Dynamique spatio-temporelle des mammifères hivernant dans ... · Estimated effects of current and lag density (previous winter) on habitat selection of snowshoe hares in the Montmorency

DYNAMIQUE SPATIO-TEMPORELLE DES MAMMIFÈRES HIVERNANT DANS UNE FORÊT BORÉALE DE L’EST DU

CANADA

Thèse de doctorat

TOSHINORI KAWAGUCHI

Doctorat en sciences forestières Philosophiae doctor (Ph.D.)

Québec, Canada

© Toshinori KAWAGUCHI, 2015

iii

RÉSUMÉ

La sélection de l'habitat par les espèces animales est rarement abordée par des études à long

terme. Basé sur 11 années de pistage sur la neige, j’ai examiné 1) s’il est possible d’élaborer

un indice de population fiable avec des dénombrements de pistes comparés à des ventes de

peaux de mustélidés, l'écureuil roux et la marte, 2) si la sélection de l'habitat du lièvre est

influencée par la densité de l’espèce, 3) si la profondeur de la neige exerce une influence sur

l'utilisation de l’habitat du lièvre, 4) et si l’association spatiale entre la martre et le lièvre est

réduite lorsque l’abondance de prédateurs concurrents, le lynx du Canada et le renard roux,

augmente.

Chaque année, 91,3 km ± 28,9 km (moyenne ± SD) de transects ont été parcourus. Pour

le premier objectif, des modèles linéaires généralisés du nombre de pistes de chaque espèce

ont été développés, en fonction de l'effet de l'année (variable catégorique) et des descripteurs

de la végétation. Les estimations des effets de l'année étaient étroitement associées avec les

ventes des peaux d'écureuil roux et de belettes. Le nombre moyen de pistes par effort

d’échantillonnage étaient associés avec les ventes de peaux de martre. La fréquentation de

jeunes peuplements (20-40 ans) était influencée par l’indice de population de lièvres durant

l'année précédente. À l’intérieur d’un hiver, le lièvre était davantage associé à feuillage au-

dessus de 2 m (données LiDAR) à mesure que la neige devenait plus profonde. Finalement,

la relation de causalité entre le lièvre, la martre, l'écureuil roux, le renard roux et le lynx a été

déterminée par l'analyse de piste (path analysis). L’association spatiale entre les lièvres et la

martre diminuait lorsque l’abondance de lynx dans l'année précédente était élevée.

Cette étude démontre l’importance de la prise en compte de la dynamique écosystémique

à long terme tel que le climat et la dynamique de la population, et de l’espèce focale, lors de

l’étude de la sélection de l’habitat. Elle incite à la prudence dans les projections à long terme

basées sur des approches simples telles que les indices de qualité des habitats. Dans un

contexte d’aménagement forestier, il est probable que les changements à court terme et à long

terme dans la végétation et l’enneigement, suite aux pratiques forestières et aux changements

climatiques, auront des effets complexes sur la répartition spatiale des mammifères

hivernants.

v

ABSTRACT

Habitat selection by animals has rarely been the focus of long term studies. Based on 11

years study of snow tracking, I investigated whether 1) population indices derived from snow

tracking agreed with pelt sales in marten, red squirrel and weasels, 2) habitat selection by

snowshoe hare is influenced by conspecific density, 3) snow depth influenced habitat use

pattern of snowshoe hare, 4) spatial association between marten and hare is reduced when

other hare predators, lynx and fox, are more abundant.

Each year, 91.3km ± 28.9 km (mean ± SD) of transects were surveyed. For the first

objective, generalized linear models were used for track count of each species as function of

year effect (categorical variable) and vegetation variables. Estimates of year effects agreed

strongly with pelt sales of red squirrel and weasels. Mean track counts by sampling effort

agreed with marten pelt sales. Hare track counts in young (20-40y) forest stands declined

with an increase of conspecific density with one year lag. Hare track counts were increasingly

associated to stands with high foliage density above 2m (measured with LiDAR), as snow

became deeper in the course of winter. Finally, path analyses of the causal relationship

between spatial distributions of hare, marten, red squirrel, red fox and Canada lynx suggested

that the hare-marten spatial association declined when lynx abundance in the previous year

was high.

This thesis underlines the importance of accounting for long term ecosystem dynamics

such as population and climate, including those of the focal species, in the study of habitat

selection. It raises questions about the validity of long-term projections based on simple

approaches such as habitat suitability indices. In a forest management context, short- and

long-term changes in the vegetation and snow cover, following forest management and

climate change, will have complex effect on wintering mammal spatial distribution.

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TABLE OF CONTENTS

RÉSUMÉ ............................................................................................................................... iii

ABSTRACT ............................................................................................................................ v

TABLE OF CONTENTS ...................................................................................................... vii

LIST OF TABLES ............................................................................................................... xi

LIST OF FIGURES ............................................................................................................. xiii

ACKNOWLEDGMENTS ................................................................................................ xvii

AVANT-PROPOS .............................................................................................................. xix

GENERAL INTRODUCTION ............................................................................................ 1

Ecosystem management and limits of short term studies ............................................. 2

Habitat selection ................................................................................................................ 3

Interspecific interactions and habitat selection .............................................................. 4

Winter and seasonal aspects of habitat selection ........................................................... 5

Objectives .......................................................................................................................... 7

STUDY SITE ......................................................................................................................... 9

Climate condition .............................................................................................................. 9

Plant species composition ................................................................................................. 9

Mammal species composition .......................................................................................... 9

Natural disturbance ........................................................................................................ 10

Ecosystem management ................................................................................................. 10

GENERAL METHODS ..................................................................................................... 19

Strengths .......................................................................................................................... 19

Weaknesses ...................................................................................................................... 20

Sampling design .............................................................................................................. 20

Survey conditions ............................................................................................................ 21

Statistical procedures for adjusting potential bias in track counts ............................ 21

CHAPTER 1 - SNOW TRACKING AND TRAPPING HARVEST AS RELIABLE SOURCES FOR INFERRING ABUNDANCE: A 9-YEAR COMPARISON .............. 27

Abstract ............................................................................................................................ 28

Introduction ..................................................................................................................... 29

Field-Site Description ..................................................................................................... 32

Methods ............................................................................................................................ 33

Results .............................................................................................................................. 37

Discussion ........................................................................................................................ 38

Acknowledgments ........................................................................................................... 40

CHAPTER 2 – INFLUENCES OF CURRENT AND RECENT CONSPECIFIC DENSITY ON HABITAT SELECTION OF SNOWSHOE HARE .............................. 49

Abstract ............................................................................................................................ 50

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Introduction .................................................................................................................... 51

Methods ........................................................................................................................... 52

Results ............................................................................................................................. 55

Discussion ........................................................................................................................ 56

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

CHAPTER 3 – VARIATION OF SNOW DEPTH AFFECTS THE SPATIAL DISTRIBUTION OF SNOWSHOE HARE ..................................................................... 63

Abstract ........................................................................................................................... 64

Introduction .................................................................................................................... 65

Methods ........................................................................................................................... 66

Study site ...................................................................................................................... 66

Assessing snowshoe hare spatial distribution .............................................................. 67

Understory cover, stand height and foliage density ..................................................... 67

Statistical analysis ........................................................................................................ 69

Results ............................................................................................................................. 70

Discussion ........................................................................................................................ 70


CHAPTER 4 – WINTER SPATIOTEMPORAL DYNAMICS OF A BOREAL PREDATOR-PREY COMPLEX ...................................................................................... 81

Abstract ........................................................................................................................... 82

Introduction .................................................................................................................... 84

Methods ........................................................................................................................... 87

Study area ..................................................................................................................... 87

Snow tracking ............................................................................................................... 87

Estimation of population indices .................................................................................. 88

Exploratory path analysis ............................................................................................ 89

Dynamics of spatial association ................................................................................... 90

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

Discussion ........................................................................................................................ 92


Appendix ....................................................................................................................... 103

Appendix 1. The best causal graphs for each year during study period, 2004-2014. 103

Appendix 2. Result of d-separation test representing all d-separation claims and its probability of independence. ...................................................................................... 107

Appendix 3. Estimated path coefficients of edges in the best graphs for each year. . 112

GENERAL CONCLUSION ............................................................................................ 117

The challenge for developping a reliable population index ...................................... 117

Feedback effect of density on habitat selection of snowshoe hare ........................... 118

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Snow depth influenced habitat use of hare ................................................................. 118

Interactions in spatial distribution among mammals ................................................ 119

Caveats ........................................................................................................................... 120

Management implications ............................................................................................ 121

Long term studies and snow tracking ......................................................................... 122

LITERATURES CITED .................................................................................................. 124

xi

LIST OF TABLES

GENERAL METHODS

Table Method 1. Sampling effort for snow tracking at the Montmorency Forest, southern Quebec, Canada, 2004-2014. ........................................................................................ 25

CHAPTER 1

Table 1. 1. Sampling effort for snow tracking and track counts for American Marten, American Red Squirrel, and weasels at the Montmorency Forest, southern Quebec, Canada, 2004-2012. ...................................................................................................... 46

Table 1. 2. Pearson correlations between pelt sales, and different population indices that were based on snow-tracking over the three species (n = 9): a) American Marten, b) American Red Squirrel, and c) weasels, in southern Quebec, Canada, 2004-2012. Year effect GLM indicates estimates of year effect from a Generalized Linear Model. .......................... 47

CHAPTER 2

Table 2.1. Sampling effort for snow-tracking of snowshoe hare (Lepus americanus) in the Montmorency Forest, southern Quebec (Canada), 2004-2014. .................................... 59

Table 2.2. Estimated effects of current and lag density (previous winter) on habitat selection of snowshoe hares in the Montmorency Forest, Québec, 2004-2014 (n = 10). Estimates are shown for models including either current or lagged effects of density. Positive estimates indicate a greater association at higher density. Adjusted R2 values can be negative, because unlike raw R2, they are penalized by the number of parameters. .... 60

CHAPTER 3

Table 3. 1. Sampling effort for snow-tracking for snowshoe hare (Lepus americanus) at the Montmorency Forest in southern Quebec, Canada, 2012 -2014. ................................. 77

Table 3. 2. List of models for testing hypothesis regarding effect of interaction between LiDAR derived tree height, penetration rate, regenerating forest and snow depth on track counts of hare. X indicates a corresponding variable included into the model. ........... 78

Table 3. 3. Model comparison among candidate models for habitat use by snowshoe hare in the Montmorency Forest, Quebec, 2012-2014. The variable YEAR was treated as a categorical variable. The w indicates Akaike weight. .................................................. 79

Table 3. 4. Estimated effects of snow depth on habitat use by snowshoe hare in the Montmorency Forest, Quebec, 2012-2014: a) hare response mean LiDAR penetration rate (n = 336) and b) hare response to proportion of understory cover (n = 180) under different snow depth. .................................................................................................... 80

CHAPTER 4

Table 4. 1. Sampling effort for snow tracking and track counts for snowshoe hare (Lepus americanus), American marten (Martes americana), red squirrel (Tamiasciurus

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hudsonicus), and Canadian lynx (Lynx canadensis) and red fox (Vulpes vulpes) at the Montmorency Forest, southern Quebec, Canada, 2004-2014. ................................... 100

Table 4. 2. Model fit for dynamics of edges in spatial distribution between Hare and Marten in the Montmorency Forest, Québec, 2004-2014. Candidate models include variables: Hare-marten path coefficients (Hare-Marten), hare-marten path coefficients in the previous year (Hare-Martent-1), marten population index (Marten population), hare-fox path coefficients (Hare-Fox), and hare-lynx path coefficient (Hare-Lynx). .............. 101

Table 4. 3. Model fit for dynamics of edges in spatial distribution between marten and squirrel in the Montmorency Forest, Québec, 2004-2014. Candidate models include variables: Hare-lynx path coefficients (Hare-Lynx), hare population index (Hare population), hare-fox path coefficients (Hare-Fox). ....................................................................... 102

Table 4. 4. Result of d-separation test representing all d-separation claims and its probability of independence. ......................................................................................................... 107

Table 4. 5. Estimated path coefficients of edges in the best graphs for each year. ............ 112

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LIST OF FIGURES

STUDY SITE

Figure Site 1. Inter-annual dyhnamics in weather condition in winter (January to March), 2004-2014: a) Snow depth and b) Temperature. Black line indicates mean value. Blue line indicates either seasonal maximum or minimum value depending on variable of interst. Grey error bar indicates standard deviation. ..................................................... 12

Figure Site 2. Vegetation maps of the study site. The map was created based on vegetation map in March 2014. ...................................................................................................... 13

Figure Site 3. Distribution of clear-cut performed during the study period, 2004-2014. Different colors are corresponding to year of clear-cut performed. ............................. 14

Figure Site 4. Inter-annual variations in proportion of area of each habitat types in Foret Montmorecy, southern Quebec, 2004-2014. ................................................................ 15

GENERAL METHOD

Figure Method 1. Spatial distribution of transects in Forêt Montmorency. ......................... 23

Figure Method 2. Schematic representation of an off-road snow-tracking transects. .......... 24

Photo 1. Effect of snow depth on understory cover represented by photographie. The photo represents that small tree was buried by deep snow. ............................................................ 16

Photo 2. Graphical representation of four types of winter habitats in the study site, the Montmorency Forest, Québec: Regenerating forest (top left), young forest (top right), mature forest (bottom left), old forest (bottom right). .................................................. 17

CHAPTER 1

Figure 1.1. Graphical representation of spatial location of snow tracking site (the Montmorency Forest) and trapping area of Furbearer Management Unit (UGAF) 39, southern Quebec, Canada, 2004 - 2012. Black area indicates the location of snow tracking sites. Gray area indicates the location of trapping area. ................................. 41

Figure 1. 2. Inter-annual dynamics of winter precipitation and winter temperature in the study sites, the Montmorency Forest and the Laurentides Wildlife Reserves (UGAF 39), southern Quebec, Canada, 2004-2012: a) Winter temperature (oC), b) winter precipitation (mm). The data for 2006 was not available. ............................................ 42

Figure 1. 3. Spatial distribution of sampling transects in the Montmorency Forest, southern Quebec, Canada, 2004-2012. Black lines indicate off-trail transects and gray lines indicate either roads or trails. ........................................................................................ 43

Figure 1. 4. Comparison of population trends between snow tracking and pelt sales across three taxa: a) American Marten, b) American Red Squirrel and c) weasels, southern Quebec, Canada, 2004-2012. Two population indices are presented: left) Year effect of a Generalized Linear Model (Year effect GLM), right) tracks/exposure time. Black lines represent pelt sales and gray lines represent population indices of year effect GLM (right) or tracks/exposure time (left). Vertical bars represent standard errors. ............. 44

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CHAPTER 2

Figure 2. 1 Estimated population index of snowshoe hare over 11 years from 2004 to 2014 in the study site. The index was developed from coefficients of the year effect estimated from generalized estimating equations (GEE). Vertical bars indicate standard errors. 61

Figure 2.2. Association of hares with 0-20y forest stands explained by a) immediate effect (Dt) only and b) with immediate and time-lag effects (Dt + Dt-1) of the density index over 10 years (2005 - 2014) in the study site (n = 10). Points with standard error bars indicate model coefficients. Dashed lines indicate 95 % confidence bands of the fitted regression line values. .................................................................................................. 62

CHAPTER 3

Figure 3. 1. Satellite snow depth over distribution of snowshoe hare (Lepus americanus). Satellite data was obtained from Canadian Meteorological Centre (Brown and Brasnett 2010). The date of measurement for the map was 1 March in 2012. The resolution was 24km x 24km. The location of the study site was represented by a black star. The histogram showed frequency distribution of snow depth over hare distribution. Snow depth at the study site was 85.1cm on this date. .......................................................... 74

Figure 3. 2. Vegetation map and sampling location in the study site, 2012-2014. The vegetation map was produced by using the one in 2012, ............................................. 75

Figure 3. 3. Effect of interaction between snow depth and vegetation structure on habitat use by hares at the Montmorency Forest, Canada, 2012-2014: A) hare response to LiDAR penetration rate, B) hare response to mean understory cover under different snow depth. High penetration rate values indicate low foliage density above 3m from ground. ..... 76

CHAPTER 4

Figure 4. 1. Population dynamics of five species over 11 years, 2004-2014: a) Snowshoe hare, b) red squirrel, c) American marten, d) Lynx and e) red fox. Gray error bars indicate standard errors. ............................................................................................................. 96

Figure 4. 2. Summary of path analysis results linking spatial distributions of predator, prey and vegetation attributes, 2004-2014. Thickness of line is proportional to the number of years with evidence for an edge. Red colored edges indicate positive coefficients and blue colored edges indicate negative coefficiens. Grey colored edges indicate that path coefficient were either positive or negative depending on study year. ........................ 97

Figure 4. 3. Dynamics of edges in spatial distributions between hare and marten, 2004 -2014 (n = 10). The graphs represent a) relationship between hare-marten spatial association in the current winter and the one in the previous winter and b) hare-marten spatial association in the current winter and lynx population index in the previous winter. Points with standard error bars indicate path coefficients. Dashed lines indicate 95 % confidence bands of the fitted regression line values. .................................................. 98

Figure 4. 4. Dynamics of edges in spatial distributions between squirrel and marten, 2004 -2014 (n = 10): a) Relationship between squirrel-marten spatial association and hare population index, b) Relationship between squirrel-marten spatial association and hare-marten association. Points with standard error bars indicate path coefficients. Dashed lines indicate 95 % confidence bands of the fitted regression line values. .................. 99

xv

Figure 4. 5. The best causal graphs for each year during study period, 2004-2014. Solid line indicates significant path (p < 0.05). Dashed line indicates non-significant path (p < 0.05). Red line indicates positive effect from a variable to the other and blue lines indicate negative. ........................................................................................................ 106

xvii

ACKNOWLEDGMENTS

I am indebted to a great number of recherchers and volunteers who have helped me

progress in this PhD project. Their collaboration and contribution made this thesis possible.

Financial support for this project was provided by a scholarship to T. Kawaguchi from

the “Leadership and Sustainable development Scholarship Program” of Laval University as

well as Foundation F.-K.-Morrow, and by a Natural Sciences and Engineering Research

Council of Canada (NSERC) Discovery grant to A. Desrochers.

I thank the Ministère des Forêts, de la Faune et des Parcs for providing us with pelt sales

data, trapping effort data and geographical information for chapter 1. Especially, I appreciate

H. Bastien for providing me useful comments and advice for the manuscript in chapter 1.

We are grateful to the 29 skilled field workers who contributed to the collection of snow

tracking data. Among 29 skilled workers, I would like to thank especially M. Lapointe for

her great contribution to data collection under harsh winter environment. I also thank C.

Villeneuve for teaching me how to drive a snowmobile safely and for his great contribution

to data collection.

I appreciate my collegues for providing me useful comments on my project and for

having discussions on ecological and statistical issues. I thank J. Marchal for providing me

useful advice on R programming. I have frequently discussed statistical issues with him

during lunch time. The discussion made me think the issues further and deeper. And I thank

J. Faure Lacroix for helping me improve my french writing and pronouciation in french

presentation in Colloque 3 and other opportunities. F. Fabianek introduced me to boreal forest

in the Montmorency Forest in 10 days of his field work. The field work was useful for me to

xviii

capture characteristics of boreal forest and to develop the initial idea of my thesis. I thank S.

Beaudoin for her assistance in correcting in my French during PhD program. I thank C. Roy,

S. Renard and N. S. Baker for their comments on the earlier version of this thesis.

I appreciate D. Fortin, L. Bélanger and C. Samson for their assistance in the design of the

study and W.F.J. Parsons in Centre d’Ètude de Forêt (CEF) for his assistance in linguistic

corrections. My special thanks was given to C. Samson for his contribution to this thesis from

the beginning to the end. His comments and questions often led me to think further and deeper.

I also appreciate I. D. Thompson and M. Mazerolle, members of jury for my defense, for

providing me with thoughtful feedbacks on the thesis.

Lastly, I would like to give my gratitude to my director, André Desrochers, for having

provided me with useful advice and comments on this thesis for this 4 years. He had been

working on data collection and initiation of snow tracking project from 2000 before I started

the PhD program. Again, I apprecite his great amount of contribution to the project.

xix

AVANT-PROPOS

Cette thèse de doctorat inclut quatre chapitres. Je suis l’auteur principal de tous les

chapitres, aussi que l’introduction générale et la conclusion générale. Toutes les analyses

statistiques ont été éffectués par moi. Mon directeur de recherche, André Desrochers, a

largement contribué à l’élaboration de cette thèse. De plus, Héloïse Bastien (Ministère des

Forêts, de la Faune et des Parcs, en cours) est coauteur du premier chapitre.

Le prémier chapitre a été accepté par la revue scientifique Northeastern Naturalist.

1

GENERAL INTRODUCTION

Ecological long term studies are often essential to detect and understand ecological

processes and patterns including population dynamics and spatial distribution of animal

species (Lindenmayer et al. 2012). Ecological patterns requiring longitudinal studies

typically include, but are not limited to, responses to climate change (Brown and Braaten

1998) and population dynamics (Fryxell et al. 1999). In the Canadian boreal forest, fur returns

were instrumental in revealing the 10-year population cycle in several mammals including

snowshoe hare (Lepus americanus) and lynx (Lynx canadensis) (Elton and Nicholson 1942).

A classical long term study in Yukon showed that population dynamics of snowshoe hare are

driven primarily by a predator, the Canada lynx (Krebs et al. 2001a). In the case of habitat

preference of snowshoe hare, monitoring habitat preferences for eight years let researchers

realize that at low population densities, hares used mature pine forest less frequently than

dense immature pine forest. However, at peak density, they used mature pine forest as

frequently as immature pine forest (Mowat 2003). Long term work in Finland empirically

demonstrated mesopredator release in a hare-fox-lynx relationship, in which the top predator

(lynx, Lynx lynx) suppressed abundance of mesopredator (red fox, Vulpes vulpes), indirectly

leading to prey population increase (hare, Lepus timidus) (Elmhagen et al. 2010).

An additional reason for the need to conduct longitudinal ecological studies is the

presence of time lags in processes such as species redistribution following landscape changes

(Metzger et al. 2009), local extinction after deforestation (Brooks et al. 1999), species

invasion (Crooks 2005), and population dynamics (Fryxell et al. 1991, Erb et al. 2001). Time

lags can be caused by long processing times following the perception of a stimulus (Brooks

et al. 1999), intervening processes between two processes of interest (Magnuson 1990), or

2

feedbacks (Framstad et al. 1997). Thus, accounting for recent ecological states is often

required to understand what causes current ecological phenomena.

Ecosystem management and limits of short term studies

In the boreal forest, forestry practices under the context of ecosystem management (defined

in Gauthier et al. 2008) attempt to emulate habitat and landscape pattern produced by natural

disturbances (Long 2009) and attempts to manage forest within natural range of variability

(Keane et al. 2009). On the other hand, the Convention on Biological Diversity (CBD)

mentioned that at least 17 per cent of terrestrial are expected to be conserved by 2020 as well

as prevention of species extiction. Under this context, a large number of studies have

investigated wildlife-habitat relationships to conserve a focal species via protecting their

preferred habitat and to establish a management ‘baseline’. Habitat relationship studies in

this context typically 1) evaluate effect of forestry practices on animal distribution (e.g.,

Ferron et al. 1998, de Bellefeuille et al. 2001, St-Laurent et al. 2008), 2) examine if forestry

practices emulate dynamics of habitat use along a stand age gradient observed in natural

disturbance (e.g., Allard-Duchêne et al. 2014) and 3) describe habitat use patterns along stand

age gradient under natural disturbance (e.g., Hodson et al. 2011). Most of these studies

substitute space for time, i.e. use a cross-sectional approach, to infer long-term relationships.

The results derived from these studies have been, and remain, important to establish a basic

understanding of habitat use, an end-result of habitat selection, and patterns of population

dynamics. However, since wildlife-habitat relationships have been shown to vary with

wildlife population density fluctuating over time (examples above), thus the studies might

potentially introduce confounding effects due to conditions prevailing at the time of those

studies.

3

Habitat selection

Habitat selection is a decision-making process whereby individuals preferentially use, or

occupy, a non-random set of available habitat (Morris 2003), which ecologists postulate as a

tool to maximize fitness. The process is influenced by various factors including conspecific

density (Fretwell and Lucas 1970), weather (Reid et al. 2012), interference or exploitation

competition (Morris et al. 2000) and predation risk (Hildén 1965, Laundré et al. 2010,

Thomson et al. 2006).

Under the assumption of ideal free distribution (Fretwell and Lucas 1970), animals move

freely among habitats of different quality, in accordance to what is expected to maximize

their fitness. Fitness per capita declines with increase of conspecific density due to

intraspecific competition. According to the isodar model (Morris 2003), individuals use the

best habitat at low density. However, as density increases, individuals are exposed to

intensive intraspecific competition. As a consequence, fitness in the best habitat can decrease

to the point where fitness in an alternative habitat is equal or higher to that in the best habitat.

Eventually, this leads individuals to move from high-density (best) to lower-density

(alternative) habitat. The effect of conspecific population density on habitat selection has

been documented for various taxa, including mammals (e.g., fat sand rat Psammomys obesus,

Shenbrot 2004; white-footed mouse Peromyscus maniculatus, Morris 1996) and birds (e.g.,

brown-headed cowbird Molothrus ater, Jensen and Cully 2005) taxa (Dreisig 1995, Haché et

al. 2012).

To this date, effects of density on wildlife-habitat relationships have been investigated in

cross-sectional approaches (Morris 2003, Hodson et al. 2010). However, given the facts that

population dynamics of animals synchronized over space (Liebhold et al. 2004), variation

4

captured in cross-sectional approach might not be as large as the one captured in longtitudinal

studies, potentially leading to poor performance of space-for-time substitution.

In addition, time lags are generally expected, and sometimes observed in species

redistribution due to lagged perception of stimuli or long processing time (Brooks et al. 1999).

Since habitat selection involves responses to stimuli, the effect of density on habitat selection

could be lagged. Despite their likely occurrence, time lag effects of density on habitat

selection per se have not been investigated.

Interspecific interactions and habitat selection

Habitat selection by preys and predators each can be influenced by interspecific

interactions modulated by prey abundance, predation risk, and intra-guild competition (Lima

2002, Gorini et al. 2012). Predation risk often leads prey to maintain anti-predator responses

to prior presence of predators, for example moose (Alces alces) – grey wolf (Canis lupus)

(Latombe et al. 2014), elk (Cervus canadensis)-wolf (Fortin et al. 2005).

In a prey-predator interaction, habitat selection by predators often translates into

occupying areas with high prey abundance, as has been shown in the lynx - snowshoe hare

case (Keim et al. 2011).

When several prey species are available, the strength of the spatial association between a

predator and a given prey species can decrease when the abundance of alternative prey

species increases (prey switching; Murdoch et al. 1975). Prey switching is exemplified with

wolves that concentrate on areas used by deer (Odocoileus virginianus) during winter, but

switch to beavers (Castor canadensis) during summer, when the latter become available

(Latham et al. 2013).

5

Habitat selection can be influenced by competition for shared resources by other species

(Morris et al. 2000, Morris 2003). For example, population density of collared lemmings

(Dicrostonyx groenlandicus) in their preferred habitat can decline with increase in density of

competitor, brown lemmings (Lemmus trimucronatus), in the same habitat (Morris et al.

2000). Competition among predators having dietary overlap often results in spatial

segregation among species as shown in the marten (Martes americana) – fisher (Pekania

pennant, formerly Martes pennanti) relationship (Fisher et al. 2013).

North American boreal forests typically host a multi-species predator-prey system

including snowshoe hare, American marten, red squirrel (Tamiasciurus hudsonicus), lynx

and red fox. To exemplify how the complexity of this system influnece habitat selection of a

species, we consider marten. Marten has been long thought to be an old-growth specialist

species (Buskirk and Powell 1994) but now known to use a wider range of habitats including

young and mature forest (Potvin et al. 2000). A deeper understanding of marten habitat

selection can be attributed to the spatial distribution of their prey, snowshoe hare and red

squirrel (Powell et al. 2003). Since those prey species have been confirmed to appear in

mature forest (40yr-) (Hodson 2011, Allard-Duchêne et al. 2014), association between

mature or old forest and marten could be due to association between habitat and those prey.

Still, as observed in a case of prey switching and competition, the strength of the spatial

association between marten and its prey itself could be subject to change due to abundance

of alternative prey and competitors.

Winter and seasonal aspects of habitat selection

The boreal forest is characterized by its harsh winter environment with extreme low

temperatures and deep snow cover (Brandt et al. 2013). Of course, the main challenges

6

encountered in winter by animals are lower food availability and high homeostasis demands.

But recent climate change (Pauli et al. 2013) may add to the existing challenges, for example,

by creating a mismatch between molt to white pelage by hares and the timing of snowfall

(Mills et al. 2013). Lower snow accumulation may also reduce survival of small mammals

such as voles which are dependent on subnivean space (Pauli et al. 2013). However, winter

is also challenging for field work, and therefore a relatively small number of studies have

been conducted during this season (Campbell et al. 2005).

Spatial distribution in summer might be different from the one in winter because

availability of required resource might be subject to vary due to weather, especially in

landscapes where snow depth regularly exceeds 1 m. Variable snow accumulation affects

accessibility to resources including food and thermal cover for both herbivore and carnivore

(Halpin and Bissonette 1988, Morrison et al. 2003). When small trees are covered by deep

snow, availability of understory cover may decline to the point where food (White et al. 2009)

and anti-predator cover (Litvaitis et al. 1985) become sparse enough to elicit changes in

habitat selection.

As described above, habitat selection can be considered as a complex process involving

multiple stimuli and potentially including lags in response to stimuli, all with potential

consequences on the spatio-temporal dynamics of mammals. For example, snowshoe hare –

habitat relationship is known to vary with conspecific density, but this reponse could be

lagged. In addition, preferred habitat of hare might vary with climate, particulary snow depth.

Given an association between prey and predators, spatial dynamics of prey would greatly

influence the dynamics of predator. Long term studies, enabling to capture large varitation

7

and allowing the investigation of lagged responses, would contribute to address those

uncertainty prevailing in species-habitat relationship studies.

Objectives

The general objective of my thesis is to gain a better understanding of spatio-temporal

dynamics of wintering mammals in an eastern Canadian boreal forest under varying

abundance, trophic and competitive interactions with accounting for potential lagged

response to these factors, based on a 11 years of snow-tracking. My thesis contains four

specific objectives, each corresponding to a chapter.

The first thesis objective (Chapter 1) was to design and evaluate different population

indices developed from snow tracking data by comparing with pelt sales data for three

mammal species: American marten, red squirrel and weasels. If the two indices describe

population changes accurately over time, I predict that they will be highly correlated, after

accounting for sources of error described above.

My second objective (chapter 2) was to investigate influences of current and recent

conspecific density on habitat selection of snowshoe hare. In this chapter, I predicted that at

high population densities, snowshoe hare distribution expands into less preferred habitat, thus

weakening the association between hares and most preferred habitat. I predicted that the the

shift in distribution would be stronger in the winter following high density rather than in the

current winter, i.e. will be lagged.

My third objective (Chapter 3) was to examine whether variation of snow depth affects

the spatio-temporal distribution of snowshoe hare. I predicted that with increasing snow depth,

snowshoe hares will less frequently use sites dominated by saplings and lower trees.

8

My fourth objective (Chapter 4) was to explore the spatio-temporal dynamics of five

mammal species: snowshoe hare – red squirrel – marten – lynx – red fox, in a temporally-

changing habitat. I tested whether the strength of the marten-hare spatial association is

affected by presence of lynx and fox, competitors, and abundance of alternative prey, red

squirrel.

9

STUDY SITE

Geographical location and topography

Snow tracking was performed in the Forêt Montmorency, about 80 km north of Quebec City

(47° 20’N, 71° 10’W). The area of this study site is 66 km2. Altitude ranges from 650 m to

1000 m and maximum degree of slope was 40o.

Climate condition

From 1999 to 2011, annual mean temperature was 0.3 °C. Annual precipitation was 1417 mm

(33 % as snow). Maximum snow depth at weather station in the Montmorency Forest ranged

from 62 to 146 cm in 2004-2014 (Figure Site 1a). Mean daily temperature ranged from -15

oC to -9 oC (Figure Site 1b). Snow accumulation in this site was high enough to bury small

trees (Photograph 2).

Plant species composition

Balsam fir (Abies balsamea) dominates second-growth mature forest stands. Black

spruce (Picea mariana), white or paper birch (Betula papyrifera), trembling aspen (Populus

tremuloides), and white spruce (P. glauca) are also common. Recent (less than 5-yr-old)

clear-cuts are generally colonized by red raspberry (Rubus idaeus), balsam fir, and white

birch (de Bellefeuille et al. 2001). Dominance of balsam fir was confirmed by the prism

survey conducted in 2012, which showed that 77% of trees captured in the survey was balsam

fir and only 9% of tree was white birch.

Mammal species composition

A lot of mammal species are commonly observed in the study site: snowshoe hare,

American marten, American red squirrel, Canadian lynx, red fox, grey wolf, weasels (mostly

10

Mustela erminea based on known species distribution), moose, beaver, river otter, and

porcupine (Bouliane et al. 2015). Mountain lion was rarely observed. There was no sighting

of coyote (Canis latrans) and bob cat (Lynx rufus). Small mammlas such as meadow vole

(Microtus pennsylvanicus) and southern red-backed vole (Clethrionomys gapperi) are also

present.

Natural disturbance

Outbreak of spruce budwarm and windthrow were main natural disturbance in the site,

which were driving sources of developing mosaic forest.

Ecosystem management

The principal strategy to protect biodiversity is to “manage natural mosaic forest within

variabily of natural primitive forest” (Bélanger 2001). Specifically, the management goal is

to be achieved through three practices: 1) employ practices maintaining natural regeneration

process, 2) emulate natural disturbances to conserve mosaic forest, 3) developping modality

to conserve ecological value.

The main forestry practice in this area is clearcut with protection of soils and regeneration

(CPRS; Bélanger et al. 1991), resulting in an ever changing landscape (Figure 1 and Figure2).

A dense road is present, with ca. 150 km of roads, i.e. over 2 km/km2. The forest has been

managed to perserve 20% of regenerating forest (1-20yr) and 20 % of youg forest (20-to-

40yr) and 20% of mature forest (40-80yr), 20 % of older forest with irregular management

(e.g., all types of partial cutting) in a landscape at a scale of 10km2. In addition, in a

management unit of 10-15km2, similar proportion of different size of harvest patches (0.5ha-

to-30ha, 10ha-to-30ha, and 30ha-to-100ha) were created. As a consequence of these

11

management, the landscape in this study site was mosaic forest and contained a large amount

of small patches. During the study period, average of actual proportion of each of habitat

type were 26.4 ± 3.5 % (mean ± standard deviation), 23.8 ± 2.9 %, and 49.7 % ± 1.1 for

regenerating forest, young forest and mature forest respectively. Targeted proportion of each

plant species (balsam fir, white spruce, black spruce, white birch) are 60, 14, 3, 10-to-25 %

separately.

In addition, the study area is also managed for other uses such as recreational and research

activity. During winter, some of roads and trails has been used for cross-country ski and

snowshoe hiking in the study years.

12

Figure Site 1. Inter-annual dyhnamics in weather condition in winter (January to March), 2004-2014: a) Snow depth and b) Temperature. Black line indicates mean value. Blue line indicates either seasonal maximum or minimum value depending on variable of interst. Grey error bar indicates standard deviation.

13

Figure Site 2. Vegetation maps of the study site. The map was created based on vegetation map in March 2014.

14

Figure Site 3. Distribution of clear-cut performed during the study period, 2004-2014. Different colors are corresponding to year of clear-cut performed.

15

Figure Site 4. Inter-annual variations in proportion of area of each habitat types in Foret Montmorecy, southern Quebec, 2004-2014.

16

Photo 1. Effect of snow depth on understory cover represented by photographie. The photo represents that small tree was buried by deep snow.

17

Photo 2. Graphical representation of four types of winter habitats in the study site, the Montmorency Forest, Québec: Regenerating forest (top left), young forest (top right), mature forest (bottom left), old forest (bottom right).

19

GENERAL METHODS

(While the following chapters provide description about the field method, this section

provides more detatiled descriptions on the field work used in the entire thesis.)

Most of this thesis was based on snow tracking, a method commonly used in northern

countries (e.g., Canada: Silva et al. 2009; Finland: Sulkava 2007; Norway: Pedersen et al.

2010). Snow tracking can be achieved in two ways, 1) searching for tracks (used here), and

2) tracking at bait stations (not used here) (Halfpenny, 1995).

Snow tracking is non-invasive and is used to monitor changes in relative population size

(Raphael 1994) as well as to determine habitat selection for many mammalian species.

Population trend derived from snow tracking surevey was corresponding to the one from

mark-repcature method. Snow tracking has been used to conduct reliable field surveys of

American marten, fisher, lynx and wolverine (Halfpenny 1995). Snow tracking and the

classic pellet count technique can show similar patterns in habitat use for the snowshoe hare

(Litvaitis et al. 1985).

Strengths

Snow tracking is often easy to implement, less expensive in comparison with other

techniques such as live trapping. In addition, this technique has no bias associated with bait.

Compared to other non invasive methods (e.g., camera trap), snow tracking can survey

extensive areas, which is potentially important in detecting rare animals and also in

investigating animals with large home range size (> 50 ha). For several mammalian species

active in winter, snow tracking has higher detection probability (Gompper et al. 2006). Given

larger variation in seasonal snow depth (approximately 50cm to 150cm) as observed in the

study area (Montmorency Forest), snow tracking would provide more stable detection rate

20

comparing with camera traps which are installed at a fixed height and are potentially buried

in deep snow.

Weaknesses

The main weakness of snow tracking is that it does not provide a direct estimate of

number of animals, given the lack of individual identification. Furthermore, the detection and

count of animal tracks is easily affected by weather such as strong wind, snow fall, and snow

quality (Gompper 2006). Snow tracking is more labor-intensive than camera traps, which

may explain why several government agencies that used to employ snow tracking as

monitoring method now use camera traps (Claude Samson, pers. comm.). Despite these

caveats, snow tracking offers a high potential for long-term, extensive studies, because of its

simplicity and relatively low cost.

Sampling design

Snow-tracking has been conducted at Forêt Montmorency each winter (date range: 20

December – 15 April) since March 2000, initially using a haphazard selection of transects

(2000-2003), followed by a more systematic design (2004-present), consisting of 67 sample

units placed on a 1-km square grid (Figure Method 1). Each grid point was numbered and

sorted randomly at the beginning of a field season to determine the temporal sampling order

within the season. In order to cover larger area and visit a site regulaly, off-road transects

sampled in the previous 2 years were removed from the pool of points available for sampling.

Off-road transects were 2 km long, except for truncated transects occurring near the border

of the study area (Figure Method 2). Additionally, some off-road transects were erased from

the sampling area because observers cannot access them securely. Road and trail transects

covers 150 km of roads and 40 km of trails. However, the actual length of transect sampled

21

depended on snow condition, weather, time of day, and personnel available (Table Method

1). Each year, snow tracking surveys are conducted from late December to late March.

Survey conditions

Snow quality and climate can affect the detection rate of animal tracks. Thus, we

conduct snow tracking when no strong wind (faster than 20m/s) and no snow fall has occurred

in approximately the last 24 h before field work. Each transect is surveyed only once each

winter. At the start of each transect, observers entered snow conditions on a GPS and start

looking for tracks by snowshoe, skis, by foot or by snowmobile at speed slower than 20 km/h.

When a track is detected, observers obtained its coordinate with a GPS and measured stride

length, width as well as footprint width and depth to help identify the species in case of doubt.

All six species studied here leave tracks that are usually easy to detect and identify, and track

misidentification is assumed to be negligible.

Personnel who participated in snow tracking included experienced field workers and

volunteers. To avoid variation in quality and capacity of observers in detection and

identifying tracks, two days of field training was required for inexperienced participants, and

volunteers were required to measure and photograph tracks of equivocal origin. Thus, this

entire thesis assumed that the quality and capacity of observers were not different among

years and within years. Number of observators per a sampling event ranged from one to two

in most sampling events, depending on difficulty of terrain (for security) and experience.

Statistical procedures for adjusting potential bias in track counts

Since snow tracking was performed on both trail and off-trail transects, surveying on

trails and roads might detect more tracks than on off-trail transect for certain species. For

22

example, red fox and wolf have been confirmed to use road as movement corridors (Coffin

2007). In addition, road has no canopy cover and lateral cover. For mammal species which

require these vegetation cover, road might not be used as much as off-trail transect, resulting

in lower track detection rate on road. Exposure time and temperature are often factors

affecting detection rate of tracks (Roy et al. 2010, Bois et al. 2012).

The following chapters integrated these factors into statistical models as fixed effects to

account for these biases in track presence or counts. While N-mixture models are often used

to account for detection probability (MacKenzie et al. 2002), this thesis did not use them

because the aim of this thesis did not require to estimate true occupancy but simply assess

whether and how track count and environmental conditions, including other wildlife,

covaried while accounting for possible bias.

23

Figure Method 1. Spatial distribution of transects in Forêt Montmorency.

24

Figure Method 2. Schematic representation of an off-road snow-tracking transects.

25

Table Method 1. Sampling effort for snow tracking at the Montmorency Forest, southern Quebec, Canada, 2004-2014.

Year

Road and trail Off-trail

Length (km)

Number of observers

Sampling events (days)

Length (km)

Number of observers


2004 109.5 4 11 44.4 4 30 2005 110.9 6 18 20.2 2 13 2006 187.5 3 21 47.5 3 21 2007 125.6 3 14 30.8 3 14 2008 146.7 5 17 47.3 4 19 2009 162.8 4 19 26.4 3 13 2010 151.4 10 13 29.6 5 10 2011 143.8 4 10 24.7 2 9 2012 135.9 2 13 69 6 21 2013 124.4 5 9 27.3 2 11 2014 124.6 5 12 22.9 2 9 Total 1523.1 51 157 390.2 36 170 Mean 138.5 4.6 14.3 35.5 3.3 15.5 SD 23.3 2.1 3.9 14.9 1.3 6.6

27

CHAPTER 1 - SNOW TRACKING AND TRAPPING HARVEST AS

RELIABLE SOURCES FOR INFERRING ABUNDANCE: A 9-YEAR

COMPARISON

Toshinori Kawaguchi1,*, André Desrochers1 and Héloïse Bastien2

1Centre d’Étude de la Forêt, Université Laval, 2405, rue de la Terrasse, Québec (Québec),

G1V 0A6, Canada. 2Direction de la Gestion de la Faune de la Capitale-Nationale et de la

Chaudière-Appalaches, Ministère des Forêts, de la Faune et des Parcs, 1300, rue du

Blizzard, local 100, Québec (Québec) G2K 0G9, Canada.

This chapter was accepted by the journal Northeastern Naturalist on 3 November 2015.

Therefore, the format in this chapter was adjusted to the guidline of the journal.

28

Abstract

Trapping harvest and snow tracking are frequently used to infer population dynamics, yet

there have been few evaluations of those indices. We developed population indices for Martes

americana Turton (American Marten), Mustela spp. (Weasels) and Tamiasciurus hudsonicus

Erxleben (American Red Squirrel) from 9 years of snow tracking data in Eastern Canada.

Population indices were mean track counts per unit effort and derived from a Generalized

Linear Model (GLM) of track counts as a function of year and covariates including forest age.

Mean track counts had significant correlation with American Marten and Weasels pelt sales.

Year effects in GLM were correlated with American Red Squirrel and Weasels pelt sales.

Both methods are in agreement therefore they likely are replicating population dynamics of

the selected species.

29

Introduction

Monitoring animal populations is a key to understanding the state of ecosystems, their

functions and their responses to anthropogenic and natural disturbances (Lindenmayer et al.

2012). Direct estimates of population densities by live trapping or mark-recapture is very

labor intensive and expensive (Gese 2001). Thus, ecologists often resort to population indices,

such as those derived from snow tracking (Pellikka et al. 2005), trapping harvest (Roberts

and Crimmins 2010), observation reports by hunters (Simard et al. 2012) and scat surveys

(Krebs et al. 2001a, Mowat and Slough 2003).

Snow tracking is non-invasive (Halfpenny et al. 1995) and is frequently used to estimate

relative abundance of wintering mammals in North America (Mowat and Slough 2003) and

Europe (Pellikka et al. 2005). Assuming that the number of tracks is proportional to

population size, some studies use tracking counts per unit effort to infer relative abundances

of mammals such as Lepus americanus Erxleben (Snowshoe Hare), Tamiasciurus hudsonicus

Erxleben (American Red Squirrel) (Jensen et al. 2012), Martes americana Turton (American

Marten) (Krebs 2011), and Mustela frenata Lichtenstein (Long-tailed Weasel) (Fitzgerald

1977). Snow tracking is ease of use (Halfpenny et al. 1995) and has higher detection rates

than other non-invasive techniques such as camera traps and track plates (Gompper et al.

2006). Also, snow tracking does not require the baits or attractants that are employed in other

population monitoring techniques (Raphael 1994).

However, there may be significant noise and bias in population indices obtained from

snow tracking data. Track counts can be affected by weather conditions such as strong winds

and recent snow falls (Raphael 1994, Gompper et al. 2006), and can vary with the activity

level of animals. For example, American Marten activity decreases at extremely low

30

temperatures in American Marten (Thompson and Colgan 1994) and weasels (Robitaille and

Baron 1987). Presence of prey can influence track counts of the predator as shown in Lynx

canadensis Kerr (Canada Lynx) (Keim et al. 2011). Observer bias and miss identification can

be biases.

In many provincial and state agencies, trapping harvest in the form of total catch and

catch per unit effort is commonly used to infer relative population sizes and trends of fur-

bearing species (Douglas and Strickland 1987), particularly American Marten and Pekania

pennanti (formaly Martes pennanti ) Erxleben (Fisher) (Jensen et al. 2012). In the United

States, harvest surveys are frequently used to monitor Lynx rufus Schreber (Bobcat)

population status (Roberts and Crimmins 2010). In Quebec, Canada, the number of pelts that

are sold is the main and sometimes the only index of population size, in the case of Weasels

composed mostly (probably more than 95% based on known species distribution) of Mustela

erminea Linnaeus (Short-Tail Weasel) and Long-tailed Weasel, Mephitis mephitis Schreber

(Striped Skunk), and Ondatra zibethicus Linnaeus (Muskrat). For Canada Lynx, American

Marten and Fisher, abundances can be inferred from the logbooks that trappers are required

to maintain. Those logbooks record the numbers of trapped animals for each species, together

with trapping effort. Numbers of pelts sold can be obtained from fur transaction reports.

Trapping harvest is mainly affected trapping effort (Fortin and Cantin 2004), which in turn

is influenced by several ecological and socioeconomic factors, such as food abundance (Ryan

et al. 2004), market prices (Weinstein 1977), government quotas (Smith et al. 1984),

landscape changes (Raphael 1994) and other disturbances over time (Raphael 1994), and

temperature (Kapfer and Potts 2012).

31

In addition to problems that are specific to each of these indirect methods, temporal

changes in sampling locations may introduce additional bias to the index. Moreover, even if

field surveys are conducted in fixed locations each year, successional patterns in vegetation

will affect the response of different species to the sampled plots (Anderson 2001). As is the

case with any model, those that are used to develop population indices should optimize the

combination of simplicity and accuracy.

Few studies have evaluated the reliability of population indices that have been obtained

through those indirect methods. In a five-year study in Ontario, Thompson and Colgan (1987)

reported different population trends from trapping harvest and snow tracking. Thompson et

al. (1989) again found no correlation between track counts and trapping harvest, but they did

find a significant correlation between track counts and live trapping data. The authors

suggested that track counts correctly described population changes over 5 years.

To evaluate population indices that were based on snow tracking and pelt sales, we

examined the correlation between the two techniques with data from three mammal species:

American Marten, weasels, and American Red Squirrel. While two indices were not direct

measure of population size, we hypothesized that if two indices describe population changes

accurately over time, they are highly correlated, after accounting for sources of error

described above. We predicted that if one or both of the indices failed to describe annual

population dynamics, correlation coefficient between two indices should not be different

from zero for the species concerned.

32

Field-Site Description

We conducted snow tracking surveys in the Montmorency Forest, a 66 km2 area, about

80 km north of Quebec City (47° 20’N, 71° 10’W), Canada (Fig. 1. 1). Trapping and hunting

for the three species were not allowed in this area. Most of the study area was originally

clearcut between 1941 and 1945 and is now managed with a combination of clear-cuts and

selective cuts. The resulting forest is composed of mature (more than 40-y-old), mid-

succession (21- to 40-y-old) and regenerating (less than 20-y-old) forest stands, which

comprise 55 %, 25 % and 20 % of the study area at the time of the study, respectively.

Locations of different-aged stands shift with time due to continuing timber harvest and forest

stand succession; mean stand age remained stable throughout the study period (42.97 ± 1.67

years, range 0 – 113 y). A dense road network is present, with about 150 km of dirt roads.

During winter, several roads were groomed by machinery for cross-country ski trails.

Elevation ranges from 650 m to 1000 m. From 1999 to 2011, annual mean temperature was

0.3 °C. Annual precipitation was 1417 mm (33 % as snow). Maximum snow depth at weather

station in the Montmorency Forest ranged from 62 to 146 cm in 1999–2011 (Vigeant-

Langlois and Desrochers 2011). Abies balsamea (Linnaeus) Miller (Balsam Fir) dominated

second-growth mature forest stands. Picea mariana (Miller) Britton, Sterns and Poggenburg

(Black Spruce), Betula papyrifera Marshall (White Birch), Populus tremuloides Michaux

(Trembling Aspen), and P. glauca (Moench) Voss (White Spruce) were also common. Recent

(less than 5 y) clear-cuts were generally colonized by Rubus idaeus Linnaeus (Red

Raspberry), balsam fir, and white birch (de Bellefeuille et al. 2001).

We obtained pelt sales data from fur transaction reports of Furbearer Management Unit

(French acronym, UGAF) 39, a 7934 km2 area (Fig. 1.1). In this area, Balsam Fir and Black

33

Spruce were dominant plant species. White Birch, Trembling Aspen, Yellow Birch and

maples were also common (Dussault et al. 2006). Similar to the Montmorency Forest, timber

harvest was conducted in UGAF 39, resulting in a heterogeneous stand mosaic (Dussault et

al. 2006). From 2004 to 2012, mean daily winter temperature ranged from -16 to -9 oC and

total winter precipitation ranged from 165 to 265mm (Environment Canada 2014; Fig. 1. 2).

In summary, the main difference between snow tracking and trapping areas was the

presence of trapping in the latter. Thus, furbearers were possibly more abundant in the snow

tracking area than in the trapping area, potentially resulting in different movement patterns

due to higher density. But we assumed that those two locations exhibited similar population

trends. We based our assumption on the facts that two areas were geographically overlapped

and had similar forest composition and forest management, in addition to the fact that wildlife

populations within 100 km have been shown to vary synchronously in various taxa including

mammals (Liebhold et al. 2004).

Methods

Snow tracking

We conducted snow tracking each winter (20 December - 31 March) from 2004 to 2012.

We counted tracks along a subset of a network including about 150 km of roads, 40 km of

trails, and 60 km of off-trail straight line transects (Fig. 1.3). None of the roads that were

surveyed had been snowplowed. Transect length depended upon snow condition, current

weather, time of day, and personnel availability (Table 1.1). Off-trail transects were randomly

selected from a systematic grid covering the entire study area at the beginning of each year.

However, in the selection process, we removed transects that had been surveyed in the

previous 2 years. For each year, we surveyed selected transects only once to cover larger area

34

as much as possible with a GPS. As a result, we surveyed an average of 91.3km ± 28.9 km

(mean ± SD) of transects each year (Table 1.1). Our surveys on transects were performed

within a 24 to 72 hours after the last snowfall exceeded 3 cm. We recorded all tracks found

within 2 m of either side of the transect lines into a GPS receiver and identified the species,

based on track pattern and size. Conspecific tracks that were within 3 m of a recorded track

were ignored. Temperature and snow depth were measured at an Environment Canada

weather station at the Montmorency Forest.

We georeferenced track and transect data with ArcGIS (Version 10.1, ESRI, 2012) and

then split transects into 200 m fixed-length segments, totaling 4123 200-m segments for the

entire study (Table 1.1). We counted tracks on each transect segment, and generated buffers

with a radius of 100 m around each segment. We selected this fixed-length as a compromise

that limits the number of zeros in the data while retaining a sufficiently large number of

sampling units. Within each buffer, we calculated the mean age of forest stands (weighted by

area), slope (the difference between minimum and maximum elevation), variance of age and

mean elevation. Mean forest stand age was calculated as follows, with age in years and w in

hectares for each forest stand in a buffer:

𝑀𝑒𝑎𝑛 𝑎𝑔𝑒 = ∑ 𝑤1𝑎𝑔𝑒1+𝑤2𝑎𝑔𝑒2+⋯+ 𝑤𝑖𝑎𝑔𝑒𝑖 𝑘=1

𝑖

∑ 𝑤1+ 𝑤2+⋯ + 𝑤𝑖𝑘=1𝑖

(1)

Since buffers occasionally included roads, rivers and lakes, we also calculated the

percentage of vegetation cover inside each buffer.

Population indices from snow tracking data

We assumed that track count is a function of population size, animal activity level,

exposure time of transect since the last disturbance. We developed two population indices.

35

The first was mean track count per 200-m transect segment per hours of exposure since the

last snowfall. This index has the advantage of simplicity, but ratios raise statistical issues

including spurious correlation (Atchley et al. 1976). Thus, we used a second index based on

a generalized linear model (GLM) with a negative binomial distribution and log link by using

package MASS in the R software (Venables and Ripley 2002). We used negative binomial

distribution instead of Poisson due to large number of zero count. In the GLMs, track counts

were function of year as a categorical variable, and combinations of the following covariates:

mean stand age, variance of stand age, mean temperature since last disturbance, exposure

time since last disturbance (snow or wind), slope, mean elevation, proportion of vegetated

area and transect type (road or off-trail). Transect type was included into the model to account

for differences in length of off-trail transect surveyed among study years. Year effect

represents a population index (or ‘anomaly’) by comparing mean counts in a given year with

that in the reference year (2004). Thus, year effect estimates reflected differences in mean

track counts between 2004 and other years, after accounting for the effect of covariates on

track counts described above. We were aware that GLMs do not account for spatial

autocorrelation which likely occurred in track counts. However, average Moran’s I for

American Marten, American Red Squirrel and weasels over study years were respectively

0.044 (range = 0.015-0.080), 0.062 (range = 0.007-0.119) and 0.024 (range = 0.006-0.040).

These values suggest weak spatial autocorrelation in track counts after accounting for

covariates. More importantly, we considered years, not transects, as sampling units for

statistical inference on the comparison of time series. Thus we do not consider spatial

autocorrelation an issue.

36

Pelt sales

We used pelt sales data as a proxy for trapping harvest, based on fur transaction reports,

which were obtained from Quebec’s Ministère des Forêts, de la Faune et des Parcs. Pelt sales

data were composed mainly of trapping harvest and of number of animals hunted. There were

112 trappers in the area who used body-gripping traps (Model 120, 160 or 220). Trapping

season was from 18 October to 1 March of the following year, and there was no harvest limit

for the three species that were studied. However, for American Marten, the ministry asked

trappers to stop trapping American Marten when they caught more females than males late

in the trapping season. We obtained data of trapping effort (number of traps x number of

nights) for American Marten from trappers’ mandatory reports and daily logbooks of the

government. Data for American Red Squirrel and weasels were not available. Average

trapping effort for American Marten from 2004 to 2012 was 70187 [range: 52046 - 87756]

traps x night. Average number of trapping logbooks submitted was 72 [range: 58 -83].

Population indices from pelt sales data

Various confounding factors such as temperature and trapping effort can influence both pelt

sales and track counts. We calculated Pearson product-moment correlations (r) between raw

pelt sales and four factors: trapping effort, daily mean temperature (January to March) (oC),

winter total precipitation (mm) and pelt price in the previous year adjusted for 2012

(Canadian dollars) in the UGAF 39. Only factors which had significant correlation with pelt

sales were accounted in testing correlation between two indices.Testing correlation between

two indices

In order to test if two indices agree, we calculated Pearson correlation between pelt sales

and each of the annual snow tracking indices. If pelt sales have significant correlation with

any of potential external factors such as trapping effort, winter temperature and winter

37

precipitation, we also modeled pelt sales as a function of the significant external factors and

one of population index derived from snow tracking data. When estimated parameter of an

annual tracking index was significantly different from zero, we deemed two indices agreed.

The procedure was replicated for each of population index based on snow tracking. The entire

statistical analysis was performed in the software R (R Development Core Team 2013).

Results

Annual track counts were 139 ± 83 (mean ± SD) [range: 66 - 329] for American Marten,

543 ± 723 (mean ± SD) [range: 63-2061] for American Red Squirrel, and 120 ± 174 (mean

± SD) [range: 11 - 575] for weasels. Mean annual pelt sales for American Red Squirrel,

weasels, and American Marten were 184 [range: 74- 319], 361 [242 - 636], and 921 [629-

1334] pelts respectively. None of the factors among trapping effort, winter temperature,

winter precipitation and pelt price in the previous year were significantly correlated with pelt

sales (-0.64 < r < 0.44, P > 0.05).

Mean of track counts hr-1 200 m-1 for American Red Squirrel, weasels, and American

Marten were 0.029 [0.004 –0.131], 0.006 [0.007- 0.027], and 0.008 [0.006 – 0.016]

respectively. There was strong agreement between mean track counts per unit effort and pelt

sales in American Marten and weasels (Fig. 1.4; Table 1.2, but the agreement was not

statistically significant in the case of squirrel (Fig. 1.4; Table 1.2).

Year effect estimates counts 200 m-1 (compared with 2004) in the best generalized linear

model ranged from -1.76 to 2.34 for American Red Squirrel, -1.54 to 1.91 for weasels, and -

0.61 to 0.81 for American Marten. Year effect estimates agreed strongly with pelt sales of

weasels and American Red Squirrel (Fig. 1.4; Table 1.2), but the agreement was not

38

statistically significant in the case of American Marten (Table 1.2). Agreement between

GLM-based snow-tracking indices and pelt sales was the highest in weasels, followed by

American Red Squirrel and American Marten (Table 1.2).

Discussion

Track counts data were generally in strong agreement with pelt sales over the nine years

of this study, which indicated that both methods capture a real signal in the three taxa that

were studied. Our results differed from those of Thompson et al. (1989), who found no

correlation between track counts and trapping harvest in Short-tailed Weasel, Vulpes vulpes

Linnaeus (Red Fox), or Lynx. Agreement between track counts and pelt sales is remarkable

especially because they were derived from completely different methods, from different sets

of locations, with contrasting trapping effort. Thus, both data sets likely captured population

phenomena occurring over the entire study area.

Nevertheless, agreement between indices may arise from common biases, such as weather

and food availability effects on exploratory behavior and movements. Thus, it could be

argued that the variation in the population indices had little to do with actual population size.

However, there was little confounding effect of trapping effort, mean daily temperatures or

precipitation (snow) on indices, because none of these factors were strongly correlated with

indices. Food-related biases may have occurred, though. Jensen et al. (2011) reported that

success rate of American Marten harvest was lower in mast year of Fagus grandifolia Ehrhart

(American Beech) and Sorbus aucuparia Linnaeus (Mountain Ash) than in year of mast

failure. When food is abundant, American Marten could be less likely to be attracted to bait

associated with trapping devices, possibly leading to underestimates of the relative

abundance. High food abundance might decrease track counts. High food abundance was

39

reported to decrease daily movement length of Mustela nivalis Linnaeus (Least Weasel)

(Klemola et al. 1999), potentially leading to fewer track counts.

American Marten track counts showed a lower degree of agreement with pelt sales than

did weasels. Lower agreement could have arisen from a policy which was implemented by

the Quebec government and the policy recommended that trappers stop trapping adults (H.

Bastien, personal communication). However, we failed to identify when the policy was

implemented. In Michigan, trapping harvest limits greatly impacted the number of fisher

harvested (Hiller et al. 2011).

American Red Squirrel track counts also exhibited a lower degree of agreement with pelt

sales than did weasels. Sales of American Red Squirrel pelts might reflect population sizes

poorly because not all pelts were sold, given that American Red Squirrel is often used as bait

for other furbearers (H. Bastien, personal communication). Furthermore, American Red

Squirrel pelts are considered of no significant commercial value by local trappers. The price

of a American Red Squirrel pelt varied between 0.65 and 1.44 Canadian dollars (CAD) from

2004 to 2012 (where 1 CAD ~ 1 USD), which was much less than the price of a weasels pelt

(range: 2.24 CAD – 8.87 CAD) or a American Marten pelt (range: 44.88 CAD – 121.71

CAD). Thus, the motivation for capture contrasted strongly between species.

Population indices are not substitutes for true estimates of abundance. The indices used

in this study were not compared with population trends obtained from direct measures of

abundance, but given the high correlations obtained, at least in the case of American Marten

and weasels, our study adds to existing support for the use of either snow tracking or pelt

sales. With those indices, one should be able to infer relative inter- annual population

40

changes of the furbearer species studied, and possibly others such as Red Fox and Canada

Lynx. Therefore, use of these indices would be useful to investigate impact of local forestry

and wildlife management on population dynamics of furbearer species.

Acknowledgments


the “Leadership and Sustainable development Scholarship Program” of Laval University, by

a scholarship to T. Kawaguchi from the Foundation F.-K.-Morrow and by a Natural Sciences

and Engineering Research Council of Canada (NSERC) grant to A. Desrochers. We are

grateful to the 29 skilled field workers who contributed to the collection of snow tracking

data, and to the Ministère des Forêts, de la Faune et des Parcs for providing us with pelt sales

data, trapping effort data and geographical information. We thank D. Fortin, L. Bélanger and

C. Samson for their assistance in the design of the study and W.F.J. Parsons in Centre d’Ètude

de Forêt (CEF) for his assistance in linguistic corrections. Lastly, we thank the anonymous

reviewers for their thoughtful comments and suggestions to improve our manuscript.

41

Figure 1.1. Graphical representation of spatial location of snow tracking site (the Montmorency Forest) and trapping area of Furbearer Management Unit (UGAF) 39, southern Quebec, Canada, 2004 - 2012. Black area indicates the location of snow tracking sites. Gray area indicates the location of trapping area.

42

Figure 1. 2. Inter-annual dynamics of winter precipitation and winter temperature in the study

sites, the Montmorency Forest and the Laurentides Wildlife Reserves (UGAF 39), southern

Quebec, Canada, 2004-2012: a) Winter temperature (oC), b) winter precipitation (mm). The

data for 2006 was not available.

43

Figure 1. 3. Spatial distribution of sampling transects in the Montmorency Forest, southern

Quebec, Canada, 2004-2012. Black lines indicate off-trail transects and gray lines indicate

either roads or trails.

44

Figure 1. 4. Comparison of population trends between snow tracking and pelt sales across three taxa: a) American Marten, b) American Red Squirrel and c) weasels, southern Quebec, Canada, 2004-2012. Two population indices are presented: left) Year effect of a Generalized Linear Model (Year effect GLM), right) tracks/exposure time. Black lines represent pelt sales

45

and gray lines represent population indices of year effect GLM (right) or tracks/exposure time (left). Vertical bars represent standard errors.

46

Table 1. 1. Sampling effort for snow tracking and track counts for American Marten, American Red Squirrel, and weasels at the Montmorency Forest, southern Quebec, Canada, 2004-2012.

Year On

road/trails (km)

Off-trail (km)


200m segment

(n)

American Marten (count)

American Red

Squirrel (count)

weasels (count)

2004 33 17 14 252 70 133 36 2005 60 5 14 325 66 187 11 2006 51 23 12 370 88 63 58 2007 71 16 14 435 140 2061 130 2008 97 23 12 602 329 441 575 2009 58 12 11 346 98 78 76 2010 91 12 8 518 106 251 35 2011 110 13 8 619 163 175 89 2012 91 39 19 656 194 1497 71 Total 662 160 112 4123 1254 4886 1081

Average 74 18 12.4 458 139 543 120 Standard deviation

25 10 3.4 146 83 723 174

47

Table 1. 2. Pearson correlations between pelt sales, and different population indices that were based on snow-tracking over the three species (n = 9): a) American Marten, b) American Red Squirrel, and c) weasels, in southern Quebec, Canada, 2004-2012. Year effect GLM indicates estimates of year effect from a Generalized Linear Model.

Population index r P a) American Marten Mean of count/exposure hours 0.71 0.032

Year effect GLM 0.55 0.12 b) American Red Squirrel Mean of count/exposure hours 0.57 0.1

Year effect GLM 0.77 0.02 c) weasels Mean of count/exposure hours 0.87 0.002

Year effect GLM 0.85 0.004

49

CHAPTER 2 – INFLUENCES OF CURRENT AND RECENT

CONSPECIFIC DENSITY ON HABITAT SELECTION OF SNOWSHOE

HARE

TOSHINORI KAWAGUCHI, Centre d’étude de la forêt, and Département des sciences

du bois et de la forêt, Université Laval, 2405, rue de la Terrasse, Québec (Québec), G1V

0A6, Canada

ANDRÉ DESROCHERS, Centre d’étude de la forêt, and Département de science du bois

et de la forêt, Université Laval, 2405, rue de la Terrasse, Québec (Québec), G1V 0A6,

Canada

Key word: habitat selection, density-dependency, time lag, ideal free distribution, Lepus

americanus

50

Abstract

Ideal free distribution theory predicts that conspecific density affects habitat selection, but

possible lags in response to the density remain poorly documented. Snowshoe hare (Lepus

americanus) are known for their marked inter-annual variation in population density,

possibly leading to strong density-dependent effects on habitat selection. Based on 11 years

of snow tracking in southern Quebec (Canada), we investigated whether snowshoe hare

habitat selection exhibits instantaneous and delayed responses to its population density. We

measured the relationship between track counts in 100 m transect segments and the

proportion of forest stands of different age classes within 50 m of the transect. We developed

an index of population density with generalized estimating equations (GEE) looking at track

counts in response to year as a categorical variable. Snowshoe hares spread significantly from

preferred forest stands when density in the previous winter was high. To our knowledge, no

previous empirical studies have documented a lagged response to population density in

habitat selection. Time lags offer a possible explanation for deviations, which have appeared

in empirical studies of density-dependent habitat selection, from the ideal free distribution.

51

Introduction

Habitat selection is a process involving responses to a large variety of stimuli, such as

vegetation structure and composition, predation risk (Hildén 1965), weather (Reid et al. 2012),

and conspecific population density (Fretwell and Lucas 1970). The effect of conspecific

population density on habitat selection has been documented for various taxa, including

mammals (e.g., fat sand rat Psammomys obesus, (Shenbrot 2004); white-footed mouse

Peromyscus maniculatus, (Morris 1996); domestic sheep Ovis aries, (Mobæk et al. 2009)),

birds (e.g., brown-headed cowbird Molothrus ater; Jensen and Cully 2005), and fish (e.g.,

brown trout Salmo trutta; Ayllón et al. 2013). Under the ideal free distribution model

(Fretwell and Lucas 1970), animals move freely and rapidly among habitats of different

quality in order to maximize their fitness (Morris 2003). According to Morris’ (2003) isodar

model, fitness decreases with increases in density, which may lead individuals to move from

high-density to lower-density habitat.

Time lags are often observed in processes such as species redistribution following

landscape changes (Metzger et al. 2009), local extinction after deforestation (Brooks et al.

1999), species invasion (Crooks 2005), and population dynamics (Fryxell et al. 1991, Erb et

al. 2001). Time lags can be caused by: 1) long processing times following the perception of

a stimulus (Brooks et al. 1999), 2) intervening processes between two processes of interest

(Magnuson 1990), and 3) feedbacks (Framstad et al. 1997).

While several empirical studies have documented the ideal free distribution (Dreisig 1995,

Haché et al. 2012), deviations from ideal free model have been reported. Those deviations

have been interpreted as resulting from limited perceptual constrains or despotic behavior

(Oro 2008). However, to our knowledge, none has examined whether responses to density

52

are lagged, which may account for departures from IFD. Yet the perception of a decrease in

fitness or an increase in density, or that of available nearby habitats (Rosenzweig 1981), is

unlikely to be an instantaneous process.

Here, we documented population dynamics of snowshoe hare (Lepus americanus) over

11 winters, evaluated whether habitat selection of hare was affected by density, and tested

whether responses to density are delayed. More specifically, we measured the strength of

association between hares and forest stands of different age classes to identify the most and

least preferred habitats, based on winter track counts. Snowshoe hare was used as a model

species because of its strong population fluctuations (Krebs 2001, 2011) and reported

deviations from an ideal free distribution (Morris 2005). We predicted that at high population

densities, snowshoe hare distribution expands into less preferred habitat, thus weakening the

association between hares and most preferred habitat. Because we expect lags in snowshoe

hare responses, we predicted that the lagged shift in distribution would be stronger in the

winter following high density rather than in the current winter.

Methods

We conducted snow tracking surveys at the Montmorency Forest, a 66 km2 boreal forest

about 80 km north of Quebec City (47°20’N, 71°10’W), Canada. Most of the study area was

originally clear-cut between 1941 and 1945 and is now managed with a combination of clear-

cuts and selective cuts. The resulting forest is composed of mature (more than 40-y-old), mid-


comprise ca. 55 %, 25 % and 20 % of the study area, respectively. Locations of different-

aged stands shift with time due to continuing timber harvest and forest stand succession:

mean stand age remained stable throughout the study period (43.32 ± 1.98 years; mean ± SD,

53

range 0 – 114 y). A dense road network is present, with about 150 km of roads, i.e., over 2

km/km2. During winter, several roads were groomed by machinery for cross-country ski trails.

Elevation ranges from 650 m to 1000 m. From 1999 to 2011, annual mean temperature was

0.3 °C. Annual precipitation was 1417 mm (33 % as snow). Maximum snow depth at weather

station in the study site ranged from 62 to 146 cm in 1999–2011 (Vigeant-Langlois and

Desrochers 2011). In our study area, balsam fir (Abies balsamea) dominates second-growth

mature forest stands. Black spruce (Picea mariana), white or paper birch (Betula papyrifera),

trembling aspen (Populus tremuloides), and white spruce (P. glauca) are also common.

Recent (less than 5-y-old) clear-cuts are generally colonized by red raspberry (Rubus idaeus),

balsam fir, and white birch (de Bellefeuille et al. 2001).


We counted tracks along a subset of a network that included about 150 km of roads, 40 km

of trails, and 60 km of straight line transects inside forest stands. None of the roads that were

surveyed had been snow-plowed. Transect length depended upon snow condition, weather,

time of day, and personnel availability (Table 2.1). Each year, we surveyed selected transects

only once to cover larger area as much as possible, and surveyed tracks along 94.73 km ±

30.57 km (mean ± SD) of transects (Table 2.1). We surveyed transects within a 24 to 72 hours

after the last snowfall exceeded 3 cm. We recorded all hare tracks that fell within 2 m of

either side of the transect lines into a GPS receiver. Hare tracks that were within 3 m of a

recorded track were ignored. Any of single track, a trail and a network of hare track was

recorded as one track.

We geo-referenced track and transect data with ArcGIS (Version 10.1, ESRI 2012) and

split transects into 100 m segments, totaling 10436 100-m segments for the entire study

54

(Table 2.1). We counted tracks along each transect segment, and generated buffers with a

radius of 50 m. Winter home range size of snowshoe hare averages 2 ha (Beaudoin et al.

2004), thus we considered 2 ha of resulting buffer size as an sufficient size that reflects spatial

scale of hare. Within each buffer, we calculated the mean age of forest stands (weighted by

area), slope (the difference between minimum and maximum elevation), mean elevation, and

the proportions of the area occupied by 4 habitat types, based on forest stand age (0-20 y, 20-

40 y, 40-80 y). Older forest stands (older than 80 y) were rare and not included in the analyses.

Since buffers occasionally included roads, rivers and lakes, we also calculated the percentage

of vegetated area inside each buffer.

For indexing population density, we used model coefficients for year as a categorical

effect in a Generalized Estimating Equation (GEE) with a negative binomial distribution and

log link by using package geeM in the R software (McDaniel and Henderson 2015). GEE

was used to account for local spatial autocorrelation. In GEE, we used transect unit before

segmetation as a cluster. The model included the following covariates: hours of exposure

since last disturbance, mean stand age, squared mean age, slope, mean elevation, mean

temperature in the previous 24 hours, month as a categorical variable and year as a categorical

variable. The squared term was added because habitat use pattern showed a peak around 40y

stand age (Hodsons et al. 2011), thus resulting in better fit. Month was included into the

model to account for potential decline of hare population over winter (Kielland et al. 2010).

We validated this approach with trapping success data that had been obtained from the same

region; annual population indices that had been derived from snow tracking were highly

correlated with trapping success (Kawaguchi et al. in press).

55

To investigate our initial prediction, a GEE was used with a negative binomial distribution

and log link. In GEE, track counts were a function of hours of exposure since last disturbance,

proportion of each habitat type (regenerating forest: 0-20 y, young forest: 20-40 y, mature

forest: 40-80 y), slope, mean elevation, transect type (forest vs road), percentage of vegetated

area within the buffer and month (categorical variable). Our categorization of habitat types

was based on hare habitat use patterns in which hare appears at high density in 20-40 y forest,

also to avoid mixing preferred and unpreferred habitats in a category.

We developed a model for each habitat type and each year, and we used model estimates

for the effect of the proportion of each habitat as measures of habitat preference. To compare

immediate and delayed effects of snowshoe hare population density on habitat preference,

we used linear models for the preference as a function of population density in current (Dt)

and previous (Dt-1) winters separately and combined. The model integrated weight for each

observation which were calculated as following:

wi = (1/SEi)/(1/SE1 + 1/SE2 + … + 1/SEk)

Where wi is a weight for measured habitat preference i, SE is a standard error of estimated

coefficient of habtiat variable. Since we used lagged effect, we excluded habitat preference

in 2004 from the analysis. A model with the highest adjusted R2 was considered as the best

model. All statistical analyses were conducted in the R statistical environment (R

Development Core Team 2014).

Results

We found 14240 snowshoe hare tracks in total and track counts/km varied from 4.9 to

23.7 (Table 2.1).

56

The association between track counts and the proportion of 0- to 20-y-old stands was

negative each year (range of model estimates: [-0.011, -0.0001]), suggesting that this habitat

was the least preferred. In contrast, the relationship between track counts and the proportion

of 20- to 40-y-old stands was positive each year (range: [0.001, 0.012]), suggesting that 20-

to 40-y-old stands were most preferred. Relationships between track counts and the

proportion of older stands were either positive or negative, depending upon the year.

The lag model for response to 20- to 40-y-old performed best among the candidate models.

The lagged effect of density was significantly negative, suggesting that hare more frequently

used 20-to-40-y-old forest in response to higher density in the previous winter (Table 2.2;

Figure 2.2). In other habitat types (0- to -20 y forest and 40-80-y-forest), neither immediate

nor lagged effect were significant (Table 2.2; Figure 2.2) while lagged effect in the other

habitat types showed positive.

Discussion

Snowshoe hares wintering at the Montmorency Forest appeared to respond spatially to

their population density with a lag of one year. The models including delayed effects

explained the dynamic associations of hares with most preferred habitats much better than

the model with immediate responses to population density. In contrast, population density

poorly explained variation in hare responses to forest stands that were 40-to-80 y and 0-to-

20y.

The preference for forest stands that were 20- to 40-y-old was consistent with other

studies (Thompson et al. 1989, Hodson et al. 2011). The avoidance of hares of 0- to 20-y-old

forest stands was also consistent with past studies (Thompson et al. 1989, Potvin et al. 1999).

57

Also, avoidance could be explained by the fact that these stands were mostly covered by snow,

thus offering few opportunities for foraging as well as high predation risk.

The signs of the model estimates in 20-40 y forest stand associations can be explained by

an overflow of individuals from the preferred habitat, in response to changes in population

density. We interpret the lagged response of hares as a ‘buffer effect’ (Brown 1969, Gill et

al. 2001). The apparent overflow of hares from high-density habitat was delayed. Presence

of delayed effect would be due to delays in perception of stimuli (increased density) or that

of nearby available habitat.

We expected a lagged shift of snowshoe hare use into the less preferred habitat (0-to-20y

and 40-to-60y stand), thus positive coefficients of the lagged effect in those habitats. Contrary

to our expectation, none of lagged effect in these habitat appeared non significant while the

sign of coeffcients showed positive. This pattern could be attributed to higher mortality in

low-density habitat, potentially resulting in offsetting increased habitat use by dispersing hare.

As younger forest stands were more open and often partly covered by a thick snow layer

(Horstkotte and Roturier 2013), hares in this habitat would be more vulnerable to predators

such as Canada lynx (Lynx canadensis). Immediate and deferred costs of dispersal are known

to lower survival rates (Stamps et al. 2005). Thus, dispersed hare might have low survival

rate.

Numerous empirical studies on density-dependent habitat selection have been performed;

however, most of these studies showed at least one case in which animals did not follow the

ideal free distribution (Morris and MacEachern 2010). For example, in a study on snowshoe

hares in northwestern Ontario, Morris (2005) indicated that they exhibited subtle density-

58

dependent habitat selection, with large residual variation. In an Idaho study, local hare

extinction was not mitigated by greater densities in neighboring areas (Thornton et al. 2012b).

As demonstrated here, using a time-lag effect can significantly improve the explanatory

power of density-dependent habitat selection models.

Acknowledgements

Financial support for this project was provided by the “Leadership and Sustainable

Development Scholarship Program” of Laval University to T. Kawaguchi, by a scholarship

to T. Kawaguchi from the Foundation F.-K.-Morrow and by a Natural Sciences and

Engineering Research Council of Canada (NSERC) grant to A. Desrochers. We are grateful

to the 29 skilled field workers who contributed to the collection of snow tracking data, and

the Montmorency Forest for logistical support. We thank D. Fortin, L. Bélanger and C.

Samson for their assistance in the design of the study, and W.F.J. Parsons in Centre for Forest

Research (CFR) for linguistic corrections.

59

Table 2.1. Sampling effort for snow-tracking of snowshoe hare (Lepus americanus) in the Montmorency Forest, southern Quebec (Canada), 2004-2014.

Year km sampled 200 m

segment (n)

Track count On

road/trails Off-trail

2004 34 19 527 597 2005 61 6 671 390 2006 52 23 751 425 2007 72 17 890 943 2008 99 24 1234 1684 2009 59 12 715 344 2010 93 12 1055 834 2011 112 14 1263 1937 2012 93 42 1352 2896 2013 113 20 1325 3147 2014 52 13 653 1043 Total 840 202 10436 14240 Mean 76 18 949 1295 SD 27 9 306 996

60

Table 2.2. Estimated effects of current and lag density (previous winter) on habitat selection of snowshoe hares in the Montmorency Forest, Québec, 2004-2014 (n = 10). Estimates are shown for models including either current or lagged effects of density. Positive estimates indicate a greater association at higher density. Adjusted R2 values can be negative, because unlike raw R2, they are penalized by the number of parameters.

Stand age

Adjusted R2 Model estimates ( ± s.e)

Current Lag Current + lag Current density (Dt) Lag density (Dt-1)

0-20 y 0.13 0.09 0.04 0.0039 ± 0.003, P = 0.16 0.0038 ± 0.003, P = 0.21 20-40 y 0.39 0.47 0.46 -0.0034 ± 0.0013, P = 0.03 -0.0034 ± 0.0011, P = 0.02 40-80y -0.11 -0.08 -0.23 0.0003 ± 0.001, P = 0.82 0.0008 ± 0.001, P = 0.58

61

Figure 2. 1 Estimated population index of snowshoe hare over 11 years from 2004 to 2014 in the study site. The index was developed from coefficients of the year effect estimated from generalized estimating equations (GEE). Vertical bars indicate standard errors.

62

Figure 2.2. Association of hares with 0-20y forest stands explained by a) immediate effect (Dt) only and b) with immediate and time-lag effects (Dt + Dt-1) of the density index over 10 years (2005 - 2014) in the study site (n = 10). Points with standard error bars indicate model coefficients. Dashed lines indicate 95 % confidence bands of the fitted regression line values.

63

CHAPTER 3 – VARIATION OF SNOW DEPTH AFFECTS THE

SPATIAL DISTRIBUTION OF SNOWSHOE HARE

TOSHINORI KAWAGUCHI,1 Centre d’étude de la forêt, Université Laval, 2405, rue de

la Terrasse, Québec (Québec), G1V 0A6, Canada

ANDRÉ DESROCHERS, Centre d’étude de la forêt, Université Laval, 2405, rue de la

Terrasse, Québec (Québec), G1V 0A6, Canada

Key words: Snow depth, Habitat use, Snowshoe hare, LiDAR

64

Abstract

Snow accumulation changes access to vegetation by herbivores. Snowshoe hare (Lepus

americanus), a keystone species in the boreal forest, is known to most frequently use young

forest stands during winter. However, snow accumulation greatly varies in time and space,

possibly affecting habitat use by snowshoe hare, especially in regions where snow

accumulation is high. We measured shifts in habitat use by snowshoe hare as a function of

snow depth at the Montmorency Forest, Quebec, Canada. We surveyed on 67 km of transects

over 3 winters, found 2239 hare tracks and measured snow depth in 336 locations. We

analyzed track counts as a response to foliage density above 2 m indexed by penetration rate

obtained from LiDAR imagery, snow depth and the interaction between snow depth and

foliage density as explanatory variables. Snowshoe hares were less frequently found in sites

with high foliage density at the middle height but were found more frequently in sites with

high foliage density when snow accumulation increased. We concluded that snow depth

dynamics may introduce significant uncertainty in spatial distribution models for the species,

and possibly its interactions with predators.

65

Introduction

Snow depth greatly varies seasonally, inter-annually and geographically (Brown and

Braaten 1998). Snow depths in boreal forests in eastern North America often exceed 1m, and

occasionally 2m (Brown and Bransnett. 2010). However, snow depth varies greatly at the

forest stand scale, particularly in response to tree height and canopy cover (Horstkotte and

Roturier 2013). Snow depth often plays a significant role for wildlife including overwinter

survival, dispersal (Kielland et al. 2010, Campbell et al. 2005) and the supply of subnivean

space (Korslund and Steen 2006), daily movements and activity levels (Fuller 1991, Murray

and Boutin 1991), as well as access to food and thermal cover (Wolff 1980, Halpin and

Bissonette 1988, Morrison et al. 2003). If small trees, herbaceous vegetation and shrubs are

buried by deep snow, availability of understory cover may change. However, deep snow may

allow small herbivores to access to browse at higher levels.

Snow depth is often predicted to vary in response to climate change (Campbell et al.

2005). As a consequence, small herbivores such as the snowshoe hare (Lepus americanus)

should respond to changes in snow depth. Hare is strongly dependent on understory cover

(Litvaitis et al. 1985), mostly found in 20 to 40 y old boreal forests in eastern North America

(Thompson et al. 1989, Hodson et al. 2011).

Approximately 10 % of the geographical range of snowshoe hare (Figure 3.1) experiences

more than 1m of snow depth on a regular basis, and may therefore exhibit greater temporal

variation in the spatial distribution of small herbivores, both within years, as snow

accumulates, and among years. An early study, without detailed statistical analysis argued

that habitat shifts from summer to winter (Wolff 1980), but we know of no quatitative studies

examining spatial dynamics of small herbivores in response to snow depth during winter.

66

Here, we investigated whether snow depth influenced habitat use by snowshoe hare in a

boreal forest of southern Quebec, Canada. We predicted that with increasing snow depth,

snowshoe hares should less frequently use sites dominated by understory cover. Deeper snow

should provide access to vegetation above 2m, thus we predicted that hares should be more

strongly associated with higher density of foliage above 2m in high snow depth.

Methods

Study site

We conducted snow tracking surveys at the Montmorency Forest, a 66 km2 boreal forest

about 80 km north of Quebec City (47° 20’N, 71° 10’W), Canada. Most of the study area was

originally clear-cut between 1941 and 1945 and is now managed with a combination of clear-

cuts and selective cuts. The resulting forest is composed of mature (more than 40-y-old), mid-


comprise ca. 55 %, 25 % and 20 % of the study area at the time of the study, respectively.

Locations of different-aged stands shift with time due to continuing timber harvest and forest

stand succession: mean stand age remained stable throughout the study period (43.3 ± 2.0 y,

range 0 – ca. 120 y). A dense road network is present, with about 150 km of dirt roads, i.e.,

over 2 km/km2. During winter, several roads were groomed by machinery for cross-country

ski trails. Elevation ranges from 650 m to 1000 m. From 2012 to 2014, mean winter

temperature was ranged from -14.5 to -9.2 °C. Maximum snow depth at the weather station

of the study area ranged from 58 to 111 cm. The Montmorency Forest exhibits higher snow

depth than in most of snowshoe hare’s range (Figure 3.1). In our study area, balsam fir (Abies

balsamea) dominates second growth mature forest stands. Black spruce (Picea mariana),

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white or paper birch (Betula papyrifera), aspen (Populus tremuloides) and white spruce (P.

glauca) are also common (de Bellefeuille et al. 2001).

Assessing snowshoe hare spatial distribution

We conducted snow tracking each winter (1 January - 31 March) from 2012 to 2014.

Each year we counted tracks along a subset of 60 km of straight line transects inside forest

stands. Transect length depended upon snow condition, weather, time of day, and personnel

availability (Table 3.1). Off-transects were randomly selected from a systematic grid

covering the entire study area (Figure 3.2). We surveyed selected transects only once, with a

GPS receiver. As a result, we surveyed an average of 22 km ± 12 km (mean ± SD) of transects

each year (Table 3.1). Our surveys on transects were performed within a 24 to 72 hours after

the last snowfall exceeding 3 cm. We recorded all hare tracks found within 2 m of either side

of the transect lines into a GPS receiver. Hare tracks that were within 3 m of a recorded track

were ignored. Any of single track, a trail and a network of hare track was recorded as one

track. During snow tracking, we measured snow depth at 100-m intervals by pushing a stick

down until it hit the ground, marking the level of the snow surface, and measuring the length

between the tip of the stick and snow level marker.

Understory cover, stand height and foliage density

We measured understory cover in 2011 and 2012 along line transects used for snow

tracking, from August to November, i.e., in the absence of snow. We defined understory

cover as a visual estimate of the proportion of ground covered by live herbaceous of shrub

and conifer vegetation lower than 1.5 m from the ground, within 2 m on each side of the line

transect, at 50 m intervals. Thus, resulting understory cover plots were rectangular, 4 m x 50

m.

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We obtained stand heights with a Light Detection and Ranging (LiDAR) image. LiDAR

is a remote-sensing technology that can indirectly characterize forest structure including

forest biomass, foliage density, canopy structure and tree height (Lim et al. 2003, Wulder et

al. 2008). 95% LiDAR return height was used as a proxy for tree height in our statistical

analysis. And LiDAR penetration rate above 2m from ground was used as an indicator of

foliage density. Airborne LiDAR data were obtained in August 2011 by using an Optech

ALTM 3100 sensor at a pulse repetition rate of 100 kHz, laser wavelength of 1046 nm,

divergence of 0.25 mrad and scan rate between 46 Hz and 56 Hz (For more details, see Racine

et al. 2014). The LiDAR image resolution was 5 m.

Georeferencing snow tracking data

We georeferenced track and transect data with ArcGIS (Version 10.1, ESRI, 2012) and

split transects into 200 m segments, totaling 336 200-m segments for the entire study (Table

3. 1). We obtained track counts on each transect segment, and generated buffers with a radius

of 100 m. Since home range of snowshoe hare ranged from 2 ha to 10 ha (Beaudoin et al.

2004), we considered 7 to 8 ha of resulting buffer size as an sufficient size that reflects spatial

scale of hare. Within each buffer, we calculated slope (the difference between minimum and

maximum elevation), mean elevation, proportion of low trees (0-6m) grids, understory cover,

mean penetration rate above 2 m and mean snow depth. Since we did not measure understory

cover for 2013 and 2014, we used 0-6 m of mean tree height as a surrogate for abundance of

understory cover because of its higher correlation with measured understory cover than either

0-2 m or 0-4 m (n = 146, r = 0.48, 0.35, 0.14 for 0-6m tree, 0-4m tree, 0-2 tree respectively).

To account for roads, rivers and lakes occasionally included in buffers, we calculated the

proportion of vegetated area.

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Statistical analysis

We modeled track counts as a response to the following covariates: exposure time,

penetration rate above 2 m, snow depth, understory cover, vegetated area, 0-6m tree grid,

year effect (categorical variable), slope, mean elevation and mean temperature. Year effect

was integrated to account for possible relative changes in hare population size among the

study years (Mowat et al. 2003). We used two models: a) penetration rate model and b)

understory cover model (Table 3.2). Each model corresponded to two predictions: a) a

negative interaction term between penetration rate and snow depth, indicative of hares

responding to deep snow by increasing their use of foliage at middle height, and b) the

interaction term between understory cover and snow depth will be significantly negative if

snowshoe hares reduce their use of sites with high understory cover when snow is deeper.

Using generalized linear models (GLMs) with a negative binomial distribution and log

link, we verified that the interaction model representing our principal hypothesises received

the highest support comparing with other candidate models which do not include the

interaction term (Table 3.3). We selected the best-supported models with an information-

theoretical approach, thus by Akaike Information Criterion (AIC) (Burnham and Anderson

2002).

In order to account for effect of local spatial correlation, we reran the best supported

model by using generalized estimating equations (GEE) procedure with a negative binomial

distribution and log link by using the package geeM (McDaniel and Henderson 2015). With

GEE, we used transect unit before segmetation as a cluster. Parameter estimation was based

on quasi-likelihood. Significance of parameters was determined by Wald tests using

estimated robust standard errors. For the model b), we used the dataset only from 2012. All

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statistical analyses were conducted in the R statistical environment (R Development Core

Team 2015).

Results

We found 2239 tracks in total, and measured snow depth at 336 locations. Averaged snow

depth in the field ranged from 33.8cm to 186.5cm, depending on advancement of winter, and

location.

Among the candidates of penetration models, the highest support evidenced by the

highest Akaike weight was given to the model including the interaction between snow depth

and penetration rate, snow depth, proportion of low tree grid, year effect, slope, elevation and

exposure time (Table 3.3a). In the best model, the interaction term between penetration rate

and snow depth wassignificantly negative (Table 3.4a), i.e., hare track counts higher at foliage

density above 2m particularly with deeper snow (Figure 3.3a).

Among the candidate models for understory cover, the highest support was received by

the model including the interaction between snow depth and vegetation cover, exposure time,

snow depth and understory cover (Table 3.3b). In the best model, the interaction term

between understory cover and snow depth was significantly negative (Table 3.4b), i.e., hare

track counts became lower at sites with high understory cover particularly with deeper snow.

Discussion

The results were consistent with our predictions, namely that snowshoe hares respond to

forest structure differently depending on snow depth. Hares exhibited strong preference

toward sites dominated by understory cover under low snow depth. The stronger association

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with understory cover was consistent with several studies from low snow depth sites and

snow-free season (Wolfe et al. 1982, Litvaitis et al. 1985). However, as snow accumulated,

lower access to understory vegetation reduced the relative use of sites dominated by

understory cover.

We interpret the strong association between hare track counts and foliage density above

2m from ground under the condition of high snow depth as a result of an “elevator effect”

giving access to browse at higher positions from ground. If foliage density above 2 m acted

simply as cover which was also an important determinant of snowshoe hare distribution

(Hodson et al. 2010a), no statistical interaction between the effects of foliage density and

snow depth would be expected.

Food availability could be a key element in explaining their habitat use pattern. It was

reported that hare put higher priority on vegetation cover than food (Hodges 1999). Hodson

et al. (2011) reported that hare population density was lower at sites with higher food

availability (deciduous twigs) than at sites with lower food availability. If food was constraint

on habitat selection of hare, it wouldn’t show lower hare density at higher food availability

site. Thus, food did not affect habitat selection of hare and food availability was unlikely to

work as a constraint in forming their spatial pattern.

Several studies have documented changes in habitat use of small mammals due to changes

in snow depth due to the significant roles of snow depth (e.g., Reid et al. 2012). The majority

of studies have focused on phenomena under the snow surface, thus looking at supply of

thermoregulation by snow rather than facilitated access to foliage and vegetation cover.

Consequently, little focus has been given to “elevator effect”. While a few studies have

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described habiat shift of snowshoe hare from summer to winter (Wolff 1980), to our

knowledge, our study is the first example that quantitatively demonstrated likely result of

“elevator effect” by high snow depth.

Our results would have important implications to wildlife management under future

climate change. Brown and Braaten (1998) reported that snow depth had increasing trend

from 1946 to 1995 in east coast of Canada while there was decreasing trend in snow depth in

other regions of Canada. Variation of the trend was also observed in the United States

(Kunkel et al. 2009). Recent studies indicated that climate change possibly affect dynamics

of snow depth (Christensen et al. 2013). Since our study demonstrated the change in habitat

use by hares due to high snow depth, fluctuation in future climate possibly affects winter

behavior and distribution of hare and in turn affects distribution of associated predators

including Canadian lynx and American marten (Apps 1999, Powell et al. 2003). We

concluded that snow depth dynamics may introduce significant uncertainty in spatial

distribution models for the species, and its interactions with predators.

Acknowledgements

Financial support for this project was provided by the scholarship “Leadership and

Sustainable Development Scholarship Program” of Laval University to T. Kawaguchi, by a

scholarship to T. Kawaguchi from the Foundation F.-K.-Morrow and by a Natural Sciences


grateful to the 9 skilled field workers who contributed to the collection of snow tracking data,

the Montmorency Forest for logistical support. We appreciated J-C. Ruel, J. Bégin, E.B.

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Racine for providing us with LiDAR data. We thank D. Fortin, L. Bélanger and C. Samson

for their assistance in the design of the study, I.D. Thompson for his advice on the manuscript.

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Figure 3. 1. Satellite snow depth over distribution of snowshoe hare (Lepus americanus). Satellite data was obtained from Canadian Meteorological Centre (Brown and Brasnett 2010). The date of measurement for the map was 1 March in 2012. The resolution was 24km x 24km. The location of the study site was represented by a black star. The histogram showed frequency distribution of snow depth over hare distribution. Snow depth at the study site was 85.1cm on this date.

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Figure 3. 2. Vegetation map and sampling location in the study site, 2012-2014. The vegetation map was produced by using the one in 2012,

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Figure 3. 3. Effect of interaction between snow depth and vegetation structure on habitat use by hares at the Montmorency Forest, Canada, 2012-2014: A) hare response to LiDAR penetration rate, B) hare response to mean understory cover under different snow depth. High penetration rate values indicate low foliage density above 3m from ground.

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Table 3. 1. Sampling effort for snow-tracking for snowshoe hare (Lepus americanus) at the Montmorency Forest in southern Quebec, Canada, 2012 -2014.

Year Track count Km sampled 200m segments (n) 2012 1227 36 180 2013 792 18 93 2014 220 13 63 Total 2239 67 336 Mean 746 22 112 SD 505 12 61

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Table 3. 2. List of models for testing hypothesis regarding effect of interaction between LiDAR derived tree height, penetration rate, regenerating forest and snow depth on track counts of hare. X indicates a corresponding variable included into the model.

Variable Models

Penetration rate Understory cover

Exposure time since disturbance X X Understory cover X 0-6m tree grids X Vegetated area X

LiDAR penetration rate X Year effect X Snow depth X X

Penetration rate x Snow depth X Understory cover x Snow depth X

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Table 3. 3. Model comparison among candidate models for habitat use by snowshoe hare in the Montmorency Forest, Quebec, 2012-2014. The variable YEAR was treated as a categorical variable. The w indicates Akaike weight.

Model K AIC w a) LiDAR penetration rate Penetration rate + 0-6m tree + Vegetated area + Snow depth + YEAR + Temperature + Slope + Elevation + Snow depth x Penetration rate

11 1883.76 0.96

Penetration rate + 0-6m tree + Vegetated area + Snow depth + YEAR + Temperature + Slope + Elevation 10 1889.88 0.04

Penetration rate + Snow depth + Vegetated area + YEAR + Temperature 9 1911 <.01

Penetration rate + Snow depth + Vegetated area + YEAR 6 1915.92 <.01 Penetration rate + Snow depth + Vegetated area + YEAR + Temperature + Slope + Elevation 8 1918.66 <.01

b) Understory cover Exposure + Understory cover + Snow depth + Understory cover x Snow depth 4 848.08 0.83

Exposure + Understory cover + Snow depth + Slope + Elevation + Understory cover x Snow depth 6 851.59 0.14

Exposure + Understory cover + Snow depth 3 855.43 0.02

Exposure + Understory cover + Snow depth + Slope + Elevation 5 859.21 <.01

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Table 3. 4. Estimated effects of snow depth on habitat use by snowshoe hare in the Montmorency Forest, Quebec, 2012-2014: a) hare response mean LiDAR penetration rate (n = 336) and b) hare response to proportion of understory cover (n = 180) under different snow depth.

Variable Value SE P a) LiDAR penetration rate

(Intercept) 3.0019 1.2815 0.02 exposure time 0.0013 0.0057 0.81

Penetration rate -0.0242 0.0246 0.33 0-6m tree 0.0415 0.0122 <0.01

Snow depth 0.0147 0.0047 <0.01 Vegetation area -0.0007 0.0079 0.93

YEAR 2013 0.2886 0.2365 0.22 YEAR 2014 -0.4065 0.2634 0.12

Slope 0.002 0.0079 0.8 Elevation -0.0018 0.001 0.07

Mean temperature 0.025 0.0179 0.16 Penetration rate x

Snow depth -0.0004 0.0001 <0.01

b) Understory cover

(Intercept) 1.0632 0.6902 0.12 Exposure time -0.0034 0.0074 0.65

Understory cover 0.0818 0.0268 <0.01 Snow depth 0.0105 0.005 0.03

Understory cover x Snow depth -0.0009 0.0002 <0.01

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CHAPTER 4 – WINTER SPATIOTEMPORAL DYNAMICS OF A

BOREAL PREDATOR-PREY COMPLEX

TOSHINORI KAWAGUCHIa, ANDRÉ DESROCHERSa,

a Centre d’étude de la forêt, Université Laval, 2405, rue de la Terrasse, Québec (Québec),

G1V 0A6, Canada

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Abstract

1. Habitat selection by mesopredators is subject to trade-offs between energy gain from prey,

competition and predation risk from larger predators. These trade-offs should lead to

variability in the spatial association between large predators, small predators, and their prey.

2. We explored spatio-temporal relationships among five mammal species including

American marten (Martes americana), snowshoe hare (Lepus americanus), red squirrel

(Tamiasciurus hudsonicus), red fox (Vulpes vulpes) and Canadian lynx (Lynx canadensis).

More specifically, we examined whether spatial association between marten, a mesopredator,

and hare was influenced by the presence of predators, fox and lynx, as well as fox-hare or

lynx-hare associations, based on 11 years of snow-tracking along 976 km of transects in

southern Quebec (Canada). We also examined whether martens responded spatially to high

squirrel abundance when larger predators were present or when hare-marten association was

weak.

3. With path analyses, we built directed acyclic graphs depicting spatial associations among

species each year separately. We modeled marten-hare path coefficients as a function of lynx-

hare coefficients, fox-hare coefficients, marten-hare coefficients in the previous winter,

marten, lynx, fox population indices in current and previous year. We modeled marten-

squirrel path coefficients as function of lynx-hare coefficients, fox-hare coefficients, marten-

hare coefficients and hare population index.

4. Spatial association between marten and hare was weak or nonexistent when lynx was

spatially associated with hare. Hare-marten association was best explained by lynx

population index in the previous year and was negatively correlated with the lynx population

index. The model for squirrel-marten association including lynx population index in the

previous winter and hare-marten association performed best among twelve candidate models.

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5. Our first prediction was not rejected, thus suggesting that habitat selection by marten, a

mesocarnivore species, was influenced by larger predator abundance in the previous year.

Evidence for switching to alternative prey was slightly weak. Presence of larger carnivores

likely led to uncertainty into predicting spatial distribution of mesocarnivores based simply

on their prey distribution.

Key words: Martes Americana, Lepus americanus, prey switching, community ecology,

competition, habitat selection

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Introduction

Habitat selection is a decision-making process whereby individuals preferentially use, or

occupy, a non-random set of available habitat (Morris 2003). The process involves responses

to a large variety of stimuli, such as vegetation structure and composition, predation risk

(Hildén 1965), weather (Reid et al. 2012, Crowther et al. 2014), conspecific population

density (Fretwell and Lucas 1970). Individuals are expected to use space to maximize their

fitness (Morris 2003). In a prey-predator interaction, habitat selection by predators often

translates into occupying areas with high prey abundance. For example in snow tracking

study, with snowshoe hare (Lepus americanus) as a main prey species, lynx (Lynx

canadensis) tracks are more likely to be found in areas with higher hare abundance indexed

by hare track count in snow tracking study (Bayne et al. 2008, Keim et al. 2011).

However, when several prey species are available, the strength of the spatial association

between a predator and a given prey species may decrease when the abundance of alternative

prey species increases (prey switching; Murdoch et al. 1975). Prey switching is exemplified

with wolves (Canis lupus) that concentrate on areas used by deer (Odocoileus virginianus)

during winter, but switch to beavers (Castor canadensis) during summer, when the latter

become available (Latham et al. 2013).

Habitat selection of a species can be influenced by competition for shared resources by

other species (Morris et al. 2000, Morris 2003). For example, density of collared lemmings

(Dicrostonyx groenlandicus) in their preferred habitat declined with increase in density of

competitor, brown lemmings (Lemmus trimucronatus), in the habitat (Morris et al. 2000).

Similarly, habitat selection by a predator species might be influenced by competition for

shared prey by another predator species. Competition among predators having dietary overlap

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often results in spatial segregation among species as shown in the marten-fisher relationship

(Fisher et al. 2013).

Habitat selection by mesopredators might be subject to change due to predation risk from

larger predators in addition to interspecific competition. Predation by large species on

mesopredators, sometimes leads to a decline in the abundance of mesopredators (Borer et al.

2003; Polis et al. 1989). For example, in a system including gray fox (Urocyon

cinereoargenteus, 3-5kg), coyote (Canis latrans, 8-20kg) and bobcats (Felis rufus, 5-15kg),

these species shared food resources such as small mammals (lagomorphs and rodents). 58%

of gray fox mortality and 40% of bobcat mortality were caused by coyotes, and gray fox

abundance was negatively associated with coyote abundance (Fedriani et al. 2000).

Because of the potential influence of competitors and alternative prey, habitat selection

by mesopredators should result from a trade-off between energy gain, predation risk,

intraspecific competition and intra-guild competition (Lima 2002, Gorini et al. 2012). When

a larger predator species occupies area with high primary prey abundance, mesopredators

might not occupy the area due to predator avoidance (Fortin et al. 2005, Latombe et al. 2014)

and a concomitant reduction in foraging success (Brown and Kotler 2004, Andruskiw et al.

2008). Instead, they might switch to occupy area with high secondary prey abundance for

energy gain. Despite this possibility, it has not been addressed how mesopredators distribute

themselves when they face with requirement of energy gain, competition and energy gain

simultaneously.

North American boreal forests typically host a predator-prey system including snowshoe

hare, marten (Martes americana), squirrel (Tamiasciurus hudsonicus), lynx and fox (Vulpes

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vulpes). Their abundance is known to fluctuate strongly (e.g., hare; Krebs et al. 2001a,

marten; Flynn and Schumacher 2009). Lynx, marten and fox prey on hare (Martin 1994,

Poole and Graf 1996, Krebs et al. 2001b, Krebs 2011) and squirrel (Apps 1999). Hare and

squirrel are considered as more important food item comparing to small mammals such as

red-backed vole because hare and squirrel composed 78.9% of total carories comsumed based

on minimum caloric estimate (Cumberland et al. 2001). Lynx and fox prey on marten (Clark

et al. 1987, Thompson 1994, Hearn 2007, Naughton 2012). Under such a system, we expect

marten habitat selection to reflect a balance between energy gain from hares and predation

risk from lynx. In particular, even though marten tends to select areas of higher hare

abundance (Vigeant-Langlois and Desrochers 2011), marten should be less associated to

areas with abundant hare when lynx is present or when lynx is spatially associated with hare

because of predator avoidance and reduction in foraging success. In such cases, marten may

increase its use of alternative prey such as squirrels.

Here, we explored spatio-temporal relationships among five mammal species including

American marten, snowshoe hare, red squirrel, red fox and Canadian lynx. More specifically,

we tested 1) whether spatial association between marten and hare decreased when spatial

association with hare by known predators, fox and lynx, was strong, 2) whether hare–marten

association was reduced under relative abundance of lynx or fox, 3) whether stronger hare-

lynx association or higher abundance of fox and lynx lead to stronger marten-squirrel

association. To account for lags in effect of conspecific density and predation risk on

behavioral response including habitat selection (Kawaguchi and Desrochers unpublished

[chapter 2], Magnuson 1990, Laundré et al. 2001), effects of abundance and spatial

associations between a paired variable in the previous winter were also examined.

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Methods

Study area

We conducted snow tracking surveys in a 66 km2 boreal forest 80 km north of Quebec

City (47° 20’N, 71° 10’W). Most of the study area was originally clear-cut between 1941 and

1945 and is now managed with a combination of clearcuts and selective cuts. The resulting

forest is composed of mature (more than 40-y-old), mid-succession (21- to 40-y-old) and

regenerating (less than 20-y-old) forest stands comprising ca. 55%, 25% and 20% of the study

area, respectively. Locations of different-aged stands shift with time due to continuing timber

harvest and forest stand succession, with a mean stand age remaining stable throughout the

study period (43.0 ± 1.7 years, range 0 – 114 yr). A dense road network is present, with 2 km

of roads/km2. During winter, several roads were groomed by machinery for cross-country ski

trails. Altitude ranges from 650 m to 1000 m. From 1999 to 2011, annual mean temperature

was 0.3 °C. Annual rainfall was 1417 mm (33 % in snow). Maximum snow depth at weather

station ranged from 62 to 146 cm in 2004-2014. Balsam fir dominates second growth mature

forest stands. Black spruce (Picea mariana), white birch (Betula papyrifera), aspen (Populus

tremuloides) and white spruce (P. glauca) are also common. Recent (less than 5 y) clearcuts

are generally colonized by raspberry (Rubus idaeus), balsam fir, and white birch (de

Bellefeuille et al. 2001).

Snow tracking


We counted tracks along a subset of a network including about 150 km of roads, 40 km of

trails, and 60 km of straight line transects. None of the roads that were surveyed had been

snowplowed. Transect length depended upon snow condition, current weather, time of day,

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and personnel availability (Table 4.1). Off-trail transects were randomly selected from a

systematic grid covering the entire study area at the beginning of each year. However, in the

selection process, we removed transects that had been surveyed in the previous 2 years. For

each year, we surveyed selected transects only once to cover larger area as much as possible

with a GPS. As a result, we surveyed an average of 92.4 km ± 29.8 km (mean ± SD) of

transects each year (Table 4.1). Our surveys on transects were performed within a 24 to 72

hours after the last snowfall exceeded 3 cm. We recorded all tracks that fell within 2 m of

either side of the transect lines into a GPS receiver and identified the species, based on track

pattern and size. Conspecific tracks that were within 3 m of a recorded track were ignored.

Any of single track, a trail and a network of conspecific tracks was recorded as one track.

We georeferenced track and transect data to ArcGIS (Version 10.1, ESRI, 2012) and split

transects into 400m fixed-length segments, totaling 2461 400-m segments for the entire study

(Table 4.1). We counted tracks on each transect segment, and generated buffers with a radius

of 200 m. Within each buffer, we calculated the mean age of forest stands (weighted by area),

proportion of area of mature forest (40-80 y).

Estimation of population indices

Population indices (henceforth, ‘abundances’) of hare, squirrel, marten and fox were

estimated from the year effect in generalized linear models in which track counts were

function of mean stand age, elevation, slope, elevation, temperature and year effect as

categorical variable (Kawaguchi et al. in press). Abundance of lynx was estimated from the

mean track count divided by exposure hours because of excessive amount of zeros in lynx

track counts from 2009 to 2011, i.e. no lynx tracks found in 2009 and 2011.

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Exploratory path analysis

To identify the most important directed acyclic graph (hereafter, DAG) representing

spatio-temporal relationships among the selected species, we conducted exploratory path

analysis through d-separation test (Shipley 1997, 2000), which allows non-Gaussian or non-

linear data. The variables used were hare track counts, squirrel track counts, marten track

presence (binary), lynx track presence, fox track presence, mean temperature, transect type

(road or straight-line) and proportion of mature forest (40-80 y) within a buffer. We used the

method by Shipley (1997) to look for evidence of causal links (undirected edges). We

produced all possible DAG by changing directions of edges with the constraint of being

biologically meaningful. We assumed 1) no directed edge from animal tracks to habitat, 3)

no directed edge with a positive regression coefficient from a predator to a prey and 4) no

directed edge with negative path coefficient from prey to predator. We applied d-separation

test for all DAGs and then calculated AIC values. A DAG with a lower AIC was considered

better (Shipley 2012). We repeated these procedures for each year. In the procedure for

determining presence of edges, we used different type I error risks (alpha = 0.05, 0.1, 0.20,

0.3, 0.4 or 0.5). To examine dependencies among paired variables (e.g., hare and lynx), we

used a Generalized Linear Model (GLM) for one variable (e.g, lynx) as a function of the other

(e.g, hare). Since switching response variables can change the p value, we switched response

variable and then ran GLM once again. Then, the lowest p value was used to determine the

independency. To account for effect of exposure time since the last disturbance (wind or

snow) on detetion rate and thus on track count and presence, exposure time was integrated

into GLM whenever track count or presence of mammal species was used as response

variable. Since our aim is not to estimate true occupancy, we did not use N-mixture model

(See Mackenzie et al. 2002) to account for detection rate but we integrated the variables as

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fixed effect in GLMs. For hare and squirrel track counts, negative binomial distribution and

log link were applied by using the package MASS in the program R. Binomial distribution

and logit link were used for transect type, marten, lynx and fox. Based on the best DAG, path

coefficients were calculated by regressing a child variable which has incoming arrows for

parent variables having outgoing arrows. GLM was used to calculate the path coefficients.

We are aware of potential spatial autocorrelation among transects segments and thus,

Moran’s I was calculated by using model residuals to verify degree of spatial autocorrelation.

Dynamics of spatial association

Path coefficients in the best graphs were used as a proxy for strength of spatial association

between two species for each year. When a direct edge was not present, a path coefficient

was set to zero. By using multiple linear models, we separately examined the response of the

marten-hare path coefficients and the response of squirrel-marten path coefficients as a

function of different combinations of covariates: the marten-hare coefficient and squirrel-

marten in the previous year marten, lynx and fox population indices in the current winter as

well as ones in the previous winter, and lynx-hare, fox-hare path coefficients. We used

adjusted r squares to compare model performance.. The model fit was performed by using

the package stats in the program R. All statistical analyses were conducted in the R statistical

environment (R Development Core Team 2015).

Results

We found and georeferenced 13186, 1307, 4916, 954 and 311 tracks in total for hare, marten,

squirrel, fox and lynx respectively (Table 4.1). Population index of lynx experienced a large

decline from 2008 to 2009. Lynx abundance was negatively correlated with fox abundance

(n = 11, r = -0.63, P = 0.04), but we found no other correlation in the dynamics of paired

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species (n = 11, P > 0.05). Average Moran’s I for marten, hare, squirrel, fox and lynx over

study years were respectively 0.03 [range: -0.005 – 0.05], 0.1 [range: 0.05 – 0.25], 0.05

[range: -0.003 – 0.09], 0.03 [range: -0.002 – 0.06] and 0.03 [range: -0.01 – 0.08]. These values

suggest weak spatial autocorrelation in track counts after accounting for covariates.

Eleven DAGs were obtained through exploratory path analyses (Appendix 1 to see the

best DAG each year, Appendix 2 for detailed result of d-separation test on the best causal

graphs). The best DAGs for each year differed in topology. Three hare-to-marten edges out

of five were significant (P < 0.05, Figure 4.2, Appendix 3 for presenting all the path

coefficients estimated). Mean path coefficients from hare to marten was 0.070, ranging from

0.031 to 0.113. One hare-to-lynx edge out of two was significant. Mean path coefficient from

hare to lynx was 0.125 [range: 0.034 – 0.215]. Two edges from hare to fox remained in the

best graphs. The mean path coefficient was 0.042 [range: 0.033 – 0.053]. There was no edge

between hare and marten when hare and lynx had direct positive path coefficient. Path

coefficients from lynx to marten were negative in three years [range: –2.01 to -0.802].

Positive edges from squirrel to marten appeared in 4 years and mean of the path coefficients

was 0.225 [range: 0.078 – 0.400] (Figure 4.2).

The model for marten-hare dynamics, including lynx abundance in the previous year and

hare-marten association in the previous winter, performed best among the candidate models

(Table 4.2). Estimated effects of both lynx abundance in the previous winter and hare-marten

association in the previous winter were negative (Figure 4.3) and the effect of lynx abundance

in the previous year was significant (Table 4.2).

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The model for marten-squirrel dynamics including hare-marten path coefficient and lynx

abundance in the previous year, performed best among the candidate models (Table 4.3). The

models including hare abundance or squirrel abundance poorly performed. Estimated effect

of hare-marten path coefficient was negative and that of lynx abundance in the previous year

was also negative (Figure 4.4). None of them appeared to be significant while hare-marten

coefficients in the best model was nearly significant.

Discussion

The spatial association between one predator, marten, and one of its main prey, hare,

depended on the abundance of larger predators in the previous year. The spatial association

between marten and an alternative prey, red squirrel, was best explained by recent abundance

of larger predators and the hare-marten spatial association rather than abundance of larger

predators and predator spatial association with the main prey.

We predicted that when lynx or fox were more strongly associated with hare, marten

would be less associated with hare and also predicted that hare-marten spatial association

should be reduced with greater lynx or fox abundance. Our first prediction was partially true

for lynx, not for fox. Our results showed that marten was not spatially associated with hare

when lynx was associated with hare. However, the second prediction received most support

by the result in which the model with lynx abundance in the previous year performed best for

explaining hare-marten spatial association. This argument is also supported by the fact that

80% of hare-marten links were concentrated in years after no detection of lynx tracks. These

results support the hypothesis that competition for shared prey between lynx and marten

influenced the hare-marten relationship, thus habitat selection of mesopredators.

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Why did lynx abundance in the previous winter exert a stronger influence on the hare-

marten relationship than current lynx abundance? Lagged responses in predator-prey

dynamics have been documented elsewhere. For example, Laundré et al. (2001) observed 1

year delays in behavioral change in elk after reintroduction of wolves in Yellowstone. We

can speculate that the higher importance of lagged effect of lynx population index was due

to delays in assessing predation risk from lynx by marten, but testing this hypothesis would

require a dedicated study.

Thr model performance for dynamics of hare-marten association was improved by

including hare-marten association in the previous winter. And its estimated effects showed

that higher spatial associations between hares and marten in the current winter tended to lead

to a lower association in the following winter. Possible explanation could be feedback effect

in which hare took avoidance behavior against marten in response to recent presence of

marten. Avoidance behavior against predator by prey in response to direct and indirect cue

was commonly observed in various taxa (e.g., moose-wolf; Latombe et al. 2014). When hare

was spatially associated with marten, hare would more frequently encounter with marten or

would find indirect cue such as tracks. The detection of predator cues by hare would lead

hares to spend less time in area with marten, potentially resulting in a weaker spatial

association.

The best model for squirrel-marten spatial association indicated that the association was

reduced with stronger hare-marten association and with higher lynx relative abundance in the

previous year. While switching to alternative prey due to decline in primary prey abundance

has been widely documented(e.g., Thompson and Colgan 1990, Randa et al. 2009), we did

not find significant switching to alternative prey due to decline of primary prey abundance

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and the models with prey relative abundance performed poorly. Rather, stronger support was

given to the hypothesis that the switching to alternative prey was due to combined effect of

decline in spatial association of predator with primary prey and also relative abundance of

larger predator. However, these factors nearly became significant potentially because of

presence of other preys such as ruffed grouse (Bonasa umbellus) (Cumberland et al. 2001) in

the study area, evidenced by detection of grouse track during the field work. Since grouses

were also available for marten, marten might allocate certain proportion of their hunting effort

(e.g., attempt to be spatially associated) to location of grouse, resulting in ambiguours

switching behavior from hare to squirrel.

In summary, our hypothesis that habitat selection by a mesocarnivore, the American

marten, was greatly influenced by larger predator abundance in the previous year was not

rejected. And our study provided weak evidence for prey switching to alternative prey.

Previous studes on prey switching have demonstrated that prey-predator spatial relationship

were dynamics rather than stable. To our knowledge, our study is the first to demonstrate a

case that prey-predator relationship can be dynamics due to larger predator. Further work on

predicting the spatial distribution of carnivores via the distribution of their prey, an area of

current interest (Trainor et al. 2013, Trainor and Schmitz 2014), should not ignore the

possible role of competitors sharing similar prey.

Acknowledgements


the “Leadership and Sustainable development Scholarship Program” of Laval University, by

a scholarship to T. Kawaguchi from the Foundation F.-K.-Morrow and by a Natural Sciences


95

grateful to the 29 skilled field workers who contributed to the collection of snow tracking

data. We thank D. Fortin, L. Bélanger and C. Samson for their assistance in the design of the

study and thank I.D. Thompson and M. Mazerolle for their advice on the manuscript.

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Figure 4. 1. Population dynamics of five species over 11 years, 2004-2014: a) Snowshoe hare, b) red squirrel, c) American marten, d) Lynx and e) red fox. Gray error bars indicate standard errors.

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Figure 4. 2. Summary of path analysis results linking spatial distributions of predator, prey and vegetation attributes, 2004-2014. Thickness of line is proportional to the number of years with evidence for an edge. Red colored edges indicate positive coefficients and blue colored edges indicate negative coefficiens. Grey colored edges indicate that path coefficient were either positive or negative depending on study year.

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Figure 4. 3. Dynamics of edges in spatial distributions between hare and marten, 2004 -2014 (n = 10). The graphs represent a) relationship between hare-marten spatial association in the current winter and the one in the previous winter and b) hare-marten spatial association in the current winter and lynx population index in the previous winter. Points with standard error bars indicate path coefficients. Dashed lines indicate 95 % confidence bands of the fitted regression line values.

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Figure 4. 4. Dynamics of edges in spatial distributions between squirrel and marten, 2004 -2014 (n = 10): a) Relationship between squirrel-marten spatial association and hare population index, b) Relationship between squirrel-marten spatial association and hare-marten association. Points with standard error bars indicate path coefficients. Dashed lines indicate 95 % confidence bands of the fitted regression line values.

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Table 4. 1. Sampling effort for snow tracking and track counts for snowshoe hare (Lepus americanus), American marten (Martes americana), red squirrel (Tamiasciurus hudsonicus), and Canadian lynx (Lynx canadensis) and red fox (Vulpes vulpes) at the Montmorency Forest, southern Quebec, Canada, 2004-2014.

YEAR On

road/trails (km)

Off-trail (km)

400m segments

(n)

Hare (count)

Lynx (count)

Marten (count)

Squirrel (count)

Fox (count)

2004 32 15 121 547 38 65 137 10 2005 58 5 159 371 17 59 178 18 2006 50 23 183 424 52 85 63 50 2007 68 15 209 880 55 136 2009 50 2008 94 21 289 1502 89 314 427 64 2009 55 11 166 310 0 93 78 114 2010 88 12 252 787 1 98 229 121 2011 106 11 295 1788 0 149 168 155 2012 89 35 312 2578 31 183 1398 188 2013 107 19 320 3001 14 100 148 113 2014 50 12 155 998 14 25 81 71 Total 797 179 2461 13186 311 1307 4916 954

Average 72 16 224 1199 28 119 447 87 Standard deviation 25 8 72 917 28 78 644 56

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Table 4. 2. Model fit for dynamics of edges in spatial distribution between Hare and Marten in the Montmorency Forest, Québec, 2004-2014. Candidate models include variables: Hare-marten path coefficients (Hare-Marten), hare-marten path coefficients in the previous year (Hare-Martent-1), marten population index (Marten population), hare-fox path coefficients (Hare-Fox), and hare-lynx path coefficient (Hare-Lynx).

Model n Adjusted R2 Variable Estimate SE P

Hare-Lynx 11 -0.04 Hare-Lynx -0.14 0.17 0.44 Hare-Fox 11 -0.1 Hare-Fox -0.13 0.38 0.74

Fox population 11 -0.05 Fox population 0.02 0.02 0.5 Lynx population 11 0.03 Lynx population -1.61 1.43 0.29 Hare-martent-1 10 -0.09 Hare-martent-1 -0.22 -0.36 0.55

Lynx population t-1 10 0.34 Lynx population t-1 -2.94 1.23 0.041 Fox populationt-1 10 0.34 Fox populationt-1 0.04 0.0172 0.044 Hare-Martent-1 +

Lynx population t-1 10 0.47 Hare-Martent-1 -0.49 0.29 0.13

Lynx population t-1 -3.46 1.18 0.02

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Table 4. 3. Model fit for dynamics of edges in spatial distribution between marten and squirrel in the Montmorency Forest, Québec, 2004-2014. Candidate models include variables: Hare-lynx path coefficients (Hare-Lynx), hare population index (Hare population), hare-fox path coefficients (Hare-Fox).

Model n Adjusted R2 Variable Estimate SE P Hare-Marten 11 0.02 Hare-Marten -1.1 1.01 0.3 Hare-Lynx 11 -0.02 Hare-Lynx 0.14 0.16 0.39

Hare population 11 -0.02 Hare population -0.07 0.08 0.39 Squirrel population 11 0.11 Squirrel population -0.06 0.04 0.17

Lynx population 11 -0.05 Lynx population -3.17 4.57 0.51 Lynx population t-1 10 -0.06 Lynx population t-1 -3.43 4.75 0.49

Fox population 11 0.05 Fox population 0.08 0.06 0.25 Hare-Lynx +

Lynx population t-1 10 -0.06

Lynx population t-1 -4.05 6.32 0.43 Hare-Lynx 0.165 0.26 0.35

Hare population + Hare-Lynx 11 -0.12

Hare population -0.05 0.1 0.67 Hare-Lynx 0.09 0.2 0.66

Hare population + Hare-Marten 11 -0.06

Hare population -0.05 0.09 0.59 Hare-Marten -0.9 1.11 0.44

Hare-Marten + Squirrel abundance 11 0.2

Hare-Marten -1.31 0.91 0.19 Squirrel population -0.06 0.04 0.12

Hare-Marten + Lynx population t-1

11 0.32 Hare-Marten -2.69 1.15 0.052

Lynx population t-1 -11.06 5.01 0.06

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Appendix

Appendix 1. The best causal graphs for each year during study period, 2004-2014.

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105

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Figure 4. 5. The best causal graphs for each year during study period, 2004-2014. Solid line indicates significant path (p < 0.05). Dashed line indicates non-significant path (p < 0.05). Red line indicates positive effect from a variable to the other and blue lines indicate negative.

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Appendix 2. Result of d-separation test representing all d-separation claims and its

probability of independence.

Table 4. 4. Result of d-separation test representing all d-separation claims and its probability of independence.

Year Dsep Claim P Distribution Link

2004

Fox_||_Lynx|{Transect type,40-80y,Temperature,Squirrel,Marten,Exposure time} 0.72 Binomial Logit

Fox_||_Squirrel|{Transect type,Hare,40-80y,Temperature,Marten,Exposure time} 0.67 Binomial Logit

Fox_||_Hare|{Transect type,40-80y,Marten,Exposure time} 0.45 Binomial Logit

Hare_||_Lynx|{Transect type,40-80y,Temperature,Squirrel,Exposure time} 0.04 Negative

binomial Log

Hare_||_40-80y|{Transect type,Exposure time} 0.15 Negative binomial Log

Marten_||_Lynx|{Transect type,40-80y,Temperature,Squirrel,Hare,Exposure time} 0.87 Binomial Logit

Marten_||_40-80y|{Transect type,Hare,Temperature,Exposure time} 0.93 Binomial Logit

Marten_||_Squirrel|{Transect type,Hare,40-80y,Temperature,Exposure time} 0.66 Binomial Logit

Temperature_||_Hare|{Transect type,40-80y} 0.59 Gaussian Identity Temperature_||_Fox|{Transect type,40-80y,Marten} 0.95 Gaussian Identity Transect type_||_40-80y|{NA} 0.77 Binomial Logit Transect type_||_Fox|{40-80y,Marten} 0.66 Binomial Logit

2005

Fox_||_Transect type|{Temperature,40-80y,Marten,Exposure time} 0.37 Binomial Logit

Hare_||_Lynx|{Squirrel,Fox,Exposure time} 0.39 Negative binomial Log

Hare_||_Fox|{Squirrel,40-80y,Marten,Exposure time} 0.13 Negative binomial Log

Hare_||_Temperature|{Squirrel,Exposure time} 0.16 Negative binomial Log

40-80y_||_Hare|{Temperature,Transect type,Squirrel} 0.42 Gaussian Identity 40-80y_||_Marten|{Temperature,Transect type} 0.06 Gaussian Identity 40-80y_||_Lynx|{Temperature,Transect type,Squirrel,Fox} 0.1 Gaussian Identity

Lynx_||_Marten|{Temperature,Squirrel,Fox,Exposure time} 0.51 Binomial Logit

Lynx_||_Transect type|{Temperature,Squirrel,Fox,Exposure time} 0.26 Binomial Logit

Lynx_||_Temperature|{Squirrel,Fox,Exposure time} 0.06 Binomial Logit Marten_||_Transect type|{Temperature,Exposure time} 0.49 Binomial Logit Marten_||_Squirrel|{Temperature,40-80y,Exposure time} 0.1 Binomial Logit

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Marten_||_Hare|{Squirrel,Temperature,Exposure time} 0.03 Binomial Logit Squirrel_||_Fox|{Temperature,40-80y,Marten,Exposure time} 0.53 Negative

binomial Log

Squirrel_||_Transect type|{Temperature,40-80y,Exposure time} 0.11 Negative

binomial Log

Temperature_||_Fox|{40-80y,Marten} 0.55 Gaussian Identity Transect type_||_Hare|{Temperature,Squirrel} 0.12 Binomial Logit

2006

Fox_||_Squirrel|{40-80y,Lynx,Exposure time} 0.79 Binomial Logit Fox_||_Temperature|{40-80y,Lynx,Exposure time} 0.35 Binomial Logit Fox_||_Hare|{Transect type,Squirrel,Lynx,Exposure time} 0.64 Binomial Logit

Hare_||_40-80y|{Transect type,Squirrel,Exposure time} 0.86 Negative binomial Log

Hare_||_Marten|{Transect type,Squirrel,40-80y,Lynx,Fox,Exposure time} 0.11 Negative

binomial Log

40-80y_||_Fox|{Lynx} 0.61 Gaussian Identity Lynx_||_Squirrel|{40-80y,Transect type,Hare,Exposure time} 0.9 Binomial Logit

Lynx_||_40-80y|{Transect type,Hare,Exposure time} 0.73 Binomial Logit Squirrel_||_Transect type|{40-80y,Temperature,Exposure time} 0.47 Negative

binomial Log

Squirrel_||_Temperature|{40-80y,Exposure time} 0.78 Negative binomial Log

Temperature_||_Marten|{40-80y,Transect type,Squirrel,Lynx,Fox} 0.8 Gaussian Identity

Temperature_||_Lynx|{40-80y,Transect type,Hare} 0.28 Gaussian Identity Temperature_||_Hare|{40-80y,Transect type,Squirrel} 0 Gaussian Identity Transect type_||_Fox|{40-80y,Temperature,Lynx} 0.95 Binomial Logit

2007

Fox_||_Lynx|{Temperature,Hare,Exposure time} 0.81 Binomial Logit Fox_||_Marten|{Temperature,Transect type,Lynx,Exposure time} 0.75 Binomial Logit

Fox_||_Squirrel|{Temperature,40-80y,Exposure time} 0.35 Binomial Logit Hare_||_40-80y|{Transect type,Squirrel,Fox,Exposure time} 0.31 Negative

binomial Log

Hare_||_Temperature|{Transect type,Squirrel,Fox,Exposure time} 0.39 Negative

binomial Log

40-80y_||_Lynx|{Temperature,Hare} 0.12 Gaussian Identity 40-80y_||_Fox|{NA} 0.18 Gaussian Identity Marten_||_Hare|{Transect type,Squirrel,Fox,Temperature,Lynx,Exposure time} 0.52 Binomial Logit

Marten_||_Squirrel|{Temperature,40-80y,Transect type,Lynx,Exposure time} 0.79 Binomial Logit

Marten_||_40-80y|{Temperature,Transect type,Lynx,Exposure time} 0.13 Binomial Logit

Squirrel_||_Lynx|{Temperature,40-80y,Hare,Exposure time} 0.46 Negative

binomial Log

Temperature_||_40-80y|{NA} 0.57 Gaussian Identity Temperature_||_Fox|{NA} 0.89 Gaussian Identity Transect type_||_Fox|{Temperature} 0.99 Binomial Logit Transect type_||_40-80y|{Temperature} 0.78 Binomial Logit

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Transect type_||_Squirrel|{Temperature,40-80y} 0.51 Binomial Logit Transect type_||_Lynx|{Temperature,Hare} 0.14 Binomial Logit

2008

Fox_||_Transect type|{40-80y,Temperature,Marten,Hare,Exposure time} 0.27 Binomial Logit

Fox_||_Squirrel|{40-80y,Temperature,Marten,Hare,Exposure time} 0.82 Binomial Logit

Fox_||_Lynx|{Transect type,Temperature,40-80y,Marten,Hare,Exposure time} 0.35 Binomial Logit

Hare_||_Marten|{40-80y,Transect type,Temperature,Exposure time} 0.38 Negative

binomial Log

Hare_||_40-80y|{Transect type,Exposure time} 0.33 Negative binomial Log

Hare_||_Lynx|{Transect type,Temperature,Exposure time} 0.6 Negative

binomial Log

Lynx_||_40-80y|{Transect type,Temperature,Exposure time} 0.9 Binomial Logit

Marten_||_Lynx|{Transect type,Temperature,40-80y,Exposure time} 0.9 Binomial Logit

Marten_||_Squirrel|{40-80y,Transect type,Temperature,Hare,Exposure time} 0.1 Binomial Logit

Squirrel_||_Lynx|{Transect type,Temperature,40-80y,Hare,Exposure time} 0.05 Negative

binomial Log

Temperature_||_Hare|{Transect type} 0.66 Gaussian Identity Temperature_||_40-80y|{Transect type} 0.21 Gaussian Identity Transect type_||_Squirrel|{40-80y,Temperature,Hare} 0.68 Binomial Logit

2009

Fox_||_Marten|{Temperature,Transect type,Squirrel,Exposure time} 0.76 Binomial Logit

Hare_||_Fox|{Temperature,Transect type,Squirrel,Exposure time} 0.47 Negative

binomial Log

Hare_||_40-80y|{Temperature,Squirrel,Exposure time} 0.67 Negative binomial Log

40-80y_||_Marten|{Temperature,Transect type,Squirrel} 0.77 Gaussian Identity 40-80y_||_Fox|{Temperature,Transect type,Squirrel} 0.62 Gaussian Identity Marten_||_Hare|{Temperature,Transect type,Squirrel,Exposure time} 0.42 Binomial Logit

Squirrel_||_Temperature|{40-80y,Transect type,Exposure time} 0.75 Negative

binomial Log

Transect type_||_40-80y|{Temperature} 0.4 Binomial Logit Transect type_||_Hare|{Temperature,Squirrel} 0.35 Binomial Logit Transect type_||_Temperature|{NA} 0.77 Binomial Logit

2010

Fox_||_Marten|{Temperature,Transect type,Squirrel,Hare,Exposure time} 0.33 Binomial Logit

Fox_||_Hare|{Temperature,Transect type,40-80y,Squirrel,Exposure time} 0.28 Binomial Logit

Fox_||_Squirrel|{Temperature,Transect type,40-80y,Exposure time} 0.34 Binomial Logit

Fox_||_40-80y|{Temperature,Transect type,Exposure time} 0.97 Binomial Logit

40-80y_||_Marten|{Temperature,Transect type,Squirrel,Hare} 0.74 Gaussian Identity

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Temperature_||_Marten|{Transect type,Squirrel,Hare} 0.93 Gaussian Identity Temperature_||_Hare|{Transect type,40-80y,Squirrel} 0.3 Gaussian Identity Transect type_||_40-80y|{Temperature} 0.19 Binomial Logit Transect type_||_Squirrel|{Temperature,40-80y} 0.75 Binomial Logit

2011

40-80y_||_Hare|{Transect type,Temperature,Squirrel,Fox} 0.08 Gaussian Identity

Marten_||_40-80y|{Transect type,Temperature,Squirrel,Fox,Exposure time} 0.79 Binomial Logit

Marten_||_Hare|{Temperature,Squirrel,Fox,Transect type,Exposure time} 0.25 Binomial Logit

Squirrel_||_Transect type|{40-80y,Exposure time} 0.7 Negative binomial Log

Temperature_||_Squirrel|{Transect type,40-80y} 0.01 Gaussian Identity Temperature_||_Fox|{Transect type,40-80y,Squirrel} 0.03 Gaussian Identity Transect type_||_Marten|{Temperature,Squirrel,Fox} 0.66 Binomial Logit

2012

Fox_||_Hare|{Transect type,40-80y,Temperature,Squirrel,Exposure time} 0.6 Binomial Logit

Fox_||_40-80y|{Transect type,Temperature,Squirrel,Exposure time} 0.38 Binomial Logit

40-80y_||_Lynx|{Temperature,Fox} 0.74 Gaussian Identity 40-80y_||_Transect type|{NA} 0.12 Gaussian Identity Lynx_||_Hare|{Transect type,40-80y,Temperature,Fox,Exposure time} 0.61 Binomial Logit

Marten_||_Squirrel|{40-80y,Temperature,Hare,Transect type,Fox,Lynx,Exposure time} 0.47 Binomial Logit

Squirrel_||_Lynx|{40-80y,Temperature,Hare,Fox,Exposure time} 0.12 Negative

binomial Log

Transect type_||_Squirrel|{40-80y,Temperature,Hare} 0.03 Binomial Logit Transect type_||_Lynx|{Temperature,Fox} 0.12 Binomial Logit

2013

Fox_||_Lynx|{40-80y,Squirrel,Transect type,Hare,Exposure time} 0.98 Binomial Logit

40-80y_||_Hare|{Temperature,Transect type,Squirrel,Lynx} 0.01 Gaussian Identity

Lynx_||_Transect type|{40-80y,Squirrel,Exposure time} 0.34 Binomial Logit Lynx_||_Marten|{40-80y,Squirrel,Temperature,Transect type,Hare,Exposure time} 0.32 Binomial Logit

Marten_||_Fox|{40-80y,Transect type,Hare,Temperature,Exposure time} 0.35 Binomial Logit

Marten_||_Squirrel|{40-80y,Transect type,Temperature,Hare,Exposure time} 0.14 Binomial Logit

Squirrel_||_Fox|{40-80y,Transect type,Hare,Exposure time} 0.51 Negative

binomial Log

Squirrel_||_Temperature|{40-80y,Transect type,Exposure time} 0.15 Negative

binomial Log

Temperature_||_Fox|{40-80y,Transect type,Hare} 0.41 Gaussian Identity Temperature_||_Lynx|{40-80y,Squirrel} 0.56 Gaussian Identity Transect type_||_Temperature|{NA} 0.85 Binomial Logit Transect type_||_40-80y|{Temperature} 0.33 Binomial Logit

2014 Fox_||_Marten|{Temperature,Transect type,Hare,40-80y,Lynx,Exposure time} 0.56 Binomial Logit

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Fox_||_Hare|{Temperature,40-80y,Squirrel,Lynx,Exposure time} 0.58 Binomial Logit

Hare_||_Transect type|{Temperature,40-80y,Squirrel,Exposure time} 0.6 Negative

binomial Log

40-80y_||_Squirrel|{Temperature,Transect type} 0.68 Gaussian Identity 40-80y_||_Marten|{Temperature,Transect type,Hare} 0.63 Gaussian Identity Lynx_||_Hare|{Temperature,40-80y,Squirrel,Marten,Exposure time} 0.9 Binomial Logit

Lynx_||_40-80y|{Temperature,Marten,Exposure time} 0.87 Binomial Logit Marten_||_Squirrel|{Transect type,Temperature,Hare,Exposure time} 0.43 Binomial Logit

Squirrel_||_Fox|{Transect type,40-80y,Lynx,Exposure time} 0.44 Negative

binomial Log

Squirrel_||_Lynx|{Transect type,Marten,Exposure time} 0.36 Negative binomial Log

Squirrel_||_Temperature|{Transect type,Exposure time} 0.63 Negative binomial Log

Temperature_||_Fox|{40-80y,Lynx} 0.86 Gaussian Identity Temperature_||_Lynx|{Marten} 0.87 Gaussian Identity Transect type_||_Lynx|{Temperature,40-80y,Marten} 0.48 Binomial Logit Transect type_||_Fox|{Temperature,40-80y,Lynx} 0.32 Binomial Logit

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Appendix 3. Estimated path coefficients of edges in the best graphs for each year.

Table 4. 5. Estimated path coefficients of edges in the best graphs for each year.

Year P (graph) Parents Child Estimate Standard error z P

2004 0.839

Transect type Hare 0.928 0.27 3.433 0.001 Exposure time Hare -0.002 0.009 -0.212 0.832

Hare Marten 0.049 0.034 1.453 0.146 Transect type Marten 1.067 0.482 2.216 0.027 Temperature Marten -0.044 0.037 -1.187 0.235

Exposure time Marten -0.008 0.016 -0.48 0.631 Hare Squirrel 0.072 0.018 3.933 <0.001

40-80y Squirrel 0.009 0.007 1.307 0.191 Transect type Squirrel -1.063 0.377 -2.823 0.005 Temperature Squirrel 0.107 0.032 3.36 0.001

Exposure time Squirrel 0.04 0.01 3.854 <0.001 Squirrel Lynx 0.323 0.134 2.412 0.016 40-80y Lynx -0.018 0.013 -1.34 0.18

Transect type Lynx -1.523 0.812 -1.876 0.061 Temperature Lynx 0.131 0.069 1.905 0.057

Exposure time Lynx 0.011 0.02 0.524 0.6 Marten Fox 1.513 0.913 1.657 0.098 40-80y Fox -0.036 0.023 -1.573 0.116

Exposure time Fox 0.052 0.034 1.567 0.117 40-80y Temperature -0.071 0.025 -2.881 0.005

Transect type Temperature -3.408 1.101 -3.096 0.002

2005 0.007

Squirrel Hare 0.244 0.046 5.325 <0.001 Exposure time Hare -0.018 0.01 -1.795 0.073 Temperature Marten 0.518 0.102 5.077 <0.001

Exposure time Marten 0.025 0.021 1.182 0.237 40-80y Squirrel 0.031 0.008 4.021 <0.001

Temperature Squirrel 0.216 0.031 6.93 <0.001 Exposure time Squirrel 0.019 0.014 1.415 0.157

Squirrel Lynx 0.225 0.081 2.788 0.005 Fox Lynx 1.458 0.769 1.895 0.058

Exposure time Lynx -0.021 0.027 -0.77 0.441 Marten Fox 1.041 0.63 1.653 0.098 40-80y Fox -0.021 0.014 -1.483 0.138

Exposure time Fox 0.03 0.024 1.259 0.208 Transect type 40-80y 15.092 6.384 2.364 0.019 Temperature 40-80y -0.511 0.258 -1.98 0.049

Temperature Transect type 0.23 0.076 3.026 0.002

2006 0.515

Squirrel Hare 0.254 0.165 1.539 0.124 Transect type Hare 0.769 0.298 2.586 0.01 Exposure time Hare -0.013 0.009 -1.427 0.154

Squirrel Marten 0.268 0.226 1.187 0.235

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Lynx Marten -1.594 0.82 -1.943 0.052 Fox Marten -1.098 0.675 -1.628 0.103

40-80y Marten 0.026 0.011 2.341 0.019 Transect type Marten 1.376 0.43 3.202 0.001 Exposure time Marten -0.047 0.016 -2.959 0.003

40-80y Squirrel 0.032 0.01 3.187 0.001 Exposure time Squirrel -0.011 0.013 -0.905 0.365

Hare Lynx 0.215 0.055 3.942 <0.001 Transect type Lynx -1.835 0.735 -2.497 0.013 Exposure time Lynx -0.012 0.014 -0.807 0.42

Lynx Fox -1.098 0.768 -1.429 0.153 Exposure time Fox -0.009 0.013 -0.673 0.501

40-80y Transect type -0.02 0.009 -2.313 0.021


40-80y Temperature 0.049 0.015 3.327 0.001

2007 0.688

Squirrel Hare 0.02 0.009 2.128 0.033 Fox Hare -0.4 0.289 -1.385 0.166

Transect type Hare 1.218 0.268 4.551 <0.001 Exposure time Hare -0.023 0.008 -2.791 0.005

Lynx Marten -0.802 0.552 -1.453 0.146 Transect type Marten 1.986 0.437 4.543 <0.001 Temperature Marten 0.089 0.03 2.975 0.003

Exposure time Marten 0.025 0.015 1.694 0.09 40-80y Squirrel 0.02 0.004 5.56 <0.001

Temperature Squirrel 0.053 0.012 4.325 <0.001 Exposure time Squirrel 0.004 0.007 0.532 0.595

Hare Lynx 0.034 0.025 1.392 0.164 Temperature Lynx -0.049 0.032 -1.538 0.124

Exposure time Lynx -0.148 0.052 -2.847 0.004

Temperature Transect type -0.128 0.036 -3.582 <0.001

2008 0.471

Transect type Hare 1.352 0.199 6.785 <0.001 Exposure time Hare 0.027 0.009 3.03 0.002

40-80y Marten 0.017 0.006 2.788 0.005 Transect type Marten 0.783 0.326 2.403 0.016 Temperature Marten 0.081 0.027 3.044 0.002

Exposure time Marten -0.003 0.014 -0.212 0.832 Hare Squirrel 0.098 0.014 6.858 <0.001

40-80y Squirrel 0.024 0.006 4.316 <0.001 Temperature Squirrel 0.049 0.025 1.959 0.05

Exposure time Squirrel 0.013 0.013 0.987 0.324 Transect type Lynx -0.784 0.507 -1.548 0.122 Temperature Lynx -0.045 0.033 -1.359 0.174

Exposure time Lynx -0.025 0.021 -1.188 0.235 Hare Fox 0.033 0.023 1.414 0.157

Marten Fox 1.065 0.416 2.561 0.01

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40-80y Fox 0.02 0.01 2.063 0.039 Temperature Fox -0.143 0.043 -3.298 0.001

Exposure time Fox -0.002 0.02 -0.106 0.916

40-80y Transect type -0.008 0.007 -1.085 0.278

Transect type Temperature -1.658 0.728 -2.277 0.024

2009 0.944

Squirrel Hare 0.254 0.121 2.105 0.035 Temperature Hare -0.032 0.026 -1.242 0.214

Exposure time Hare -0.022 0.017 -1.292 0.196 Squirrel Marten 0.158 0.131 1.206 0.228

Transect type Marten 0.588 0.485 1.211 0.226 Temperature Marten 0.04 0.034 1.198 0.231

Exposure time Marten -0.05 0.023 -2.211 0.027 40-80y Squirrel 0.016 0.009 1.804 0.071

Transect type Squirrel 0.61 0.561 1.087 0.277 Exposure time Squirrel -0.026 0.024 -1.118 0.264

Squirrel Fox 0.294 0.155 1.896 0.058 Transect type Fox -1.078 0.708 -1.521 0.128 Temperature Fox 0.134 0.045 2.94 0.003

Exposure time Fox 0.001 0.023 0.041 0.967 Temperature 40-80y -0.414 0.283 -1.466 0.145

2010 0.73

Squirrel Hare 0.199 0.043 4.583 <0.001 40-80y Hare -0.007 0.005 -1.505 0.132

Transect type Hare 1.464 0.305 4.795 <0.001 Exposure time Hare -0.01 0.008 -1.359 0.174

Hare Marten 0.114 0.034 3.378 0.001 Squirrel Marten 0.078 0.065 1.203 0.229

Transect type Marten 1.307 0.478 2.735 0.006 Exposure time Marten -0.015 0.012 -1.235 0.217

40-80y Squirrel 0.023 0.007 3.078 0.002 Temperature Squirrel 0.054 0.062 0.869 0.385

Exposure time Squirrel 0.023 0.011 2.121 0.034 Transect type Fox -1.014 0.563 -1.801 0.072 Temperature Fox 0.052 0.068 0.758 0.448

Exposure time Fox -0.005 0.011 -0.43 0.667 Temperature 40-80y 1.259 0.537 2.345 0.02

Temperature Transect type -0.133 0.063 -2.117 0.034

2011 0.028

Squirrel Hare 0.075 0.046 1.642 0.101 Fox Hare -0.207 0.143 -1.446 0.148

Transect type Hare 0.597 0.212 2.823 0.005 Temperature Hare -0.031 0.012 -2.603 0.009

Exposure time Hare -0.041 0.006 -7.026 <0.001 Squirrel Marten 0.396 0.116 3.419 0.001

Fox Marten -0.982 0.343 -2.866 0.004 Temperature Marten 0.058 0.026 2.203 0.028

Exposure time Marten -0.028 0.013 -2.179 0.029 40-80y Squirrel 0.022 0.007 3.367 0.001

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Exposure time Squirrel -0.035 0.012 -2.974 0.003 Squirrel Fox 0.138 0.095 1.447 0.148 40-80y Fox 0.013 0.007 1.954 0.051

Transect type Fox -1.35 0.57 -2.366 0.018 Exposure time Fox -0.046 0.013 -3.537 <0.001 Transect type 40-80y 8.211 3.995 2.055 0.041 Temperature 40-80y -0.445 0.219 -2.03 0.043 Transect type Temperature 1.689 1.061 1.593 0.112

2012 0.106

40-80y Hare -0.004 0.002 -1.737 0.082 Transect type Hare 0.711 0.119 5.958 <0.001 Temperature Hare -0.037 0.014 -2.588 0.01

Exposure time Hare 0.005 0.004 1.273 0.203 Hare Marten 0.061 0.019 3.262 0.001 Lynx Marten -2.005 1.163 -1.725 0.085 Fox Marten -0.42 0.326 -1.29 0.197

40-80y Marten -0.006 0.006 -0.882 0.378 Transect type Marten 0.74 0.308 2.4 0.016 Temperature Marten -0.056 0.038 -1.457 0.145

Exposure time Marten 0.015 0.01 1.444 0.149 Hare Squirrel 0.096 0.008 11.538 <0.001

40-80y Squirrel 0.025 0.004 6.852 <0.001 Temperature Squirrel 0.1 0.022 4.558 <0.001

Exposure time Squirrel 0.001 0.006 0.194 0.846 Fox Lynx 1.01 0.725 1.393 0.164

Temperature Lynx -0.37 0.1 -3.716 <0.001 Exposure time Lynx 0.061 0.028 2.196 0.028

Squirrel Fox 0.087 0.02 4.302 <0.001 Transect type Fox -1.001 0.361 -2.775 0.006 Temperature Fox -0.092 0.036 -2.559 0.01

Exposure time Fox 0.044 0.01 4.263 <0.001 40-80y Temperature -0.02 0.011 -1.881 0.061

Transect type Temperature -2.383 0.51 -4.671 <0.001

2013 0.184

Squirrel Hare 0.168 0.051 3.289 0.001 Lynx Hare -0.877 0.33 -2.658 0.008

Transect type Hare 0.679 0.158 4.298 <0.001 Temperature Hare -0.025 0.006 -3.99 <0.001

Exposure time Hare 0.009 0.006 1.664 0.096 Hare Marten 0.032 0.017 1.869 0.062

40-80y Marten 0.012 0.008 1.473 0.141 Transect type Marten 1.219 0.385 3.169 0.002 Temperature Marten -0.023 0.019 -1.217 0.224

Exposure time Marten 0.021 0.016 1.32 0.187 40-80y Squirrel 0.017 0.006 2.627 0.009

Transect type Squirrel 0.982 0.313 3.134 0.002 Exposure time Squirrel 0.041 0.013 3.222 0.001

Squirrel Lynx 0.274 0.2 1.371 0.17 40-80y Lynx -0.036 0.016 -2.211 0.027

Exposure time Lynx 0.015 0.034 0.435 0.664

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Hare Fox 0.053 0.016 3.29 0.001 40-80y Fox -0.007 0.007 -1.004 0.315

Transect type Fox -0.991 0.496 -1.998 0.046 Exposure time Fox 0.037 0.015 2.462 0.014 Temperature 40-80y -0.748 0.117 -6.369 <0.001

2014 0.982

Squirrel Hare 0.149 0.065 2.293 0.022 40-80y Hare -0.01 0.005 -1.93 0.054

Temperature Hare 0.061 0.017 3.648 <0.001 Exposure time Hare 0.004 0.008 0.452 0.651

Hare Marten 0.096 0.042 2.255 0.024 Transect type Marten 1.148 0.69 1.665 0.096 Temperature Marten -0.13 0.064 -2.034 0.042

Exposure time Marten 0.023 0.036 0.648 0.517 Transect type Squirrel -1.25 0.59 -2.12 0.034 Exposure time Squirrel -0.001 0.017 -0.052 0.958

Marten Lynx 1.474 0.919 1.604 0.109 Exposure time Lynx -0.065 0.028 -2.283 0.022

Lynx Fox -1.503 1.132 -1.328 0.184 40-80y Fox 0.016 0.011 1.438 0.151

Exposure time Fox -0.043 0.017 -2.499 0.012 Temperature 40-80y 0.329 0.254 1.297 0.197

40-80y Transect type -0.017 0.014 -1.234 0.217


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GENERAL CONCLUSION

This thesis addressed spatio-temporal dynamics of wintering mammals, with a focus on

snowshoe hare and marten. Habitat selection by snowshoe hare was influenced by current

density and recent density and they tended to move to older habitat in deeper snow. The long

term data combined with multiple species addressed habitat selection by mesopredators as

the likely result of trade-offs between energy gain, predation avoidance, and competitor

abundance, leading to changes in habitat selection depending on community dynamics.

The challenge for developping a reliable population index

Chapter 1 played a key role in the thesis, by providing evidence for the reliability of track

counts as a proxy for species dynamics. In this chapter, I estimated year effect in the

regression from snow tracking data, and showed a good agreement with pelt sales in red

squirrel and weasels and mean track counts accounting for exposure time agreed with marten

pelt sales. Besides supporting the reliability of track counts, those results should be of interest

to furbearer managers, because they support the use of pelt sales as a population index.

Therefore, pelt sales can be useful to investigate the effect of forestry on population trends of

mammals. Pelt sales have been recorded over the Quebec province since 2003 (the Ministère

des Forêts, de la Faune et des Parcs: https://www.mffp.gouv.qc.ca/faune/statistiques/chasse-

piegeage.jsp).

Then, in chapter 4, the result indicated track counts of marten were not only influenced

by habitat but also by track counts of prey, snowshoe hare and squirrel, and of competitor,

lynx and fox. Therefore, estimated year effect could be higher in the year when the space

highly used by preys was intensively surveyed. Incorporating prey track counts into the

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regression analysis might increase agreement with pelt sales due to removal of bias associated

with prey and competitor, potentially leading to obtain better popualtion index.

Feedback effect of density on habitat selection of snowshoe hare

In chapter 2, I addressed the possible time lag effect of conspecific density on habitat

selection. Immediate effects of density combined with time lagged effect best performed in

explaining use of regenerating and young forests. Habitat use of hare has been frequently

investigated (e.g., Thronton et al. 2012, Bois et al. 2012). Though, it rarely addressed effect

of conspecific density on habitat selection and rarely showed population trends around study

years. Therefore, it would be difficult to perform further investigation if current or recent

density can have impact on observed habitat use. It is recommended that, where possible,

habitat studies should show population trend of a species in interest to allow comparisons

among studies.

In contrast to younger forests, I was unable to explain the use of older forests by

conspecific density. The result from chapter 4 revealed that hare tracks were spatially

associated with squirrel tracks. Allard-Duchene et al. (2014) also showed that hare and

squirrel had similar responses to stand age after fire disturbance, in which two species had

peaks at almost the same age. Since the use of mature forests was mediated by squirrels, this

might reduce predictive power of ideal free distribution, resulting in poor performance of

ideal free distribution models for this habitat.

Snow depth influenced habitat use of hare

In chapter 3, I found that snowshoe hare more frequently used foliage in the middle height

when snow was deeper. Stand ages at the peak habitat use of hare were variable with winter

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habitat use studies. Since maximum snow depth varied with regions, variation of snow depth

may explain variations in hare habitat use pattern. Forestry practices including thinning and

clear-cut are also known to influence snow depth (Horstkotte and Roturier 2013). Difference

in forestry practice among regions would additionaly explain the variations.

Predators are often spatially associated with prey (e.g., lynx – snowshoe hare; Keim et al.

2011) as shown in chapter 4. In the studies of prey-predator spatial distribution, prey location

was often assumed to be fixed (e.g., Latham et al. 2013) even though we do know that the

spatial distribution of prey is dynamic, even at short time scales, as shown in Chapter 3.

Besides, as shown in chapter 2, prey (snowshoe hares) expanded their distribution toward

less preferred habitat in years of high abundance. Monthly and yearly spatial dynamics are

therefore likely to cause significant uncertainty in prey-predator studies that assume

negligible or nonexistent prey location shifts at timescales considered. Chapter 3 also has

implications in the longer run, if regional climate change does influence regional snow

precipitation in decadal time scales (Christensen 2013). Not only decadal dynamics in snow

depth change snowshoe hare distribution, but also that associated predators, marten and lynx.

Interactions in spatial distribution among mammals

In chapter 4, I hypothesized that the spatial association between a mesocarnivore (marten)

and an herbivore (hare) tended to decrease with higher abundance of competitor, lynx, in the

previous year. Lowered spatial association appeared to lead marten towards areas with an

alternative prey, red squirrel.

Prey switching has been investigated as function of ratio of primary prey and secondary

prey. However, as observed in chapter 4, larger predator abundance might have contributed

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more to explain prey switching indirectly by decreasing spatial relationship between

predation and primary prey, rather than prey abundance.

Caveats

In my thesis, I investigated density-dependent habitat selection at yearly scale, implying

that population size of hare was assumed to be constant within a winter. However, it is known

that winter population size of snowshoe hare declines along with time (Kielland et al. 2010).

Thus, the inter-season declines in population size might affect habitat selection.

An important assumption in this thesis is that observed behavioral decision making by

animals should lead to fitness gain or maintain fitness. However, in this thesis, I did not

address fitness components (e.g., survival and population growth rate). Thus, my general

question regarding fitness is that « Does observed behavioral response to stimuli contribute

to fitness gain? And the spatial distribution of animals as the end result of the response can

contribute to fitness gain at the whole population level? ». More specific questions will be:

1) Which habitat had the most contribution to fitness (local population growth rate) of

snowshoe hare?

2) Did high local abundance of snowshoe hare or red squirrel contribute to increasing

local population growth rate or maintaining local population of marten?

3) When marten switched to the alternative prey, was its population size declined or

stable?

Chapter 4 focused on spatial association between preys and predators because strong

spatial asspciation was assumed to lead to successful hunting and energy gain, possibly

resulting in fitness gain. Since spatial association does not necessary mean hunting success

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(Lima and Dill 1990), this assumption would be supported by providing an evidence that

frequency of occurrence of a certain prey in predator diet is increased with spatial association.

Diet analysis (e.g., scat survey) would enable to verify that the frequency of a certain prey

(e.g., hare) in mesocarnivore diet is declined in presence of larger predatopr sharing the prey

item. Such a verification would give strong support to the result observed in chapter 4.

My thesis addressed the questions about fundamental ecology but did not address

application aspect. A question is “Does the knowledge obtained throughout the entire thesis

improve prediction of spatial distribution of a animal species?”. In order to examine

applicability of the knowledge to wildlife management, it is important to conduct snow

tracking in another year in the study site and then compare observed track counts in 2015

with track counts that the knowledge obtained throughout the thesis predicts.

Management implications

Studies on spatial relationship between a species and forest stand types should ideally

address population trends of the species during study period and the previous year of first

study year in order to avoid confounding effects that would otherwise lead to possibly biased

assessments of preferred habitat. Though a lot of studies have addressed effect of forestry

practices including site preparation and commercial thinning on animal distribution (e.g.,

Thornton et al. 2012a), their interpretations of results are likely to vary with population status

during study period, thus leading to changes in management implication to forestry.

Conserving different types of habitat (stand age) with similar proportion in a landscape

as in the study site would be favorable for mammal community, at least a part of community,

and would increase resilience of the community to climate change. The first reason for this

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statement is due to the result that preferred habitat of snowshoe hare varied with snow depth.

Given future fluctuation of snow precipitation (either increase or decrease), preparing

different habitat in terms of stand age would enable hare to switch to different habitat

depending on fluctuating snow depth. Since abundance of squirrel indexed by track count

was high in mature forest (seen in other literatures Fisher and Wilkinson 2005), presence of

different habitat can enable different prey items to exist in a landscape, potentially offering

chance of prey switching to carnivores.

Conserving carnivores, for example marten, might require setting conservation areas to

include high prey abundance areas. Protection of a focal species by setting conservation areas

would be efficient when strong association between a focal species and its preferred habitat

is validated. Marten is often considered as an indicator species (Thompson 1991) and is

registered in endangered species act in New Foundland (The Newfoundland Marten

Recovery Team 2010) and is considered as a species dependent on mature and over mature

forest. In chapter 4, marten showed variable response to mature habitat (40-80yr) which is

also observed in other literature (Potvin et al. 2000). On the other hand, marten was more

frequently (frequency of direct links occurred) spatially associated with prey species. Setting

conservation area might require to entail preferred habitat of prey item of marten.

Long term studies and snow tracking

While long term studies have been acknowledged as important to reveal ecological

processes, their success is often hindered by limited funding (Nelson et al. 2008) which is

required to assure personnel availability. For example, a 40y-study on demography of

seabirds, which had been supported financially, was closed due to termination of funding for

the project leading to lack of personnels (Birkhead 2014). Snow tracking study would have

123

potentials as a long term study due to less cost and its simplicity which might allow to

integrate citizen participation into long term monitoring (Dickinson et al. 2012) for assuring

personnels. In addition to these advantages, this thesis based on snow tracking has reavealed

important ecological process and therefore snow tracking would also have high capacity to

address complex ecological issues such as time lag and interspecific interaction, leading to

further understanding how a part of biodiversity is maintained.

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