaires et d’une étendue d’eau. Toutefois, il y avait des

différences au niveau de la magnitude des coefficients. Ils ont généralement évité les

chemins tertiaires, les peuplements de feuillus tolérants et l’intérieur des coupes totales.

Les individus qui sont morts au cours de l’hiver (n = 7) ont fait une sélection qui tendait à

être différente de celle des individus vivants (n = 9) à l’échelle du paysage (p = 0.135).

Cependant, aux échelles des domaines vitaux et des mouvements journaliers, ils ont fait une

sélection similaire (p = 0.82, p = 0.93 respectivement). Nos résultats indiquent que les

facteurs indépendants de la densité pourraient influencer la survie hivernale d’une

population nordique de cerfs et que cette influence varierait en fonction de l’échelle

étudiée. En effet, la plus grande différence observée entre les valeurs moyennes à l’échelle

du paysage, même si elle n’était pas significative, indique que les facteurs ayant le plus

grand potentiel de limiter l’aptitude phénotypique des cerfs influencent la sélection

d’habitat à plus grande échelle. Cette hiérarchisation de la sélection d’habitat et des

facteurs limitants a aussi été constatée dans les coefficients des FSR. Cette situation

démontre toute l’importance qu’il faut accorder à la présence de ravage de qualité à

l’échelle du paysage, mais aussi à plus petite échelle pour accroître le taux de survie

hivernale. Toutefois, d’autres analyses sont nécessaires pour savoir si les résultats non

significatifs proviennent d’un échantillon trop petit ou d’une relation inexistante entre la

sélection d’habitat et la survie.

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Abstract Understanding how habitat and habitat selection influences survival within a population can

be a powerful tool for managing wildlife populations. Using a meta-analytical process and

resource selection functions (RSF), we examined white-tailed deer (Odocoileus

virginianus) winter habitat selection and how this selection varied at three different scales

(landscape scale, home range scale and daily movements). To test the RSF approach and

one of its main assumptions, that habitat selection matters for survival, we compared RSF

population model residuals between the dead and alive individuals. Selection was similar

at all spatial scales with selection for areas closer to water and to primary roads; although

there were some differences in the coefficient’ magnitude. They generally avoided areas

close to unutilized roads, tolerant hardwoods stands and interior of the clear cuts. Dead

individuals (n = 7) had a selection that tended to be different than alive individuals (n = 9)

at the landscape scale (p = 0.135). However, at the home range and the daily movements

scale, both groups selected in a similar way (p = 0.82, p = 0.93 respectively). Our results

suggest that density-independent landscape factors may influence winter survival for a

northern deer population and that the magnitude of this influence may vary with scale.

Indeed, the largest observed difference, even if it was not significant, happened at the

landscape scale, which indicates that factors with the highest potential to limit fitness are

hypothesized to influence broad-scale selection. This hierarchical habitat selection and

limiting factors was also observed within the RSF coefficients. This situation shows all the

importance of providing adequate overwintering areas at the landscape scale but also at

finer scales to maintain an increasing winter survival rate. However, further analysis are

needed in order to know if the non significant results arise from a too small sample size or a

relationship between survival and habitat selection that does not exist.

15

Introduction Habitat can be defined as the distinctive set of environmental components used by an

animal to meet its life requirements such as survival and reproduction (Block and Brennan

1993). As for habitat selection, it can be considered as using a set of environmental

components more or less often than would be by random use (Johnson 1980). Ultimately,

habitat selection patterns are a consequence of the influence of habitat selection on survival

and reproduction (Southwood 1977). Disproportionate use of habitats implies the quality

and abundance of resources in a given area, which in turn reflects fitness in that habitat

(Fretwell and Lucas 1969), although there are exceptions (Van Horne 1983). However, the

cumulative effect of a season’s worth of habitat choices or at a life time scale on survival

remain unclear (McLoughlin et al. 2007).

One common way of assessing habitat selection is using a resource selection function

(RSF), which is defined as any statistical model that is proportional to the probability of an

area being used (Manly et al. 2002). It usually compares used sites to unused or random

sites (i.e. available) using environmental variables to predict the probability of use by a

species, which is assumed to be proportional to the value of the resources. RSF have

largely been used to identify distribution and abundance of organisms (Boyce and

McDonald 1999), to detect differential selection of habitats in terms of parameter estimates

among populations, between the sexes (Ciarniello et al. 2007) and among different species

in the same study area (Jenkins et al. 2007) and to assess the importance of scale in habitat

selection (Boyce 2006, Gustine et al. 2006, Ciarniello et al. 2007). Indeed, habitat selection

studies should be not only scale dependent (Boyce 2006) but also multi-scale since patterns

of selection and the environmental variables that influence decisions can change from one

scale to the next (Jenkins et al. 2007). However, despite the fact that an RSF is relatively

easy to execute and understand, this statistical method has some flaws. With this analysis,

we are not able to relate the changes in internal state of the animal such as feeding and

breeding to habitat selection and thus understand why the animal chose that particular

habitat. Also, RSF is considered as a spatially implicit model and hence, all areas within

the defined available area are assumed to be equally accessible to each individual

(Moorcroft and Barnett 2008). Moreover, what is lacking at this point is an approach that

16

relates the RSF approach to survival and thus directly reflects the central assumption of

habitat selection that an individual’s fitness is influenced by habitat selection.

Survival within a population is often modeled with a Cox proportional-hazards regression

(Cox and Oakes 1984, Farmer et al. 2006, Aldridge and Boyce 2007). This method takes

the survival times of a group of subjects and generates a survival curve, which shows how

many of the individuals remain alive over time, and relates that curve to environmental

variables. In spite of the appropriateness of that technique, we utilized RSF models

because we wanted to simultaneously test this widespread methodology and an important

assumption of the method. The approach we developed here consists in analyzing residuals

obtained from RSF models, which are the difference between the observed data and the

fitted model (Cook and Weisberg 1982). The fitted model refers to the average habitat

selection of each individual in the model, which can be considered as the mean population.

So this way, we could compare grouped or single individuals to the average population;

higher mean residuals values for a group denoting a selection that is more different from

the population. Therefore, when studying survival in a population, one would expect

individuals that did not survive to have higher residuals values since they should act more

differently from the population than individuals who survived. This implemented RSF

technique, in spite of its simplicity, allows researchers to go on further in their

understanding of biological processes such as survival or sexual segregation.

For many northern ungulate species, winter is a period of elevated mortality because of

increased risks in predation and starvation (Mech et al. 1987, DelGiudice et al. 2002),

especially the period of late winter when fat reserves are at their lowest level. In particular,

winter is known to have a significant influence on habitat selection by ungulates (Parker et

al. 1984, Rettie and Messier 2000). We have assumed that during winter white-tailed deer

at the northern limit of their range congregate into yards with specific habitat features that

improve survival (Nelson and Mech 1981;1986). Deer yards have specific habitat

characteristics such as mature coniferous trees that provide thermal cover, shallower snow

conditions and increased sublimation and high deer population densities usually ranging

from 16 to 23 deer/km2 (Nelson and Mech 1981). Yarding behavior is advantageous

because forage abundance and quality are reduced by snow cover (Dumont et al. 1998). As

17

a result, winter energy expenditure from locomotion through deep snow combined with

cold temperatures can overcome energy intake (Hobbs et al. 1981, Berteaux et al. 1998). In

addition, late winter period worsen this situation as prolonged congregation in the yard

causes a gradual diminution in browse supply and deer’s fat reserve are at their lower level

(Dumont et al. 2000). Furthermore, higher deer population density yields extended trail

networks that facilitate movement and escape from predators (Nelson and Mech 1986,

Dumont et al. 1998). Thus, increased risks of starvation and predation have led to

behavioral mechanisms and altered habitat selection that help to offset seasonally

heightened energetic demands in white tailed deer. However, the fact that utilization of

survival enhancing mechanisms vary widely by individual and that we assumed a close

relation between the species and specific habitat features make this species, amongst others,

a prime candidate for studying the influence of environmental variables on survival.

Because of the previous criticism and owing to RSF popularity, we will assess winter

selection of the environmental variables available from GIS layers and coverage maps

using RSF in a high density overwintering area. To test RSF models and the assumption

that habitat selection matters for survival, we took advantage of the high level of mortality

and abundant GPS points to calculate whether the cumulative sum of deviations from the

average habitat selection is different between the deer that survived and those that died.

We will also assess selection at three scales (landscape scale, home range scale and daily

movements scale) and test whether survival-habitat relationships depend on scale.

Materials and methods

Study area

The study area is located in the Lake David overwintering deer yard (46°39’ N, 75°15’ W,

Quebec, Canada), 130km north of Ottawa, Canada (Figure 1). Mean annual temperature is

3°C with 900 to 1000 mm precipitation of which 25 to 30% is snow (Robitaille and Saucier

1998). The average cumulative index of sinking depth as measured by a penetrometer that

mimicks a deer hoof was 5744 cm/d in the 2007-2008 winter and 4939 in the 2008-2009

winter, but the average value over a 20 year period (1989-2008) was 3866 cm/d (Verme

1968). Deer population density was estimated at 19.2 deer/km2 in January 2008 with aerial

18

surveys (Michel Hénault, Québec Ministry of Natural Resources and Wildlife, pers.

Comm.). Predation risk can be important in this area. Primary predators include wolves

(Canis lupus), coyotes (C. latrans) and black bear (Ursus americanus).

The winter yard (292km2) has a mean elevation of 340m, ranging between 240 and 510m,

and 5% of the area is composed of lakes or rivers. Its trees are mostly sugar maple (Acer

saccharum) and yellow birch (Betula alleghaniensis) on mesic sites typically located on

hill sides, cedar (Thuja occidentalis) on organic soils as well as balsam fir (Abies

balsamea) and red maple (Acer rubrum) in valley bottoms (Robitaille and Saucier 1998).

The yard is divided in half by a river which is the limit between public and private land.

The public land is entirely forested with unused logging roads and a snowmobile trail

network that is used extensively all winter. On private land, the yard is mainly forested

with scattered villages and agricultural land interspersed. Since forestry is the main

industry in the area, 26.8 % of the yard has been modified by logging operations varying

from clear cuts in the late 80s to selection cuts in the late 90s. A portion of the yard

(approximately 5%) is also originating from a wildfire that occurred in 1921.

19


Canada. Light grey lines are the major highways.

20

Deer capture and monitoring

Nineteen white-tailed deer were captured in early February 2008 and January 2009 using

modified Stephenson box traps (Anderson and Nielsen 2002) in the Lake David yard and

monitored from deployment to the initiation of spring migration out of the yard on or about

April. Captures and handling methods were approved by the Laval University Animal Care

Committee, following the Canadian Council on Animal care guidelines and principles

(Authorization certificate 2007047-1). Handling time of deer varied from 1 min to 3 min.

We deployed GPS collars equipped with mortality sensors (Quantum 5000, Telemetry

Solutions, California, USA) on five males and six females in 2008 and eight females in

2009, all at least 1.5 years old. Captured deer were aged as adults or fawns based on their

size. GPS fixes were randomly spread in time using a schedule drawn from a negative-

binomial distribution. This randomization allows a good variation of the fix interval with

fixes very close in time and others hours away. This approach balances the tradeoff

between battery life and frequent fixes, and removes the problem that regular fixes can

impose an unwanted scale signature that is unrelated to habitat selectivity (Burdett et al.

2007). Deer captured in 2009 were monitored using the same randomized schedule method

but in a more intensive way. In order to keep a similar mean time interval between the two

years of monitoring, we only kept a randomly selected sample of the 2009 deer locations

for the analysis. To reduce measurement error in GPS points, we removed all points that

had PDOP (Position Dilution Of Precision) value higher than 4 with a 2D-fix and 7 with a

3D-fix, which consisted of 15% of the dataset, after testing the collar under different types

of cover (D'Eon and Delparte 2005). We established this threshold while minimizing the

collar imprecision without creating a bias against dense cover locations by comparing

locations obtained from collars on dead animals to their real location and after testing the

collars under various types of cover.

Land use and vegetation data

We used the Quebec Ministry of Natural Resources (MNR) vector GIS layers from the 3rd

decennial survey to obtain the cover information as well as the water system. These data

consisted of 137 different forest types grouped in 7 categories, stand density and age. We

21

classified forest types using a gradient of coniferous cover modified from Dumont et al.

(1998). Stand density was represented with 4 categories of canopy coverage ranging from

25 to 40% to more than 80%. Forest stand age was ranged from 10 to 120 years old. Past

and present logging operations were obtained and categorized according to their intensity

and age. Partial and selection cuts younger than 20 years were grouped together. All

treatments that harvested more than 60% of the cover were grouped as clear cuts. Selection

cuts and clear cuts were managed the same way for the analysis. Buffers were created for

each cut with radii of 40, 80 and 120m and then the area categorized with values ranging

from 1 (inside the cut) to 5 (farther than 120m from the edge). Road network and their

relative utilization layers were also obtained from the MNR and confirmed with the main

forest companies and the snowmobile club in the area. Roads were classified in three

categories, from primary to unused roads, following their utilization and the Euclidean

distance between each point and the closest road of each category was calculated. We used

the same method to calculate the distance to water, either stream or lake. All the

environmental variables used in the analysis are presented in table 1.

Resource Selection

We examined habitat selection by using conditional logistic regression (Ciarniello et al.

2007) with the function clogit in survival package from the program “R” (R

Development Core Team 2008). A conditional logistic regression does an analysis very

similar to the usual logistic regression except it keeps track of which presence is paired

with which absence (Hosmer and Lemeshow 2004). In other words, conditional logistic

regression compares a specific presence point only to its paired absence, not against all

absence points. Since it can be seen as a logistic regression of the differences between a

used point and an available point, there was no spatial autocorrelation and thus we could

use all points. This method allows for control of variation among individuals since each

random point is drawn from the area deemed available to that presence point (Compton et

al. 2002). We defined the extent of the available area separately for each spatial scale (see

scales of analysis for details).

22

Scales of analysis

Landscape scale

In this design, we examined habitat selection at the population-level, i.e. location of white-

tailed deer home ranges on the landscape and thus, location of the overwintering area.

Availability was defined, with a circle shape, as all known locations of marked animals

plus a buffer of 2.5km delineated from the extreme locations so as to encompass the entire

overwintering area and parts outside it (Boyce 2006). The circle had an area of 420 km2.

We randomly generated one random point for each used point throughout the circle.

Home range

In this scale, we explored what deer selected within their home range. Home ranges

delineation were estimated using program “R” function Estimation of Kernel Brownian

Bridge Home-Range (BBHR) in the package adehabitat (Horne et al. 2007, R Development

Core Team 2008). We used the BBHR function instead of other known methods such as

minimum convex polygons because BBHR takes into account the time interval between

fixes to define the areas that were most utilized by the animal and thus, is more realistic

(Bullard 1991). This function created home ranges that incorporated clusters of points by

utilizing 2 smoothing parameters that account for the animal’s speed and the imprecision of

the relocations (i.e. measurement error). Measurement error was estimated by using the

standard deviation of the distances between a known location (determined with a GPS

averaging the location) and the fixes taken by 3 different collars over a period of one

month. We generated one random point for each used point throughout the home range of

each animal yielding one used point paired with one available point.

Daily movements

Habitat selection during daily movements, given that they are restricted by the selection of

the home range within the larger landscape, will be assessed at this scale. Since we had

irregular time intervals, we could not use the distance traveled between fixes because it

would have incorporated a variation not related to the habitat or the animals. We used the

23

speed between each fix to find a mean winter speed for each individual. Then, we used that

speed to calculate the distance moved during one peak of the major activity cycle, which

consisted of a time interval of 5 hours (Ozoga and Verme 1970). The speed will be

underestimated and less precise as time intervals increase since an animal’s path will likely

be more tortuous than the straight line between two fixes. However, time intervals longer

than 2 hours represent less than 10% of the dataset, thus decreasing the risk of

underestimating the speed. The calculated distance (700m) was then used to draw a buffer

around each used point and then each of them was paired with one random point inside the

buffer.

Individual variation

Rather than perform a mixed effect conditional logistic model in one step, which can easily

create convergence problems during optimization (Bolker 2008), we performed a separate

conditional logistic regression for each individual and estimated population-level regression

parameters using standard meta-analysis approaches (Lipsey and Wilson 2001). To do this,

we weighted regression parameters from each individual with the inverse of its variance

and calculated a mean with these new values. The mean constituted the population-level

regression parameter.

Model selection and analysis

Predictor variables were screened for collinearity. If the Spearman correlation coefficient

was ≥ 0.6, one of the predictor variables was not included in the model (Ciarniello et al.

2007). All covariates not explicitly included in the models but present in Apppendix 1

represent the reference category. Categorical dummy variable coding required one category

to be removed. This was the case for the last category for each categorized variables,

which were the forest types, density, selection and clear cuts.

We selected the most parsimonious model using Akaike Information Criteria (AIC)

(Burnham and Anderson 2002). Since we are using a meta-analytical process, we could not

select the best model for each individual since all models had to be the same to compute

population-level regression parameters. Hence, we selected the model that was better for

24

most of the individuals. During that process, models were considered better if ΔAIC was >

2.0. The population-level model was evaluated using a k-fold cross validation with k=5

(Boyce et al. 2002). With this technique, a Spearman-rank correlation between area-

adjusted frequency of cross-validation points within individual bins and the bin rank was

calculated for each cross-validated model. We divided RSF predictions into 10 ordinal bins

using quantile breakpoints representing progressively more and more selected habitat

classes (Johnson et al. 2006).

Table 1 - Description of environmental variables from GIS layers used in the models for

white-tailed deer in the Lake David yard, Quebec, Canada (2008)

Variable Type Description Forest type categorical

Conifers > 75% of conifers or mixed stand with conifers dominance (>50%)

Intolerant hardwoods mixed with conifers

Mixed stand with intolerant hardwoods dominance (>50% intolerant hardwoods)

Tolerant hardwoods mixed with conifers

Mixed stand with tolerant hardwoods dominance (>50% tolerant hardwoods)

Tolerant hardwoods >75% of tolerant hardwoods Tolerant hardwoods mixed with intolerant hardwoods

>75% of tolerant or intolerant hardwoods

Water Water, agricultural fields, swamps

Misc. Alder, island, power line Stand density categorical Canopy coverage percentage (A :

more than 80%; B : 60 – 80%, C : 40 – 59%, D : 25 – 39%)

Stand age linear Stand age in years Distance to primary, secondary and unused road

linear Straight line distance to the nearest primary, secondary or unutilized road (primary refers to plowed or highly used snowmobile trails, secondary refers to seldom used forest road and unutilized refers to never used by snowmobiles.

Distance to water linear Straight line distance to the nearest body of water,

25

stream or lake Selection cuts categorical 1 (inside the cut), 2 (distance 1m

to 40m), 3 (distance of 41m to 80m), 4 (distance of 81m to 120m, 5 (more than 121m)

Clear cuts categorical 1 (inside the cut), 2 (distance 1m to 40m), 3 (distance of 41m to 80m), 4 (distance of 81m to 120m, 5 (more than 121m)

Resource selection and survival

We used the population-level regression parameters at each scale to calculate the residuals

for each presence point. We used model residuals instead of a categorical factor because

we did not have a single model for all individuals. In simulation tests, these two

approaches give approximately the same result on simulated data sets (data not shown). In

this study, individuals were grouped according to their winter fate i.e. those who survived

and those who died and a t-test was performed to test the difference between the mean

residuals from each group (t.test function in package stats from the program ‘’R’’ and

heterogeneous variances (R Development Core Team 2008). A higher mean residual value

indicates that this group of individuals made a selection that was more different from the

model reflecting the average population habitat selection than the other group. Residuals

were not squared as usual since we did not use them to assess the fit of the model but to

know the group tendency in relation to the other group. This is why we only used the

presence point and not all the points. Since our RSF models only incorporated

environmental variables, a significant difference between the groups would be the direct

consequence of a difference in the influence of habitat selection on survival.

Results We used 27,129 locations from 16 collared deer during deployment and locations number

ranged from 301 to 2697 locations per deer (mean = 1596, SD = 819, n = 16). We did not

used data fro deer no 95 since we were not able to recover enough fixes to be included in

the analysis. Deployment duration ranged from 19 to 92 days (mean = 61, SD = 18.9, n =

26

16) and time interval between fixes from 3 to 2580 min (mean = 52, SD = 167.3). Three

2008 collars malfunctioned and stopped taking fixes after 31, 37 and 58 days. Two of these

collars were recovered on a deer that died. We decided to use data from these collars since

the malfunction had no apparent link with the previous fixes and we did not detect blatant

inaccuracies in the location data. We were not able to recover any data from 3 of the 2009

collars. Furthermore, we suffered a relatively high mortality rate of 36% during the first

year with 4 deer dying in April 2008 (1 male and 3 females) and 38% during the second

year with 1 deer dying in January and 2 in March 2009. However, deer with

malfunctioning collars had a known fate since their VHF beacon was properly working and

could emit a mortality signal. Carcasses exhibited no characteristic signs of predation such

as bite marks, claw marks, caching, chewed or scattered bones. In the absence of evidence

of predation we assume that death was a result of starvation. Deer use of their winter range

appeared to vary widely between individuals as we can see on figure 2.

Figure 2 – Example of two different overwintering area use for white-tailed deer in the

Lake David yard, Canada (2009). Green circles represent locations from a deer that

27

survived to the winter and black triangles represent locations from a deer that died. Black,

red and dotted black lines represent, respectively, primary, secondary and unused roads.

Using ΔAIC for individual deer, we selected the model that was the best for most of the

deer, which was the model with all the covariates presented in table 1 for all spatial scales.

No predictor variables were correlated enough to be dropped from the analyses since

Spearman correlation coefficients ranged from 0.0005 to 0.35. Results from the population

models are shown in table 2. Area-adjusted frequencies from the cross-validation analysis

displayed significant positive rank values (Spearman-rank correlation) for all spatial scales

except for 2 home range sets (Appendix 1). Lower correlation values and the 2 non-

significant values indicate that the home range scale model was not as good as the other

scales, indicating that environmental variables important for white-tailed deer habitat

selection were not included in the model.

Resource Selection

Landscape scale

In the establishment of their winter home range, white-tailed deer showed a strong selection

for primary and secondary roads, being closer than random to these roads and avoided

unused roads (table 2). Deer also selected for areas closer to water with a strong

conditional logistic regression coefficient of -4.101, which was stronger than the distance to

primary and secondary roads. Deer avoided clear cuts as well as selection cuts. As for

habitat categories, the most avoided habitat category was tolerant hardwoods with an

avoidance of almost twice as the avoidance any other habitat types. The least avoided

habitat type that was significant was tolerant hardwoods mixed with conifers. A dense

cover with more than 80% of canopy coverage (density A) was the most selected density.

Deer also selected for older stands. However, since the square term of the stand age was

significant and negative, they did not selected for the oldest stand age.

Home range

While in their home range, deer showed a weaker selection for areas closer to primary

roads than to areas closer to water. They showed a stronger avoidance of secondary and

28

unused roads. Deer avoided the interior of clear cuts but this avoidance gradually shifted to

a weak selection with regard to the area comprised between 81 to 120m from the edge.

Nevertheless, they showed a weak selection for selection cuts. Water was the most avoided

habitat type and intolerant hardwoods mixed with tolerant hardwood were the most selected

type. At this scale, deer strongly selected more open habitat types with canopy coverage

between 40 and 60%, which may be related to a dense shrub layer under the sparse canopy.

Daily movements

For their food searching behavior and their inter-patch movements, deer selected areas

closer to primary roads and farther to unused roads. They selected for areas closer to water

but this selection was more than three times less important than the distance to primary

roads. Their avoidance of the interior of the clear cuts had the same strength as the

selection for the interior of the selection cuts. Tolerant hardwoods were the most avoided

habitat category and the water category was the most selected habitat type.

Table 2 - Resource selection functions at three spatial scales for 11 white-tailed deer in the

Lake David yard, Quebec, Canada (2008)

Variable Landscape Home range Daily movements

β SE β SE β SE

Stand age 0.982 0.222 -0.125 0.137 0.104 0.088

Stand age2 -0.532 0.165 -0.079 0.102 -0.130 0.062

Distance to water -4.101 0.498 -0.797 0.226 -0.864 0.169

Distance to primary roads -2.476 0.154 -0.578 0.115 -2.919 0.098

Distance to secondary roads -1.633 0.081 1.012 0.076 -0.134 0.081

Distance to unused roads 0.413 0.329 0.829 0.180 0.762 0.132

Conifers and conifers mixed with hardwoods -0.991 0.509 0.060 0.364 -0.342 0.199

Intolerant hardwoods mixed with conifers -1.530 0.467 1.461 0.172 -0.173 0.168

Tolerant hardwoods mixed with conifers -1.370 0.500 -0.032 0.248 -0.555 0.214

Tolerant hardwoods -2.975 0.520 1.478 0.223 -1.130 0.259 Intolerant hardwoods mixed with tolerant hardwoods -1.612 0.530 1.940 0.278 -0.195 0.192

Water 0.388 0.865 -2.541 0.917 0.938 0.385

Clear cut (inside) -1.116 0.349 -0.979 0.192 -0.583 0.126

Clear cut (1-40m) -0.144 0.236 -0.720 0.082 -0.070 0.079

Clear cut (41-80m) -0.561 0.232 -0.039 0.068 0.123 0.069

Clear cut (81-120m) 0.129 0.207 0.180 0.058 0.256 0.063

Selection cut (inside) -0.518 0.220 0.351 0.173 0.425 0.105

29

Selection cut (1-40m) 0.165 0.277 -0.001 0.124 0.042 0.091

Selection cut (41-80m) -0.325 0.313 -0.011 0.115 -0.168 0.090

Selection cut (81-120m) -0.741 0.292 0.297 0.101 -0.063 0.084

Density A 2.513 0.562 0.540 0.168 0.929 0.220

Density B 2.314 0.530 0.317 0.134 0.455 0.188

Density C 1.903 0.543 4.078 1.926 0.234 0.214 Note: Regression coefficients (β) in boldface italic type had confidence intervals that did

not include 0.

Resource selection and survival

When comparing the mean residual of each group, we note that habitat selection had no

influence on survival at either scale. However, even tough the difference is not significant

at the landscape scale, the p value of 0.148 is not far from the significant threshold of 0.1.

However, at all spatial scale, the mean residuals for the dead individuals were higher than

for alive individuals, indicating a closer to the mean population selection for alive

individuals. Lower residuals values at the landscape scale indicate that this model explains

really well the data. It also means that the landscape scale included habitat that is strongly

avoided during winter and thus the delineation of the yard is well defined (see figure 3).

High residuals values along with lower or non-significant Spearman-rank correlation

coefficient at the home range scale indicate that this model is not very good. However, a

hierarchical Bayes model with exactly the same environmental variables, but with more

explicit random effects had lower residuals values, thus indicating that the lower fit may

arise from a different structuring of random effects and not from the variables (unpublished

data).

Table 3 - T-test summary for winter RSF meta-analysis for 16 white-tailed deer in the Lake

David yard, Canada, 2009

Mean Residuals (SE)

Scale Alive Individuals

(n=9) Dead Individuals

(n=7)

d.f. T Value P Value

Landscape scale 0.063 0.085 11.741 -1.55 0.148

Home range 0.471 0.479 8.136 -0.21 0.839

Daily movements 0.424 0.426 12.473 -0.086 0.933

30

Figure 3 - Relative probability of presence for white-tailed deer at the landscape scale in the

Lake David yard, Canada (2008).

31

Discussion We examined differential resource selection obtained from conditional logistic regressions

and our results show that the cumulative effect of selection of density-independent factors

may be important for survival. Selection for primary roads and areas closer to water,

avoidance for unused roads and clear cuts were amongst the most important results from

our models of white-tailed deer habitat selection. Tolerant hardwoods, tolerant hardwoods

mixed with conifers, intolerant hardwoods mixed with tolerant hardwoods and water were

also important variables to describe white-tailed deer habitat selection.

Deer selection for primary roads at all spatial scales and for areas closer to water at the

landscape scale may in part be explained by the regional toposequence and the fact that

primary roads are usually built in the valley’s bottom even tough elevation did not

significantly improve model fit and thus was not included in the models. The regional

toposequence indicates that conifer and cedar stands are commonly found near the rivers

and the valley bottom and as you gain elevation, stands get more and more deciduous with

sugar maple and yellow birch on the hill’s summit (Robitaille and Saucier 1998) . Also,

deer overwintering habitat is often found in the vicinity of swamps and river sides where

cedar is dominant and where there is a more important coniferous cover (Habeck 1960,

Nelson and Mech 1981). Hence, deer may indirectly select primary roads while selecting

for typical yard characteristics. Even though the correlation between the distance to

primary roads and each habitat types was not important enough to be considered, tolerant

hardwoods, a habitat type not typically associated with primary roads following the

toposequence since it is localized at higher elevations, was the more correlated habitat type

(Spearman correlation coefficient = 0.35). However, primary roads and their hardened

surface can be selected independently of other landscape attributes as they can facilitate

deer displacements (Lavigne 1976). This selection may seem contradictory to the road

ecology literature describing diverse adverse effects such as altered behavioral patterns of

animals living near roads, increased vigilance and fleeing behavior or barrier effects

(Mader 1984, Freddy et al. 1986, Buchanan 1993, Manor and Saltz 2005). Also, it

demonstrates that an animal that would otherwise be disturbed by motorized activity can

32

get habituated. Thus, it confirms how variable a population response to a landscape feature

can be.

Even if it was not significant, the greatest difference between the two groups appeared to be

at the landscape scale indicating that individuals who survived the winter season selected

habitat characteristics in the establishment of their home range in a way that was closer to

the mean population than dead individuals. The higher importance of resource selection on

survival at the landscape scale is consistent with the literature and the idea of hierarchical

habitat selection (Johnson 1980) and limiting factors, which recognizes that factors with the

greatest potential to limit fitness are hypothesized to influence large-scale selection,

whereas selection at finer scale reveals less critical factors (Rettie and Messier 2000). For

example, Dussault et al. (2005) and Johnson et al. (2001) suggest that large herbivores

select their home range within the landscape to minimize predation risk whereas habitats at

the home range scale are selected to maximize forage intake (Johnson et al. 2001, Mansson

et al. 2007). This hierarchical habitat selection and differences between scales is also found

within the RSF coefficients. The predominance of the selection for areas closer to water,

and more resinous cover according to the toposequence, over the distance to primary roads

and the selection of tolerant hardwoods mixed with conifers at the largest scale analyzed

suggest that resinous cover may be a limiting factor. Indeed, at the finer scale, selection for

primary roads was more important than the distance to water and the selection for water

suggests a selection for a less limiting factor not related to resinous cover. Thus, for white-

tailed deer situation in northern regions, the availability of adequate overwintering areas

can be considered as a factor that has the potential to limit fitness and survival rate in the

landscape.

Our non-significant results do not necessarily imply that habitat selection does not have

influence on white-tailed deer winter survival. Thus, Klaver et al. (2008) explored

relationships between white-tailed deer weekly survivorship and several habitat variables

and indicated which habitat characteristic would promote survivorship. McLoughlin et al.

(2007) also found that lifetime reproductive success of roe deer (Capreolus capreolus), a

fitness indicator, was related to lifetime home range locations. In our situation, if the

influence exists but is small, a larger sample size would be necessary to detect the

33

difference. A larger sample size would also be necessary if the lack of significance is due

to the variation inherent to biological studies. Also, our results may arise from the fact that

we did not include understory in our models or other habitat variables, which can have an

impact on deer winter habitat selection. However, the t test result differences observed

between scales, even if they were not significant, indicate that our method can detect

situations where habitat has reduced or greater influence on the studied process, i.e.

survival.

Management implications

The importance of winter habitat selection at the landscape scale is a key factor in survival.

This means for deer management that the selection, delineation and protection of deer

winter yards as a whole is important for successful deer overwintering and population

growth, not just for the sake of having deer habitat. It is not only a religious vow; it plays

an important role in their winter survival. Also, the presence of used snowmobile trails in

the yard does not necessarily imply that white-tailed deer will be displaced. However,

further research is needed in order to quantify the biological impact of snowmobile

disturbance on deer energy budget.

Acknowledgements We thank the many people who helped in the field, particularly David Poulin, Jean-Pascal

Trudeau and Marianne Moffat. Financial support for this project was provided by Natural

Sciences and Engineering Research Council of Canada (NSERC, McIntire), the Canada

Research Chair program (McIntire), the Canada Foundation for Innovation (McIntire), and

MC Forêt inc. E.Allard was also supported by a grant from NSERC and Fondation

Héritage-Faune. We received GIS layers and logistical support from Quebec Ministry of

Natural Resources.

34

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38

Appendix Appendix 1 - Cross-validated Spearman-rank correlations (rs) between bins ranks and area-

adjusted frequencies for individual model sets

Landscape scale Home range scale Daily movements scale Set

rs P rs P rs P

1 1 < 0.0001 0.55 0.10 0.96 < 0.0001 2 1 < 0.0001 0.77 0.01 0.98 < 0.0001 3 0.99 < 0.0001 0.35 0.32 0.99 < 0.0001 4 0.99 < 0.0001 0.68 0.03 0.99 < 0.0001 5 1 < 0.0001 0.61 0.06 0.99 < 0.0001

Average 1 < 0.0001 0.73 0.02 0.99 < 0.0001

39

Appendix 2 - Monitoring summary for white-tailed deer RSF analysis in the Lake David

yard, Canada 2009

Monitoring Deer identification Sex Start End

Estimated date of death

Number of days

Number of locations used

1 F 2008-02-02 2008-04-05 - 63 1786

2 F 2008-02-03 2008-04-13 2008-04-14 70 2166

3 F 2008-02-01 2008-03-21 - 49 1214

4 F 2008-02-08 2008-04-17 2008-04-18 70 2312

5 F 2008-02-08 2008-04-05 2008-04-25 58 1686

6 F 2008-02-22 2008-05-01 - 70 2709

7 M 2008-02-17 2008-04-18 - 62 2399

8 M 2008-02-09 2008-04-24 - 76 2697

9 M 2008-02-10 2008-04-20 - 71 2177

10 M 2008-02-02 2008-03-11 2008-04-01 37 674

11 M 2008-02-10 2008-03-11 - 31 618

91 F 2009-01-02 2009-04-01 - 92 1829

92 F 2009-01-10 - - - -

94 F 2009-01-02 2009-01-21 2009-01-22 19 301

95 F 2009-01-10 2009-01-18 - 7 0

97 F 2009-01-10 2009-03-17 2009-03-17 67 1512

98 F 2009-01-10 2009-04-01 2009-04-02 81 1821

99 F 2009-01-04 - - - -

910 F 2009-01-02 2009-03-02 - 59 1228

The use of irregular time intervals to detect movement

bouts and the influence of primary roads

41

Résumé Le mouvement des organismes au sein des paysages hétérogènes n’est pas encore très bien

compris puisque différentes variables environnementales peuvent l’influencer soit

localement ou encore à distance. Par exemple, les routes primaires traversant un ravage de

cerf de Virginie (Odocoileus virginianus) peuvent attirer les cerfs à partir d’une bonne

distance puisque leur surface compactée facilite les déplacements. Par contre, le

dérangement causé par les véhicules peut causer l’effet inverse. Pour comprendre le

mécanisme des interactions entre les cerfs et les routes, nous avons utilisé une marche

aléatoire corrélée pour modéliser le mouvement. L’utilisation d’une échelle continue pour

nos points GPS, comparativement à un intervalle de temps choisi arbitrairement, nous a

permis d’être capable d’examiner et de trouver un changement dans les échelles de

mouvement. Nous avons sélectionné des trios de points GPS avec un intervalle de temps

variant de 30 à 72 minutes avec des incrémentations de 6 minutes pour estimer les distances

parcourues et les angles de virage (n = 17 chevreuils). En utilisant un intervalle de temps

de 54 minutes, nous avons détecté un corridor de 116m autour des chemins primaires où les

distances parcourues sont 12% plus courtes qu’à l’extérieur de cette zone. Cependant,

aucune différence n’a été détectée avec d’autres intervalles. L’utilisation d’intervalles de

temps croissant suggère un changement non relié à l’habitat dans le comportement des

cerfs. En effet, la présence d’un plateau dans les distances parcourues aux intervalles de 54

à 66 minutes indique que les cerfs peuvent avoir changé leur comportement, suggérant ainsi

que différents moments de mouvement ont lieu sous la barre des 54 minutes. Nos résultats

supportent l’importance que l’on doit accorder à l’inclusion des caractéristiques de l’habitat

pour modéliser les mouvements d’une façon plus mécanistique. Notre utilisation d’un

intervalle de temps continu pour les points GPS est innovatrice et les résultats montrant des

différences au niveau des paramètres de mouvement démontrent l’utilité d’enlever les

incréments de temps fixés arbitrairement dans les données GPS.

42

Abstract Movement of organisms in heterogeneous landscapes is still not well understood since

different environmental variables may influence it locally or from a distance. For example,

primary roads passing through a white-tailed deer (Odocoileus virginianus) overwintering

area may attract deer from afar as their hardened surface facilitates easy movement, but

harassment from vehicles may have the opposite effect. To understand the mechanistic

interactions between deer and roads, we used a correlated random walk to model

movement. Because our GPS fixes were on a continuous scale (i.e., not arbitrarily chosen

at a particular time interval), we were able to examine and to find scales of movement

change. We selected trios of GPS fixes with varying time interval (30 min to 72 min, with

6-min increments) to estimate move distances and turn angles (n = 17 deer). At the 66-min

interval, we detected a significant buffer of 116 m around primary roads within which

move lengths were 12% smaller than outside the buffer. However, no differences were

detected for other time intervals. The use of varying time intervals suggests a change in the

internal behavioral state of the animal. Indeed, the presence of a plateau at 54-66-min

intervals in the move lengths might indicate that deer changed their behavior, suggesting

movement bouts that occur below this time interval. Our results reinforce the importance

of incorporating habitat features in modeling movement and achieving more mechanistic

understanding of ecological systems. Our use of a continuous time interval for GPS fixes is

novel and the results showing differences in movement parameters demonstrate the

usefulness of removing the arbitrarily fixed temporal increments of time in GPS data.

43

Introduction Animal movement has long been a concern in population ecology since it is the basis of

dispersal and population distribution. However, the interactions between an organism and

heterogeneous landscapes are still not well understood (Lima and Zollner 1996, Morales

and Ellner 2002). For example, there are still concerns whether animals directly interact

with the landscape or with particular habitat features within the landscape. Most

movement studies to date have been on small organisms, such as insects and butterflies,

and at fine spatial and temporal scales (Zollner and Lima 1999). These studies mostly rely

on experimental designs or direct observations and rarely describe movements as

mechanistic rules that can be used to predict movement in new landscapes (Schultz and

Crone 2001). However, such observational studies set the basis for further and

complementary movement analysis. The difficulty of directly observing nocturnal or

cryptic species limitation narrows the range of species that can be studied. Even fewer

studies have been done at a broad spatial scale and on vertebrates. Among these, Fortin et

al. (2005a) studied elk movements in Yellowstone National Park by comparing observed

and random moves, where they found that elk adjusted their foraging to fine-scale habitat

structure. Also, Bergman et al. (2000) used a correlated random walk to model caribou

movement at two different spatial scales. Yet, the latter did not account for the influence of

the landscape on movement.

Recent advances in technology such as GPS collars allow researchers to model movements

of larger animals mechanistically, i.e., not just phenomenologically, like resource selection

functions, and at broader spatial scales. Among all likely mechanistic movement models, a

correlated random walk (CRW) is a very general approach that has widespread potential

application. Typically, continuous movement paths of individuals are broken down by

researchers into discrete steps, each of which is characterized by its distance and direction

(Turchin 1998). The correlated component of a random walk indicates that the direction of

a given move is related to the direction of the previous one (Kareiva and Shigesada 1983).

Despite the wide use of this method, considering animals found in spatially heterogeneous

landscapes as correlated random movers irrespective of the environment may not be

appropriate. Animals, through behavior, respond to landscape structure (Bélisle 2005) and

44

for example, may congregate around concentrations of food resources by modifying their

speed or turning frequency rates (Bovet and Benhamou 1988, Walsh 1996). Relating

changes in path characteristics to changes in the environment will allow the development of

more realistic movement models and perhaps better forecasts of movement in a changing

landscape.

Ungulates’ intra-patch winter movements, defined as fine temporal and spatial scale

movements related to a food searching behaviour, have not been studied as much as

migration and dispersal movement patterns despite their potential influence on habitat use

and survival. One potential reason for this omission of intra-patch movements may be the

incongruence of data and the fine scale of local interactions. Indeed, the temporal scale at

which data are collected has to agree with the process studied (Boyce 2006). This historic

problem with studying fine scale movements was the lack of methods necessary to collect

such data. One common way of collecting data is to survey several animal paths with snow

tracking methods (Steventon and Major 1982). However, this time-consuming method is

only realized under specific conditions and is characterized by the inability to identify the

quantity of individuals tracked. Other widely used methods include radio-telemetry and

GPS telemetry. Nevertheless, these often sacrifice frequent fixes that would allow the

characterization of fine-scale movement to maximize battery life (Fortin et al. 2005a,

Hebblewhite and Merrill 2007). Notably, Burdett et al. (2007) demonstrate the influence of

sampling interval on the delineation of home ranges, higher sampling frequencies giving

better accuracy and greater home ranges size. Also, these studies generally use an

arbitrarily fixed time interval such as 2 or 5 h. Their scale of analysis is restricted to

multiples of the fixed interval, thus restricting the processes to be studied. Hence,

limitations in data collection caused a gap in ecological knowledge of fine-scale movement.

Roads are known to affect wildlife movements, but there is still widespread uncertainty and

variation in the nature of these effects. The road-effect zone is defined as the area over

which significant ecological effects extend outward from a road and is typically wider than

the road surface and its roadsides (Forman 1995). Ecological effects can be as diverse as

an increased presence of exotic plant species, altered behavioral patterns of animal living

near roads and barrier effects that divide population (Mader 1984, Buchanan 1993, Watkins

45

et al. 2003). For the ungulates, negative effects can consist of increased vigilance and

fleeing behaviour, altered space use and higher risks of collisions (Freddy et al. 1986,

Conover et al. 1995, Manor and Saltz 2005). Distance to which ungulates modify their

behaviour in response to vehicle harassment is highly variable and depends on many factors

such as wind direction, group size and directions and visibility of the vehicles (Stankowich

2008). Nevertheless, in some situations, effects may be positive such as an increase of

deciduous shrub growth in the openings created by roads, augmentation of ecotones,

presence of groomed roads which may act as traveling corridors and moose attraction to de-

icing salt pools (Lavigne 1976, Laurian et al. 2008). However, in the absence of direct

observations and vehicle-ungulates encounter, we do not know the size of the road-effect

zone where behaviour is modified. Thus, mechanistic interactions between ungulates

movements and used roads are not well understood.

Our first objective is to assess the influence of the time interval across a continuum of time

scales used to discretized white-tailed deer movement paths on the parameters of the CRW.

Varying the time interval will let us verify the presence of different scales of movement,

even at a fine temporal scale. The second objective is to understand the interactions

between deer winter movements and used roads in a deer yard at fine temporal scales. We

hypothesized that primary roads would influence deer movement and that deer would

modify their behaviour (i.e. the correlated part of their movement and the step lengths)

closer to the roads. Establishing the parameters of a correlated random walk (CRW) model

for fine-scale movement will allow us to better understand movement within the yard in the

presence of primary roads. This understanding is important for yard managers since

movements in winter are restrained and energetically costly.

Materials and methods

Study area

The study area is located in the Lake David overwintering deer yard (46°39’ N, 75°15’ W,

Quebec, Canada), 130km north of Ottawa, Canada (Figure 4). Mean annual temperature is

3°C with 900 to 1000 mm precipitation of which 25 to 30% is snow (Robitaille and Saucier

1998). The average cumulative index of sinking depth as measured by a penetrometer that

46

mimicks a deer hoof was 5744 cm/d in the 2007-2008 winter and 4939 cm/d in the 2008-

2009 winter, with a 20-year average of 3886 cm/d (Verme 1968). Deer population density

in the winter yard was estimated to19.2 deer/km2 in January 2008 from aerial surveys

(Michel Hénault, Québec Ministry of Natural Resources and Wildlife, pers. Comm.).

Predation risk may be important in this area, though the mortalities of our animals did not

appear to be due to direct predation. Primary predators include wolves (Canis lupus),

coyotes (C. latrans).

The winter yard (292km2) has a mean elevation of 340m, ranging between 240 and 510m,

and 5% of the area is composed of lakes or rivers. Its trees are mostly sugar maple (Acer

saccharum) and yellow birch (Betula alleghaniensis) on mesic sites typically located on

hill sides, cedar (Thuja occidentalis) on organic soils as well as balsam fir (Abies

balsamea) and red maple (Acer rubrum) in valley bottoms (Robitaille and Saucier 1998).

The yard is divided in half by a river which is the limit between public and private land.

The public portion of the yard is entirely forested with unused logging roads and a

snowmobile trail network that is used extensively all winter. On the private part, the yard

is mainly forested with scattered villages and agricultural land interspersed. Since forestry

is the main industry in the area, 26.8% of the yard has been modified by logging operations

varying from clear cuts in the late 80s to selection cutting in the late 90s. A portion of the

yard (approximately 5%) is also originating from a wildfire that occurred in 1921.

47


Canada. Light grey lines are the major highways.

48

Deer capture and monitoring

Nineteen white-tailed deer were captured in early February 2008 and January 2009 using

modified Stephenson box traps (Anderson and Nielsen 2002) in the Lake David yard and

monitored from deployment to the initiation of spring migration out of the yard on or about

April. Captures and handling methods were approved by the Laval University Animal Care

Committee, following the Canadian Council on Animal care guidelines and principles

(Authorization certificate 2007047-1). Handling time of deer varied from 1 min to 3 min.

We deployed GPS collars (Quantum 5000, Telemetry Solutions, California, USA) on five

males and six females in 2008 and on eight females in 2009, all at least 1.5 years old.

Captured deer were aged as adults or fawns based on their size. GPS fixes were randomly

spread in time using a schedule drawn from a negative-binomial distribution. This

randomization produced good variation in the fix interval with many fixes close in time and

others hours away. This approach balances the tradeoff between battery life and frequent

fixes, while removing an unwanted scale signature that is unrelated to habitat selectivity

and characteristic of regular fix intervals (Burdett et al. 2007). To reduce measurement

error in GPS points, we removed all points that had PDOP (Position Dilution Of Precision)

value higher than 4 with a 2D-fix and 7 with a 3D-fix, which consisted of 15% of the

dataset, after testing the collar under different types of cover (D'Eon and Delparte 2005).

We established this threshold to minimize the collar imprecision without creating a bias

against dense cover locations by comparing locations obtained from collars on dead

animals to their real location and after testing the collars under various types of cover.

Road data

We used the Quebec Ministry of Natural Resources (MNR) vector GIS layers from the 3rd

decennial survey to obtain the road network and their relative utilization layers. These

were confirmed with maps obtained from the main forest companies and the snowmobile

club in the area. Roads were classified following their relative utilization, which were

verified in the field. Only plowed paved roads or highly used groomed snowmobile trail

were classified as primary roads and used in the analysis.

49

Analysis

Observations

To minimize the error superposition on two successive localizations on this analysis, we

did not use data points that were <27 min apart. We estimated data points separated by

approximately 30 min would have sufficiently small GPS error influence, though this is

difficult to determine precisely. Thus, the smallest scale that minimized the errors

consisted of a selection of trios of fixes with a time interval ranging from 27.1 to 33 min

(mean = 30). A minimum of three consecutives fixes is needed to compute the CRW

parameters. To understand the influence of the time interval on white-tailed deer CRW

movement parameters, we sampled our dataset with time interval incrementing of 6 min

from 30 to 72 min. To maintain a complete separation between different time intervals and

thus, overlap of trios, we used a range of 3 min before and after each time interval. We

used 6-min increments in order to obtain a large enough number of trios per time interval.

We stopped at 72 min because we assumed that beyond that time interval, other processes

could be involved and variation in move length would be too important. Since movement

is energetically expensive in winter, displacements are limited and so, longer time intervals

will not necessarily correspond to longer distance moved, i.e. they may come back to their

starting point. However, longer moves may also arise from inter-patch movement. The

randomization of our fix schedule throughout the day assured us of a random sampling of

deer movement, without any bias for the time of day, the day of the week or the proximity

to roads. A starting and ending point was assigned to each trio of points according to the

direction of movement. Step lengths and their absolute direction were calculated. Turning

angles, which express how much deer deviated from the previous direction, were calculated

for each trio. For each fix, the Euclidean distance was calculated to the nearest primary

road and the absolute angle was also calculated. This distance was calculated with a spatial

join in program ArcGIS, release 9.2 (ESRI 2009).

Parameter estimates

We assumed that white-tailed deer movement can be explained within the framework of a

CRW (Fortin et al. 2005b). For each time interval, move lengths and deviations in turning

angles were estimated using standard methods for correlated random walk analysis inside

50

and outside the road-effect zone, except that an additional parameter was estimated for the

size of the road-effect zone for each parameter (Kareiva and Shigesada 1983). We divided

the landscape in two categories: outside the road-effect zone and inside the road-effect

zone. The size of the road-effect zone was an empirically estimated parameter and

indicated at which distance from the road behavior was changing. We used the function

breakpoints in package strucchange in program “R” (R Development Core Team 2008)

to find this parameter, as this function determines if the data are better explained with a

breakpoint. We treated the trios as independent data points for the regression because they

spanned the many months of movement data. So, while there may be variation among

individual deer, we believe that the variation across a many months within a single deer

would be larger than the variation among deer. We also make this assumption because, to

our knowledge, mixed-effect models are not yet developed for piecewise regression. Trios

were considered inside the road-effect zone if the distance to road of their middle fix, which

was associated with deviation in turning angle, was lower than the break value. We

obtained the best distribution describing move lengths and deviations in turning angles

using Akaike Information Criterion (AIC) (Burnham and Anderson 2002). We tested

exponential and lognormal distributions for move lengths and beta, wrapped normal and

Von Mises distributions for the turning angles. A given distribution was considered better

if ΔAIC for the second best model was greater than 2. To understand the influence of

primary roads when deer are approaching them, we evaluated another model which only

included trios inside the road-effect zone with the first step approaching the road.

Results We obtained varying numbers of independent trios depending on the time interval between

steps with a mean of 1051 trios for 17 deer (min.: 739, max.: 1452, SD = 256). We were

not able to recover data from 2 of the 19 collared deer. Therefore, the analysis will only be

done with 17 deer. For all deer, distance to primary roads ranged between 0 m and 3422 m

(mean: 264.4, SD = 244.48). Each deer used the vicinity of primary roads and their mean

distance to these roads is shown in table 4.

51

Table 4 - Distance to primary roads for each white-tailed deer monitored in the Lake David yard, Canada, 2009

Deer ID Sex Mean distance (m) Standard deviation1 F 150.6 90.7 2 F 443.8 433.3 3 F 95.8 79.2 4 F 166.9 98.5 5 F 403.0 157.4 6 F 139.2 146.7 7 M 340.3 215.0 8 M 551.3 317.9 9 M 221.0 222.7 10 M 266.9 365.6 11 M 312.9 256.7 91 F 187.9 90.1 94 F 127.3 121.1 95 F 256.7 107.1 97 F 483.3 258.9 98 F 162.7 139.2 910 F 158.6 149.4

Distance

Move lengths were distributed following a log-normal distribution, thus indicating a

majority of short moves and few longer moves. When considering a time interval of 66

minutes, move lengths were best defined with a breakpoint at 116 m (95% confidence

interval: 82 - 175m, n = 1180 trios) from primary roads (Table 5). The breakpoint

corresponds to the road-effect zone where the studied parameter is modified. However, no

breakpoints were detected for the other time intervals. Move lengths were longer outside

the buffer of 116 m along primary roads than inside, from 22.4 to 25.1m (Figure 5, Table

5). There were no differences between the trios inside the breakpoint value and those

approaching the road (Table 6). In general, as the time interval increased, the distance

moved between each step also increased. We noted the presence of a plateau between the

time intervals of 54 and 66 minutes, where move lengths stopped increasing (Figure 6).

After that plateau, the time interval of 72 shows an increase in move length. However, this

52

plateau was not significant when analysed with the function segmented from the R

package segmented (R Development Core Team 2008)

Figure 5 – Distribution of move lengths and deviation in turning angle inside and outside

the breakpoint value of 116m for a time interval of 66 min (a and b respectively).

Lognormal distributions were fit to move length data and Von Mises distributions were fit

to deviation in turning angle data. Bars are the observed densities.

53

Figure 6 – Mean move lengths, calculated with lognormal distribution for white-tailed deer

winter movements with varying time interval sampling. Move length for the 66 minutes

time interval was calculated with all trios, without considering the 116m zone. Error bars

represent the 95% confidence interval.

Angles

Deviations in turning angles followed a Von Mises distribution, the circular equivalent of

the Normal distribution for linear quantities, with equal probabilities of turning right or left

(parameters). Von Mises distributions are characterized with a mean (μ) and a

concentration parameter (kappa) of turning angles. Higher kappa value indicates that

angles are more concentrated around the mean. We detected an angular correlation

between moves. However, this correlation was negative since the confidence intervals for

54

all time interval included 180° (Table 7). This situation implies that deer had the tendency

to move in the opposite direction of the previous move and do frequent turns. We were not

able to detect a significant breakpoint in the data for the deviations in turning angles. When

using the breakpoint determined for step lengths (i.e., 116m), the angles for the 66 minutes

interval model were found to be different inside and outside this road zone. The mean

deviation in turning angle inside the road zone was different than the mean outside the

zone. However, we did not detect a difference for the concentration parameter, turning

angles outside the breakpoint being similar to those inside. The concentration parameter

was not different when we compared trios inside the buffer getting closer to primary roads

to those only located inside (0.24 and 0.31 respectively, Table 5).

Table 5 - Parameter estimates for a biased correlated random walk indicating the presence

of a breakpoint with a time interval of 66 minutes for white-tailed deer in the Lake David

yard, Canada (2009).

Parameter Estimate95% confidence

interval Number of

trios Break value (m) 115.6 82.2 - 174.9 1180 Move length (m)

Inside break 22.4 20.3 - 24.7 433 Outside break 25.1 23.4 - 26.9 747

Deviation in turning angle (degrees) Inside break 433

mean 208.4 176.4 - 240.4 kappa 0.24 0.10 - 0.37

Outside break 747 Mean 159.2 109.5 - 208.9 kappa 0.12 0.02 - 0.22

55

Table 6 - Parameters of a biased correlated random walk with a time interval of 66 minutes

for trios inside the breakpoint distance getting closer to primary roads for white-tailed deer

in the Lake David yard, Canada (2009).

Parameter Estimate95% confidence

interval Number of

trios Break value (m) 116 1180 Move length (m)

26.7 23.3 - 30.5 236 Deviation in turning angle (degrees)

mu 208.5 175.0 - 242.0 236 kappa 0.31 0.13 - 0.49 236

Table 7 - Variation in deviation of turning angles for white-tailed deer in the Lake David

yard, Québec, 2008

Time interval (min)

Mu (95% CI)

Kappa (95% CI)

Number of trios

30 180.8

(168.3 - 193.3) 0.35

(0.28 - 0.43) 1318

36 186.2

(173.9 - 198.4) 0.34

(0.27 - 0.42) 1452

42 175.5

(158.0 - 193.0) 0.31

(0.22 - 0.41) 862

48 185.4

(168.7 - 202.1) 0.34

(0.24 - 0.44) 785

54 177.8

(153.8 - 201.8) 0.21

(0.12 - 0.30) 981

60 192.8

(168.4 - 217.2) 0.20

(0.11 - 0.28) 1087

66 186

(154.5 - 217.5) 0.15

(0.07 - 0.23) 1180

72 141.3

(89.6 - 193.0) 0.11

(0.01 - 0.22) 739

Discussion Proximity of primary roads in the yard impacted deer movement patterns and thus

movement for the 66-min time interval was better explained when including this important

anthropogenic habitat feature. However, longer move lengths outside the breakpoint value

56

are contrary to what is found in the literature. Snowmobile harassment and the presence of

groomed trails that ease deer locomotion should have cause an increase in move length or

the displacement of deer from the areas close to roads (Lavigne 1976, Freddy et al. 1986).

Indeed, these features should increase the animal’s speed and thus, the move length. One

potential reason explaining the lowered speed can arise from the road openings. They can

create an area with a more important deciduous shrub cover, thus increasing browse

availability. Animals may spend more time foraging, thus lowering the distance moved for

the same time unit. Our method detected a break in the data at only one time interval, thus

revealing the importance of the time interval used (Johnson et al. 2002). Therefore,

primary roads seemed to influence broader scale movement, i.e. movements occurring

within a longer time interval, since we were unable to detect an effect at finer temporal

scales. However, even if it was detected at one of the broadest time interval, a time interval

of 66 min can be considered as a fine scale movement. It demonstrate the need to include

habitat heterogeneity in movement models since several different habitat characteristics

may modify all or only some movement parameters (Schtickzelle et al. 2007). As well as

small organisms such as insects, white-tailed deer reacted to its landscape even at the time

of the year when movements are the most reduced.

Values of deviation in turning angles including 180° in their confidence interval show that

winter deer movements are somewhat restrained in space and that they have a high

tendency to reverse direction. This result is not an artifact of a sample size too small or the

result of the GPS error since it is consistent through all time intervals. Usually, the

correlation component refers to the tendency of individuals to move in the same direction

as the previous move (Schultz and Crone 2001). However, our results demonstrate that

deer have the tendency to move in the opposite direction as the previous move, which can

also be considered as a correlated random walk since the direction of each move is not

independent of the previous move. High probability of reversing movement direction

suggests that deer tend to maximize their habitat use while minimizing their net

displacement, which are impeded by deep snow (Parker et al. 1984). This restriction in

space use corresponds to the fact that during winter, deer congregate into yards with

specific habitat features and elaborate an extended trail network to facilitate movement

(Nelson and Mech 1981;1986). The maintenance of this network requires that deer often

57

follow the same trail, thus increasing its turning frequency. Furthermore, small

displacements will restrict their capacity to exploit distant resources. This situation may

easily lead to the over-exploitation of available resources seen in deer yards. This over

exploitation can be enhanced by the already present scarcity of food in a degraded yard or

one extensively used over a long period of time (Dumont et al. 1998).

The 6-min variation in the time interval, which ranged from 30 min to 72 min, used to

sample the complete dataset allowed us to gain insight into the different winter movement

patterns for white-tailed deer. As the time interval increased, the value of the concentration

parameter diminished, thus indicating a lower correlation component. This situation

underlines the increasing presence of different behaviors occurring at longer time intervals

since movement are less directed. Different behaviours are also reflected in the move

lengths. Normally, we should have observed a constant and gradual increase in move

lengths with a constant time interval augmentation, not a plateau between the 54-min and

the 66-min time interval where move lengths are, at the same time, similar and different

from the previous and next section. However, the fact that it was not significant may arise

from a too large variation in move length between the time intervals or a too small sample

size. This situation suggests that studying white-tailed deer winter movements with a time

interval longer than 54 minutes may be capturing different behaviors, and will average over

finer scale movement bouts. This switching in behavior state and the differences in the

concentration parameter along different time intervals are one of the reasons why CRW

often tend to fail over long time horizons since the animal is not always moving (Firle et al.

1998). Several studies related different behaviors (eg. resting, foraging, traveling) by

means of movement parameters to landscape characteristics (Jonsen et al. 2003, Frair et al.

2005, Forester et al. 2007). However, our models did not include habitat variables,

indicating that the change is internal to the animal. This and the presence of the breakpoint

in the step lengths showing the influence of primary roads demonstrate how complicated it

can be to accurately model movement in heterogeneous landscape.

This kind of movement pattern at fine temporal scale would not have been detected if we

had used fixed time interval for our GPS fixes. Indeed, the use of a continuous time

interval allowed us to use different continuous time increments since we were not

58

restrained to the same time interval. Animals do not necessarily obey to a strict and regular

schedule for their activity and behavior patterns. Thus, the use of a regular time interval

may not capture movement or habitat selection pattern depicting uncommon or irregular

but essential activities.

Acknowledgements We thank the many people who helped in the field, particularly David Poulin, Jean-Pascal

Trudeau and Marianne Moffat. Financial support for this project was provided by Natural

Sciences and Engineering Research Council of Canada (NSERC, McIntire), the Canada

Research Chair program (McIntire), the Canada Foundation for Innovation (McIntire), and

MC Forêt inc. E.Allard was also supported by grants from NSERC and Fondation

Héritage-Faune. We received GIS layers and logistical support from Quebec Ministry of

Natural Resources.

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Conclusion générale

La situation du cerf de Virginie au Québec est loin d’être qualifiée de préoccupante.

Cependant, dans certaines régions, la rigueur des hivers rencontrés peut fortement

influencer les populations puisque plus de 40% des individus peuvent ne pas survivre à

l’hiver (Mech et al., 1987). La présence d’un ravage de superficie suffisamment importante

et composé des caractéristiques recherchées par les cerfs peut donc être crucial pour les

individus de ces régions. Cependant, les types d’habitat les plus sélectionnés et leurs

interactions avec d’autres caractéristiques de l’habitat telles que les chemins primaires et les

ruisseaux demeurent encore peu connus. Il en est de même pour l’échelle à laquelle cette

sélection s’effectue. Aussi, la relation entre cette sélection d’habitat et la survie des cerfs

suite à la saison hivernale mérite d’être étudiée plus en profondeur. Non seulement il est

important de comprendre cette relation pour aménager adéquatement les ravages de cerf, il

est aussi important de comprendre comment ils se déplacent à l’intérieur de ceux-ci.

Malgré la sévérité des hivers rencontrés au cours de cette étude, nous n’avons pu démontrer

avec certitude la présence d’une relation entre la sélection d’habitat au cours de l’hiver et la

survie hivernale des cerfs de Virginie. Cependant, les résultats à l’échelle du paysage

permettent de supposer qu’une telle relation pourrait exister à cette échelle, ce qui

concorderait avec la théorie de la hiérarchie des facteurs limitants (Rettie and Messier,

2000). En effet, cette théorie indique que les facteurs ayant le plus grand potentiel de

limiter la survie et la reproduction sont évités à de plus grandes échelles alors que les

facteurs moins décisifs sont évités aux plus petites échelles. La sélection plus importante

pour les peuplements plus résineux à l’échelle du paysage qu’aux autres échelles viendrait

donc confirmer cette théorie. La présence de peuplements à dominance résineuse, une des

composantes principales associées aux ravages de cerfs de Virginie, dans le paysage serait

donc favorable pour les populations de cerfs. Aussi, la présence et la proximité des

chemins primaires ont été des variables environnementales importantes dans la

modélisation de la sélection d’habitat des cerfs. En effet, leur influence s’est fait sentir

sous la forme d’une sélection assez forte à chacune des échelles spatiales, quoique la force

de cette influence ait été différente en fonction de l’échelle. Cette influence s’est également

répercutée sur les mouvements à fine échelle puisque les cerfs ont diminué la longueur

62

moyenne de leur mouvement à proximité des chemins. Les chemins utilisés à l’intérieur

des ravages pourraient donc ne pas être si néfastes au niveau de la sélection d’habitat.

L’étude des mouvements à plusieurs échelles temporelles a permis de mieux comprendre

l’utilisation du ravage par les cerfs. Les valeurs moyennes des angles de virage, toutes

situées autour de 180º, démontrent que les cerfs maximisent l’utilisation de leur espace en

effectuant plusieurs changements de direction. Cette façon de faire facilite également

l’entretien des sentiers présents dans le ravage, ces derniers permettant d’aider à éviter les

prédateurs et facilitant les déplacements (Nelson and Mech, 1986). La planification de

l’aménagement des ravages doit donc tenir compte de cette importante particularité. Ainsi,

la création d’une mosaïque de plusieurs micro-peuplements pourra favoriser la situation du

cerf dans les ravages.

Cette étude a permis de démontrer, une fois de plus, l’importance devant être accordée au

choix des échelles, autant spatiale que temporelle, et au fait d’en inclure plusieurs

imbriquées une dans l’autre (Boyce, 2006). En effet, l’observation des différences au

niveau de la magnitude et de la direction des coefficients n’a pu être réalisée qu’avec

l’utilisation de trois échelles spatiales différentes pour les fonctions de sélection des

ressources. De même, l’utilisation d’une échelle temporelle augmentant graduellement

pour l’analyse des mouvements a permis de valider un processus observé à fine échelle.

Ainsi, la variation des échelles utilisées dans les analyses permet d’observer et de quantifier

les variations inhérentes aux processus biologiques ou de valider l’existence réelle d’un

processus.

Compte tenu de certains résultats non significatifs obtenus dans cette étude et de la quantité

impressionnante de données pouvant être obtenues avec la technologie des colliers GPS, de

nombreuses perspectives de recherche future sont présentes. Malgré le fait que nous avons

observé une sélection pour les chemins primaires et une diminution de la distance

parcourue, l’étude des effets physiologiques reliés à cette proximité serait approprié. En

effet, les effets du compromis réalisés par les cerfs ne sont pas connus et pourraient s’avérer

néfastes pour leur budget d’énergie. Aussi, l’inclusion des données du sous-couvert dans

les modèles pourrait faire en sorte d’améliorer significativement les modèles, et ainsi

d’encore mieux comprendre les interactions entre le cerf de Virginie et son habitat.

63

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Utilisation hivernale des ravages du cerf de Virginie · 2018-04-17 · selection may influence white-tailed deer winter survival. In this study, I showed that the influence of habitat