Conservation Genetics of Woodland Caribou in The

CONSERVATION GENETICS OF WOODLAND CARIBOU IN THE

CENTRAL BOREAL FOREST OF CANADA

A dissertation submitted to the Committee on Graduate Studies

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the Faculty of Arts and Science

TRENT UNIVERSITY

Peterborough, Ontario, Canada

Environmental and Life Sciences Ph.D. Graduate Program

May 2015

ABSTRACT

CONSERVATION GENETICS OF WOODLAND CARIBOU IN THE CENTRAL BOREAL FOREST OF CANADA

Laura M. Thompson

Maintaining functional connectivity among wildlife populations is important to ensure genetic diversity and evolutionary potential of declining populations, particularly when managing species at risk. The Boreal Designatable Unit (DU) of woodland caribou

(Rangifer tarandus caribou) in Ontario, Manitoba, and Saskatchewan has declined in southern portions of the range because of increased human activities and has been identified as 'threatened' by the Committee on the Status of Endangered Wildlife in

Canada (COSEWIC). In this dissertation, I used ten microsatellite DNA markers primarily from winter-collected fecal samples to delineate genetic structure of boreal caribou in declining portions of the range and increase understanding of the potential influence of the non-threatened Eastern Migratory DU of woodland caribou on genetic differentiation. Eastern migratory caribou are characterized by large home ranges compared to boreal caribou and migrate seasonally into portions of the Boreal DU range.

A regional- and local-scale analysis using the spatial Bayesian clustering algorithm in program TESS delineated four regional clusters and 11 local clusters, with the majority of local clusters occurring along the southern periphery of the range. One of those clusters in Ontario corresponded spatially with the seasonal overlap of boreal and eastern migratory caribou and was characterized by substantial admixture, suggesting that the two DUs could be interbreeding. Next, I decoupled the impacts of historical and contemporary processes on genetic structure and found that historical processes were an

important factor contributing to genetic differentiation, which may be a result of historical patterns of isolation by distance or different ancestry. Moreover, I found evidence of introgression from a currently unsampled population in northern Ontario, presumably barren-ground caribou (R. t. groenlandicus). Finally, because our analysis suggested recent processes were also responsible for genetic structure, I used a landscape genetics analysis to identify factors affecting contemporary genetic structure. Water bodies, anthropogenic disturbance, and mobility differences between the two DUs were important factors describing caribou genetic differentiation. This study provides insights on where conservation and management of caribou herds should be prioritized in threatened portions of the boreal caribou range and may have implications for future delineation of evolutionarily significant units.

Keywords: Approximate Bayesian computation, Bayesian clustering model, boreal forest, Canada, Designatable Unit, gene flow, genetic structure, microsatellite DNA, landscape genetics, landscape resistance, Rangifer tarandus, woodland caribou

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To my father, Robert M. Thompson, Jr.

for teaching me that anything is possible

PREFACE

The chapters in this dissertation will be submitted to peer-reviewed journals and each has, therefore, been written in manuscript format. Some redundancy among chapters occurs, particularly in the introduction sections, and the use of "we" has been used where appropriate because the work was conducted in collaboration with others.

The co-authors have been listed at the beginning of each chapter.

ACKNOWLEDGEMENTS

I would like to thank my Ph.D. advisors Drs. Paul Wilson and Micheline Manseau for giving me the opportunity to come and study caribou in the "Great White North". It has been a wonderful experience and I have truly grown both professionally and personally as a result. My committee members Drs. Bruce Pond and Jim Schaefer provided valuable advice and support along the way and I am very grateful to them. I am also thankful for the many people who helped in the lab, including Taryne Chong, Marina

Kerr, and Karen Smith. I would like to thank the other students, post docs, and research associates working on caribou, including Laura Finnegan, Paul Galpern, Peter Hettinga,

Sones Keobouasone, and Cornelya Klütsch, for providing consultation and encouragement. The work by Mark Ball carved the path for my study and I am grateful for his contributions. Additionally, this study would not have been possible without the many people who provided caribou DNA samples, funds for this project, and expertise on caribou biology, including Ken Abraham, Glen Brown, Natasha Carr, Chris Chenier,

Peter Davis, Steve Kingston, Gerry Racey, and Art Rodgers from Ontario Ministry of

Natural Resources, Dennis Brannen, Dale Cross, Vicki Trim, and Kent Whaley from

Manitoba Conservation, Al Arsenault, Gigi Pittoello, and Tim Trottier from

Saskatchewan Ministry of the Environment, and Dan Frandsen, Fiona Moreland, Richard

Pither, and Norman Stolle from Parks Canada. The Natural Sciences and Engineering

Research Council of Canada and Manitoba Hydro were also important contributors.

I would like to thank Natasha Carr and Steve Kingston for my first helicopter trip and allowing me to enjoy the beautiful Lake Superior coast scenery. Vicki Trim took me on my first ice-fishing expedition in Thompson, Manitoba and we enjoyed fantastic

walleye (or pickerel?). Also, a big thanks goes out to Gerry Racey for the magical trip to

Webequie.

I owe a debt of gratitude to the wonderful people I met along the way, in addition to those mentioned above (in alphabetical order): John Benson, Jim Castle, Andrea Cee,

Cindy Chu, Mark and Heather Dzurko, Danielle Ethier, Fritz Fischer, Tina Fridgen, Colin

Garroway, Matt Harnden, Josh Holloway, Megan Hornseth, Karen Hussey, Eric Koen,

Karen Loveless, Stacey Lowe, Andrea Maxie, Erica Newton, Agnes Pelletier, Jenn Paul,

Lucy Poley, Stephen Petersen, Glynis Price, Erin Rees, Terin Robinson, Jeff Row, Chris

Sharp, Lindsay Spenceley, Lindsay Thomson, Aaron Walpole, Kaitlin Wilson, and

Joanna Zigouris. I would also like to thank my friends from the Peterborough Ultimate

League.

My previous mentor, Dr. Scott Schlarbaum, has been a guiding light along the way. I'm also thankful for my current supervisors Drs. Shawn Carter and Doug Beard who have provided a tremendous amount of support while I worked on my degree. A special thanks goes to Cindy Thatcher for reviewing earlier drafts of my dissertation chapters. Finally, I would not be at this stage in my career or in life without my wonderful family. I love you all so much! You too, Wayah!

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TABLE OF CONTENTS

ABSTRACT ...... ii

PREFACE ...... v

ACKNOWLEDGEMENTS ...... vi

LIST OF FIGURES ...... xii

LIST OF TABLES ...... xiv

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

1.1 Background ...... 1

1.2. General Problem Statement ...... 4

1.3. Justification ...... 7

1.4. Objectives...... 8

1.5. References ...... 9

CHAPTER 2. DELINEATING GENETIC STRUCTURE OF WOODLAND CARIBOU IN THE CENTRAL BOREAL FOREST OF CANADA ...... 19

2.1. Abstract ...... 20

2.2. Introduction ...... 21

2.3. Methods ...... 24

2.3.1. Study area and sampling ...... 24

2.3.2. Genotyping ...... 26

2.3.3. Measuring genetic structure ...... 27

2.3.4. Analysis of genetic groups ...... 30

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2.4. Results ...... 31

2.4.1. Study area, sampling, and genotyping ...... 31

2.4.2. Measuring genetic structure ...... 32

2.4.3. Analysis of genetic groups ...... 34

2.5. Discussion...... 35

2.6. References ...... 44

CHAPTER 3. SEPARATING IMPACTS OF HISTORICAL AND CONTEMPORARY PROCESSES ON GENETIC STRUCTURE OF WOODLAND CARIBOU IN THE CENTRAL BOREAL FOREST OF CANADA ...... 67

3.1. Abstract ...... 68

3.2. Introduction ...... 69

3.3. Methods ...... 73

3.3.1. Study area and sampling ...... 73

3.3.2. Genetic differentiation and network analysis ...... 74

3.3.3. Isolation by Distance...... 76

3.3.4. Approximate Bayesian Computation...... 77

3.4. Results ...... 78

3.4.1. Genetic differentiation and network analysis ...... 78

3.4.2. Isolation by Distance...... 80

3.4.3. Approximate Bayesian Computation...... 80

3.5. Discussion...... 81

3.6. References ...... 88

CHAPTER 4. MODELING LANDSCAPE RESISTANCE TO GENE FLOW FOR WOODLAND CARIBOU POPULATIONS WITH VARYING MOBILITY ...... 105

4.1. Abstract ...... 106

4.2. Introduction ...... 107

4.3. Methods ...... 111

4.3.1. Study Area and Sampling ...... 111

4.3.2. Genetic Data and Analysis ...... 112

4.3.3. Factors Hypothesized to Affect Gene Flow ...... 113

4.3.4. Resistance Surface Optimization and Calculation of Resistance Distances ... 115

4.3.5. Statistical Analysis ...... 118

4.4. Results ...... 119

4.4.1. Genetic Data and Analysis ...... 119

4.4.2. Resistance Surface Optimization and Statistical Analysis ...... 120

4.5. Discussion...... 123

4.6. References ...... 131

CHAPTER 5. CONCLUSIONS ...... 152

5.1. Synthesis...... 152

5.2. Conservation Implications ...... 154

5.3. References ...... 155

Appendix A...... 160

Appendix B...... 161

Appendix C...... 162

Appendix D...... 165

Appendix E...... 166

Appendix F...... 167

LIST OF FIGURES

Fig. 2.1. Sampling locations of unique woodland caribou individual genotypes in Ontario, Manitoba, and Saskatchewan, Canada, 2012...... 61

Fig. 2.2. Average DIC values (dashed line, left y-axis) and the second order rate of change of DIC calculated based on Evanno et al. (2005; solid line, right y-axis) plotted against the number of woodland caribou groups (Kmax) from the top 10% of runs in program TESS ...... 62

Fig. 2.4. Bar plots of average assignment probabilities calculated from the A) no- admixture model and the B) BYM model in program TESS for woodland caribou individuals sampled in Manitoba and Saskatchewan (Kmax = 6)...... 64 Fig. 2.5. Bar plots of average assignment probabilities calculated from the A) no- admixture model and the B) BYM model in program TESS for woodland caribou individuals sampled in Ontario (Kmax = 5) ...... 65 Fig. 2.6. Location of woodland caribou groups inferred by the no-admixture model in TESS (polygons) for (A) all three provinces combined, (B) Manitoba and Saskatchewan, and (C) Ontario...... 66 Fig. 3.1. Location of the 28 woodland caribou herds (center point), 11 genetic clusters (C1-C10), and three caribou Designatable Units (DUs) in Ontario, Manitoba, and Saskatchewan, Canada, 2014...... 101 Fig. 3.2. The four historic scenarios used in the ABC analysis to explore the impacts of divergence and admixture on genetic structure of woodland caribou ...... 102

Fig. 3.3. Network of RST with a percolation threshold of 0.019...... 103

Fig. 3.4. Network of FST with a percolation threshold of 0.011 ...... 104 Fig. 4.1. Locations of woodland caribou herds in Ontario, Manitoba, and Saskatchewan, Canada ...... 148

Fig. 4.2. The plotted relationship between FST and each of the significant landscape models for the full data set ...... 149

Fig. 4.3. The plotted relationship between FST and each of the significant landscape models for the truncated data set ...... 150

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Fig. 4.4. Optimized composite landscape resistance surfaces and current maps for the full and truncated data sets and the variables considered for influencing contemporary genetic structure of woodland caribou ...... 151

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LIST OF TABLES

Table 2.1. The number of individuals (N), observed and expected heterozygosity, average number of alleles, allelic and private allelic richness (rarefied based on sample size), and distance to the current southern range margin for the woodland caribou groups identified by the local TESS analysis in Ontario, Manitoba, and Saskatchewan, 2013...... 58

Table 2.2. Genetic differentiation calculated between woodland caribou groups inferred by the no-admixture model in TESS. FST was calculated between the 4 groups inferred at the uppermost hierarchical level of structure (RC1-RC4) in Saskatchewan, Manitoba, and Ontario, Canada, 2013...... 59

Table 2.3. Genetic differentiation calculated between woodland caribou groups inferred by the no-admixture model in TESS. FST was calculated between groups inferred at a lower hierarchical level of structure (LC1-LC10) in Saskatchewan, Manitoba, and Ontario, Canada, 2013...... 60

Table 3.1. Significant pair-wise permutation tests that suggested observed RST between herds (and associated genetic clusters) is greater than the permuted RST (pRST, an FST equivalent)...... 98

Table 3.2. Degree, clustering coefficient (CC), and betweenness centrality (BC) for networks with nodes based on the woodland caribou herds in Ontario, Manitoba, and Saskatchewan, Canada when edges are weighted with FST (Dpe = 0.011) and RST (Dpe = 0.019)...... 100

Table 4.1. Linearized FST values among woodland caribou herds in Ontario, Manitoba, and Saskatchewan, Canada...... 143

Table 4.2. The R2, T statistic(s), F statistic, P-value(s), and AIC for the linear and quadratic regression models calculated between the resistances distances from the univariate resistance surfaces and FST for the full data set (27 herds) in Ontario, Manitoba, and Saskatchewan, Canada...... 144

Table 4.3. The R2, T statistic(s), F statistic, P-value(s), and AIC for the linear and quadratic regression models calculated between the resistances distances from the composite resistance surfaces and FST for the full data set (27 herds) in Ontario, Manitoba, and Saskatchewan, Canada...... 145

Table 4.4. The R2, T statistic(s), F statistic, P-value(s), and AIC for the linear and quadratic regression models calculated between the resistances

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distances from the univariate resistance surfaces and FST for the truncated data set (24 herds) in Ontario, Manitoba, and Saskatchewan, Canada...... 146

Table 4.5. The R2, T statistic(s), F statistic, P-value(s), and AIC for the linear and quadratic regression models calculated between the resistances distances from the composite resistance surfaces and FST for the truncated data set (24 herds) in Ontario, Manitoba, and Saskatchewan, Canada...... 147

CHAPTER 1. GENERAL INTRODUCTION

1.1 Background

Rebuilding landscape connections among detached populations and ensuring that connectivity is maintained among populations that are still intact are important for management of species at risk of extirpation or extinction. Landscape connectivity supports dispersal, which is the movement of individuals between discrete locations or populations and is an important process for ensuring persistence of wildlife populations

(Schtickzelle et al. 2006, Riley et al. 2014, Simpson et al. 2014). Dispersal has the potential to supplement declining populations, recolonize habitat patches, and facilitate gene flow between different portions of a species range (e.g., Arthur et al. 1993, Boyd and Pletscher 1999, Roach et al. 2001). Wildlife populations often exhibit biological limits to dispersal, preventing gene flow over large scales (i.e., isolation by distance;

Wright 1943). That process has the potential to create genetic structure, which occurs when groups of individuals found within a geographic area are more closely related to one another than would be expected if randomly sampled throughout the species range

(Avise 1992). Although genetic structure is a common phenomenon (Mayer et al. 2009), increased genetic differentiation due to external forces (e.g., environmental constraints, dispersal barriers) is cause for concern for wildlife managers because it suggests that connectivity has been lost or minimized. Resulting fragmented populations often develop low genetic diversity that can affect evolutionary potential and, in extreme cases, reduced population sizes that are at increased risk of local extinctions due to stochastic effects from demographic (Lande 1988, Wootton and Pfister 2013), environmental (Saether et al.

1998), or genetic factors (Mills and Smouse 1994, Higgins and Lynch 2001, Jaquiéry et

2 al. 2009, Hostetler et al. 2013). Consequently, there is a large amount of interest in understanding patterns of genetic structure and the forces that are driving them, which are now feasible because of advances in molecular technology.

A number of techniques to delineate patterns of genetic structure have been explored. Older methods involved grouping individuals based on a priori knowledge of sampling location and, subsequently, calculating genetic differentiation among groups of individuals (Pritchard et al. 2000). However, a disadvantage of that approach is that it may not capture genetic structure that exists within a particular sampling location. More sophisticated techniques have the ability to delineate genetic structure patterns without a priori knowledge of where an individual was sampled. Edge detection methods (e.g., wombling, Monmonier’s algorithm) can be used to identify regions where sharp changes in allele frequencies occur, providing insights on genetic structure patterns (Manel et al.

2003). Also, Bayesian clustering models that reveal an unknown number of populations and assign individuals probabilistically to each of those inferred groups (Pritchard et al.

2000, Guillot et al. 2005a, François et al. 2006, Chen et al. 2007, Corander et al. 2008b,

Durand et al. 2009) have recently been developed and applied. A large number of

Bayesian clustering models have been developed that, for example, allow for linkage disequilibrium and correlated allele frequencies (Falush et al. 2003) or the incorporation of geographic information (Guillot et al. 2005b, François et al. 2006, Chen et al. 2007,

Corander et al. 2008a, Durand et al. 2009, Hubisz et al. 2009). Simulation results have shown that Bayesian clustering models (with geographic information) are superior to edge detection techniques for identifying boundaries to gene flow (Safner et al. 2011).

Additionally, Bayesian clustering models are advantageous because they have the ability

3 to identify hybrid or admixture zones in addition to zones with sharp changes in allele frequencies (Durand et al. 2009, François and Durand 2010).

Once genetic structure has been delineated, it is important to understand factors that may be generating those patterns. The influence of the landscape has received a large amount of attention in the last decade (Manel et al. 2003, Holderegger and Wagner

2006, Manel et al. 2007, Storfer et al. 2010, Manel and Holderegger 2013). Specifically, the landscape is represented as a raster where each pixel is assigned a cost or resistance value that corresponds to hypothesized reductions in gene flow. Resistance or least-cost distances can be subsequently calculated among individuals or populations and compared with measures of genetic differentiation. Because habitat fragmentation is one of the greatest concerns for wildlife populations (Andrén 1994, Fahrig 1997), many studies have focused on the impacts of anthropogenic features as causes of landscape fragmentation. For example, a large number of studies have explored the impact of roads as potential barriers to gene flow (e.g., Epps et al. 2005, Riley et al. 2006, Frantz et al.

2010, van Manen et al. 2012, Prunier et al. 2014, Sawaya et al. 2014). Additionally, natural barriers, such as water or topographic features, have been shown to inhibit gene flow of some species (e.g., Funk et al. 2005, Perez-Espona et al. 2008, Frantz et al. 2010,

Perez-Espona 2012, Cosic et al. 2013, Kimble et al. 2014). Those types of ‘isolation-by- barrier’ hypotheses are often compared with isolation by distance to understand the relative contribution of barriers and a species’ dispersal distance on genetic differentiation (Cushman et al. 2006). An expansion of this approach is to also consider the varying environmental conditions (or gradients in landscape resistance) perceived by animals as they move between populations in a landscape (Zeller et al. 2012). That type

4 of hypothesis is referred to as ‘isolation by landscape resistance’ (Cushman et al. 2006,

McRae 2006).

Although the hypotheses of landscape barrier or gradients of landscape resistance are often compared to isolation by distance to determine the most important factors driving genetic structure, it is also important to consider other factors that can contribute to genetic structure patterns to avoid wrongly attributing them to human-caused or other landscape factors. Recent research has considered the importance of ecological processes on genetic differentiation. Isolation by environment or isolation by ecology occurs when gene flow across differing environmental conditions is reduced because of the process of selection (Shafer and Wolf 2013, Wang et al. 2013, Sexton et al. 2014). Finally, present- day populations have the potential to carry genetic signatures that resulted from historical processes, such as signatures of differing refugia that were occupied during the last glacial maximum (Hewitt 2000, Zellmer and Knowles 2009). Consequently, accounting for the impacts of phylogeographic history in landscape genetic models can improve prediction power of spatial and ecological factors (Dyer et al. 2010).

1.2. General Problem Statement

A portion of a caribou (Rangifer tarandus) subspecies range (i.e., woodland caribou; R. t. caribou) has been identified by the Committee on the Status of Endangered

Wildlife in Canada (COSEWIC) as ‘Threatened’ (COSEWIC 2000). This threatened boreal population or Boreal Designatable Unit (DU; COSEWIC 2011) is also known as the ‘sedentary’ or ‘forest-dwelling’ ecotype based on limited movement and dispersion of females during calving (Bergerud 1988) and the selection of boreal forest habitats year- round. The Boreal DU (or boreal caribou) has declined substantially in recent decades

(McLoughlin et al. 2003, Schaefer 2003, Vors et al. 2007), primarily in the southern portion of the range where human development is most prevalent (Schaefer 2003). In those areas, populations have been either reduced substantially in size or extirpated

(Mallory and Hillis 1998). Logging has converted forests to early successional stages, making habitats ideal for moose (Alces alces) and deer species (Odocoileus spp.; Rempel et al. 1997). Additionally, roads and trails may facilitate travel by predators (e.g., gray wolves; Canis lupus), leading to increased predation on caribou (Bergerud 1974).

Studies have examined genetic connectivity patterns of boreal caribou in Alberta and British Columbia where populations were highly fragmented and spatially structured

(McLoughlin et al. 2004, Serrouya et al. 2012, Weckworth et al. 2012). Additionally, structure among boreal herds was explored using non-genetic clustering techniques in northern Alberta, northern British Columbia, the Northwest Territories, and Yukon and showed that the annual ranges were significantly smaller than those of other caribou DUs

(Nagy et al. 2011). Alternatively, studies in Quebec and Labrador suggested a largely connected population (Couturier 2007, Boulet et al. 2007), potentially due to the presence of a different DU (the Eastern Migratory DU) that has larger home ranges than boreal caribou and may increase gene flow among boreal populations in the region (Boulet et al.

2007). Although the Eastern Migratory DU in our study area is also considered the woodland caribou subspecies, it has migratory behaviors similar to the barren-ground caribou (R. t. groenlandicus) subspecies (or Barren-ground DU), spending winters in the boreal forest (overlapping with the Boreal DU) and migrating long distances to tundra areas along the Hudson and James Bay coasts during calving season.

A large amount of research has also explored potential factors that may be influencing connectivity among woodland caribou populations. For example, landscape alterations have shown to affect connectivity (e.g., Smith et al. 2000, Dyer et al. 2001,

Mahoney and Schaefer 2002, O'Brien et al. 2006, Fall et al. 2007), but most studies were conducted at a local or regional scale. Exceptions are more recent studies that have used genetic data (or genetic combined with radio-collar data), which may be more feasible to collect at large scales. For example, habitat suitability was found to be the most important predictor of caribou genetic relatedness in Quebec and Labrador (as opposed to isolation by distance) and the importance of landscape permeability peaked during the calving and breeding seasons (Yannic et al. 2014). Although habitat suitability and landscape features (e.g., large valleys) were also important for describing genetic structure of western (and spatially fragmented) populations of boreal caribou (in Alberta) and the Southern, Central, and Northern Mountain DUs of woodland caribou in Alberta and British Columbia, other demographic and ecological factors (population size, genetic drift) were also strongly correlated with genetic structure (Serrouya et al. 2012,

Weckworth et al. 2013).

Despite the large amount of woodland caribou research in eastern and western portions of the subspecies range, very few studies have focused specifically in the central boreal forest regions. Ball (2008) was the first broad-scale woodland caribou population genetic study in those regions. He used microsatellite DNA collected primarily from fecal pellets to characterize the genetic structure of caribou in Ontario, Manitoba, and

Saskatchewan (and in Manitoba and Saskatchewan only; Ball et al. 2010). Although most of the sampling occurred along the southern periphery of the boreal caribou range

7 where anthropogenic fragmentation is greatest, his results showed distinct differences in the amount of genetic structure in the eastern (Ontario) and western (Manitoba and

Saskatchewan) portions of the study area. Specifically, five genetic clusters collected from eight herds were detected in Saskatchewan and Manitoba, while only one distinct cluster was detected from the five geographic areas in Ontario (Ball 2008). Although sampling intensities differed considerably among the three provinces, the results suggested an east-west pattern in the degree of genetic differentiation. Consequently, there is uncertainty regarding potential factors that may be responsible for differences in genetic structure patterns between Ontario and Manitoba and Saskatchewan. Galpern et al. (2012) found that both natural and anthropogenic disturbances were important for describing genetic connectivity in the Smoothstone-Wapeweka caribou herd in

Saskatchewan and O’Brien et al. (2006) found an association between the distribution of woodland caribou and connectivity of winter habitat in the Owl-Lake and Kississing herds in Manitoba. Additionally, older research has illustrated the importance of fire on caribou in the boreal forest regions of Manitoba (Schaefer and Pruitt 1991). However, because those studies were conducted at local scales, it is not clear whether those factors are important at the herd level only or could be driving differences in genetic structure at the broader scales that Ball (2008) and Ball et al. (2010) examined.

1.3. Justification

Identifying genetic connectivity and populations with low genetic diversity can provide insights on areas where management actions are needed. Therefore, an understanding of the genetic structure of boreal caribou is necessary for developing effective conservation measures for the species. Additionally, an understanding of

8 landscape factors and other biological and ecological processes (historical and contemporary) that are driving the genetic structure and how they may vary across central regions of the boreal forest is necessary to focus the type of management efforts. Finally, because the Eastern Migratory DU overlaps with boreal animals in northern Ontario and parts of northeastern Manitoba, there is potential for interbreeding between the two DUs

(similar to the Quebec and Labrador animals) and may be an important factor for understanding genetic connectivity among populations. A broad-scale spatial structure analysis that incorporates a large number of caribou populations from multiple regions

(e.g., at the range margin and in the core of the range) and from a large portion of the boreal and eastern migratory DUs (ecotype level) has the potential to provide valuable insights for local and provincial management of caribou (Morrison 2012).

1.4. Objectives

The objectives of this dissertation were to build on the work of Ball (2008) and

Ball et al. (2010) by increasing our understanding of woodland caribou connectivity in central portions of the boreal forest and identifying factors that are important for limiting connectivity. A large amount of fecal pellet sampling has occurred in northern regions of

Manitoba and Ontario since the work by Ball (2008) and Ball et al. (2010). Therefore, microsatellite DNA sampled from woodland caribou in Ontario, Manitoba, and

Saskatchewan was again used to characterize connectivity by analyzing genetic structure; the previously and newly sampled regions encompassed areas where both the Boreal and

Eastern Migratory DUs of woodland caribou are found, as well as areas where only boreal populations (both self-sustaining and not self-sustaining based on the percentage of disturbed habitat; Environment Canada 2012) are found. Because genetic data can

9 carry signatures of historical and contemporary processes, it is important to consider decoupling the impacts of historical and contemporary factors that may be driving gene flow patterns. The specific objectives of this study were to:

1) Characterize genetic structure of woodland caribou in Ontario, Manitoba, and

Saskatchewan,

2) Separate the impacts of potential historical and contemporary factors affecting genetic

structure, and

3) Identify factors that may be driving recent gene flow patterns.

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CHAPTER 2. DELINEATING GENETIC STRUCTURE OF WOODLAND CARIBOU IN THE CENTRAL BOREAL FOREST OF CANADA

Laura M. Thompsona, Cornelya F. C. Klütscha, Micheline Manseaub,c, and Paul J.

Wilsona

aNatural Resources DNA Profiling and Forensic Centre, Trent University, DNA Building,

2140 East Bank Drive, Peterborough, Ontario, Canada K9J 7B8 bNatural Resources Institute, University of Manitoba, 70 Dystart Road, Winnipeg,

Manitoba, Canada R3T 2N2 cOffice of the Chief Ecosystem Scientist, Parks Canada, 30 Victoria St., Gatineau,

Quebec, Canada J8X 0B3

2.1. Abstract

Delineating structure of wildlife populations is important for managing species at risk, particularly those with complex demographic histories. The Boreal Designatable

Unit (DU) of woodland caribou (Rangifer tarandus caribou) in Ontario, Manitoba, and

Saskatchewan has declined in southern portions of the range because of increased human activities and has been identified as 'threatened' by the Committee on the Status of

Endangered Wildlife in Canada (COSEWIC). A second Eastern Migratory DU of woodland caribou that is not threatened migrates seasonally into portions of the boreal

DU range (i.e., Ontario and northeastern Manitoba), confounding management of threatened populations. Because of uncertainty regarding connectivity of populations in declining portions of the caribou range, as well as the potential interaction between boreal and eastern migratory DUs, we used 10 microsatellite DNA markers from winter- collected fecal samples to characterize genetic structure. A regional- and local-scale analysis using the non-spatial Bayesian clustering algorithm in program STRUCTURE revealed at least 2-4 genetic groups. However, the spatial Bayesian clustering models in program TESS, which are more robust when genetic differentiation is weak, delineated four regional groups and 11 local groups. One of those local groups in Ontario corresponded with the seasonal overlap of Boreal and Eastern Migratory DUs and delineated a large admixture zone, suggesting that the two DUs could be interbreeding.

Subsequent analyses indicated reduced genetic diversity and greater differentiation among groups situated at the southern range periphery, identifying the need for increased management actions in those areas. Our findings illustrate the utility of genetic data in

21 combination with Bayesian clustering models for informing conservation of threatened species with unresolved management questions.

2.2. Introduction

Gene flow among wildlife populations is important to maintain genetic diversity and potentially increase evolutionary potential by spreading advantageous alleles and hampering the impacts of mutation, genetic drift, and selection (Ellstrand 2014).

However, various processes can affect genetic connectivity among groups. Although most species exhibit biological limits to dispersal, creating an isolation-by-distance pattern of genetic structure (e.g., Schmidt et al. 2009, Kanno et al. 2011, Koen et al.

2012, Pelletier et al. 2012), breaks in gene flow, or genetic discontinuities, can also occur because of social structure and sex-biased dispersal (e.g., Daly-Engel et al. 2012,

Holekamp et al. 2012, Rollins et al. 2012), natural and unnatural barriers on the landscape

(e.g., Leidner and Haddad 2010, Hapeman et al. 2011), and environmental or climatic conditions (e.g., Gray et al. 2014, Wang and Bradburd 2014). Additionally, wildlife populations have the potential to undergo significant demographic changes, such as range retractions and expansions (Pertoldi et al. 2007, Excoffier et al. 2009, Arenas et al. 2012), and the impacts of such shifts are of increased interest to the scientific and conservation communities because of the potential impacts of land use and projected climate changes.

The distribution of the Boreal Designatable Unit (DU) of woodland caribou

(Rangifer tarandus caribou; herein referred to as boreal caribou) has retracted substantially in recent decades (McLoughlin et al. 2003, Schaefer 2003, Vors et al. 2007).

The majority of the decline has occurred in the southern portion of the range because of increased human activities (Schaefer 2003). In those areas, populations have been either

22 reduced substantially in size or extirpated (Mallory and Hillis 1998). As a result, boreal caribou have been designated as ‘threatened’ by the Committee on the Status of

Endangered Wildlife in Canada (COSEWIC 2000). Boreal caribou are sometimes referred to as the ‘sedentary’ ecotype of woodland caribou based on limited movement and dispersion of females during calving (Bergerud 1988) and overlap of ranges among seasons (Darby and Pruitt 1984, Mallory and Hillis 1998, Rettie and Messier 2001); telemetry research has shown that female annual home ranges can vary from approximately 200-4,000 km2 (Rettie and Messier 2001, Racey et al. 1997, Brown et al.

2003). Those behaviors differ substantially from the Eastern Migratory DU of woodland caribou (herein referred to as eastern migratory caribou) that is found in northeastern

Manitoba, Ontario, Quebec, and Labrador. That DU spends winters in northern portions of the boreal forest (overlapping with boreal caribou), but migrates long distances to tundra areas along the Hudson and James Bay coasts during summer (Berglund et al.

2014). Annual home range sizes of some eastern migratory females have shown to vary from approximately 20,000 to >200,000 km2 (Schaefer and Wilson 2002, Wilson 2013), potentially as a result of local environmental differences (Avgar et al. 2013).

Additionally, eastern migratory populations occur at higher densities than boreal caribou and are not protected under the Species at Risk Act (SARA; www.sararegistry.gc.ca).

Studies have examined the genetic structure patterns of boreal caribou in areas where populations ranged from fragmented and spatially structured (Alberta;

McLoughlin et al. 2004) to a largely connected population (Quebec and Labrador; Boulet et al. 2007, Couturier 2007). In the latter case, it was suggested that the presence of the eastern migratory DU could increase gene flow among populations of boreal caribou in

23 the region (Boulet et al. 2007). The advantages of genetic estimators of population structure is that they encapsulate the functional connectivity resulting from effective gene flow (Michels et al. 2001, Vos et al. 2001, Stevens et al. 2006), are inclusive of both male and female animals, and are informative over broad geographic scales not readily detectable by other means, such as VHF telemetry and aerial surveys (Coulon et al.

2006). Here we expanded on a recent consideration of the genetic structure of boreal caribou from central Canada (i.e., in Saskatchewan and Manitoba; Ball et al. 2010) by including other putative populations from this region and extending sampling into the

Ontario caribou range that contains seasonally overlapping boreal and eastern migratory caribou. The study area contained a region where the two DUs are present (in the Far

North of Ontario) and a region that is presumed to be only boreal caribou (Manitoba,

Saskatchewan, and the southern range in Ontario; Fig. 2.1), but several of those boreal populations were located in human-modified landscapes at the current southern range margin. This study has the potential to provide insights regarding the impacts of the past range retraction and current anthropogenic activities on woodland caribou populations, as well as determine the potential interaction between the two caribou DUs.

We hypothesized that genetic structure would be greatest at the southern range margin, where the presence of anthropogenic activity is greatest. Additionally, those populations may be less connected because boreal caribou are characterized by sedentary behaviors (i.e., small dispersal distances), leading to presumably less connected and more differentiated units in the south. Second, we hypothesized that genetic connectivity among boreal herds would be greater in portions of the range where they overlap with eastern migratory caribou because of potential interbreeding between the two DUs; the

24 mobility of eastern migratory caribou would homogenize what would, otherwise, be differentiated boreal caribou herds. However, the alternative is that behavior and/or non- overlap during the critical rut period will demonstrate low genetic admixture between the two ecotypes but high geographic admixture (i.e., individuals from the two DUs would be found in the same regions but would not be interbreeding). In this case, we expected to see differentiation between what is presumed to be boreal and eastern migratory caribou because low levels of interbreeding conserved different genetic signals (i.e., high genetic assignments to different groups).

2.3. Methods

2.3.1. Study area and sampling

The study area consisted of the Hudson Bay coast and boreal forest in Ontario,

Manitoba, and Saskatchewan and is comprised of three ecozones representing geological, vegetative, and climatic differences that exist throughout the region (Wiken 1986). The

Hudson Plains ecozone covers northern Ontario and extends into northeastern Manitoba and western Quebec. It is characterized by extensive wetlands and coastal marshes with vegetation ranging from arctic tundra to boreal forest transition types (Ecological

Stratification Working Group 1996). The boreal shield is Canada’s largest ecozone and consists of large regions of coniferous forests interspersed by exposed bedrock features.

A large number of lakes are also present (Ecological Stratification Working Group 1996).

The boreal plains ecozone is located to the south and west of the boreal shield and differs in that it is not dominated by bedrock outcrops and has fewer lakes (Ecological

Stratification Working Group 1996). Additionally, despite the presence of some coniferous forest, large portions of deciduous forest, wetlands, and peat bogs exist. The

25 wetland and bog areas make up as much as 25-50% of the boreal plains ecozone

(Ecological Stratification Working Group 1996). Both the boreal plain and shield can be characterized by frequent fire occurrence; however, natural fire occurrence generally decreases from the southern to the northern boundary of the boreal forest and from west to east (Johnson 1996).

DNA was extracted primarily from woodland caribou fecal samples collected during the winter seasons between 2005 and 2011 by the Ontario Ministry of Natural

Resources, Manitoba Conservation, Saskatchewan Ministry of the Environment, and

Parks Canada; a small number of samples came from caribou blood and tissue collected opportunistically by the Ontario Ministry of Natural Resources. Most sampling resulted from systematic aerial survey flights throughout the study area in search of caribou activity. Flights were conducted to identify craters (foraging locations; Ball et al. 2010), which were subsequently visited by helicopter to collect fecal material. Some regions in

Ontario were visited only by helicopter or by foot. Samples came from 20 management units (herds/ranges) delineated by the provincial governments. However, because the management unit in northern Ontario is large (Far North range; Fig. 2.1), we partitioned the region (and areas farther north of the Far North range) into sampled areas (i.e.,

Attawapiskat, Big Trout Lake, Fort Severn, Keewaywin, Marten Falls, Moosonee,

Peawanuck, Weagamow, Webequie), for a total of 28 caribou herds or ranges or sampled areas (herein referred to as herds; Fig. 2.1). Each sample was enclosed in individual containers to prevent DNA contamination and stored at -20°C. All samples were shipped frozen to the Natural Resources DNA Profiling and Forensics Centre at Trent University in Peterborough, Ontario for DNA analysis to identify unique individuals.

2.3.2. Genotyping

DNA extraction and quantification followed the protocol outlined by Ball et al.

(2007). We amplified DNA using 10 polymorphic, microsatellite markers (Rt6, Rt7, Rt9,

Rt24, and Rt30, Wilson et al. 1997; Map2C and BM848, Moore et al. 1992; BM888 and

RT5, McLoughlin et al. 2004; BMS1788, Cronin et al. 2005) as part of multiplex sets.

Each reaction was composed of a volume ranging from 7-10 l and contained: 1x PCR buffer, 2.0 mM MgCl2, 0.2 g/ml of BSA, 0.4-0.5 M of each primer (forward primer fluorescently labeled with NED, FAM, or HEX; Applied Biosystems or ABI, Foster City,

California, USA); 0.2 M of each dinucleotide triphosphate; 1 unit of Taq polymerase

(Invitrogen Life Technologies, Carlsbad, California, USA) and 2.0 l of DNA template.

The amplification cycle consisted of an initial denaturing of 94 C for 5 min followed by

30 cycles of 94 C denaturing for 30 seconds, 56-60 C annealing for 30 seconds, and 72

C extension for 30 seconds. The cycling culminated with a final extension of 60 C for

45 minutes. Thermal cycling was performed in an MJ DNA Engine PTC 200 (MJ

Research, Watertown, Massachusetts, USA) configured with a heated lid.

Following amplification, 0.5 μl of each reaction was added to 10 μl of deionized formamide and 0.002 μl of the internal size standard GENESCAN-500 (ROX; ABI).

That mixture was subjected to capillary electrophoresis on an ABI 3730 Genetic

Analyzer (i.e., automated sequencer) and GENEMARKER AFLP/Genotyping Software

(version 1.6; Soft Genetics LLC®, State College, Pennsylvania, USA) was used to score, bin, and output allelic (and genotypic) designations for each caribou sample. Because some areas were more heavily sampled than others (increasing the potential for

27 recaptures), we used the package ALLELEMATCH (version 2.03; Galpern et al. 2012a) in R (R Core Team 2013) to identify unique multilocus genotypes.

We determined the genotyping error rate by analyzing a random subset of samples

(N = 92) from the North Interlake region in Manitoba using the same PCR and capillary electrophoresis methods described above (DNA extraction was not replicated). Three different laboratory personnel were required to blindly score the random subset of samples, providing a second set of scores that could be compared with the original. From this, we estimated the per-locus error rate to range from 0.00 to 0.01 for each of the 10 loci (Hettinga et al. 2012). Additionally, we used MICRO-CHECKER (version 2.2.3; van Oosterhout et al. 2004) to test for the presence of null alleles (one or more alleles that fail to amplify during PCR), large allele dropout (large alleles do not amplify as efficiently as small alleles), or genotyping errors related to stuttering (potential mutations cause slight changes in the allele size during PCR; van Oosterhout et al. 2004).

2.3.3. Measuring genetic structure

We used a Bayesian clustering algorithm implemented in program STRUCTURE

(version 2.3.3; Pritchard et al. 2000) to delineate the distribution of genetic variation throughout the caribou range. That program uses Markov Chain Monte Carlo (MCMC) methods to non-spatially delineate genetic structure based on the observed genotypes (X).

We assumed an admixture model where K was the number of populations and Q represented the admixture proportions (probability that an individual has ancestry to a particular parental group) for each individual (Q values ranged from 0.0 to 1.0 representing no ancestry and perfect ancestry, respectively). We ran the model with correlated allele frequencies a total of five times with K ranging from one to 15 and the

28 burn-in length (number of early iterations to establish starting values) and number of

MCMC repetitions set to 500,000 and 1,000,000, respectively. For each simulation, the program calculates a likelihood value (posterior probability of X given K). The program ran on a high-performance computing cluster (https://www.bioportal.uio.no/; Kumar et al. 2009). We evaluated the second order rate of change (∆K) in likelihood values, proposed by Evanno et al. (2005), to identify the most likely number of caribou groups.

Once the most appropriate K was inferred, we averaged the assignment values for each of the five replicates in program CLUMPP (version 1.2; Jakobsson & Rosenberg 2007).

Hierarchical structure was delineated by analyzing data collected in Ontario separately from data collected in Manitoba and Saskatchewan using the same parameters as described for the analysis of all provinces combined, except that the maximum K was set to ten. Although it is customary to choose a maximum K value based on the number of sampled units (i.e., herds; Evanno et al. 2005), the likelihood values from preliminary runs in STRUCTURE began to level off at smaller K values and suggested increasing K to >15 (or >10 in the case of hierarchical structure) was unnecessary.

Program STRUCTURE (and other non-spatial clustering algorithms) maybe inaccurate when sampling is discrete along clines. (Chen et al. 2007)). Additionally, non- spatial clustering algorithms may have difficulty detecting structure when the degree of genetic differentiation between groups is weak (Latch et al. 2006). Therefore, comparing model results with those generated from a spatial Bayesian clustering algorithm is recommended (Chen et al. 2007). We used the program TESS (version 2.3.1; Chen et al.

2007, Durand et al. 2009) to infer the number of genetic groups based on the genotypes and geographic coordinates of individuals (François et al 2006). Durand et al. (2009)

29 recommends starting with the no-admixture model to estimate an upper bound on the number of genetic clusters and, also, because, it may create patches along clines. The no- admixture model uses a hidden Markov random field as a prior distribution and assumes that individual allele frequencies at a particular location are more similar to allele frequencies at neighboring locations than to those at distant locations (François et al

2006, Chen et al. 2007). The probability that two individuals belong to the same cluster is controlled by the interaction parameter and any non-zero value introduces spatial dependence (Durand et al. 2009). We ran the no-admixture model with the interaction parameter set to 0.6 a total of 50 times with the maximum number of populations (Kmax) ranging from two to 15 and the number of MCMC iterations set to 50,000 and a burn-in length set to 40,000. For each simulation, TESS calculates the deviance information criterion (DIC), which is a measure of both model fit and model complexity; smaller DIC values indicate that a particular model is more appropriate for the data (Spiegelhalter et al. 2002). For each value of Kmax, we selected and averaged the top 5% of runs based on DIC and plotted them against Kmax. Again, the second order rate of change, calculated for DIC, was used to determine the most likely number of genetic groups. We then ran the BYM model, an admixture model that is also available in TESS, with a linear trend a total of 50 times with the same number of iterations and burn-in length as above to determine the proportion of each individual's genome that belonged to the inferred number of groups identified based on the no-admixture model (Durand et al. 2009). The assignment probabilities from the top 5% of runs (based on DIC) were averaged in

CLUMPP. Again, we delineated hierarchical structure by analyzing data collected in

Ontario separately from data collected in Manitoba and Saskatchewan using the same

30 models and parameters as described for the analysis of all provinces combined, except that Kmax ranged from two to 10.

2.3.4. Analysis of genetic groups

There are multiple biological processes that can cause Bayesian clustering programs to imply clusters when randomly mating groups may not actually exist.

Therefore, it is important to validate results of Bayesian clustering models by ensuring that each of the inferred clusters comply with the assumptions of Hardy-Weinberg and linkage equilibrium (Guillot et al. 2009). We used the probability test in program

GENEPOP (version 3.1; Raymond and Rousset 1995) to determine if the frequencies of sampled genotypes were consistent from one generation to the next (i.e., Hardy-Weinberg expectations). The genotypic linkage disequilibrium test, also in program GENEPOP, was used to test whether the genotypes at one locus were independent from genotypes at another locus. We used sequential Bonferroni adjustments (Rice 1989) to determine significance of multiple tests. For each of the local clusters, we calculated observed and unbiased expected heterozygosity in program GENALEX (version 6.4; Peakall and

Smouse 2006) and program HPRARE (version June 6, 2006; Kalinowski 2005) was used to estimate the average number of alleles, allelic richness, and private allelic richness (a measure of genetic distinctiveness); that program uses rarefaction to correct the observed number of alleles for sample size differences among groups. Finally, we measured

Euclidean distance from the center of each of the inferred groups (based on the no- admixture model in TESS) to the closest point along the current southern range margin to allow comparison between genetic diversity values and proximity to the range margin.

Guillot et al. (2009) also suggested an important method for validating Bayesian clustering results is to ensure the inferred clusters are substantially differentiated from each other. Therefore, we computed FST values based on the uniform method of Weir and Cockerham (1984) in SPAGEDI (version 1.3; Hardy and Vekemens 2002) to determine whether genetic differentiation increased when pooling individuals based on genetic clusters, as opposed to the caribou herds. Additionally, because Bayesian clustering methods have the potential to overestimate genetic structure due to underlying patterns of isolation by distance (Frantz et al. 2009, Schwartz and McKelvey 2009,

Francois and Durand 2010, Safner et al. 2011, Meirmans 2012), we calculated a regression slope of FST values on spatial distances in SPAGEDI to determine any impacts of isolation by distance on our results; the slope (b) was tested for a significant difference from zero based on 1000 permutations, which is the equivalent of carrying out a Mantel test (Mantel 1967, Hardy and Vekemans 2002). This test was conducted for the regional and local clusters and caribou herds. Finally, we calculated isolation-by-distance tests among individuals within clusters. Specifically, a Mantel test was used to compare individual-based genetic distance with Euclidean distance among individuals in

GENALEX.

2.4. Results

2.4.1. Study area, sampling, and genotyping

We identified 1134 unique caribou genotypes in Ontario, Manitoba, and

Saskatchewan (Fig. 2.1). Five hundred ten unique genotypes were sampled in Ontario and 624 unique genotypes were sampled in Manitoba and Saskatchewan (Fig. 2.1).

MICRO-CHECKER detected the potential presence of null alleles or genotyping errors

32 related to stuttering at the RT30 and RT24 loci in the majority of genetic groups identified by the Bayesian clustering analyses (7 and 5 out of the 10 groups for RT30 and

RT24, respectively). Although simulations have shown that the presence of null alleles only modestly reduces power of Bayesian clustering methods to correctly assign individuals (Carlsson 2008), we chose to run the TESS models with and without those two loci. The results were not affected and we report findings based only on the ten loci.

2.4.2. Measuring genetic structure

The ∆K values calculated from program STRUCTURE suggested that the highest order of structure was K = 2 when caribou individuals from all three provinces were pooled (Appendix A); however, the ∆K value was also large for K = 3 and the likelihood values continued to increase to K>10, suggesting the presence of hierarchical structure

(Appendix A). Two groups indicated a separation between the Saskatchewan-northern

Manitoba animals and the southern Manitoba-Coastal animals and individual assignment to the two groups was not organized geographically among other Ontario herds

(Appendix B). When confining our analysis to Manitoba and Saskatchewan, the ∆K peaked at K = 2; however, the likelihood values continued to increase to K>10, again suggesting hierarchical structure (Appendix A). When individuals were clustered into two groups, the assignment values suggested a northern Manitoba and Saskatchewan group and a southern Manitoba group, which was similar to the results produced by Ball et al. (2010; Appendix B). When confining our analysis to Ontario, the ∆K values suggested the most likely number of groups to be K = 2 (Appendix A). Two groups separated the Coastal animals from the remainder of Ontario (Appendix B).

The second order rate of change in DIC values from the no-admixture model in

TESS suggested the most likely number of groups was Kmax = 4 when individuals from the 3 provinces were pooled (regional clusters; Fig. 2.2a). The admixture values calculated from the BYM model suggested strong assignment to a Saskatchewan and northern Manitoba group (Smoothstone-Wapeweka, Reed, Kississing, Wheadon,

Wimapedi, Wabowden, Naosap, and a portion of the Norway House individuals; regional cluster 1 or RC1), a Coastal group (RC2), a southern Manitoba group (The Bog and

North Interlake; RC3), and an Ontario group (Kesagami, Brightsand, Sydney, Churchill,

Nipigon, Berens, Pagwachuan, Far North, Tundra, God's Lake, and a portion of the

Norway House individuals; RC4; Figs. 2.3 and 2.6). When confining our analyses to caribou sampled in Manitoba and Saskatchewan, the no-admixture model identified six groups (local clusters; Fig. 2.2b), including a Smoothstone-Wapeweka group (local cluster 1 or LC1), a The Bog and upper North Interlake group (LC2), a Kississing group

(LC3), a lower North Interlake group (LC4), a northern Manitoba group (Reed, a portion of Kississing, Wheadon, Wimapedi, Wabowden, Naosap, and Norway House; LC5), and a Pasquia group (LC11). The assignment probabilities calculated by the BYM model showed strong assignment to each of those groups, except for some admixture in LC2,

LC3, and LC5 (Figs. 2.4 and 2.6). When confining our analysis to Ontario, the second order rate of change in DIC values calculated by the no-admixture model suggested that the most likely number of groups was Kmax = 5 (Fig. 2.2c); those groups included an

Ontario south group (LC6), a Kesagami group (LC7), an Ontario north group (LC8), a

Coastal group (LC9), and a Nipigon group (LC10; Figs. 2.5 and 2.6). The assignment

34 probabilities calculated by the BYM model, however, showed a large amount of admixture in LC8 (Figs. 2.5 and 2.6).

2.4.3. Analysis of genetic groups

We used groups identified by the no-admixture model in TESS to test for the random union of gametes and linkage equilibrium. The number of loci that departed from HWE in the 4 regional groups (based on the no-admixture model in TESS) identified when individuals from all 3 provinces were pooled ranged from four in RC2 and RC4 to seven in RC1 (Appendix C). However, the number of departures decreased substantially when calculated for the local groups, ranging from zero departures in LC7 and LC9 to four departures in LC1 and LC3 (Appendix C). When the three provinces were pooled, the number of associations between loci (linkage disequilibrium) ranged from one in RC1 to eight in RC3 out of 45 comparisons (Appendix C). The number of associations decreased when calculated for the local groups, ranging from zero for LC1,

LC6, LC9, and LC10 to five for LC2 (Appendix C). The loci and loci pairs that exhibited departures from Hardy-Weinberg and linkage equilibrium were not consistent across genetic clusters at the different scales. Therefore, no loci were removed from the

Bayesian clustering analyses based on these results.

The observed heterozygosity ranged from 0.570 for the Lower North Interlake group (LC4) to 0.699 for the Kesagami group (LC7; Table 2.1). The average number of alleles and allelic richness ranged from 4.60 and 3.96, respectively, for the Coastal group

(LC9) to 11.10 and 7.58, respectively, for the Ontario north group (LC8; Table 2.1). In general, heterozygosity, average number of alleles, and allelic richness decreased as distance to the range margin decreased (Table 2.1). The Coastal group (LC9) exhibited

35 the lowest private allelic richness value, whereas the largest value occurred in the northern Ontario (LC8) group (Table 2.1).

The average FST value calculated for the 26 caribou herds was 0.038 and ranged from 0.000 for eight comparisons to 0.157 between the Coastal and North Interlake herds

(Appendix D). The average FST for the four TESS groups identified from the pooled data set was 0.056, with the minimum FST value (0.014) calculated between RC1 and RC4 and the maximum FST value (0.110) calculated between RC2 and RC3 (Table 2.2).

When calculated among local groups, the average FST value increased to 0.072 and ranged from 0.013 between LC6 and LC8 to 0.166 between the LC9 and LC4 (Table

2.3). Mantel tests indicated that no correlations existed between FST and geographic distance for the 26 herds (b < 0.001, P = 0.056), the four regional groups (b = 0.001, P =

0.189), or the ten local groups (b < 0.001, P = 0.449), suggesting isolation by distance was not an important factor contributing to genetic structure. However, an isolation-by- distance pattern was detected among individuals within three of the regional clusters, including RC1 (r = 0.077, P = 0.001), RC2 (r = 0.514, P = 0.001), and RC4 (r = 0.068, P

= 0.001). Additionally, isolation-by-distance patterns were detected among individuals within the LC3 (r = 0.144, P = 0.014), LC4 (r = 0.253, P = 0.005), LC5 (r = 0.068, P =

0.010), LC8 (r = 0.083, P = 0.003), and LC9 (r = 0.120, P = 0.039) clusters.

2.5. Discussion

Developing an understanding of how wildlife populations are spatially structured is critical to management. Our study provides insights on the genetic structure of woodland caribou in central portions of the boreal forest, an area that has seen drastic declines in caribou numbers during the last century and, yet, minimal information exists

36 regarding the connectivity among groups of these animals. Our results support the hypothesis that genetic structure patterns in caribou vary from northern to southern portions of the study area. As predicted, genetic clusters, or regions of sharp changes in allele frequencies, occurred primarily along the southern periphery of the study area, which is also the southern range limit of the species. This is a pattern that corresponds with what would be expected under the abundant-centre model, where populations at the periphery of a species range are generally smaller and more spatially isolated and exhibit reduced genetic diversity and increased genetic differentiation (Eckert et al. 2008).

Indeed, estimates of caribou allelic richness and heterozygosity were lowest within groups situated along the southern range periphery (Table 2.1). Moreover, genetic differentiation, measured based on FST, was greatest between groups in those regions

(Tables 2.2 and 2.3). Similar genetic patterns have been detected in woodland caribou at the range margin in Alberta (Weckworth et al. 2012). The sedentary behavior that is characteristic of boreal populations of caribou may have contributed to our detected patterns because those animals are generally found in southern portions of the range.

However, the lack of an isolation-by-distance pattern among clusters and herds suggested other factors that are potentially driven by the landscape are more important.

Landscape alterations as a result of anthropogenic activities are common in southern portions of the study area (Schaefer 2003, Vors et al. 2007) and likely have large impacts on the detected patterns. Previous research has shown that anthropogenic features can negatively impact caribou through direct habitat loss (Murphy and Curatolo

1987, Dyer et al. 2001, Mahoney and Schaefer 2002) and associated disturbances (e.g., highway noise) have the potential to cause avoidance by caribou in regions near an

37 anthropogenic feature (Dyer et al. 2001, Seip et al. 2007, Vistnes and Nellemann 2008,

Galpern et al. 2012b). A positive relationship between the degree of woodland caribou avoidance and the intensity of anthropogenic disturbance has been detected in portions of the range (Leblond et al. 2013), with avoidance distance thresholds often ranging from 4-

10 km depending on the intensity of disturbance (Leblond et al. 2013, Vors et al. 2007).

Additionally, linear features, including roads, trails, and power lines, may facilitate travel by predators (e.g., gray wolves; Canis lupus) and increase access to caribou habitat and the potential for caribou mortality (James and Stuart-Smith 2000). Forestry practices, including forest cutovers, may also impact caribou populations by changing the composition and configuration of the forest, including a loss of old-growth pine (Pinus spp.) and spruce (Picea spp.) forests (Smith et al. 2000, Vors et al. 2007). Forest cutovers can alter caribou food supplies and increase moose (Alces alces) and deer

(Odocoileus spp.) numbers, which also increases predator numbers.

Genetic structure was detected in the northwest portion of Manitoba (the

Kississing and Naosap-Reed herds; Figs. 2.4 and 2.6b). Although those populations are not located near the range margin, anthropogenic activity (e.g., hydroelectric development) is greater in those regions compared to other northern regions of the study area (Environment Canada 2011). Moreover, natural landscape disturbance, such as frequent fire occurrence, can reduce lichen abundance (Joly et al. 2003, Dunford et al.

2006) and affect habitat use by caribou in some regions (e.g., Schaefer and Pruitt 1991,

Joly et al. 2003). Consequently, it is reasonable to assume that fire may hinder caribou movements throughout the landscape, potentially causing gene flow patterns to vary between regions where fire is prevalent and where it is less common. Kurz and Apps

(1999) showed that fire was the primary disturbance factor west of the Manitoba-Ontario border, burning 9 times more forest area within the last 40 years than east of that border.

It has also been suggested that wolf densities are greater in the southern and western portions of the boreal forest (i.e., central Manitoba and Saskatchewan; Darby et al. 1989) where the occurrence of fire is also prevalent. Caribou in those areas tend to select peat bogs surrounded by coniferous forest (Stuart-Smith et al. 1997, Rettie and Messier 2000) to provide some separation from predators (Rettie and Messier 2000).

The no-admixture model in TESS delineated a large genetic group in the Far

North region of Ontario where eastern migratory animals overlap with boreal animals and suggested that the region is genetically differentiated from animals sampled farther south in Ontario (LC6 and LC8; Fig. 2.6c). However, it is important to note that the majority of our samples were collected noninvasively and it is unknown whether unique individuals exhibited distinct eastern migratory or boreal behaviors. Therefore, we assumed that our DNA sampling was representative of both the boreal and eastern migratory animals. The clustering results suggested a potential genetic signal differentiating between the two DUs. The admixture model, however, indicated that caribou individuals were not strongly assigned to LC8 and exhibited a large amount of ancestry from both LC6 and LC8 (Fig. 2.6c). This result supports our hypothesis of potential interbreeding between the two DUs, as opposed to the alternative hypothesis of geographic admixture where individuals have high genetic assignment to different groups. Durand et al. (2009) stated that the no-admixture model has a tendency to create patches along clines and may be useful (in addition to the admixture model) for identifying where potential clines exist. Although genetic clines can result from

39 adaptation along an environmental or climatic gradient, it is more likely that detection of a cline in a mobile species like caribou would be the result of recent contact and interbreeding of individuals from more than one parental population. Such patterns can occur when populations become genetically differentiated due to spatial processes, including range retractions, and then come back into contact after a range shift or expansion (Durand et al. 2009). Klütsch et al. (2012) used mitochondrial DNA to explore phylogeographic history of caribou in Ontario and Manitoba and detected haplotypes from the woodland caribou subspecies that expanded from refugia located south (in what is now the eastern and mid-western United States) of the Laurentide ice sheet, as well as barren-ground caribou (R.t. groenlandicus) that expanded from northern refugia (Flagstad and Røed 2003, Røed 2003) after the last ice age. Consequently, eastern migratory caribou have potentially intermixed with both the Boreal and Barren- ground DUs, or may even represent a secondary contact zone between the 2 lineages.

Secondary contact zones are not an uncommon phenomenon and have been identified in wild populations of plant (e.g., Godbout et al. 2005, Arnaud-Haond et al. 2007) and animal species (e.g., Zamudio and Savage 2003, Phillips et al. 2004, Dubey et al. 2008,

Barlow et al. 2013). Indeed, LC8 exhibited high allelic and private allelic richness values, which has been described in other contact zones (e.g., Arnaud-Haond et al. 2007,

Haanes et al. 2011b).

Although Hardy-Weinberg and linkage disequilibrium were reduced when data were reorganized from regional clusters to local clusters, we still detected departures in some local clusters (Appendix C). This finding may suggest additional structure that was not detected by our models. For example, The Bog and upper North Interlake group

(LC2) exhibited three and four departures from Hardy-Weinberg and linkage equilibrium, respectively (Appendix C). Ball et al. (2010) used several different Bayesian clustering methods to infer genetic structure in the region and found that The Bog herd clustered with the upper North Interlake herd in two of the models, whereas the remaining models separated The Bog from the upper North Interlake. Consequently, the departures we detected may be the result of a Wahlund effect (the pooling of two groups with differing allele frequencies; Wahlund 1928). Indeed, the admixture (BYM) model in TESS suggested mixed ancestry between the two regions (Figs. 2.4b and 2.6b). A potential explanation for the occurrence of Wahlund effects in our study area is periodic long range movements by young caribou males that ultimately contribute to the gene pool of neighboring populations. Most mammal species exhibit male-biased dispersal

(Greenwood 1980), including ungulates (e.g., Haanes et al. 2011a). Although departures from Hardy-Weinberg equilibrium can disappear after a single generation, it may take several generations to obtain linkage equilibrium after a migration event (Hartl and Clark

1997) and this could explain why some clusters exhibited several linkages among loci

(Appendix C). Finally, several isolation-by-distance tests within clusters (between genetic distance and geographic distance of caribou individuals) were significant, which could also explain some of the departures from random mating.

Other factors, such as genetic drift, may have impacts on our results. For example, Ball et al. (2010) suggested that the North Interlake herd is genetically fragmented into an upper and lower North Interlake cluster (and was supported by our results), potentially due to a road network (Fall et al. 2007). Hettinga et al. (2012) estimated demographic parameters for the North Interlake herd and found a declining

41 population trend for both the upper and lower North Interlake clusters, suggesting that immigration from other populations is minimal. The herd as a whole is separated from other caribou populations by large lakes to the east and west and a hydro reservoir to the north (Hettinga et al. 2012). The only exception is some connection between the upper

North Interlake and The Bog herd. Thus, the lower North Interlake, in particular, may be susceptible to genetic drift and inbreeding. The same processes are likely acting on the

Coastal herd as well. Sampling in that region occurred primarily on the Slate, Pic, and

Michipicoten islands in Lake Superior where animals are geographically isolated. Our results showed that both the lower North Interlake (LC4) and Coastal (LC9) clusters exhibited low heterozygosity and allelic richness values (Table 2.1). It should be noted, however, that Pic Island is closer to the Ontario mainland, which may allow for gene flow between Pic Island animals and mainland animals when lake ice is plentiful. Our genetic structure results suggested that Pic Island clustered more closely with the southern Ontario cluster (LC6) than the Coastal cluster (LC9; Figs. 2.5 and 2.6c).

Animals on Michipicoten Island were translocated from the Slate Islands in 1982 (Gogan and Cochrane 1994), which may explain why additional clustering was not detected between those two regions despite geographic isolation.

The Smoothstone-Wapeweka and Kesagami herds are also found close to the southern range periphery (Fig. 2.1). Although the local analysis separated those clusters

(LC1 and LC7) from other regions, the regional analysis grouped the Smoothstone-

Wapeweka herd in the northern Manitoba cluster (RC1; Figs. 2.3 and 2.6a) and the

Kesagami herd in the Ontario cluster (RC4; Figs. 2.3 and 2.6a). Therefore, some level of gene flow is occurring between those herds and other groups, which was supported by

42 greater genetic diversity values for those local clusters (Table 2.1). Interestingly, both

LC1 and LC7 exhibited relatively high private allelic richness, which may be a result of genetic distinctiveness (Table 2.1). Mitochondrial DNA results have suggested western groups of boreal caribou, including those in Saskatchewan, potentially expanded from a different refugium than those in Ontario and some groups in Manitoba (Klütsch et al.

2012). Similar to the northern Ontario cluster (LC8), the Kesagami cluster (LC7) may be influenced by eastern migratory animals, as the landscape along the James Bay coast is similar to that found along the Hudson Bay coast (Wiken 1986). Brown et al. (2003) used telemetry information to characterize range size and seasonal movement patterns and found that ranges were generally much larger than other boreal populations found across Canada. Additionally, the cluster may exchange genes with caribou herds found in Quebec.

Our study complements and builds on work by Ball et al. (2010) by extending sampling into other regions of the boreal forest, as well as areas where eastern migratory caribou are typically found (in the Ontario Far North). We were able to improve our understanding of how the different DUs interact and showed that eastern migratory caribou likely influence genetic differentiation of boreal herds found in Ontario, which is similar to findings in Quebec (Boulet et al. 2007). Unlike Boulet et al. (2007), however, we did not have DNA from collared animals with known migratory behaviors.

Consequently, our study relied on the assumption that sampling, such as samples collected from the Hudson Bay coast (Peawanuck and Fort Severn) where it is believed that only eastern migratory caribou exist, is representative of animals from the Eastern

Migratory DU. Additionally, we assumed that any sample size of eastern migratory

43 caribou would be large enough to genetically discriminate from boreal caribou and determine whether admixture was occurring between the two DUs. For example, sample size, as well as a variety of other factors (e.g., number of loci) can affect the power to detect genetic differentiation (Ryman and Palm 2006). This study also provides valuable insights on regions where gene flow is inhibited among caribou populations. Although

FST values suggested that there is limited connectivity between some of the clusters found along the southern periphery of the range (e.g., LC4, LC9), our results suggest that substantial gene flow exists over large areas in all three provinces (Appendix D).

Because this study focused on understanding the genetic structure patterns, we can only speculate on potential factors that may be driving gene flow. Thus, a quantitative approach for testing the impacts of various hypothesized gene flow inhibitors is needed for this study area (Thompson et al., Chapter 4). Additionally, this study utilizes genetic information for understanding connectivity among populations, which has the potential to carry remnants of evolutionary or other historical factors and can affect current-day genetic structure patterns (e.g., Hewitt 2000, Dyer et al. 2010). Therefore, it is also important to explore whether historical factors may be influencing our results in addition to potential contemporary factors, such as landscape alterations.

In general, genetic differentiation was greatest among clusters in southern portions of the study area where the woodland caribou range has retracted substantially.

Therefore, our study provides valuable information on where provincial management of caribou herds should focus to either restore connectivity among clusters or ensure that existing connectivity is maintained. Additionally, this study provides spatial information on the extent of potential interbreeding between the two caribou DUs in Ontario. This

44 information is important given that eastern migratory caribou are not protected under

SARA, giving managers insights on where protection of boreal caribou should occur.

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Table 2.1. The number of unique individuals (N), observed and expected heterozygosity, average number of alleles, allelic and private allelic richness (rarefied based on sample size), and distance to the current southern range margin for the ten woodland caribou groups identified by the local TESS analysis in Ontario, Manitoba, and Saskatchewan, 2013. Descriptive statistics were only calculated for groups with ≥5 individuals (i.e., LC11 was removed).

Unbiased Distance to Observed expected southern heterozygosity heterozygosity Average Private range Genetic (standard (standard number of Allelic allelic margin group N error) error) alleles richness richness (km)

LC1 121 0.691 (0.036) 0.738 (0.029) 9.6 7.17 0.29 78.97 LC2 91 0.593 (0.040) 0.674 (0.025) 7.9 6.06 0.10 111.23 LC3 62 0.647 (0.062) 0.739 (0.033) 7.8 6.49 0.13 177.92 LC4 56 0.570 (0.043) 0.619 (0.027) 5.5 4.61 0.09 51.77 LC5 289 0.672 (0.043) 0.735 (0.030) 10.7 7.14 0.24 244.72 LC6 185 0.652 (0.035) 0.696 (0.023) 10.7 6.73 0.23 104.32 LC7 77 0.699 (0.023) 0.733 (0.019) 8.6 6.69 0.28 88.01 LC8 140 0.697 (0.031) 0.738 (0.023) 11.1 7.58 0.36 418.44 LC9 85 0.597 (0.045) 0.621 (0.048) 4.6 3.96 0.01 128.72 LC10 23 0.626 (0.041) 0.672 (0.035) 5.3 5.12 0.16 50.22

Table 2.2. Genetic differentiation (FST) calculated between woodland caribou groups inferred by the no-admixture model in TESS. FST was calculated between the four groups inferred at the uppermost hierarchical level of structure (RC1-RC4) in Saskatchewan, Manitoba, and Ontario, Canada, 2013. All FST values were significantly different from zero.

RC1 RC2 RC3 RC4 RC1 0 RC2 0.082 0 RC3 0.037 0.110 0 RC4 0.014 0.062 0.036 0

Table 2.3. Genetic differentiation (FST) calculated between woodland caribou groups inferred by the no-admixture model in TESS. FST was calculated between genetic groups inferred at a lower hierarchical level of structure (LC1-LC10) in Saskatchewan, Manitoba, and Ontario, Canada, 2013. Genetic differentiation measures were only calculated between groups with ≥5 individuals (i.e., LC11 was removed). All FST values were significantly different from zero.

LC1 LC2 LC3 LC4 LC5 LC6 LC7 LC8 LC9 LC10

LC1 0 LC2 0.048 0 LC3 0.043 0.057 0 LC4 0.093 0.066 0.095 0 LC5 0.022 0.037 0.026 0.079 0 LC6 0.025 0.036 0.046 0.086 0.019 0 LC7 0.036 0.044 0.060 0.091 0.030 0.025 0 LC8 0.034 0.034 0.034 0.090 0.016 0.013 0.022 0 LC9 0.084 0.105 0.133 0.166 0.087 0.073 0.073 0.078 0 LC10 0.090 0.082 0.099 0.128 0.088 0.093 0.099 0.090 0.136 0

Fig. 2.1. Sampling locations (circles) of unique woodland caribou individual genotypes in Ontario, Manitoba, and Saskatchewan, Canada, 2012. Unique individuals are coded by the woodland caribou herds (colors); several individuals share the same geographic location.

76000 600 75000 500

74000 400

73000

72000 300 DIC 71000 Δ 200 Average DIC Average 70000 100 69000 68000 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Kmax A.

41500 500 450 41000 400 40500 350 300 40000 250

39500 200 DIC Δ

Average DIC Average 39000 150 100 38500 50 38000 0 2 3 4 5 6 7 8 9 10 Kmax B.

31800 200 31600 180 160

31400 140 31200

120

31000 100 DIC

30800 80 Δ

Average DIC Average 60 30600 40 30400 20 30200 0 2 3 4 5 6 7 8 9 10 Kmax C. Fig. 2.2. Average DIC values (dashed line, left y-axis) and the second order rate of change of DIC calculated based on Evanno et al. (2005; solid line, right y-axis) plotted against the number of woodland caribou groups (Kmax) from the top 5% of no-admixture model runs in program TESS. Plots were generated for individuals sampled in (A) Ontario, Manitoba, and Saskatchewan, (B) Manitoba and Saskatchewan only, and (C) Ontario only.

Fig. 2.3. Bar plots of average assignment probabilities calculated from the A) no-admixture model and the B) BYM model in program TESS for woodland caribou individuals sampled in Ontario, Manitoba, and Saskatchewan (Kmax = 4). Each individual caribou is represented by a single vertical bar separated into multiple colored segments with respective lengths proportional to the assignment values of each inferred cluster.

Fig. 2.4. Bar plots of average assignment probabilities calculated from the A) no- admixture model and the B) BYM model in program TESS for woodland caribou individuals sampled in Manitoba and Saskatchewan (Kmax = 6). Each individual caribou is represented by a single vertical bar separated into multiple colored segments with respective lengths proportional to the assignment values of each inferred cluster.

Fig. 2.5. Bar plots of average assignment probabilities calculated from the A) no- admixture model and the B) BYM model in program TESS for woodland caribou individuals sampled in Ontario (Kmax = 5). Each individual caribou is represented by a single vertical bar separated into multiple colored segments with respective lengths proportional to the assignment values of each inferred cluster.

RC1 RC4

RC3

RC2

LC1 LC3 LC5

LC2 LC11 LC4

LC8

LC6 LC7 LC10

LC9

C. Fig. 2.6. Location of woodland caribou groups inferred by the no-admixture model in TESS (polygons) for (A) all three provinces combined, (B) Manitoba and Saskatchewan, and (C) Ontario. Circles represent individual caribou locations and colors correspond to the groups inferred by the BYM admixture model in TESS. The size of the circles represent the strength of assignment, with the small circles representing assignment values ranging from 0.00 to 0.50, medium circles representing assignment values ranging from 0.51 to 0.80, and large circles representing assignment values ranging from 0.81 to 1.00.

CHAPTER 3. SEPARATING IMPACTS OF HISTORICAL AND CONTEMPORARY PROCESSES ON GENETIC STRUCTURE OF WOODLAND CARIBOU IN THE CENTRAL BOREAL FOREST OF CANADA

Laura M. Thompsona, Cornelya F. C. Klütscha, Micheline Manseaub,c, and Paul J.

Wilsona

aNatural Resources DNA Profiling and Forensic Centre, Trent University, DNA Building,

2140 East Bank Drive, Peterborough, Ontario, Canada K9J 7B8 bNatural Resources Institute, University of Manitoba, 70 Dystart Road, Winnipeg,

Manitoba, Canada R3T 2N2 cOffice of the Chief Ecosystem Scientist, Parks Canada, 30 Victoria St., Gatineau,

Quebec, Canada J8X 0B3

3.1. Abstract

Disentangling contemporary and historical genetic structure patterns of wildlife populations is necessary to elucidate impacts of recent anthropogenic pressures, particularly for species of conservation concern. The Boreal Designatable Unit (DU) of woodland caribou (Rangifer tarandus caribou) has declined in southern portions of the range because of increased human activities and has been identified as 'threatened' by the

Committee on the Status of Endangered Wildlife in Canada (COSEWIC). A genetic structure analysis based on Bayesian clustering algorithms identified approximately 10 clusters in Ontario, Manitoba, and Saskatchewan. However, the Boreal DU in that region is inclusive of a large number of caribou herds with a possible diversity of ancestries and interacts with another DU (the Eastern Migratory DU), which migrates seasonally into portions of the boreal range (i.e., Ontario and northeastern Manitoba). Those processes have the potential to influence genetic structure patterns in addition to recent landscape alterations, and we, therefore, attempted to decouple the genetic structure influenced by historical and contemporary processes using 10 microsatellite DNA markers. A comparative test of FST and RST to determine the relative importance of drift

(contemporary processes) and mutation (historical processes), respectively, supported historical processes as a relevant factor contributing to genetic differentiation (RST>FST,

P-value = 0.030). A network constructed based on RST revealed 2 clusters (a Manitoba and Saskatchewan cluster and a southern Manitoba and Ontario cluster). However, a comparative network based on FST suggested a large amount of contemporary gene flow among those regions, with exception to herds along the southern periphery of the range where structure due to reduced gene flow and genetic drift is likely. Approximate

Bayesian Computation validated the historical patterns, supporting a hypothesis where northern Manitoba and Saskatchewan populations descended from a different ancestral population than southern Manitoba and Ontario. Additionally, we found evidence of introgression from a currently unsampled population in northern Ontario where there is seasonal overlap between boreal and eastern migratory animals. A Mantel test between

RST and spatial distance suggested isolation by distance may also be responsible for the historical patterns. Our findings illustrate the importance and utility of separating historical and contemporary genetic structure to identify patterns that should be of conservation focus and patterns that resulted from evolutionary or other historical processes. Additionally, understanding evolutionary history of populations may have implications for delineating evolutionary significant units for species with conservation status.

3.2. Introduction

Dispersal is a critical process for distributing genetic variation among populations and for minimizing loss of diversity through genetic drift. Identifying factors that reduce gene flow is a common focus of conservation studies, particularly for species at risk, because the causes are often anthropogenic in nature (e.g., Kyle and Strobeck 2001,

Tracy and Jamieson 2011, Hartmann et al. 2013). However, other processes, such as recolonization after vicariance events, can also affect the signal of genetic differentiation due to incomplete lineage sorting or secondary admixture (i.e., introgression; Nittenger et al. 2007). For example, glacial-induced vicariance may be responsible for genetic differentiation and subsequent recent admixture in a large number of North American mammals (e.g., Aubry et al. 2009, Shafer et al. 2011, Reding et al. 2012). Additionally,

70 isolation by distance has the potential to affect genetic structure (Dyer et al. 2010).

Therefore, disentangling the different underlying causes of genetic structure is necessary for management to ensure differentiation is not erroneously attributed to factors that inhibit contemporary gene flow.

Understanding the history of populations may also have implications for defining evolutionary significant units. The Committee on the Status of Endangered Wildlife in

Canada (COSEWIC) has outlined specific criteria for delineating taxonomic entities below the species level (i.e., Designatable Units or DUs) and can include units that encompass the entire range of a subspecies, as well as units that are found to be both discrete and significant (COSEWIC 2014). Although discreteness of populations can be defined based on genetic distinctiveness, significance requires evidence of phylogenetic divergence that is typically measured with evolutionary-informative markers (e.g., mitochondrial DNA sequences; COSEWIC 2014). However, markers with high mutation rates (e.g., microsatellites) have the potential to complement and build on analyses of

DNA sequences by being able to distinguish between incomplete lineage sorting and recent introgression (Qu et al. 2012). Microsatellites have been used to understand both divergence history and recent admixture of, for example, moose (Alces alces) in Norway and Finland (Haanes et al. 2011, Kangas et al. 2013).

The distribution of the Boreal DU of woodland caribou (Rangifer tarandus caribou; herein referred to as boreal caribou) has retracted substantially in recent decades

(McLoughlin et al. 2003, Schaefer 2003, Vors et al. 2007). The majority of the decline has occurred in the southern portion of the range because of increased human activities

(Schaefer 2003). In those areas, populations have been either reduced substantially in

71 size or extirpated (Mallory and Hillis 1998). As a result, boreal caribou have been designated as ‘threatened’ by COSEWIC (2000). Boreal caribou are sometimes referred to as the ‘sedentary’ ecotype of woodland caribou based on limited movement of females during calving (Bergerud 1988) and overlap of home ranges among seasons (Darby and

Pruitt 1984, Mallory and Hillis 1998, Rettie and Messier 2001); telemetry research has shown that female annual home ranges can vary from approximately 200-4,000 km2

(Racey et al. 1997, Rettie and Messier 2001, Brown et al. 2003, Arsenault and Manseau

2011). Those behaviors differ substantially from the Eastern Migratory DU of woodland caribou (herein referred to as eastern migratory caribou) that is found in northeastern

Manitoba, Ontario, Quebec, and Labrador. Eastern migratory caribou spend winters in northern portions of the boreal forest (overlapping with boreal caribou), but migrate long distances to tundra areas along the Hudson and James Bay coasts during summer

(Schaefer 2003). Annual home range sizes of some eastern migratory females ranged from approximately 20,000 to >200,000 km2 (Schaefer and Wilson 2002, Wilson 2013).

Additionally, eastern migratory populations occur at higher densities than boreal caribou and are not protected under the Species at Risk Act (SARA; www.sararegistry.gc.ca).

Microsatellite DNA extracted from woodland caribou tissue collected in Ontario,

Manitoba, and Saskatchewan (the boreal and eastern migratory DUs) was used to delineate genetic structure at two scales (Thompson et al., Chapter 2), including four regional clusters (Figs. 2.3 and 2.6a in Thompson et al., Chapter 2) and 11 local clusters throughout the region (Figs. 2.4, 2.5, and 2.6b,c in Thompson et al., Chapter 2). The majority of those clusters were located along the southern periphery of the range.

Additionally, differentiation was detected between Manitoba and Saskatchewan and

Ontario, with a large admixture zone located in northern Ontario where the boreal and eastern migratory animals overlap seasonally. It is unclear whether evolutionary history or other historical factors may be responsible for some of those patterns. A recent study using mitochondrial DNA suggested that caribou in Ontario and Manitoba are descendants of caribou that expanded from multiple refugia located both north and south of the Laurentide ice sheet after the last ice age (Klütsch et al. 2012). Therefore, it is reasonable to assume that phylogeographic history may have an impact on genetic structure in addition to contemporary processes (e.g., reduced gene flow, drift).

Additionally, although an isolation-by-distance pattern was not detected based on contemporary measures of genetic differentiation (Thompson et al., Chapter 2), historical isolation-by-distance patterns have the potential to affect contemporary genetic structure patterns. It is important to discriminate between those processes to inform future work regarding evolutionary significant units and elucidate impacts of recent anthropogenic pressures on caribou populations.

In this study we attempted to partition the historical impacts on genetic differentiation of woodland caribou from recent ecological processes. We predicted that both historic and contemporary patterns would have an impact on genetic structure, but structure caused by contemporary processes would occur primarily at the southern periphery of the range where anthropogenic activity is greatest. The remainder of genetic structure would be attributed to historical factors from incomplete lineage sorting, isolation by distance, and/or potential introgression from other DUs of caribou. The objectives of our study were to 1) delineate genetic structure caused by historical and contemporary processes and 2) validate our results through tests of isolation by distance

73 and by estimating the timing of divergence or admixture events in providing a preliminary reconstruction of the population history.

3.3. Methods

3.3.1. Study area and sampling

The study area consisted of the Hudson Bay coast and boreal forest in Ontario,

Manitoba, and Saskatchewan and is comprised of three ecozones as a result of geological, vegetative, and climatic differences that exist throughout the region (Wiken 1986). The

Stratification Working Group 1996). The boreal shield is Canada’s largest ecozone and consists of large regions of coniferous forests interspersed by exposed bedrock features.

A large number of lakes are also present (Ecological Stratification Working Group 1996).

The boreal plains ecozone is located to the south and west of the boreal shield and differs in that it is not dominated by bedrock outcrops and has fewer lakes (Ecological

Stratification Working Group 1996). Additionally, despite the presence of some coniferous forest, large portions of deciduous forest, wetlands, and peat bogs exist. The wetland and bog areas make up as much as 25-50% of the boreal plains ecozone

DNA was extracted primarily from woodland caribou fecal samples collected during the winter seasons between 2005 and 2011 by the Ontario Ministry of Natural

Resources, Manitoba Conservation, Saskatchewan Ministry of the Environment, and

Attawapiskat, Big Trout Lake, Fort Severn, Keewaywin, Marten Falls, Moosonee,

Peawanuck, Weagamow, Webequie), for a total of 28 caribou herds or ranges or sampled areas (herein referred to as herds; Fig. 3.1). Each sample was enclosed in individual containers to prevent DNA contamination and stored at -20°C. All samples were shipped frozen to the Natural Resources DNA Profiling and Forensics Centre at Trent University in Peterborough, Ontario for DNA analysis to identify unique individuals.

We used the same microsatellite data from 1,134 unique woodland caribou individuals that were described in Thompson et al. (Chapter 2).

3.3.2. Genetic differentiation and network analysis

We used two measures of genetic differentiation to delineate contemporary and historical gene flow. FST (Wright 1965) measures genetic differentiation based on allele

75 identity (i.e., allelic states) and is an appropriate measure when genetic drift is the primary cause of structure (Hardy et al. 2003). RST (Slatkin 1995) is an analog of FST, but measures genetic structure based on allele size (i.e., the number of repeats between microsatellite alleles) and is appropriate when stepwise mutation contributes to genetic differentiation (Hardy et al. 2003). An increase in the number of mutation events suggests an increased time lapse since common ancestry (Hardy et al. 2003). Therefore, phylogeographic history may be an important cause of genetic differentiation when RST is greater than FST (Hardy et al. 2003). We calculated pairwise FST (Weir and Cockerham

1984) and RST (Michalakis and Excoffier 1996) between caribou herds using program

SPAGEDI (version 1.3; Hardy and Vekemans 2002). We compared observed RST values among caribou herds with the distribution of RST obtained after 10,000 allele size permutations (pRST), which is analogous to FST, also in program SPAGEDI.

Networks are useful tools to visualize and compare differences in clustering among populations (Rozenfeld et al. 2008, Garroway 2008). We constructed two networks where nodes represented the caribou herds and edges were weighted with FST and RST to better understand how caribou clustered based on the measures of genetic differentiation. Community algorithms are commonly used to identify clusters in a network (e.g., Girvan and Newman 2002, Newman and Girvan 2004) because they can detect a large number of edges linking a group of nodes with relatively fewer edges connecting it to other groups of nodes in the network (Fortunato 2010). However, identifying communities on a matrix of genetic differentiation can be problematic because every pair of nodes is connected by a unique edge. An alternative approach involves calculating a percolation threshold (Dpe; Stauffer and Aharony 1994) that can

76 be used to reduce an edge set so that biologically meaningful clusters can be identified

(Rozenfeld et al. 2008, Moalic et al. 2011, Moalic et al. 2012, Cowart et al. 2013).

Specifically, the algorithm determines the point where long-range connectivity disappears and the network begins to fragment into smaller components (Stauffer and Aharony

1994). Moalic et al. (2012) found that the algorithm showed greater resolution and cluster discrimination than community algorithms when identifying clusters in a complete graph.

We compared networks based on a series of node metrics. Degree is the number of edges connected to each node and represents the level of connectivity of a particular node (Kivelä et al. 2015). Betweenness centrality is the number of shortest paths passing through a node that also pass through other nodes (Freeman 1977) and measures how important a particular region is for maintaining gene flow (Kivelä et al. 2015). The clustering coefficient (or transitivity measure) is the ratio of connected node triplets compared to the possible number of node triplets in the network (Watts and Strogatz

1998, Newman et al. 2001, Girvan and Newman 2002). A high average clustering coefficient over all nodes suggests that a network is non-random and has hierarchical structure (Kivelä et al. 2015). Program EDENETWORKS (Kivelä et al. 2015) was used to construct the networks and calculate Dpe and node metrics for both FST and RST.

3.3.3. Isolation by Distance

Thompson et al. (Chapter 2) tested for an association between FST and spatial distance for our study area and found that isolation distance was not an important factor.

However, because historical patterns of isolation by distance have the potential to impact genetic structure patterns (Hewitt 2000, Dyer et al. 2010), we also wanted to determine

whether RST may be related with spatial distance. We calculated a regression slope of

RST values on spatial distances in SPAGEDI to determine any historical impacts of isolation by distance; the slope (b) was tested for a significant difference from zero based on 1000 permutations, which is the equivalent of carrying out a Mantel test (Hardy and

Vekemans 2002).

3.3.4. Approximate Bayesian Computation

We inferred population history using the Approximate Bayesian Computation algorithm implemented in DIYABC (version 2.0; Cornuet et al. 2014), which is a coalescent-based program that looks back in time at genealogies until the most common ancestor is reached (Cornuet et al. 2008). The program simulates data sets for a specified set of scenarios and the posterior distribution of parameters for each scenario can be compared to the summary statistics via a Euclidean distance (Beaumont et al. 2002) from the observed data (Cornuet et al. 2008). We used the results of the network analyses

(above) to develop 4 scenarios with various splitting and admixture events that included one unsampled population (Fig. 3.2). A reference table of 106 simulated data sets was generated using wide priors (default) for all of the parameters. We chose two one-sample summary statistics, including mean number of alleles and mean gene diversity (Nei

1987), and five two-sample summary statistics, including mean number of alleles (2 samples), mean gene diversity (2 samples), FST (Weir and Cockerham 1984), shared allele distance (Chakraborty and Jin 1993), and (δµ)2 distance (Goldstein et al. 1995), which were transformed by linear discriminant analysis to reduce dimensionality of explanatory variables (Estoup et al. 2012). The sequence of events was set so that time one (t1) was greater than times two and three (t2 and t3) and time two was greater than or

78 equal to time three. A polychotomous logistic regression on 1% of simulated data sets closest to the observed data was used to estimate the posterior probabilities for each scenario (Cornuet et al. 2008, 2010). We assessed confidence in selecting the most probable scenario by simulating 500 test data sets with values similar to our observed data (drawn from prior distributions; Cornuet et al. 2010). The type-I error rate was estimated as the proportion of times the selected scenario did not provide the highest posterior probability among competing scenarios. Additionally, the type-II error rate was estimated by simulating 500 data sets for each alternative scenario and determining the proportion of times a lesser likely scenario was incorrectly selected as the best model

(Cornuet et al. 2010).

3.4. Results

3.4.1. Genetic differentiation and network analysis

FST ranged from 0.000 for 8 comparisons to 0.157 between the Coastal and North

Interlake herds (Appendix D). RST ranged from 0.000 for 19 comparisons to 0.198 between the Wheadon and Peawanuck herds (Appendix D). The global (observed) RST value of 0.059 for herds was significantly greater than the permuted value (0.044, P =

0.030), suggesting a signal of mutation in our data. Significant differences when considering pair-wise comparisons suggested patterns of differentiation that were consistent with a portion of the Bayesian clustering results (see Thompson et al., Chapter

2). Specifically, 74 out of 325 tests were significant at an alpha value of 0.05 and 27 of those were still significant at an alpha value of 0.01 (Table 3.1). Significant tests occurred primarily between herds sampled in the northern Manitoba cluster (C5) and several other groups, including the northern Ontario cluster (C8; 5 tests), the southern

Ontario cluster (C6; 10 tests), the Kesagami cluster (C7; 1 test), and overlapping areas of the southern Ontario cluster (C6) and the northern Ontario cluster (C8; 3 tests).

Additionally, herds sampled in the Smoothstone-Wapeweka cluster (C1) were different from the northern Ontario cluster (C8; 1 test) and the southern Ontario cluster (C6; 2 tests). One test was significant between the southern Ontario cluster (C6) and the

Kesagami cluster (C7). Only two tests were significant within a cluster (the northern

Ontario cluster; C8) and a test was significant between herds sampled in the northern

Ontario cluster (C8) and the overlapping areas of the northern and southern Ontario clusters (C6 and C8). Finally, there was one significant test between the North Interlake herd (C2 and C4) and the northern Manitoba cluster (C5; Table 3.1). We also performed the test based on genetic clusters to confirm that mutation is responsible for the genetic structure results. Two pair-wise comparisons out of 45 were significant at an alpha of

0.01 (C5 and C8, C5 and C6; data not shown).

The network weighted with RST was thinned based on a Dpe of 0.019 and revealed 2 clusters, including a northern Manitoba and Saskatchewan group (n = 6) and an Ontario group (n = 19; Fig. 3.3). The Coastal herd was the only disconnected component (Fig. 3.3). Interestingly, The Bog and North Interlake herds clustered with

Ontario and suggested that the ancestry of those groups is different from the remainder of

Manitoba. Although an increasing number of herds became disconnected after experimenting with smaller cutoff values for long-range connectivity (below Dpe), no more clusters were apparent. The average clustering coefficient was 0.65 and ranged from 0.00 for The Bog and Coastal herds to 1.00 for the Wabowden, Nipigon, Kesagami,

Sydney, and Attawapiskat herds (Table 3.2). The average degree was 5.69 and ranged

80 from zero for the coastal herd to 13 for the Moosonee herd (Table 3.2). Finally, the average betweenness was 17.46 and ranged from zero for The Bog, Wabowden, Nipigon,

Kesagami, Sydney, Coastal, and Attawapiskat herds to 111.73 for the Norway House herd (Table 3.2).

The FST network was thinned using a Dpe of 0.011 and revealed only one cluster that connected the majority of herds (n = 19), with exception to Smoothstone-Wapeweka,

Naosap-Reed, Kississing, and several herds at the southern periphery of the range

(Coastal, Nipigon, The Bog, and North Interlake; Fig. 3.4). The average clustering coefficient was much lower (0.29) than for RST, ranging from 0.00 for all of the Manitoba and Saskatchewan herds and the Sydney, Pagwachuan, Coastal, Nipigon, and Kesagami herds in Ontario to 1.00 for Fort Severn and Brightsand (Table 3.2). The average degree was 3.38 and ranged from 0 for The Bog, North Interlake, Kississing, Nipigon,

Smoothstone, Naosap-Reed, and Coastal to 11 for Peawanuck, Berens, and Webequie

(Table 3.2). Average betweenness was 6.96 and ranged from 0.00 for all of the Manitoba and Saskatchewan herds (except Wheadon) and 7 of the Ontario herds to 67.00 for the

Berens herd (Table 3.2).

3.4.2. Isolation by Distance

A Mantel test indicated a correlation between RST and geographic distance for the

26 caribou herds (b = 0.003, P < 0.001). This suggests that isolation by distance may have been an important factor contributing to historical genetic structure.

3.4.3. Approximate Bayesian Computation

We used the results of the RST/FST analysis to develop four scenarios to compare with the observed data set. Because the pair-wise comparisons from the permutation test

81 were similar to the genetic clustering results, we only used the genetic clusters for this analysis. The RST patterns suggested that Manitoba and Saskatchewan may have a different evolutionary history than Ontario, and that southern Manitoba (The Bog and

North Interlake) may have ancestry more similar to Ontario. Therefore, we compared the likelihood that Manitoba and Saskatchewan were different from Ontario (adding The Bog and North Interlake clusters to either Manitoba and Saskatchewan or to Ontario; scenarios 1 and 2; Fig. 3.2) with the likelihood that all clusters diverged from the same ancestor at the same time (scenario 3; Fig. 3.2). Additionally, because of the large amount of admixture in the Ontario North cluster (C8), we also developed a scenario similar to scenario 2 except that the Ontario North cluster has undergone admixture from an unsampled population (scenario 4; Fig. 3.2).

The logistic regression supported scenario 4 as the most likely population history, with a posterior probability (PPr) of 0.971. The 95% confidence interval (CI) ranged from 0.954 to 0.988 and did not overlap with the confidence intervals of the competing scenarios (scenario 1, PPr = 0.002, CI = 0.000-0.575; scenario 2, PPr = 0.001, CI =

0.000-0.574; scenario 3, PPr = 0.026, CI = 0.011-0.041). The proportion of times that competing scenarios had the highest posterior probability (or type-I error rate) was 0.318

(scenario 1, PPr = 0.028; scenario 2, PPr = 0.124; scenario 3, PPr = 0.166). The proportion of times that the scenario 4 (the chosen scenario) had the highest posterior probability after simulating data sets for alternative scenarios (type-II error rate) was

0.032 when data were simulated for scenario 1, 0.184 when data were simulated for scenario 2, and 0.084 when data were simulated for scenario 3.

3.5. Discussion

The impacts of recent landscape alterations in the boreal forest have been of utmost concern to managers focused on the decline of woodland caribou in the boreal forest (e.g., Schaefer 2003, Vors et al. 2007, Environment Canada 2011, Festa-Bianchet et al. 2011). Although contemporary changes to the landscape are likely causing major impacts on genetic connectivity, our study supports the hypothesis that historical factors are also influencing genetic structure patterns within the study area. Specifically, the permutation test showed that RST was significantly greater than FST, a signal of mutation in our data. Thus, both RST and FST are potentially informative measures for describing genetic structure. The RST network showed a clear clustering pattern (Fig. 3.3) where

Ontario caribou herds were differentiated from Manitoba and Saskatchewan herds; the only exception is that southern Manitoba animals were more related to Ontario than northern Manitoba animals (Fig. 3.3). The partition in the RST network closely corresponds with partitions delineated by the Bayesian clustering results (Thompson et al., Chapter 2), where Ontario animals clustered differently than Manitoba animals when samples from all three provinces were incorporated into the analysis (Figs. 2.3 and 2.6a in Thompson et al., Chapter 2). This finding could be a result of a historical isolation-by- distance pattern. Indeed, RST exhibited a strong relationship with spatial distance (b =

0.003, P < 0.001). However, the average clustering coefficient of 0.65 suggested hierarchical structure (and the RST clustering coefficients for herds were greater than those calculated based on FST; Table 3.2), which would not be expected if the processes affecting genetic structure were related to isolation by distance alone (Kivelä et al. 2014).

Vicariance could provide another explanation for the RST clustering patterns (and

Bayesian clustering results). A mitochondrial DNA study by Klütsch et al. (2012) found

83 that Manitoba animals may have colonized the region after expanding from a different refugium than animals in Ontario. Specifically, the authors indicated that while both

Manitoba and Ontario animals expanded from refugia found south of the Laurentide ice sheet during the last glacial maximum, the Ontario animals likely expanded from a refugium in the Appalachian Mountains, while Manitoba animals likely expanded from a refugium found west of the Appalachian Mountains and east of the Mississippi River

(Klütsch et al. 2012). A reconstruction of the population history by the ABC analysis supports both the RST and Bayesian clustering results, showing that the majority of

Manitoba and Saskatchewan animals may have descended from a different lineage or utilized different post-glacial colonization routes than those in Ontario (Fig. 3.2). Thus, it is reasonable to assume that a portion of the structure delineated by the RST network and

Bayesian clustering analysis could be related to incomplete lineage sorting as a result of ancestry from different glacial refugia or post-glacial colonization routes.

The Bayesian clustering analysis (Thompson et al., Chapter 2) also revealed a southern Ontario and northern Ontario cluster (Fig. 3.1), the latter of which was highly admixed. Although the percolation threshold used to thin the RST network did not differentiate between southern Ontario and northern Ontario, several of the pairwise RST comparisons between herds sampled in the northern Ontario cluster (C8) and the southern

Ontario cluster (C6; or in areas where the 2 clusters overlap) were significant at an unadjusted alpha (α = 0.05) or at α = 0.01 (14 comparisons; Table 3.2). Klütsch et al.

(2012) detected haplotypes in Ontario that are most commonly found in the barren- ground caribou subspecies, suggesting that there may be introgression from barren- ground caribou into the woodland caribou range in northern Ontario. This hypothesis

84 was validated by the ABC analysis where the most supported scenario suggested that the northern Ontario cluster (C8) resulted from an admixture event between the southern

Ontario cluster (C6) and an unsampled population, with barren-ground caribou as the likely candidate (Fig. 3.2).

Although our results suggested that the southern Manitoba herds (North Interlake and the The Bog) may have historical similarities to Ontario, the FST results indicated that contemporary processes have likely impacted the genetic structure of those herds because they are highly differentiated from both Ontario and northern Manitoba (Table

2.2 in Thompson et al., Chapter 2). This also closely corresponds with the regional

Bayesian clustering results, which delineated a southern Manitoba cluster separate from both Ontario and Northern Manitoba and Saskatchewan (Figs. 2.3 and 2.6a in Thompson et al., Chapter 2). Additionally, both the FST values (Table 2.3 in Thompson et al.,

Chapter 2; Appendix D) and local Bayesian clustering results suggested that several other herds found along the southern periphery of the range have become differentiated due to a lack of gene flow, including the Coastal, Nipigon, and Smoothstone-Wapeweka herds

(Fig. 3.4). The southern periphery of the range is characterized by a large amount of anthropogenic activity; previous research has shown that anthropogenic features can negatively impact caribou through direct habitat loss (Murphy and Curatolo 1987, Cronin et al. 1998, Dyer et al. 2001, Mahoney and Schaefer 2002). Additionally, linear features, including roads, trails, and power lines, may facilitate travel by predators, such as gray wolves (Canis lupus), and increase access to caribou habitat and the potential for caribou mortality (James and Stuart-Smith 2000). Moreover, forestry practices, including forest cutovers, may also impact caribou populations by changing the composition and

85 configuration of the forest, including a loss of old-growth pine (Pinus spp.) and spruce

(Picea spp.) forests (Smith et al. 2000, Vors et al. 2007). Forest cutovers can alter caribou food supplies and increase moose (Alces alces) and deer (Odocoileus spp.) numbers, which also increases predator numbers. Finally, ecological factors associated with the range margin itself may have an influence on our detected patterns. For example, the abundant-centre model suggests that populations at the periphery of a species range are generally smaller and more spatially isolated and exhibit reduced genetic diversity and increased genetic differentiation (Eckert et al. 2008).

Genetic structure was detected in the northwest portion of Manitoba (the

Kississing and Naosap-Reed herds; Figs. 2.4 and 2.6b in Thompson et al., Chapter 2).

Although those populations are not located near the range margin, anthropogenic activity is greater in those regions compared to other northern regions of the study area

(Environment Canada 2011). Additionally, a large amount of fragmentation may exist because of natural disturbance due to wildfire. Kurz and Apps (1999) showed that fire was the primary disturbance factor west of the Manitoba-Ontario border, burning nine times more forest area within the last 40 years than east of that border. It has also been suggested that wolf densities are greater in the southern and western portions of the boreal forest (i.e., central Manitoba and Saskatchewan; Darby et al. 1989) where the occurrence of fire is also prevalent. Caribou in those areas tend to select peat bogs surrounded by coniferous forest (Stuart-Smith et al. 1997, Rettie and Messier 2000) to provide some separation from predators (Rettie and Messier 2000). It is important to note, however, that impacts of fire and predators are not likely to be recent factors only.

Caribou have evolved with fire and predators (Klein 1982, Musiani et al. 2007) and those factors may have impacts on both historical and contemporary genetic structure.

With exception of the differentiated herds at the southern periphery of the range and in northwest Manitoba, the pair-wise FST estimates suggested contemporary connectivity is greater among other herds (Appendix D). A potential explanation is that the habitat in northern regions is more contiguous, particularly in northern Ontario, than at the southern periphery of the range. Additionally, the presence of eastern migratory caribou likely increases gene flow among boreal herds (Thompson et al., Chapter 2), which is similar to findings in Quebec (Boulet et al. 2007). Betweenness centrality values based on FST were high for many of the northern Ontario herds (i.e., Marten Falls,

Peawanuck, and Webequie; Table 3.2), suggesting they may provide important connections between regions. Interestingly, the contemporary betweenness centrality value was greatest for the Berens herd (BC = 67.0; Table 3.2). Although that region falls along the southern periphery of the range and there is considerable anthropogenic activity south of the region, many of the samples were collected from Woodland Caribou

Provincial Park where habitat is protected from extensive anthropogenic activity. Thus, animals in that region may provide important connections between Ontario herds and the boreal Manitoba herds west of the Ontario border. Contemporary degree values (based on FST) were large for several of the northern Ontario herds (i.e., Attawapiskat, Marten

Falls, Peawanuck, Webequie, and Big Trout Lake), where there is considerable overlap between Boreal and Eastern Migratory DU ranges (Fig. 3.1) and suggests that those groups exchange large numbers of migrants with other herds (Kivelä et al. 2014).

However, the degree values were also high for several of the northern Ontario herds

based on RST (i.e., Moosonee, Keewaywin, and Fort Severn; Table 3.2) and may indicate that eastern migratory caribou had historical influences in addition to contemporary influences on gene flow. It is important emphasize, however, that degree may be sensitive to differences in sampling intensity (Kivelä et al. 2014). Likewise, betweenness centrality may be sensitive to noisy data in that the importance of nodes can be susceptible to small perturbations in edge weights (Kivelä et al. 2014, Segarra and

Ribeiro 2014). Thus, results for both measures should be interpreted with caution.

Our findings illustrate the importance of considering both historical and contemporary processes when interpreting genetic structure patterns. Such information is necessary when using genetic data to inform functional connectivity for caribou management. Without separating those impacts, it might be concluded that all of the detected structure is a result of contemporary changes to the landscape. In general, contemporary patterns of genetic differentiation were greatest among clusters in southern portions of the study area where the woodland caribou range has retracted substantially.

Provincial management of caribou herds should focus in those areas to either restore connectivity among herds or ensure that existing connectivity is maintained.

Additionally, this study supported recent research based on mitochondrial DNA that suggested boreal populations of woodland caribou may have expanded from different refugia after the last ice age (Klütsch et al. 2012). This information may be important to inform future delineation of evolutionary significant units. Finally, this work has implications for conservation and land protection, particularly for caribou groups that may be important for contemporary gene flow (based on betweenness centrality; e.g.,

Webequie, Marten Falls, Peawanuck, Berens). Webequie and Marten Falls, in particular,

88 are located in close proximity to the McFaulds Lake Project or “Ring of Fire”, which is a large deposit of chromite (and other minerals) that has the potential to encourage development in the region as a result of concentrated mining activities (OMNDM 2015).

Thus, any proposed development should carefully consider possible impacts to caribou connectivity.

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Table 3.1. Significant pair-wise permutation tests that suggested observed RST between herds (and associated genetic clusters calculated based on the Bayesian clustering algorithm in TESS; see Thompson et al., Chapter 2) is greater than the permuted RST (pRST, an FST equivalent). Codes for herds are as in Figure 3.1.

Cluster Cluster I Herd I J Herd J Observed Permuted P-value C1 SW C6, C8 Wea 0.052 0.018 0.039 C10 Nip C1 SW 0.089 0.042 0.026 C10 Nip C5 WW 0.133 0.064 0.023 C10 Nip C5 Whe 0.114 0.053 0.031 C2 TB C5 Wab 0.073 0.035 0.028 C2 TB C5 WW 0.097 0.039 0.017 C2 TB C5 Whe 0.075 0.039 0.029 C3 Kis C8 Pea 0.092 0.034 0.017 C4 NI C5 WW 0.133 0.055 0.006* C4 NI C5 Whe 0.117 0.059 0.029 C5 NR C10 Nip 0.107 0.052 0.032 C5 NR C4 NI 0.119 0.061 0.018 C5 NR C6 Pag 0.124 0.046 0.008* C5 NR C8 Pea 0.155 0.036 0.002* C5 NR C8 Web 0.057 0.020 0.009* C5 WW C8 Web 0.072 0.026 0.039 C6 Ber C5 NR 0.067 0.028 0.036 C6 Ber C5 WW 0.072 0.021 0.003* C6 Ber C5 Whe 0.074 0.013 0.002* C6 Ber C6, C8 MF 0.055 0.010 0.011 C6 Ber C8 Web 0.030 0.000 0.021 C6 Bri C1 SW 0.106 0.025 0.000** C6 Bri C3 Kis 0.083 0.038 0.030 C6 Bri C5 NR 0.117 0.041 0.000** C6 Bri C5 Wab 0.084 0.026 0.004* C6 Bri C5 WW 0.129 0.024 0.000** C6 Bri C5 Whe 0.126 0.026 0.000** C6 Bri C6, C8 MF 0.065 0.021 0.017 C6 Bri C8 Web 0.048 0.013 0.024 C6 Chu C5 WW 0.075 0.020 0.002* C6 Chu C5 Whe 0.074 0.027 0.010 C6 Chu C6, C8 MF 0.041 0.013 0.041 C6 Chu C7 Kes 0.073 0.026 0.007* C6 Chu C7 Moo 0.038 0.014 0.038 C6 Chu C8 Pea 0.051 0.007 0.015 C6 Pag C1 SW 0.101 0.030 0.003* C6 Pag C5 Wab 0.092 0.042 0.034 *Indicates significance at an alpha of 0.01; **indicates significance at an alpha of 0.01 and sequential Bonferroni adjustment.

Table 3.1. (continued).

Cluster I Herd I Cluster J Herd J Observed Permuted P-value C6 Pag C5 WW 0.122 0.033 0.000** C6 Pag C5 Whe 0.122 0.035 0.002* C6 Pag C8 Web 0.073 0.019 0.013 C6 Syd C8 Web 0.046 0.013 0.025 C6, C8 Kee C1 SW 0.079 0.030 0.012 C6, C8 Kee C5 NR 0.097 0.046 0.039 C6, C8 Kee C5 WW 0.099 0.028 0.004* C6, C8 Kee C5 Whe 0.099 0.031 0.006* C6, C8 Kee C7 Kes 0.038 0.016 0.044 C6, C8 MF C5 NR 0.071 0.030 0.033 C6, C8 MF C5 WW 0.080 0.026 0.041 C6, C8 MF C5 Whe 0.070 0.028 0.047 C6, C8 MF C6 Pag 0.073 0.017 0.011 C6, C8 MF C8 Pea 0.066 0.006 0.008* C6, C8 Wea C5 Whe 0.076 0.026 0.009* C7 Kes C5 WW 0.076 0.034 0.035 C7 Kes C5 Whe 0.072 0.033 0.038 C7 Kes C6, C8 MF 0.042 0.011 0.027 C7 Moo C5 Wab 0.060 0.021 0.025 C7 Moo C5 WW 0.082 0.027 0.009* C7 Moo C5 Whe 0.071 0.026 0.016 C8 Att C6 Ber 0.044 0.002 0.011 C8 Att C6 Bri 0.063 0.015 0.012 C8 Att C6 Pag 0.066 0.019 0.015 C8 Att C8 Pea 0.081 0.006 0.000** C8 FS C5 NR 0.083 0.037 0.030 C8 FS C5 WW 0.077 0.037 0.048 C8 FS C5 Whe 0.082 0.032 0.024 C8 Pea C1 SW 0.135 0.032 0.003* C8 Pea C2 TB 0.087 0.030 0.016 C8 Pea C5 Wab 0.110 0.012 0.002* C8 Pea C5 WW 0.146 0.016 0.002* C8 Pea C5 Whe 0.165 0.021 0.000** C8 Pea C6 Syd 0.072 0.017 0.020 C8 Pea C8 Web 0.053 -0.006 0.002* C8 Web C5 Whe 0.067 0.019 0.011 C9 Coa C6, C8 MF 0.130 0.057 0.028 *Indicates significance at an alpha of 0.01; **indicates significance at an alpha of 0.01 and sequential Bonferroni adjustment.

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FST RST

Node Degree CC BC Degree CC BC Att 7 0.67 5.92 4 1.00 0.00 Ber 11 0.27 67.00 9 0.58 8.43 BTL 7 0.67 5.92 9 0.64 9.89 Bri 4 1.00 0.00 6 0.60 4.19 Chu 5 0.80 1.58 6 0.73 2.56 Coa 0 0.00 0.00 0 0.00 0.00 FS 4 1.00 0.00 11 0.55 18.85 Kee 5 0.50 2.83 11 0.58 14.82 Kes 1 0.00 0.00 2 1.00 0.00 Kis 0 0.00 0.00 4 0.50 95.00 MF 7 0.48 18.33 6 0.87 2.13 Moo 4 0.83 2.00 13 0.40 52.33 NR 0 0.00 0.00 5 0.60 27.67 Nip 0 0.00 0.00 2 1.00 0.00 NI 0 0.00 0.00 3 0.33 23.00 NH 1 0.00 0.00 9 0.56 111.73 Pag 1 0.00 0.00 6 0.60 10.07 Pea 11 0.42 29.33 7 0.67 6.15 SW 0 0.00 0.00 4 0.67 1.00 Syd 1 0.00 0.00 3 1.00 0.00 TB 0 0.00 0.00 1 0.00 0.00 Wab 1 0.00 0.00 2 1.00 0.00 WW 1 0.00 0.00 4 0.83 6.67 Wea 4 0.50 1.75 10 0.49 50.14 Web 11 0.42 29.33 7 0.81 2.72 Whe 2 0.00 17.00 4 0.83 6.67

Fig. 3.1. Location of the 28 woodland caribou herds (center point), 11 genetic clusters identified based on the Bayesian clustering

algorithm in TESS (C1-C11; see Thompson et al., Chapter 2), and three caribou Designatable Units (DUs) in Ontario, Manitoba, and 101 Saskatchewan, Canada, 2014.

102

Fig. 3.2. The four historic scenarios used in the Approximate Bayesian Computation analysis to explore the impacts of divergence and admixture on genetic structure of woodland caribou. Scenario 1 is that the Manitoba and Saskatchewan clusters diverged from a different ancestor than Ontario, scenario 2 is the same as scenario 1 except that The Bog and North Interlake clusters have the same ancestor as Ontario, scenario 3 is that all ten clusters diverged from one common ancestor, and scenario 4 is the same as scenario 2 except that the Ontario North group (cluster 8) resulted from admixture between the Ontario South group (cluster 6) and an unsampled population. The names C1-C10 correspond with Cluster 1-Cluster 10 (see Fig. 3.1; C11 was not included in this analysis because of ≤5 individuals).

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Fig. 3.3. Network of RST with a percolation threshold of 0.019. The nodes represent caribou herds and are sized and colored based on betweenness values. Edges are weighted by RST, with green edges representing smaller RST values (greater connectivity) and blue edges representing larger RST values (less connectivity). Codes for herds are as in Figure 3.1; GL and Pas were excluded because of ≤5 individuals.

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Fig. 3.4. Network of FST with a percolation threshold of 0.011. The nodes represent caribou herds and are sized and colored based on betweenness values. Edges are weighted by FST values, with green edges representing smaller FST values (greater connectivity) and blue edges representing larger FST values (less connectivity). Codes for herds are as in Figure 3.1; GL and Pas were excluded because of ≤5 individuals.

105

CHAPTER 4. MODELING LANDSCAPE RESISTANCE TO GENE FLOW FOR WOODLAND CARIBOU POPULATIONS WITH VARYING MOBILITY

Laura M. Thompsona , Micheline Manseaub,c, and Paul J. Wilsona

aNatural Resources DNA Profiling and Forensic Centre, Trent University, DNA Building,

2140 East Bank Drive, Peterborough, Ontario, Canada K9J 7B8 bNatural Resources Institute, University of Manitoba, 70 Dystart Road, Winnipeg,

Manitoba, Canada R3T 2N2 cOffice of the Chief Ecosystem Scientist, Parks Canada, 30 Victoria St., Gatineau,

Quebec, Canada J8X 0B3

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4.1. Abstract

Modeling the influence of landscape factors on genetic connectivity of wildlife populations generally assumes that populations respond similarly to various hypothesized factors. However, measuring impacts can be problematic when populations exhibit marked differences in mobility. We developed resistance surfaces based on a suite of landscape variables hypothesized to affect gene flow of the Boreal Designatable Unit

(DU) of woodland caribou (Rangifer tarandus caribou) in Ontario, Manitoba, and

Saskatchewan. Because a second DU (Eastern Migratory DU) of woodland caribou with greater mobility than boreal caribou occurs in the northern part of the study area, we also considered whether resistance decreased as distance to the eastern migratory range decreased using a distance to the eastern migratory range variable (or Dmig). A matrix regression between each set of resistance distances and FST (calculated based on ten microsatellite markers from 27 caribou herds) suggested that water bodies, anthropogenic disturbance, and Dmig were the most important factors affecting caribou gene flow.

However, only water bodies and anthropogenic disturbance were important in a composite model, which was empirically optimized after iteratively combining multiple landscape variables into a single layer. Geographic distance between herds was not a significant factor. Although the Dmig variable did not substantially improve the optimized composite model when all sampled herds were incorporated into the analysis, a truncated data set that removed three herds potentially influenced by large lakes in

Manitoba suggested that the Dmig variable was the most important descriptor of gene flow, followed by anthropogenic disturbance, water bodies, fire, and snow depth.

However, only Dmig, anthropogenic disturbance, and water bodies were important in the

107 optimized composite model, which was still significant after removing the effect of geographic distance. Our results show that accounting for differences in mobility is an important consideration for fully understanding the influence of landscape factors on gene flow of wildlife populations with varying life history traits.

4.2. Introduction

The structure of the landscape has the potential to affect animal movements and either impede or facilitate gene flow among wildlife populations (e.g., Manel et al. 2003,

Cushman et al. 2006, Storfer et al. 2007, Manel and Holderegger 2013). For example, a large number of studies have explored the impact of anthropogenic activity on gene flow of wildlife (e.g., Epps et al. 2005, Riley et al. 2006, Frantz et al. 2010, van Manen et al.

2012, Prunier et al. 2014, Sawaya et al. 2014). Additionally, natural landscape features, such as water or topographic features, may inhibit or facilitate gene flow of some species

(e.g., Funk et al. 2005, Perez-Espona et al. 2008, Frantz et al. 2010, Perez-Espona 2012,

Cosic et al. 2013, Kimble et al. 2014). Those different landscape factors are often combined into a resistance surface, which is a map with values assigned to various features based on the corresponding hypothesized relationship with animal movements

(Spear et al. 2010), and are either built or validated with measures of effective dispersal

(i.e., gene flow; Garroway et al. 2010, Koen et al. 2012, Shirk et al. 2010). More recent studies have expanded the focus of landscape genetics to include the impacts of ecological or demographic processes (Serrouya et al. 2012, Weckworth et al. 2013), as well as local environmental conditions (e.g., climate) that generate genetic structure due to selection gradients (Wang et al. 2013, Row et al. 2014). Because most species have biological limits to dispersal that prevent gene flow over large scales, creating an

108 isolation-by-distance (IBD) pattern of genetic structure (Wright 1943), genetic differentiation is often compared with Euclidian distance (in addition to landscape resistance distances) to test the importance of IBD on genetic differentiation. However, that strategy assumes a particular species exhibits similar movement characteristics throughout the area of interest. A number of life history and behavioral traits can cause intraspecific variance in how a species uses space (Maher and Burger 2011).

Consequently, it is important to consider potential variance in movement behavior when parameterizing a landscape resistance surface.

The distribution of the Boreal Designatable Unit (DU) of woodland caribou

(Rangifer tarandus caribou; herein referred to as boreal caribou) has retracted substantially in recent decades (McLoughlin et al. 2003, Schaefer 2003, Vors et al. 2007).

The majority of the decline has occurred in the southern portion of the range because of increased human activities (Schaefer 2003). In those areas, populations have been either reduced substantially in size or extirpated (Mallory and Hillis 1998). As a result, boreal caribou have been designated as ‘threatened’ by the Committee on the Status of

Endangered Wildlife in Canada (COSEWIC 2000). Boreal caribou are sometimes referred to as the ‘sedentary’ ecotype of woodland caribou based on limited movement of females during calving (Bergerud 1988) and overlap of home ranges among seasons

(Darby and Pruitt 1984, Mallory and Hillis 1998, Rettie and Messier 2001); telemetry research has shown that female annual home ranges can vary from approximately 200-

4,000 km2 (Racey et al. 1997, Rettie and Messier 2001, Brown et al. 2003). Those behaviors differ substantially from the Eastern Migratory DU of woodland caribou

(herein referred to as eastern migratory caribou) that is found in northeastern Manitoba,

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Ontario, Quebec, and Labrador. Eastern migratory caribou exhibit greater mobility than boreal caribou, spending winters in northern portions of the boreal forest (overlapping with boreal caribou) and migrating long distances to tundra areas along the Hudson and

James Bay coasts during summer (Schaefer 2003). Annual home range sizes of some eastern migratory females ranged from approximately 20,000 to >200,000 km2 (Schaefer and Wilson 2002, Wilson 2013). Additionally, eastern migratory populations occur at higher densities than boreal caribou and are not protected under the Species at Risk Act

(SARA; www.sararegistry.gc.ca).

Thompson et al. (Chapter 2) used Bayesian clustering models to delineate genetic structure patterns of woodland caribou sampled in Ontario, Manitoba, and Saskatchewan.

One of those groups was indicative of a large admixture zone that corresponded spatially with the seasonal overlap of the boreal and eastern migratory animals in Ontario, suggesting that the two DUs could be interbreeding. The models also delineated several local groups that occurred primarily along the southern range periphery and suggested that the majority of Ontario caribou were differentiated from Manitoba and Saskatchewan caribou (Thompson et al., Chapter 2). An attempt to disentangle the influence of historical and contemporary processes on those patterns indicated that the latter were only responsible for a portion of the genetic groups. Specifically, an RST network representing mutation (or historical processes) revealed two clusters that closely aligned with the differentiation between Ontario caribou and Manitoba and Saskatchewan caribou

(Thompson et al., Chapter 3). Those patterns may have been a result of historical patterns of isolation by distance, or even vicariance during the last glacial period

(Thompson et al., Chapter 3). However, a comparative network based on FST

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(representing contemporary processes) indicated a large amount of recent gene flow among northern portions of the provinces and restricted gene flow primarily among groups along the southern periphery of the range (Thompson et al., Chapter 3).

Consequently, although the provincial patterns were likely a result of historical patterns, an understanding of factors that may have contributed to contemporary structure (FST) is still needed. However, the seasonal presence of eastern migratory caribou, which may be a mixture of boreal caribou and the highly mobile barren-ground caribou (R. t. groenlandicus) subspecies (Thompson et al., Chapter 3), has the potential to confound the influence of factors affecting contemporary gene flow because of marked differences in movement behavior.

Here, we attempted to identify factors affecting contemporary genetic differentiation of woodland caribou in Ontario, Manitoba, and Saskatchewan. For this, we developed a resistance surface that represents the likelihood a species will be able to navigate throughout a region and the associated resistance distances are regressed against measures of genetic differentiation (e.g., FST). We considered a range of landscape factors hypothesized to impact FST using a model selection framework similar to Shirk et al. (2010) that considers the importance of landscape variables individually and when multiple variables are combined into a composite layer. Additionally, we hypothesized that impacts of landscape factors would be less important in northern regions of the study area, largely because of the presence of eastern migratory caribou. Therefore, we incorporated a variable representing distance to the Eastern Migratory DU range to account for differences in mobility between the two DUs of caribou. We predicted that

111 relationships between landscape variables and genetic differentiation would improve by adjusting the resistance surface for differences in mobility.

4.3. Methods

4.3.1. Study Area and Sampling

The study area consisted of the Hudson Bay coast and boreal forest in Ontario,

Manitoba, and Saskatchewan and is comprised of three ecozones as a result of geological, vegetative, and climatic differences that exist throughout the region (Wiken 1986). The

Stratification Working Group 1996). The boreal shield is Canada’s largest ecozone and consists of large regions of coniferous forests interspersed by exposed bedrock features.

A large number of lakes are also present (Ecological Stratification Working Group 1996).

The boreal plains ecozone is located to the south and west of the boreal shield and differs in that it is not dominated by bedrock outcrops and has fewer lakes (Ecological

112

DNA was extracted primarily from woodland caribou fecal samples collected during the winter seasons between 2005 and 2011 by the Ontario Ministry of Natural

Resources, Manitoba Conservation, Saskatchewan Ministry of the Environment, and

Ontario were visited only by helicopter or by foot. Each sample was enclosed in individual containers to prevent DNA contamination and stored at -20°C. All samples were shipped frozen to the Natural Resources DNA Profiling and Forensics Centre at

Trent University in Peterborough, Ontario for DNA analysis to identify unique individuals.

4.3.2. Genetic Data and Analysis

Our data set consisted of 1,069 woodland caribou individuals sampled in Ontario,

Manitoba, and Saskatchewan and genotyped at ten microsatellite loci (Fig. 4.1, Table 4.1; see Thompson et al., Chapter 2 for a description of the molecular techniques). Samples were acquired from 20 management units (herds/ranges) delineated by the provincial governments. However, because the management unit in northern Ontario is large (Far

North range; Fig. 4.1), we partitioned the region into sampled areas (i.e., Attawapiskat,

Big Trout Lake, Keewaywin, Marten Falls, Moosonee, Weagamow, Webequie) for a total of 27 caribou herds or ranges or sampled areas (herein referred to as herds; Fig. 4.1). We calculated FST based on Weir and Cockerham (1984), which was then linearized (FST/1-

113

FST) based on Rousset (1997). Linearized FST was calculated between all caribou herds sampled in the three provinces using GENEPOP (version 4.0.1; Raymond and Rousset

1995, Rousset 2008).

4.3.3. Factors Hypothesized to Affect Gene Flow

We considered a suite of landscape variables to identify potential factors affecting contemporary genetic structure (FST; Fig. 4.4). Previous research has suggested that woodland caribou prefer large areas of mature, coniferous forests that provide food sources and protection/separation from predators (Environment Canada 2012) and we assumed that gene flow would be greater in those habitat types. However, anthropogenic disturbance, including roads and forestry practices may negatively impact caribou populations (Murphy and Curatolo 1987, Cronin et al. 1998, Dyer et al. 2001, Mahoney and Schaefer 2002, Vors et al. 2007), and potentially gene flow, through direct and indirect habitat loss. Linear features, in particular, may facilitate travel by predators, such as gray wolves (Canis lupus), and increase mortality rate (James and Stuart-Smith

2000, Environment Canada 2012). Natural habitat fragmentation caused by forest fires can also cause habitat loss and research has suggested it may take 40 or more years after a disturbance for forests to return to conditions that resemble caribou habitat (Schaefer and Pruitt 1991, Joly et al. 2003, Dunford et al. 2006). Other natural landscape factors include the presence of lakes and rivers, which may have negative impacts on caribou gene flow. Caribou have been associated with frozen lakes during winter (Ferguson and

Elkie 2005), but habitat selection often shifts to coniferous forests and peat bogs at other times of the year (Rettie and Messier 2000). In some regions and in calving season in particular, caribou may select habitats on lake peninsulas and islands to avoid predation

114

(Bergerud et al. 1990), but we assumed that most gene flow would occur around or along larger water bodies (e.g., barriers to gene flow). Differences in snow depth also have the potential to impact gene flow of caribou populations. Mayor et al. (2007) found that woodland caribou in Newfoundland consistently selected habitats with reduced snow depth across multiple scales. Finally, we considered dispersal distance represented as geographic distance between herds, as well as a mobility variable (measured in distance) that increased as proximity to the Eastern Migratory DU range increased.

The impacts of human activities on caribou gene flow were assessed using polygonal (e.g., cut areas, mines, agriculture, settlement) and linear anthropogenic disturbances (e.g., roads, railways, pipelines, power lines) digitized from Landsat imagery for the entire Boreal DU range (Environment Canada 2011). Features in that layer were buffered with a 500-m radius to account for the zone of influence on caribou

(Environment Canada 2011). Although Environment Canada (2011) explored a range of buffer widths, the 500-m buffer was the minimum width that exhibited a strong relationship with a measure of caribou recruitment (Environment Canada 2011). Forty- year fire disturbance was obtained from the National Large Fire Database (Natural

Resources Canada 2014), which is a collection of fire polygons provided by national parks and fire management agencies from each province and territory. The database includes burns ranging from 1917 to 2013; therefore, we extracted fires that occurred between 1965 and 2005 (the earliest collection year of caribou samples) to represent 40- year fire disturbance. A water layer was extracted from the land cover database produced by the Canada Centre of Remote Sensing from Moderate Resolution Imaging

Spectroradiometer (MODIS) satellites (250-m resolution). The maximum snow depth

115 averaged over 18 winter seasons (1979-1997) was acquired from Natural Resources

Canada (2006); snow depths were typically at a maximum in January or February in

Southern Canada and later in the year farther north (Natural Resources Canada 2006).

We digitized the Eastern Migratory DU range from COSEWIC (2011) and distance to the range was calculated using the Spatial Analyst extension in ArcGIS (version 10.1; ESRI

Redlands, California, USA). Finally, we created a layer where all pixels were equal to one, providing a Euclidean distance model that is bounded by the study area (Lee-Yaw et al. 2009) to assess the importance of IBD. All variables had the same extent as the sampled area with an additional 100-km buffer bounded by a rectangle; a 1-km resolution was chosen to facilitate downstream analyses.

4.3.4. Resistance Surface Optimization and Calculation of Resistance Distances

We used a method similar to Shirk et al. (2010) to empirically optimize resistance surfaces (where larger values represented greater resistance to gene flow) and calculate pair-wise resistance distances. We conducted the analysis in two rounds. First, we considered each variable individually (univariate optimization) and reclassified layers based on a range of costs (i.e., 10, 100, 200, 400; Galpern et al. 2012). The categorical variables (i.e., water, fire, and anthropogenic variables) were reclassified by simply assigning costs to the features of interest (e.g., water bodies) and all other areas were assigned a cost of one (representing no resistance to gene flow). Because anthropogenic activities were not mapped beyond the Boreal DU range (Environment Canada 2011), we added a polygon to encompass the area south of the range and coded it with the same costs as anthropogenic disturbances mapped within the range. Indeed, extensive human- related activity occurs in those areas. Koen et al. (2010) showed that artificial boundaries

116 can overestimate landscape resistance when no barriers actually exist. However, we were concerned that landscape resistance may be underestimated if areas south of the Boreal

DU range were assigned a cost of one, similar to areas without anthropogenic activity; preliminary tests suggested that incorporating a polygon with the same cost as anthropogenic disturbances had a stronger relationship with genetic differentiation than the inclusion of an artificial boundary (or an absolute barrier to gene flow). A mathematical function was used to determine resistance (R) values for the distance to the migratory range variable (Dmig):

, where C is the cost value and Dmigmax is the maximum distance from the migratory range

(or the maximum pixel value in the grid). The average maximum snow depth (SD) variable was reclassified so that areas with <30 cm of snow were assigned a rank (SDrank) of one, areas with 30-49 cm of snow were assigned a rank of two, and areas with 50-99 cm of snow were assigned a rank of three. Costs were assigned based on:

However, the cost values were slightly adjusted (i.e., 5, 10, 50, 200) so that the areas with greater snow depths would have a similar range of costs as the other four variables.

Effective resistance distances based on each of the resistance surfaces were calculated in program CIRCUITSCAPE (version 4.0; McRae et al. 2013). That program uses circuit theory and treats each pixel in a raster as a node in a network that is connected to adjacent nodes through resistors (McRae 2006, McRae et al. 2008), which were weighted with cost values from each of the resistance surfaces. The technique differs from a least-cost path model (Adriaensen et al. 2003) in that multiple pathways on

117 the cost surface are computed (as opposed to a single path; McRae 2006, McRae et al.

2008). Calculation of landscape resistance in CIRCUITSCAPE has shown to be more appropriate than least cost paths for populations at coarse scales (McRae and Beier 2007).

Because herds encompassed a large area, we used regions as focal nodes (as opposed to points) where multiple cells represented the spatial footprint of each herd and cells may or may not be contiguous depending on the spread of samples within a particular herd.

The pair-wise option in CIRCUITSCAPE was used to iterate average resistance distances across all focal nodes (woodland caribou herds) based on a cell connection scheme of four neighbors (to prevent connection across linear features that are not aligned in a cardinal direction; Shirk et al. 2010).

We summed important resistance surfaces identified from the univariate analysis

(based on the relationship with FST; Shirk et al. 2010) to develop composite resistance surfaces, where multiple variables with varying costs were combined into a single layer.

We accounted for interactions among variables by again considering a range of cost values for each variable. However, as Shirk et al. (2010) pointed out, considering a factorial number of alternative cost values would make the analysis intractable.

Therefore, we experimented with cost values one variable at a time while holding the cost values of other variables constant (Shirk et al. 2010). The optimization was performed in the order of importance of each variable identified from the univariate analysis. That is, the variable with the strongest relationship with FST was optimized first while the other variables were held constant. Once the optimal cost was identified, we optimized costs for the second most important variable while holding cost values for other variables constant. These steps were repeated until cost values for all variables had been

118 optimized. We used the same set of costs for the composite optimization that were used for the univariate optimization, but we also considered whether the resistance surface improved without including a particular variable (0 cost). The univariate and composite optimization was repeated for a smaller subset of herds so that the impacts of landscape factors would not be obscured by the large lakes in Manitoba (e.g., Lake Winnipeg; Fig.

4.1). This truncated data set consisted of 24 herds (all herds except for Smoothstone-

Wapeweka, The Bog, and North Interlake).

4.3.5. Statistical Analysis

The Mantel test (Mantel 1967) is commonly used for landscape genetics studies to determine relationships between genetic distance and geographical or landscape resistance distances (Balkenhol et al. 2009). However, that technique assumes that relationships are linear (Legendre and Fortin 2010). Wang (2013) showed that standard regression techniques may also be applied when variables are in the form of distance matrices by implementing a randomization procedure to account for the non- independence of the matrix elements. Specifically, that study used a Multiple Matrix

Regression with Randomization (an R function available from the Dryad Data

Repository; doi:10.5061/dryad.kt71r) to identify relationships between FST and geographic and environmental distances (Wang 2013). Although similar to a Mantel test, an advantage of using standard regression techniques is that the model specification can be modified to account for nonlinear relationships (Lichstein 2007). We used the script provided by Wang (2013) to estimate coefficients of the relationships between FST and resistance distances calculated from CIRCUITSCAPE. Although our study optimized both univariate and composite models, only one set of resistance distances was calculated

119 for each resistance surface and we, therefore, modified the R function to consider only one independent variable (simple matrix regression with randomization). Additionally, the regression formula was modified to consider curvilinear relationships between FST and the resistance variables by adding a quadratic term to each regression model.

Because the IBD model consisted of a layer with all values equal to one, summing the layer with other resistance surfaces would not reveal the influence of IBD on our results.

Therefore, we computed the residuals from regressions between the optimized composite models and geographic distance; those residuals were then regressed against FST to determine if a relationship still existed after removing the impact of geographic distance.

The residual variation from the geographic distance model was also regressed against FST after partitioning out the impact of the optimized composite models. This approach is similar to the causal modeling framework outlined by Cushman et al. (2006), except that we used regressions instead of correlations. All regressions were calculated based on

1,000 permutations for both the full and truncated data sets. Finally, although we considered only one independent predictor variable for each regression model, we wanted to understand correlations among the landscape variables to facilitate interpretation of the composite models. We used Mantel tests to calculate pair-wise correlation values between landscape resistance matrices computed for each landscape variable for both the full and truncated data sets. Mantel tests were calculated using the EcoDist package in R

(R Core Team 2013).

4.4. Results

4.4.1. Genetic Data and Analysis

120

The average FST value calculated for the 27 caribou herds was 0.030, with the minimum FST value (0.000) calculated for five comparisons and the maximum FST value

(0.107) calculated between the North Interlake and Norway House herds (Table 4.1).

The average value of FST for the truncated data set was 0.028 and ranged from 0.000 for five comparisons to 0.080 between the Nipigon and Atiko herds (Table 4.1).

4.4.2. Resistance Surface Optimization and Statistical Analysis

The linear regression analyses performed based on the univariate optimization suggested that resistance distances calculated for water with a cost of 400 had the

2 strongest relationship with FST (R = 0.214, P = 0.001; Table 4.2). Resistance distances from anthropogenic and Dmig were the only other variables exhibiting a relationship with

2 FST; the anthropogenic variable was important at a cost of 10 (R = 0.058, P = 0.027;

Table 4.2) and had borderline significance at a cost of 100 (R2 = 0.074, P = 0.051; Table

4.2), whereas the Dmig variable was not sensitive to different cost values and all of the regressions suggested a similar relationship (R2 = 0.125, P ranged from 0.019 to 0.034,

AIC = -2826.27; Table 4.2). Adding a quadratic term to the regression model, however,

2 improved the FST relationship for the anthropogenic (cost = 100, R = 0.120, P = 0.04,

AIC = -2822.25) and Dmig (all costs, R2 = 0.182, P ranged from 0.001 to 0.012, AIC = -

2847.86) variables (Table 4.2).

Because the univariate optimization for the full data set indicated a relationship between FST and the water, Dmig, and anthropogenic variables, we summed those layers to develop a composite resistance surface and optimized cost values in order of importance based on the univariate analysis. Specifically, the optimization proceeded one variable at a time in the order of water (initial cost = 400), Dmig (initial cost = 10),

121 and anthropogenic (initial cost = 100); an initial cost value of ten was arbitrarily chosen for Dmig because all of the costs had similar performance (Table 4.2). The linear regressions performed based on the composite optimization suggested that the best model for the full data set was water (cost = 400) and anthropogenic (cost = 100); the Dmig variable did not improve model performance (R2 = 0.423, P = 0.001, AIC = -2972.27;

Table 4.3). The quadratic regression was not an improvement over the linear model

(Table 4.3).

When using the truncated data set (no Smoothstone-Wapeweka, The Bog, and

North Interlake herds), all of the variables had a linear relationship with FST (Table 4.4); none of the quadratic terms for the non-linear regressions were significant (Table 4.4).

The Dmig variable was most important (R2 = 0.283, P = 0.001, AIC = -2365.35), followed by the anthropogenic (cost = 100, R2 = 0.227, P = 0.001, AIC = -2344.80), water (cost = 400, R2 = 0.145, P = 0.001, AIC = -2317.02), fire (cost = 10, R2 = 0.103, P

= 0.002, AIC = -2303.78), and snow depth (R2 = 0.075, P ranged from 0.21 to 0.26, AIC

= 2295.43) variables (Table 4.4). The Dmig and snow depth variables were insensitive to cost values (Table 4.4).

The composite optimization for the truncated data set proceeded in the order of

Dmig (initial cost = 10), anthropogenic (initial cost = 100), water (initial cost = 400), fire

(initial cost = 10), and snow depth (initial cost = 5; Table 4.5). Again, costs for the Dmig and snow depth variables were arbitrarily chosen because of similar performance of cost values in the univariate optimization. The best composite model for the truncated data set was Dmig (cost = 400), anthropogenic (cost = 100), and water (cost = 400); the fire

2 and snow depth variables did not improve the relationship with FST (R = 0.306, P =

122

0.001, AIC = -2374.35). However, a large number of models had similar performance, as indicated by the coefficients of determination and delta AIC values less than 2.0

(Burnham and Anderson 2002; Table 4.5). Like with the full data set, the linear regression model was the better descriptor of the relationship between FST and the composite resistance distances (Table 4.5).

IBD was not an important predictor of genetic structure when using the full data set (R2 = 0.021, P = 0.137, AIC = - 2786.93; Table 4.2). Additionally, the residual variation from the best composite model after partitioning out the influence of the

2 geographic distance model had a strong relationship with FST (R = 0.409, P = 0.002,

AIC = -2964.03), whereas the residual variation from the geographic distance model after partitioning out the influence of the composite resistance model had no relationship with

2 FST (R = 0.007, P =0.422, AIC = -2782.04). When using the truncated data set, there

2 was a relationship between FST and IBD (R = 0.096, P = 0.002, AIC = - 2301.39; Table

4.4). However, the residual variation from the best composite model still had a

2 relationship with FST after partitioning out the influence of geographic distance (R =

0.212, P = 0.009, AIC = -2339.43), whereas the residual variation from the geographic distance model was not significant after partitioning out the influence of the composite resistance model (R2 = 0.002, P = 0.687, AIC = -2275.65).

Correlations between the most important landscape variables in the univariate models from the full data set were smallest between water (cost = 400) and Dmig (cost =

10; r = 0.438) and largest between anthropogenic (cost =100) and Dmig (cost = 10; r =

0.764; Appendix E). For the truncated data set, the correlation values were smallest

123 between snow depth (cost = 5) and Dmig (cost = 10; r = 0.411) and largest between snow depth (cost = 5) and geographic distance (r = 0.977, Appendix F).

4.5. Discussion

Our study considered the importance of landscape factors to understand the emergence of contemporary genetic structure of woodland caribou in central portions of the boreal forest. Although IBD is a commonly detected phenomenon among wildlife populations (Meirmans 2012), our results suggested that it was not an important factor contributing to genetic structure for the full or truncated data sets. However, we found that accounting for differences in caribou mobility among regions (Boreal DU range vs. the Eastern Migratory DU range) was an important factor describing genetic structure.

Although the Dmig variable was not incorporated into the composite resistance layer calculated for all 27 herds, the univariate analysis suggested a relationship with FST and it was the most important variable in both the univariate and composite analyses when we tested relationships for the truncated data set. A number of factors can contribute to variance in mobility within a species, including differences in life-history (e.g., Steele et al. 2009) that are potentially maintained by selective forces that counterbalance gene flow

(Peterson and Denno 1997). Indeed, telemetry research has suggested marked differences in home range sizes of females between the two DUs (Wilson 2013). Our results suggest that landscape genetics studies should consider potential variance in movement behavior when parameterizing resistance surfaces, particularly when differences in life history traits may exist.

A number of other variables we considered were also important for describing caribou gene flow. Water, in particular, had a strong linear relationship with resistance

124 values that continued to increase with the largest FST values, whereas other variables

(i.e., anthropogenic, Dmig) were better described by a quadratic relationship with a trend line peaking at an FST value of ~0.04 for both variables; Fig. 4.2). Because some of the largest FST values were calculated among herds found in southern Manitoba (e.g., North

Interlake; Table 4.1), it is likely that the large lakes in the region (e.g., Lake Winnipeg,

Lake Manitoba; Fig. 4.1) are responsible for a portion of the variance in genetic structure.

The retraction of the southern range margin has potentially exacerbated those impacts as the range no longer extends south of the lakes and movements are restricted by Lake

Winnipeg (Fig. 4.1). Short Bull et al. (2011) emphasized how multiple spatial comparisons in different landscapes (i.e., replication) may be necessary to ensure the same inferences can be made for a particular landscape variable throughout the entire study area. Although we did not perform our analysis across multiple landscapes, we analyzed a truncated data set with three herds sampled west of Lake Winnipeg removed

(e.g., The Bog, North Interlake and, Smoothstone-Wapeweka herds), which suggested that all of the other factors we considered exhibited a linear relationship with FST and anthropogenic and Dmig were more important than water (Tables 4.4). Thus, although our results suggest water is an important factor influencing caribou gene flow (in both the full and truncated data sets), the size of water bodies and their proximity to other barriers

(e.g., range margin) may be particularly important.

Anthropogenic disturbance was an important variable when the full data set of herds was used and was a particularly important factor in the composite model, improving the coefficient of determination of the water only model (cost = 400) from

0.214 to 0.423 (Tables 4.2-4.3). This was not surprising given that anthropogenic

125 activities are likely a key factor in driving woodland caribou declines along the southern periphery of the range (Schaefer 2003, Vors et al. 2007). However, the anthropogenic variable was still significant for describing gene flow among herds in the truncated data set (R2 = 0.305, P = 0.001, AIC = 2373.85). The largest impacts were likely occurring in

Ontario herds sampled along the southern periphery of the range, as well as in northern regions of Manitoba where anthropogenic activities are increasing (Fig. 4.1; Environment

Canada 2011). Although the optimized composite model for the truncated data set included anthropogenic disturbance in addition to water and Dmig, it was not a substantial improvement over the model that included only water and Dmig as indicated by a delta AIC <2.0 (Table 4.5). Anthropogenic disturbance was correlated with water

(Appendix E), which may explain why it did not improve the composite model substantially. Fire and snow depth also had relationships with FST when using the truncated data set, but did not substantially improve the composite model (Tables 4.4-

4.5). Pair-wise correlations calculated between each of the variables suggested that fire and snow depth were highly correlated with geographic distance and water, but fire became less correlated at larger cost values (Appendix F). However, fire disturbance was not as important for describing FST at the larger cost values (Table 4.4-4.5), suggesting that any relationships were a result of the correlation with geographic distance. Although many studies have shown that caribou are negatively associated with 40-year fire disturbance (Klein 1982, Schaefer and Pruitt 1991), our results suggest that other factors are much more important for describing genetic structure at the scale we examined. This finding is similar to the study by Vors et al. (2007), which found that after considering several variables, including fire, forest cut blocks were one of the best predictors of

126 caribou occupancy in Ontario (Vors et al. 2007). However, it is important to point out that we considered only one spatial scale. Identifying relationships between patterns and processes can be scale dependent and landscape genetic relationships generally improve as the grain becomes finer (Cushman and Landguth 2010). Therefore, our chosen pixel size of 1 km may not have been fine enough to adequately describe the relationship between fire and FST. Galpern et al. (2012) used individual-based analyses to illustrate how relationships between landscape variables and genetic distance can be overlooked without considering a range of different grains.

We also considered whether our approach may have temporal limits by being unable to separate a non-effect from those that are too premature to detect (i.e., 40-year fire disturbance and average maximum snow depth). Landguth et al. (2010) performed a simulation analysis to determine the amount of lag time between when a barrier is created and the ability to detect that barrier using genetic data. Those simulations were based on genetic differentiation between populations (FST), as well as individuals (the proportion of shared alleles via Mantel’s r). Although their findings suggested that FST increased linearly with the initiation of a barrier, it took approximately 200 generations to reach

50% of the FST equilibrium value and as many as 400 generations to reach 90% of the equilibrium value (Landguth et al. 2010). Therefore, it is not surprising that FST had weaker relationships with recent fires and maximum snow depth (averaged over 18 winters) when compared to the relationship for persistent landscape features (e.g., water).

It is likely that there has not been sufficient time since the initiation of recent fires to cause substantial increases in FST. Moreover, the lack of a strong relationship between

FST and snow depth may be a result of the breeding season not coinciding with the timing

127 of the heaviest snow fall. However, the temporal scale likely only affects populations in the southern-most portions of the study area; animals in the northern regions are likely to encounter snow conditions for as much as 8 months of the year (Pruitt 1959).

We used the univariate optimization to reduce the dimensionality of the composite analysis by excluding variables that were unimportant and prioritizing those with the strongest relationship with FST (Shirk et al. 2010). Indeed, this step reduced the number of variables considered for the composite analysis based on the full data set and improved the regression models substantially (Tables 4.2-4.3). Surprisingly, the optimal cost values did not change a large amount between the univariate and the composite analyses, except that the best composite model excluded the Dmig variable (Table 4.3).

This result differs from the analysis performed by Shirk et al. (2010), who detected several changes in optimal cost values between the univariate and multvariate analyses.

A potential explanation for why our optimal cost values were similar between the two stages is that we used a set of fixed cost values (e.g., 10, 100, 200, 400), whereas Shirk et al. (2010) started with parameters based on expert opinion and optimized costs until a unimodal peak of support was detected. Therefore, our study likely identified a range that contained the unimodal peak of support as opposed to the peak value itself. The only exception is when optimal parameters were identified as 400 and it is possible that peak value was actually much larger. However, using a similar approach to Shirk et al. (2010) would not have been feasible because of the large size of our grids (~2.7 million pixels) and the use of regions as opposed to points for focal nodes (n), which increases the number of calculations from n calculations to n(n-1)/2 calculations (McRae et al. 2013).

All of the variables we considered when analyzing the truncated data set were included in

128 the composite optimization because of univariate relationships with FST. Again, the optimal cost values did not change between the univariate and composite analyses, except that the Dmig variable became more important when accounting for interactions of other variables (Tables 4.4-4.5).

A potential limitation of our study is that we used distance matrices in a regression analysis to understand the importance of landscape-based resistance among sampling sites, which may have less power than computing a relationship between two rectangular data tables (e.g., the untransformed allele frequency and site-based landscape data; Legendre and Fortin 2010). However, because our hypotheses were focused on the impacts of landscape factors affecting gene flow between caribou herds, the use of distance matrices was appropriate (Legendre and Fortin 2010). Nevertheless, the impacts of local environmental conditions at a particular site also has the potential to influence gene flow of an organism (Balkenhol et al. 2009, Pflüger and Balkenhol 2014).

Specifically, measuring impacts of the landscape among sites only focuses on the transient stage of dispersal, but local conditions may be important for the emigration and immigration stages of dispersal (Pflüger and Balkenhol 2014). For example, Murphy et al. (2010) used gravity models to show that predation in ponds may impact functional connectivity of frog populations in addition to terrain and habitat permeability that existed between ponds. Therefore, by only measuring impacts of landscape factors among caribou herds, we assumed that local environmental conditions, as well as stochasticity associated with mating and inheritance (Graves et al. 2013), were not important factors affecting caribou gene flow. Finally, although AIC has been used for landscape genetics studies that are based on distance matrices (e.g., Garroway et al. 2011,

129

Blair et al. 2013), the validity of the technique has been questioned because of the potential lack of independent observations (Goldberg and Waits 2009). However, the use of caribou herds as opposed to individuals as the sampling unit may minimize issues related to autocorrelation. Moreover, the R2 values provide a model ranking consistent with AIC when only one independent variable is used.

Shirk et al. (2010) acknowledged several other limitations of the framework we used for optimizing univariate and composite resistance surfaces. First, we did not test the entire factorial of composite resistance surfaces due to intractability of the analysis, so it is possible the optimal composite resistance surface went untested (Shirk et al. 2010).

Additionally, the selected resistance model may depend on the starting cost values in cases where a species' response to the landscape is multimodal (Shirk et al. 2010). That is, the detection of a peak in the AIC or coefficient of determination values may result from responses to local conditions and detection of the global peak in support is, therefore, overlooked (Shirk et al. 2010). Future work may want to consider an optimization procedure to avoid incorrect landscape genetic inferences when a species' response the landscape is multimodal (Graves et al. 2013).

Our study identified landscape factors that may be important for affecting contemporary gene flow of woodland caribou at the scale we considered and suggested that large water bodies may be important gene flow inhibitors, as well as anthropogenic disturbance. Therefore, future management of woodland caribou should consider strategic planning of human-related activities and avoid development or disturbance that might exacerbate the impacts of natural water bodies on gene flow. The resistance surfaces provide insights on regions where maintaining genetic connectivity may be

130 particularly important. For example, there are several narrow corridors delineated by the landscape resistance surfaces in Manitoba with high current values (suggesting the potential for large amounts of gene flow; Fig. 4.5); management of those areas is extremely important to ensure genetic connectivity is maintained and evolutionary potential is maximized, which is critical in the case of potential climate and other land- use changes. The delineation of those corridors may be particularly useful for mapping future hydroelectric developments in Manitoba, so that impacts on caribou connectivity are minimized.

Several regions exhibited moderate current values, such as the majority of northern Ontario (Fig. 4.5); the current values in those areas were likely less concentrated because of the large amount of contiguous habitat and suggest that the region may be an important core area for woodland caribou. However, a large deposit of chromite (and other minerals), referred to as the McFaulds Lake Project or “Ring of Fire”, exists in the middle of that region and has the potential to encourage development as a result of concentrated mining activities (OMNDM 2015). Similar types of development have occurred in other parts of the woodland caribou range, including the oil sands, which are a collection of crude oil reserves in northern Alberta (Alberta Energy 2015). Human development associated with the oil sands have led to an increase in deer and wolf densities, which have caused caribou declines as a result of incidental predation (Latham et al. 2011). Thus, any proposed development in the Ring of Fire should carefully consider impacts to the structure and configuration of the boreal forest so that the creation of ideal habitat types for deer and other temperate ungulates are minimized. The use of

131 strategic development plans may help to control potential increases in wolf densities and allow for sustainable caribou populations in the region.

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Table 4.1. Linearized FST values among woodland caribou herds in Ontario, Manitoba, and Saskatchewan, Canada.

Ati Att BMB BON BTL Bri CL Chu Kee Kes Kis MF Moo NR Nip NI NH OF Pag SW Syd TB Wab WW Wea Web Whe

Ati 0.000

Att 0.026 0.000

BMB 0.054 0.018 0.000

BON 0.029 0.005 0.018 0.000

BTL 0.033 0.000 0.015 0.008 0.000

Bri 0.049 0.019 0.031 0.005 0.027 0.000

CL 0.067 0.013 0.014 0.007 0.014 0.014 0.000

Chu 0.047 0.019 0.019 0.007 0.022 0.010 0.035 0.000

Kee 0.040 0.012 0.014 0.016 0.012 0.016 0.019 0.009 0.000

Kes 0.058 0.021 0.031 0.022 0.032 0.032 0.034 0.026 0.016 0.000

Kis 0.072 0.027 0.052 0.030 0.029 0.035 0.036 0.056 0.039 0.049 0.000

MF 0.045 0.000 0.012 0.014 0.007 0.023 0.012 0.014 0.005 0.009 0.039 0.000

Moo 0.045 0.014 0.023 0.007 0.025 0.019 0.021 0.015 0.013 0.011 0.059 0.009 0.000

NR 0.073 0.029 0.043 0.026 0.035 0.035 0.041 0.041 0.044 0.040 0.021 0.029 0.050 0.000

Nip 0.080 0.053 0.044 0.040 0.049 0.049 0.072 0.039 0.046 0.060 0.061 0.040 0.054 0.049 0.000

NI* 0.095 0.064 0.085 0.056 0.073 0.058 0.087 0.063 0.060 0.064 0.066 0.066 0.077 0.061 0.070 0.000

NH 0.043 0.013 0.029 0.019 0.022 0.021 0.009 0.037 0.020 0.035 0.041 0.022 0.023 0.050 0.074 0.107 0.000

OF 0.043 0.025 0.029 0.005 0.030 0.019 0.035 0.011 0.032 0.032 0.060 0.027 0.029 0.045 0.068 0.105 0.038 0.000

Pag 0.070 0.027 0.036 0.009 0.030 0.012 0.016 0.031 0.029 0.029 0.040 0.021 0.033 0.045 0.067 0.068 0.035 0.023 0.000

SW* 0.056 0.028 0.043 0.013 0.028 0.025 0.037 0.027 0.030 0.032 0.033 0.028 0.030 0.032 0.046 0.069 0.032 0.026 0.029 0.000

Syd 0.058 0.022 0.032 0.000 0.033 0.030 0.025 0.026 0.032 0.039 0.055 0.030 0.027 0.041 0.071 0.077 0.039 0.022 0.028 0.044 0.000

TB* 0.038 0.029 0.047 0.031 0.035 0.041 0.063 0.036 0.030 0.037 0.055 0.031 0.035 0.045 0.049 0.031 0.061 0.062 0.046 0.041 0.050 0.000

Wab 0.041 0.017 0.022 0.022 0.017 0.028 0.029 0.018 0.021 0.032 0.042 0.014 0.020 0.026 0.053 0.075 0.031 0.029 0.044 0.025 0.037 0.035 0.000

WW 0.054 0.020 0.044 0.019 0.028 0.022 0.039 0.019 0.026 0.031 0.033 0.021 0.026 0.024 0.061 0.056 0.033 0.034 0.033 0.019 0.038 0.033 0.020 0.000

Wea 0.033 0.011 0.011 0.004 0.004 0.021 0.022 0.016 0.005 0.023 0.031 0.016 0.019 0.039 0.034 0.064 0.025 0.022 0.020 0.018 0.034 0.036 0.023 0.032 0.000

Web 0.042 0.000 0.008 0.003 0.004 0.011 0.007 0.007 0.004 0.015 0.029 0.000 0.005 0.020 0.045 0.066 0.020 0.016 0.028 0.030 0.013 0.033 0.011 0.021 0.013 0.000

Whe 0.055 0.022 0.045 0.011 0.028 0.023 0.028 0.028 0.034 0.033 0.026 0.027 0.026 0.013 0.050 0.059 0.026 0.033 0.036 0.017 0.035 0.039 0.020 0.010 0.028 0.018 0.000 *Denotes herds that were removed from the full data set to create the truncated data set.

Table 4.2. The R2, t statistic(s), F statistic, P-value(s), and AIC for the linear and quadratic regression models calculated between the resistances distances from the univariate resistance surfaces and FST for the full data set (27 herds) in Ontario, Manitoba, and Saskatchewan, Canada.

Linear model Quadratic model

Variable/cost R2 t stat F stat Pr > |t| AIC R2 t stat t stat 2 Pr > |t| Pr > |t|2 F stat P-value AIC anthro10 0.058 4.62 21.35 0.027 -2800.37 0.090 4.48 -3.50 0.001 0.027 17.15 0.017 -2810.53 anthro100 0.074 5.28 27.84 0.051 -2806.48 0.120 5.55 -4.26 0.006 0.044 23.69 0.040 -2822.35 anthro200 0.068 5.04 25.39 0.084 -2804.18 0.111 5.36 -4.11 0.028 0.082 21.71 0.048 -2818.81 anthro400 0.057 4.60 21.13 0.131 -2800.16 0.093 4.86 -3.71 0.072 0.157 17.83 0.125 -2811.78 fire10 0.020 2.70 7.28 0.088 -2786.78 0.031 2.47 -1.97 0.191 0.347 5.60 0.188 -2788.65 fire100 0.016 2.39 5.70 0.106 -2785.22 0.026 2.29 -1.84 0.261 0.395 4.57 0.258 -2786.63 fire200 0.015 2.30 5.30 0.128 -2784.83 0.025 2.31 -1.87 0.270 0.405 4.43 0.278 -2786.35 fire400 0.014 2.24 5.02 0.151 -2784.55 0.024 2.33 -1.91 0.297 0.426 4.34 0.306 -2786.19 Dmig10 0.125 7.05 49.71 0.032 -2826.27 0.182 6.83 -4.92 0.002 0.011 38.60 0.008 -2847.86 Dmig100 0.125 7.05 49.71 0.021 -2826.27 0.182 6.83 -4.92 0.001 0.018 38.60 0.012 -2847.86 Dmig200 0.125 7.05 49.71 0.019 -2826.27 0.182 6.83 -4.92 0.001 0.012 38.60 0.012 -2847.86 Dmig400 0.125 7.05 49.71 0.034 -2826.27 0.182 6.83 -4.92 0.001 0.013 38.60 0.001 -2847.86 SD5 0.008 1.66 2.74 0.356 -2782.28 0.009 0.92 -0.58 0.561 0.732 1.53 0.526 -2782.28 SD10 0.008 1.66 2.74 0.338 -2782.28 0.009 0.92 -0.58 0.574 0.762 1.53 0.575 -2780.61 SD50 0.008 1.66 2.74 0.356 -2782.28 0.009 0.92 -0.58 0.574 0.752 1.53 0.586 -2780.61 SD200 0.008 1.66 2.74 0.350 -2782.28 0.009 0.92 -0.58 0.550 0.735 1.53 0.576 -2780.61 water10 0.083 5.63 31.71 0.001 -2810.06 0.115 4.64 -3.52 0.003 0.040 22.56 0.003 -2820.33 water100 0.178 8.71 75.80 0.001 -2848.53 0.185 3.44 -1.61 0.028 0.376 39.37 0.001 -2849.14 water200 0.200 9.33 87.10 0.001 -2857.74 0.201 2.84 -0.86 0.084 0.611 43.89 0.001 -2856.48 water400 0.214 9.75 95.08 0.001 -2864.11 0.214 2.39 -0.30 0.134 0.848 47.46 0.001 -2864.11 geog 0.021 2.73 7.43 0.137 -2786.93 0.035 2.76 -2.25 0.040 0.168 6.30 0.107 -2790.01 anthro = anthropogenic disturbance, fire = fire disturbance, Dmig = distance to the eastern migratory range, SD = average maximum snow depth, water = water bodies, geog = geographic distance.

144

Linear model Quadratic model Variables/costs R2 t stat F stat P-value AIC R2 t stat t stat 2 Pr > |t| Pr > |t|2 F stat P-value AIC water0+Dmig10+anthro100 0.098 6.15 37.80 0.071 -2815.63 0.169 7.23 -5.45 0.002 0.018 35.32 0.024 -2842.40 water10+Dmig10+anthro100 0.161 8.18 66.88 0.017 -2841.07 0.258 9.15 -6.74 0.001 0.003 60.43 0.002 -2882.17 water100+Dmig10+anthro100 0.318 12.77 163.05 0.002 -2914.09 0.352 7.83 -4.26 0.001 0.032 94.60 0.002 -2929.93 water200+Dmig10+anthro100 0.373 14.41 207.73 0.001 -2943.46 0.379 5.79 -1.85 0.009 0.395 106.28 0.002 -2944.87 water400+Dmig10+anthro100 0.416 15.77 248.54 0.001 -2968.29 0.416 4.70 -0.17 0.063 0.952 123.94 0.001 -2966.31 water400+Dmig0+anthro100 0.423 15.98 255.37 0.001 -2972.27 0.426 3.45 1.47 0.208 0.589 129.18 0.001 -2972.44 water400+Dmig100+anthro100 0.276 11.54 133.21 0.001 -2893.01 0.329 8.51 -5.23 0.001 0.009 85.31 0.001 -2917.57 water400+Dmig200+anthro100 0.238 10.43 108.83 0.002 -2874.81 0.301 8.55 -5.59 0.001 0.007 74.79 0.001 -2903.03 water400+Dmig400+anthro100 0.204 9.46 89.55 0.003 -2859.70 0.271 8.28 -5.64 0.001 0.006 64.61 0.003 -2888.38 water400+Dmig0+anthro0 0.214 9.75 95.08 0.001 -2864.11 0.214 2.39 -0.30 0.134 0.848 47.46 0.001 -2864.11 water400+Dmig0+anthro10 0.371 14.35 205.89 0.001 -2942.29 0.378 1.97 1.94 0.357 0.377 105.63 0.001 -2944.05 water400+Dmig0+anthro200 0.416 15.75 248.12 0.001 -2968.04 0.420 3.32 1.64 0.235 0.531 125.99 0.001 -2968.72 water400+Dmig0+anthro400 0.398 15.17 230.26 0.002 -2957.38 0.405 2.74 2.16 0.317 0.405 118.66 0.001 -2960.03 anthro = anthropogenic disturbance, fire = fire disturbance, Dmig = distance to the eastern migratory range, SD = average maximum snow depth, water = water bodies, geog = geographic distance.

145

Table 4.4. The R2, T statistic(s), F statistic, P-value(s), and AIC for the linear and quadratic regression models calculated between the resistances distances from the univariate resistance surfaces and FST for the truncated data set (24 herds) in Ontario, Manitoba, and Saskatchewan, Canada.

Linear model Quadratic model Variable/cost R2 t stat F stat Pr > |t| AIC R2 t stat t stat 2 Pr > |t| Pr > |t|2 F stat P-value AIC anthro10 0.172 7.56 57.11 0.001 -2325.91 0.183 3.52 -1.92 0.006 0.198 30.67 0.001 -2327.59 anthro100 0.227 8.98 80.58 0.001 -2344.80 0.236 3.91 -1.77 0.006 0.290 42.17 0.002 -2345.95 anthro200 0.221 8.82 77.80 0.001 -2342.63 0.228 3.70 -1.55 0.042 0.404 40.29 0.002 -2343.04 anthro400 0.200 8.27 68.42 0.006 -2335.17 0.204 3.22 -1.23 0.130 0.546 35.02 0.005 -2334.69 fire10 0.103 5.62 31.60 0.002 -2303.78 0.109 2.40 -1.27 0.192 0.539 16.64 0.008 -2303.39 fire100 0.095 5.35 28.62 0.002 -2301.07 0.098 2.08 -0.97 0.305 0.634 14.78 0.020 -2300.02 fire200 0.091 5.25 27.55 0.002 -2300.09 0.095 2.08 -0.99 0.315 0.640 14.26 0.032 -2299.07 fire400 0.089 5.17 26.74 0.003 -2299.35 0.092 2.09 -1.01 0.345 0.646 13.88 0.031 -2298.39 Dmig10 0.283 10.39 107.98 0.001 -2365.35 0.299 5.35 -2.50 0.001 0.130 58.15 0.001 -2369.59 Dmig100 0.283 10.39 107.98 0.001 -2365.35 0.299 5.35 -2.50 0.004 0.131 58.15 0.002 -2369.59 Dmig200 0.283 10.39 107.98 0.001 -2365.35 0.299 5.35 -2.50 0.004 0.128 58.15 0.001 -2369.59 Dmig400 0.283 10.39 107.98 0.001 -2365.35 0.299 5.35 -2.50 0.004 0.123 58.15 0.001 -2369.59 SD5 0.075 4.72 22.29 0.004 -2295.23 0.083 2.49 -1.48 0.052 0.328 12.28 0.026 -2295.43 SD10 0.075 4.72 22.29 0.005 -2295.23 0.083 2.49 -1.48 0.059 0.354 12.28 0.021 -2295.43 SD50 0.075 4.72 22.29 0.003 -2295.23 0.083 2.49 -1.48 0.055 0.391 12.28 0.022 -2295.43 SD200 0.075 4.72 22.29 0.005 -2295.23 0.083 2.49 -1.48 0.064 0.355 12.28 0.022 -2295.43 water10 0.126 6.28 39.42 0.001 -2310.75 0.134 2.93 -1.62 0.036 0.354 21.14 0.004 -2311.39 water100 0.142 6.74 45.40 0.002 -2315.97 0.148 2.79 -1.37 0.068 0.414 23.71 0.001 -2315.85 water200 0.144 6.80 46.18 0.001 -2316.64 0.150 2.77 -1.33 0.087 0.443 24.04 0.001 -2316.42 water400 0.145 6.83 46.63 0.001 -2317.02 0.151 2.75 -1.30 0.103 0.466 24.22 0.005 -2316.74 geog 0.096 5.38 28.97 0.002 -2301.39 0.107 2.96 -1.88 0.023 0.240 16.38 0.008 -2302.93 anthro = anthropogenic disturbance, fire = fire disturbance, Dmig = distance to the eastern migratory range, SD = average maximum snow depth, water = water bodies, geog = geographic distance. 146

Linear model Quadratic model Variables/costs R2 t stat F stat Pr > |t| AIC R2 t stat t stat 2 Pr > |t| Pr > |t|2 F stat P-value AIC Dmig0+anthro100+water400+fire10+SD5 0.198 8.23 67.75 0.001 -2334.64 0.203 3.07 -1.23 0.053 0.471 34.69 0.002 -2334.15 Dmig10+anthro100+water400+fire10+SD5 0.219 8.76 76.75 0.001 -2341.81 0.223 3.22 -1.25 0.035 0.492 39.24 0.001 -2341.38 Dmig100+anthro100+water400+fire10+SD5 0.283 10.39 108.02 0.001 -2365.37 0.290 4.08 -1.61 0.002 0.300 55.63 0.001 -2366.00 Dmig200+anthro100+water400+fire10+SD5 0.298 10.79 116.43 0.001 -2371.39 0.307 4.50 -1.86 0.004 0.250 60.47 0.001 -2372.86 Dmig400+anthro100+water400+fire10+SD5 0.304 10.95 119.94 0.001 -2373.85 0.315 4.85 -2.09 0.002 0.166 62.89 0.001 -2376.24 Dmig400+anthro0+water400+fire10+SD5 0.303 10.91 118.97 0.001 -2373.18 0.311 4.53 -1.82 0.004 0.230 61.64 0.001 -2374.5 Dmig400+anthro10+water400+fire10+SD5 0.303 10.92 119.14 0.001 -2373.29 0.312 4.57 -1.85 0.004 0.221 61.80 0.001 -2374.72 Dmig400+anthro200+water400+fire10+SD5 0.304 10.95 119.82 0.001 -2373.77 0.318 5.12 -2.32 0.001 0.158 63.57 0.001 -2377.18 Dmig400+anthro400+water400+fire10+SD5 0.303 10.91 119.05 0.002 -2373.23 0.320 5.49 -2.66 0.004 0.097 64.37 0.001 -2378.28 Dmig400+anthro100+water0+fire10+SD5 0.286 10.49 109.97 0.001 -2366.78 0.303 5.18 -2.53 0.001 0.097 59.28 0.001 -2371.19 Dmig400+anthro100+water10+fire10+SD5 0.288 10.52 110.61 0.001 -2367.24 0.304 5.14 -2.50 0.001 0.109 59.49 0.001 -2371.48 Dmig400+anthro100+water100+fire10+SD5 0.295 10.71 114.72 0.001 -2370.17 0.309 5.00 -2.32 0.003 0.130 60.97 0.001 -2373.56 Dmig400+anthro100+water200+fire10+SD5 0.300 10.83 117.23 0.001 -2371.96 0.312 4.93 -2.21 0.002 0.137 61.90 0.001 -2374.86 Dmig400+anthro100+water400+fire0+SD5 0.305 10.96 120.04 0.001 -2373.93 0.316 4.91 -2.14 0.003 0.149 63.11 0.001 -2376.54 Dmig400+anthro100+water400+fire100+SD5 0.302 10.88 118.33 0.001 -2372.73 0.310 4.57 -1.83 0.005 0.251 61.34 0.001 -2374.08 Dmig400+anthro100+water400+fire200+SD5 0.298 10.79 116.31 0.001 -2371.30 0.305 4.41 -1.68 0.003 0.303 59.95 0.001 -2372.14 Dmig400+anthro100+water400+fire400+SD5 0.292 10.62 112.80 0.001 -2368.81 0.298 4.24 -1.53 0.009 0.332 57.84 0.001 -2369.16 Dmig400+anthro100+water400+fire0+SD0 0.306 10.98 120.64 0.001 -2374.35 0.320 5.47 -2.42 0.009 0.116 64.31 0.002 -2378.19 Dmig400+anthro100+water400+fire0+SD10 0.294 10.67 113.83 0.001 -2369.54 0.302 4.43 -1.86 0.003 0.241 59.16 0.001 -2371.03 Dmig400+anthro100+water400+fire0+SD50 0.214 8.65 74.76 0.001 -2340.24 0.220 3.30 -1.44 0.017 0.367 38.55 0.001 -2340.31 Dmig400+anthro100+water400+fire0+SD200 0.137 6.58 43.33 0.001 -2314.17 0.144 2.94 -1.57 0.042 0.366 23.01 0.003 -2314.64 anthro = anthropogenic disturbance, fire = fire disturbance, Dmig = distance to the eastern migratory range, SD = average maximum snow depth, water = water bodies, geog = geographic distance. 147

Fig. 4.1. Locations of woodland caribou herds in Ontario, Manitoba, and Saskatchewan, Canada. The herds are represented by different colors and the herds removed from the full data set (SW, TB, and NI) are coded as circles, whereas the truncated data set is coded as squares. All herds represent managed units except for those within the Ontario Far North range that have been categorized based on sampled areas. 148

2 0.1 y = -0.0013x + 0.0124x + 0.0096 0.12 y = 0.0102x + 0.0112

0.08 0.1

0.08 ST

0.06 ST

- -

/1 0.06

/1 ST

ST 0.04 F F 0.04

0.02 0.02

0 0 0 2 4 6 8 10 0 1 2 3 4 5 Anthropogenic (cost=100) Water (cost=400)

0.1 y = -0.0026x2 + 0.0187x + 0.0068 0.12 y = 0.0025x + 0.0166

0.08 0.1

0.08 ST

ST 0.06

F F -

- 0.06

/1 /1 ST

ST 0.04 F F 0.04

0.02 0.02

0 0 0 2 4 6 8 0 5 10 15 20 25 Dmig (cost=10) Composite (anthropogenic + water)

Fig. 4.2. The plotted relationship between FST and each of the significant landscape models for the full data set: A) anthropogenic disturbance, B) water bodies, C) distance to the migratory range (Dmig), and D) the optimized composite model.

149

150

y = 0.0076x + 0.0149 y = 0.0055x + 0.0131

0.1 0.1

0.08 0.08

ST F

F 0.06 0.06

0.04 /1 0.04

ST F 0.02 F 0.02 0 0 0 1 2 3 4 0 2 4 6 8 Fire (cost=10) Anthropogenic (cost=100)

0.1 y = 0.0075x + 0.014 0.1 y = 0.0007x + 0.017

0.08 0.08

ST ST

F F -

- 0.06 0.06 /1

/1 0.04 0.04

ST ST F F 0.02 0.02 0 0 0 2 4 6 0 10 20 30 40 Water (cost=400) Snow depth (cost = 5)

y = 0.0103x + 0.0153 y = 0.0061x + 0.0116

0.1 0.1

0.08 0.08

ST ST F

F 0.06 0.06

- -

/1 0.04 0.04

ST ST F F 0.02 0.02 0 0 0 1 2 3 0 2 4 6 8 Geographic Dmig (cost=10)

y = 0.0001x + 0.0108 0.1

0.08 ST

F 0.06

/1 0.04 ST

F 0.02 0 0 100 200 300 400 Composite (Dmig + anthropogenic + water)

Fig. 4.3. The plotted relationship between linearized FST and each of the significant landscape models for the truncated data set: A) fire, B) anthropogenic disturbance, C) water bodies, D) snow depth, E) geographic distance, F) distance to the migratory range (Dmig), and G) the optimized composite model.

151

A. C. E.

2.*^

.*^ B. D. 3

5.^

Fig. 4.4. Optimized composite landscape resistance surfaces (A-B) and current maps (C- D) for the full (top) and truncated (bottom) data sets (the extent of the resistance surfaces for the full and truncated data sets are outlined by the black, solid and gray, dotted lines, respectively). The variables considered for influencing contemporary genetic structure of woodland caribou were E1) fire disturbance, E2) anthropogenic disturbance, E3) water, E4) average maximum snow depth, and E5) distance to the eastern migratory range (Dmig). The symbols indicate whether the variables were included into the optimized composite resistance surface for the full (*) or truncated (^) data set.

152

CHAPTER 5. CONCLUSIONS

5.1. Synthesis

This dissertation provides valuable insights toward understanding genetic structure of woodland caribou within central portions of the Canadian boreal forest and likely has implications for taxonomic designations at the subspecies level, as well.

Relationships between the boreal and eastern migratory DUs of woodland caribou were unclear prior to this work, particularly in western portions of the eastern migratory range

(COSEWIC 2011). This study has shown that there is likely interbreeding between boreal and eastern migratory populations of woodland caribou in Ontario. Other investigators have speculated that eastern migratory caribou may have arisen from admixture between boreal populations of woodland and barren-ground caribou (Boulet et al. 2007, Bergerud et al. 2008). This study does not reject that hypothesis, as a reconstruction of the population history showed that caribou in the Far North region of

Ontario may have resulted from an admixture event between caribou in southern Ontario and an unsampled population, with barren-ground caribou as the likely candidate.

Additionally, this work identified potential historical differences between groups of boreal caribou, which may also have implications for future delineation of evolutionarily significant units. Specifically, boreal caribou in Ontario were differentiated from caribou in Manitoba and Saskatchewan based on a measure of allele size, representing historical differentiation. That finding is consistent with results from a mitochondrial DNA study conducted by Klütsch et al. (2012) that indicated boreal populations of caribou in the central boreal forest may have expanded from two different refugia found south of the

Laurentide ice sheet and east of the Mississippi River. However, historical patterns of

153 isolation by distance were also evident, which have the potential to obfuscate this interpretation.

This work also has implications for conservation and management at the herd level. We detected a large amount of contemporary gene flow (FST) in northern regions of our study area, suggesting that boreal herds are well-connected. However, the presence of eastern migratory caribou that have greater mobility than their boreal counterparts likely increase gene flow in overlapping portions of the range.

Differentiation was greatest in southern portions of the study area where the woodland caribou range has retracted substantially. Therefore, our study provides valuable information on where conservation management actions supporting caribou herds should focus. Additionally, our study provides information on factors that are important to consider for managing herds. For example, anthropogenic disturbance was an important variable in all of our landscape models and may especially be problematic in areas where persistent landscape features (i.e., water bodies) are already limiting gene flow.

This study provides methodological considerations regarding the parameterization of landscape resistance surfaces. Specifically, we showed how variance in life history traits (such as the differing movement behaviors possessed by boreal and eastern migratory caribou) is important to consider when identifying factors affecting gene flow.

A large number of recent landscape genetics studies are incorporating biological and ecological variables (e.g., Weckworth et al. 2013, Serrouya et al. 2012, Wang 2013) or performing replication (Short Bull et al. 2011) to improve model performance. However, this is the first known study to our knowledge that adjusted a resistance surface for differences in movement behaviors. This work builds on recent work by Wang (2013)

154 that utilized regression models with randomization for identifying relationships between distance matrices. Although a large number of studies account for non-linearity through the use of transformations (e.g., Shirk et al. 2010, Epps et al. 2013), very few studies have considered polynomial regressions for cases where the relationships between variables are believed to be curvilinear (but see Hanks and Hooten 2012). The use of polynomial regressions as opposed to transformations has the potential to reduce the number of runs in CIRCUITSCAPE, making analyses with a large number of variables more tractable.

5.2. Conservation Implications

Studies have suggested that the drastic decline of woodland caribou in the central boreal forest is anthropogenic in nature (Schaefer et al. 2003, Vors et al. 2007, Festa-

Bianchet et al. 2011). Our study suggests that anthropogenic disturbance may also limit gene flow, which has the potential to lead to genetic impacts (e.g., low genetic diversity) in addition to demographic impacts. This is consistent with findings for mountain populations of woodland caribou (Weckworth et al. 2013). The human population size is expected to increase in our study area (Ontario, Manitoba, and Saskatchewan) in the next

25 years (Statistics Canada 2014), which will likely lead to increases in anthropogenic disturbances throughout the region. Additionally, mining activity around the “Ring of

Fire”, a large chromite deposit, has the potential to increase human development and impact caribou populations in northern Ontario. Our study identified that region has being a potentially important area for promoting caribou connectivity. Consequently, careful planning is critical to minimize impacts of human development if self-sustaining woodland caribou populations is a desired outcome.

155

Climate change is an additional stressor that has the potential to exacerbate the impacts of anthropogenic disturbance on caribou gene flow as a result of synergistic feedbacks (Staudt et al. 2013). That is, effects of separate processes may have greater impacts on connectivity than the sum of single processes alone (Brook et al. 2008).

Climate change projections suggest that temperatures in the boreal forest may increase as much as 4-5 C by 2100 (Price et al. 2013), causing physiological stress to caribou in some regions as a direct effect (Sharma et al. 2009) and increased habitat changes as a result of fire and loss of snow cover, leading to predation, reduced weaning mass, nutritional decline, and disease as indirect effects (Weladji and Holand 2003, Post and

Forchhammer 2008, Couturier et al. 2009, Vors and Boyce 2009, Latham et al. 2011,

Pickles et al. 2013, Gustine et al. 2014, Hendrichsen and Tyler 2014). Thus, adopting measures to reduce vulnerability of caribou to future climate change (IPCC 2007; adaptation) , in addition to reducing impacts of land use change, will be critical in future management plans. However, because climate change will have continued impacts and the degree of those impacts is uncertain, it is important to realize that adaptation may be an ongoing process as opposed to an endpoint (Stein et al. 2013).

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160

Appendix A.

-34000 60 -34500 50 -35000

-35500 40

-36000

30 K

-36500 Δ -37000 20

ln Pr(X|K) ln -37500 10 -38000 -38500 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 A. K

-18500 100

-19000 80

-19500

-20000 K 40 Δ

-20500 ln Pr(X|K) ln -21000 20 -21500 0 1 2 3 4 5 6 7 8 9 10 K B.

-14500 180 -15000 160 140 -15500 120 -16000

100 K

-16500 80 Δ ln Pr(X|K) ln 60 -17000 40 -17500 20 -18000 0 1 2 3 4 5 6 7 8 9 10 K C. Average likelihood values (lnP(X|K), dashed line, left y-axis) and the second order rate of change of lnP(X|K) calculated based on Evanno et al. (2005; solid line, right y-axis) plotted against the number of woodland caribou groups from the five runs in program STRUCTURE. Plots were generated for individuals sampled in (A) Ontario, Manitoba, and Saskatchewan, (B) Manitoba and Saskatchewan only, and (C) Ontario only.

Appendix B.

Bar plots of average Q values calculated from the non-spatial admixture model in program STRUCTURE for woodland caribou individuals sampled in Ontario, Manitoba, and Saskatchewan (top, K = 2), Manitoba and Saskatchewan only (bottom left, K = 2), and

Ontario only (bottom right, K = 2). Each individual caribou is represented by a single vertical bar separated into multiple colored 161 segments with respective lengths proportional to the Q values of each inferred cluster.

Appendix C.

P-values from the Hardy-Weinberg (A-B) and linkage (C-D) equilibrium tests calculated for woodland caribou in Ontario, Manitoba, and Saskatchewan, Canada. Tests were calculated for the four regional clusters (RC1-RC4; A and C) and the ten local clusters (LC1- LC10; B and D). An asterisk (*) indicates significance after sequential Bonferroni adjustment and a peak (^) indicates significance at an alpha of 0.01. Tests were only calculated for groups with ≥5 individuals (i.e., LC11 was removed).

A. Locus RC1 RC2 RC3 RC4 BM848 0.000*^ 0.001*^ 0.016 0.074 BM888 0.000*^ 0.001*^ 0.000*^ 0.006*^ BMS1788 0.161 0.006*^ 0.076 0.097 Map2C 0.033 0.029 0.057 0.001*^ RT24 0.000*^ 0.023 0.000*^ 0.196

RT30 0.000*^ 0.037 0.000*^ 0.000*^ RT5 0.001*^ 0.033 0.147 0.001*^ RT6 0.041 0.472 0.844 0.445 RT7 0.000*^ 0.014 0.000*^ 0.134 RT9 0.010*^ 0.000*^ 0.000*^ 0.660

B. Locus LC1 LC2 LC3 LC4 LC5 LC6 LC7 LC8 LC9 LC10 BM848 0.016 0.345 0.000*^ 0.153 0.033 0.610 0.817 0.222 0.241 1.000 BM888 0.093 0.016 0.0003*^ 0.366 0.000*^ 0.407 0.505 0.047 0.036 0.032 BMS1788 0.336 0.056 0.496 0.242 0.281 0.976 0.200 0.296 0.039 0.096 Map2C 0.001*^ 0.103 0.275 0.237 0.103 0.012 0.208 0.896 0.049 0.003*^ RT24 0.854 0.000*^ 0.000*^ 0.000*^ 0.000*^ 0.355 0.577 0.192 0.514 0.435 RT30 0.023 0.003*^ 0.011 0.007^ 0.000*^ 0.000*^ 0.054 0.000*^ 0.102 0.016 RT5 0.583 0.918 0.004*^ 0.579 0.489 0.269 0.045 0.498 1.000 0.087 RT6 0.001*^ 0.770 0.991 0.643 0.812 0.089 0.722 0.450 0.020 0.493 RT7 0.002*^ 0.005*^ 0.400 0.092 0.022 0.923 0.090 0.680 0.049 0.626

RT9 0.000*^ 0.035 0.184 0.003*^ 0.475 0.174 0.468 0.937 0.677 0.447 162

163

Appendix C. (continued)

Locus I Locus J RC1 RC2 RC3 RC4 BM848 BM888 0.855 0.005^ 0.010 0.525 BM848 BMS1788 0.264 0.039 0.174 0.417 BM848 Map2C 0.725 0.527 0.010^ 0.013 BM848 RT24 0.342 0.074 0.602 0.224 BM848 RT30 0.425 0.549 0.071 0.457 BM848 RT5 0.016 0.114 0.292 0.213 BM848 RT6 0.010^ 0.536 0.184 0.106 BM848 RT7 0.568 0.304 0.240 0.241 BM848 RT9 0.570 0.008^ 0.886 0.039 BM888 BMS1788 0.361 0.183 0.291 0.438 BM888 Map2C 0.705 0.052 0.000*^ 0.608 BM888 RT24 0.328 0.420 0.509 0.077 BM888 RT30 0.251 0.113 0.234 0.189 BM888 RT5 0.173 0.123 0.000*^ 0.476 BM888 RT6 0.758 0.210 0.789 0.000*^ BM888 RT7 0.950 0.085 0.132 0.947 BM888 RT9 0.005^ 0.018 0.000*^ 0.000*^ BMS1788 Map2C 0.724 0.221 0.000*^ 0.000*^ BMS1788 RT24 0.040 0.459 0.385 0.637 BMS1788 RT30 0.295 0.235 0.053 0.499 BMS1788 RT5 0.000*^ 0.076 0.018 0.004^ BMS1788 RT6 0.016 0.184 0.292 0.939 BMS1788 RT7 0.130 0.001*^ 0.005^ 0.949 BMS1788 RT9 0.105 0.064 0.336 0.000*^ Map2C RT24 0.693 0.693 0.048 0.008^ Map2C RT30 0.148 0.667 0.511 0.470 Map2C RT5 0.631 0.227 0.000*^ 0.999 Map2C RT6 0.323 0.411 0.857 0.041 Map2C RT7 0.648 0.034 0.099 0.984 Map2C RT9 0.476 0.153 0.000*^ 0.292 RT24 RT30 0.100 0.165 0.332 0.113 RT24 RT5 0.044 0.378 0.000*^ 0.000*^ RT24 RT6 0.039 0.583 0.640 0.265 RT24 RT7 0.308 0.060 0.892 0.313 RT24 RT9 0.516 0.031 0.037 0.107 RT30 RT5 0.476 0.147 0.007^ 0.478 RT30 RT6 0.432 0.168 0.120 0.369 RT30 RT7 0.755 0.268 0.828 0.215 RT30 RT9 0.566 0.199 0.335 0.238 RT5 RT6 0.093 0.162 0.127 0.865 RT5 RT7 0.868 0.468 0.000*^ 0.271 RT5 RT9 0.110 0.000*^ 0.195 0.183 RT6 RT7 0.085 0.899 0.550 0.916 RT6 RT9 0.746 0.623 0.026 0.281 RT7 RT9 0.028 0.037 0.148 0.210

164

Appendix C. (continued)

Locus I Locus J LC1 LC2 LC3 LC4 LC5 LC6 LC7 LC8 LC9 LC10 BM848 BM888 0.966 0.000*^ 0.592 0.210 0.976 0.623 0.064 0.631 0.058 0.402 BM848 BMS1788 0.214 0.361 0.148 0.596 0.848 0.310 0.104 0.158 0.104 0.687 BM848 Map2C 0.266 0.325 0.073 0.113 0.219 0.783 0.017 0.629 0.909 0.198 BM848 RT24 0.797 0.500 0.963 0.312 0.643 0.071 0.559 0.464 0.142 0.236 BM848 RT30 0.698 0.899 0.736 0.008^ 0.531 0.575 0.900 0.972 0.849 0.321 BM848 RT5 0.542 0.269 0.825 0.309 0.074 0.023 0.672 0.338 0.493 0.038 BM848 RT6 0.387 0.598 0.055 0.475 0.118 0.470 0.001*^ 0.681 0.290 0.332 BM848 RT7 0.681 0.067 0.070 0.451 0.328 0.597 0.350 0.521 0.710 0.598 BM848 RT9 0.114 0.417 0.513 0.941 0.635 0.173 0.023 0.458 0.324 0.879 BM888 BMS1788 0.346 0.667 0.730 0.274 0.981 0.915 0.385 0.207 0.448 0.771 BM888 Map2C 0.685 0.001*^ 0.310 0.990 0.915 0.871 0.235 0.027 0.321 0.831 BM888 RT24 0.169 0.828 0.620 0.041 0.274 0.401 0.096 0.504 0.771 1.000 BM888 RT30 0.289 0.235 0.358 0.809 0.679 0.881 0.150 0.151 0.341 0.858 BM888 RT5 0.081 0.659 0.880 0.122 0.766 0.000*^ 0.726 0.293 0.621 0.583 BM888 RT6 0.711 0.945 0.284 0.594 0.867 0.312 0.545 0.679 0.070 0.435 BM888 RT7 0.612 0.150 0.800 0.384 0.867 0.788 0.822 0.249 0.543 0.965 BM888 RT9 0.152 0.000*^ 0.359 0.118 0.000*^ 0.804 0.209 0.017 0.322 0.685 BMS1788 Map2C 0.000*^ 0.044 0.189 0.285 0.775 0.158 0.398 0.127 0.629 0.752 BMS1788 RT24 0.044 0.823 0.310 0.137 0.044 0.738 0.128 0.092 0.636 0.573 BMS1788 RT30 0.357 0.011 0.792 0.032 0.163 0.078 0.728 0.939 0.338 0.170 BMS1788 RT5 0.018 0.298 0.433 0.151 0.355 0.786 0.517 0.075 0.288 0.966 BMS1788 RT6 0.111 0.459 0.072 0.925 0.137 0.657 0.777 0.948 0.268 0.437 BMS1788 RT7 0.504 0.065 0.122 0.007^ 0.667 0.075 0.317 0.939 0.023 0.969 BMS1788 RT9 0.289 0.115 0.378 0.882 0.494 0.011 0.872 0.926 0.396 0.823 Map2C RT24 0.382 0.029 0.674 0.678 0.025 0.000*^ 0.721 0.394 0.833 0.086 Map2C RT30 0.978 0.511 0.032 0.322 0.133 0.042 0.176 0.620 0.861 0.559 Map2C RT5 0.292 0.000*^ 0.716 0.271 0.000*^ 0.687 0.470 0.589 0.827 1.000 Map2C RT6 0.218 0.446 0.043 0.726 0.171 0.610 0.010^ 0.000*^ 0.231 0.345 Map2C RT7 0.864 0.622 0.189 0.198 0.179 0.521 0.092 0.964 0.084 0.877 Map2C RT9 0.201 0.014 0.00258^ 0.017 0.383 0.806 0.613 0.636 0.734 0.487 RT24 RT30 0.076 0.798 0.897 0.047 0.434 0.040 0.446 0.374 0.315 0.951 RT24 RT5 0.725 0.020 0.924 0.260 0.413 0.103 0.067 0.063 0.839 0.860 RT24 RT6 0.201 0.311 0.389 0.746 0.261 0.554 0.441 0.128 0.557 0.392 RT24 RT7 0.930 0.894 0.282 0.614 0.547 0.646 0.028 0.421 0.074 0.981 RT24 RT9 0.235 0.142 0.260 0.047 0.794 0.862 0.085 0.999 0.212 0.025 RT30 RT5 0.834 0.478 0.872 0.174 0.182 0.564 0.103 0.130 0.289 0.996 RT30 RT6 0.392 0.293 0.188 0.454 0.566 0.594 0.531 0.250 0.200 0.812 RT30 RT7 0.786 0.613 0.069 0.940 0.900 0.112 0.509 0.616 0.349 0.120 RT30 RT9 0.483 0.154 0.361 0.336 0.563 0.371 0.590 0.885 0.561 0.670 RT5 RT6 0.079 0.776 0.160 0.063 0.314 0.991 0.905 0.375 0.128 0.993 RT5 RT7 0.253 0.137 0.895 0.001*^ 0.939 0.113 0.122 0.141 0.954 0.867 RT5 RT9 0.555 0.523 0.890 0.737 0.051 0.687 0.072 0.753 0.049 0.042 RT6 RT7 0.095 0.374 0.000*^ 0.521 0.168 0.843 0.776 0.153 0.798 0.199 RT6 RT9 0.740 0.069 0.257 0.250 0.991 0.428 0.599 0.596 0.534 0.919 RT7 RT9 0.076 0.639 0.382 0.070 0.654 0.125 0.875 0.891 0.630 0.271

Appendix D.

Genetic differentiation of the woodland caribou herds in Ontario, Manitoba, and Saskatchewan. Pair-wise FST values (top matrix) represent genetic differentiation caused by genetic drift and pair-wise RST values (bottom matrix) represent genetic differentiation caused by mutation. Genetic differentiation was only calculated among herds with >5 individuals (i.e., God's Lake and Pasquia were removed).

Att Ber BTL Bri Chu Coa FS Kee Kes Kis MF Moo NR Nip NI NH Pag Pea SW Syd TB Wab WW Wea Web Whe

Att 0.000 0.005 0.000 0.019 0.020 0.091 0.008 0.013 0.021 0.027 0.000 0.015 0.031 0.057 0.070 0.012 0.029 0.008 0.029 0.022 0.030 0.019 0.021 0.011 0.000 0.023 Ber 0.046 0.000 0.008 0.005 0.009 0.073 0.012 0.016 0.024 0.030 0.014 0.007 0.027 0.041 0.059 0.018 0.009 0.006 0.013 0.000 0.032 0.022 0.020 0.004 0.003 0.011 BTL 0.006 0.016 0.000 0.027 0.023 0.091 0.008 0.012 0.033 0.029 0.006 0.026 0.035 0.051 0.078 0.022 0.031 0.010 0.029 0.035 0.036 0.018 0.029 0.005 0.005 0.029 Bri 0.067 0.012 0.033 0.000 0.011 0.091 0.032 0.017 0.033 0.038 0.023 0.019 0.037 0.053 0.064 0.022 0.014 0.000 0.027 0.031 0.046 0.028 0.024 0.021 0.011 0.024 Chu 0.034 0.008 0.015 0.034 0.000 0.072 0.035 0.009 0.028 0.060 0.014 0.015 0.043 0.040 0.066 0.039 0.034 0.007 0.029 0.026 0.037 0.019 0.020 0.019 0.007 0.029 Coa 0.150 0.059 0.081 0.086 0.061 0.000 0.101 0.068 0.076 0.130 0.059 0.061 0.111 0.095 0.157 0.081 0.082 0.078 0.078 0.090 0.097 0.078 0.098 0.058 0.078 0.103 FS 0.028 0.000 0.000 0.020 0.018 0.045 0.000 0.019 0.034 0.027 0.025 0.035 0.039 0.054 0.082 0.015 0.043 0.010 0.039 0.028 0.051 0.038 0.033 0.022 0.009 0.033 Kee 0.035 0.016 0.008 0.002 0.009 0.043 0.000 0.000 0.016 0.041 0.005 0.014 0.046 0.049 0.064 0.021 0.031 0.000 0.031 0.033 0.030 0.022 0.027 0.006 0.004 0.035 Kes 0.048 0.044 0.032 0.066 0.079 0.057 0.017 0.040 0.000 0.052 0.008 0.012 0.042 0.062 0.068 0.037 0.032 0.023 0.034 0.041 0.039 0.033 0.032 0.023 0.015 0.035 Kis 0.042 0.054 0.033 0.090 0.076 0.110 0.047 0.073 0.033 0.000 0.040 0.063 0.022 0.066 0.072 0.042 0.042 0.038 0.034 0.058 0.060 0.044 0.034 0.033 0.031 0.026 MF 0.000 0.059 0.000 0.070 0.043 0.149 0.014 0.025 0.043 0.053 0.000 0.009 0.030 0.042 0.070 0.022 0.022 0.004 0.029 0.031 0.033 0.014 0.021 0.017 0.000 0.027 Moo 0.009 0.009 0.002 0.017 0.040 0.096 0.000 0.012 0.015 0.042 0.009 0.000 0.053 0.057 0.083 0.024 0.034 0.007 0.031 0.028 0.037 0.021 0.026 0.018 0.005 0.027 NR 0.048 0.072 0.055 0.132 0.072 0.153 0.090 0.108 0.085 0.016 0.077 0.080 0.000 0.051 0.065 0.051 0.047 0.036 0.033 0.042 0.048 0.026 0.025 0.042 0.021 0.013 Nip 0.046 0.026 0.030 0.015 0.032 0.101 0.027 0.019 0.070 0.102 0.054 0.013 0.120 0.000 0.073 0.080 0.073 0.062 0.047 0.076 0.051 0.055 0.063 0.036 0.047 0.052 NI 0.059 0.050 0.078 0.046 0.059 0.098 0.064 0.040 0.067 0.115 0.082 0.035 0.135 0.028 0.000 0.119 0.073 0.066 0.073 0.083 0.032 0.079 0.059 0.070 0.070 0.062 NH 0.022 0.019 0.000 0.025 0.030 0.080 0.000 0.004 0.026 0.019 0.010 0.005 0.058 0.060 0.087 0.000 0.037 0.009 0.033 0.040 0.065 0.032 0.034 0.025 0.021 0.027 Pag 0.070 0.036 0.056 0.012 0.052 0.063 0.028 0.005 0.041 0.086 0.078 0.015 0.142 0.024 0.000 0.038 0.000 0.021 0.030 0.030 0.050 0.046 0.035 0.021 0.030 0.037 Pea 0.088 0.008 0.035 0.000 0.054 0.080 0.000 0.000 0.050 0.101 0.071 0.019 0.183 0.046 0.066 0.016 0.016 0.000 0.034 0.017 0.036 0.012 0.014 0.022 0.000 0.024 SW 0.055 0.047 0.049 0.119 0.050 0.101 0.068 0.085 0.070 0.032 0.075 0.068 0.009 0.098 0.109 0.058 0.112 0.157 0.000 0.046 0.043 0.026 0.020 0.019 0.031 0.017 Syd 0.046 0.003 0.046 0.057 0.008 0.058 0.038 0.032 0.065 0.076 0.075 0.045 0.069 0.052 0.034 0.055 0.046 0.078 0.035 0.000 0.054 0.038 0.039 0.035 0.014 0.036 TB 0.042 0.027 0.059 0.070 0.050 0.102 0.055 0.062 0.057 0.085 0.071 0.028 0.085 0.024 0.016 0.089 0.037 0.095 0.059 0.021 0.000 0.036 0.034 0.037 0.034 0.040 Wab 0.028 0.047 0.026 0.092 0.024 0.097 0.047 0.046 0.063 0.027 0.041 0.064 0.017 0.099 0.100 0.021 0.101 0.123 0.013 0.030 0.078 0.000 0.020 0.025 0.011 0.020 WW 0.069 0.077 0.059 0.148 0.081 0.147 0.083 0.109 0.082 0.008 0.087 0.090 0.009 0.154 0.153 0.046 0.138 0.171 0.019 0.081 0.108 0.019 0.000 0.033 0.022 0.011 Wea 0.033 0.000 0.022 0.019 0.008 0.038 0.005 0.000 0.030 0.053 0.044 0.012 0.080 0.041 0.017 0.011 0.001 0.012 0.054 0.000 0.029 0.030 0.073 0.000 0.014 0.029

Web 0.000 0.031 0.000 0.050 0.022 0.120 0.009 0.018 0.038 0.046 0.000 0.015 0.060 0.042 0.071 0.011 0.079 0.056 0.062 0.048 0.054 0.026 0.077 0.028 0.000 0.018 165 Whe 0.051 0.080 0.056 0.145 0.080 0.164 0.089 0.110 0.077 0.013 0.075 0.076 0.000 0.129 0.133 0.054 0.139 0.198 0.003 0.074 0.081 0.019 0.003 0.083 0.071 0.000

Appendix E.

Correlations between resistance distances calculated from each of the landscape variables for the full data set.

anthro anthro anthro anthro fire fire fire Dmig Dmig Dmig Dmig water water water water SD 10 100 200 400 fire10 100 200 400 10 100 200 400 10 100 200 400 SD5 SD10 SD50 200 geog anthro10 1.000 anthro100 0.912 1.000 anthro200 0.844 0.988 1.000 anthro400 0.765 0.955 0.989 1.000 fire10 0.901 0.722 0.643 0.567 1.000 fire100 0.854 0.685 0.612 0.541 0.989 1.000 fire200 0.844 0.676 0.605 0.535 0.985 1.000 1.000 fire400 0.837 0.670 0.599 0.530 0.983 0.999 1.000 1.000 Dmig10 0.689 0.764 0.732 0.676 0.457 0.449 0.441 0.435 1.000 Dmig100 0.689 0.764 0.732 0.676 0.457 0.449 0.441 0.435 1.000 1.000 Dmig200 0.689 0.764 0.732 0.676 0.457 0.449 0.441 0.435 1.000 1.000 1.000 Dmig400 0.689 0.764 0.732 0.676 0.457 0.449 0.441 0.435 1.000 1.000 1.000 1.000 water10 0.926 0.772 0.700 0.627 0.950 0.910 0.903 0.898 0.459 0.459 0.459 0.459 1.000 water100 0.864 0.756 0.700 0.639 0.864 0.827 0.821 0.817 0.453 0.453 0.453 0.453 0.968 1.000 water200 0.842 0.744 0.692 0.634 0.839 0.803 0.797 0.793 0.445 0.445 0.445 0.445 0.953 0.999 1.000 water400 0.826 0.734 0.685 0.630 0.820 0.785 0.779 0.775 0.438 0.438 0.438 0.438 0.941 0.996 0.999 1.000 SD5 0.867 0.618 0.520 0.429 0.933 0.884 0.876 0.871 0.347 0.347 0.347 0.347 0.919 0.809 0.779 0.758 1.000 SD10 0.867 0.618 0.520 0.429 0.933 0.884 0.876 0.871 0.347 0.347 0.347 0.347 0.919 0.809 0.779 0.758 1.000 1.000 SD50 0.867 0.618 0.520 0.429 0.933 0.884 0.876 0.871 0.347 0.347 0.347 0.347 0.919 0.809 0.779 0.758 1.000 1.000 1.000 SD200 0.867 0.618 0.520 0.429 0.933 0.884 0.876 0.871 0.347 0.347 0.347 0.347 0.919 0.809 0.779 0.758 1.000 1.000 1.000 1.000 geog 0.927 0.730 0.646 0.564 0.975 0.932 0.924 0.919 0.426 0.426 0.426 0.426 0.970 0.880 0.854 0.835 0.973 0.973 0.973 0.973 1.000

Appendix F.

Correlations between resistance distances calculated from each of the landscape variables for the truncated data set.

anthro anthro anthro anthro fire fire fire Dmig Dmig Dmig Dmig water water water water SD 10 100 200 400 fire10 100 200 400 10 100 200 400 10 100 200 400 SD5 SD10 SD50 200 geog anthro10 1.000 anthro100 0.928 1.000 anthro200 0.861 0.986 1.000 anthro400 0.774 0.945 0.986 1.000 fire10 0.937 0.840 0.781 0.710 1.000 fire100 0.896 0.817 0.766 0.702 0.990 1.000 fire200 0.887 0.810 0.760 0.697 0.986 1.000 1.000 fire400 0.881 0.804 0.755 0.694 0.984 0.999 1.000 1.000 Dmig10 0.646 0.690 0.643 0.565 0.497 0.502 0.496 0.490 1.000 Dmig100 0.646 0.690 0.643 0.565 0.497 0.502 0.496 0.490 1.000 1.000 Dmig200 0.646 0.690 0.643 0.565 0.497 0.502 0.496 0.490 1.000 1.000 1.000 Dmig400 0.646 0.690 0.643 0.565 0.497 0.502 0.496 0.490 1.000 1.000 1.000 1.000 water10 0.956 0.873 0.822 0.759 0.976 0.942 0.936 0.931 0.468 0.468 0.468 0.468 1.000 water100 0.938 0.897 0.863 0.815 0.956 0.928 0.922 0.918 0.470 0.470 0.470 0.470 0.991 1.000 water200 0.931 0.898 0.868 0.824 0.950 0.922 0.917 0.913 0.466 0.466 0.466 0.466 0.987 1.000 1.000 water400 0.925 0.899 0.872 0.831 0.944 0.918 0.912 0.908 0.463 0.463 0.463 0.463 0.983 0.999 1.000 1.000 SD5 0.926 0.755 0.670 0.579 0.931 0.880 0.872 0.866 0.411 0.411 0.411 0.411 0.949 0.907 0.895 0.886 1.000 SD10 0.926 0.755 0.670 0.579 0.931 0.880 0.872 0.866 0.411 0.411 0.411 0.411 0.949 0.907 0.895 0.886 1.000 1.000 SD50 0.926 0.755 0.670 0.579 0.931 0.880 0.872 0.866 0.411 0.411 0.411 0.411 0.949 0.907 0.895 0.886 1.000 1.000 1.000 SD200 0.926 0.755 0.670 0.579 0.931 0.880 0.872 0.866 0.411 0.411 0.411 0.411 0.949 0.907 0.895 0.886 1.000 1.000 1.000 1.000 geog 0.953 0.824 0.754 0.675 0.975 0.935 0.927 0.923 0.441 0.441 0.441 0.441 0.987 0.958 0.950 0.943 0.977 0.977 0.977 0.977 1.000

167