The abundance and distribution ofbeavers (Castor canadensis) in

Québec, Canada

Stacey Isabelle Jarema Department ofNatural Resource Sciences McGill University, Montréal

August 2006

A thesis subrnitted to McGill University in partial fulfilrnent of the requirernents of the degree of

Master of Science

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While these forms may be included Bien que ces formulaires in the document page count, aient inclus dans la pagination, their removal does not represent il n'y aura aucun contenu manquant. any loss of content from the thesis. ••• Canada ABSTRACT

The importance of spatial variation in abundance for the assessment of climate change impacts was examined using the North American beaver (Castor canadensis) in Québec as a model species. A preliminary characterization of the beavers' range edge improved the core-sampling bias and revealed that beavers are present at low densities in shrubby riparian habitats as far north as the communities of Tasiujaq and Umiujaq. Spatial variation in beaver abundance across the province follows a roughly logistic pattern, with abundance peaking in southern Québec, declining steeply around 49°N, and remaining uniformly low as far as 58°N. Although climate sensitivity ofbeaver abundance and the greatest changes in future beaver density are predicted to occur near the middle of their range, beavers are expected to occupy most of the province by 2055. These results highlight the value of incorporating density estimates from across a species' range into climate envelope models.

li RÉSUMÉ

L'importance de la variation spatiale de l'abondance pour évaluer les conséquences des changements climatiques a été examinée en utilisant le castor nord-américain (Castor canadensis) au Québec comme espèce modèle. Une caractérisation préliminaire des limites de l'aire de répartition des castors a contribué à améliorer le biais d'échantillonnage qui existait en faveur du centre de l'aire et a révélé qu'on retrouve de faibles populations de castors dans des habitats arbustifs aussi loin au Nord que les communautés de Tasiujaq et d'Umiujaq. La variation spatiale de l'abondance des castors à travers toute la province montre une tendance plus ou moins logistique. L'abondance atteint son plus haut niveau dans le sud du Québec, elle diminue sensiblement près du 4ge parallèle nord et elle demeure uniformément basse jusqu'au 58e parallèle nord. Bien qu'on s'attend à ce que la sensibilité des castors aux effets du climat et les plus grands changements à la densité future de leur population aient lieu autour du milieu de leur aire de répartition, il est prévu que les castors habiteront la majeure partie de la province d'ici 2055. Ces résultats soulignent l'importance d'incorporer les estimations de densité de l'aire de répartition au complet d'une espèce dans les modèles d'enveloppe climatique.

iü PREFACE

Contribution of authors

The following thesis is divided into two manuscripts intended for publication (Chapter II and Chapter III). Chapter II presents the results from a preliminary characterization of the beavers' range edge in northern Québec, and is co-authored by Murray M. Humphries and myself. Chapter III, co-authored by Murray M. Humphries, Jason Samson and myself, examines the beavers' abundance pattern across Québec and how the input of such density estimates into species-c1imate envelope models can affect our prediction of c1imate change impacts. Murray M. Humphries, my master's supervisor, was responsible for the initial ideas behind both manuscripts, and provided analytical and editorial guidance throughout the writing process. Jason Samson also provided analytical advice and conducted an important statistical analysis in Chapter III. 1 collected the data (field work and reports), and was responsible for data selection, analysis (e.g., GIS work) , and writing the initial drafts of the manuscripts. For literature citations and formatting, 1 used the guidelines provided by "Arctic" pub li shed by the Arctic Institute of North America, University of Calgary.

iv ACKNOWLEDGEMENTS

First and foremost 1 would like to thank my supervisor Murray M. Humphries; to whom 1 will always be grateful for taking a chance on me, constantly finding time to discuss my project (even when impossibly busy!), being extraordinarily patient with my MANY questions, helping me with the concepts, analyses and writing of this thesis, and giving me the support and encouragement 1 needed, when 1 needed it most. Next, 1 would like to thank my lab mates (Tricia Kerr, Troy Pretzlaw, Jason Samson), field assistant (Mike Ross) and splendid work-study student (Hsin-Hui Huang). l've been so lucky to have Tricia's warrn support from the very start. She has listened to my worries, understood my frustrations, offered words of encouragement, gave me wonderful advice, and reminded me to take deep breaths through it aH; for this, 1 thank her. Troy and Jason managed to explain modeling and statistical concepts 1 could never have understood from a textbook, and provided awesome feedback whenever 1 asked; for this, 1 thank them. Mike was an awesome field assistant and 1 thank him for his patience, company, and hard work while in Kuujjuaq. Hsin-Hui taught me everything 1 needed to know about GIS and without her, it would have taken me at least another year to finish this project! 1 appreciate aH her hard work and helpful advice. There were also several organizations and people outside McGill that were instrumental in the completion of this thesis: 1 need to thank the Natural Science and Engineering Research Council (i.e., Northem Research Intemship Prograrn and PGS M) for facilitating my stay in Kuujjuaq, subsidizing the costs associated with working in the North and providing financial support during the second year of my masters. 1 am also indebted to the Fonds Québécoise de la Recherche sur la Nature et les Technologies, as weB as Stewart and Anne Brown for providing much needed financial assistance while writing my thesis. A very special thanks goes out to Dr. Bill Doidge (Director, Nunavik Research Centre) and Makivik Corporation for providing logistical support, allowing me to use their facilities, offering important advice and GIS support, giving me the opportunity to volunteer with the CWS, and the li st goes on! Without you guys, it wouldn't have been possible for me to conduct my field work in northem Québec.

v Dr. Serge Payette and the Centre d'Études Nordiques were responsible for helping me with my research on the Hudson's Bay coast. 1 wish to extend my most heartfelt thanks and appreciation to Dr. Payette, for supporting me (emotionally, logistically and financially), kindly encouraging me, and inspiring me to be the best researcher 1 can be; he has helped shape the person 1 have become and made my experience in the North something 1 will cherish forever. Thanks to Christian Pilon (Direction du développement de la faune, Société de la faune et des parcs du Québec), René Lafond (Chef d'équipe à la gestion intégrée des ressources, Direction du développement de la faune, Société de la faune et des parcs du Québec), Pierre Canac-Marquis (Coordonnateur Piégeage, Société de la faune et des parcs du Québec) and Hélène Jolicoeur (Direction du développement de la faune, Secteur Faune Québec) for encouraging my research, answering my many questions, and providing me with important results from their beaver research studies conducted in southem Québec. l' d like to send out a huge thanks to Dr. Dan McKenney and Pia Papadopol (Great Lakes Forestry Centre, Natural Resources Canada, Govemment of Canada) for providing me with the selected modeled c1imate data for point locations, and answering my sometimes arduous requests! Last, but definitely not least, 1 must thank my family and friends for there incredible support and encouragement from the very beginning. Most importantly, 1 have to thank my wonderful husband and my beautiful mom for their unconditional love, patience, and faith in me. As a small token of my love for them, and of my gratitude for all that they have sacrificed for me, 1 dedicate this thesis to them.

vi TABLE OF CONTENTS

ABSTRACT ...... ii RÉSUMÉ ...... iii PREFACE ...... iv Contribution of authors ...... iv ACKNOWLEDGEMENTS ...... v TABLE OF CONTENTS ...... vii LIST OF FIGURES ...... xi LIST OF TABLES ...... xii GENERAL INTRODUCTION ...... 1 CHAPTER 1: LITERATURE REVIEW ...... 2 1. Understanding species' distributions and range limits ...... 2 2. Variation in local abundance across species ranges ...... 6 3. General beaver ecology ...... 10 4. Beavers at their northem range limit...... 14 LITERATURE CITED ...... 19 CHAPTER II: PRELIMINARY CHARACTERIZATION OF THE BEAVERS , (Castor canadensis) NORTHERN RANGE LIMIT IN QUÉBEC, CANADA ...... 35 ABSTRACT ...... 35 INTRODUCTION ...... 36 METHODS ...... 38 1. Study site selection and description ...... 38 1.1. Koksoak River ...... 3 8 1.2. Lac Guillaume-Delisle ...... 39 1.3. Lac à L'Eau Claire ...... 41 2. Beaver Surveys ...... 42 2.1. Aerial Surveys ...... 42 2.2. Ground Surveys ...... 44 3. Estimating Relative Abundance ...... 44 4. Habitat Selection ...... 45

vü 4.1. Habitats used by beavers ...... 45 4.2. Use vs. Availability ...... 45 RESULTS ...... 46 1. Establishing the beavers' northem range limit ...... 46 2. Quantifying the present-day abundance ofbeavers at their northem range limit..47 3. Habitat Selection ...... 47 3.1. Habitat used by beavers ...... 47 3.2. Use vs. Availability ...... 48 DISCUSSION ...... 49 1. Establishing the beavers' northem range limit ...... 49 2. Quantifying the present-day abundance ofbeavers at their northem range limit..50 3. Habitat Selection ...... 52 LITERATURE CITED ...... 54 CONNECTING STATEMENT ...... 68 CHAPTER III: SPATIAL VARIATION IN ABUNDANCE ACROSS THE BEAVERS' (Castor canadensis) NORTHEASTERN RANGE AND ITS IMPLICATIONS FOR ASSESSING THE IMPACTS OF CLIMATE CHANGE ...... 69 ABSTRACT ...... 69 INTRODUCTION ...... 70 METHODS ...... 74 1. Study Areas ...... 74 1.1. Collection ofreports ...... 74 1.2. Data selection ...... 74 1.3. Aerial survey methodology ...... 74 1.4. Rendering data compatible for GIS ...... 75 2. Climate Variables ...... 76 2.1. Point estimates (P.E.)for temperature minimums and maximums, precipitation totals, and agroclimatic indices ...... 76 2.2. Temperature Normals ...... 77 3. Non-climate variables ...... 77 3.1. Physical parameters ...... 77

viii 3.2. Land-caver parameters ...... 78 3.3. Predatar densities and average number ofharvested beaver pelts ...... 78 4. Model Selection ...... 79 5. Climate sensitivity, climate change, and density change ...... 80 RESULTS ...... 80 DISCUSSION ...... 82 LITERA TURE CITED ...... 86 GENERAL CONCLUSION ...... 105 LITERATURE CITED ...... 107 APPENDIX lA-l ...... 109 Table lA-l.l: Density estimates for North American beavers (Castor canadensis) in Canada and United States expressed as the average number ofbeaver colonieslkm2 .109 Table lA-l.2: Density estimates for North American beavers Cc. canadensis) in Canada and United States expressed as the average number ofbeaver colonieslkm of stream or river ...... 119 APPENDIX lA-2 ...... 122 1. Classification ofbeaver habitat in North America ...... 122 2. Validation of classification keys and habitat suitability index models ...... 130 Table lA-2.1: Codes used in the ecological maps obtained from the Service des Inventaires Ecologiques ...... 133 Table lA-2.2: Summary of classes devised for beaver habitat potential...... 134 Table lA-2.3: Summary ofmost important variables used to determine beaver habitat suitability ...... 135 Table lA-2.4: Summary ofresults from the validation of certain classification keys and habitat suitability index model created by Allen (1983) ...... 137 APPENDIX 2A-l ...... 138 Table 2A-l.l: Aerial survey conditions, Koksoak River study area, Québec ...... 138 Table 2A-l.2: Aerial survey conditions, Lac Guillaume-Delisle study area, Québec. 139 Table 2A-l.3: Aerial survey conditions, Lac à L'Eau Claire study area, Québec ...... 140 Table 2A-l.4: Description of habitats frequented by beaver (biological and physical factors) in the Koksoak River study area ...... 14l

ix Table 2A-1.5: Description ofhabitats frequented by beaver (biological and physical factors) in Lac Guillaume Delisle study area ...... 142 Table 2A-l.6: Description ofhabitats frequented by beaver (biological and physical factors) in Koksoak River study area ...... 143 Codes for Tables 2A-1.4 to 1.6 ...... 144 APPENDIX 3A-l ...... 145 Table 3A-l.1: Bioclimatic parameters used for the Se1ected Modeled Climate Data for Point Locations ...... 145 Table 3A-1.2: Land cover classes from the Mosaïque du Québec, obtained from the Photo cartothèque Québécoise (1: 2 500 000) and the final categories used in our analysis ...... 147 Table 3A-1.3: Land cover classes from the Spatiocarte Portrait du Québec Forestier Méridional, obtained from the Direction des Inventaires Forestiers (1:1250000) and the final categories used in our analysis ...... 148 Table 3A-l.4: Climate model and emissions scenario comparisons ...... 149 LITERATURE CITED ...... 150

x LIST OF FIGURES

Figure 2.1: Location ofstudy areas and flight paths in Northem Québec ...... 63

Figure 2.2: Map of northem Québec illustrating our accumulated knowledge of the beavers' northem range limit...... 64

Figure 2.3: Location of survey quadrats and observed active and abandoned beaver signs in a) Koksoak River, b) Lac Guillaume-Delisle, and c) Lac a L'Eau Claire study areas ...... 65

Figure 2.4: Habitat selection by beavers of (a) aquatic habitat and (b) vegetation cover in the Koksoak River study area ...... 66

Figure 2.5: Representative photos of a) Koksoak River, b) Lac Guillaume-Delisle, and c) Lac a L'Eau Claire study area ...... 67

Figure 3.1: Local abundance of North American beavers (c. canadensis) across the province of Québec ...... 97

Figure 3.2: Variation in local beaver density as a function of (a) latitude (decimal degrees) (b) average maximum March-April-May temperature (c) average annual temperature and (d) average maximum September-October-November temperature across Québec ...... 98

Figure 3.3: Predicted changes in (1) beaver density, with a 1°C increase in temperature (climate sensitivity), (2) temperature from present to the year 2055 (climate change) and (3) the number ofbeaver colonies per krn2 from present to the year 2055 (density change) across Québec, based on three climatic variables with the best-fit models ...... 99

xi LIST OF TABLES

Table 3.1: Climate and non-c1imate variables evaluated as potential predictors of beaver density across Québec ...... 100

Table 3.2: Results from univariate regression between c1imate variables and non-c1imate variables against beaver density; statistical outliers not removed ...... l 01

Table 3.3: Partial regression analysis estimating the variation in beaver density explained by c1imate and non-c1imate variables ...... 102

Table 3.4: Results from univariate regresslOn between c1imate variables and beaver density, with statistical outliers removed ...... 103

2 th th th Table 3.5: R values explaining the variation in the 10 , 50 and 90 percentile ofbeaver densities using 10 of the top 15 univariate c1imate predictors and three different models (normal, 2nd order polynomial and linear) ...... 104

xü GENERAL INTRODUCTION

Species' responses to spatial climate variability are frequently used as a basis for predicting the impacts of the temporal phenomenon of climate change. However, due to the paucity of information on how abundance varies across species' ranges, nearly aIl predictions of climate change impacts on species'distributions and regional biodiversity are based on presence/absence data. Furthermore, among the few studies that have examined variation in abundance across the range, most are characterized by a core­ sampling bias (i.e., higher number of surveys performed near the core versus the edge of the range). Thus, there is an urgent need to evaluate how better characterization of the edge of species' ranges and incorporation of spatial variation in abundance will influence climate change predictions. Using North American beavers (Castor canadensis) within Québec as a model species, the following thesis provides a comprehensive literature review (Chapter I) and explores the above mentioned issues in two separate but related manuscripts (Chapter II and III). The objectives of Chapter II are to (a) establish the present day range limit of beavers in two northern regions of Québec, (b) quantify present-day beaver abundance in the vicinity of these range limits, and (c) evaluate habitat selection by beavers near the edge of their range. Aerial beaver surveys on the north-eastern and north-western coasts of Québec, and interviews with the locals were conducted in order to achieve these objectives. We hypothesize that (a) the beavers' northern range limit near the western coast of Ungava Bay and eastern coast of Hudson's Bay will coincide with the northern tree-line, (b) the density ofbeaver colonies per km2 will be lower than densities observed further south, and (c) similar to southern populations, beavers at their northern range limit will select for stable waterbodies with a minimum of forest coyer. The objectives of Chapter III are to (a) examine the spatial variation in beaver abundance across the north-eastern portion of their range, (b) evaluate the extent to which climate and non-climate variables could explain this variation, and (c) use a climate envelope model that includes spatial variation in abundance to predict the beaver' s responses to projected climate change. These objectives were met by extracting beaver density estimates from reports produced by Hydro Québec and the Québec Government,

1 obtaining c1imate and non-c1imate variables for localities with density estimates, selecting the c1imate variables explaining the most variation in beaver abundance, developing species-c1imate envelope models, and estimating c1imate sensitivity, c1imate change and density change for the best c1imate variables. We predict that beaver abundance will dec1ine in a logistic fashion from the core to the edge of their range and will be strongly correlated with c1imate variables that dec1ine linearly across the same gradient. Thus, we hypothesize that the c1imate sensitivity of beaver abundance (change in abundance per unit change in c1imate) will be highest in the mid-range and lowest at the core and edge of the range. There are many potential implications of this research. By establishing how far north beavers are presently located, how abundant they are, and what habitats they are selecting, it will be possible to detect whether the beavers' range limit is expanding, population densities are changing, and/or habitat selection is shifting in the face of ongoing c1imate change. By examining the spatial variation in present beaver abundance across Québec and deciphering to what extent c1imate and non-c1imate variables explain this variation, we will be in a better position to understand the mechanisms underlying the beavers' geographical abundance pattern. Finally, by integrating beaver density estimates into species-c1imate envelope models, we can predict where the largest impacts of climate change may take place, illustrating the importance of incorporating a spatial component to population-c1imate research.

CHAPTER 1: LITERATURE REVIEW

1. Understanding species' distributions and range limits The question of why species occur where they do has intrigued ecologists and evolutionary biologists for centuries (Grinnell, 1922; Andrewartha and Birch, 1954; Hutchinson, 1957; MacArthur and Wilson, 1967; MacArthur, 1972; Brown, 1984; Caughley et al., 1988; Gaston, 1990; Krebs, 1994; Lawton et al., 1994; Brown et al., 1996; Gaston, 2003). A renewed interest in this question has been sparked by the urgency to understand and anticipate the effects of c1imate change on plant and animal populations (Ludwig et al., 2001). Average surface temperatures have increased 0.6 ± 0.2°C since the

2 late 19th century and are expected to rise from 1.4°C to 5.8°C over the next century (Houghton et al., 2001). Moreover, the rate and duration of warming during the 20th century has likely been the largest in the last 1,000 years, with the 1990's being the warmest decade of the millennium in the Northem Hemisphere (Houghton et al., 2001). Recently observed ecological responses to this rapid change in climate (Hughes, 2000; McCarty, 2001; Walther et al., 2002; Parmesan and Yohe, 2003; Root et al., 2003), and evidence from fossil records (Davis and Shaw, 2001) tends to support the biogeographical premise that climate has a profound influence on the natural distribution and abundance of species (Pearson and Dawson, 2003). It is now possible, with improvements in the efficiency of climate data interpolation and modeling techniques, to predict the distributions of several organisms based on climate variables (Olwoch et al., 2003). One of the most popular modeling strategies used to predict the potential impacts of climate change on species' distributions is called the species-climate envelope approach (a.k.a. bioclimate envelope, climate space, ecological niche modeling approach; Box, 1981; Sutherst and Maywald, 1985; Austin, 1992; Huntley et al., 1995; Carey, 1996;Sykes et al., 1996; Iverson and Prasad, 1998; Bakkenes et al., 2002; Berry et al., 2002; Erasmus et al., 2002; Pearson et al., 2002; Peterson et al., 2002; Thuiller, 2003; Araujo et al., 2004; Skov and Svenning, 2004; Thomas et al., 2004). Species-climate envelope modeling is based on the concept of the ecological niche (Pearson and Dawson, 2003). Hutchinson (1957) defined a species' fundamental ecological niche as comprising aIl environmental dimensions within which a species can survive and reproduce. Due to competition and other biotic interactions, species may be excluded from certain parts of their fundamental ecological niche. This reduced niche is referred to as the realized niche (Hutchinson, 1957; Austin et al., 1990). Species-climate envelope models that use physiological constraints to provide a mechanistic basis for determining climatic limits on species distributions (physiologically based models: Prentice et al., 1992; Sykes et al., 1996) aim to identify the fundamental ecological niche, while those based on empirical relationships between observed species distributions and climate variables (correlative models: Huntley et aL, 1995; Peterson et aL, 2001; Bakkenes et al., 2002; Pearson et al., 2002), attempt to define the realized ecological niche (Pearson and Dawson, 2003). Although both models have been criticized (Hampe,

3 2004), they remain the most important approach to studying the possible consequences of a changing environment on species distributions (Olwoch et al., 2003). The main sources of climate data for species-climate envelope models are climate surfaces, generated by interpolating observed climate data sampled at varying intensities from across a region, while the primary method of simulating climates is through the use of general circulation models (GCMs; coupled ocean-atmosphere models) which provide three-dimensional simulations of the atmosphere (Olwoch et al., 2003). Species' distributions are linked to climate most frequently with Generalized Linear Models (GLMs) because they have a solid statistical foundation and an ability to realistically model ecological relationships (Thuiller, 2003; Elith et al., 2006). Other correlative models using regression approaches include Generalized Additive Models (GAMs), Generalized Dissimilarity Models (GDMs), and Multivariate Adaptive Regression Splines (MARS). GAMs use non-parametric data-defined smoothers to fit non-linear functions, making them more flexible and capable of handling complex ecological response shapes; GDMs, use matrix regression, generalized linear modeling and kernel regression algorithms, allowing them to capture realistic ecological relationships between dissimilarity and ecological distance and to estimate likelihoods of occurrence of a species; and MARS models use piecewise linear fits rather than smooth functions, making them faster to implement than GAMs (Elith et al., 2006). Alternative ru1e-based approaches, which can be used to predict current and future potential distributions (Thuiller, 2003; Elith et al., 2006), have also emerged, including Classification and Regression Tree analysis (CART; De' Ath and Fabricius, 2000; De' Ath, 2002; Bourg et al., 2005), Artificial Neural Networks (ANN; Rip1ey, 1996, e.g., SPECIES; Pearson et al., 2002), and genetic algorithms for rule-set predictions (GARP; Stockwell and Noble, 1992; Peterson et al., 2002; BIOCLIM; Nix, 1986). Finally, in contrast to models of a correlative nature, examples of mode1s based on physiologica1 limits to a species' climatic tolerances have included studies examining the effects of temperature on the distribution of vegetative species (Sykes et al., 1996; Prentice et al. 1992), and mamma1s (grey seals (Halichoerus grypus); Hansen and Lavigne, 1997; hibernating mamma1s; Humphries et al., 2002; and Virginia opposums (Didelphis virginiana); Kanda, 2005).

4 There are several reasons to exercise caution when interpreting the results from species-climate envelope models, whether they are correlative or physiologically based. With correlative models, spurious relationships may lead to over-predictions, confounding effects may mask the true determinant(s) (Parmesan et al., 2005), species distributions may not be determined primarily by climate, the assumption that species distributions are at equilibrium with current climate conditions may be unrealistic (Araùjo et al., 2005), and correlations based on current conditions may not apply in the future (Pearson and Dawson, 2003). Although physiologically based models may be more robust under changing climate scenarios because they are based on mechanistic considerations, the fact that they ignore intra-species variation, and aim to identify an ecological niche that will never be realized (i.e., the fundamental niche), limits their potential (Pearson and Dawson, 2003). Both models overlook biotic interactions, evolutionary change and species dispersal (Pearson and Dawson, 2003; Hampe, 2004). Using microcosm experiments with fruitfly assemblages, Davis et al. (1998a), demonstrated that inter-species interactions (e.g., competition) can greatly influence a species abundance and distribution and because these factors are ubiquitous in nature, species-climate envelope models may lead to serious errors. The assumption that the tolerance range of a species remains the same as it shifts its geographical range, fails to take into account the potential importance of rapid evolutionary change in response to climate change (e.g., Thomas et al., 2001), and as such, may lead to wrong predictions for species that experience sufficiently rapid adaptation (Pearson and Dawson, 2003). Although species-climate envelope models aim to predict the potential range of organisms under climate change, species that fail to migrate at the same pace as the changing climate because of individual dispersal characteristics, natural barriers, or habitat fragmentation may render these predictions erroneous. According to Hampe (2004) due to these oversights and the fact that current statistical methods used for model validation overestimate model fit due to pseudoreplication, species-climate envelope models are prone to pro duce artificially optimistic scenarios of future climate change impacts on species distributions. Finally, one additional concem with respect to species­ climate envelope models is that they most often rely on presence-absence data (Erasmus et al., 2002; Huntley et al., 2004; Araujo et al., 2005). Although presence/absence range

5 maps provide a useful indication of the broad regional occurrence of a given species, they exclude information about how local abundance varies across the range. As a result, climate-envelope approaches are capable of predicting future species range shifts but not future changes in abundance across the range. Considering these limitations, climate­ envelope models should be seen as an important first step in a broader modeling framework (Pearson and Dawson, 2003) and a first approximation or "null model" of the potential effects of climate change on biota at large spatial scales (Davis et al., 1998b; Lawler et al., 2006); instead of accurate predictions of future distributions of individual species.

2. Variation in local abundance across species ranges According to Sagarin and Gaines (2002a), several ecological and evolutionary hypotheses are based on the assumption that the population density of a species is highest at the core of its geographical range, and dec1ines gradually towards its range edges (e.g., Andrewartha and Birch, 1954; Whittaker, 1956; Rapoport, 1982; Hengeve1d and Haeck, 1982; Brown, 1984; Brussard, 1984; Gaston, 1990; Brown et al., 1995). This pattern has recently been referred to as the "abundant centre hypothesis" (ACH; Sagarin and Gaines, 2002a, b; Sorte and Hofmann, 2004). Yet despite the numerous theoretical models that can logically explain the ACH, and its status as a biogeographical rule, whether it is a widespread pattern observed in natural populations, has recently been brought into question (Sagarin and Gaines, 2002a,b; Brewer and Gaston, 2002). The dilemma stems from the fact that there remains a remarkable paucity of acceptable studies quantifying how abundance varies from the core to the periphery of species' ranges. This is especially the case for mammals, whose commonly broad geographic distributions have proven inherently difficult to sample. Theoretical support for the ACH is often rooted in the idea that population densities are coupled with niche axes (Brown, 1984; Brown et al., 1995). According to Brown (1984), each species has certain ecological requirements, which include a combination of spatially autocorrelated biotic and abiotic variables. The population density of a species is assumed to be highest where the combination of environmental variables most closely corresponds to their requirements. As the species moves away

6 from this point, the combination of environmental variables changes, niche requirements are met less frequently and as such, abundance dec1ines. Thus the variation in population density of a species over space is assumed to reflect the probability density distribution of the required combinations of biotic and abiotic variables (Brown, 1984). Although Brown's (1984) model is frequently interpreted as synonymous with the ACH, the model' s original and subsequent formulations (Brown, 1984; Brown et al., 1995), explicitly predicted departures from a normal distribution if 1) a sharp discontinuous change in one environmental variable violates the assumption that the abundance and distribution of a species is determined by a combination of environmental variables, and 2) environmental patchiness violates the assumption that environmental variables are spatially auto-correlated. Although other explanations and theoretical models have been proposed (Grinnell, 1922; Cain, 1944; Levins, 1969; Hanski, 1982; Cox and Moore, 1985; Williams, 1988; Maurer and Brown, 1989; Hall et al., 1992; Hengeveld, 1993; Maurer, 1999) Brown's theory is still most often referred to in the literature when attempting to explain the internaI structure of a species range (i.e., Brown, 1984; cited 710 times according to Web ofScienee database 2006).

The strongest empirical support for the ACH has undoubtedly come from studies examining geographical abundance patterns in bird species. Telleria and Santos (1993) studied European birds and found that 5 out of 6 species (83.3%) examined, showed support for the ACH. Em1en et al. (1986) and Curnutt et al. (1996) examined abundanee patterns in North American birds and discovered that 8 out of 18 (44.4%) species and 4 out of 6 (66.6%) species, respective1y, also supported the ACH. More evidence for this pattern of abundanee has been provided by North American Breeding Bird Surveys and Christmas Bird Counts, which have allowed researchers to create interpolated maps of the variation in abundanee for numerous bird species (Root, 1988; Priee et al., 1995). In most cases, the maps show a peak near the center of the bird species' range and a graduaI decline towards most boundaries (Brown, 1984; Root, 1988; Brown et a1., 1995; Priee et aL, 1995). Using these same surveys, Brown et al. (1995) aiso discovered that widely distributed species are abundant in a few "hot spots" which tend to be concentrated towards the center of the range. This pattern even held up when tested amongst plants (Eriogonum abertianum, Casearia corymbosa), arthropods {agonid fig wasp, isopod

7 crustaceans (Porcellio laevis), beetles (Dyschirius globosus)} and ciliate parasites (Trichodina sp), over various spatial scales (Brown et al., 1995). Several other species' population densities follow a nonnal distribution when plotted as a function of the distance from the center of their range (e.g., along a latitudinal gradient) including certain northern European beetles (Hengeveld and Haeck, 1982; Svensson, 1992), British bumble bees (Williams, 1988), leafhoppers (e.g., Erythroneura Homoptera; McClure and Price, 1976; Whitcomb et al., 1994), moths (Coleophora alticolella Lepidoptera; Randall, 1982), intertidal dogwhelk (Nucella canaliculata Mollusca; Sorte and Ho finann , 2004), North American butterflyweed (Asclepias tuberosa; Woodson, 1964; Sagarin and Gaines, 2002a), stemless and melancholy. thistle (Cirsium acaule and Cirsium heterophyllum; lump and Woodward, 2003), annual grass (Vulpia ciliata spp. ambigua.; Carey et al., 1995), Argentinian palm trees (Rapoport, 1982), various European plants (Hengeveld and Haeck, 1982), eastern cottontails from Kansas (Sylvilagus floridanus; Williams et al., 2003), and Australian grey kangaroos (Macropus fuliginosus and Macropus giganteus; Caughley et al., 1988). But for as many empirical tests that have shown support for the ACH, a myriad of species' population densities do not follow a nonnal distribution when plotted with respect to position within their geographical range. Sagarin and Gaines (2002a) compiled a comprehensive reVlew of direct and indirect empirical evidence for the ACH. They discovered that out of 22 direct empirical tests of the ACH, most studies inadequately sampled the species' range (91%), and only 39% supported the hypothesis (including most of the studies mentioned in the previous paragraphs). Furthennore, the studies that managed to gather data on abundance across the entire range, often under-sampled the range edges by using data collected from transects running through only parts of the range (Sagarin and Gaines, 2002a). Finally, complexities in the application of analytical techniques rendered certain results from the analysis of abundance data difficult to interpret (Brewer and Gaston, 2002). Alternative patterns of variation in abundance included no evident pattern, a graduaI increase in abundance from the southern periphery to the northem periphery (referred to as ramped north), a graduaI increase in abundance from the northem periphery to southem periphery (referred to as ramped south) and a graduaI decrease in abundance from the southem

8 periphery to the core and then a graduaI increase in abundance from the core to the northern periphery (referred to as abundant edge; Sagarin and Gaines, 2002b). Indirect tests examining ecological or evolutionary expectations of the ACH were inconc1usive (Sagarin and Gaines, 2002a). Our understanding of fundamental Issues m ecology and evolution, and our consequent ability to manage and conserve species in the face of c1imate change, rests on our understanding of geographical abundance patterns. We are currently missing high­ quality data on the variation of abundance across most species' ranges. To date, Caughley et al. (1988), Rodriguez and Delibes (2002), and Williams et al. (2003), have been the only researchers to investigate variation in abundance across the geographical range of a mammal species. Caughley et al. (1988), examined the abundance of western grey kangaroos (Macropus fuliginosus) and eastern grey kangaroos (Macropus giganteus) across Australia, and Williams et al. (2003) examined the pattern in eastern cottontails (Sylvilagusfloridanus) in Kansas (U.S.A). The local abundance ofboth kangaroo species and eastern cottontails rose progressively towards the core of their range. Rodriguez and Delibes (2002) examined the internaI structure in the geographic range of the Iberian lynx (Lynx pardinus) and discovered that their geographical abundance pattern did not follow a normal probability density distribution of abundance (Brown, 1984) with lynx densities dec1ining rapidly to zero and having multimodal centers of abundance concentrated in the eastern half of the range (Rodriguez and Delibes, 2002). The North American beaver (Castor canadensis) is well-suited to exammmg abundance patterns and c1imate change impacts because their local abundance can be accurately assessed via aerial surveys of dams, lodges, and autumn food caches (Fuller and Markl, 1987; Hay, 1958; Bergerud and Miller, 1977; Novak, 1987; Banfield 1974), their general habitat requirements (deciduous and shrubby vegetation along waterways) can be identified from landcover classifications (Atwater, 1940; Slough and Sadleir, 1977; Allen, 1983; Howard and Larson, 1985; Novak, 1987 Tecsult Ine. 2000, 2002, 2004), and theyhave been extensively surveyed (see Tables lA-l.l and lA-l.2).

9 3. General beaver ecology The North American beaver (C canadensis) is a large semi-aquatic rodent found in streams, ponds and lakes throughout Canada and the United States, except for sections of the Midwest, peninsular Florida, the southwestem deserts and the Arctic tundra (J enkins and Busher, 1979; Novak, 1987). Able to withstand c1imatic conditions ranging from -50°C to 38°C, beavers are found in small isolated pockets along the U.S.-Mexican border at their southem limit (Novak, 1987; Gallo-Reynoso, 2002), and up to the treeline in Alaska, Northwest Territories, Québec and Labrador at their northem limit (Nash, 1951). In general, beaver distribution is limited by (a) sufficient quantities of deciduous vegetation, and (b) stable, secure aquatic habitats (Novak, 1987; Slough and Sadleir, 1977; Muller-Schwarze and Sun, 2003). Relief, surface materials, anthropogenic factors and predation rates may also affect the relative quality of a habitat as reflected by the density ofbeaver colonies. Beaver population density is usually expressed in colonies per unit area or length of stream/river (Hill, 1982). Beavers are "choosy generalist" herbivores (Harper, 1969). They eat the leaves, twigs, and bark of woody plants, as well as many different kinds of herbaceous species (e.g., aquatic vegetation; Jenkins and Busher, 1979; Allen, 1983). The number of different food species eaten, decreases from the beavers' southem range limit to their northem range limit (alpine and Arctic; Novak, 1987). Jenkins (1979) explains that food preferences vary throughout the year, and from year to year, due to the variation in nutritional value of the food resources. North American beavers selected, in order of preference, aspens (Populus tremuloides), willows (Salix spp.), cottonwoods (P. balsamifera), and alders (Alnus spp.). Although beavers use coniferous trees, they cannot survive for a prolonged period of time without deciduous trees and shrubs (Allen, 1983; Novak, 1987). Herbaceous species, when available, are preferred over woody species (Jenkins, 1981). However, beaver establishment is not likely limited by the actual biomass of herbaceous vegetation but rather by the total biomass of accessible winter food cache plants (i.e., woody species; Boyce, 1981; Allen, 1983). It is assumed that forests having canopy closures between 40 and 60%, trees ranging from 2.5 to 15.2 cm dbh, and shrubs higher than 2 m are prime habitats for beavers (Allen, 1983). Conversely, forests having crown closures exceeding 60%, trees less than 2.5 cm dbh, shrubs less than

10 2 m in height, and/or shore1ine vegetation composed sole1y of coniferous trees, are assumed to be less suitable for beaver establishment (Allen, 1983). In general, beavers forage up to 50 m from the water's edge (Traversy, 1975, 1976; Hall, 1970; Willis, 1978; Banville, 1978; Nault and Gascon, 1983; Novak, 1987; Fryxell and Doucet, 1991,1993), but under poor conditions, they have been observed foraging 100 m (J enkins, 1980; Howard and Larson, 1985; Bordage and Filion, 1988) to 200 m away from the water (Bradt, 1938; Hammond, 1943; Northcott, 1971). The farther beavers move away from the water, the more selective they become, generally se1ecting trees of smaller diameter (Jenkins, 1979, 1980). According to Jenkins (1981), the types offood species present may be less important in determining habitat quality than hydrologic and physiographic factors. Beavers can survive in regions where their preferred food species are uncommon or absent, but they cannot survive in areas where the water supply fluctuates or is fast moving (Novak, 1987). Beavers will colonize aquatic habitats where water depth and stability can be controlled and where the water supply is permanent (Slough and Sadleir, 1977; Allen, 1983). Large rivers and lakes, where these factors cannot be controlled or where there is excessive wind and wave action, are either avoided or can be colonized if protected areas such as bays and islands are available (Hill, 1982; Allen, 1983; Brodeur et al., 1977; Le Groupe Roche Boreale, 1991; Alliance Environnement Inc., 2004). The most important factor determining the suitability of riverine habitats is stream gradient (Slough and Sadleir, 1977). According to Retzer et al. (1956) streams with a gradient of 15% or more, and valleys only as wide as the stream channel were both unsuitable. Finally, it is assumed that a minimum of 0.8 km of stream and 1.3 km2 of lakes or marshland habitat must be available before these areas are considered suitable for beaver colonization (Allen, 1983). Physiographic factors, as well as soil/rock types and human disturbance can influence where beavers will establish. Beavers seem to prefer flat terrain (Hill, 1982), and gently sloping banks (Brodeur et al., 1977; Le Groupe Roche Boreale, 1991; Tecsult Inc., 2000; FORAMEC, 2004; Alliance Environnement Inc., 2004). A uniform topography ensures that waterways are slow-moving. Gently sloping banks are beneficial for beavers because: (1) in association with fine soil partic1es, they permit the

11 establishment of weIl developed shoreline vegetation, producing better quality food for beavers (Hydro Québec, 1982) (2) in the event of a forest fire, they allow the water table to protect the shoreline vegetation and adjacent forest such that they can continue being used by beavers (Hydro Québec, 1982). Although beavers generally avoid steep slopes when cutting woody vegetation (Hiver, 1938; Robb, 1942) they have been observed cutting on steep slopes when foraging further away from the water, because steep slopes provide an easy escape from predators and sometimes facilitate the transportation of branches (Novak, 1987). Rocky waterways and shorelines, watersheds consisting of shale, and waterways underlain by porous limestone are usually avoided by beavers (Packard, 1947; Retzer et al., 1956; Brodeur et al., 1977; Novak, 1987). Instead, beavers favor waterways bordered by organic and fine sediments because they promote the establishment of preferred shoreline vegetation (Banville, 1978; Bider, 1979; Banville, 1979; Le Groupe Roche Boreale, 1991), and watersheds made up of glacial till, schist and granite because they are highly resistant to erosion and retain water collected from temporary or seasonal runoffs (Retzer et al., 1956; Novak, 1987). Human disturbance (e.g., agriculture, urban sprawl) can deter beavers from several otherwise suitable habitats, and predation by mammalian predators {e.g., humans (Homo sapiens), wo1ves (Canis lupus) and black bears (Ursus americanus)} can have a particularly significant effect on beaver densities (e.g.,Voigt et al., 1976; Shelton and Peterson, 1983; Novak, 1987). The first habitat suitability studies were qualitative in nature (e.g., Atwater, 1940; Packard, 1947; Fuller 1953), but several authors have since quantitatively classified beaver habitat (Appendix 1A-2). In Canada, a land capability rating was developed for large areas across the country during the early 1970' s (Perret, 1970; Novak, 1987). In Québec, dichotomous classification keys based on ecological maps were developed to set up systems defining the areas most likely to be inhabited by beavers (Table 1A-2.1; Traversy, 1974; Banville and Traversy, 1977; Levasseur and Mondoux, 1977; Environnement Illimité Inc., 1981; SOMER, 1982). In the United States, a habitat suitability index model was established by Allen (1983), evaluating the suitability of habitats for beavers (0= unsuitable to 1= optimum habitat) based on what are thought to be key habitat variables that affect beaver populations. Similarly, the U.S. Forest Service

12 (1973) created a rudimentary habitat model for beavers consisting of five habitat variables, where each variable was rated as either suitable or unsuitable. In Colorado, after examination of several stream sections, it was established that four physical features were very important in determining whether a site was suitable for beavers (Retzer et al., 1956; Rutherford, 1964). Another approach to characterizing beaver habitats used throughout North America describes the relationship between beaver density and various physical and vegetative parameters using multiple regression (stepwise linear and logistic), discriminant analysis, canonical correlations, and/or principal components regression (Slough and Sadleir, 1977; Boyce, 1981; Howard and Larson, 1985; Beier and Barrett, 1987; Consortium Gauthier and Guillemette, 1990; Le Groupe Roche Boreale, 1991). The variables explaining the most variation in beaver density varied depending on the region, resulting in certain simple predictive equations (e.g., latitude and fire alone were able to explain 39% of the variation in beaver density near Eastmain in Québec; Le Groupe Roche Boreale, 1991), and more elaborate ones (e.g., eight physical and vegetative pararneters were needed to explain 66% of the variation in colony site number on strearns in British Columbia; Slough and Sadleir, 1977; see Appendix lA-2). The majority of studies, whether they attempted to mathematically explain the variation in beaver densities or not, established three to five classes of habitat potential ranging from the most ideal beaver habitats, to marginal and/or unsuitable habitats (Table lA-2.2). A summary of the most important variables used to decipher the suitability of habitats for beaver can be found in Table IA-2.3. When studies attempted to validate certain habitat suitability models, the correlation between the habitat potential classes and beaver densities tended to be very weak or non-existent, highlighting the fact that habitat suitability models for beavers tend to be site specifie (Table IA-2.4). The fundarnental unit of a beaver population is the colony, which most often consists of four to eight related individuals occupying an aquatic habitat and sharing one cornrnon food supply (i.e., a food cache; Bradt, 1938; Bergerud and Miller, 1977). A typical colony in midwinter consists of an adult pair, two to four kits from the previous spring litter, two or three yearlings, and occasionally one or more 2.5 year olds depending on the quality of the habitat (Novakowski, 1965; Svendsen, 1980; Hill, 1982). The average number ofbeavers per colony in Canada ranges from 2.7 to 7.5 beavers (Gunson,

13 1970; Nordstrom, 1972; Traversy and McNicoll, 1976; Novak, 1977; SOTRAC, 1978, 1980, 1983; Gauthier and Lafleur, 1979; Payne, 1982; Nault, 1984; Pilon and Daig1e, 1984; Brunelle and Bider, 1987; Potvin et al., 1993; Bourbonnais, 1994). The home range of a beaver colony can vary anywhere from 0.3 km to 2.5 km of shoreline habitat (Novakowski, 1965; Nordstrom, 1972; Bergerud and Miller, 1977; Brooks, 1977; Boyce, 1981) and 0.031 km2 (fall) to 0.103 km2 (surnrner) inland habitat (Wheatley, 1994). Because there is only one food cache per colony, the most reliable way of obtaining an index of relative beaver abundance is to count the number of food caches, and divide it by the area surveyed, to ob tain the number ofbeaver colonies per km2 (Hay, 1958; Bergerud and Miller, 1977; Fuller and Markl, 1987; Novak, 1987). The highest success in locating beaver colonies using auturnn food caches has been in areas where beaver populations depend on this method of food storage throughout the winter (i.e., northem regions; Fuller, 1953; Hay, 1958; Bergerud and Miller, 1977; Swenson et al., 1983). The average number of beaver colonies per km of stream or river in North America ranges from 0.01 to 1.55, while the average number of beaver colonies per km2 ranges from 0.01 to 3.51 (see Tables lA-LI and 1.2).

4. Beavers at their northern range limit Beaver habitat studies have been carried out throughout southem Canada and the United States, but only a few studies have examined beavers at their northem range limit. The sparse selection of woody and herbaceous vegetation available at these latitudes incites beavers to search for a minimal amount of deciduous vegetation (Fuller, 1953; Novakowski, 1965; Aleksiuk, 1970; Dennington and Johnson, 1974). However the rarity of preferred species {e.g., trernbling aspen (Populus tremuloides)} do es not seem to hamper or discourage the development of important beaver populations in northem environrnents (Northcott, 1971) but instead, beavers use woody species as a function of their availability (e.g., willows (Salix spp.), spruce (Picea spp.), and plants from the heath family (Ericaceae); Novakowski, 1965; Aleksiuk, 1970; Northcott, 1971; Dennington and Johnson, 1974; Nault and Gascon, 1983; Consortium Gauthier and Guillemette, 1992). In fact, even with the prevalence of aquatic habitats considered to be unsuitable for beavers (e.g., large lakes and rivers), and nonexistent to low occurrence of arborescent species in

14 the north, beavers have adjusted to the biophysical characteristics of the habitats available, despite and considering the rarity of favorable habitats (Consortium Gauthier and Guillemette, 1990). Nault and Gascon (1983) point out that the beavers' resourcefulness renders it difficult to predict what habitats they will occupy. Similarly, Environnement Illimité Inc. (1981) suggest that because favorable habitats are relatively rare at higher latitudes, causing the variation in average beaver densities estimated per unit surface, and the number of zero observations to be high in northem regions, classification keys should be used for small scale studies in order to identify prime habitats. According to SOTRAC (1980), models that were developed based on beaver habitat requirements near the core oftheir range (e.g., HSI model; Allen, 1983), may not be suitable for assessing habitats at their range edge, because certain factors in northem environments may be more important compared to southem regions. For example, the harsh northem climate may be a limiting factor with respect to the quality of food resources, forcing the beavers to relocate more frequently because of overexploitation (Aleksiuk, 1970; SOTRAC, 1980). Certain additions have also been suggested to improve the fit of classification models developed based on habitat selection by southem beavers, including bioclimatic factors, and information relating to trapping and hunting (Consortium Gauthier and Guillemette, 1989). According to Novakowski (1965), who studied beavers in Wood Buffalo National park and the Mackenzie River drainage north of the 60th parallel, willows (SaUx spp.) are the predominant species present at northem latitudes, and as such, good beaver habitat is based on the density of willows (SaUx spp.) rather than the density of coniferous trees in the northem boreal forest. In his study area, the bog drainage area, with its numerous, secure, small ponds and slow moving streams, provided a more stable environment for lodge and dam building, and a constant food source including water-lilies (Nuphar spp.) and sedges (Carex spp.). Similarly, Novak (1987) states that the best habitats in the North occur in the deltaic complex of the lower Mackenzie River, thermokarst lakes within lacustrine basins, and on streams bordering gently sloping flanks of large plateaus. Novakowski (1965) believes that at northem latitudes, edaphic conditions (which directly influence food quantity and quality) , and topography, rather than northem c1imatic conditions were the major factors determining the abundance and distribution of the

IS species. Food was not limiting population increase or density, with feeding areas being utilized on a sustained yield basis and at levels that could indefinitely support a much larger population. Despite this fact, beavers at these latitudes spent most of the year (average of 150 days) under the ice in relative poverty. It appears that beavers store enough food in their food caches to prevent starvation, but not enough to me et the full maintenance energy requirements of the colonies (Novakowski, 1967). Consequently, methods of energy conservation including reduced activity, huddling, insulation from ambient temperatures through the construction of lodges, increase in fur insulation and fat deposits, weight loss in adults, and perhaps dormancy, provide the necessary mechanisms for survival at northern latitudes, where beavers experience long periods of under-ice existence (Novakowski, 1967). Aleksiuk (1968, 1970) and Aleksiuk and Cowan (1969 a, b) also studied beavers

III the Mackenzie Delta, Northwest Territories. Aleksiuk (1968) established that the population dynamics of beavers at their northern range limit are similar to populations furthersouth, except that a greater number of young are produced each year for recruitment, and self-regulatory mechanisms operate to prevent explosive population growth. Aleksiuk (1970) examined the seasonal food regime of Arctic beavers and discovered that northern beavers prefer the leaves and growing tips of willows (Salix spp.) in the summer months and the bark ofwillows in the winter months {e.g., willow was the predominant food species in the food cache (76%) after poplar (Populus balsamifera; 14%) and aIder (Alnus crispa; 10%)}. He states that northern beavers have adapted to seasonal variation in protein availability by using high-protein willow leaves in the summer, and have adapted to low energy availability in the winter by storing food in the autumn, chewing through the ice before break-up in the spring to collect more food, and lowering food intake. The author believes that the beavers' heavy reliance on willows at these latitudes may lead to long-term cycles in beaver numbers, with beaver densities declining as willows are degraded, and increasing once willows have re-populated the area during periods of low beaver population density (Aleksiuk, 1970). Aleksiuk and Cowan (1969a, b) studied winter metabolic depression and seasonal energyexpenditure in northern beaver populations. They concluded, based on growth cessation, low food intake, low thyroid activity, normal body temperature, and behavioral lethargy in two

16 northern beaver kits subjected to total darkness, that Arctic beavers experience a winter metabolic depression induced by decreasing light intensity in the autumn. They believe that northern beavers possess an inherent annual pattern of metabo1ic activity, with high metabo1ic energy expenditure in the summer and 10w metabolic energy expenditure in the winter, and indicate that this pattern is roughly attuned to the annual environmental energy avai1ability (Aleksiuk and Cowan, 1969b). Simi1arly, Smith et al.'s (1991) study revea1ed a significant reduction in the mean dai1y body temperature of adult beavers from 36.3°C in autumn to 35.3°C in the winter and early spring, an adaptation that according to the authors may faci1itate surviva1 during extreme resource scarcity. Contrary to these findings, MacArthur (1989) and Dyck (1991) found no evidence of torpor or metabolic depression in adult beavers tested in the summer and autumn respective1y. Likewise, Dyck and MacArthur (1992) discovered that beavers body temperatures remained at 3rC from June through to March, and exp1ained that through a combination of physio10gica1 and behavioral mechanisms for retarding heat 10ss and minimizing immersion hypothennia (e.g., rise in body temperature of 1-3°C prior to nest departure) northern beavers are able to economically and precise1y regulate their body temperature throughout the year. The authors a1so found that northern beavers experience daily rhythms in activity and body temperatures before freeze-up, with an increase in body temperature and near-continuous occupation of the lodge during the day and a drop in body temperature and 10dge occupancy during the night, but found no obvious daily trends in body temperature after freeze-up. Dennington and Johnson (1974) undertook beaver habitat studies in the Mackenzie Valley and northern Yukon as part of the Canadian Wildlife Service wildlife habitat eva1uation pro gram a10ng the proposed pipeline corridor. Similar to Novakowski (1965) and Novak (1987), they discovered that beavers make use of high density lake complexes in glacial-1acustrine basins, hilly ground moraines and river deltas, most 1ikely due to the avail ab ilit y of food species (paper birch, Betula payrifera; willows, Salix spp.; and water lily, Nymphaea spp.), water-1evel stabi1ity, and the high ratio of shoreline to surface area. Interesting1y, severa1 small, c10sely spaced 1akes with abundant food resources seemed ideal for beaver colonization, but remained unoccupied. The authors explain that these lakes may have been too shallow and exposed to support overwintering

17 beaver colonies. Small meandering, low-gradient streams which were shallowly incised within broad floodplains occupying glacial meltwater channels and stream configurations that increased the available shoreline length provided increased habitat potential. The high incidence of these types of streams, and the relatively high fluctuations in water levels in areas of permafrost, resulted in streams being frequented to a much greater extent in the southem and central regions of the Mackenzie Valley rather than streams in the lower Mackenzie Valley and northem Yukon. Willows (Salix spp.) once again were the most frequently utilized food species. In sorne areas however, other species, such as birch (B. papyrifera) and water-lily (Nymphaea spp.) were used exc1usively, while balsam poplar (Populus balsamifera), alders (Alnus spp.), aspen (Populus spp.) and spruce (Abies spp.) were used occasionally. According to the authors, it remains questionable whether spruce was used as a food source, and it is possible it was only used to coyer and submerge more desirable food species within the food cache. In the end, the relative quality of habitats in the north (as reflected by beaver densities) were enhanced by an optimum distribution of land and water (i.e., maximum available shoreline), stable water levels and sufficient quantities of one or more food species. The Great Whale Region in Québec, located at the beavers' northem range limit, has been extensively studied by Hydro Québec. According to Bider (1979), Hydro Québec (1982), Consortium Gauthier and Guillemette (1989), and Consortium Gauthier and Guillemette (1990), the optimum habitat for beavers at these latitudes were areas rich in small lakes bordered by forests offering a minimum of coyer. Bider (1979) explains that higher beaver densities were found on small shallow lakes, which were highly populated by white water lily (Nymphaea spp.), and had shoreline gradients of <10%, allowing the growth of large bands of deciduous shrubs {bayberry (Myrica spp.) and willows (SaUx spp. minimum dbh 5cm)} and facilitating the construction of dams. Higher beaver densities were also found on little streams originating from head lakes, because they created organic matter deposits that favored the growth of alders (Alnus spp.) and willows (Bider, 1979). Conversely, the harsh c1imatic influence of Hudson's Bay, short growing season, cold air pockets, variable and unpredictable flow on big rivers, severe fires, large valleys with stunted willows, and scarcity of deciduous trees diminished the capacity to support beavers in northem Québec. Consortium Gauthier and Guillemette

18 2 (1989) affirmed that prime beaver habitats were associated with smalllakes «1 km - 100 ha), gentIy sloping banks, fine surface deposits, narrow slow-moving streams, aquatic vegetation made up primarily of Nymphaea spp., moderately abundant shoreline vegetation, and adjacent forest coyer composed of black spruce and mosses and/or lichens. They also agreed with Bider (1979) that severe fires diminished the favorability of a habitat for beaver establishment. Although Consortium Gauthier and Guillemette (1990) confirmed the previous findings, they discovered that, contrary to previous studies, the presence of fine deposits, abundance of streams, and relief of the region did not appear to influence the colonization ofbeavers at these latitudes. In summary, it would seem that northern beavers inhabit aquatic habitats similar to southern populations (small, secure and stable) but have developed a heavy reliance on shrubs, namely willows (SaUx spp.), and aquatic vegetation as their primary food resources. As such, it would seem that beavers at their range boundary are limited by quality food species rather than by physical parameters. Northern beavers have adapted to the cold and prolonged periods of time below the ice by building lodges, increasing fur insulation and fat deposits, huddling, caching food, conserving energy by reducing activity, and perhaps expressing a form ofwinter dormancy.

LITERATURE CITED

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34 CHAPTER II: PRELIMINARY CHARACTERIZATION OF THE BEAVERS' (Castor canadensis) NORTHERN RANGE LIMIT IN QUÉBEC, CANADA

ABSTRACT

There is a need to better characterize the edges of species' ranges in order to adequately describe abundance patterns and accurately predict species' responses to c1imate change. The objectives of this study were to (a) establish the beavers' (Castor canadensis) present day range limit in two northern regions of Québec (b) quantify present-day beaver abundance in the vicinity of these range limits and (c) evaluate habitat selection by beavers near the edge of their range. The northemmost beaver inhabitants in Québec on the eastern coast of Ungava Bay and western coast of Hudson's Bay are foundjust below the tree-line near Tasiujaq (58°42'N,69°56'W) and Umiujaq (56°33'N, 76°33'W) respectively. The number ofbeaver colonies per krn2 in the vicinity ofthese northern sites was on average lower than sites found further south, most likely because beavers at these latitudes are restricted to riparian zones with shrubby shoreline vegetation (i.e., alders and willows). Similar to habitat selection described in the interior of the range, beavers at the 2 range periphery selected for small lakes «1 krn ) bordered by forests. The lack of deciduous and mixed forests likely limit the CUITent northem beaver populations and therefore if the tree-line advances further north as a result of c1imate change, we Can expect to see a concomitant shift and increase in beaver densities at their present range limit.

35 INTRODUCTION

Evidence that climate change is having a profound impact on the abundance and distribution ofspeeies (Hughes, 2000; MeCarty, 2001; Walther et al., 2002; Parmesan and Yohe, 2003; Root et al., 2003) has led to a growing demand for ecologists to predict the response of natural systems to ongoing environmental change (Ludwig et al., 2001). An understanding of the factors shaping species' ranges and a clear idea of their internaI structure is essential to predicting the response of plant and animal populations to c1imate change (Parmesan et al., 2005). Geographie range limits provide a unique opportunity to understand the ecological niche and threshold responses to a changing environment (HoIt and Keitt, 2005). Furthermore, because the majority of studies examining geographic abundance patterns under-sampled the range edges (Sagarin and Gaines, 2002), there is a need to better characterize the edges of species' ranges, in order to adequately describe the patterns of abundance and accurately predict species' responses to c1imate change. Characterization of the edge of a species' range requires defining the range boundary by identifying the spatial extent of occupied 10calities, estimating abundance in the vicinity of the boundary, and describing the habitats that support the establishment of the species at these extremes. The majority of distribution maps are based on occurrence data that have been collected without special attention to the completeness of single ranges (Udvardy, 1969). Instead, occurrence data for most species have been documented without planned sampling schemes, and have been derived from presence-only records in the literature, biological surveys, and museum/herbarium collections (Brown et al. 1996, Elith et al., 2006). Consequently, the intents and methods of collecting such data are rarely known, and absences cannot be inferred with any certainty (Elith et al., 2006). To accurately characterize the edge of a species range, high-resolution data would need to be collected systematically to decipher where species are present and absent (Udvardy, 1969; Fortin et al. 2005). However, even with organized research and the employment of a uniform system, it remains inherently difficult to decipher whether a species is actually absent from a location, or whether they have simply been overlooked in the sampling process (Fortin et a1., 2005). Hence the majority of distribution maps are simplified "outline

36 maps" that fail to depict holes within the range boundaries where a species do es not occur (Brown et al. 1996). The North American beaver (Castor canadensis) is well-suited to characterizing northern range limits because of the ease in detecting their colonies, their well­ documented habitat requirements, and their familiarity to indigenous communities. Relative beaver abundance can be determined by conducting aerial surveys of waterways and counting the number of active beaver signs per colony (e.g., well-maintained dams, lodges, food caches etc.). This method also allows researchers to record abandoned signs and to decipher where the species is absent. Many aerial surveys have been carried out across Québec by the governrnent and Hydro Québec, with beaver densities ranging from 0.01 colonies per km2 near the future Great Whale Complex, to 1.36 colonies per km2 near Pikauba Reservoir in Saguenay Lac St-Jean (See Table lA-1.l). The general consensus from studies that have classified beaver habitats in North America is that beavers require deciduous trees and/or shrubs in sufficient quantities, as weIl as a stable water supply in order to survive (Appendix lA-2). These habitat requirements are straightforward and relatively easy to characterize. Beavers continue to be an important food species and fur bearer for many indigenous people s, including certain Cree communities still structured around the Cree tallymen (Whiteman and Cooper, 2000), but many Inuit communities located at the beavers' northern range limit consider them a nuisance (P. May, pers. comm. 2004). Whether beavers are considered to be a vital, culturally significant source of fur and food or a nuisance species, northern people are usually weIl aware of where beavers live, and thus Traditional Knowledge can be used to help delimit the beavers' northern range limit. The objectives of this study were to (a) establish the beavers' present day range limit in two northern regions of Québec (Koksoak River and Lac Guillaume-Delisle) (b) quantify present-day beaver abundance in the vicinity of these range limits and (c) evaluate habitat selection by beavers near the edge of their range. We hypothesize that (a) the beavers' northern range limit near the western coast of Ungava Bay and eastern coast of Hudson's Bay will coincide with the northern tree-line, (b) the density of beaver colonies per km2 will be lower than densities observed further south, and (c) similar to southern populations, beavers at their northern range limit will select for stable

37 waterbodies offering a mInImUm of forest coyer. The results from this study will complement a limited number of previous studies, published only as theses and reports, which have been conducted near the beavers' northem range limit.

METHODS

1. Study site selection and description The Koksoak River (58°32'N, 68°10'W), Lac Guillaume-Delisle (56°15'N, 76° 17'W) and Lac à L'Eau Claire (56°10'N, 74°25'W) were selected as our study sites because they are close to the beavers' proposed northem range limit, they had never been surveyed, and our collaborators, Makivik Corporation and Le Centre d'Études Nordiques, have research facilities in Kuujjuaq (58°06'N, 68°24'W) and Kuujjuaraapik (55°17'N, 77°45'W) respectively (Fig. 2.1).

1.1. Koksoak River The Koksoak river is approximately 137 km long, rises at the junction ofthe Caniapiscau and Mélèzes rivers (57°40'N, 69°30'W), and empties into the southem section of Ungava Bay (Breton-Provencher, 1982). It is sorne 700 m wide at its source, and becomes 2 km wide downstream of Cailloux Stream. The river has an annual flow of 2327 m3/s. The tide affects the water levels up to 85 km from the mouth of the Koksoak (around Cailloux Stream). The average spring tide amplitude at the mouth of the Koksoak is 8.7 m and the maximum spring tide amplitude is 13.9 m. The ice free period usually begins by mid-June and ends by mid-November (Breton-Provencher, 1982). Kuujjuaq mean January and July temperatures are -24.3°C and 11.3°C respectively, while the average annual temperature is -5.7 oC (Environment Canada, 2002). Total annual precipitation is 530 mm, 48% of which falls in the form of snow (Environment Canada, 2002). The growing season is 100 days long (Breton-Provencher, 1982). From the confluence of the Caniapiscau and Mélèzes rivers to the mouth of the Koksoak, two major vegetation zones (boreal and arctic) and sub-zones (forest-tundra and southem arctic) are crossed (Saucier, 2003). The lands surrounding the southem portion

38 of the Koksoak are characterized by patchily distributed stands of black spruce (Picea mariana) and tamarack (Larix laricina), covered with reindeer moss (Cladina rangiferina; Levasseur and Laframboise, 1978; Groupe Dryade, 1984). The majority of the banks of the Koksoak (60%) and several of its tributaries, are lined with a dense riparian strip of low lying willows (SaUx spp.), green aIder (Alnus viridis or crispa), and dwarf birch (Betula pumila var. glandulifera; Fig. 2.5a; Levasseur and Laframboise, 1978; Groupe Dryade, 1984). Moving north-east towards Ungava Bay, the stands of black spruce (P. mariana) and tamarack (L. laricina) become increasingly scattered and restricted to the valleys, individual trees become smaller in height and girth (i.e., dwarfed), and sm aIl peat bogs coyer larger expanses (Guimont and Laverdière, 1980; Breton-Provencher, 1982). North of the tree-line, the lands surrounding the Koksoak are characterized by bare rock, herbaceous species, mosses, and lichens (Levasseur and Laframboise, 1978). Vegetation in these zones is restricted to the fractures of exposed rock and protected valleys rich in marine and/or glacial deposits (Levasseur and Laframboise, 1978). ArcView 9.0 was used to construct a 200 m buffer around aIl waterways within a 40 km by 80 km area along the Koksoak River from Kuujjuaq to where the river splits 2 2 into the Caniapiscau and Mélèzes rivers. This area (648 km ) was then divided into 4 km quadrats for a total of 162 quadrats. Kuujjuaq locals, including employees from the Makivik Coporation {i.e., two wildlife technicians (Peter May, Sandy Suppa), an employee at the Nunavik Research Center (Alix Gordon), and the President of Makivik Corporation (Johnny Peters)}, and a local hunter (Simeonie Berthe), were asked to delineate on topographic maps (1: 50 000) outlining the study area, where they had seen and/or hunted beavers. Fifteen quadrats were randomly selected from within the regions designated by the locals and 15 quadrats randomly selected from the rest of the study area, for a total of 30, 4 km2 quadrats (Fig. 2.3a).

1.2. Lac Guillaume-Delisle 2 Lac Guillaume-Delisle (also known as Richmond Gulf) is a vast (702 km ), triangular, brackish lake that collects water from six major tributaries and is linked to the eastem shore of Hudson's Bay by a long narrow channel (500 m wide, 10 km long) called

39 Le Goulet (Fig. 2.3; Von Mors and Begin, 1993; Archambault, 1997). Located 125 km north of Kuujjuaraapik (55°l7'N, 77°45'W), and a few kilometres south-east of Umiujaq (56°33'N, 76°33'W), Lac Guillaume-Delisle is separated from the Hudson's Bay by a ridge of high (330 m), narrow, Proterozoic cuestas (Von Mors and Begin, 1993; Archambault, 1997). Diurnal tides affect the water levels of the entire lake. The average spring tide amplitude is 60 cm, and the maximum spring tide amplitude is 1.9 m (Von Mors and Begin, 1993). The ice-free period usually begins by mid-June and ends by mid­ December (Von Mors and Begin, 1993). January and July mean monthly temperatures, estimated by interpolating data from Kuujjuaraapik and Inukjuaq weather stations, are -24.1°C and 10°C respectively, while the average annual temperature is -5.7 oC (Environment Canada, 1993,2002). Total annual precipitation is approximately 550 mm, 40% of which falls in the form of snow, early on in the season (60%; Archambault, 1997). Although freezing can occur at any time throughout the year, the growing season is approximately 100 days long (Archambault, 1997). The lake crosses two major bioclimatic zones (hemiarctic and arctic; Hydro­ Québec 1993) and vegetation sub-zones (forest-tundra and southern arctic; Saucier, 2003). There is a latitudinal, longitudinal and altitudinal affect on the vegetation surrounding the lake. From Kuujjuaraapik to the southern tip of the lake, wherever soil conditions are favourable, the boreal forest is open, semi-continuous and composed mainly of black spruce (P. mariana) and tamarack (L. laricina; Fig. 2.5b; Archambault, 1997). From the southern tip of the lake to the northern tip, 20 km in from the Bay, the forest is patchy due to unfavourable soil conditions (peat lands, rocks), and the challenges of re-growth after tire (Hydro Québec, 1993). Isolated pockets of clonaI balsam poplar (Populus balsamifera) can also be found in this region (Archambault, 1997). From the northern tip of the lake to Umiujaq, the forest gives way to shrubs, {willows (Salix spp.), dwarf birch (B. pumila var. glandulifera) and green aIder (A. viridis or crispa)}, herbaceous species, mosses and lichens (Hydro Québec, 1993). The shrubs, which mark the tree-line, are restricted to protected areas at the bottom of valleys or at the foot of mountains (Hydro Québec, 1993). The Low Peninsula, extending from the shores of the Hudson Bay inwards to Lac Guillaume-Delisle, and from Le Goulet up to Umiujaq (Fig.

40 2.3b), is exposed to the unfavourable climatic conditions of the Bay (Archambault, 1997). As such, the west side of the lake is characterized by small scattered thickets of arborescent white spruce (Picea glauca), krummholz tundra {black spruce (P. mariana)}, rocky outcrops, shrubs, mosses, lichens and peat lands (i.e., fens; Archambault, 1997). FinaIly, along the coast, there is an absence of conifers in elevated areas (i.e., altitudinal tundra over the cuestas) creating an altitudinal effect (Payette 1975, 1983). When Hydro Québec was researching the feasibility of the Great Whale project (Consortium Gauthier and Guillemette, 1990), they carried out several aerial beaver surveys near Lac Guillaume-Delisle. To compliment these surveys, we focused on the small areas not yet surveyed by Hydro Québec. Bill Doidge (Director, Nunavik Research Center, Makivik Coporation) while working with Beluga (Delphinapterus leucas) at Little Whale River (56°0'N, 76°45'W) during the summer of 2004, asked local George Luste and his crew to delineate on al: 50 000 topographie map of Lac Guillaume-Delisle, where they had observed/hunted beavers or heard beavers were located. Dr. Serge Payette (Le Centre d'Études Nordiques) was also consulted and asked to identify areas that support a high density of deciduous trees and/or shrubs. Once these areas were delineated, ArcView 9.0 was used to create a 200 m buffer around aIl waterways generating an area 2 2 of 544 km • The area was then divided into 136, 4 km quadrats and 31 quadrats randomly se1ected for the aerial survey (Fig. 2.3b).

1.3. Lac à L'Eau Claire Lac à L'Eau Claire (also known as Clearwater Lake), is the second largest naturallake in Québec after Lac Mistassini, and consists of two roughly circular basins separated by a fringe of islands (Fig. 2.3c; Bostock, 1969; Archambault, 1997). The lake, formed by meteor impact sorne 285 to 300 million years ago, is approximate1y 33 km long and 71 km wide, with a surface area of 1269 km2 (Denee, 1981; Begin and Payette, 1989; Boudrealt et a1., 2003). It sits at an altitude of 240 m and is located 70 km west of Lac Guillaume-De1isle, at approximate1y the same latitude (Bostock, 1969; Boudrealt et a1., 2003). Mean January and July temperatures fluctuate around -24°C and 11°C respectively, while the average annual temperature for the region is between -4.3 oC

41 (Kuujjuaraapik) and -6.7 oC (lnukjuaq; Begin and Payette, 1989; Archambault, 1997). Average annual precipitation is approximately 640 mm, with 40% falling in the form of snow, mostly in November (Boudreault et al., 2003). The growing season is between 80 and 100 days (Begin and Payette, 1989). The lake is usually ice-free from the end of June to the beginning of November, but in sorne years has stayed frozen until July (Archambault, 1997). Vegetation in the Lac à L'Eau Claire region is characteristic of the hemiarctic zone (bioclimatic zone), more specitically the hemiarctic interior (ecoclimatic region) and forest-tundra region (vegetation zone), where scattered forest formations are accompanied by numerous expanses of tundra (Hydro Québec, 1993). The patchy mosaic of open tundra dominated by lichen, and forests dominated by stunted black spruce (P. mariana) and accompanied by tamarack (L. laricina) and balsam poplar (P. balsamifera) in more humid regions, are a result of a long history of forest tires (Fig. 2.5c; Begin and Payette 1989). Because the lake is vast, creating a cold windy mesoclimate, the islands and eastem shores of the lake are composed of tundra, palsa and dune complexes (Hydro Québec, 1993). Aiso worth noting is that near Crafton Bay, a healthy population of clonaI balsam poplar (P. balsamifera), little leaf buttercup (Ranunculus abortivus), common strawberry (Fragaria virginiana), and pin cherry (Prunus pensylvanica) thrive, indicating that the temperatures were once much warmer in this region (Archambault, 1997). Based on the information derived from the literature and the advice provided by Dr. Serge Payette (Le Centre d'Étude Nordiques) regarding regions that would most likely support beaver populations, a 50 km2 quadrat around Crafton Bay was outlined on a topographic map (1: 50 000) for the aerial survey (Fig. 2.3c). Because no indigenous communities were located around Lac à L'Eau Claire Study area, the aim here was to establish whether this area, where Dr. Payette claims high densities ofbalsam poplar can be found (P. balsamifera; one of the beavers' preferred food species), would support any beaver populations.

2. Beaver Surveys 2.1. Aerial Surveys

42 Aerial surveys of the Koksoak River study area were conducted on 5 July 2004 in an Astar-350 helicopter flying at 60-80 m above ground level, and at a speed of approximately 90 km/ho The team consisted of an experienced pilot, 1 navigator/observer (front left), and 1 observer (rear left). Aerial surveys of the Lac Ouillaume-Delisle study area were conducted on 15 July 2004 (one quadrat) and 16 July 2004 (30 quadrats) in a Bell 206 Long Ranger helicopter flying at 60-80 m above ground leve1 and at a speed of approximately 110 km/h. The team consisted of an experienced pilot, 1 navigator/observer (front left), and 3 observers (rear). Aerial surveys of the Lac à L'Eau Claire study area took place on 17 July 2004 in a Bell 206 Long Ranger helicopter flying at 30-60 m above ground level and at a speed of approximately 200 km/h. The team consisted of an experienced pilot, 1 navigator/observer (front left), and 2 observers (rear). For alliocations, aerial survey conditions were noted at every quadrat (Table 2A- 1.1 to 1.3) including the start and finish time, the meteorological conditions (cloud coyer, wind, etc.), altitude and speed of the aircraft. Aerial survey conditions were optimal for aIl study areas, with overcast skies, excellent visibility and gentle winds. All quadrats were delineated on 1: 50 000 topographic maps that were scanned and imported into the Ozi Explorer OPS Mapping Software (version 3.95.3f). A Oarmin eMap OPS unit was connected to a laptop and the "Moving Map" option activated so that the pilot and navigator could see where the helicopter was flying, ensuring that the selected quadrats were flown. AlI waterways (wetlands, lakes, rivers and streams) were flown within the limits of each quadrat. When a sign ofbeaver presence was observed, the helicopter made several passes over the site, allowing the navigator/observer to note the OPS coordinates and describe the characteristics of the area. During this time the observer took pictures of the site and surrounding habitat with a digital camera (Nikon Coolpix 3200). The physical description of the area was based solely on visual observations including the status of the signs (active vs. abandoned), nature of the signs (e.g., dam, food cache, lodge, downed trees, peeled sticks, runway etc.), the type of aquatic habitat (e.g., lake, stream, river etc.), exposure to wind and waves (little vs. high), type of lake (head lake vs. chain lake), area 2 oflake « or> 1 km ), width ofwater body « or> 10 m), through-flow ofwater body (slow vs. fast), aquatic vegetation (present vs. absent), width of shoreline vegetation «5 m, 5-10 m, > 10 m) and type of shoreline vegetation (river shrubs, black spruce (P.

43 mariana), tamarack (L. laricina); Table 2A-1.4 and 1.5). The different types of aquatic habitats were delineated by the following characteristics: a wetland was differentiated from a lake by the absence of tributaries on a map at a scale of 1: 50 000 and streams were differentiated from rivers by a single versus double line at the same scale. In addition to these systematic surveys, any evidence of beaver presence (past or present) was noted during incidental flights in these regions. While working as a volunteer with the Canadian Wildlife Service during the summer of 2004 ( 11-17 June), we flew from Kuujjuaq to Puvimituq (600 02'N, 77°17'W), and from Kuujjuaq up to the southem shores of Leaf Bay (east of Tasiujaq, 58°42'N, 69°56'W; Fig. 2.1). As we flew out of Kuujjuaq, in an Astar-350 helicopter, the pilot flew at a low altitude and speed so that we might informally look for signs of beaver presence. Very quickly the landscape changed from small scattered forests of black spruce (P. mariana) and tamarack (L. laricina), into barren rock, lichen and peat lands (tundra). Similarly, as we were stationed in Kuujjuaraapik for our surveys on the west coast of Québec, we flew out of Kuujjuaraapik and had the opportunity to look for signs ofbeaver presence on the way to Umiujaq and Lac à L'Eau Claire (Fig. 2.1: flight paths).

2.2. Ground Surveys In the Koksoak River study area, from 7-11 July 2004, 21 signs observed from the helicopter (located using a Garmin eMap handheld GPS unit) and 6 signs not seen from the helicopter were visited by boat and foot. At each site, a precise GPS reading was taken, biological and physical factors were verified and new characteristics noted (i.e., slope of banks, surface deposits on the embankments, composition of food cache, composition of shoreline vegetation, and adjacent coyer (dominant and present; Table 2A-1.6). No ground surveys were conducted in the Lac Guillaume-Delisle and Lac à L'Eau Claire study areas.

3. Estimating Relative Abundance We conducted aerial surveys during the surnmer, whereas the best time to estimate relative abundance of beaver is in autumn after the leaves have fallen from the trees but before freeze up (Novak, 1987). In the autumn, active lodges can be easily distinguished

44 from abandoned lodges by the presence of fresh food caches (Swank and Glover, 1948; Crissey, 1949; Payne, 1981; Novak, 1987), permitting an estimate of relative abundance based on the number of active colonies per km2 (Bergerud and Miller, 1977). Due to the summer timing of our surveys, we had to rely on other signs of activity (freshly applied mud, peeled sticks, well-maintained dams with fresh vegetation etc.) and the average summer home range size for beavers in North America which covers 0.3 to 2.5 km of shoreline and 0.103 km2 ofland (Novak, 1987; Hydro Québec, 1993; Wheatley, 1994) to delineate distinct, active beaver colonies (Table 2A-1.4 to 1.6). In order to render results comparable to previous surveys, we divided the total number of active colonies in the 2 2 study area by the total area surveyed (km ), to yield the density ofbeaver colonieslkm •

4. Habitat Selection 4.1. Habitats used by beavers To establish the general characteristics of habitats used by beavers at their northem range limit we recorded the biological and physical features surrounding aIl signs observed within our study sites (as described in sections 2.1 and 2.2). These observations were documented during our aerial and ground surveys.

4.2. Use vs. A vailability To evaluate habitat selection ofbeavers near the edge oftheir range, we compared the use (observed values) versus the availability (expected values) of aquatic environments (lakes, rivers, streams), and vegetation {shrubs, conifers, others (tundra, transition zone, and mixed forest)} within our study sites. These factors were chosen because habitat suitability for beavers is govemed by water reliability and food availability (Atwater, 1940; Slough and Sadleir, 1977; Allen, 1983; Howard and Larson, 1985; Novak, 1987). Due to the low number of observations ofbeaver signs in Lac Guillaume-Delisle and Lac à L'Eau Claire study areas, our analysis of habitat selection is limited to the Koksoak River study area. However, in the discussion, we compare our findings to those of Hydro Québec, which includes an analysis of habitat selection ofbeaver populations close to and south of Lac Guillaume-Delisle and Lac à L'Eau Claire. To quantify use, we once again relied on the documented biological and physical

45 factors associated with each of the beaver signs observed during aerial and ground surveys in Koksoak River study area (Table 2A-IA and 1.6). Abandoned and active signs were pooled and analysed because both were used at one time by beavers. Signs located outside our study quadrats were not included in our analysis. To quantify availability, we used vector data depicting the aquatic habitats and vegetation categories, and XTools Pro in ArcView 9.0. Using maps from the National Topographic DataBase (NTDB 1:250 000), we calculated the total length of shoreline (km) for rivers, lakes and streams within each quadrat of the Koksoak River study area. For food availability, because beavers usually forage within 50 m of a waterway, and rarely forage beyond 200 m (Bradt, 1938; Hammond, 1943; Northcott, 1971; Allen, 1983), a 200 m buffer was created around aIl waterways within the quadrats, and using the Mosaïque du Québec (1: 2 500 000), the area of each of the vegetation categories were calculated. The Mosaïque du Québec, created using satellite images acquired in 1999-2000 from the SPOT satellite (1 km resolution) and map data, is the best available data for northem Québec and contains 15 land coyer classes, including 5 for non-forested northem habitats, 5 for different forest types, 2 for different agricultural practices, 1 for urban centers, 1 for peat bogs, and 1 for waterways (Québec Govemment/Ministère des Ressources Naturelles Faune et Parcs, 2003). Once the observed and expected values were calculated, a Chi-square Goodness of fit test was performed to see if there was a difference between observed and expected values. If the Chi-square value was significant, simultaneous Bonferroni confidence intervals were used to decipher which of the waterways and/or vegetation categories were being selected for, against or used in proportion to availability (Neu et al., 1974).

RESULTS

1. Establishing the beavers' northern range limit The locations reported to support northem beaver populations are illustrated in Figure 2.2. On the Ungava coast, the locals claim that beaver densities are rising and that beaver colonies are moving further north, with one family spotted near Tasiujaq. Kuujjuaq locals believe beaver densities are higher on the northem shores of the Koksoak River, and that

46 in order to avoid going hungry, beavers chew through the ice in March. One of the most active Kuujjuaq hunter's states that beaver colonies can be found along most northem tributaries off the main channel of the Koksoak River. A beaver was reportedly shot on Mackays Island which is located very close to the tree-line in the Koksoak River, 21-22 km from Kuujjuaq where the waters become progressively salt y, while 5 to 6 years ago, two beavers were spotted near the mouth of the Koksoak River, close to Ungava Bay. On the coast of Hudson's Bay, locals report that beavers can be found as far north as the northeastem tip of Lac Guillaume-Delisle.

2. Quantifying the present-day abundance of beavers at their northern range limit In the Koksoak River study area, a total of 22 out of 30 quadrats presented no sign of beaver presence (active or abandoned). Within the remaining 8 quadrats, 47 signs were observed (Fig. 2.3a). Of these signs, 27 were active and 20 abandoned (Table 2A-1.4). The study quadrats supported a density of 0.08 beaver colonies/km2 when results from the 2 ground surveys were included (10 coloniesl120 km ). In the Lac Guillaume-Delisle study area, a total of 5 out ofthe 31 quadrats surveyed displayed signs ofbeaver activity (active and/or abandoned; Table 2A-1.5, Fig. 2.3b). A total of 10 signs were observed from the air. Ofthese signs, 1 was active (i.e., a well-maintained dam with freshly applied mud and vegetation) while the rest were abandoned. The study quadrats supported a density of 2 2 0.01 colonies/km (1 colony/ 124 km ). In the Lac à l'Eau Claire study area, no beaver activity was observed in the 50 km2 quadrat, generating a density estimate of 0 2 colonies/km • In addition, no beaver activity was observed during flights to and from Puvimituq and LeafBay, or during flights to and from Umiujaq and Lac à l'Eau Claire.

3. Habitat Selection 3.1. Habitat used by beavers A total of 47 signs were observed from the air and by foot in the Koksoak River study area, while a total of 12 signs were noted just outside the study quadrats. Of these 59 signs, 33 were active and 26 were abandoned. The majority of active signs were observed on lakes smaller than 1 km2 (67%), while 10 signs were found on slow moving streams and one in the protected regions of the Koksoak River (Table 2A-1.4). The majority of

47 abandoned signs were found on slow-moving strearns (58%), while 10 signs were observed on lakes smaller than 1 km2 and one sign observed in the protected regions of the Koksoak River. As a general rule, the waterways used by beavers were greater than 10 m in width (93.2% ) and protected from the wind and waves (98.3%). No aquatic vegetation was found at any of the sites and the width of the shoreline vegetation was greater than 10 m at 96.6% of the sites. The most striking feature when locating signs on foot (Table 2A-I.6) was the density, height and extent of the shrubs surrounding the dams, lodges and burrows. The shrubs typically reached 2 m in height and forrned thickets so dense that walking through them proved very difficult. Willows (SaUx spp.) were found at 93 % of the sites, green aIder (A. viridis or crispa), at 85% of the sites and dwarf birch (B. pumila var. glandulifera) at 33.3 % of the sites. Similarly, the old food caches were composed ofwillows and alders. Black spruce (P. mariana) and tamarack (L. laricina) were found in the immediate surroundings at 34% and 29% of the sites respectively. Within the Lac Guillaume-Delisle study area, the majority of abandoned signs (88%) were found on sheltered, slow moving, meandering streams. The one active dam 2 was located on a small «1 krn ) sheltered, head-Iake. One abandoned bank lodge was located in a sheltered portion of a fast moving river (i.e., Rivière à L'Eau Claire). The width of the shoreline vegetation surrounding the shores where beaver signs were located was more than 10 m in 60% of the cases, and was composed of willows (SaUx spp.). In general however, the river shrubs lining the shores of the waterways were sparse throughout the majority of the study area. No aquatic vegetation was found at any of the sites. Black spruce (P. mariana) and tarnarack (L. laricina) were found in the immediate surroundings at 30% and 10% of the sites respectively.

3.2. Use vs. Availability The locations of active and abandoned beaver signs in the Koksoak River study area indicate that beavers are strongly selecting for lakes, against rivers, and using strearns in proportion to their availability (i = 63.07, d.f.= 2, P <0.001; Fig. 2.4a). The sarne aquatic habitat selection patterns emerge if only active signs are incIuded in the analysis. The presence of abandoned and active signs also indicate beavers are selecting for areas

48 classified as coniferous vegetation, selecting against areas classified as tundra, mixed forests, or transition zones, and using areas classified as shrubs in proportion to their availability (t =13.66 d.f.= 2, P = 0.005; Fig. 2.4b). When analyses were limited to active signs, coniferous and shrub habitats were found to be selected and all other habitats were used in proportion to their availability.

DISCUSSION

1. Establishing the beavers' northern range limit The beavers' CUITent northern range limit on the western coast of Ungava Bay is close to Tasiujaq (58°42'N, 69°56'W) and on the eastern coast of Hudson's Bay is close to Umiujaq (56° 33'N, 76° 33'W). For the Ungava Bay coast, our results are based on Traditional Knowledge and our flights over the area. For the Hudson's Bay coast, the delimitation is based on Traditional Knowledge, our own surveys and surveys carried out by Hydro Québec (Consortium Gauthier and Guillemette, 1990; Table 14). On both coasts, these locations mark the northern most coordinates where beavers have been observed, and most likely represent colonies that have dispersed beyond the beavers' traditional range boundaries. Traditional knowledge suggests that, in these regions, beavers continue to move further north and that their densities continue to increase. The fact that beavers are patchily distributed (the majority of our study quadrats had no sign of beaver presence {73% on the Koksoak River and 83.9% around Lac Guillaume­ Delisle)}, are located at sites completely devoid of deciduous trees, have been seen chewing through the ice in March to prevent starvation (P. May, pers.comm. 2004) and have been observed in brackish waters near Ungava Bay (A. Gordon, pers. comm. 2004), indicates that beavers are indeed occupying marginal habitats at their northern range limit. As hypothesized, the beavers' northern range limit near the eastem Ungava Bay coast and Hudson's Bay coast seems to coincide with the northem tree-line (Fig. 2.2). According to Payette (1993) the tree-line is defined as the northern most position of individual trees (height of 5 m or more) located at the limit of the forest-tundra, which is the transition zone between the boreal forest and the Arctic tundra zones. At these latitudes, black spruce (P. mariana) and tamarack (L. laricina) are the dominant tree

49 species. Although beavers have been observed cutting down spruce and tamarack across North America (Novak, 1987), it is most likely that they are using the tall shoreline shrubs as their food supply and the larger spruce and tamarack trees as coyer (Consortium Gauthier and Guillemette, 1990). This could explain the fact that no beavers were found close to Nastapoka and Lac des Loups Marin during the aerial surveys done by Hydro Québec (Fig. 2.2 Areas surveyed; beaver absent; Consortium Gauthier and Guillemette,

1990), even though both locations ~re found well be10w the tree-line. In these areas, although trees are present, they are for the most part stunted and sparse1y distributed, and the shore1ine vegetation is underdeve10ped (Consortium Gauthier and Guillemette, 1990). As such, it would seem that 'be1ow the treeline' is a necessary but not sufficient condition for the presence of beavers. Aerial surveys throughout the interior of northern Québec would be necessary to establish where the beavers' northern range limit is found between Ungava and Hudson's Bay.

2. Quantifying the present-day abundance of beavers at their northern range limit Densities at the beavers' northern range limit (0.08, 0.01, and 0.00 colonies per km2 at the Koksoak River, Lac Guillaume-Delisle and Lac à l'Eau Claire study areas respectively) fall within the lower scope of abundances in Québec (see Lafond et al., 2003). A decrease in the number of beaver colonies per km2 from south to north within study areas and as a general pattern observed between study areas in Québec, has been attributed to a parallel drop in the quality of habitat (Bovet et al., 1973; Traversy, 1974; Traversy, 1975; Traversy and Morasse, 1975; Traversy, 1976; Brodeur et al., 1977; Banville, 1978; Pelletier and Lizotte, 1982; Consortium Gauthier and Guillemette, 1990; Le Groupe Roche Boreale, 1991; Consortium Gauthier and Guillemette, 1992; Tecsult Inc., 2000; Lafond et al., 2003). Habitats in the North are considered to be of lower quality because deciduous forest vegetation is rare (Consortium Gauthier and Guillemette, 1990). As such, it is believed that available food is depleted at a faster rate than optimum habitats further south, forcing beavers to relocate more often and limiting population growth (Consortium Gauthier and Guillemette, 1990). Thus, although the absence of deciduous forest vegetation does not prevent beavers from successfully occupying sites, it does appear to prevent them from attaining high densities at these sites. More research is

50 required to resolve whether this density minimization in northern regions results from high mortality and rapid turnover of colonies or territorial and spacing behaviour of surviving beavers. The Koksoak River study area had the highest beaver densities among the three study regions, even though Lac Guillaume-Delisle and Lac à L'Eau Claire had several habitats that appeared ideal for beaver establishment (i.e., presence of shoreline shrubs and adjacent forest coyer, slow moving meandering streams, small lakes etc.). The discrepancy between beaver densities may be explained by the land coyer, topography and waterways within Koksoak River study area versus the other two study regions. In the Koksoak River study region, the density, height, and expanse of the shoreline shrubs bordering the banks of the waterways was much higher than in the other two study regions (Fig. 2.5). Other studies have demonstrated the importance of shoreline shrubs for beaver establishment, particularly in northern regions close to our study sites (Great Whale Region: Bider, 1979; Hydro Québec, 1982; Eastmain Region: Le Groupe Roche Boreale, 1991; Sainte Marguerite Reservoir: Consortium Roche Associés Ltée/Desssau Inc., 1995; Nottaway, Broadback and Rupert Complex: Gauthier and Guillemette, 1992; Peribonka and Manouane Rivers: Tecsult Inc., 2004). At Lac Guillaume-Delisle and Lac à L'Eau Claire, although certain waterways did harbour a high density of shoreline shrubs, the incidence, height and expanse of these shrubs was inferior and the occurrence of spruce (P. mariana or glauca) and/or tamarack (L. laricina) higher. AIso, at Lac à L'Eau Claire, rocky outcrops covered a good portion of the study area (Fig. 2.5c). The topography in the Koksoak River study area was for the most part, flat; a characteristic favouring beaver establishment because gentle gradients allow beavers to easily control the flow and fluctuations in water levels by ensuring that water velocity remains manageable even during flooding (Retzer et al. 1956; Rutherford, 1964). On the other hand, the relief in the other two study regions was more variable and corrugated making food retrieval and transport more difficult (Slough and Sadleir, 1977; Novak, 1987; Bordage and Filion, 1988). Finally, where the lakes and streams were deep and c1ear in the Koksoak: River study region, preventing the waters from freezing to the bottom over the winter, the waters in several meandering streams and lakes near Lac Guillaume

51 Delisle were very shallow and muddy, decreasing the suitability of these habitats for beaver colonization. Beaver densities observed in our study areas are comparable to those observed by other authors at the beavers' northem range limit in Québec. For example, in the Great Whale Region, average beaver densities ranged from 0 to 0.05 colonies/km2 (Bider, 1979; Hydro Québec, 1982; Consortium Gauthier and Guillemette, 1989; Consortium Gauthier and Guillemette, 1990). On the other hand, in the Northwest Territories, beaver densities observed at northem latitudes tended to be higher than those observed in our study areas. 2 In the Mackenzie Delta, Novakowski (1965) reported an average of 0.4 colonies per km , 2 Aleksiuk (1970) between 0.15 and 0.38 colonies per km , Dennington and Johnson 2 2 (1974) 0.17 colonies per km , Poole and Croft (1990) 0.26 colonies per km and Popko et 2 al. (2002) between 0.22 and 0.75 colonies per km . This discrepancy may be as a result of the flat deltaic complex of the Mackenzie lowlands which features many stable, slow moving bog lakes and streams lined with willows (Salix spp.) and supporting high densities of aquatic vegetation, known to be an important food resource for beavers at northem latitudes (Novakowski, 1965; Dennington and Johnson, 1974; Bider, 1979; Consortium Gauthier and Guillemette, 1989).

3. Habitat Selection The aquatic habitats, shoreline vegetation and adjacent coyer characterizing the sites colonized by beavers in our study areas are similar to other northem sites. Bider (1979), Hydro Québec (1982), Consortium Gauthier and Guillemette (1989) and Consortium Gauthier and Guillemette (1990) studied beavers in the Great Whale Region and discovered that the optimum beaver habitat at northem latitudes were lakes less than 1 km2 and narrow, slow-moving streams bordered by moderately abundant shoreline vegetation, and forests (usually black spruce (P. mariana)) offering a minimum of cover. Novakowski (1965), Aleksiuk (1970), and Dennington and Johnson (1974) noted the sparse selection of both woody and herbaceous vegetation available to beavers and the predominant use of willows (Salix spp.) and aquatic vegetation in the Far North. In our 2 study areas, beavers also colonized small lakes «1 km ) and slow moving streams with abundant shoreline vegetation composed mainly of willows and alders, and adjacent

52 coyer composed of black spruce (P. mariana) and/or tamarack (L. laricina). Contrary to other studies however, the presence of aquatic vegetation did not seem to influence beaver colonization in our study areas, as no aquatic vegetation was observed at any of our sites. Beaver colonies in the Koksoak River study area were observed selecting lakes 2 «1 km ) bordered by coniferous trees. The fact that beavers were selecting lakes, using streams in proportion to their availability and avoiding rivers is most likely attributable to the stability of the water. Smalliakes rarely experience major fluctuations in water levels and most streams in this region were secure and slow moving. The Koksoak River however, is wide, fast moving and variable (e.g., tides, spring melt etc.), making it impossible for beavers to control and thus difficult to colonize. Several studies of southem beaver populations have noted the importance of water stability in site selection for beavers (Retzer et al., 1956; Nixon and Ely, 1969; Dennington and Johnson, 1974; Slough and Sadleir, 1977; Potvin et al., 1993; Le Groupe Roche Boreale, 1991; Tecsult Inc., 2000; FORAMEC, 2004). Our finding that northem beavers select for areas classified as coniferous habitat likely reflects the importance of coniferous trees as shelter for beavers as well as limitations imposed by the spatial resolution of land classification available for northem Québec. Although beavers can consume conifers, they cannot survive for prolonged periods oftime without shrubby or arborescent deciduous vegetation (Novak, 1987). With the high density of shoreline shrubs lining the shores where beaver colonies were found, it is most likely that the coniferous trees were used as a form of coyer for beavers rather than as a food supply (Bider, 1979; Hydro Québec, 1982; Consortium Gauthier and Guillemette, 1989; Consortium Gauthier and Guillemette, 1990). However, one of the limitations of the methods used for our analysis is the resolution of the available land coyer data (1 km resolution, SPOT satellite imagery), which frequently prevents detection of shoreline shrubs. These important habitats thus can be found lumped into the adjacent land coyer classes such as forests (mixed, coniferous or deciduous), peat bogs and other non-forested classes. This same limitation was noted by several studies using low­ resolution satellite imagery to quantify the available land cover types (Tecsult Inc. 2000, 2002b, 2004), and may explain why we did not detect any selection for shrub habitats. It

53 is also possible that beavers that were observed selecting habitats classified as coniferous forests were in fact selecting for shoreline vegetation adjacent to these coniferous habitats. Acquiring and classifying high-resolution Quickbird satellite images (60 cm) may have allowed us to clarify which habitats beavers were selecting. Usage is said to be selective if components are used disproportionately to their availability (Johnson 1980). Consequently, in order to decipher whether selection is taking place, the availability of resources must be calculated and compared with usage. In southem Québec, use vs. availability of aquatic habitats for beaver colonies near Kenogami Lake revealed that beavers were selecting streams and avoiding aIl other aquatic habitats (Tecsult Inc., 2002a), while beaver colonies near Romaine River were selecting streams, lakes and wetlands, bordered by mixed and deciduous forests (Tecsult Inc., 2002b). At more northem latitudes, beaver colonies near Péribonka Reservoir had no preference for any particular aquatic habitat (aIl were used in proportion to availability) but they did use riverside shrubs more often than expected. These results illustrate how at northem latitudes, beavers have adapted to the biophysical characteristics of the habitats available, despite and considering the rarity of their preferential habitats (Consortium Gauthier and Guillemette, 1990). As such, the availability of quality vegetation for food and construction, to the detriment of physical factors, has become more important in determining where northem beavers will establish themselves (Novakowski, 1965; Nault and Gascon, 1983; Consortium Roche Associés Ltée /Dessau Inc., 1995; Tecsult Inc., 2000; Tecsult Inc., 2004), According to Grace et al. (2002), because macrofossils and palynological evidence show that tree-lines have always been dynarnic, they expect tree-lines to continue being responsive to climate change and to experience large advances in the near future. If this advance do es take place, and beavers at northem latitudes are presently limited by the low occurrence of deciduous trees, we would expect a concomitant range expansion, increase in beaver density and shift in habitat selection. The information gathered in this study, will thus serve as a benchmark from which to measure and detect whether such a changes are taking place.

LITERATURE CITED

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61 WHEATLEY, M. 1994. Boreal Beavers (Castor canadensis): Horne Range, territoriality, food habits and genetics of a mid-continent population. Unpublished PhD. Thesis, University of Manitoba: 350 pp. WHITEMAN, G., and COOPER, W.H. 2000. Ecological embeddedness. Academy of Management Journal 43(6):1265-1282. VON MORS, 1., and BEGIN, Y. 1993. Shoreline shrub population extension in response to recent isostatic rebound, eastern Hudson Bay, Québec, Canada. Arctic and Alpine Research 25(1):15-23.

62 U5"O'O'W UO"O'O'W 75"0'O'W 65"0'O'W 60"0'O'W 55"O'O"W

~ p Puvirnituq P D ca

~ P D in Schefferville 10

QUÉBEC

Legend

_ Koksoak River Study Area Montreal ~ Lac Guillaume-Oelisle Study Are a o Lac à L'Eau Claire Study Area -- Northern tree-line • - • - Flight paths

75"0'0""" 70"0'0""" 65"0'0""" sa"a'a"""

Figure 2.1: Location ofbeaver study areas and flight paths in Northem Québec

63 1$'O'C"W 7COO'O"W

Puvirnituq •

Tasiujaq r~ Kuujjuaq'" • . ] NORTHERN 1 UÉBEC

z

1ftt

7S·0·O·'W 7000'O'W Legend

Areas surveyed: beENer absent ..... _--'

Figure 2.2: Map of northem Québec illustrating our accumulated knowledge of the approximate northem range limit for beaver in the province.

64 U"I CP ao q Z

A.env€! sites

... Abandoned sites c=J 4 x 4 km' Quadrats ~ f\oksoak River Study Area

76"3IJO'W 74°45"O'W 74"3fY'O'W

Figure 2.3: Location of survey quadrats and observed active and abandoned beaver signs in a) Koksoak River, b) Lac Guillaume-Delisle, and c) Lac à L'Eau Claire study areas.

65 100 Z2: 63.07 • Avallability. P< 0,001, df =2, n =47 so

70 +

2Q ~

10;

a) Aquatic Habitat

100 ············· ...... ··1 '1..2 : 13.66 00 ,005, df = 2. n '" :ln

;i} 1/1 !! ()() tJ) 'S() 0 = - 40 0~ :;0

2!}

li}

()

b) Vegetation Type

Figure 2.4: Habitat selection by beavers of a) aquatic habitat and b) vegetation cover in the Koksoak River study area. Selection for a given habitat is indicated by +, selection against by -, and use in proportion to availability by =, which was determined based on whether the expected proportion of use (i.e., availability) feH below, above, or within the limits of the Bonferonni confidence intervals (error bars) respectively. Use includes active and inactive beaver signs, but similar patterns emerge if only active signs are included.

66 Figure 2.5: Representative photos of a) Koksoak River, b) Lac Guillaume-Delisle, and c) Lac à L'Eau Claire study areas, including occupied beaver habitat in a) and b) and potential but unoccupied beaver habitat in c).

67 CONNECTING STATEMENT

One of the largest criticisms of studies examining geographical abundance patterns is that they under-sampled the species' range edges, resulting in a core-sampling bias (see Sagarin and Gaines 2002a). The majority of aerial beaver surveys in Québec were carried out below the 50th parallel, once again leaving the species' range edges relatively unexplored except for a limited number of studies carried out by Hydro Québec. In order to adequately describe beavers' geographical abundance pattern in Québec and accurately predict their response to c1imate change (i.e., objectives for Chapter III), we improved this core-sampling bias by carrying out three aerial beaver surveys in northern Québec (Chapter II). The beaver density estimates obtained from these aerial surveys thus complemented and formed an important addition to the surveys we collected throughout the province for the following manuscript.

68 CHAPTER III: SPATIAL VARIATION IN ABUNDANCE ACROSS THE BEAVERS' (Castor canadensis) NORTHEASTERN RANGE AND ITS IMPLICATIONS FOR ASSESSING THE IMPACTS OF CLIMATE CHANGE

ABSTRACT

Species-climate envelope models rely on presence/absence data to predict the impacts of climate change on species distributions and regional biodiversity. As a result we have little idea wh ether climate change modeling and monitoring efforts focused on the periphery of species' ranges are over-estimating or under-estimating the impacts of climate change across species' ranges. Using beavers (Castor canadensis) as a model species, the main objectives of this study were to (a) examine the spatial variation in abundance across the north-eastern portion of their range, (b) evaluate the extent to which climate and non-climate variables could explain this variation and (c) use a climate envelope model that includes spatial variation in abundance to predict beavers responses to projected climate change. Beaver abundance across Québec follows a roughly logistic pattern, with high but variable abundance across the southern portion of the province, a sharp decline in abundance at about 49~, then a long tail of low abundance extending as far as 58~. Several climatic and non-climatic variables were strong predictors of variation in beaver abundance, and 90% of the variation explained by non-climate variables could be accounted for by climate variables. We found general support for our hypothesis that the c1imate sensitivity of beaver abundance peaked in the mid-range. Combining our best c1imate envelope models of beaver abundance with CUITent GCM projections of future c1imate change, beavers are predicted to expand their range to encompass almost an of Québec by 2055 and to undergo the greatest increases in abundance near the middle of their range. Our central conclusion is that there is much to be gained by incorporating information about how abundance varies across species ranges when using spatial climate variability as a basis for predicting the temporal phenomenon of climate change.

69 INTRODUCTION

Climate is a major determinant of the distribution and abundance of species (Andrewartha and Birch, 1954; J effree and J effree, 1994; Lomolino et al., 2005). With global average surface temperatures having increased 0.6 ± O.2°C since the late 19th century and expected to rise from 1.4°C to 5.8°C over the next century (Houghton et al., 2001), there is a need to develop models that link species distributions to scenarios of climate change in order to anticipate the effects of a changing climate on plant and animal populations (Ludwig et al., 2001; Lawler et al., 2006). Climate envelope approaches are being used extensively to predict how climate change will alter species distributions (Box 1981, Sutherst and Maywald 1985, Austin 1992, Huntley et al. 1995, Carey 1996, Sykes et al. 1996, Iverson & Prasad 1998, Bakkenes et al. 2002, Berry et al. 2002; Erasmus et al. 2002, Pearson et al. 2002, Peterson et al. 2002, Thuiller 2003, Araujo et al. 2004, Skov and Svenning 2004, Thomas et al. 2004). Essentially, this method attempts to relate current species distributions with current climatic conditions, and using predicted future climate scenarios usually derived from general circulation models, predict the associated shift in species' geographic distributions (Davis et al., 1998; Lawler et al. 2006). Species-c1imate envelope models rely on presence/absence data to predict the impacts of climate change on species distributions and regional biodiversity (Erasmus et al., 2002; Huntley et al., 2004; Araujo et al., 2005). Although presence/absence range maps provide a useful indication of the broad regional occurrence of a given species, they exclude information about how local abundance varies across the range. As a result, c1imate-envelope approaches are capable of predicting future species range shifts but not future changes in abundance across the range. Although many c1imate envelope models assume a ramp of suitability or occurrence probability near range boundaries, the absence of data regarding how abundance actually varies between range boundaries, limits climate change predictions to the periphery of species' ranges. Similarly, monitoring of species responses to recent c1imate change is focusing on species range expansions and contractions, with little attention paid to changes in abundance between range boundaries (Root et al., 2003; Parmesan and Yohe, 2003; Martinez-Meyer et al., 2004; Araujo et al., 2005). Thus, at present, we have little idea whether c1imate change modeling and

70 monitoring efforts focused on the periphery of species' ranges are over-estimating or under-estimating the impacts of climate change across species' ranges. Our ability to provide more sensitive and/or representative assessment of climate change impacts thus rests on our understanding of geographical abundance patterns. Numerous ecological and evolutionary hypotheses are based on the assumption that the population density of a species is highest at the core of its geographical range, and declines gradually towards its range edges (Sagarin and Gaines, 2002a; e.g., Andrewartha and Birch, 1954; Whittaker, 1956; Rapoport, 1982; Hengeveld and Haeck, 1982; Brown, 1984; Brussard, 1984; Gaston, 1990; Brown et al., 1995). This pattern has recently been referred to as the "abundant centre hypothesis" (ACH; Sagarin and Gaines, 2002a, b; Sorte and Hofmann, 2004). Theoretical support for the ACH is often rooted in the idea that population densities are coupled with niche axes (Brown, 1984; Brown et al., 1995). According to Brown's (1984) influential conceptual model, if each species has multiple ecological requirements, which include a combination of spatially auto­ correlated biotic and abiotic variables, and its population density is highest where the combination of these environmental variables most closely corresponds to its requirements, then local abundance should follow a normal distribution across each species' range, with a graduaI dec1ine in density from the highest abundance in the core towards the range edges. Although Brown's (1984) model is frequently interpreted as synonymous with the ACH, the model's original and subsequent formulations (Brown, 1984; Brown et al., 1995), explicitly predicted departures from a normal distribution if 1) a sharp, discontinuous change in one critical environmental variable violates the assumption that the abundance and distribution of a species is determined by a combination of environmental variables or 2) environmental patchiness violates the assumption that environmental variables are spatially auto-correlated.

Sagarin and Gaines (2002a) compiled a comprehensive review of direct and indirect empirical evidence for the ACH. Of 22 direct empirical tests of the ACH, the majority of studies inadequately sampled the species' range (91 %), and only 39% supported the hypothesis. Alternative patterns of variation in abundance across species' ranges included no evident pattern (flat), abrupt changes in the core of the range (step), a graduaI increase in abundance from one periphery to the other (ramped), and a graduaI

71 decrease in abundance from the southern periphery to the core and then a graduaI increase in abundance from the core to the northern periphery (abundant edge; Sagarin and Gaines, 2002b). The strongest empirical support for the ACH came from studies which examined the abundance of bird species (Emlen et al., 1986; Telleria and Santos, 1993; Curnutt et al., 1996), in particular studies based on breeding and winter bird surveys conducted in North America (Brown, 1984; Root, 1988; Brown et al., 1995; Priee et al., 1995).

There remains a remarkable paucity of studies quantifying how abundanee varies across species' ranges. This is especially the case for mammals, for which only three studies have evaluated geographical abundanee patterns. Caughley et al. (1988), examined the abundance of western grey kangaroos (Macropus fuliginosus) and eastern grey kangaroos (Macropus giganteus) across Australia, Williams et al. (2003) examined the pattern in eastern cottontails (Sylvilagus floridanus) in Kansas (USA), and Rodriguez and Delibes, 2002) examined the internaI structure in the geographic range of the Iberian lynx (Lynx pardinus). The local abundance of both kangaroo species and the eastern cottontail rose progressively from the edge to the core of their range, whereas the Iberian lynx had multimodal centers of high abundance concentrated in the eastern half of the range from which density rapidly declined to zero (Rodriguez and Delibes, 2002). Henee, although data are sparse, and support for the ACH as a biogeographical rule is weak, there are theoretical and empirical reasons to expect that most species will be characterized by sorne pattern of systematic variation in local abundance across their range, and that this pattern will frequently include a tail of low abundance near the periphery of the range. An important consequence of this tail of low abundance is that the change in abundance per unit distance will tend to decrease as the range boundary is approached. Further, because most climatic variables are spatially autocorrelated, the change in abundance per unit change in climate (i.e., the species' local climate sensitivity) will also tend to decrease as the range boundary is approached. Consequently, for species with a tail of low abundance at the periphery of their range, climate-envelope models incorporating variation in abundance across the range should predict weak impacts of climate change at the periphery of the range, and stronger impacts where the tail ramps upwards to higher abundanee. Predictions of climate change impacts focused on

72 presence-absence data and range edges may therefore underestimate the magnitude of species responses to climate change in the interior of species ranges. In the present study, we incorporate spatial variation in relative abundance into a climate envelope model to test the hypothesis that predicted species responses to climate change will be larger near the core of the range rather than the edge of the range. We test this hypothesis using a distinctive data set involving 161 surveys of the regional abundance of North American beaver (Castor canadensis) covering 74% of their 1.1 million km2 range in Québec, Canada. Beavers are well-suited to examining abundance patterns and climate change impacts because their local abundance can be accurately assessed via aerial surveys of dams, lodges, and autumn food caches (Fuller and Markl, 1987; Hay, 1958; Bergerud and Miller, 1977; Novak, 1987; Banfield 1974), their general habitat requirements (deciduous and shrubby vegetation along stable waterways; Atwater, 1940; Slough and Sadleir, 1977; Allen, 1983; Howard and Larson, 1985; Novak, 1987) can be identified from landcover classifications, and they have been extensively surveyed (see Tables lA-LI and lA-1.2). Despite better than typical survey efforts, equivalent estimates of local abundance are not available across their entire range, which encompasses most of North America. Thus, although we are unable to evaluate whether North American beavers support or refute the ACH, the volume and extent of the data available across Québec provides a unique opportunity to examine how beaver abundance varies from the northeastern core of their range to the northeastern edge of their range, and how this variation might influence our assessment of c1imate change impacts. The main objectives of this study were to (a) examine the spatial variation in beaver abundance across the north-eastem portion of their range, (b) evaluate the extent to which climate and non-climate variables could explain this variation and (c) use a climate envelope model that includes spatial variation in abundance to predict the beaver' s responses to projected climate change. We predict that beaver abundance will decline in a logistic fashion from the core to the edge of their range and will be strongly correlated with climate variables that decline linearly across the same gradient. Thus, we hypothesize that the climate sensitivity of beaver abundance (change in abundance per unit change in climate) will be highest in the mid-range and lowest at the core and edge of the range.

73 METHODS

1. Study Areas

1.1. Collection ofreports Beaver density estimates were derived from reports obtained from the Direction de l'Aménagement de la Faune de l'Outaouais (Christian Pilon, Gatineau, Québec), the Direction de l'Aménagement de la Faune de Mauricie (Trois-Rivières, Québec), and the documentation centers at the Québec Ministry of Environment (Québec, QC), and Hydro Québec (Montréal, Québec). The keywords (i.e., "inventaire" (survey), "aérien" (aeria1) and "castor" (beaver)) used at the documentation centers, and the comprehensive review of beaver surveys obtained from Lafond et al. (2003), allowed us to 10cate 138 reports. The survey information gathered from these reports can be found in Table lA-LI. The results from our own aeria1 beaver surveys (see Chapter II) were a1so included in this collection.

1.2. Data selection We included on1y helicopter surveys in our ana1ysis because plane surveys can overlook an important number of beaver signs (correction factor up to 75%; Traversy, 1974; Traversy and Morasse, 1975; Banville, 1978; Banville, 1979a,b; Canac-Marquis, 1981; Payne, 1981; Houde, 1982; Potvin and Breton, 1982; Cloutier, 1983; Michaud, 1984). Ifa study region was surveyed in more than one year, and the survey coverage was within 20% of the maximum survey coverage, beaver densities were averaged, and the standard deviation as well as the standard error ca1cu1ated. Otherwise, only the beaver density estimated from the most extensive survey was included in the ana1ysis.

1.3. Aerial survey methodology The majority of study areas were surveyed in the autumn, after deciduous 1eaves had fallen and before freeze up, when beavers were comp1eting their food caches. The survey teams consisted of a pilot and a minimum of one observer/navigator in a he1icopter flying

74 at low altitude «100 m) and speed «140 kmlh). Both active and abandoned sites were recorded, with three active categories including (1) lodge with fresh food cache (2) fresh food cache without the presence of a lodge (3) other obvious signs of beaver presence (e.g. peeled sticks, well maintained dams, runways and burrows, beaver etc.) In general, areas were surveyed in one of two ways; total coverage or sub-sampling. Total coverage involved surveying all the waterways within the study area. A total of 76.9% of the surveys included in our study used this type of survey method. Sub-sampling involved di vi ding the study zone into equally sized quadrats and surveying all the waterways within a percentage of these quadrats. The remaining 23.1 % of the surveys included in

2 2 2 2 our report divided their study areas into 4 km , 9 km , 25 km or 50 km quadrats, of which 9 to 23% were randomly selected and surveyed. The beaver reserves were not surveyed as systematically as other regions, but densities were estimated by Lafond et al. (2003; appendix 3) based on incidental surveys conducted within and around the boundaries of the reserves. Whether the entire study area was surveyed, or a sub-sample of quadrats was surveyed, the total number of active beaver colonies observed was divided by the total 2 area surveyed, to yield the average number of beaver colonies/ km • Thus, an important determinant of the average number of beaver colonies per km2 might be the amount of shoreline in the survey area. We therefore evaluate measures of water and shoreline area in the survey regions as potential non-climatic predictors of beaver density (See section 3.1: Physical parameters).

1.4. Rendering data compatible for GIS For the recreational and protected areas (e.g., ZECS (Controlled Harvesting Zones), wildlife reserves, outfitting operations, national parks, and ecological reserves) we purchased the vector data (Limites des territoires récréatifs et protégés 1: 250 000) from the Photo cartothèque Québécoise. The divisions and free zones were digitized in ArcView 8.2 using digital maps imported from Lafond et al.'s (2003) report. The beaver reserves and the rest of the study areas included in our report were digitized in ArcView 8.2 by scanning figures taken from the corresponding studies referenced in Table lA-l.l. Using the table operations option in X Tools Pro 2.0.1, the area, perimeter, length,

75 hectares, and mid-point x-y coordinates were calculated for each of the study areas. In the end, 161 study zones (i.e., polygons) were included in our analysis.

2. Climate Variables

2.1. Point estimates (P.E.) for temperature minimums and maximums, precipitation totals, and agroclimatic indices We used Selected Modeled Climate Data for Point Locations created by The Landscape Analysis and Application Section (LAAS), Great Lakes Forestry Centre (GLFC), Canadian Forest Service (CFS), and Natural Resources Canada (NRCan; 2006). The originators used a software package of programs called ANUCLIM to obtain estimates of monthly mean climate variables, bioclimatic parameters, and indices relating to crop growth (Houlder et al., 2000). Using mathematical descriptions (i.e., climate surfaces) of the way a set of c1imate variables changes across a region, ANUCLIM estimated monthly mean values for minimum temperature, maximum temperature, precipitation, solar radiation, evaporation and others, at the center point of each study area polygon (Houlder et al., 2000). One of the main components of the package, BIOCLIM, used bioc1imatic surrogate parameters derived from the c1imate surfaces to approximate energy and water balances at given locations (e.g., mean temperature of warmest period, precipitation of driest quarter etc.; Table 3A-1.1; Nix, 1986). As a predictive system, BIOCLIM needed to use a digital elevation model (DEM) and SEEDGROW provided selected bioc1imatic variables (LAAS, GLFC, CFS, NRCan, 2006). To get a finer start-time and end-time granularity for the period and quarter based parameters, the c1imate surfaces describing the c1imate variables spatially for each month were normally interpolated into weekly values of the cumulative monthly totals during the year, using a cubic Bessel interpolation technique (De Boor, 1978; LAAS, GLFC, CFS, NRCan, 2006). The c1imate surfaces were generated by the ANUSPLIN package (Hutchinson, 1999), a separate package to ANUCLIM, from long-term monthly averages of the c1imate variables at fixed points within a region (Houlder et al., 2000). LAPPNT (one of the nine programs making up the ANUSPLIN package) calculated values and Bayesian standard error estimates, of partial thin plate smoothing spline surfaces at points supplied in our file (Hutchinson, 2004).

76 2.2. Temperature Normal~' For average temperatures we used the Canadian Gridded Climate Data (Hopkinson, 2001). These data, which included aIl monthly mean temperatures for the period 1961 to 1990, were extraeted from the Canadian Climate Archive and interpo1ated, using an inverse square distance weighting scheme, to a 50 km grid, true at 60 0 N on a polar stereographic secant projection aligned with 111°W (Hopkinson, 2001). Validation conducted by the author confirmed that the gridded estimates of monthly mean temperature and precipitation are representative of the station data, but one of the major limitations of the dataset is that there is no adjustment for elevation, such that gridded values in elevated terrain are unlikely to reflect the full orographie influence on these parameters (Hopkinson, 2001). Onee the gridded values were imported into ArcView 8.2, they were projected to NAD 1983 Québec Lambert, interpo1ated to a raster image using Inverse Distance Weighted in 3D Analyst, reclassified at intervals of 1.0°C and finally eonverted from a raster image to a feature using Spatial Analyst. The final product was intersected with aIl study area polygons.

3. Non-climate variables Potential non-climate predictors ofbeaver density were se1ected based on previous beaver habitat studies (Appendix 1A-2).

3.1. Physical parameters The length, area and perimeter of waterways (rivers, lakes, and wetlands) within each study area were estimated from 92 National Topographic Digital maps (1: 250 000) Buffers around all waterways, 200 m in width to inc1ude the maximum inland foraging distance of beavers (Bradt, 1938; Allen, 1983; Müller-Schwarze and Sun, 2003), were constructed using BufferWizard in ArcView 8.2. Slopes within the 200 m buffer zones were calculated from the same National Topographie Digital maps using ArcView 8.2 3D Analyst to create a TIN from the contour lines, and the SLOPE function in Surface Analysis to derive the slopes in degrees. The image was then re-classed using defined

77 intervals of 4° (0°-4.0°,4.1 °_8.0°, 8.1 °-12.0°, > 12.1 0). After downloading SLC version 2.2 (PQC 001, 002, 003, and 004) from the Canadian Soil Information (CanSIS) website, we calculated the dominant value for the kind of surface materials within each study area (KINDMAT field) by following the instructions given by CanSIS on how to map component table attributes in ArcView 3.2. The area ofbuilt up regions (populated zones where buildings are so close together that, for cartographic purpose, they are represented by a built-up area outline) and the length of roads used for vehicles as well as limited­ used roads (roads whose conditions vary depending on the season or to which public access is denied), were derived, once again, from the National Topographic Digital maps.

3.2. Land-cover parameters Land cover within the 200 m buffer zones was estimated for study areas north of the 52nd parallel from the Mosaïque du Québec (Photo cartothèque Québécoise, 1: 2 500 000 scale, 15 land cover classes (Table 3A-1.2); Québec Government/Ministère des Ressources Naturelles Faune et Parcs, 2003a) and for study areas south of the 52 nd parallel from the Spatiocarte Portrait du Québec Forestier Méridional (Direction des Inventaires Forestiers, 1: 1 250 000 scale, 22 land cover classes (Table 3A-1.3); Québec GovemmentiMinistère des Ressources Naturelles, 2003b).

3.3. Predator densities and average number ofharvested beaver pelts Wolf (Canis lupus) densities were calculated by dividing the number of wolves found in each administrative region, by the area of the administrative region (Lariviere et al., 1998; Jolicoeur and Heneault, 2002), whereas black bear (Ursus americanus) densities were calculated by dividing the number of black bears found within each trapping zone by the area of each trapping zone (Jolicoeur, 2005). The average number of beaver pelts harvested/km2 was calculated by dividing the average number of pelts harvested in regions referred to as "libre" (private lands and certain crown lands where trapping is carried out with no particular constraints) and "structuré" (crown lands subdivided into trapping territories where exclusive trapping rights are leased to certain trappers), by the area of these zones within each administrative region (P. Canac-Marquis, pers. comm., 2004).

78 4. Model Selection AU proportional data were arcsine transfonned before the analysis. The 63 variables included in our study were classed under the climate or non-climate category. Each climate and non-climate variable' s potential as a predictor of beaver density was initially evaluated by comparing the adjusted R2 values of 26 univariate linear regression models with a climate variable as the explanatory variable, 37 univariate linear regression models with a non-climate variable as the explanatory variable, and beaver density as the response variable in both cases. A partial regression analysis including the highest univariate climate predictor and two non-climate predictors ofbeaver density was used to estimate the independent explanatory power of each group. Once the statistical outliers were removed, and the univariate linear regression models with climate variables re­ calculated, 10 of the 15 variables with the highest adjusted R2 values were selected to model their relationship with beaver density. In order to deal with the heteroscedasticity of the data and the core-sampling bias (i.e., higher number of surveys perfonned near the core vs. the edge of the range), we constructed a predictive model ofbeaver density based on the best variables by dividing each variable into 8 equal categories and calculating the th th th 10 , 50 and 90 percentiles of beaver density for each category. We described the t th th relationship between each variable and the 10 \ 50 and 90 percentiles ofbeaver density for each category based on three functions;

Linear Density = a + b *(z)

nd 2 Order Polynomial Density = a + b (z) - b/(2*c) * (zi

Nonnal y...J2II

Where z = best predictor(s). Using SYSTAT version 10.2 (SYSTAT, 2002), we estimated the free pararneters for the 50th percentile by least-square function for non-linear regression. The value of c resulting from this procedure was used in the calculation of the distribution models for the 10th and 90th percentiles. We compared distribution models 2 using adjusted coefficients of detennination (mean-corrected R ).

79 5. Climate sensitivity, climate change, and density change th th The best models for the 1oth, 50 and 90 percentiles were used to predict present and future beaver densities in Québec. Gridded climate data for 1961-90 (present) and scenarios for 2040-69 periods (future) were used (Bootsma and McKenney, 2005). This climate data included monthly maximum and minimum values for temperature and precipitation, as weIl as growing degree-days and potential evapotranspiration; average annual temperature was calculated from monthly averages of minimum and maximum temperatures. The model and scenario used to predict future climate was described by Flato et al. (2000) and Boer et al. (2000 a, b: CGCMI GAl). To evaluate the generality of this model and emission scenario we compared it with two other models, each with two emission scenarios (i.e., CGCM2 A2, B2 (Flato and Boer, 2001) and HADCM3 A2, B2 (Gordon et al., 2000; Pope et al. 2000)).

To estimate the predicted change III beaver density with a 1°C increase in temperature (climate sensitivity), the predicted change in temperature from the present until 2055 (climate change) and the predicted change in beaver density from the present until 2055 (density change) across the province of Québec, longitudes and latitudes with their respective estimates were imported into ArcView 8.2, interpolated to a raster image using Splining in 3D Analyst, reclassified to 8 classes using the Manual function in 3D Analyst, and finaIly converted from a raster image to a feature using Spatial Analyst. The final products were then intersected with a vector file of Québec.

RESULTS

The highest beaver densities in Québec are found in the southwestem portion of the province (black and dark grey zones; Fig. 3.1). In other southem portions of the province, beaver densities are variable but generally de cline from west to east. Moving northward,

beaver densities decline sharply around 49~ (Fig. 3.2a), then form a long tail of low abundance spanning more than 9° of latitude The climate and non-climate variables evaluated as potential predictors of beaver density are found in Table 3.1, and the adjusted R 2 values and standard coefficients

80 obtained when these variables were regressed independently against beaver density are found in Table 3.2. The top climate predictors included certain agro-climatic indices (i.e., potential evapotranspiration and growing degree days), and temperature variables (i.e., maximum, minimum and average seasonal temperatures). The top non-climate predictors included land cover variables (i.e., coniferous, deciduous and mixed forests; shrub, lichen, moss and rock coverage), as well as latitude, black bear (U americanus) densities, and length of roads. Partial regression revealed that each group (i.e., climate and non­ climate) explained about the same proportion of the variability in beaver density, and that 90% of the variation explained by non-climate variables (0.45) could be accounted for by climate variables (Table 3.3) The top climate and non-climate predictors remained the same when the statistical outliers were removed, with the top 15 climate variables explaining between 19% (average winter temperature) and 49% (growing degree days) of the variation in beaver th th density (Table 3.4). When 10 ofthese climate variables were used to predict the 10 , 50 , and 90th percentiles of beaver densities, a normal model provided a beUer fit (based on mean-corrected R2 values) than a linear or 2nd order polynomial model in 25 of 30 instances (Table 3.5). On the whole, the normal model outperformed the other models a minimum of 80% of the time for all the three percentiles. t th Overall the best three predictors of the 1Oth, 50 \ and 90 percentiles collectively and the 50th percentile in particular, were average maximum September-October­ November temperature, average maximum March-April-May temperature, and average annual temperature (Table 3.5). Each of these three climate variables assume a normal relationship with percentiles of beaver density, with the slope of the curve peaking at intermediate climate values corresponding to the approximate mid-point of beavers' distribution in Québec, then flattening to varying extents at warmer climate values corresponding with southem Québec (Fig. 3.2b,c,d). For the top three climate variables, the climate sensitivity of beaver density (predicted change in density per unit CC) change in climate) peaks at intermediate latitudes and declines northward and southward of these latitudes (Fig. 3.3). However, GCM projections of the change in these climate variables expected to occur between now ,- and 2055, peaks at high latitudes and generally diminishes southward (Fig. 3.3). The

81 expected change in all three climate variables is comparable to other GCM projections (Table 3A-l.4). Combining c1imate sensitivity of beaver density and projections of climate change, the expected future change in beaver density differs somewhat between the various models, but the largest absolute changes in density (future density - present density) are consistently predicted to occur in the southern half of Québec (Fig. 3.3). Nevertheless, despite the small density changes expected to occur at the periphery of the range, the range boundary is predicted to expand drastically by 2055 to the farthest reaches of northern Québec (Fig. 3.3). Predicted range boundary extension are similar, whether projected climate values are applied to normal equations that relate CUITent beaver densities and climate conditions or climatic isotherms that delineate areas cUITently occupied and unoccupied by beavers. Thus, beavers are presently restricted to regions with >-5°C average annual temperature, >-5.5°C maximum spring temperatures, and> 1.5°C maximum autumn temperatures, and these conditions are expected to occur in all areas of Québec by 2055, except for a very small region along the northern portion of the Québec-Labrador border.

DISCUSSION

Beaver abundance across Québec follows a roughly logistic pattern, with high but variable abundance across the southern portion of the province, a sharp de cline in abundance at about 49°N, then a long tail of low abundance extending as far as 58~. Although these results do not directly support the ACH because only a portion of the range was examined (Sagarin and Gaines, 2002a), there is a strong tendency for mean abundance to decrease from the core of the range to the edge of the range in a roughly normal fashion (Brown, 1984; Brown et al., 1996). Furthermore, when local abundance is plotted as a function of distance from the center (i.e., latitude), all points fall within a triangular constraint space with the maximum abundance, mean abundance and variation in abundance being highest near the core of the range, and remaining uniformly low near the range edge (Fig. 3.2a; Brown et al., 1995; Enquist et al., 1995). Similar to Brown et al.'s (1995) findings, there are also a few beaver "hot spots" (e.g., Reservoir Pikauba,

82 2 1.36 beaver colonies/km ) that are close to the core of the range, where environmental conditions c1early satisfy the niche requirements for high beaver densities to be attained. Several climatic and non-climatic variables were strong predictors of variation in beaver abundance, and 90% of the variation explained by non-climate variables could be accounted for by climate variables. The range limits of many plants and animaIs appear to coincide with climatic isotherms (Root, 1988) and to shift spatially in response to temporal climate change (Root et al., 2003; Parmesan and Yohe, 2003; Martinez-Meyer et al., 2004), with climatic predictors of range distributions often outperforming non­ climate predictors (Thuiller et al., 2004), regardless of the trophic level under consideration (Huntley et al., 2004). We selected climate variables for modeling purposes because they were slightly better predictors of beaver density and are more commonly and consistently projected in climate change scenarios. However, we could have explained nearly as much variation in beaver density with several land-coyer variables and the variation explained would have overlapped extensively with that explained by climate variables. In other words, the independent effect of climate on beaver density (i.e., variation in climate not correlated with variation in non-climate variables) was very weak. These results emphasize that climate variables can serve as an effective proxy for the suite of climatic and non-climatic factors that determine animal abundance, but the validity of using climate proxies to project future changes in animal abundance hinges critically on the persistence of current correlations between climate, habitat, and other environmental features (Pearson and Dawson, 2003; Lawler et al. 2006). We found general support for our hypothesis that the climate sensitivity ofbeaver abundance (change in abundance per unit change in climate) peaked in the mid-range. The most pronounced change in the density of beavers across Québec occurs in the vicinity of the O°C T avg ann isotherm. Given the importance of ice coyer and deciduous vegetation to beavers, it might seem logical that beaver densities decline precipitously in locations where water is frozen and vegetation does not grow for a majority of the year.

However, the O°C T avg ann isotherm does not impose a range limit beyond which beavers

cannot persist {beavers are present in regions of Québec where T avg ann is -SoC, growing season is only 100 days long (see Chapter II), and lakes are free of ice for fewer than 131 days per year (Koksoak River; Lenormand et al., 2002)}, but instead a high density limit

83 beyond which beavers invariably occur at low abundance. Thus, the more pertinent question for locations north of the ODC T avg ann isotherrn is what environmental factors allow beavers to persist but prohibit them from attaining high densities in these habitats. This is a more complicated question that may relate to variation in temporal occupancy and population dynamics across the range. Although identifying the mechanisms that generate the observed pattern ofbeaver density across Québec is not the objective of this study, it is noteworthy that the O°C Tavg ann isotherrn roughly corresponds to the northemmost extent of mixed wood forest in Québec {Mosaïque du Québec (Québec Govemment/Ministère des Ressources Naturelles Faune et Parcs, 2003a)}. Low beaver densities north of the O°C T avg ann isotherrn in Québec have been attributed to the scarcity of deciduous vegetation and high density of less desirable coniferous species (Canac­ Marquis, 1980; Desrosiers, 1982; Pelletier and Lizotte, 1982; Michaud, 1984; Brunelle and Bider, 1987; Potvin and Breton, 1992; Potvin and Breton, 1997; Tecsult Inc., 2000; Alliance Environnement Inc., 2002, 2004). When beaver density is plotted as a function of the prevalence of coniferous forests within our study areas, it can also be seen that the lower the beaver density, the higher the proportion of land covered by coniferous forests. As such, the northern beavers' heavy reliance on shoreline shrubs (willows, alders, dwarf birch) and scattered aspen stands present within coniferous forests may be sufficient to support low densities of beavers, but the low nutritional quality and long regeneration time ofthese bore al habitats may prec1ude beavers from reaching high densities. Combining our best c1imate envelope models of beaver abundance with current GCM projections of future c1imate change, beavers are predicted to exp and their range to encompass almost all of Québec by 2055 and to undergo the greatest increases in abundance near the middle of their range. It is important to acknowledge that although we use a species-c1imate envelope approach in this study, we are not suggesting that this is an adequate basis for predicting the future fine-scale distribution of beavers across Québec. We are aware that there are numerous limitations in using a correlative approach, inc1uding the fact that we fail to account for biotic interactions, evolutionary change, or dispersal (Pearson and Dawson, 2003) and that the present relationship between a species' distribution and climate, may not remain the sarne in the future (Lawler et al. 2006). Consequently, in using this approach we assume that the relationship between

84 climate and beaver abundance distribution reflects sorne direct or indirect form of causality, that this causality will remain the same in the face of climate change, and that beaver responses and climate change will occur at a similar pace. Based on their known habitat requirements (Atwater, 1940; Slough and Sadleir, 1977; Allen, 1983; Howard and Larson, 1985; Novak, 1987) we therefore suspect that beavers will only exp and their range into the northem reaches of the province if there is a concomitant shift in their preferred food species (shrubby and arborescent deciduous vegetation), they can disperse at a fast enough rate to keep up with this shi ft, habitat fragmentation does not affect their ability to disperse, stable secure waterways are accessible and available, and there are an adequate number of ice-free days to gather enough food to last them the winter months. Similarly, the largest changes in density are expected to occur in the southem half of Québec, only if other forms of environrnental change (e.g., conversion of forests into agriculturallands, urban sprawl, changes in fire frequency) do not override the effects of climate change in this region. Once again, although the driving mechanisms behind the expected mid-range increase in density have not been examined in this study, it is likely

that the factors responsible for the current decline in density near the O°C T avg ann isotherm, will be involved. As such, since we believe that the drop in density at the O°C

T avg ann isotherm may be re1ated to the scarcity of deciduous vegetation, increased temperatures willlikely increase the percentage of deciduous vegetation at these latitudes, and consequently allow beaver densities to experience a significant increase in abundance at mid-latitudes. Our central conclusion is that there is much to be gained by incorporating information about how abundance varies across species ranges when using spatial climate variability as a basis for predicting the temporal phenomenon of climate change. Species­ climate envelope models re1y on presence/absence data which can he1p predict expected range shifts in the face of climate change, but cannot detect where the largest changes in density will occur. The ensuing emphasis on monitoring range boundaries to detect expansions or contractions has led to the discovery of sensitive bioindicators of the impacts of c1imate change, and has improved our understanding of the ecological niche, threshold responses to environrnental change, the nature of adaptation, speciation and co­ evolution, species interactions, and invasion dynarnics (Parmesan and Yohe, 2003; HoIt

85 and Keitt, 2005; Perry et al., 2005; Wilson et al., 2005). However, the CUITent importance placed on monitoring range edges may cause the largest impacts of climate change to go undetected if normal variation in abundance and linear variation in climate renders relationships between climate and density weakest at the periphery of the range. In this way, even though certain species may currently be experiencing major changes in abundance and associated ecosystem impacts near the core of their range, they are likely being overlooked because changes in relative abundance are less frequently monitored by researchers and less easily perceived by the general public. Achieving good measures of relative abundance across adequate spatial scales is difficult, in particular for species that are widely distributed, highly mobile, and difficult to observe directly. Population ecologists have overcome these difficulties to generate excellent abundance estimates for many populations, but due to research priorities and constraints, have tended to conduct these estimates year-after-year in one or a very few localities. To adequately answer the questions posed by climate change, we need to add a spatial component to population-c1imate research that encompasses the range of c1imate variability projected by GCM's. Given the current paucity of data on how the abundance of several species varies with spatial c1imate variability, progress in this important area of research requires capitalizing on currently available coarse indices of abundance as well as generation ofnew and better data on variation in species' abundance across space.

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96 i.! <~~~ i'(:;:- ~> ~-.> Ji

1~ ::t: 1', !S ~~ 0 t~ S;

Legend Average # beaver colonies! knY "000 .. 0.01-027 .. 0.28-054 ... 0.55·0.82 0$::'-109 CJ 110- 136 Uninhabited by beaver ~ Inhablted by beaver but nct surveyed Figure 3.1: Local abundance of North American beavers (C canadensis) across the province of Québec. Densities were derived from 161 aerial beaver surveys conducted between the years 1976 and 2004. The average number of beaver colonies per km2 for each study site was calculated by locating active colonies from the air (helicopter) and dividing this number by the total area of the study region or quadrats surveyed.

97 (a) • (b) '$ ':: ~ "::t » :':{ • • • ::" • • ;; ~ ~< ,n D "> ::> ~, ':; .:)(-. ..:>, ? ,: ~

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~·····,,,..w,,.·~·,,,,,,,·'t'·' .... ~·~·».'''''''rM.,'''.,..,W• .,.v.,..='t:''V.'w."'''~.WNNM~m'".Nf'~".=~ .... r" ••, •. "._.-r, ~~'""' • -"'M'M"~'''' __~!"''''~ .e. .~ .: (: ; 4 f. 2 4 1(~ ~I:

Figure 3.2: Variation in local beaver density as a function of (a) latitude (decimal degrees) (b) average maximum March-April-May temperature CC) (c) average annual temperature CC) and (d) average maximum September-October-November temperature (OC) across Québec. The caption in Figure 3.1 explains how average beaver densities for each study site were calculated. Point estimates for temperatures were calculated based on the middle point of each study region. Lines represent the normal equations that best described the relationship between the temperature categories (values for aIl three climate variables were divided into 8 equal categories each), and the lOth (dashed line), 50th (solid line) and 90th (dashed lines) percentiles ofbeaver density within each category.

98 1 CUmate sensitivity. 2)c:;limate c!iai1ge

a) TMAXMAM (a.1)

b) TAVGANN (b.1)

c)TMAXSON

Legend Legend Legend Uninhabited r.; beaver- present A temp.C"CI from present-2055 Uninhabited by beaver- 2055 A be.ver densityM"C A tempo 0.001 - 0.74 A beaver densityfrom present-2055 -0.110--0.001 0.75 - 1.47 -0.239 - -0.001 0000- 0.016 1.48- 2.20 0000·0037 0.017 - 0.033 III 2.21- 2.94 .~ 0038-0.074 • 0.034 - 0.049 .2.95-3.67 0.D75- 0.110 _ 0.050 - 0.065 .3.68-4.41 .0.111-0.147 _ 0.066 - 0.082 _4.42-515 _ 0.148-0.184 .5.16-5.89 Figure 3.3: Predicted changes in (1) beaver density, with a 1°C increase in temperature (climate sensitivity), (2) temperature from present to the year 2055 (climate change) and (3) the number of beaver colonies per km2 from present to the year 2055 (density change) across Québec, based on three climatic variables with the best-fit models (i.e., (a) average maximum March-April-May temperature, (b) average annual temperature and (c) average maximum September-October-November temperature). To calculate predicted changes in temperatures, we used interpolated gridded data for present temperatures (1961-1990; Bootsma and McKenney 2005), and projected gridded data obtained from the CGCMI GAI model (Flato et al. 2000, Boer et al. 2000a,b) for future temperatures (2040-2069; Bootsma and McKenney 2005). To calculate predicted changes in beaver densities, this same temperature data was used in conjunction with the best predictive models for the 50th percentile of beaver densities. The black and dark grey areas indicate where the largest changes will occur, with the most pronounced changes in beaver density expected to occur in the lower half of Québec, and in temperature expected to transpire in the northern half. The white areas indicate regions not inhabited by beavers at present (column 1; climate sensitivity) and in the future (column 3; density change). These areas were delineated using _5°C average annual temperature because it coincided with the present-day range limit for beavers.

99 Table 3.1: Climate and non-climate variables evaluated as potential predictors of beaver density across Québec.

Non-Climate Definition Non-Climate Oefinition Clîmate Definition Climale Variable Definition Variable Vanable Variable IntPE1 Irterpolaled poterttal PETminmam Poirt esttmate for Average Mlnlffium Bilrivers Totat area oflhe 200 m buffer around ail Totdec Totalarea of land covered bydeclduousforest evapotransp!ration 1 (accl.ffIulated Marctt-Aprtl-May Temperatl.l'e III oC ( nvef'ii ln the study area. <ÎI'Ilded by the ltncludtng dectduous regrowth) tn Ihe 200 m buffer o~ same peood as GDD) Hl mm total area of the study area (8) zone around ail rive,"", lakes and wellands in the usmg Spltning Technique (1) Slucty area, dlvided by the total area of the 200 m PEGDD POint estimate for Grcrwtng Degree PETminjja POirt estlmate for Average Minimum buffer around aH rlvers,iakes and wetiands tn the Days abOV1! base ternperalIJe for the June-July-August Temperatll"e tn·C study area (8) ertlre grcrwtng Seilison (6) Bl.llakes Total area ofthe 200 m buffer around ait Total area of land covered by mixed forest (tncludng Totmix PETavgann Peu" estlmale for Average Annual PETminson Part esbmide for A\IeI'iIge Mtnlmum lakes in the stlKfy arN. dlVlded bythe mixed regrowth. mixed dominated by young Temperalll"e tn degrees celsius (6) Seplember-Ocl.obef-Nowmber area of the study area (8) total conîferous and mixed domtnaled by young Temp~!Se tn OC (6) deciduous) in the 200 m buffer zone around iiiI waterways in the study area, divided by the total Ta>.ogdjf 111erpolaled Awrage Oecember- PETIIaO Part estimate of the Meen Ot .... nili are.; of the 200 m buffer around ail waterways 111 the January-FebfuaryTernperatll"eln Range divided by the Annulli sludy area (a) degrees ceiStUS using Inverse Temperatl.l'e Range (6) Distance Weigl1ed Technique (3) B\lwetland Total area of the 200 m buffer around Totmossroc Total area of land covered by moss and rock in Ihe weUands ln Ihe study area. divided by the 200 m buffer zone afound ail waterways ln the study T8\7118f11 Interpotated AVEnge March-Apnl- PETS.. Part esttmale of the standard QeoI.Iiall total area ofthe study af'ea (8) area, divided by the lot.11 araa. of the 200 m buffer May Temperat .... e in GC using In\lerse of the mor1h1y man temparatlllM around ail waterways in the stu';" area (8) Distance Weig:l1ed Technique (3) expressed as a % of the man oflho! temperatll"es,coefftciert 01 variation ( Retrivshor Totallan of shoreline 41long nvers in the Totrock Total area of land covered by rocks in lhe 200 m of V), 6) stuÔf area. dvided by Ihe total km of buffer zone around ail waterways in the study area, shorellne (rh/ers, lakes. wetlan(5) in the dlvided by the total area of the 200 m butTer around TaYlija ! .... erpolated Aw:rage June-JuIy- PEAN1Prec: POh1 esllmale fur the sum of ail tne study area (a) ail waterways tn the study arei! (8) August Temperalll"e tn oC U5tng morthly preaplation estlmales in mn' Inverse Distance Welgtted (S) Retakeshor Total km of sha-ellne alonglakes ln Ihe Totstnblich Total area of land covered by 5htubs and lIChens ln Technique (3) study are3. divided by Ihe total km of the 200 m buffer zone around ail waterways tn the shoreline (rivers, lakes, wetlands) in the study area, divided by the total area of the 200 m Tavgson Irterpolated Average September- PElrlprecdjf Poirt estimale for Average Oecembel stud')' areit(B) bu1'rer around aU waterways in the study area (8) October-No~ber T ernperature ln January-February Precipitation in mIT oc IJStng I"",",se Distance Wegl1ed (S) Refwetshor Total km of shorehne along wellands in TCltstr"tbmoss Tdal ars of land covered bystlf'ubs and mosses in Technique (3) the stud)' area, divlded by lhe total km of the 200 m bUffer zone ilround aU watefWays ln Ihe 5horeline (rivers, lakes, wetlands) tn the study area, divlded bo; the total area of the 200 m PETmu4f Poirt estimale for Average Maltimum PE~Kmam Point estimai. for Average March-Apt stu6t' area(8) buffer around aH waterways in the stucty are; (8) Oecember-January-Februrary May Preapitation ln mm (6) Temperatll"e in oC (6) ".eS100 Allakes v.ithin the study polygon Imt Toturban Total area of land occupied by populated areas are less lhan or equal 10 1 km' (8) wiltlln the 200 m buffet zone around ail waterways in PETmaxma POInt estimale for Average Maximum PEaygrecjja Poirt estlmate for AV'efiI~ June-July. the study area, dlvided bythe lotal area of the 200 m Marctt-Aprli-May Temperalll"e in OC August Preapitation ln mm (6) buffer around aO waterways in the study area (8) (6) PETmlDCjja Poirt estimate for Average Maximum PElI'I4'I'ecson POInt esbm.e for A ...... ge $eptembe IIakes >100 AI I*es within Ihe study polygon lhal To" Taal areil of land having a stope ofless lhan or June-July-August Temperalll"e in oC Oclob«·NOYember Precipitation in m aregreiterthan1krrf(8) equal to 2 de!Tees wiltin the 200 m buffer zone ,a) ,a) around ail waterways inthe slu~ area, divided by the total area of the 200 m buffer around al PETrnDXson Part estimale for A\lWilge Maximt.m PEPreSeas Poin estimale of the standard devillii wateM'olYS in the study area (8) September-Odoba'-No_mber dit.. morthly mean l.rnp...... Temperalll"e in "C (6) eitpl'essed as a % of the meen of thot Totawi Total area of land use

Mineralsol Total arel of surface matenal made up 1ot31 Total area of t.nd having a sIope of !;Jeaterlhan 30 preciominantly of minerai particles degees wilhin the 200 m buffer zone around ail eorQinlng <30% organlc matler as watel'Nays ln the study area, divided bylhetotal measll"ed by weîgtt, divided lotal area of area of the 200 m btAfer arOU(ld al wil.erw;rys in the .udy ",,~gon (2) study area (8) Organicsoil Total area of strface material COltaining Totbllf2 The area within the sludy polygon oceupied by a >30% O'ganie matter as measured by slope less than CI equal to 2", dvtdedbythe total weight, livide<:! by total area of study area of the sludy pàygon (8) polygon(2) Softrock Total area of surfac:e matenal made up of Totbtlfi0 The area.....nthin the study pofygon occupled by a rockthal can be dugwilh a shovel O.e. slope Jess than or equal to 10', dlW:led by the total undîfferentialed shales, upper aTH of the study pàygon (8) Cretaceous and Tertiary matertals), divided by Iml ara of study potygon (2) H ..drocad Total area of surface material composed Tolbl.lf30 The area wilhirllhe sludy polygon occupied by a of!7l1ni1e, dMded by the total area dthe slope less than or equal to 30", divided by the tatal ."",,,,,1ygoo(2) ilrea of the study polygon (8) Hardrocbas Total area of surface matenal composed Totbllf31 The area within the study polygon occupied by a of lmeslone, civided bythe total area of slope greater than 30·, divlded by the total area of the study polygon (2) the study polygon (8)

H ..drock Total area of surface rnatenal compose

Blank TOial area that is undetermined, divlded aowo .... NOOIbef' of Black Bear"$ per km squared per trap~ng ~ the lotal .-ea of the stud)' po~gon (2) zone (5) AvgHarvest Average HaIV•• al Beaver Pels for Limiledroads km of road whon conditiom v-.y depending on the Structured or Free ZOfles in the HlSon or to whidl public access is denied, divided Acmnistrâhlll Regions ri Ouebec (9) by total area cA siuet,. area po1ygon (8) dvided by Ma of Structlr'ed 0( Free zone'i'llthin the Aâninistrative Regions of QuO'" BulIIup Iem~ of populated zones where buildings .oa'" lem of roads for the movemert of mator vehicles, are so close together tnat, for civided bytotal area ofstudyare;1 potygon (8) cartogrllphlc purpose, they .re repreaenl:ed by. boit-up area oLlline, dvidedbythe total area of the study area ""lygon(B) (1) Bootsma A., and McKenney D, 2005. GrIdded Interpolated cUmate scenarIOS for 1961-90 baseline and 2010-39 and 2040-69 perIods (500 aro.second grid) Cl Agric.ure and ~Food Canada (A.AFC) and Nattnl ResolrCfl Canada (NRCan) (avaiable at· tlttp./M'Yow .elcs. uvle.calscen.loeIlndeJl. c:gI?Other _Data) (2) Centre br Land and Biologleal Resoll"ces Research. 1996. Soli Landscapes ofClInada, v.2.2, Researeh BnuK:h, Ag-lculure and A.gri-Food Canada. Ottawa

(3) Hoplanson R. 2001. Griclded observed climatologies for 1961-1990 (50 km gricI) CI Environmert Canada (available at: http:/MwN.cics.lNle.calscen~lndex.cgi?Olher_Data ) (4) Jolicoew, H., et Henuul, N, 2002. Repartlion g60graphique du loup el du coyote au sud du 52e paralèle et estimation de la population de Io~s au Quêbec.SQÇi~é de Il 6u.,e et des parcs du QIJ4!bec:.56 p.

(5) JoIicoeLf, H. 2005. Oireçtkm dudéveloppemed. de la faune Secteur Faune Quebec, Ministà'e des Ressources natlKetles el: de la Faune, Plan de gestion de rOIn mirau auMlec

(8) LandscapeAnalysilandApplcation Section (LAAS,Great lakes Forestry Centre (GLFC), Canadan Forest Service (CFS), Nlf:ural ResolA'Ces Canada (NRCan).2008.Selecteci Modeied Cimate Data for Poirt loc:ations..Saul Ste. Marie LAAS (7) Lariviere, S ,H. Jokoearet M, Cret., 1998. DensMes et tendance dëmograplllque du loup (Canis lupus) dans les rtservesfaunlques du Qtébec erm-e 1983 et 1997. Quibec, Ministère de rEl1\/lronnemert de la Faune, Direction de la faune et des ha~ats. 33 p. (8) Natural Resources Canada. 2008. c.ntre br TopoD"8Phic Irmmation: Glossary for NTBD data 1:250 000, ttttp:/MwN.cls.mcan.gc.calci/serMt/CJTlsleJo-01&page)d ..1-002-001.ttmllb (9) Pierre Canac-Marquis Coordomalell" PléQeage Faune el Parcs Ou6bec

100 Table 3.2: Results from univariate regression between c1imate variables and non-c1imate variables against beaver density; statistical outliers not removed.

Climale Variables Mean sidA sibB Non-climale Variables Mean sidA sibB correcled corrected R' R' InlPE1 0.3946 -0.2693 0.0013 Totdec 0.3594 0.1317 0.5423 PETrnaxüa 0.3790 -0.8315 0.0538 Lalitude 0.3100 2.3298 -0.0420 PETrnaxmam 0.3748 0.0683 0.0375 Totconf 0.2990 0.4436 -0.3873 PEGDD 0.3596 -0.1755 0.0004 BearDens 0.2640 0.0006 0.7414 PETrnaxson 0.3337 -0.1035 0.0514 Tolmix 0.2099 0.0983 0.2426 Tavgjja 0.3250 -0.5625 0.0549 Totshrublich+Tolshrubmoss 0.2021 0.3084 -0.4539 PETavgann 0.2997 0.2486 0.0437 AvgHarvest 0.1543 0.1523 0.5147 Tavgrnam 0.2990 0.2656 0.0345 Limit roads 0.1512 0.1328 0.3483 PETlso 0.2876 -1.1867 6.0435 Totmossroc 0.1326 0.2921 -1.4248 PETminjja 0.2843 -0.2759 0.0622 Roads 0.1185 0.1696 0.3615 Tavgson 0.2685 0.0329 0.0580 Longitude 0.0963 -1.0368 -0.0179 PETminrnam 0.2641 0.4802 0.0333 BufRivers 0.0592 0.0406 0.4802 PETrnaxdjf 0.2594 0.5589 0.0304 WolfDens 0.0459 0.1591 1.5717 PETminson 0.2527 0.3530 0.0676 Totrock 0.0270 0.2783 -0.4501 PETSeas 0.1706 0.4586 -0.0076 Tot3O 0.0226 -1.2218 0.9660 Tavgdjf 0.1654 0.6255 0.0249 Tol31 0.0226 0.2955 -0.9660 PETmindjf 0.1483 0.7429 0.0227 Relakeshor 0.0208 0.3604 -0.1167 PEavprecmam 0.1071 -0.0068 0.0038 TotbuffJO 0.0198 -1.2455 0.9799 PEPreSeas 0.0713 0.9815 -0.1618 Totbuff31 0.0198 0.2937 -0.9799 PEAnnPrec 0.0618 -0.0438 0.0003 Buflakes 0.0163 0.3380 -0.2019 PEavprecdjf 0.0596 0.0897 0.0026 #lakes >100 0.0148 0.2710 -0.0001 PEavgrecüa 0.0537 -0.1349 0.0038 Bufwetland 0.0145 0.2409 0.3169 PEavprecson 0.0027 0.1677 0.0010 Totbuff10 0.0127 0.0039 0.1872 #lakeS100 0.0124 0.2705 0.0000 Relwetshor 0.0088 0.2470 0.1116 Hardrock 0.0079 0.2672 -0.9594 Softrock 0.0073 0.2670 -0.2051 Relrivshor 0.0070 0.2156 0.0714 Totbuff2 0.0036 0.1876 0.0653 Tot10 0.0028 0.1604 0.0762 Hardrocbas 0.0020 0.2630 0.0638 Organicsoil 0.0018 0.2684 -0.0548 Builtup 0.0013 0.2670 -0.3954 Hardrocaci 0.0006 0.2616 0.0146 Totagri 0.0005 0.2633 0.0351 Mineralsoil 0.0003 0.2765 -0.0092 Tot2 0.0000 0.2705 -0.0051 Taturban 0.0000 0.2647 0.0113

101 Table 3.3: Partial regression analysis estimating the variation in beaver density explained by c1imate and non-c1imate variables. Controlling for c1imate was potential evapotranspiration, and for non-c1imate were longitude and the proportion of land covered by coniferous forests, moss and rocks. Each group explains about the same proportion of the variability in beaver density, with 90% of the variation explained by non-c1imate variables, being accounted for by c1imate variables.

Predictors Proportion of Variation Explained

Climate 0.05 Non-cli mate 0.052

Cli mate and Non-cli mate 0.42 Unexplained Variation 0.48

102 Table 3.4: Results from univariate regresslOn between climate variables and beaver density, with statistical outliers removed.

Adjusted W Variable

0.4919 PEGDD * 0.4699 IntPE1 * 0.4390 PETmaxjja *

0.4329 PETmaxmam * 0.3893 PETmaxson * 0.3891 Tavgjja * 0.3472 Tavgmam 0.3467 PETlso * 0.3464 PETavgann * 0.3347 Tavgson * 0.3244 PETminjja 0.3028 PETmaxdjf

0.3024 PETminmam 0.2902 PETminson * 0.1962 Tavgdjf 0.1924 PETSeas

0.1715 PETmindjf 0.1067 PEavpreemam 0.0834 PEPreSeas 0.0568 PEavpreedjf 0.0536 P EAnn Pree 0.0395 PEavgrecjja 0.0001 PEavprecson

* Selected for modelling

-

103 2 t th th Table 3.5: Adjusted R values explaining the variation in the 10 \ 50 and 90 percentile of beaver densities using 10 of the top 15 univariate climate predictors and three different models (nonnal, 2nd order polynomial and linear). The shaded values indicate the highest R2 values, with the nonnal model perfonning best in 25 out of30 cases (83.3%).

Variables Percentile Normal 2nd order Polynomial Linear Mean Corrected R' TMAX SON' Densily loth percentile 0.&24 : 0.597 0.604 Density 50th percentile 0.995 ! 0.990 0.976 Densily 90th percentile 0.$36 1 0.917 0.912 T MAX MAM* Densily loth percentile 0.866 i 0.911 0.752 Densily 50th percentile 0.993 0.992 0.917 Densily 90th percentile 0.971 0.969 0.897 TAVGANN' Densily loth percentile 0.454 , 0.326 0.424 Densily 50th percentile 0.989 0.891 1 0.967 Density 90th percentile U22 0.907 0.918 PET Densily loth percentile 0.765 i 0.761 0.778 Densily 50th percentile 0.975 ! 0.973 0.952

Densily 90th percentile 0.866 1 0.950 0.917 TISOTHERM Densily loth percentile o.9tO 1 0.760 0.759 Densily 50th percentile 0.920 0.778 0.770 Densily 90th percentile 0.663 0.649 0.856 1 TMINSON Densily loth percentile 0.512 i 0.414 0.421 Densily 50th percentile o.tI81 0.971 0.970 Densily 90th percentile 0.883 0.865 0.867 TAVGJJA Densily loth percentile 0.451 0.327 '0;505 Densily 50th percenlile 0.987 0.979 0.860 Densily 90th percentile 0.909 0.882 0.885 TMAXJJA Densily loth percentile 0.433 0.331 0.440 Densily 50th percentile 0.864 0.962 0.917 Densily 90th percentile 0.948 0.940 0.926 GDD Densily loth percentile 0.415 , 0.298 0.387 Densily 50th percenlile 0.838 . :..0.885 0.776 Densily 90th percentile 0.778 1).883 0.748 TAVGSON Densily loth percentile 0.713 0.687 0.712 Densily 50th percentile 0.954 1 >1).1l8s 0.960 Densily 90th percentile 0.893 0.889 0.886

• Highest R' value for 50th percentile

.-

104 GENERAL CONCLUSION ln Chapter II, 1 examined the beavers' range limit in Québec by carrying out three aerial beaver surveys and gathering Traditional Ecological Knowledge from the local communities. In the process, 1 confirmed that the beavers' northern range limit on the north-eastern and western coasts of Québec lies just below the tree-line near the communities of Tasiujaq (58°42'N, 69°56'W) and Umiujaq (56°33'N, 76°33'W) respectively. In these northern localities, average beaver densities are lower than densities observed closer to the core of the range and northern populations occupying the Mackenzie Delta (NWT). The fact that beavers are restricted to riparian zones offering shrubby shoreline vegetation in northern Québec, may explain why beaver densities are so low. Among the three study regions, beaver densities were highest in the Koksoak River study area, most likely due to the high density of shoreline shrubs, flat topography and prevalence of stable waterways. Habitat selection by northern beavers for stable 2 waterways (i.e., small lakes <1 km ) providing a minimum of forest coyer resembles selection taking place in southern populations, except that mixedldeciduous forests, selected for by southern beavers, provide a source of food, building materials and shelter, whereas coniferous forests, selected for by northern beavers, likely provide shelter alone. Furthermore, selection for coniferous habitats by northern beavers may reflect limitations imposed by the spatial resolution of land classification available for northern Québec. In the end, deciphering how far north beavers are found on the north-eastern and western coasts of Québec, how abundant they are in the vicinity of these regions, and what habitats they are selecting for, has placed us in a better position to detect whether beavers are expanding northward, experiencing density changes, or selecting different habitats in the face of ongoing climate change. Collation and analysis of a unique data set involving 161 surveys of the regional abundance of beavers across Québec, revealed that beaver abundance follows a roughly logistic pattern across the province, with high but variable abundance across the southern portion ofthe province, and a sharp decline in abundance at about 49~ with a long tail of low abundance extending more than 9° of latitude. Univariate regression models revealed that several climatic and non-climatic variables were strong predictors of variation in beaver abundance, and partial regression showed that 90% of the variation explained by

105 non-climate variables could be accounted for by climate variables. These results emphasize that although c1imate variables can sometimes serve as an effective proxy for the suite of climatic and non-climatic factors that determine a species abundance, current correlations between climate and non-climate variables must persist in the future if they are to be used to project changes in a species density. We found general support for our hypothesis that the climate sensitivity of beaver abundance (change in abundance per unit change in climate) peaked in the mid-range, with the most pronounced change in the density of beavers across Québec occurring in the vicinity of the O°C Tavg ann isotherm. Interestingly, although identifying the mechanisms underlying this pattern was not the objective of this manuscript, it can be seen that the O°C Tavg ann isotherm roughly corresponds to the northern limit of mixed forests in Québec, and that low beaver densities in the vicinity of this isotherm were attributed to lower densities of preferred forage species (i.e., deciduous trees). By incorporating the observed spatial variation in beaver abundance into our best climate envelope models and combining them with current GCM projections of future climate change, we predict the greatest increases in beaver abundance will occur near the middle of their range, and that they may expand their range to encompass almost all of Québec by 2055. Predicted changes in abundance will only occur if other forms of environmental change do not supersede the effects of climate change at mid-latitudes and the extent and rate of realized range shifts will depend on dispersal, evolutionary flexibility, and species interactions (Lawler et al. 2006). Overall, these results highlight the importance of incorporating information about how abundance varies across species ranges when using species-climate envelope models to predict the effects of c1imate change on plant and animal populations. Because the internaI structure ofmany species' ranges are currently unknown, the need for researchers to make the most of the available density estimates and to invest in creating more comprehensive abundance records has never been greater. Although the ultimate goal in macroecology is to understand the pro cesses responsible for creating observed patterns (Brown 1995), it remains one of the least investigated problems in ecology. My thesis research project will form the foundation for future research into the mechanisms responsible for generating the spatial variation in beaver abundance across Québec. Whether we believe the beavers' abundance pattern in

106 Québec is governed by a physiological response, dispersal, multidimensional niche or tradeoffs model (McGill, 2006), suggestions for future research inc1ude (a) conducting aerial beaver surveys throughout the interior of northern Québec to eliminate the core­ sampling bias and establish where the beavers' northern range limit is located between Ungava and Hudson's Bay (b) investigating whether low beaver densities in northern regions are a result of high mortality and rapid turnover of colonies or territorial and spacing behaviour of surviving beavers (c) conducting a more systematic examination of local scale habitat selection at northern latitudes to decipher whether beavers are selecting shrubby shoreline vegetation within lands c1assified as coniferous habitats (d) investigating the genetic relatedness of northern beaver populations in order to gain a better understanding of the role of dispersal and gene flow in maintaining CUITent range limits (e.g., Kirkpatrick and Barton, 1997) (e) improving upon the c1imate and non­ c1imate variables collected and conducting a more thorough analysis of the predictors of beaver abundance throughout Québec using Classification Tree Analysis (De' Ath and Fabricius, 2000; De' Ath, 2002; Bourg et al., 2005; Lawler et al., 2006), (t) conducting mark-recapture studies and collaborating with Québec trappers and hunters to obtain information on fecundity/recruitment, survivorship, diet, body-condition, and equilibrium population size along latitudinal transects across the province. In the end, understanding the beavers' (and other species') abundance patterns will have major implications for basic ecology (e.g., may help to explain sorne of the most important patterns in macroecology), and conservation biology (e.g., may help in decision making processes such as where to situate reserves; Sagarin and Gaines, 2002; McGill, 2006).

LITERATURE CITED BOURG, N.A., MCSHEA, W.J., GILL, D.E. 2005. Putting a cart before the search: successful habitat prediction for a rare forest herb. Ecology 86(10):2793-2804. BROWN, J. H. 1995. Macroecology. Chicago: University of Chicago Press. DE' ATH, G. 2002. Multivariate regression trees: a new technique for modeling species­ environment relationships. Ecology 83(4): Il 05-1117. DE'ATH, G., and FABRICIUS, K.E. 2000. Classification and regresslOn trees: a powerful yet simple technique for ecological data analysis. Ecology 81 :3178-3192.

107 KIRKPATRICK, M., and BARTON, N.H. 1997. Evolution ofa species range. American Naturalist 150:1-23. LAWLER, J.1., WHITE, D., NEILSON, R.P., and BLAUSTEIN, A.R. 2006. Predicting c1imate-induced range shifts: model differences and model reliability. Global Change Biology 12:1-17. MC GILL, B.1. 2006. Structure of abundance across species ranges: synthesis, evidence, mechanisms (in revision). SAGARIN, R.D., and GAINES, S.D. 2002. The "abundant centre" distribution: to what extent is it a biogeographical rule? Ecology Letters 5:137-147.

108 APPENDIX lA-l

Table lA-1.l: Density estimates for North American beavers (Castor canadensis) in 2 Canada and United States expressed as the average number ofbeaver colonieslkm • When a study area was surveyed more than once throughout the years, the density estimates were averaged (n= number of independent density estimates generated)

Avg# Location Lat. Long. Year n 2 Reference(s) colon.lkm UNITED STATES Stroud's Run State Park, 39.33 -82.10 1974-78 1 0.57 Svendsen 1980 Athens, Ohio Jackson Cou nty, Colorado 40.78 -106.38 1954-55 1 0.59 Hay 1958 PrescoU Peninsula, Busher and Lyons 1999, Quabbin Reservation, 42.25 -72.33 1975/52-96 2 0.68 Brooks et al. 1980 Massachusetts Allegany State Park, New Muller-Schwarze and Schulte 42.92 -78.20 1989-93 1 0.21 York 1999 Bearville Study Area, Longley and Moyle 1963, 47.75 -93.25 1941-56 2 0.41 Minnesota Fuller and Markl 1987 Kenai Peninsula, Alaska 60.55 -151.27 1984 1 0.02 Peterson 1984 CANADA New Brunswick Trapped population, New 46.44 -65.70 1972 1 0.33 Nordstrom 1972 Brunswick Untrapped population, New 46.44 -65.70 1972 1 1.09 Nordstrom 1972 Brunswick Québec Lafond, Pilon and Leblanc Zec Louis- Gosford 45.31 -70.86 1984/94-92 2 0.22 2003, Levesque 1984 Lafond, Pilon and Leblanc Free zone Monteregie 45.38 -73.10 1990-91 1 0.04 2003 Lafond, Pilon and Leblanc Free zone Estrie 45.48 -71.67 1988/89-91 2 0.23 2003 Lafond, Pilon and Leblanc 2003, Potvin and Breton Gatineau National Park 45.58 -76.03 1973-88/92 12 0.78 1982, Desjardins 1982, Desjardins 1981, Desjardins 1979, Giroux 1979 Lafond, Pilon and Leblanc Plaisance Wildlife Reserve 45.60 -75.19 1986 1 0.85 2003 South of Ste Agathe des 45.90 -74.25 1982 1 0.15 Mathieu 1982 Monts Lafond, Pilon and Leblanc Frontenac National Parc 45.96 -71.15 1983 1 0.43 2003 Lafond, Pilon and Leblanc Free zone and Zec Jaro 45.99 -70.37 1995/97 1 0.09 2003 Zec Jaro 45.99 -70.37 1979 1 0.10 Carrier 1980 Lafond, Pilon and Leblanc 2003, Potvin et al.1993, Papineau-Labelle Wildlife 46.10 -75.28 1992-94/88-78 15 0.75 Breton et Macquart 1984, Reserve Potvin and Breton 1982, Banville 1979b North of Ste-Theodore de 46.14 -73.82 1982 1 0.19 Mathieu 1983 Chertsey

109 Avg# Location Lat. Long. Year n 2 Reference(s) colon./km Lafond. Pilon and Leblanc Free zone Outaouais 46.27 -76.32 1979/86/89-90 3 0.56 2003. Potvin and Breton 1982 Lafond. Pilon and Leblanc Zec Saint- Patrice 46.28 -77.32 1987/92 2 0.50 2003 Lafond. Pilon and Leblanc Division Fort- Coulogne 46.37 -76.74 1988/93 2 0.47 2003 Zec Rapides- des- Lafond. Pilon and Leblanc 46.41 -77.63 1992/97 2 0.36 Joachims 2003 Lafond. Pilon and Leblanc Zec Maganasipi 46.42 -78.46 1993 1 0.46 2003 Lafond. Pilon and Leblanc Free zone Laurentides 46.43 -74.98 1989-90 1 0.39 2003 Mont Tremblant National Lafond. Pilon and Leblanc 46.44 -74.34 1988 1 0.39 Park 2003 Lafond, Pilon and Leblanc Zec Pontiac 46.45 -76.57 1987/92-94 2 0.56 2003 Lafond, Pilon and Leblanc Zec Lavigne 46.45 -73.91 1987/92 2 0.30 2003 Lafond, Pilon and Leblanc Zec Dumoine 46.46 -78.00 1993 1 0.46 2003 Outaouais registered 46.48 -76.98 1978 1 0.19 Potvin and Breton 1982 trappinQ zone Free zone Chaudiere- Lafond, Pilon and Leblanc 46.48 -70.62 1982/89-91 1 0.05 Appalaches 2003, Duchesneau 1983 Lafond, Pilon and Leblanc Zec des Nymphes 46.52 -73.63 1987/92 2 0.48 2003 Lafond, Pilon and Leblanc Free zone Lanaudiere 46.56 -73.28 1989-90 1 0.31 2003 Division Temiscamingue, Lafond, Pilon and Leblanc Zees Maganasipi, Dumoine, 46.58 -78.40 1984 1 0.32 2003 Restiao Lafond, Pilon and Leblanc Zec Bras-coupe-Desert 46.59 -76.35 1987/92 2 0.64 2003 Lafond, Pilon and Leblanc Zec Restigo 46.70 -78.37 1993 1 0.46 2003 Wildlife Reserve Lafond, Pilon and Leblanc 46.70 -73.42 1983/92 2 0.47 Mastiaouche 2003 Division Temiscamingue et Ville-Marie, Zees Lafond, Pilon and Leblanc 46.71 -78.07 1988 1 0.31 Maganasipi, Dumoine, 2003, Jutras 1989 RestiQo Rouge-Mattawin Wildlife Lafond, Pilon and Leblanc 46.73 -74.48 1987/92 2 0.23 Reserve 2003 Lafond, Pilon and Leblanc Division Temiscamingue 46.74 -78.76 1993 1 0.46 2003 Division Kipawa 46.78 -78.98 1978-79 1 0.22 Pilon and Daigle 1985 Lafond, Pilon and Leblanc Mauricie National Park 46.79 -72.97 1975/87 2 0.32 2003, Pares Canada 1988 Lafond, Pilon and Leblanc ZecCollin 46.79 -74.13 1987/92 2 0.46 2003 Lafond, Pilon and Leblanc Zec Maison-de- Pierre 46.81 -74.80 1987/92 2 0.29 2003 Lafond, Pilon and Leblanc Zec Boulle 47.02 -74.33 1987/92 2 0.24 2003 Lafond, Pilon and Leblanc Zec Chapeau-de Paille 47.03 -73.49 1981/82/92 3 0.19 2003, Cloutier 1983, Houde 1982 Wildlife Reserve Saint- Lafond, Pilon and Leblanc 47.06 -73.14 1981/92 2 0.33 Maurice 2003, Houde 1982 Lafond, Pilon and Leblanc Zec Tawachiche 47.06 -72.50 1982/92 2 0.26 2003Cloutier 1983 Lafond, Pilon and Leblanc Zec Petawaga 47.08 -75.90 1993 1 0.34 2003

110 Avg# Location Lat. Long. Year n 2 Reference(s) colon./km Wildlife Reserve Mastigouche, St-Maurice, Zees Wessonneau, Gros- 47.08 -73.39 1986 3 0.35 Pilon and Milette 1986 Brochet et Chapeau-de- Paille Lafond, Pilon and Leblanc Zec Mazana 47.11 -74.68 1987/92 2 0.21 2003 Zec Bastican-Neilson Lafond, Pilon and Leblanc 47.12 -71.87 1978/85 3 0.28 (Trapping Territories only) 2003, Banville 1979a Free zone Mauricie -Bois- Lafond, Pilon and Leblanc 47.15 -72.93 1989/91/94 1 0.22 Francs 2003 Lafond, Pilon and Leblanc Structured Zone Portneuf 47.18 -72.29 1995/97 1 0.05 2003 Lafond, Pilon and Leblanc 2003, Banville and St-Onge Portneuf Wildlife Reserve 47.18 -72.29 1975/84 2 0.32 1985, Traversy and Morasse 1976 Lafond, Pilon and Leblanc Zec Chapais 47.22 -69.73 1982/93 2 0.27 2003, Pelletier and Uzotte 1982c Lafond, Pilon and Leblanc Zec Lesueur 47.24 -75.45 1993 1 0.13 2003 Lafond, Pilon and Leblanc Division Ville-Marie 47.24 -78.76 1976-81/93-94 2 0.47 2003, Pilon and Daigle 1985 Lafond, Pilon and Leblanc Zec Mitchinamecus 47.25 -75.16 1993 1 0.37 2003 Lafond, Pilon and Leblanc Zec Wessonneau 47.29 -73.16 1980/81/93 3 0.37 2003. Houde 1982, Cloutier 1981 Manouane Wildlife Reserve 47.31 -74.05 1981 1 0.11 Hardy 1981 La Verendrye Wildlife Lafond. Pilon and Leblanc 47.32 -77.04 1981/94 2 0.33 Reserve 2003/ Crete 1983 Lafond. Pilon and Leblanc Parc de la Jacques-Cartier 47.32 -71.35 1982/84/85 0.16 (no trapping) 3 2003, Brunelle and Bider 1987 Lafond. Pilon and Leblanc Zec Gros-Brochet 47.33 -73.72 1992-93 1 0.35 2003 Lafond,Pilon and Leblanc Zec Normandie 47.35 -74.77 1995/97 1 0.30 2003 Lafond, Pilon and Leblanc Zec de la Riviere-Blanche 47.35 -72.07 1978/82/86 4 0.39 2003, Boivin 1982, Banville 1979a Free zone Capitale- Lafond, Pilon and Leblanc 47.37 -71.30 1989-91 1 0.19 Nationale 2003 Lafond, Pilon and Leblanc Zec Jeannotte 47.40 -72.27 1980/92 2 0.59 2003, Cloutier 1981 Lafond, Pilon and Leblanc Zec de la Bessonne 47.40 -72.52 1980/81/93 4 0.43 2003, Houde 1982, Cloutier 1981 Botonnais-Vermillon 3 47.41 -73.92 1982 1 0.22 Cloutier 1983 Region Zec Flamand 47.42 -73.53 1982 1 0.22 Cloutier 1983 Lafond. Pilon and Leblanc Zec Fremont 47.59 -73.69 1981/93 2 0.16 2003. Cloutier 1983 Botonnais-Vermillon 1 47.60 -73.28 1982 1 0.44 Cloutier 1983 Region Botonnais-Vermillon 4 47.61 -72.36 1982 1 0.42 Cloutier 1983 Region Rapide Sept West 47.62 -78.68 1990-92/94 2 0.30 Potvin and Breton 1997 Rapide Sept East 47.62 -78.31 1990-92/94 2 0.24 Potvin and Breton 1997 Botonnais-Vermillon 2 47.65 -73.11 1982 1 0.39 Cloutier 1983 Region

111 Avg# Location Lat. Long. Year n 2 Reference(s) colon./km Reservoir Pikauba 47.67 -71.25 2000 1 0.35 T ecsult 2002a Lafond, Pilon and Leblanc Zec La Croche 47.70 -72.82 1980/81/93 3 0.41 2003, Houde 1982, Cloutier 1981 Lafond, Pilon and Leblanc 1982/84/85/92- Laurentian Wildlife Reserve 47.73 -71.44 4 0.15 2003, Brunelle and Bider 93 1987 Lafond, Pilon and Leblanc Zec Owen 47.74 -68.48 1982/93 2 0.22 2003, Pelletier and Lizotte 1982d Lafond, Pilon and Leblanc Zec Menokeosawin 47.75 -72.40 1980/81/93 3 0.39 2003, Houde 1982, Cloutier 1981 Lafond, Pilon and Leblanc Zec des Martres 47.80 -70.58 1980/87 2 0.16 2003 Lafond, Pilon and Leblanc Zec Festubert 47.85 -76.03 1993 1 0.39 2003 Lafond, Pilon and Leblanc Zec Kiskissink 47.85 -72.18 1980/81/93 4 0.29 2003, Houde 1982, Cloutier 1981 Lafond, Pilon and Leblanc Zec du Lake au Sable 47.86 -70.26 1980/87 2 0.11 2003 Windigo Region 47.88 -73.27 1982 1 0.45 Cloutier 1983 Lafond, Pilon and Leblanc Zec Borgia 47.88 -72.56 1980/81/93 3 0.42 2003, Houde 1982, Cloutier 1981 Rapides des Coeurs Sector 47.89 -73.64 1993 1 0.47 Brunelle and Ouzilleau 1994 Lafond, Pilon and Leblanc Division Oskelaneo East 47.91 -74.81 1994 1 0.27 2003 Bassin du Haut saint Maurice (future flooded 47.92 -73.50 1993 1 0.72 Brunelle and Ouzilleau 1994 zone; Rapides-des-Couers) Lafond, Pilon and Leblanc Structure Charlevoix 48.00 -70.38 1995/97 1 0.47 2003 Riviere Kinojevis/ Lake 48.01 -78.76 1990-92/94 2 0.50 Potvin and Breton 1997 Caron Lafond, Pilon and Leblanc Division Oskelaneo West 48.04 -76.03 1980/93 2 0.32 2003, Pilon and Daigle 1985 Proposed limits of Pikauba 48.05 -71.47 2000 1 1.36 T ecsult 2002a Reservoir (low) Proposed limits of Pikauba 48.05 -71.47 2000 1 1.09 Tecsult 2002a Reservoir (high) Le Chasseur Outfitting Lafond, Pilon and Leblanc 48.06 -67.92 1984 1 0.29 Operation 2003 Lafond, Pilon and Leblanc Zec Buteux-Bas-Saguenay 48.07 -69.93 1980/87 2 0.30 2003 Lafond, Pilon and Leblanc 2003, Potvin and Breton Division Rouyn Noranda 48.07 -78.73 1977-82/90/92 5 0.44 1992, Pilon and Daigle 1985, Traversy and McNicoli 1977 Free zone Abitibi- Lafond, Pilon and Leblanc 48.10 -77.78 1987/89-90 2 0.60 Temiscamingue 2003 Division Senneterre, Lafond, Pilon and Leblanc 48.10 -76.52 1986 1 0.22 Oskelaneo, Zec Festubert 2003 Lafond, Pilon and Leblanc Zec Lake Brebeuf 48.11 -70.57 1982/93 2 0.14 2003 Lafond, Pilon and Leblanc Duchenier Wildlife Reserve 48.14 -68.13 1982 1 0.27 2003, Pelletier and Lizotte 1982e Lafond, Pilon and Leblanc Zec Mars-Moulin 48.17 -70.97 1980/82/83/84/93 5 0.32 2003 Lafond, Pilon and Leblanc Zec du Bas St-Laurent 48.18 -68.02 1982-83/93 2 0.16 2003, Pelletier and Lizotte 1982b

112 Avg# Location Lat. Long. Year n 2 Reference(s) colon./km Lafond, Pilon and Leblanc Port Daniel Wildlife Reserve 48.18 -64.97 1982/92 2 0.38 2003, Desrosiers 1982b Lafond, Pilon and Leblanc Rimouski Wildlife Reserve 48.20 -65.79 1982/86/93 3 0.27 2003, Pelletier and Uzotte 1982a Lafond, Pilon and Leblanc Zec de l'Anse-Saint-Jean 48.22 -70.15 1982/93 2 0.14 2003 Lafond, Pilon and Leblanc South of Kenogami Lake 48.25 -71.15 1983 1 0.34 2003 afond, Pilon and Leblanc Haut Saint-Maurice Bassin 48.25 -73.87 1993 1 0.40 2003, Brunelle and Ouzilleau (non-f1ooded zone) 1994 Lafond, Pilon and Leblanc Free zone Bas St-Laurent 48.33 -68.67 1989-91 1 0.09 2003 Lafond, Pilon and Leblanc Zec la Uevre 48.34 -72.68 1990/83/84/94 3 0.34 2003 Division Amos, La Sarre, Lafond, Pilon and Leblanc Rouyn, Senneterre, Free 48.35 -78.46 1985 1 0.44 2003 zone # 13 Saguenay National Park (west, north, south of Lafond, Pilon and Leblanc 48.37 -70.59 1990 1 0.46 Saguenay) total park 283 2003 km2 Lafond, Pilon and Leblanc Zec des Anses 48.37 -64.91 1984/92 2 0.26 2003 Lafond, Pilon and Leblanc Zec Casault 48.38 -67.00 1983/87/92 3 0.08 2003, Uzotte 1984 Lafond, Pilon and Leblanc Grand Lake Victoria Beaver 48.38 -77.62 1980/81/90/95/97 4 0.20 2003, Potvin et Breton 1992, Reserve Girard 1981, Charette 1980 Lafond, Pilon and Leblanc Division La Sarre 48.40 -79.23 1992/83-77 4 0.54 2003, Pilon and Daigle 1985, Traversy and McNicoli 1977 Rapides de-la-Chaudiere 48.42 -74.00 1993 1 0.26 Brunelle and Ouzilleau 1994 Sector Lafond, Pilon and Leblanc Division Senneterre 48.42 -77.51 1993-94/77 2 0.20 2003, Traversy and McNicoli 1977 Saguenay Trapping Traversy, Leblanc and 48.43 -70.87 1978 1 0.41 Territorv # 29 Marquis 1979 Lafond, Pilon and Leblanc Zec Chauvin 48.44 -70.07 1992/83 2 0.15 2003 Indian Reserve 48.45 -70.88 1981 1 0.10 Cadieux 1981 Ouiatchouan Saguenay Trapping Traversy, Leblanc and 48.47 -70.57 1978 1 0.03 T erritorv # 68 Marquis 1979 Haut Saint-Maurice Bassin (future f100ded zone; 48.50 -74.00 1993 1 0.48 Brunelle and Ouzilleau 1994 Rapide -de-Ia Chaudiere) Lafond, Pilon and Leblanc Aiguebelle National Park 48.51 -78.74 1977 1 0.52 2003, Mathieu 1978 Lafond, Pilon and Leblanc Division Amos 48.51 -78.37 1992/82-77 4 0.36 2003, Pilon and Daigle 1985, Traversy and McNicoli 1977 Lafond, Pilon and Leblanc Zec Nordique 48.54 -69.85 1993 1 0.38 2003 Lafond, Pilon and Leblanc Zec Iberville 48.58 -69.49 1992-93 1 0.38 2003 Saguenay Trapping Traversy, Leblanc and 48.59 -70.07 1978 1 0.09 Territory # 83 Marquis 1979 Obedjiwan Indian Reserve 48.61 -75.03 1981 1 0.04 Hardy 1981 Lafond, Pilon and Leblanc Zec Martin-Valin 48.65 -70.53 1993/86/83/82 5 0.10 2003

113 Avg# Location Lat. Long. Year n 2 Reference(s) .- colon./km Saguenay Trapping Traversy, Leblanc and 48.68 -70.57 1978 1 0.02 Territorv # 97 Marquis 1979 Free zone Gaspesie- lIes- Lafond, Pilon and Leblanc 48.70 -65A2 1989-91 1 0.07 de-la-Madeleine 2003 Lafond, Pilon and Leblanc Division St-Laurent ouest 48.72 -70.36 1986/92-93 2 0.13 2003 Region of the forestfire near 48.73 -69.57 1991 1 0.22 Guay 1991 Forestville Lafond, Pilon and Leblanc Matane Wildlife Reserve 48.73 -66.92 1982/87/92 3 0.22 2003, Desrosiers 1982a Zac Laval (Terrains de Consortium Roche Associés 48.73 -69.55 1978-86/87 6 OAO piegeage = 6289 km2) Ltée /Dessau 1NC. 1995 Saguenay Trapping territory Traversy, Leblanc and 48.75 -69.50 1978 1 0.14 (9) Marquis 1979 Saguenay Trapping Traversy, Leblanc and 48.77 -70A7 1978 1 0.08 T erritorv # 69 Marquis 1979 Lafond, Pilon and Leblanc Zec York-Baillargeon 48.78 -64.84 1984/92 2 0.08 2003 Saguenay Trapping Traversy, Leblanc and 48.80 -69.12 1978 1 OA4 Territorv # 75 Marquis 1979 Division Tadoussac- Lafond, Pilon and Leblanc 48.85 -69.55 1992-93 1 0.38 Bersimis 2003 Outfitting Operation Poulin Lafond, Pilon and Leblanc 48.86 -70A7 1988 1 0.10 de Courval 2003 Lafond, Pilon and Leblanc Zec Lake de la Boiteuse 48.87 -71.25 1982/93 2 0.19 2003 Lafond, Pilon and Leblanc Zec Cap-Chat 48.89 -66.81 1992 1 0.12 2003 Parcs Canada 1988 (page Forillon National Park 48.90 -64.35 1987 1 0.09 70, Table 23) Saguenay Trapping Traversy, Leblanc and 48.91 -69.97 1978 1 0.05 Territorv # 74 Marquis 1979 Lafond, Pilon and Leblanc Zec Forestville 48.95 -69.33 1992-93 1 0.38 2003 Chics Chocs Wildlife Lafond, Pilon and Leblanc 48.97 -65.67 1984-86/92 2 0.06 Reserve 2003 Outfitting Operations Lafond, Pilon and Leblanc Clauparo, Degelis, 48.98 -70.27 1986 1 0.38 2003 Laflamme et Archer Lafond, Pilon and Leblanc Zec Onatchiway 49.05 -70.82 1981/92 2 0.13 2003 Saguenay Trapping Traversy, Leblanc and 49.05 -69.98 1978 1 0.01 Territorv # 96 Marquis 1979 Saguenay Trapping Traversy, Leblanc and 49.05 -69.55 1978 1 0.25 Territorv # 86 Marquis 1979 Lafond, Pilon and Leblanc Zec des Passes 49.20 -71.56 1981/93 2 0.30 2003 Wildlife Reserve Baie- 49A9 -67A7 1984-80 1 0.52 Roy 1985 Trinite Lafond, Pilon and Leblanc Anticosti Island 49.50 -63.00 1982 1 0.09 2003, Belisle 1982 Consortium Gauthier and Southem Part of NBR 49.50 -75.50 1990 1 0.29 Guillemette .G.R.E.B.E. 1992 Lafond, Pilon and Leblanc Zec Trinite 49.52 -67.38 1992-93 1 0.17 2003 Lafond, Pilon and Leblanc Zec Varin 49.56 -68.67 1993 1 0.17 2003 Division Ragueneau/Baie Lafond, Pilon and Leblanc 49.56 -68.60 1992-93 1 0.17 Comeau 2003 Zec Riviere- Aux-Rats/Hors Lafond, Pilon and Leblanc 49.57 -72.26 1982/89/94 3 0.69 Wildlife Reserve a castor 2003 Division GodboutlPort Lafond, Pilon and Leblanc 49.58 -67.50 1979-84/92 2 0.14 Cartier 2003, Roy 1985

114 Avg# Location Lat. Long. Year n 2 Reference(s) colon.lkm Peribonka 49.63 -71.22 2001 1 0.21 T ecsult 2004 Nottaway-Broadback- Brunelle, Bernard and Rupert Complexe: 49.75 -77.17 1989 1 0.17 Labonte 1989 Waswanipi-Matagami Free zone Saguenay Lake Lafond, Pilon and Leblanc 49.87 -71.75 1989-90 1 0.18 St-Jean 2003 Lafond, Pilon and Leblanc Roberval Beaver Reserve 49.96 -72.53 1965/95/97 3 0.12 2003, Beaudet, 1966 T oulnustuc Future 50.03 -67.98 2003 1 0.09 Foramec 2004 Reservoir Sainte-Marguerite bassin Consortium Roche Associés discharging north of SM-3 50.14 -66.61 1994 1 0.04 Ltée /Dessau INC. 1995 dam Consortium Roche Associés Zac Moisie et Natashquan 50.14 -65.88 1982 1 0.09 Ltée /Dessau 1NC. 1995 Port Cartier-Sept lIes- Lafond, Pilon and Leblanc 50.18 -67.55 1979-84/92-93 2 0.14 Wildlife Reserve 2003, Rov 1985 Consortium Roche Associés Zac Moisie et Natashquan 50.23 -62.79 1982 1 0.10 Ltée /Dessau INC. 1995 Lafond, Pilon and Leblanc Abitibi Beaver Reserve 50.25 -76.25 1965/80/88/95/97 5 0.18 2003, Charette 1980, Beaudet1966 Pointe de Poste Reserve, 50.29 -66.43 1981 1 0.04 Cadi eux 1981 Seot-Iles Lafond, Pilon and Leblanc Division Lake Ste-Anne 50.30 -68.01 1992 1 0.12 2003 Nottaway Beaver Reserve 50.33 -78.17 1966 1 0.07 Beaudet1966 Nottaway-Broadback- Consortium Gauthier and Rupert Complexe: 50.36 -76.77 1990 1 0.13 Guillemette .G.R.E.B.E. 1992 Waswanioi Romaine River study area 50.38 -63.22 2001 1 0.28 Tecsult Environ Inc. 2002b Proposed limits of Tecsult Environ Inc. 2002b, 50.38 -63.22 1999/2001 2 0.11 Romaine-1 reservoir Tecsult 2000 Lafond, Pilon and Leblanc Bersimis Beaver Reserve 50.41 -68.62 1995/97 1 0.15 2003 Administrative region Lafond, Pilon and Leblanc 50.47 -59.60 1995/97 1 0.12 number 09 2003 Consortium Gauthier and Center Part of NBR 50.50 -75.50 1990 1 0.10 Guillemette .G.R.E.B.E. 1992 Consortium Gauthier and Western Part of NBR 50.50 -78.50 1990 1 0.14 Guillemette .G.R.E.B.E. 1992 Consortium Gauthier and Center Part of NBR 50.50 -77.01 1990 1 0.13 Guillemette .G.R.E.B.E. 1992 Consortium Gauthier and Eastern Part of NBR 50.50 -74.00 1990 1 0.07 Guillemette .G.R.E.B.E. 1992 Lafond, Pilon and Leblanc Zec Matimek 50.57 -66.72 1984/92 2 0.13 2003, Roy 1985 NBR Complexe - Beaver Lafond, Pilon and Leblanc Reserves Abtibi, Rupert, 50.73 -77.04 1977/90 2 0.14 2003, Banville 1978 Mistissini, Nottaway Mistassini Beaver Reserve 50.83 -74.67 1966 1 0.08 Beaudet 1966 Consortium Roche Associés Mingan Region 50.85 -63.83 1981 1 0.04 Ltée /Dessau INC. 1995 Lafond, Pilon and Leblanc Free zone Cote Nord 50.87 -65.82 1989-90 1 0.18 2003 Nottaway-Broadback- Rupert Complexe: Consortium Gauthier and 51.07 -77.96 1990 1 0.09 Waskaganish and Guillemette .G.R.E.B.E. 1992 Nemiscau

115 Avg# Location Lat. Long. Year n 2 Reference(s) colon./km Nottaway-Broadback- Consortium Gauthier and 51.13 -75.14 1990 1 0.07 Rupert Complexe: Mistissini Guillemette .G.R.E.B.E. 1992 Consortium Roche Associés Zac Manicoua9an 51.13 -68.75 1980 1 0.18 Ltée /Dessau 1NC. 1995 Consortium Roche Associés Zac Manicouagan et Moisie 51.18 -67.67 1984 1 0.21 Ltée /Dessau INC. 1995 Consortium Roche Associés Zac Moisie 51.21 -66.75 1979 1 0.08 Ltée /Dessau INC. 1995 Division Port Cartier/ Havre Lafond, Pilon and Leblanc 51.30 -65.90 1992 1 0.12 St-Pierre 2003 Lafond, Pilon and Leblanc 2003, Consortium Roche SM-3 Reservoir 51.31 -66.75 1994 1 0.12 Associés Ltée /Dessau INC. 1995 Indian Reserve Pointe 51.36 -62.04 1981 1 0.03 Cadieux 1981 Parent Consortium Gauthier and Northern Part of NBR 51.50 -75.50 1990 1 0.01 Guillemette .G.R.E.B.E. 1992 Mistassini Beaver Reserve Lafond, Pilon and Leblanc 51.50 -73.25 1987 1 0.08 (partiel) 2003 Consortium Roche Associés Zac Natashquan 51.50 -62.27 1981 1 0.16 Ltée /Dessau INC. 1995 Mecatina/Brador Sector 51.52 -58.60 1981 2 0.18 Michaud 1984 Rupert and Nottaway Beaver Reserves, Division 51.57 -77.00 1966 1 0.09 Beaudet1966 Nemaska Trapping territory on the 51.66 -58.75 1981 1 0.11 Cana-Marquis 1981 Indian Reserve La Romaine Rupert Beaver Reserve 51.79 -77.38 1965/66 2 0.06 Beaudet 1966, Drolet 1965 Future Roads (Nemiscau- La Groupe Roche Boreal 51.95 -77.25 1990 1 0.10 Centrale EM 2) 1991 Eastmain Region 52.00 -75.50 1990 1 0.07 FORAMEC 1992 (Table 2) Eastmain 1 Reservoir: Lake La Groupe Roche Boreal 52.07 -75.58 1990 1 0.07 Quindele 1991 Eastmain 1 Reservoir: La Groupe Roche Boreal 52.09 -75.93 1990 1 0.13 Grand Detour 1991 Eastmain 1 Reservoir: La Groupe Roche Boreal 52.13 -75.58 1990 1 0.04 Aviron Brisé 1991 Eastmain 1 Reservoir: La Groupe Roche Boreal 52.16 -74.99 1990 1 0.01 Troncon Amont 1991 La Groupe Roche Boreal Lakes Village 52.17 -75.33 1990 1 0.00 1991 East part of future Eastmain 52.19 -75.89 1980 2 0.12 FORAMEC 1992 (Table 2) Reservoir La Groupe Roche Boreal East part of future Eastmain 52.19 -75.89 1981/90 3 0.16 1991, FORAMEC 1992 Reservoir (Table 2) Eastmain 1 Reservoir: Lake La Groupe Roche Boreal 52.24 -75.48 1990 1 0.00 Clarkie 1991 Eastmain 1 Reservoir: La Groupe Roche Boreal 52.27 -75.57 1990 1 0.18 Lakes K 1991 Future Opinaca Reservoir 52.65 -76.33 1978 1 0.09 FORAMEC 1992 (Table 2) La Groupe Roche Boreal Opinaca Region 52.65 -76.33 1978 1 0.14 1991 Vieux Comptoir and Traversy 1976, Traversy and 52.75 -73.93 1974/75 2 0.12 Mistassini Beaver Reserves Morasse 1975 Vieux-Comptoir Beaver Lafond, Pilon and Leblanc Reserve (division Wemindji 52.87 -78.25 1989 1 0.10 2003 VC17 et 20) Lakes Boyd and Sakami 53.01 -76.73 1977 2 0.20 FORAMEC 1992 (Table 2)

116 Avg# Location Lat. Long. Year n Reference(s) colon./km2 Lafond, Pilon and Leblanc Saguenay Beaver Reserve 53.18 -62.57 19979/95/97 2 0.10 2003, Canac-Marquis, 1980 Caniapiscau area/ Mistassini Beaver Reserve 53.50 -69.75 1964-73 10 0.05 Lehoux 1974 Trapping Territory #1 Fort George Beaver 53.75 -76.37 1973 2 0.17 Traversy 1974 Reserve Future La Grande 3 Lafond, Pilon and Leblanc 53.77 -75.50 1979 1 0.06 Reservoir 2003 Future La Grande 2 FORAMEC 1992 (Table 2), 53.78 -77.55 1977/78 3 0.18 Reservoir Brodeur et al. 1977 Future La Grande 4 53.90 -73.25 1982 1 0.05 FORAMEC 1992 (Table 2) Reservoir Future Caniapiscau 54.17 -69.83 1979 1 0.01 Nault 1983 Reservoir Laforge 1 and de Fontages 54.69 -70.76 1982 1 0.01 FORAMEC 1992 (Table 2) Regions Proposed Great Whale Consortium Gauthier and 54.89 -77.31 1980/89 2 0.03 Road Running North-South Guillemette GREBE 1989 Consortium Gauthier and Proposed Roads 54.91 -76.36 1989 1 0.005 to 0.13 Guillemette GREBE 1989 Proposed Great Whale Consortium Gauthier and 54.94 -74.80 1981/89 2 0.04 Road Running West-East Guillemette GRE BE 1989 Sampling on Great Whale Consortium Gauthier and Complexe Territory; 54.97 -73.02 1989 1 0.02 Guillemette GREBE, 1990 Bienville West Sampling on Great Whale Consortium Gauthier and Complexe Territory; Great 54.98 -74.82 1981/89 2 0.03 Guillemette GREBE 1990, Whale2 Hydro Québec 1982 Sampling on Great Whale Consortium Gauthier and Complexe Territory; 55.02 -75.59 1989 1 0.01 Guillemette GREBE, 1990 Elisabeth-Kakupis Sampling on Great Whale Consortium Gauthier and Complexe Territory; 55.08 -72.66 1989 1 0.04 Guillemette GRE BE, 1990 Bienville Center Great Whale Region 55.09 -75.84 1979 1 0.49 Bider 1979 Sampling on Great Whale Complexe Territory; Rest of Consortium Gauthier and 55.20 -74.37 1989 1 0.04 the Great Whale Complexe Guillemette GRE BE, 1990 Territory (Control) Sampling on Great Whale Consortium Gauthier and Complexe Territory; 55.23 -72.05 1989 1 0.02 Guillemette GREBE, 1990 Bienville Est Futures reservoirs GW 1, 55.32 -75.03 1981 1 0.04 Hydro Québec 1982 GW2,GW3 Sampling on Great Whale Consortium Gauthier and Complexe Territory; Great 55.38 -76.78 1981/89 2 0.05 Guillemette GREBE 1990, Whale 1 Hydra Québec 1982 Sampling on Great Whale Consortium Gauthier and Complexe Territory; Mollet- 55.51 -73.94 1989 1 0.01 Guillemette GRE BE, 1990 Vaujours-Saindon Sampling on Great Whale Consortium Gauthier and Complexe Territory; Great 55.60 -73.50 1981/89 2 0.03 Guillemette GREBE 1990, Whale 3 Hydra Québec 1982 Sampling on Great Whale Consortium Gauthier and 55.76 -74.16 1989 1 0.05 Complexe Territory Guillemette GREBE, 1990 Sampling on Great Whale Consortium Gauthier and Complexe Territory; Petits 55.91 -73.35 1989 1 0.00 Guillemette GREBE, 1990 Lake des Loups Marins Sampling on Great Whale Consortium Gauthier and Complexe Territory; 56.03 -72.55 1989 1 0.01 Guillemette GREBE, 1990 Amichinatwayach

117 Avg# Location Lat. Long. Year n 2 Reference(s) colon.lkm Clearwater Lake Region 56.1 -74.25 2004 1 0.00 Jarema et al., in prep., 2004 Umiujaq Study Region 56.27 -76.22 2004 1 0.01 Jarema et al., in prep., 2004 Sampling on Great Whale Consortium Gauthier and Complexe Territory; Lake 56.65 -73.36 1989 1 0.00 Guillemette GREBE, 1990 des Loups Marins Sampling on Great Whale Consortium Gauthier and Complexe Territory; 57.55 -74.56 1989 1 0.00 Guillemette GREBE, 1990 Nastapoka Sampling on Great Whale Consortium Gauthier and Complexe Territory; Mouth 57.70 -76.88 1989 1 0.00 Guillemette GRE BE, 1990 of Nastapka Kuujjuaq Study Area 57.93 -69 2004 1 0.08 Jarema et al., 2004 in prep Ontario Larson and Gunson 1983, Algonquin Park 45.56 -48.57 1939-40/68-55 2 1.05 Robb 1942 Pakesley 45.85 -80.62 1965-68 1 0.77 Larson and Gunson 1983 Elliott Lake 46.39 -82.65 1969 1 0.89 Larson and Gunson 1983 Manitoba Riding Mountain National 50.53 -99.47 1973-80 1 1.07 Larson and Gunson 1983 Park Saskatchewan Northern Saskatchewan 55.10 -105.28 1965-67 1 0.25 Larson and Gunson 1983 Churchill River 58.80 -94.20 1965-67 1 0.34 Larson and Gunson 1983 Alberta Blackfoot and Ministik 53.29 -110.18 1975 1 0.85 Larson and Gunson 1983 GrazinQ Reserves Minburn Grazing reserve 53.42 -111.65 1968 1 3.51 Larson and Gunson 1983 Elk Island National Park 53.62 -111.12 1973-76 1 0.93 Larson and Gunson 1983 Mackay River 57.17 -111.63 1981 1 0.90 Larson and Gunson 1983 Wood Buffalo National Park 59.42 -113.00 1983 1 0.40 Larson and Gunson 1983 Northwest Territories/ Yukon Liard North 60.23 -123.47 1989 1 0.12 Poole and Croft 1990 TroutWest 60.43 -121.25 1989 1 0.25 Poole and Croft 1990 Kakisa 60.93 -117.42 1989 1 1.00 Poole and Croft 1990 Trout East 61.32 -119.85 1989 1 0.17 Poole and Croft 1990 Sanctuary North 61.65 -116.97 1989 1 0.00 Poole and Croft 1990 Simpson West 61.86 -121.35 1989 1 0.17 Poole and Croft 1990 Mink Lake 61.90 -117.67 1989 1 0.16 Poole and Croft 1990 Martin River 61.92 -121.58 1989 1 0.58 Poole and Croft 1990 Dettah East 62.32 -114.13 1989 1 0.46 Poole and Croft 1990 Yellowknife West 62.45 -114.39 1989 1 0.37 Poole and Croft 1990 Ft. Rae East 62.65 -115.83 1989 1 0.27 Poole and Croft 1990 WiliowLake 65.08 -125.27 1989-2001 1 0.53 Popko et al. 2002 8rackett Lake 65.22 -125.33 1989 1 0.50 Poole and Croft 1990 Oscar Lake 65.48 -127.08 1989-2001 1 0.22 Popko et al. 2002 Ramparts River 66.18 -129.04 1989-2001 1 0.75 Popko et al. 2002 Dennington and Johnson Richardson Mountains 68.33 -135.75 1973 1 0.17 1974 Larson and Gunson 1983, Mackenzie Delta 68.87 -133.25 1962-65/65-67 2 0.30 Aleksiuk 1968 Mackenzie River Valley 69.35 -133.90 1973 1 0.13 Larson and Gunson 1984

118 Table lA-1.2: Density estimates for North American beavers (c. canadensis) in Canada and United States expressed as the average number ofbeaver colonies/km of stream or nver.

Location Lat. Long. Year Avg.# Reference colon./km MEXICO Rio Bavispa-Yaqui 30.25 -108.90 1999 0.10 Gallo-Reynoso et al. 2002 UNITED STATES Nutras Creek, Colorado 38.06 -106.80 1956-54 1.25 Hay 1958 Green River, Colorado 38.19 -109.88 2000- 0.55 Breck et al. 2001 1997 Saline River, Kansas 38.86 -97.51 1993-89 0.30 Robel and Fox 1996 Solomon River, Kansas 38.90 -97.37 1998-89 0.48 Robel and Fox 2001 Smokey Hill River, Kansas 39.06 -96.80 1994-89 0.18 Robel and Fox 1997 Republican River, Kansas 39.06 -96.80 1992-89 1.24 Robel and Fox 1995 Blackwater River, Beaver Creek, 39.12 -79.47 1947 0.33 Glover 1948 Stony river and Red Creek, West VirQinia Kansas River, Kansas 39.12 -94.61 1991-89 0.24 Robel and Fox 1994 Big Blue River, Kansas 39.19 -96.53 1990-89 0.56 Robel and Fox 1993 S. Fork Solomon River, Kansas 39.47 -98.43 1997-89 0.32 Robel and Fox 2000 N. Fork Solomon River, Kansas 39.47 -98.43 1996-89 0.36 Robel and Fox 1999 Truckee River,Califomia and 39.85 -119.44 1985 0.38 Beier and Barret! 1989 Nevada Mill Creek, Kansas 39.92 -96.93 1995-89 0.44 Robel and Fox 1998 Lost Creek, Colorado 40.42 -105.85 1955-54 1.00 Hay 1958 Yampa River, Colorado 40.53 -108.98 2001- 0.35 Breck et al. 2002 1997 South Pass City and Fox Park, 42.47 -108.80 1988 1.41 Osmundson and Buskirk 1993 Wyoming Allegany State Park, New York 42.92 -78.20 1993-89 0.55 Muller-Schwarze and Schulte 1999 Yellowstone River, Montana 45.50 -110.62 1980-79 1.06 Swenson et al. 1983 Tongue River, Montana 46.41 -105.87 1981-79 0.98 Swenson et al. 1983 Voyageurs National Park, 48.50 -92.88 1986-61 1.55 Broschart et al. 1989 Minnesota Chena River, Alaska 64.80 -147.91 1980 0.47 Boyce 1981a Birch Creek, Alaska 66.27 -145.04 1980 0.35 Boyce 1981a CANADA Québec Pikauba River 48.33 -71.45 2000 0.05 Tecsult 2002a Portneuf River (at limes of weak 48.67 -69.20 2001 0.17 Alliance 2002b flow) Sault aux Cochons River 48.72 -69.17 2001 0.31 Alliance 2002a Riviere aux Sables (at limes of 49.05 -70.65 2000 0.27 Alliance 2002b strong flow) Lionnet River and stream of the 49.33 -69.90 2001 0.00 Alliance 2002a new discharge system Grand Detour River 49.44 -70.55 2002 0.10 Alliance 2004 Peribonka river 49.45 -71.22 2001 0.11 Tecsult 2004

119 Location Lat. Long. Year Avg.# Reference colon./km NBR Large Lakes; Lake 49.57 -76.65 1990 0.00 Consortium Gauthier and Waswanipi Guillemette .G.R.E.B.E. 1992 Manouane River 49.57 -71.13 2001 0.27 Tecsult 2004 Manouane River 49.57 -71.13 2002 0.29 Alliance 2004 NBR Large Lakes; Lake au 49.78 -76.80 1990 0.03 Consortium Gauthier and Goeland Guillemette .G.R.E.B.E. 1992 NBR Large Lakes; Lake Matagami 49.88 -77.50 1990 0.00 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Large Lakes; Lake 49.93 -76.65 1990 0.00 Consortium Gauthier and Maicasagi Guillemette .G.R.E.B.E. 1992 NBR Tributaries; Harricana 50.03 -78.99 1990 0.19 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Large Lakes; Lake 50.17 -76.92 1990 0.06 Consortium Gauthier and Poncheville Guillemette .G.R.E.B.E. 1992 NBR Large Lakes; Lake 50.25 -77.45 1990 0.02 Consortium Gauthier and Soscumina Guillemette .G.R.E.B.E. 1992 NBR Large Lakes 50.26 -77.07 1990 0.02 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 Romaine River 50.30 -63.80 2001 0.00 Tecsult Environ Inc. 2002b NBR Tributaries; Nottaway 50.83 -78.17 1990 0.01 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Large Lakes; Lake Dana 50.88 -77.33 1990 0.01 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Large Lakes; Lake Evans 50.92 -77.00 1990 0.02 Consortium Gauthier and Guillemette .GRE.B.E. 1992 NBR Tributaries; Broadback 50.92 -77.02 1990 0.08 Consortium Gauthier and Guillemette .GRE.B.E. 1992 NBR Rivers; Harricana 50.93 -79.53 1990 0.06 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Tributaries 50.97 -77.81 1990 0.06 Consortium Gauthier and Guillemette .GRE.B.E. 1992 NBR Large Lakes; Lake du tast 51.00 -77.37 1990 0.00 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 Southern region of Ste-Marguerite 51.02 -66.50 1994 0.08 Consortium Roche Associés River LIée IDessau INC. 1995 NBR Rivers 51.35 -77.38 1990 0.06 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Rivers; Broadback 51.35 -70.87 1990 0.07 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Rivers; Nottaway 51.37 -78.92 1990 0.03 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Tributaries; De Rupert 51.43 -77.37 1990 0.03 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Rivers; De Rupert 51.50 -78.75 1990 0.03 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 Ste-Marguerite River 51.50 -66.61 1994 0.09 Consortium Roche Associés LIée IDessau INC. 1995 Center region of Ste-Marguerite 51.51 -66.625 1994 0.12 Consortium Roche Associés River LIée IDessau INC. 1995 NBR Rivers; Pontax 51.60 -78.82 1990 0.11 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 NBR Tributaries; Pontax 51.62 -77.50 1990 0.01 Consortium Gauthier and Guillemette .G.R.E.B.E. 1992 Northern region of Ste-Marguerite 51.99 -66.57 1994 0.77 Consortium Roche Associés River LIée IDessau INC. 1995 l'Aviron Brise Stream 52.10 -75.54 1990 0.00 La Groupe Roche Boreal 1991 Lakes Village 52.17 -75.33 1990 0.00 La Groupe Roche Boreal 1991 Eastmain River 52.24 -78.56 1990 0.02 La Groupe Roche Boreal 1991 Clearwater River 56.20 -75.90 1990 0.04 La Groupe Roche Boreal 1991 Northwest Territories Fort Smith 60.00 -111.88 1951-49 0.13 Fuller 1953

120 Location Lat. Long. Year Avg.# Reference colon./km Fort Laird 60.23 -123.47 1956 0.3 Novakowski 1965 Wood Buffalo National Park 60.27 -114.17 1958 0.4 Novakowski 1965 Liard North/Muskeg River 60.32 -123.35 1989 0.27 Poole and Croft 1990 Hay River- Fort Providence 60.82 -115.80 1955 0.5 Novakowski 1965 Kakisa River 61.07 -119.95 1989 0.45 Poole and Croft 1990 Fort Providence 61.35 -117.35 1951-49 0.16 Fuller 1953 Birch River 61.35 -122.07 1989 0.67 Poole and Croft 1990 Fort Simpson 61.86 -121.35 1956 0.2 Novakowski 1965 Laferte River 61.88 -117.73 1989 0.31 Poole and Croft 1990 Mink Lake Horn River 61.90 -117.67 1989 0.45 Poole and Croft 1990 Martin River 61.92 -121.58 1989 0.45 Poole and Croft 1990 Wrigley 63.27 -123.61 1956 0.1 Novakowski 1965 Fort Norman 64.57 -125.30 1955 0.4 Novakowski 1965 Fort Franklin-Déline-Great Bear 65.20 -123.42 1955 0.3 Novakowski 1965 Lake Fort Good Hope 66.26 -128.63 1955 0.4 Novakowski 1965 Fort McPherson 67.44 -134.88 1957 0.2 Novakowski 1965 Arctic Red River 67.45 -133.75 1957 0.2 Novakowski 1965

121 APPENDIX lA-2

1. Classification of beaver habitat in North America

1.1 Canada The Canada Land Inventory Program produced a land capabi1ity rating for large areas across the country in the 1970's (Perret 1970, Novak 1987). Rating was done on small physiographic units using data collected from aeria1 surveys, photographs, and other on­ site surveys (Novak 1987).

1.2.1. Québec Traversy (1974), usmg eco10gica1 maps obtained from the Service des Inventaires Ecologiques (S.I.E., previously known as S.E.E.R) and his own observations made during aeria1 beaver surveys, was the first to develop a classification system defining the areas most likely to be inhabited by beavers in Québec (Travers y, 1974; Traversy and Banville, 1977). The eco10gica1 maps divided the territory into eco10gic celIs, based on various parameters including soi1 type, slope, hydro10gy, and vegetation (Table 1A-2.1). Each cell was given a code or system of codes, referring to these various parameters. For example, R1-1A5P-1b34 would be a description of the relief (R), the thickness of the free materials (1), nature of the surface materia1s (lA5P), order number (1), and unit y with respect to aquatic ecosystems and wetlands (b34). In the end, the most important variables included in the classification systems devised by Traversy (1974) and Traversy and Banville (1977), were relief, aquatic ecosystem category, abundance of streams and wetlands, and landforms (Table 1A-2.1). Classification codes identifying the beaver potential of each S.I.E. code were then established and divided into three potentia1 classes (Table 1A-2.2) and one special class: Classes 2* and 3*: The description ofthese classes is the same as the descriptions for classes 2 and 3 with one major exception. Lakes bigger than 5 km2 (500 ha) covering over 15% of a water system are considered to be non-productive for beaver. However, it can happen that certain lakes are more productive than others due to certain characteristics of the shorelines that are not evaluated by the

122 description of the ecological system; representing a hidden potential for beaver. As such, classes 2 * and 3 * can, in certain cases, represent classes 1 and 2 respectively. Levasseur and Mondoux (1977) proposed a key based on the same parameters as Banville and Traversy (1977) but excluding a parameter that was insignificant (i.e., landforms). Therefore their key included relief, aquatic ecosystems, and abundance of streams and wetlands. Environnement Illimité Inc. (1981) created two keys (one for James Bay Region and one for Cote-Nord Region), and added two parameters to the key developed by Banville and Traversy (1977; i.e., ecological region and geologic surface materials). The S.I.E codes for the classification keys developed by Environnement Illimité Inc. (1981) were divided into five classes instead ofthree (Table lA-2.2). SOMER (1982) produced a classification key for the Great Whale Region similar to the ones previously established which included aquatic ecosystems, streams and riparian habitats, and surface deposits. The major difference with SOMER's key was the addition of the categories "physiography" and "adjacent forest cover". The 5 classes used by SOMER were the same as the classes established by Environnement Illimité Inc. (1981); see Table lA-2.2. Consortium Gauthier and Guillemette (1990) also examined the potential of beaver habitats surrounding the proposed Great Whale Complex and discovered that four variables were able to predict the number of beaver colonies per km2 in these regions. They included (1) the number of lakes smaller than 0.1 km2 (l0 ha) (2) the importance of arborescent cover (3) the number oflakes between 0.1 km2 (10 ha) and 1 km2 (100 ha) (4) the number of lakes 5 km2 (500 ha) or more. The R2 value for this model was 0.447 (p

123 bums to more recent bums. In the zone of the future reservoir, the following predictive model explained 71 % of the variation in beaver densities: y (density) = -0.64 + 1.12 (FRD) + 0.88(DRG) + 0.52(HA25100) The zones of deciduous and coniferous trees (FRD), zones in regeneration (DRG), and the number of lakes from 0.25 km2 (25 ha) to 1 km2 (100 ha; HA 25100) represent parameters that allow one to predict beaver density in this sector. The number of colonies increased with increasing number of zones with high densities of deciduous and 2 2 coniferous trees, sites in regeneration, and number oflakes between 0.25 km and 1 km •

1.2.2. Northwest Territories Fuller (1953) examined all parts of the Mackenzie District to evaluate the quality of beaver habitat. The author rated beaver habitat in a very general sense (took notes on topography, vegetation, recent tires, and other observations) while carrying out aerial surveys for food caches. Although the method used was rapid, and easily implemented, the information he gathered was unquestionably qualitative in nature (Willis, 1978).

1.2.3. British Columbia Slough and Sadleir (1977) took a different approach to characterizing beaver habitat. They described the relationship between colony site numbers to various physical and vegetative parameters using backwards stepwise multiple regression analysis. The dependent variable was land use by beaver, in number of colony sites per lake or stream Cf). The independent variables inc1uded in the model were lake perimeter (Pd, and area (Ad, area vs. perimeter ratio (Rd, stream width (Ws) and length (Ls), water leve1 stability index (Wd, flow rate index (Fs), gradient index (Gs), length of shoreline covered by

different forest types (e.g. aspen TA Lor s), length of nonproductive brush (NL or s) and

swamp shore1ine (SL or s). The predictive equations that resulted from their regressions were as follows: 2 2 (1) Y(lakes) = -3.84 - 0.781 (Pd + 1.43F 3(Ad + 0.555(ALI/ ) - 5.10F 4(RL ) + 2 1.24(Wd + 1.24(TAL l/ ) + 6.32 (Nd 2 (2) Y(stream sections) = 74.2 + 24. 1(Ls) - 0.554(L/) - 98.5 (Ls I/ ) + 56.2(loglO Ls} 1 2 - 2.43F 4(Ws 2) + 4.42(GS- ) +0.954(TAs) + 0.600(NSs )

124 The most significant lake variables were those quantifying food (i.e., length of 12 nonproductive brush shoreline (Nd and transforrned length of aspen shoreline (TAL / )) together explaining 90% of the total variation in number of colony sites on lakes. Lake II2 area (Ad, perimeter {Pd, transforrned area (A L ) and transforrned area: perimeter ratio 2 (R L ) explained another 2%, and water level stability index (Wd explained less than 1%. The most siginificant stream variable was transforrned nonproductive brush and swamp shoreline (NSs 2), which explained 40% of the variation in colony site number on streams. 1 The transforrned width (Ws 2), length (Ls, L/, Ls1l2) and gradient index (GS- ) of streams accounted for 24% of the variation, while length of aspen shoreline (TAs) accounted for 2%. The authors state that it is possible to subjectively distinguish beaver land capability classes by using the known or predicted density of beaver colonies on lakes and stream sections, taking into account the limiting subclasses derived from biophysical factors included in both models (e.g., high wave action, inability to dam waterways, absence of food species) and making alIowances for special subclasses derived from inspecting outliers (e.g., human disturbance, steep relief, shalIow waterways),.

1.2. United States Atwater (1939), one of the first to develop a classification system for beaver in America, relied primarily on descriptive and arbitrary habitat units to classify streams in Montana. He established five classes ofhabitat suitability based on the arnount and quality of forage (woody species in forage area = area of concentrated beaver feed) , area available for expansion, and reliability of the water supply. AlI variables were calculated from the standpoint of future possibilities and present conditions. As such, Atwater was able to predict an undesirable spread of the species almost a decade before it becarne a large scale problem (Willis, 1978). The five classifications of waterways he developed are explained in Table 1A-2.2. Packard (1947) also used qualitative descriptions to evaluate and classify North Arnerican beaver habitat in Rocky Mountain National Park (Colorado). AlI strearns and tributaries within the park were surveyed to deterrnine the location of beaver colonies, and to record ecological data including the number of lodges and bUITows, carrying capacity, and abundance of foodlbuilding materials. Assuming six beaver per lodge and

125 two per burrow, Packard was able to predict the deviation of the present population size from carrying capacity. The author states that this national park probably supports more beaver than any other park because there is an abundance of food and building materials on many important streams, and almost every stream that can support beavers is at carrying capacity, or is overpopulated. Packard (1947) also states that a lack of aspen or willow may hamper the occupation of otherwise habitable regions in the park and that beaver were considered to compete with deer and elk on the supply of these shrubby shoreline species. Buckley (1950) in New York and MacDonald (1956) in Colorado studied carrying capacity of beaver based on woody food requirements (see Novak, 1987), but it was Retzer et al. (1956) that were the first to quantitatively assess physical features of beaver habitat. Of the 433 km of high-elevation streams in the Rocky Mountains of Colorado which they examined, 47% was occupied, 22% was abandoned, and 31 % had never been occupied. They discovered that three physical features were very important in determining whether a site was suitable for beaver (1) valley grade (2) valley width and (3) rock type. In their study, beaver never occupied streams whose grades exceeded 15%, and the majority of beaver colonies (70%) were found in valley grades between 0-6%. The width of the valley was only found to be important when it was as narrow as the channel itself, with suitability increasing as the valleys widened, apparently without limit. The authors state that differences in stream channel erosion were related to differences in the kinds of rocks making up the watershed. The most stable watersheds were made up of glacial till, schist and granite; moderately stable watersheds were composed of rhyolytic rocks; and the least stable watersheds were composed of shale. As such, the highest incidence of dams breaking as a result of channel erosion was observed on watersheds made of shale. Retzer et al. (1956) stress that the suitability classes developed for the region, based on their findings, are only concemed with physical factors and should be complemented with other factors based on beavers' food and water requirements. Rutherford (1964) also studied beaver habitat in Colorado, and he added spatial coverage of aspens and willows to Retzer et al.' s physical factors in evaluating beaver habitat (Novak 1987). According to Rutherford (1964), in the absence of competition

from livestock and big game, and inc1uding wastage, the ~bility for an area to support

126 beaver can be expressed in terrns of km2 per colony of food type and quality. Average or better than average beaver habitat typically had both aspen and willow present, with the willow stands occupying the valley bottom and aspen occurring on adjacent slopes. The V.S. Forest Service (1973) created a rudimentary habitat model for beaver. Their model consisted of five habitat variables including water suppl y, gradient, valley width, food supply, and general ecology. Each variable was rated as being either suitable or unsuitable, and if one of the variables was rated as being unsuitable, it was classified as having 0 beaver colonies (i.e., beaver-free). Boyce (1981 b) used a mathematical model to predict beaver colony density on watersheds in Alaska. The variables originally used in his model included the nearest neighbour (distance to the nearest neighbouring colony in kilometers), mean neighbour distance (average of the nearest upstream neighbouring colony and the nearest downstream colony), DIVERSITY (the diversity of vegetation types within AREA: defined as the area of the waterway plus 60m on either side of the waterway), EVENNESS (the evenness of vegetation types within AREA), BIOMASS (the non­ woody biomass ofbeaver food trees found within the AREA), GRA VEL (area of sand or gravel bars found within the AREA), RIVER (the proportion of AREA occupied by water), and land coyer types (the proportion of vegetation within AREA in the willow, gravel-willow, bog, birch, spruce, poplar, muskeg, alder-birch bog, alder-birch, alder­ willow, birch-poplar habitat types). Discriminant analysis revealed that habitats occupied by beavers for one year were surrounded by vegetation types containing significantly lower food tree biomass than habitats occupied during both years of the study. Multiple regression and canonical correlation revealed that the density of beaver colonies was positively correlated with the degree of bifurcation of the stream channel, the biomass of available winter food cache materials, and the diversity of the vegetation types. The most important land coyer types were alder-birch bogs, spruce bogs, aIder, water and willow (i.e., 76.5% of the variability in lodge density was accounted for when these land coyer types were included in the regression). Willis (1978) created a sophisticated mathematical model for Truckee River Basin (Nevada) which discriminated between desirable and unused habitats (Novak 1987). His model began with 31 physical and vegetation characteristics of beaver foraging habitat,

127 and with discriminant analysis, 13 variables were selected to distinguish between

desirable and unused habitats. The most important factors ln decreasing order of discriminating power were: neighbor cut status (cut or uncut, X4), tree species (XI), streambank slope (XII), tree circumference (X3), percent forbs between focus tree and river (XI4), percent cottonwood and wood debris in the immediate area (X27 and X2S), number of trees within 9 m of the focus tree (X21), neighbor aspect (i.e., compass bearing from the focus to the nearest tree, X8), percent grass in the immediate area (X23), total percent understory cover between the focus tree and the river (x3d, aspect of the focus tree (i.e., direction of the shortest path between focus tree and river, XI2), and shrub cover in the immediate area (X30). In the final run however, only 12 variables were used to generate the discriminant function coefficients, with neighbor aspect (i.e. compass bearing from the focus to the nearest tree, X8), aspect of the focus tree (i.e. direction of the shortest path between focus tree and river, XI2), percent grass in the immediate area (X23), and shrub cover in the immediate area (X30) being removed, and distance to water (XIO), percent willows between the focus tree and the river (XI8), and percent sedges between the focus tree and the river (XI9) being added. According to the author, the final equation can significantly separate desirable from unused habitat on the middle and lower Truckee River: D (Discriminant score) = XI (-2.395) + X3 (0.557) + X4 (0.982) + XIO (0.018) + XII (0.006) + XI4 (-0.042) + XI8 (0.156) + XI9 (-1.234) + X21 (-0.003) + X25 (0.019) + X27 (-1.018) + X31 (0.062) + 1.75742 Allen (1983) produced a beaver Habitat Suitable Index (HSI) model in the United States. The model, which includes key habitat variables affecting beaver populations, evaluates the suitability of habitat for beaver (0= unsuitable to 1= optimum habitat), but does not estimate beaver densities. The HSI model was developed for year-round application throughout the entire beaver range, and for several land cover types. When eva1uating the riverine, lacustrine and wetlands beaver habitats, the model considers the area of the cover type plus a 200m band of habitat on each si de of the rivers, lakes and wetlands. A total of nine variables were included in the model including; percent stream gradient, average water fluctuation on an annual basis, shoreline development factor, percent tree canopy closure, percent shrub canopy closure, percent trees in the 2.5 to 15.2

128 cm dbh size classes, average height of shrub canopy, specles composition of woody vegetation, and percent lacustrine surface dominated by white and/or yellow water lily. The HSI model for beaver considers the quality of life requisites for the species in each cover type, with waterand winter food being the only life requisites considered, because cover and reproductive needs are met ifwater requirements are satisfied. Howard and Larson (1985) developed a stream classification system for beaver for Prescott Peninsula New Salem, Massachusetts. Using principal components regression (PCR) and discriminant analysis, they predicted the maximum density of active beaver colonies on streams. The 14 potential habitat variables selected for their study defined water reliability and food availability. They were chosen based on their importance according to the literature and their ease in being obtained. The final PCR model that was developed to predict the maximum number of active colonies per km of stream (Y): Y = 1.034 + 0.001 (WSS) + 0.106 (SW) - 0.058 (GRAD) - 0.263 (SOD) - 0.003 (AF1) + 0.005 (Hl) + 0.005 (H2) In mixed coniferous-deciduous forest habitat, the percentage of hardwood vegetation (Hl and H2), watershed size (WSS) and stream width (SW) had significant positive effects on beaver densities, while stream gradient (GRAD) and progressively well drained soils (SOD) had negative effects. In field-tests, the PCR and discriminant models were 80% and 75% reliable in predicting active colony density. Three land capability classes for beaver on stream habitat in central Massachusetts were distinguished based on the authors' results (Table 1A-2.2) Beier and Barrett (1987) used stepwise logistic regression to identify factors important for habitat use by beaver on streams in Truckee River Basin (Nevada, Califomia). The following variables were included in their model; stream gradient(%), bank slope (Slope class: 1 = 0-30, 2 = 31-40, 3 = 41-60,4 = > 60%), bare soil (%), stream width (m), stream depth (Depth class: 1 = 1-23, 2 = 24-46, 3 = 47-76, 4 = > 76 cm), elevation (m), riparian zone width (m), litter cover (%), the abundance of aspen, aIder, cottonwood, willow, Lodgepole pine, Jeffrey pine, fir, and dogwood measured with an abundance index (0 = absent, 1 = 1-5,2 = 6-20, 3 = >20 trees or shrubs in riparian zone/ 100 m of stream length), and the abundance of grasses and forbs also measure with an abundance index (0 = absent, 1 = rare, 2 = moderate, 3 = abundant). Based on the

129 frequency of their inclusion in the functions and the magnitude of their coefficients, stream gradient, stream depth and stream width were c1early the most important factors related to beaver habitat use, with decreasing stream gradient, increasing stream width and depth having the biggest positive effect on beaver densities. Stream gradient and width were also the most important physical factors related to colony density for Slough and Sadleir (1977) and Howard and Larson (1985). A summary of the most important variables used to determine the suitability of habitats for beaver is provided in Table lA-2.3.

2. Validation of classification keys and habitat suitability index models

2.1. The Canada Land In ven tory Program Novak (1970) , in the Tweed Forestry district of Ontario, used the land capability rating produced by Canada Land Inventory and discovered that although soils were deemed to be the most important factor in rating areas for beaver, this was not the case for beaver in southeastem Ontario. In fact, an inverse correlation was found between number of colonies and beaver capability ratings (Novak 1970). The author states that this may have resulted from the fact that the presence of deeper soils generally resulted in less surface water (Novak 1970, 1987).

2.2. Traversy (1974), Traversy and Banville (1977), Levasseur and Mondoux (1977) Environnement Illimité Inc. (1981), after a thorough analysis of the parameters (category of aquatic ecosystems, abundance of streams and humid environments, presence of mountainous relief) decided that the key constructed by Traversy (1974), Traversy and Banville (1977) and Levasseur and Mondoux (1977) defined the global importance of different habitats but could be improved by adding certain parameters (i.e. ecological region and geologic surface materials). Fontaine (1979) found a strong correlation between the indices of potential determined by Levasseur and Mondoux (1977) and the average number of beaver colonies for the trapping territories surveyed by Banville (1978a), south of Eastmain River. Similarly, Brodeur et al. (1977) also found an excellent correlation. Bider (1979)

130 however, found ~hat beaver presence near Great Whale River was not limited by the same factors as those described by Traversy and Banville (1977). He found that sites classified in the key as having the capacity to support high beaver densities, in reality, did not. He attributed this fact to the presence of shrub species that were little utilized by beaver. Similarly, SOMER (1980) did not find any significant difference between the average density observed for the three different potential classes near LG2 reservoir and those attributed with the key developed by Traversy and Banville (1977).

2.3. Environnement Illimité Inc., 1981 Nault and Gascon (1983) near Eastmain used the key created by Environnement Illimité Inc. (1981) and found no significant difference between the densities associated with each potential class. However Hydro-Québec (1982) in the Great Whale Region found a highly significant correlation between the 5 potential classes obtained from Environnement Illimité Inc. (1981) and the average density of active lodges. When certain sub-classes were rearranged and consolidated, the model explained 97% of the variation (Hydro Québec, 1982). As such, it was concluded that although classification keys can be valuable in regions they are validated, they annot be used as predictive tools for other regions. Furthermore, the authors state that climate (not included in any classification keys within Québec), may be a very important factor to consider at the beavers' northem range limit (Hydro Québec 1982).

2.4. SOMER, 1982 Consortium Gauthier and Guillemette (1989) used the classification key created by SOMER (1982) and found that the habitat potential classes were very weakly related to the actual densities estimated for active beaver sites in the Great Whale region. The authors suggested that including bioclimatic factors, and information relating to trapping and hunting may improve the predictive ability of the classification keys. AIso, they state that the potential of different regions should be evaluated with more precision and at a smaller scale in order to identify favorable habitats at these higher latitudes, seeing as these ideal habitats are relatively rare.

131 Consortium Gauthier and Guillemette (1990) used the key created by SOMER (1982) and were unable to establish a strong relationship between the potential attributed to each study quadrat and the actual utilization of the quadrats estimated using the number of active sites and the density of colonies.

2.5. Allen, 1983 (HSI) Robel et al. (1993) hypothesized that high-quality beaver habitats should have more beaver colonies than low-quality habitats and one would expect values produced by HSI models to be positively correlated with beaver colony ground counts in Kansas. In the end, the HSI values were only poorly correlated with colonieslkm data. The HSI model output was associated with 17% of the variation in beaver densities, leaving 83 % of the variation in ground counts unaccounted for by the HSI model and caused by factors that were not included in the model. Even after being modified, the HSI model only accounted for less than 33% of the variation in ground counts. Development of a regression model using the 4 major vegetative variables in the HSI model was also unsuccessful. The authors suggest that water quality, stream and river substrates, agricultural activity, and other factors should be included in the HSI model to improve the fit. Le Groupe Roche Boreale (1991) tested the HSI model and found no relationship between the HSI indices ca1culated and the density of beaver colonies seen in the Eastmain study region (correlation Pearson r=-0.069 P=0.599). Although the HSI model was developed to apply to the entire distribution of the beaver, the authors believe that factors further north may be more important to beavers than in habitats further south. For example the harsh climate may be a limiting factor with respect to the quality of habitat, forcing the beaver to displace more frequently because of overexploitation. A summary of the results from the validation of the Canada Land Inventory Program, classification keys of Québec and the Habitat Suitability Index model created by Allen (1983) is provided in Table 1A-2.4. Overall, it can be seen that the classification of beaver habitat is rather site specifie and therefore models built for particular regions are not weIl supported when tested in other areas within the beaver' s range.

132 Table IA-2.1: Codes used in the ecological maps obtained from the Service des Inventaires Ecologiques. These maps were the foundation for the classification keys developed in Québec (Traversy, 1974; Traversy and Banville, 1977; Levasseur and Mondoux, 1977; Environnement Illimité Inc., 1981; SOMER, 1982)

NOTE: E.S. = Ecological system Ecological region Abundance of riparian habitats D Lac Delorme 1 absent or or very few L Lac Le Grand 2 low abundance S Monts Schefferville 3 medium abundance 0 Monts otish 4 very abundant H Lac Hippocampe 5 extremely abundant

0 Lac Opi scoteo Landfonns

Relief 1 More than 20% or surface materials are fine deposits e.g. clay and si~ F Flat 2 More than 20% of surface materials are medium deposits e.g. sand More than 200,4, of surface materials are large deposits e.g. rocks and U Undulating \ 3 gravel R Hummocky Surface materials (Geological) H Hilly or rugged 1 Till M Very hilly or rugged 1" Till of Cochrane

Aquatic Ecosystem Category 2 Fluvio-glaciale deposits Less than 5% of the E.S. 's surface occupied by aquatic a 4 Glacio- riparian si~y-clayey deposits areas 5% to 15% of the E.S. 's surface occupied by lakes < 250 b 4" Glacio- riparian and sandy fluviatile deposits ha More than 15% of the E.S.'s suface occupied by lakes < c 5 Clayey-marine deposits 250 ha 1 The E.S. comprises or borders lakes >250 ha and <500 ha 6 Littoral deposits The E.S. comprises or borders lakes >500 ha and < 1000 7 Ombrotrophic organic deposits 9 ha The E.S. comprises or borders lakes > 1000 ha and < n 7* Minerotrophic organic deposits 2500 ha r The E.S. comprises or borders lakes > 2500 ha 8 Moutainside deposits h The E.S. borders rivers that 20 m to 60 m in width 9 Wind-bome deposits i The E.S. borders rivers that are > 60 m in width 0 Rocks in place

The E.S. borders in part, rivers that are affected by the m !ides j The E.S. borders James-Bay or Hudson's Bay Abundance of stresns 1 absent or very few 2 low abundance 3 medium abundance 4 very abundant 5 extremely abundant

133 Table lA-2.2: Summary of classes devised for beaver habitat potential

REFERENCE(S) CLASS 1 CLASS2 CLASS3 CLASS4 CLASS 5 Canada

• Traversy (1974) Terrains of this type have Terrains of this type have Terrains of this type have few limitations on beaver significant limitations on limitations of such • Traversy and production. Their potential beaver production. The importance that the Banville (1977) is very high. The water limitations consist in a beaver production is - Levasseur and courses are numerous. combination oftwo or almast non-existent. The and theïr characteristics more factors including ground is inadequate and Mondoux (1977) comply with the general topography, thickness of characterized by rocky requirements for beaver laose materials, number and/or sandy soils. The of lakes and water topography is inadequate courses, size of lakes while the characteristics etc.. water and earth are of water courses make it important factors in this hard for the beaver ta case flounsh - Environnement Very high patential for High potential for beaver Medium patential for Weak potential for beaver Very weak patential for Illimte (1981) beaver establishment; establishment; these beaver establishment: establishment; these beaver establishment or these systems present systems present certain these systems present systems present very uninhabitable: these • SOM ER (1982) very few limitations limitations, especially with certain limitations with important limitations with systems present such · Consortium respect to the Quaüty of respect to the Quality and respect to the Quality and strong constraints with Gauthier and habitats available Quantity of favorable Quantity of favorable respect ta the qua lit y and habitats habitats Quantity of favorable Guillemette (1990) habitats such that the densities will rarely exceed O.025coIonieslkm2

• Siough and Sadleir No biophysicallimitations Slight limitations; number Moderate limitations; Severe limitations: Limitations preelude (1977) affect beaver production; of beaver colony sites per number of beaver cok>ny number of beaver colony beaver production: number of beaver colony shoreline of lake (km) = sites per shoreline of lake sites per shoreline of lake number of beaver colony sites per shoreline of lake 3.22· 4.81 and stream = (km) =1.61 • 3.21 and (km) =<1.60 and stream sites per shoreline of lake (km) ;; 4.82+ and stream 6.44· 9.65 stream =3.22 • 6.43 =<3.21 (km) =0 and stream =0 = 9.66+ United States

• Atwater (1939) Most favorable location Favorable location roc Fair location for beaver Marginal location for Unfavorable for beaver establishment; beaver establishment; establishment; forage na beaver establishment; plenty of preferred same requirements as as p1entiful or made up to forage made up of less species available and class 1 except that sorne extent of Jess desirable species, accessible, rcom for expansion is limited by favorable species, room topography steep and expansion, reliable water- topography, supports at for expansion strictly rocky, water-supply supply, favorable least 2.48 colonieslkm limited, waler-supply unreliable, supports only topography for the variable, supports at least scattered beaver colonies construction of dams, 1.24 colonies! km lodges and food caches, supports at least 3.73 colonies! km.

• Retzer et al. (1956) Excellent beaver habitat. Gaod beaver habitat. Questionable beaver Unsuitable beaver * Acreage of aspen and Valley grades bet'Neen 0- Valley grades bet'Neen 7- habitat. Valley grades habitat. Valley grade of wi!ows (.dded by • ·Rutherford (1964) 6% (improving with 12% (improving with belween 13-15% (CI-15% more than 15% and a Rutherford, 1964) decreasing grade), a decreasing grade, for shale), a valley width vaHey width nct much valley width of more than includes 0-12% for wider than the 'Nidth of wider than the stream 46 m (increasing with rhyolite), a valley width the channel, but usuaUy itsetf, and rock types increasing width) and wider than the 'Nidth of narrow, and rock types of become irrelevant rock types of glacial til/, channel (improving as glacial till, schist, granite, because valley grade and schist, or granite ( in that INidth increases), and rhyolite, or shale (in that width are unsuitable. order). rock types of glacial till, order). schist, granite, or rtlyolite (in that order).

• Howard and Larson Excellent habitat that can Suit.bIe habitat thal can Unsuitable habitat that (1985) support 2 or more support 1 active colony cannat support actrve colonies per km perkm colonies (0 coloniesJkm)

134 Table lA-2.3: Summary ofmost important variables used to determine beaver habitat suitability.

PHYSICAL PARAMETERS LAND-CaVER PARAMETERS OTHER Aquatic environment Forests ana trees Latitude (7)

Aquatic Ecosystem category (1, 2, 3, 4, 5) Adjacent forest cover (5) General ecology (11) Abundance of lakes, streams, vvetlands and/or Importance of arborescent cover/woody vegetation Competitive effect of deer and elk riparian habitats (1,2,3,4,5,18,19,22,25,26, (6, 60, 61, 62), zones of deciduous and coniferous (60) 28,31,32,33,35,37,43,45,56) trees (7), percentage of hardwood vegetation (15)

# lakes < 0.1 km' (6, 23) Species composition of woody vegetation (14, 19, Trapping and hunting intensity (18, 20,21,24,25,26, 27, 28,31,35, 37, 43, 44, 45, 28,31,33,34,35,36,37,38,41, 46,49,50,52,53,54, 56,57,58, 59) and 49, 51, 52, 56, 58) abundance of certain preferred species of woody vegetation (8, 1D, 13, 16) # lakes betvveen 0.1 km' and 1 km' (6) Percent tree canopy closure (14) Human disturbance (22, 25, 39, 56) # lakes 5 km' or more (6) Tree species (13) Predation (40, 51, 58) # lakes 0.25 km2 -1 km 2 (7) Tree circumference (13) Precipitation (45) Lake pelimeter (8) Percent trees in 2.5 to 15.2 cm dbh size classes (14) Lake area (8) Number of tree within 9 m of the focus tree (13) Lake area vs. perimeter ratio (8) Shrubs Stream length (8) Percent shrub crown closure (14) Valley/streamwidth (8, 9,10,11,15,16,20) Shrub cover in immediate surroundings (13) Valley! stream gradient (8, 9,10,11,14,15,16) Average height of shrub canopy (14) Stream depth (16, 20) Herbaceous ana unaerstory plants Degree of bifurcation of the stream channel (12) Percent sedges between the focus tree and the river (13) Bank slope (13,16,20,32, 49, 54, 57,58) % forbs between focus tree and river (13)

Average water fluctuation on an annual basis (14, Abundance of grasses and forbs measured with 49, 51, 54, 58) an abundance index (16) Shoreline development factor (14) Percent grass in immediate surroundings (13)

Watershed size (15, 25, 29, 45, 49, 54) Total percentage of understory cover between focus tree and river (13) Watershed depth (57) Percent lacustrine surface dominated by yellow and/or white water liIy (14) Reliability of water supply (11, 59) Shoreline vegetation characteristics Speed ofwaterflow (20,21,49,54,55,57,58) Riparian zone width (16) Roc/{ and soils Length of non productive brush along shoreline (8) Landforms or geologic surface matelials/deposits Development and composition of shoreline (1, 2, 4, 5, 17, 20, 21, 22, 23) vegetation (25, 32, 37, 42, 44, 46, 49, 54, 55, 57) Rock type (9, 10) Miscellaneous Well-drained soils (15) Diversity of vegetation types (12) Miscellaneous Biomass of available winter food cache materials (12) Relief! Physiography (1, 2, 3, 4, 5, 17, 21, 22, 23, Land-cover types (12) 25, 26, 32, 33,41,43, 45,49, 54, 56) Neighbor aspect (13) Food supply (11) Aspect of focus tree (13) Fire class (Le. old vs. new bums), zones in regeneration, and clearcutting (7, 17, 18, 25, 32, 33,36,41,45,46,47,48,49,56,58) Distance tram tocus tree te water (13) Neighbor cut status (eut or uncut; 13) Elevation (16, 55) Presence of bogs (18, 25, 42) Area available for expansion (59) Depletion of food resources (30, 45) Exposure te wind (49) Presence of islands and bays (20, 57)

135 Cont. Table 1A-2.3:

QUANTITA TlVE beaver habitat studies:

Canada

1) Traversy, 1974 2) Traversy and Banville, 1977 3) Levasseur and Mondoux, 1977 4) Environnement Illimite. 1981 5) SOMER, 1982 6) Consort Gauthier-Guillemette, 1990 7) Le Groupe Roche Boreale. 1991 8) 810u9h and Sadleir, 1977

9) Retzer et al., 1956 10) Rutherford, 1964 11) U.S. Forest Service, 1973 12) Boyce. 1981 13) Willis, 1978 14) Allen, 1983 15) Howard and Larsen, 1985 16) Beier and Banett, 1987

aUALIT AT IVE beaver habitat studies:

17) Fuller. 1953 18) Drole!. 1005 19) Traversy, 1976a 20) Brodeur et al. 1977 21) Banville, 1978 22) Banville 1979b 23) Bider, 1979 24) Charette, 1980 25) Canac-Marquis, 1980 26) Desjardins, 1980 27) Cadieux, 1981 28) Girard, 1981 29) Hardy, 1981 30) Belisle, 1982 31) BoMn, 1982 32) Desrosiers, 1982a 33) Pelletier and Lizotte, 1982a 34) Pelletier and Lizotte, 1982b 35) Pelletier and Lizotte. 1982c 36) Pelletier and Lizotte, 1982d 37) Pelletier and Lizolte, 1982e 38) Crete and Samson, 1983 39) Duchesneau, 1983 40) Breton and Macquart, 1984 41) Lizotte, 1984 42) Michaud, 1984 43) Roy, 1985 44) BruneUe and Bider, 1987 45) Parcs Canada, 1988 46) Brunelle et al. 1989 47) Cotton, 1990 48) Guay. 1991 49) Le Groupe Roche Boreal, 1991 50) Potvin and Breton, 1992 51) POOin et al, 1993 52) Brunelle and Ouzilleau. 1994 53) POOin and Breton, 1997 54) Tecsult Inc., 2000 55) Alliance Environnement Inc., 2002a 56) Lafond et al. 2003 57) Alliance Environnement Inc. 2004 58) FORAMEC, 2004

59) Atwater, 1939 60) Packard, 1947 61) Buckley, 1950 62) MacDonald, 1956

136 Table lA-2.4: Summary of results from the validation of certain classification keys and habitat suitability index model created by Allen (1983).

Classification Kell or Habitat SuitabiliPilndex Supported (Strong correlation) Not supported (Weak or Model no correlation)

The Canada Land Inventory Program (1)

Traversy (1974), Traversy and Banville (1977), (2), (3), (4) (5), (6) Levasseur and Mondoux (1977)

Environnement Illimite (1981) (8) (7)

SOMER (1982) (9), (10)

Allen (1983) (11), (12)

(1) Novak (1970) (2) Environnement Illimité Inc. (1980) (3) Fontaine (1979) (3) Brodeur et al. (1977) (5) Bider (1979) (6) SOMER (1980)

(7) NauR and Gascon (1983) (8) Hydro Québec (1982) (9) Consortium Gauthier Guillemette (1989)

(10) Consortium Gauthier Guillemette (1990) (11) Robel et al. (1993) (12) Le Groupe Roche Boreale (1991)

137 APPENDIX 2A-l

Table 2A-1.1: Aerial survey conditions, Koksoak River study area, Québec,

Elld />,,'1 Spet'd Date Top(·-0/f. p·n-m)

05-Jul-i):1 AS-:.50 241'\1-069 dnzzle 25 (8W) -;. 00 913 60-80 ,31)-IQO

05-Jul-04 AS-350 141\ 1-022 ~(HO 70-60 25 ($W) :;, 18 925 60-80 80-100

05-Jul-04 A$-3S0 241-\2-020 5, 241 25 ($W) 931 938 60-80 BO-lI)O

24K2-07Q 57 70 241 939 60·80 $1)-100

05·Jul-04 AS-35Q 24F15-Ql1 30 70 241 25(5'11) ~ 53 10013 60-80 60-100

OS-Jul-04 AS-350 24Fl5-Ql0 30 70 241 26 (SW) 1009 10 1~ 1)0-30 30-100

05-JuH)4 AS-3'50 24F14E-Qô 30 7'0 241 26 (SW) 1016 10 ,~ 80-100

05-Jul-04 AS·350 24F14E-Q24 50 ~·o 70 241 26 (SW) 1020 1028 60-80 80-100

OS-Jul-04 AS-350 24f14E-Q28 50 30 70 241 26 (SW) 1030 1036 60-80 80-100

05-Jul-04 AS-3'50 24f14E-Q38 10 50 30 70 241 26 (SW) 1036 1039 60-80 80-100

0'5-Jul-04 A&-350 24F14W-OJ8 10 30 70 241 26 (SW) 1042 1046 60-80 80-100

CtS-JuI-04 AS-350 14f14W-Q3 10 'Sü 30 70 26 (SW) lü47 1050 60-80 80-100 '" 1 (Y.,...JuI-04 AS-2SQ ::!4F 14W-QSO 30 241 26 (S'N) 1052 60-80 80-(1)(1 05-JuH)4 AS-350 .24F14W-Q45 " 50 30 70 241 26 (SW) 10'57 110,) 60-80 80-.101) 05-Jul·04 AS-3<:;O 24F 14W-Q44 11 .?·o 70 241 31 (NW) 1103 1109 60-80 80-100

Il 70 241 31 (NW) 1114 111i} 60-80 80-100

(J'S.Jul-û4 AS-350 14Fl1-QI 12 52 70 241 dnzzle 3!(NW) 11 ~1 1132 eQ-su 80-100

05-Jul-04 AS-350 24FI4E-Q3Q 12 52 30 70 241 31 (NW) 1137 1139 60-80 80-100

OS-JuI-04 AS-350 24F14E·Q36 Il 70 241 31{NW) 1140 114i 80-80 80-100

05-Jtd-04 AS-35û nymg back 12 52 10 70 241 dl'lzzle 31 (NW) 1147 1204 92 200

0'5-Jul-04 AS-350 24K2-Q19 31) 70 241 dnzZle 11 9(E) 1352 1358 60-80 $0-100

05-.A.J!-04 AS-350 24K2-Q1S 14 54 30 70 241 dnzzle 14 (SE) 1401 1415 80-80 80-100

05-Jul-04 A.S-3':iO 24F1S-û 14 70 241 14 (SE) 1416 1430 60-80 30-100

05-JuI-04 .%-350 24FE~Q62 14 54 30 70 241 14 (SE) 1434 1441 60-80 80-100

OS-JuI-04 A5-350 24F15-Q53 14 54 30 70 241 14 (SE) 1441 1443 60-80 80-100

05-Jul-04 AS-3':îO 24Fl5-Q25 14 30 70 '41 14 (SE) 1446 1443 SO-100

05-Jul-04 AS-350 24F 14E-Q? 14 54 30 70 241 14 (SE) 1450 145':1 60-80 80-100

05-Jul-04 AS-J'50 24Fl4E-Oa 14 30 7û 141 9(E) 1500 1'50i 60-80 80-100

05-Jul-04 A5-3S0 24F14E,QS 14 56 30 70 241 9(E) 1509 1'513 80-80 80-100

05-Jul-04 AS-.3S0 24F 14E-Q46 14 30 70 241 9 (E) 1515 1'518 60-80 80-100

05-JuI-04 AS-3S0 24F14E-Q33 14 56 30 70 '41 9(E) 1519 1530 60-80 80-100

CODE EXPLANATION

Topo-O# Nabonal Topographie System map sheet and quadrat number

Temp Oaily temperature

Rel Hum % Relative Humidlty in % is the ratio of the quantity of water va pour the air eontains eompared to the max. amount It can hold at that partieular temperature % Sun % of the time sunshine

% Cloud % of the time cloudy

Visibility (km) Visibility in km is the distance at which objects of suitable size can be seen and identified Almospheric visibility can be reduced by precipitation,log, haze or other obstructions 10 visibility such as blowing snow or dus

'Mnd Speed (kmlh) The speed 01 motion of air in kmlhr, usually observed at 10 m above the ground_

'Mnd Oir 10's 0 The direction (true or geographic, not magnetic) trom which the wind blows. Expressed in ten's of degrees, 9 me ans 90 degrees true or an easl wind, 36 means 360 degrees true or a wind blowing Irom Ihe geographie north pole A value of zero (0) denoles a calm wind.

138 Table 2A-1.2: Aerial survey conditions, Lac Guillaume-Delisle study area, Québec.

R.,I TE-mp Hum Start Av Albtude A.v Spe'l,j Date ("C) % %Sun %CIoUu VISlorllty(lo'm) Ram (cm) (m) (I-m/h) tnpupto 1" Jul-l14 8.,11 ~ür) LR UmluJd'l 60 100 241 ":4 (N\'\j 1703 1-;' ';·0 1"3(,-2UO

\'5-Jul-lJ4 8,,11206 LR 34C3-0111 100 241 Il J6 (~JJ t8('<; I~ 11 60-30 110

16-.Jul-04 800'11206 LR 100 2'+1 6(1·(:0 110

16-Jul-l)4 E'e1l206 LR 34('9-1)123 100 241 .!.2,(tJW) \2-17 1240 110

16-Jul-1)4 6",1I206lR 34(9·'-1125 96 100 241 23 32 (NW) 1249 1'::50 60--80 110 l&-.lul-04 B811'::iJ6lR 96 la gO '" 1250 60-S0 110 16-Jul-04 Bell 206 LR :::4C9-011'j 10 90 241 32 (NW) 1254 1258 110

16-Jul-04 Bell 2ü6 LR 34(;-048 30 70 241 28 32 (l'JW) 1414 1417 60-30 110

16-Jul-(14 Bell 206 LR 34CI·(J41 80 30 70 241 28 32 (NW) 1417 \420 60-80 110

16-Jul-04 Bell 206 lR 34Cl-0.31 BO .30 70 241 32{NW) 1421 142'; 60-$0 110

16-Jul-04 Bell 206 lR 34Cl-t)33 30 70 241 32 (NW) 1425 142'3 ôO-i'JO 110 16-Jul-04 Bell 206lR 3Kl-Q16 30 3JJ 70 241 2" 32 (l'J'W) 1430 1435 60.30 llU 16-~lul-04 8~1I 106 lR 80 30 70 :!41 32 (N'N) 1436 14 3~ 60-S0 110

16-Jul-04 8el1206lR 34Cl-04 80 30 241 23 32 (NW) 1441 1443 60-80 110

16-Jul-04 Bell.206lR 34C9-045 80 3JJ 70 241 32 (NW) 1447 1451 60-30 110

I&-Jul-04 BBlI206 LR 34C9-057 80 3JJ 70 241 28 32(NWl 1457 1<.; 00 60-80 110

lf"... JuI-04 8911206lR 34B4-f)56 65 30 70 241 31 (NV'/) 1500 60-3') 16-Jul--04 Bell206lR 3464-Q60 10 " 30 70 241 32 (N'N) 1507 1') 10 80-30 110 16-Jul-04 Bell 206 LR 3484-061 10 65 30 70 '41 28 32 (NW) 1512 1515 60-80 110 16-Jul-04 Bell 206 LR 384-()69 10 05 70 241 os 32(N'vV) 1511 110

I&-Jul-04 Btlil 206lR 3484-053 10 65 30 70 241 28 32 (f#'J) 1522 00-80 110

16-Jul-04 Bell ::08lR 3484-074 10 65 30 70 241 28 32 (NW) 1524 1527 60-30 110

16-Jul-04 Bell 206 lA: 34C8-07.>3 10 76 30 70 241 28 32 (NVv') 1604 1600 00-80 110

16-Jul-04 Bell 206 lR 34C8-Q79 IQ 76 30 70 241 28 32{NW) 1606 1608 60-80 110

16-Jul-04 Bell 206 lR 34(;8-082 10 76 30 70 241 2S 31(NW) 16"09 11'519 60-80 110

16-JuI-Q4 Bell 206 lR 34C8-Q87 10 76 30 70 241 28 32(NW) 1620 1625 00-80 110

16-Jul-ù4 Bell 206 LA: 34(;8-091 10 30 70 241 32 (NW) 1626 1627 ro-Sû 110

16-Jul-04 8~1 206 lR 34C8-Ql05 10 76 30 70 241 28 32 (NW) 1628 1630 60.30 110

16-Jul-04 Bell 206 LR 34C3-1)100 10 76 3JJ 70 241 28 31(NWI 1632 16.3'5 60.80 110 16-Jul-04 Bell 206 lR 10 76 30 70 241 28 32 (NW) 1635 1638 60-80 110

1f5-Jul-04 Bell 206 lR 34C8-093 10 76 30 70 241 32 {NWI 1658 17(1) 60-30 110

16-JuI-Q4 Bell 206 LR ~C9-Q133 10 76 70 241 28 32(1'M') 1705 1714 80-80 110

CODE EXPLANATION

Topo-O# National Topographic System map sheet and quadrat number

Temp Daily temperature

Rel Hum% Relabve Humidity in % is the ratio 01 the quantity 01 water va pour the air contains compared to the max. amount it can hold at that particular temperature

% Sun % 01 the ti me sunshine

% Cloud % 01 the tlme cloudy

Visibility (km) Vislbility in km is the distance at whlch obJects 01 suitable size can be seen and identilied. Atmosphenc visibility can be reduced by precipitation,log, haze or other obstructions to visibility such as blowing snow or dus

Wnd Speed (kmlh) The speed 01 motion of air in kmlhr, usually observed at 10 m above the ground.

Wnd Dir 10's 0 The direction (true or geographic, not magnetic) trom which the wind blows. Expressed in tan's of degrees, 9 maans 90 degrees!rua or an east wind, 36 means 360 degrees !rue or a wind blowing from the geographic north pole. A value 01 zero (0) denotes a calm wind.

139 Table 2A-1.3: Aerial survey conditions, Lac à L'Eau Claire study area, Québec.

R>;un %':k,u,j !hml P31n (hm/h) (,.m) sur ....ev lm} (km'h!

17·,lul·l)-l 8~112i)~, lI=< 3461-01 43 3~, ')0 1(1 24 1 l') ~ô (N'I'I) 'J .~.:: 1104 ~O·61j 1(10

17-Jul-04 Bell 206 LR 3482-01 62 " g" 10 24 1 10 n(NW) li 51 1'120 Sl2 191

CODE EXPLANATION

Topo-Q# National T opographic System map sheet and quadrat number

Temp Daily temperature

Rel Hum % Relative Humldlty ln % IS the ratio of the quantlty of water vapour the air contalns compared ta the max amount It can hold at that partlcular temperature

% Sun % of the tlme sunshlne

% Cloud % of the tl me cloudy

Vislbility (km) Vlslbllity ln km IS the distance at which obJects of sUitable slze can be seen and Identlfled Atmospheric vislblilty can be reduced by preclpltatlon,fog, haze or other obstructions to visibillty such as blowlng snow or dus

Wind Speed (km/h) The speed of motion of air in km/hr, usually observed at 10 m above the ground.

Wind Dir 10's 0 The direction (true or geographic, not magnebc) from whlch the wind blows Expressed ln ten's of degrees, 9 means 90 degrees true or an east wind, 36 means 360 degrees true or a wmd blowing from the geographic north pole A value of zero (0) denotes a calm wmd

140 Table 2A-1.4: Description of habitats frequented by beaver (biological and physical factors) in the Koksoak River study area, including active (first section; ends with a dashed line), abandoned (second section) and signs outside the study quadrats (grey shaded cells) observed during aerial and ground surveys.

Ouad. SUrYty Statu! Type Expo Type Arn Wldth Sp ..d Aq V.g 'Nid. Sh. v. Coordlnates pl ~thod 1------':::::.:...... ,---1 of Lake Lah (ml (m) D.clmal"s Aqu. (ha)

141 Table 2A-1.5: Description of habitats frequented by beaver (biological and physical factors) in Lac Guillaume Delisle study area, inc1uding active (first section- ends with a dashed line) and abandoned (second section) signs observed during aerial surveys.

Colony" Ou.d. Survey StatU$ Sign Type of Aqu Expo Type- Are2l4 Wldlh Speed Aq. Yeg. Wld. Sh. V. SI'\. V. Coordlnetes Point Method Lake Lake (m) (m) Decimal degr.es

OL 00 RS BS l,' long

4l-pt 1 Aenal Adlve 1 lk Little HI <100 >10 MS8n! >10 '56092 -7n 269 .- . _,- _._. ._._ .r-' ._.- _._._. 120-pt2 A~nal AbJndc,n"d Sinn >)(1 S p..bs",nt 5-10 56545 -7f;4'S7

llO-pi 6 Aenal Atoandoned Strm LittlE- >10 AbSBnt :~10 56545 -76457

120-pll Aenal Abandon .. d Strm Little >10 .Absent ')-10 56'543 -76452

120.- pt3 Aenal Almndone,j Stlm Little >10 Absent >10 56543 .16444

tl2-pt3 Aenal AbandonE-d ~trm Llttl", 'la Abs8nt >10 58377 ·7f..144

82-pt4 Aenal .Aban.joned Strm Little >10 Absent <5 56376 -75 \41

93- pt2 Aenal Aban.:lone,j Strm Little 20-45 Absent >10 56421 -7()191

9!.-pt '1 Aendl .Aband''JnE-d Slrrn lIttl.,. 20-45 Absent >10 56421 76190

56--pt2 Aenal Ab-:lndone-,j R" Little- 20-45 P.,b:.enl >10 56224 -7601.3

142 î

::;-:!:"7l..., ___~ SC"~ ~ '"1 - ~::1 lZl ~ Colony. Ou". SUnoy StIltUI' SlgrI. 1,-yp. of Ellipo. Slop. IO.posits Typ •• Ar.a· Wldlh Sp •• d Comp. Aq. V.g. W1d. Sh. V. Comp. Adjacent cour Coordlnates pt Motho. (1 ... r. cache Sh. V. Oecimeld.gre.s 0.. --- N Actlv. 1 AbW1d.. ,\qu. , Lo'.. (ha) Dominent Present §. Eï ~ FLILWlolslOl.. Allwl DB AL w 1 OS c 1 MC 1 RS 1 AL 1 wl DB BS 1 T 1 Lat. 1 Long. ~_ ....

28-pt .. Lk Little >20 1 Sa Chi <100 >10 Abs~nt >10 '57339 -.;;~192 ::1 ~ ."""',""". .."" 0.. S':;>;' •• Aanal o lZl t-1 24-pt3 +Ground Lk L1t1~ >20 1 Sa Chi <100 >10 Absent >10 57 .;4~. ;;;9177 .."" ::1 0 '-' _. (0 ~ (0 24-pt1 +Ground Acbve Lk L1t11e >20 1 Sa Chi <100 >10 Absent >10 S7iJ.l7 '77 o..:;>;'~ AéoO 7-pt17 +Ground Acbve Lk L1ttl.. >20 l 'Sa-Lü Chi <100 >10 Ab5ent >10 57 .~~, -"3D71 ~ ::::0::!. (0 _.'"0 Aanal (") c:s I-pt1g +Gfound Acbv!? lk lIttle <11) 1 Sa Chi <:'00 '10 :>ILl 57":':" -t;9 C'6~ c. o '"1 0 AeoO ::1 lZl ::1 7-pt20 +Ground Acbve lk Utile >20 1 Sa Chi <100 >10 At·senl >1[' 5(:;122 -r~S!ùG8 0..2 0 7-pt6 GrOlJnd .."" "", Litlle >20 1 Sa >10 Absent >10 5793:' -6906;:1 C/J0..H; (0 ...... ::r' 7-pt7 Active lk Lltlle >20 1 Sa <100 :>10 Abs .. n! >10 '5iS22 ·E;S06', $4. ...., ~ "'oood_. Œ _.~ 0'" o '"1 _. 8-pt22 +GfOU"ld .."" lk Litlle >20 1 Sa Chi <100 >10 >10 11 '579,6 ·r:9f1;;;1 -", ___::1 p Cl) a...... 11-pt22 +GrOl.Jld-. lk Little <10 1 Sa Chi <100 >10 AbsoiInl >10 579'57 -1)t",9')6 .."" CIJ ,..... CIJ ANi. _.:::s ::t> +Gn;",J"Id "",,, Little >20 1 Sa >10 >10 ':;7 9~$ ·l'~:j";", 11-pt21 lk w <100 Absent <§ $2. (0 _. C/J""'.,c 11,pt23 +GrOtXld Acbve ck Llllle <10 1 Sa HI <100 >1U Absenl >10 1 1 57 9~1 -';'",94.j .... ~ 0... (0~ ""o. o _. ~ 11-pt24 +Groood ""',. lk Little >20 1 33 <100 >10 Absent ~1O 11S79f>2 -1$2949 O"':::s :::s W _. C/J (JQ ......

14-pt15 +GrOl.Jld ACbve Strm Lltlle <20 1 Sa >10 Absent >10 '57S109 -~i;'.39~1 (0 ~ (0 ~ (") 0.. 14-pt25 Gcooad Acb" sv, Llltle tu-20 1 Sa "0 Msenl >10 57 9~O ·c~·:=le,,', (0 ...... 0'" MoO 0.. ::;. '< 14-pt6 +Gfound AcHv& lk LIIll .. <20 1 Sa >10 Absenl >10 1157869 -E;S.S3:: 0..(00'" Mo' ~,-...(O 14-pt7 +Ground <20 1 Sa Absent >10 '5796:01 -t3S;20 1 Sa c. >10 Abse.n! >10 57913>1 -':;83':'1 :::s lZl (0 (JQ ...... '""1 Mo. lZl 15-pt2 +GrOl.Jld Acbve SInn Llttla <20 1 Se-BI >10 .10 57971 ~e,: rro '-'" _'"'1o (")Cl) 0'"_. 15-pt27 G((JUnc! ""'" Strs Little >20 1 Sa >10 >10 ':7972 ·6~;;,1)3 ~ c. 0 Mo. :::s 0 15-pt4 +GI'OU"Id Acbve $1<, LIIII€! >1(1 AtlSE>nt >10 '579 7 ;. -132.,'61 0" '_I_.-l_2-. ·_:?~l-·~~-· o.? (JQ Ab..,' C/J (0 ;:;. 24-pt2 Mo' lk LIIlIe >20 1 Sa c_ <100 >10 Ab~,Hll >10 5784, ·(>9177 Aband El :::s ~ !\on'...... Ground lk L,tlie >20 1 Sa w <100 "hl Atlso:-nt >10 57 r• .?~, -':0')53 <0.. "~25 (0 lZl ~ Ab"" :"-<73 't-pt14 "o. Strm lill!& >20 1 Sa >10 A.tIsent >10 57 ~::'5 1';'> '< ~ :::s C/J _.0- Abon' ...... '"0 15-p1:28 "'ouad Strs 1It1\.;:t <10 1 Se >10 Absent: >1(1 578'1 .~t!(,t;." :::s"'::r' Aenal+ Aban' ~'< 15-pt3 Ground lk uttle <20 1 Sa-BI c. <100 >10 Absenl >11) 579 7 0 .'J-:i:r:::,.j 0.. ~. Aena+ Ab..,' 10 ~ (") 14-pt8 Groond Str, <20 1 Sa >10 AJ:,s!211) '579 7(1 -,:;~.?S~ '"~ lZl ~ :::s"' ...... Cl) 0.. Codes for Tables 2A-1.4 to 1.6

CODe SIGNIFICANCE Colony# Division of signs into distinctIVe colonies Quad. (Point) Quadrat 1# (Point #)

$urvey Method Aerial survey and/or ground survey

Status Active vs. Abandoned Sîgns

Activa signs of beaver preserœ

FL Active lodge with food cache trom prevlQus year

LW Active todge without food cache

BB Burrow with signs of peeled sticks o Active dam B Beaver Abandt:Jœd signs of beaver pmserat

DL Abandoned lodge DO Abandoned dam Type olAqu Type of AQualic Milieu

Lk Lake = has tributanes and or drainage channels Pd Pond = has no tributaires or drainage channels Riv River =represented as two paraleillines on a topo 1:50 000 Strm Meandering Stream =represented as a single line on a topo map 1·50 000 Strs Straight Stream;:; represented as a single line on a topo map 1:50 000 Expo. Exposure to wind and waves

Little Little exposure

Medium Medium exposure High High exposure Siope S10pe of the baM. (usually 10-20 degrees)

Deposits : Sufface deposits 00 the embankments

Lo Clay and Ioam

S. Sand BI Boukjers Type-Lake Type oflake HI Head of lake Chi Chain lake Wldth WlCbh ofwater body in meters Speed (Sp.): Through-_ or wolor body F Fast: S Slow

Composition food cache AL Green ;alœr (A/nus viridis or cri.sps )

W \Nilow (Sa/ix spp.)

DB Dwarfbirch (BtKu/a pumiJa var. glandulifera) Aq. Veg.: AquMk __tatJon

Abs. Absent Pres. Present Wid. Sh. V. Width of the shoreline vegetation (<5 m, 5-10 m, >10 m)

Sh.V. Shoreline Vegetation

RS River shrubs (including willow, green aider and/or dwarf birch)

BS Black spruee (Picee mariana) Tamarack (Lam larieina) Composition Sh. v.: Compc»i/ion orthe shonJ/ine ""9OIatiOn AL 1 Green AIder (A/nU$" viridi:s or crispa ) W 1WHow (Sa/il< spp.) DB 1 Dwarfbireh (8ra)

Dominant C 1 ConII...... MC 1 Mx.d wlth domin.nç. conifaroua RS 1 River shrubs

AL Aider (Alnus viridis or cri$pIJ) W Wiow (Salix spp.)

DB Dwarfbireh(8ra) as Black spruc:e (Picee manana) T Tamarack (Lam JarieN)

144 APPENDIX 3A-l

Table 3A-1.1: Bioclimatic parameters used for the Selected Modeled Climate Data for Point Locations (LAAS, GLFC, CFS, NRCan, 2006)

Bioclimatic parameter Definition

1. Annual Mean Temperature The mean of ail the weekly mean temperatures. Each weekly mean temperature is the mean of that week's maximum and minimum temperature.

2. Mean Diurnal Range (Mean(period max-min)) The mean of ail the weekly diurnal temperature ranges. Each weekly diurnal range is the difference between that week's maximum and minimum temperature.

3. Isothermality 2/7 The mean diurnal range (parameter 2) divided by the Annual Temperature Range (parameter 7). 4. Temperature Seasonality (C of V) The temperature Coefficient of Variation (C of V) is the standard deviation of the weekly mean temperatures expressed as a percentage of the mean of those temperatures (i.e. the annual mean). For this calculation, the mean in degrees Kelvin is used. This avoids the possibility of having to divide by zero, but does mean that the values are usually quite smal!.

5. Max Temperature ofWarmest Period The highest temperature of any weekly maximum temperature. 6. Min Temperature of Coldest Period The lowest temperature of any weekly minimum temperature. 7. Temperature Annual Range (5-6) The difference between the Max Temperature of Warmest Period and the Min Temperature of Coldest Period.

8. Mean Temperature of Wettest Quarter The wettest quarter of the year is determined (to the nearest week), and the mean temperature of this period is calculated.

9. Mean Temperature of Driest Quarter The driest quarter of the year is determined (to the nearest week), and the mean temperature of this period is calculated.

10. Mean Temperature of Warmest Quarter The warmest quarter of the year is determined (to the nearest week), and the mean temperature of this period is calculated.

11. Mean Temperature of Coldest Quarter The coldest quarter of the year is determined (to the nearest week), and the mean temperature of this period is calculated.

12. Annual Precipitation The sum of ail the monthly precipitation estimates. 13. Precipitation of Wettest Period The precipitation of the wettest week or month, depending on the time step. 14. Precipitation of Driest Period The precipitation of the driest week or month, depending on the time step. 15. Precipitation Seasonality(C of V) The Coefficient of Variation (C of V) is the standard deviation of the weekly precipitation estimates expressed as a percentage of the mean of those estimates (Le. the annual mean).

16. Precipitation of Wettest Quarter The wettest quarter of the year is determined (to the nearest week), and the total precipitation over this period is calculated.

145 Bioclimatic parameter Definition

17. Precipitation of Driest Quarter The driest quarter of the year is determined (to the nearest week), and the total precipitation over this period is calculated.

18. Precipitation of Warmest Quarter The warmest quarter of the year is determined (to the nearest week), and the total precipitation over this period is calculated.

19. Precipitation of Coldest Quarter The coldest quarter of the year is determined (to the nearest week), and the total precipitation over this period is calculated.

20. Annual Mean Radiation The mean of ail the weekly radiation estimates. 21. Highest Period Radiation The largest radiation estimate· for ail weeks. 22. Lowest Period Radiation The lowest radiation estimate for ail weeks. 23. Radiation Seasonality (C of V) The Coefficient of Variation (C of V) is the standard deviation of the weekly radiation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean).

24. Radiation of Wettest Quarter The wettest quarter of the year is determined (to the nearest week), and the average radiation over this period is calculated.

25. Radiation of Driest Quarter The driest quarter of the year is determined (to the nearest week), and the average radiation over this period is calculated.

26. Radiation of Warmest Quarter The warmest quarter of the year is determined (to the nearest week), and the average radiation over this period is calculated. 27. Radiation of Coldest Quarter The coldest quarter of the year is determined (to the nearest week), and the average radiation over this period is calculated.

28. Annual Mean Moisture Index The mean of ail the weekly moisture index values. 29. Highest Period Moisture Index The maximum moisture index value for ail weeks. 30. Lowest Period Moisture Index The minimum moisture index value for ail weeks. 31. Moisture Index Seasonality (C of V) The Coefficient of Variation (C of V) is the standard deviation of the weekly moisture index values expressed as a percentage of the mean of those values (i.e. the annual mean).

32. Mean Moisture Index of Highest Quarter MI The quarter of the year having the highest moisture index value is determined (to the nearest week), and the average moisture index value is calculated.

33. Mean Moisture Index of Lowest Quarter MI The quarter of the year having the lowest moisture index value is determined (to the nearest week), and the average moisture index value is calculated.

34. Mean Moisture Index of Warmest Quarter The warmest quarter of the year is determined (to the nearest week), and the average moisture index value is calculated.

35. Mean Moisture Index of Coldest Quarter The coldest quarter of the year is determined (to the nearest week), and the average moisture index value is calculated

146 Table 3A-1.2: Land co ver classes from the Mosaïque du Québec, obtained from the Photocartothèque Québécoise (1: 2 500 000) and the final categories used in our analysis.

ORIGINAL CLASSES FINAL CATEGORY DEFINITION (Translated) (Translated) (used in anlysis)

Rock Rock (Totrock) Zone of rocky outcrops, fields of boulders or movable deposits

Mosses and rock Moss and rock Territory covered with a mixture of mosses, rock and rock (Totmossroc) deposits with the presence of herbaceous species.

Shrubs and mosses Shrubs mosses and Territory dominated by the shrubs and mosses with the lichens (Totshrubmoss + presence of herbaceous species shrublich) Shrubs and lichens Shrubs mosses and Territory dominated by the shrubs and lichens with presence of Iichens(Totshrubmoss + herbaceous species shrublich) Coniferous and Coniferous forest Forest made up of 10 to 40 % coniferous trees whose ground lichens (Totconf) is covered with lichens Deciduous forest Deciduous forest Forest made up of more than 75 % of deciduous trees (Totdec) Mixed forest Mixed forest (Totmix) Forest made up of deciduous and coniferous trees in variable proportion. The concept of a mixed forest on an image with a resolution of 1 km is very broad. It can include/understand a mixture of the pure settlements.

Coniferous forest Coniferous forest Forest made up of more than 75 % coniferous trees. (Totconf) Coniferous forest and Coniferous forest Forest made up of 10 to 40 % coniferous trees whose ground mosses (Totconf) is covered with mosses Transition zone NIA Territory where regeneration and shrub cover is more or less important following logging or natural disturbances (insects, disease, fire)

Urban centers Urbanized area Territory occupied by great urban centres (Toturban) Intensive culture Agriculture (Totagri) Territory dominated by the agricultural activities where one finds beyond 75 % of fields assigned to monoculture

Extensive farming Agriculture (Totagri) Territory with ha If cultivated fields and ha If forests and waste lands. Peat bogs NIA Territory dominated by peat bogs. A peat bog are found in hum id regions and are produced by the accumulation of partially decomposed organic matter. Water NIA Territory dominated by waterways N.B at a 1 km resolution, only the major waterways are detected. Unclassified zone NIA This class contains the sectors which are hidden by clouds, shade, atmospheric veils or snow

147 Table 3A-1.3: Land coyer classes from the Spatiocarte Portrait du Québec Forestier Méridional, obtained from the Direction des Inventaires Forestiers (l:1 250000) and the final categories used in our analysis.

CLASSES (Translated)* FINAL CATEGORY DEFINITIONS (Translated) (used for analysis)

Rocky outcrop Rock (Totrock) Rocky outcrop Recent burn NIA Plantation burned less than 10 years ago and has not regenerated Windfall NIA An area or group of trees blown over by high wind Partial clearcut (cut in NIA Partial cut by thinning, leaving a mosaic or small bands bands or into a mosaic) Total clearcut NIA Territory where more than 75% of the stems were removed 7m of which were commercial species Water NIA Important lakes and waterways Severe epidemic NIA Deciduous plantation where more than 50% of the stems have been killed after an insect epidemic Deciduous regrowth Deciduous forest Deciduous plantation where commercial species of (Totdec) deciduous regrowth are more than 2 m in height Intolerant deciduous Deciduous forest Deciduous plantation dominated by poplars and white (young and ripe) (Totdec) birch Tolerant dedduous (young Deciduous forest Deciduous plantation dominated by maples and yellow and ripe) (Totdec) birch Island NIA Island Lichens (Cladonia) NIA Territory covered in Cladonias where the density is less than 25% Mixed forest dominated by Mixed forest (Totmix) Young or ripe mixed plantation where deciduous trees deciduous (young and ripe) occupy over 50% of the groung surface

Mixed forest dominated by Mixed forest (Totmix) Young or ripe mixed plantation where coniferous trees coniferous (young and ripe) occupy over 50% of the groung surface

Mixed regrowth Mixed forest (Totmix) Mixed plantation where regrowth is comprised of commercial species over 2 m in height Agricultural area Agriculture (Totagri) Territory dominated by agricultural activities Urbanized area Urbanized area Territory dominated by urban centers and industries (Toturban) Coniferous regrowth Coniferous forest Plantation where regrowth is comprised of non- (Totcont) commercial stems greater than 2 m in height Young coniferous Coniferous forest Coniferous plantation in the earlier stages of growth (Totcont) where the majority of the stems have a DBH greater than 9cm Coniferous (ripe and Coniferous forest Ripe coniferous plantation where the density is more than dense) (Totconf) 60% Coniferous (ripe and open) Coniferous forest Ripe coniferous plantation where the density is between (Totcont) 25% and 60% Peat bog NIA Humid, bare milieu or a milieu where the density of forest .. cover is less than 25% * MInimum area of 200 ha ; 2 km'

148 ) )

"0 ...... 0(1)(1) ~0-3 rA - S S o =C'" (Jq'<"0"0 (1) (1) o '"1 '"1 --~ ~ ...... ~ ::l a §. Ul ~ 1=' o > p:> '"1 '"1 o., (") (1) (1) --~ '"1 Ul Ul 8=5- o ~ ~ o ~ Ul '"1 Ul P:> Cf} 0 n ::l (1) ::1 ...... 10 0.. "0 (") _. ~ ...... (t°s (1)- c" ::r'sc"P:> (1) ~ c" ~ ct o (1) (1) :.:;:: s "0'"10(1)0.. ';1 (1) 0 ,g. (") ::l (1) (1) ...... "0 ...... Model CGCMl CGCM CGCM IV\DCM IV\DCM CGCMI CGCM CGCM HADCM HADCM CGCMI CGCM CGCM HADCM HADCM (") 0 '"1 p:> ...... c"0::l 2..~~0.. Scenu1. GAI Al 82 A2 82 GAI Al 82 Al 82 GAI Al 82 Al 82 1 ::l (1) ...... O'z'"1 0 0.. s_. N -< P:> rA TImo 2040-2069 2041-2070 2041-2070 2041-2070 2041·2070 2040-2069 2041-2070 2041-1070 2041-2070 2041-2070 2040-2069 2041-2070 20-11-2070 2041-2070 2041-1070 o (1) '< rA Lo...... de (d.d) Latltude (d.d) TMAXMAM TMAXSON TAVANN -!:::o.S ~ o· o c" -< ::l ~ -73.11 45.38 13.09 13.41 13.01 13.16 13.25 14.83 15.06 14.82 15.77 15.04 8.61 8.89 8.48 9.01 8.67 1 (1) 0 rA '"1 '"1 rA ~ -78.57 46.45 10.88 11.28 10.93 11.17 11.30 12.55 12.40 12.20 13.25 12.54 5.87 5.72 5.35 5.82 5.49 N \C o ...... ~(") -73.14 47.06 7.94 10.30 10.04 10.52 10.52 10.45 11.12 10.78 11.73 11.23 4.58 5.23 4.79 5.38 5.11 -...l (1) (TQ (1) os 0 ::l -73.67 47.60 8.95 8.55 8.32 8.88 8.88 10.77 10.37 10.18 11.07 10.38 3.51 3.31 2.89 3.41 3.12 C'. "0 a e; -67.92 48.06 7.81 8.12 7.91 8.42 8.27 10.44 10.18 9.84 10.79 10.31 4.38 4.03 3.66 4.20 3.95 t:! (1) ~ ..... (1)P P:>'"1 ~_. 0 -78.95 48.48 8.00 8.47 8.25 8.89 8.96 10.01 9.82 9.60 10.69 10.01 3.31 3.19 2.80 3.40 3.11 ...... t:! (") -65.78 48.95 6.61 6.38 6.16 6.57 6.35 9.00 8.20 7.94 8.82 8.20 2.46 1.83 1.43 1.99 1.71 "O~PO (1) (1) ~ a -63.22 50.48 5.40 5.49 5.27 5.72 5.53 9.04 9.06 8.64 9.57 9.35 2.99 3.08 2.71 3.19 2.99 o::1. Ul a 'ï:I -69.57 54.44 0.63 0.04 -0.13 0.42 0.35 3.85 3.79 3.17 4.11 3.86 -1.66 -1.96 -2.59 -2.21 -2.41 0..P:>7~ -69.00 57.93 -1.19 -1.27 -1.55 -1.35 -1.50 3.74 4.42 3.78 4.77 4.55 -2.41 -1.64 -2.04 -2.37 ::l ,::::., ::1. -?:_~------O'o..~rA'"1>(=l§ --<::r'rA o~>-:::.: o...... , (JqP:>"O .... 2" O(1):::':~ ~ ...... l '"1 '"1"-7a::l ,::::., _. ~ ::l ~ ::l ~ ~ '< (JO o..p:> '< - ET- (1) ~ P:> LITERATURE CITED ALEKSIUK, M. 1968. Scent-mound communication, territoriality and population regulation in beaver (Castor canadensis Kuhl). Journal of Mammalogy 49(4):759- 762. ALLEN, A.W. 1983. Habitat suitability index models: Beaver. U.S. Fish Wildlife Service FWS/OBS-Wl 0.30 Revised. ALLIANCE ENVIRONNEMENT !NC. 2002a. Dérivation partielle de la rivière du Sault aux Cochons. Suivi environnemental 2001. État de référence. Colonies de castor. Hydro-Québec Environnement et Services techniques. Montréal. ALLIANCE ENVIRONNEMENT !NC. 2002b. Dérivation partielle de la rivière Portneuf. Suivi environnemental 2000-2001. État de référence. Colonies de castor. Hydro-Québec. Environnement et Services techniques. Montréal. ALLIANCE ENVIRONNEMENT !NC. 2004. Derivation partielle de la riviere Manouane. Suivi environnemental 2002. État de référence colonies de castor. Hydro Québec Environnement et Services techniques. Montréal. ATWATER, M.M. 1940. South Fork (Montana) Beaver Survey 1939. Journal Wildlife Managemnt 4(1): 100-1 04. BANVILLE, D. 1978. Inventaire aérien des colonies de castors au sud de la rivière Eastmain - octobre 1977. Ministère du Loisir, de la Chasse et de la Pêche, Direction de la Recherche Faunique pour la Société d'Energie de la Baie James. Québec. BANVILLE, D. 1979a. Inventaire aérien des colonies de castors actives dans les zec Rivière Blanche et Batiscan-Neilson, octobre 1978. Ministère du Tourisme, de la Chasse et de la Pêche, Direction Générale de la Faune, Direction de la Recherche Faunique. Québec. BANVILLE, D. 1979b. Inventaire aérien des colonies de castors dans la réserve Papineau-Labelle, octobre 1978 : rapport d'étape. Ministère du Tourisme, de la Chasse et de la Pêche, (Direction de la Recherche Faunique). Québec. BANVILLE, D., and TRAVERSY, N. 1977. Classement du potentiel pour l'habitat à castor de la Baie James: 3ième approximation. Ministère du Tourisme, de la Chasse et de la Pêche, Direction Générale de la Faune pour la Société de Développement de la Baie James. Québec.

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