Managing human footprint with respect to its effects on large mammals: implications of spatial scale, divergent responses and ecological thresholds

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Managing human footprint with respect to its effects on large mammals: Implications of spatial scale, divergent responses and ecological thresholds

by

Mary Toews

BSc, University of British Columbia, 2011

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCE

in the Faculty of Biology

 Mary Toews, 2016 University of Victoria

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Supervisory Committee

Managing human footprint with respect to its effects on large mammals: Implications of spatial scale, divergent responses and ecological thresholds

by

Mary Toews

BSc, University of British Columbia, 2011

Supervisory Committee

Dr. A. Cole Burton (Department of Biology)

Co-Supervisor

Dr. Francis Juanes (Department of Biology)

Supervisor

Dr. Jason Fisher (School of Environmental Studies)

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Abstract

The environmental problems facing the world today are largely attributable to anthropogenic activities and landscape change. Addressing these challenges in an evidence-based way requires an understanding of precisely how species and ecosystems are responding to human impacts. Discerning linkages between stressors and their ecological repercussions, and using this to inform conservation, can be challenging due to the complexity and uncertainty of ecological research. I focused on the responses of five wide-ranging large mammal species – gray wolf (Canis lupus), Canada lynx (Lynx canadensis), coyote (Canis latrans), white-tailed deer (Odocoileus virginianus) and moose (Alces alces) – to human footprint (measure of human infrastructure and landscape change), using 12 years (2001-2013) of snowtrack surveys

conducted across the boreal forest of Alberta. I explored three key challenges to discerning the linkages between ecological dynamics and management actions. First, I asked whether the direction and magnitude of species responses vary depending on the spatial extent and grain of the study. Second, I asked whether these species respond more strongly to individual footprint features or to the cumulative effects of footprint (measured as total footprint), and whether responses to footprint are consistent across species. Third, I evaluated the utility of thresholds for large mammal management and asked whether there is evidence for consistent threshold

responses to total footprint across scales. In addressing the first two questions, I evaluated a set of generalized linear mixed effects models (GLMM) relating the relative abundance of each species to individual and cumulative effects of human footprint, using an information-theoretic approach. I compared the direction of species responses across our regional study area

(approximately 400,000 km2) to those reported in previous smaller-extent studies (median 1,525 km2), and compared responses across three spatial grains (250m, 1500m, and 5000m transect buffers). In addressing the third question, I conducted a review on the utility of ecological thresholds, described as abrupt changes in the response to a continuous driver, for large mammal management. I further tested for thresholds in species responses to total footprint by comparing linear models (logistic regression) to piecewise regression models. I compared threshold values between two grains (approximately 33km2 - 1500m transect buffer, and 5500km2 - grouping transects into clusters), and across four regions (boreal forest extent, three landscape planning units). I found that the direction of species responses varied with spatial extent, but not grain, and

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that species responded strongly to a broad suite of footprint features, indicating the need to manage for cumulative effects. Despite the appeal of ecological thresholds, using these as targets is challenging and the success of doing so has rarely been evaluated. I found threshold models to be better supported than linear ones across species, but due to variability and uncertainty in threshold values, the results are more suited as guidelines or hypotheses to be further tested, as opposed to specific management targets. Translating research on complex ecological systems into management actions is a continuing challenge, yet, ongoing biodiversity monitoring and adaptive management may refine our existing tools, and ultimately lead to better environmental stewardship.

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List of Tables

Table 2.1. Footprint and landcover features used as explanatory variables in species abundance models, from the ABMI human footprint map (2007, 2010 and 2012; ABMI HFP), the ABMI Wall-to-wall Landcover map (2000 and 2010; ABMI LC), or from the Alberta Environment and Sustainable Resource Development historical wildfire layer (AESRD HW). ... 15 Table 2.2. Explanatory variables in model sets. For each species, a set of additional non-footprint variables were included based on preliminary model selection (see Models). Days Since Snow and Year as a random effect were also included in all models. ... 21 Table 2.3. Summary of hypothesized species-footprint responses, based on previous studies. Symbols indicate the expected direction of response (positive or negative), and whether this might change with grain (small | large). ... 23 Table 2.4. Responses summarized from previous small-extent studies (from Table 2.3) are shown for each species (“Small” column). A change in response with grain is indicated by two

responses separated by a line (e.g. S|L, or S|M|L; as in Table 2.3). For the responses found at the regional extent of my study (“Large” column), a “w” denotes a weak response (confidence intervals overlap zero)... 27 Table 2.5. Responses summarized from previous small-extent studies (from Table 2.3) are ... 31 Table 3.1. Explanatory variables in model sets. For each species, a set of additional non-footprint variables were included based on preliminary model selection (see above). Days since snow and year as a random effect were also included in all models... 53 Table 3.2. Model selection showing those within 2 QAIC, or within more to show closest

individual footprint feature if total model was selected (deer). Year as a random effect and days since snow are included in all models, and certain reference variables are included; these are not shown in the table. ... 55 Table 3.3. Comparison of the parameter coefficient and quasi-adjusted standard error for the total footprint model for each species, and the individual footprint feature with the greatest coefficient magnitude (with confidence intervals not overlapping zero) from the top model, for each species. ... 56

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Table 4.1. Results of literature review for studies indicating thresholds in relation to large mammals, using the search terms “threshold” in combination with terms related to large

mammals or specific mammal species. Literature cited from the initially located papers and from reviews are also included. Ordered alphabetically by author. ... 80 Table 5.1. Results of model selection for each grain (bold heading) and extent (italics heading) using QAIC, between the piecewise “threshold” model with a breakpoint determined based on maximum likelihood, and a simple regression model, both using GLMMs. ... 111

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List of Figures

Figure 2.1. The study area spans the lower foothills and boreal forest regions of Alberta (dark and medium grey), and excludes all other ecoregions (light grey). The ABMI transect locations within these regions (central points of transects) are shown in black... 10 Figure 2.2. Spatial extents of previous studies which examined response to footprint for any of the five focal large mammal species (from studies used to complete Table 2.3). The first panel shows only studies at scales <40,000 km2, while the second shows all studies. ... 25 Figure 2.3. Variables with confidence intervals not overlapping zero for each species from the regional extent models. These include variables from all models within 6QAIC for each species (but including total footprint at all scales even when >6 QAIC). This shows strong responses outlined in Table 2.4, with those which were unexpected based on previous smaller scale studies denoted with a “x”, and those expected with a “o”. ... 28 Figure 2.4. All variables in wolf models within 6 QAIC at all three spatial grains, using

coefficient estimates from the highest ranked model (grey labels indicate which model from Table 2.2 the coefficient for that variable is from). Total footprint is shown at all grains, even though this model was not strongly supported (>6 QAIC) in model sets at the 1500m and 5000m scale... 33 Figure 2.5. Responses to total footprint, at all scales, for all five species, regardless of the level of support for the total footprint model. Shows the variability in strength of response across spatial grains, and is representative of the overall variability in responses to individual footprints... 34 Figure 3.1. Model coefficients (as standardized proportions) from the total footprint model for each species. Error bars show the 95% confidence intervals, based on quasi-adjusted standard errors. ... 57 Figure 3.2. Coefficient estimates for human footprint variables included in the surveillance model compared across species. Although agriculture and roads were not included in the same models due to collinearity, for each species, the coefficients shown are from the best supported model (with either Ag or Rd), and either Rd or Ag shown from the less supported model (See B2). Error bars represent 95% confidence intervals, calculated from adjusted standard error. Only the variables for which confidence intervals do not overlap zero are shown. ... 58

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Figure 4.1. Conceptual diagrams of threshold types (threshold locations shown in broken line), based on common distinctions found in literature review. Some distinctions are between point and zone (1a and 1b), or between abrupt and smooth (1b and 1c). Another distinction is between continuous (either no threshold, or defined as in 1d) and discontinuous (either defined as any threshold as in 1a, 1b and 1c, or specifically to hysteresis; 1b). ... 72 Figure 5.1. Land use regions of Alberta and transect locations within the boreal forest and lower foothills natural regions. ... 103 Figure 5.2. Clusters of the ABMI snowtrack transects within the boreal forest and lower foothill region, used to model the relationship between species abundance and footprint at the cluster grain. The clusters with only one sample point (transect) were removed. ... 107 Figure 5.3. Plot displaying top selected wolf models at both grains (transect and cluster) and two regions for which the wolf models successfully converged (boreal forest, Lower Athabasca), with estimated threshold values from maximum optimization (used in piecewise models) shown in broken lines (for details on the Lower Peace and Upper Athabasca regions, see C5). ... 112 Figure 5.4. Plot displaying top selected lynx models at all grains (transect and cluster) and all regions (boreal forest, Lower Athabasca, Upper Athabasca and Lower Peace), with estimated threshold values from maximum optimization (used in piecewise models) shown in broken lines. ... 113 Figure 5.5. Plot displaying top selected coyote models at all grains (transect and cluster) and most regions (boreal forest, Lower Athabasca and Lower Peace). The simulations from the coyote model for the Upper Athabasca region were unreasonable given the input data range and are partially outside the range of this plot (found in C4). The estimated threshold values from maximum optimization (used in piecewise models) are shown in broken lines. ... 114 Figure 5.6. Plot displaying top selected deer models at all grains (transect and cluster) and most regions (boreal forest, Lower Athabasca and Lower Peace). The simulations from the deer model for the Upper Athabasca region were unreasonable given the input data range and are partially outside the range of this plot (found in C4). The estimated threshold values from maximum optimization (used in piecewise models) are shown in broken lines.. ... 115

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Figure 5.7. Plot displaying top selected moose models at all grains (transect and cluster) and all regions (boreal forest, Lower Athabasca, Upper Athabasca and Lower Peace), with estimated threshold values from maximum optimization (used in piecewise models) shown in broken lines. ... 116 Figure 5.8. Stylized diagrams of the four general types of thresholds relationships found across all species, scales and regions, with species response on the y-axis and total footprint on the x-axis. These represent the general shape of the piecewise thresholds model, particularly in relation to the relative values of the slope below the threshold (lower footprint) and above the threshold (higher footprint). No constraints were placed on the model which required the segments to align at the breakpoint, and both slope and intercept varied. ... 117 Figure 5.9. A redundancy analysis correlation triplot showing the relationship between species relative abundance and total footprint (low and high), conditioning on year, days since snow and latitude. The angles in this plot represent correlations among species, and between species and footprint. Distances between points do not represent Euclidean distances, however those closer to the cluster of points are either present across the gradient of sites or neutrally related to footprint (Borcard et al., 2011). ... 118

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Acknowledgments

I owe my success in completing this Masters Thesis not only to my own hard work, but also to the many people who provided support and guidance throughout. Firstly, I’d like to thank my husband, Graham, for providing support while I managed the more challenging periods and for accompanying me on adventures when I needed breaks. I’d also like to thank my parents for being so supportive and encouraging, and for my siblings, their spouses and their kids for the fun weekends away together. I am lucky to have had a large and supportive network of peers at UVic, both in my lab group and elsewhere – thanks for the stats help, the coffee breaks and the beers. Also thanks to my roomies in Victoria and a great group of friends in Campbell River to take my mind off my thesis while I was working from home.

Thanks for the input throughout the many stages of this thesis from Dr. Jason Fisher as my committee member, and thanks to the Volpe-Fisher lab welcoming me into your group. Thanks for the input from Dr. Natalie Ban as my external member. I’ve also received much

administrative support from the Biology Secretary, Michelle Chen. The long days spent

processing the data sets from the Alberta Biodiversity Monitoring Institute were made bearable thanks to the assistance from Shawn Morrison and Dr. Branislav Hricko of ABMI. I’d also like to thank Dr. Julia Baum for encouraging the thresholds review (Chapter 4) as a component of her Advanced Ecology course (UVic), and providing valuable feedback at early stages. This research would not have been possible without funding provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Alberta Innovates – Technology Futures (AITF). Finally, I’d like to thank my fantastic supervisors Dr. Cole Burton and Dr. Francis Juanes, particularly for providing so much of your time at the early stages and so much constructive (and useful!) feedback throughout. I am immensely grateful for the time you’ve put into my

development as an ecologist, and am lucky to have found supervisors who could provide so much, and all with patience and kindness! Thank-you!

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Chapter 1: Introduction

Globally, the scale and urgency of environmental issues such as climate change, landscape change and biodiversity loss (Dirzo et al., 2014; Fahrig, 2003; Sanderson et al., 2002) continue to intensify, and along with them there is a growing need to understand, and thus manage for, the human causes of these issues. With this comes an increased call from researchers, decision-makers, and the general public for evidence-based conservation – translating science directly into policy and management (Dicks, Walsh, & Sutherland, 2014; Sutherland, Pullin, Dolman, & Knight, 2004; Svancara et al., 2005). As managers attempt to adopt this evidence-based approach, ecologists are often asked for concrete conservation advice, often to directly inform conservation targets, policy approaches and management actions (Toms & Villard, 2015). The task of collecting data on a vast range of ecological systems and distilling the complex and uncertain findings into management direction, is daunting and often unrealistic. Part of the challenge is one of resources and funding; creating the regional and national systems to collect and interpret this amount of ecological data requires a large number of resources, and thus in many cases there is simply insufficient evidence with which to make a conservation decision (Dicks et al., 2014; Legge, 2015).

Besides the issue of resources, there are a number of other hurdles to effective evidence-based conservation, and it is these fundamental challenges that I intend to explore. I do so through the lens of tackling broad-scale environmental issues, particularly that of landscape change, which is one of the greatest conservation challenges today (Fahrig, 2003; Gonzalez, Rayfield, & Lindo, 2011; Sanderson et al., 2002). I focus throughout on the impact of human footprint, a measure of human-caused landscape change (e.g. forestry, agriculture) and structures (e.g. roads, buildings), which is used as a proxy for many human impacts (Sanderson et al., 2002). My focus is on the response of large mammals to human footprint; this group is ecologically important and sensitive to broad-scale habitat change (Ripple et al., 2014; Ripple, Newsome, et al., 2015), and as such large mammals have been used as indicators to guide conservation for a broader suite of biodiversity (Morrison, Sechrest, Dinerstein, Wilcove, & Lamoreux, 2007).

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In terms of addressing large-scale cumulative effects of an expanding human footprint, I have identified three key research gaps. The first is the scale-specific nature of species-footprint relationships. It is clear that patterns and processes can vary with scale (Levin, 1992; Wheatley & Johnson, 2009), but there is little understanding of the degree of that variation, and thus there is uncertainty with respect to the importance of multi- and large-scale research in addressing large-scale ecological problems. Since one of the identified hurdles of evidence-based

conservation is the lack of broad-scale research, this is a key component to understanding the resource investments needed for biodiversity monitoring (Dicks et al., 2014).

The second key research problem is that, even if we have large-scale research to inform conservation, identifying the specific management actions to take in limiting human footprint can be challenging. For example, there is much debate on whether it is better to focus on a small number of indicator or umbrella species, or multi-species targets (Lambeck, 1997). One way to narrow down the focus is to understand which footprint features are the strongest drivers of changes in species distributions, and whether species respond similarly to these features – so that a multi-species approach is justified – or whether responses are species-specific, requiring a species-by-species conservation strategy.

The third area of research acknowledges that, even if the species and features on which to focus management actions are clear, translating this knowledge to concrete management targets requires a balance between socioeconomic and ecological factors (Huggett, 2005; Weber, Krogman, & Antoniuk, 2012). Identifying ecological thresholds in the amount of footprint, beyond which species distributions change abruptly, could inform evidence-based management by allowing quantifiable management targets (Cook, de Bie, Keith, & Addison, 2016; Svancara et al., 2005). Accordingly, the search for such thresholds is both a key area of research and a subject of intense debate (Huggett, 2005; Johnson, 2013; Lindenmayer & Luck, 2005). In the following four chapters, I will address these three research themes, with the intent of providing further understanding of the tools and tribulations in the quest for evidence-based management.

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Chapter 2: Mammal responses to human footprint vary with spatial

extent but not with spatial grain

Abstract

Ecological patterns and processes vary with spatial scale, causing uncertainty when applying small-scale or single-scale studies to regional or global management decisions. Conducting research at large extents and across multiple scales requires additional time and effort, but may prove necessary if it uncovers novel patterns or processes. Knowing the degree to which patterns vary between spatial extents and spatial grains provides insight into the importance of

considering scale. I evaluated variation across spatial extents and grains in large mammal responses to human footprint, a measure of all human infrastructure (e.g. roads, buildings) and landscape change (e.g. agriculture, forestry). I focused on the response of five key boreal mammal species – gray wolf (Canis lupus), Canada lynx (Lynx canadensis), coyote (Canis latrans), white-tailed deer (Odocoileus virginianus) and moose (Alces alces). Firstly, I asked how the direction of responses measured at the regional extent of the boreal forest of Alberta (approximately 400,000 km2) compared to those reported in previous studies conducted at smaller spatial extents (median 1,525 km2). Secondly, I tested whether there were differences in the direction or strength of responses to footprint across three spatial grains (250m, 1500m or 5000m radii). Using 12 years (2001-2013) of snowtrack survey data and a human footprint map from the Alberta Biodiversity Monitoring Institute, I evaluated a set of generalized linear mixed effects models relating the relative abundance of each species to individual and cumulative effects of human footprint. I found a strong response to human footprint at the regional extent, uncovering species-footprint relationships that differed from those identified in smaller extent studies, and thereby highlighting scale-dependent patterns. By contrast, I found little variation in direction and strength of responses across spatial grains, indicating that analyses across multiple grain sizes may not be necessary. My results reinforce the need for regional studies to

complement those conducted at smaller extents in order to fully understand, and thus manage for, the impacts of human footprint on mammalian biodiversity.

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Introduction

Scale in ecology and management

It is increasingly recognized that patterns and processes of species and ecosystems can vary with spatial scale (Levin, 1992; Wheatley & Johnson, 2009). As such, spatial scale is often an

essential consideration in ecology, perceived both as a challenge and a unifying tool (Levin, 1992; Wiens, 1989; Wu, 2004). In landscape ecology, the variations in patterns across spatial scales can partly be explained by habitat heterogeneity (Beasley, Devault, Retamosa, & Rhodes, 2007; Levin, 1992; Wu, 2004), since the area within which features are measured (extent) and the resolution of that measure (grain) will influence the composition of habitat metrics (Jelinski & Wu, 1996). Therefore, variations in species responses across spatial scales can also provide a deeper understanding of species ecology and of interacting ecological processes. For example, species may respond differently to their environment at different scales, due to differences in each species’ perception of the landscape, such as through hierarchical selection orders (Johnson, 1980) that vary with body size and dispersal ability (Fisher, Anholt, & Volpe, 2011; Holling, 1992). Moreover, different patterns emerge due to unique structuring components at different scales (Allen & Holling, 2002; Holling, 1992; Levin, 1992), interacting and accumulating processes (Ewers & Didham, 2006), and variation in the relative importance of different processes between scales (Gotelli, Graves, & Rahbek, 2010; McGill, 2010).

In order to measure ecological processes across scales, there need to be clear ways in which to define spatial scale. The two most common ways to delineate scale are in terms of spatial extent and spatial grain (Wheatley & Johnson, 2009), however the interpretations of these vary. Here, I follow the commonly used terms in landscape ecology in defining the buffer or radius around sample point as grain (Meyer & Thuiller, 2006; Wheatley & Johnson, 2009; Wiens, 1989), and the size of the study area as extent (Beck et al., 2012; Boyce, 2006; Wu, 2004). Spatial extent has also been used to refer to the buffer distance around sample points (Fuller & Harrison, 2010; Holland, Bert, & Fahrig, 2004; Leblond et al., 2011), while in some cases grain is used to refer to the resolution of the spatial data, such as pixel size or level of detail (Boyce, 2006; Wheatley & Johnson, 2009; Wu, 2004).

Given the many reasons to expect variation in patterns across scales, ecology as a discipline is shifting towards analyses that explicitly consider spatial scale. Two key areas for inquiry are

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conducting analyses at multiple spatial scales, and expanding the understanding of responses at larger landscape and regional extents (Jelinski & Wu, 1996; McGill, 2010). Multi-scale analyses can uncover novel information on species biology (Wheatley & Johnson, 2009), such as how they perceive and select habitat (e.g. DeCesare et al., 2012; Fuller & Harrison, 2010; Lundy et al., 2012). This, in turn can lead to better science-based management, as it is increasingly recognized that research should be conducted at appropriate scales that match those of management (Angelstam et al., 2004; Elith & Leathwick, 2009). Furthermore, through an accumulation of multi-scale studies, ecologists may reach the ultimate goal of being able to predict across scales (Levin, 1992; Wheatley & Johnson, 2009), scaling patterns and processes up and down, and using scale as a unifying principle (Johnson & St-Laurent, 2011; Levin, 1992). The second key area for investigation is that of analyses over larger spatial extents. Many

ecological analyses are conducted at relatively small spatial extents (i.e. local scales), likely due to logistical constraints, such as the lack of resources to sample thoroughly across large areas. And yet, given that patterns and processes can vary with scale, addressing today’s challenging suite of regional- and global-scale conservation issues requires large-scale research. Promisingly, the emerging sub-field of macroecology is tackling these continental and global questions, and paving the way forward to understanding how patterns and processes play out at the largest spatial extents (Beck et al., 2012). What is urgently needed are more landscape and regional extent studies, which bridge the gap between local and continental scales, and are relevant to provincial (or state/territory) and national management decisions.

Measuring responses to human impacts at a landscape scale

A key conservation challenge is the accumulating impact of human use on the landscape, through habitat loss, fragmentation and transformation (Fahrig, 2001, 2003; Gonzalez et al., 2011). These impacts, over regional and global scales, can reach unanticipated levels of cumulative effects, with the global human footprint estimated as covering over 80% of the world’s surface (Sanderson et al., 2002). This cumulative human footprint, defined as the combined imprint of human structures and land-uses, can be used as a proxy for many other environmental issues (e.g. pollution, direct impacts of human activity; Sanderson et al., 2002). It is also a definable measure of habitat loss, fragmentation and change (Leu, Hanser, & Knick,

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2008; Sanderson et al., 2002), which combined account for a large part of biodiversity loss (Fahrig, 2003).

Human footprint can impact a diverse range of ecosystem components, from soil and hydrology, to vegetation structure and biodiversity; yet the effects of human footprint on large mammals merits specific consideration. Based on life-history characteristics and historical human impacts, large mammals are often more sensitive than other species to land-use changes (Johnson, 2002). This is partly because large mammals tend to have lower rates of reproduction and exist at lower densities (Cardillo et al., 2005; Damuth, 1981), making them less resilient to disturbance. They also generally require large home ranges to meet their needs for foraging and reproductive habitat (Lindstedt, Miller, & Buskirk, 1986; Woodroffe & Ginsberg, 1998). Consequently, cumulative land-use change from human footprint across vast areas can severely limit the availability of habitat of sufficient quantity or quality for such large home ranges (Hagen et al., 2012). Moreover, the impacts of human footprint can be exasperated by direct human-caused mortality, such as from hunting pressure or persecution due to perceived threats (Clark, Paquet, & Curlee, 1996; Ray, 2010). Large mammals are also economically, culturally, and ecologically important (Ripple et al., 2014; Ripple, Newsome, et al., 2015), and have been widely used as management tools (e.g. indicator, flagship and umbrella species). Therefore, managing the human footprint with respect to its impacts on large mammals may have greater biodiversity benefits (Clark et al., 1996; Morrison et al., 2007).

Due to the widespread nature of human footprint, management of the cumulative effects of multiple land-uses needs to be addressed at a large spatial extent, such as across management regions (Johnson et al., 2011). Implementing effective management at these regional scales could be achieved through a strong understanding of the response of certain indicators, such as large mammals, to footprint (Burton et al., 2014; Morrison et al., 2007). While finer-scale, behavioural responses of large mammals to some footprints have been well-studied compared to many other taxonomic groups, there are few insights into larger extent, population-level impacts (Northrup & Wittemyer, 2013; Venier et al., 2014).

Research objectives and hypotheses

Although there is a need to consider analyses at multiple scales and at regional extents, conducting these analyses requires additional time and effort. In some cases, or for certain

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ecological questions, the added effort of multi-scale or regional extent analyses might not be necessary. My objective was to investigate whether conducting studies at regional extents and at multiple spatial grains provides novel ecological information compared to single-grain or small-extent analyses. I used the example of large mammal responses to human footprint, focusing on the response of five species at the regional extent of the boreal forest of Alberta, Canada: gray wolf (Canis lupus), Canada lynx (Lynx canadensis), coyote (Canis latrans), white-tailed deer (Odocoileus virginianus) and moose (Alces alces). These five species have ranges extending across the boreal forest of Canada and are a representative sample of the boreal large mammal community, with some habitat specialists (e.g. lynx; Mowat & Slough, 2003) and generalists (e.g. deer and coyote; Gompper, 2002; Hewitt, 2011), and a combination of herbivores,

omnivores and carnivores. These species are all economically important and are classed as fur-bearers or game species (Government of Alberta, 1997), while some are also of conservation concern (e.g. lynx listed as Threatened in United States; United States Fish and Wildlife Service, 2000 and Sensitive in Alberta; Government of Alberta, 2013).

Specifically, I asked (1) do the responses observed at the regional extent of our study area (400,000km2) differ from responses at smaller spatial extents from previous studies? Observing novel patterns compared to studies conducted at smaller extents, particularly in terms of direction of response (positive or negative), would support the need for large-scale studies to guide

regional and landscape scale management.

Furthermore, I asked (2) does the response to human footprint vary with spatial grain (the extent of the buffer around each transect within which footprint is measured)? Specifically, ‘variation’ in responses across grains could be seen in (a) the set of footprint features which best explain relative abundance (i.e. model selection), (b) different strengths of response for a particular footprint feature, and/or (c) different direction of response. I expect to observe some variation in species’ responses among grains, due to a highly heterogeneous landscape, which could reflect differences in habitat selection with scale (e.g. Leblond et al., 2011). Variation across grain would indicate that there is a risk of arriving at different conclusions depending on the choice of spatial grain, thus conducting analyses at multiple spatial grains is necessary to gain a full understanding of a given relationship.

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Methods

Responses characterized at smaller spatial extents

I conducted a literature review to summarize known responses of the five focal species to individual types of human footprint and to total footprint (see Explanatory Variables section). In this search, I focused on studies that measured responses to specific footprint features, total footprint, or habitat loss in general. I did not constrain the search by spatial scale; however, I recorded the spatial extent for each study area (including estimates when not explicitly stated). In many cases, I generated expected responses by combining and comparing results of numerous studies, distilling this information into one hypothesis (positive or negative response to

footprint), which occasionally was expected to change with spatial grain. If there was insufficient information to summarize a predicted response, that species-footprint relationship was omitted from testing at the regional extent (see Models below).

Study area

The boreal forest and lower foothills natural regions of Alberta together cover over 400,000 km2 (approximately 65%) of the province, encompassing the northern half and extending southward along the eastern side of the Rocky Mountains, below the city of Edmonton (Natural Regions Committee, 2006; Figure 2.1). The elevation ranges from 150m to 1500m above sea level, and the climate is variable from north to south, although average daily temperatures exceed 15°C in only a few months, while for several months in winter they are below 10°C in most areas or -20°C in northern subregions (Natural Regions Committee, 2006). The deciduous, coniferous and mixed boreal forests are primarily composed of trembling aspen (Populous tremuloides), balsam poplar (Populous balsamifera), white spruce (Picea glauca), black spruce (Picea mariana) and jack pine (Pinus banksiana), while black spruce, shrubs (e.g. Salix spp.) and sedges (Carex spp.) are abundant in wetlands (Natural Regions Committee, 2006). In the lower foothills region, located northeast of the Rocky Mountains, lodgepole pine (Pinus contorta) grows on mesic sites, and there is a higher diversity of forest types, with an abundance of tamarack (Larix laricina) in addition to the above-listed common boreal species (Natural Regions Committee, 2006). These regions are the site of numerous land-uses, namely in the forestry, agriculture, and energy

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and the three administrative Oil Sands Areas (Athabasca, Cold Lake and Peace River) cover 21% of Alberta (Alberta Biodiversity Monitoring Institute, 2014). Within the OSR, the total human footprint covers 13.8%, and includes agriculture (7.4%), forestry (2.9%), and energy structures, such as well sites, open pit mines, and seismic lines (2.2%; Alberta Biodiversity Monitoring Institute, 2014). Human land-use in the lower foothills region includes forestry, grazing and agriculture in the lower fringe, open-pit coal mines, and oil and gas development (Natural Regions Committee, 2006).

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Figure 2.1. The study area spans the lower foothills and boreal forest regions of Alberta (dark and medium grey), and excludes all other ecoregions (light grey). The ABMI transect locations within these regions (central points of transects) are shown in black.

Response variable: Relative abundance from snowtrack surveys

Species occurrence data were obtained from the Alberta Biodiversity Monitoring Institute (ABMI). The ABMI conducted snowtrack surveys to monitor larger mammals as part of their provincial biodiversity monitoring program (www.abmi.ca). The ABMI sampling design is based on a systematic 20 x 20 km sampling grid, with a subset of grid sites sampled in a

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particular year (Bayne, Gray, & Litke, 2006; Burton et al., 2014). Targeted “off-grid” sites are also sampled each year to inform specific management goals or to provide a more complete gradient of coverage (Burton et al., 2014). From 2001-2004, prior to the formal launch of the ABMI program, a set of off-grid sites were sampled as part of the Integrated Land Management (ILM) Program (Bayne, Moses, & Boutin, 2005). These ILM surveys consisted of 9 km

triangular transects (3 km per side), whereas the subsequent (2005-2013) ABMI surveys

involved linear 10-km transects. For all transects, species occurrence data (presence or absence) were recorded for each 1-km segment (for specific protocol details see ABMI, 2012b; Bayne et al., 2005). The surveys were generally conducted 3-6 days (range 1-15) after a “track obliterating snow” (>1cm snowfall) between November 1 and March 31. The survey year stated is that of the start of the sampling season (i.e. 2013 survey year includes some surveys conducted in 2014). I omitted any transects which were repeated within the same season (retained 11 transects which were repeated in different years; see Appendix A1 for details). I included 669 surveys in the analysis, from transects surveyed between November 2001 and March 2014.

I created an index of relative abundance from the snowtrack data as the proportion of 1km segments in which a species is detected out of the total number of segments sampled for each transect. An index is assumed to correlate with true abundance, but does not provide an actual population estimate or correct for detectability (O’Brien, 2011). Despite some controversy around the use of indices (e.g. Johnson, 2008; Sollmann, Mohamed, Samejima, & Wilting, 2013), in many cases they are more cost effective (O’Brien, 2011) and are useful for

management and conservation decisions (Güthlin, Storch, & Küchenhoff, 2014). In the case of snowtracking surveys, the ability to use an index of abundance may be an advantage over other non-invasive survey techniques (Gompper et al., 2006). Although it is challenging to test whether the index has a monotonic relationship with true abundance, often indices provide similar estimates, indicating that these may be related to true abundance (e.g. Güthlin, Storch, & Küchenhoff, 2014; Kojola et al., 2014). For snowtracking surveys, a higher relative abundance is assumed to be primarily due to higher actual abundance of that species in that area, and that the differences in relative abundance between sites reflect ratios of actual abundance (Pellikka, Rita, & Lindén, 2005). Indeed, given the large distance sampled by ABMI transects, these surveys may be more likely to measure local abundance than intensity of use (i.e. 10km may span

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numerous home ranges; Gompper et al., 2006). However, I acknowledge that this index could reflect both the number of individuals of a species around a transect (local abundance), as well as their movement behaviour, which could change in response to footprint (see Discussion for more on the use of this index).

The ABMI snowtrack monitoring program does not distinguish between white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) tracks. However, white-tailed deer are much more prevalent in most of boreal Alberta (Latham, Latham, McCutchen, & Boutin, 2011) and thus I follow Dawe, Bayne, & Boutin (2014) in assuming that samples are from white-tailed deer. These two species have also been considered as an ecologically similar group when sampling techniques cannot distinguish them (Hebblewhite, Munro, & Merrill, 2009; Nielsen, Bayne, Schieck, Herbers, & Boutin, 2007).

Explanatory variables: Biotic interactions and climate

I included certain ‘non-footprint’ explanatory variables in all models, in recognition that a wide variety of factors influence species distributions, with some more relevant at certain spatial scales (Beck et al., 2012; McGill, 2010). For example, certain abiotic factors, such as regional climate, may be more relevant at large spatial extents (Menge & Olson, 1990), and are known drivers for white-tailed deer (Dawe et al., 2014) and moose (Lenarz, Nelson, Schrage, & Edwards, 2009). Biotic interactions (e.g. predation, competition) are also important in driving patterns of species distributions (Beck et al., 2012), yet until recently they have been considered less important at larger scales (Menge & Olson, 1990; Wisz et al., 2013) as they may be poorly correlated with distribution (Beck et al., 2012; Elith & Leathwick, 2009) due to weak

interactions (McCann, Hastings, & Huxel, 1998) and feedback between species (Franklin, 2010). Climatic variables, including mean annual temperature and mean annual precipitation, were obtained from ABMI for each snowtrack sampling site, sourced from PRISM (parameter-elevation regressions on independent slopes model; Daly, Gibson, Taylor, Johnson, & Pasteris, 2002) and WorldClim interpolations (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005). Due to high collinearity across sites among measures of mean annual precipitation, mean annual temperature, and latitude (>0.7 Spearman or Pearson correlation coefficients), only latitude was included in models as a proxy for climatic variation. Accounting for climatic variation allows for a more standardized comparison of footprint effects across species, and may also account for

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issues of spatial autocorrelation in the models, since some causes of autocorrelation are regional climate gradients (e.g. Maestre et al., 2012). To account for the potential influence of biotic interactions, I included the relative abundance of interacting species (e.g. predators, prey, competitors) in models for each species (see Models below).

Explanatory variables: Human footprint and non-footprint habitat

Human footprint data were obtained from the ABMI human footprint map (www.abmi.ca), using versions from 2007 (version 4.3), 2010 (version 1.3), and 2012 (version 2.0). Each version is spatially accurate to within 7.5 to 10m and includes footprint features which existed up to the version year (i.e. the 2007 version does not include features developed after 2007; ABMI, 2012a). Individual polygons in the human footprint maps were derived from Government of Alberta footprint layers, forestry data from an industry partnership, and the Alberta Vegetation Index (AVI), or created through interpretation of SPOT 5 and IRS images (ABMI, 2012a). Additional explanatory variables describing natural land cover were obtained from the ABMI Wall-to-wall Landcover Map, using versions from 2000 (version 2.1) and 2010 (version 1.0), or from the Alberta ESRD Historical Wildfire Perimeter Spatial Data (Government of Alberta, 2015).

All footprint and landcover variables were measured as the proportion of the buffered area around each transect covered by that feature (with buffer radii of 250m, 1500m, and 5000m; see next section). The human footprint and landcover data for the 250m grain were provided by the ABMI; they are based on the human footprint and wall-to-wall landcover maps, but were corrected using visual assessment of satellite imagery so that they are accurate for the year in which a transect was sampled. I processed and assembled data at the 1500m and 5000m scales using the closest temporal periods to the time of sampling (see next section). All explanatory variables were standardized by subtracting the mean and dividing by the standard deviation, providing a mean of zero and a standard deviation of one, which facilitates analysis and comparison of effects sizes across variables. I omitted footprint features at all scales that were poorly represented at any scale in the buffered datasets (<100 non-zero values), thus ensuring that a gradient in footprint intensity was assessed. This cut-off is roughly based on

recommendations suggesting that sample size should be 20-40 times the number of predictor variables, and that a ratio of 1:1 presence-absence is ideal (Franklin, 2010). At this stage, some

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less prevalent footprint features were not included as individual footprint components in the analysis, but were included in ‘total footprint’ (e.g. mine sites, railways, urban areas). Footprint and landcover variables were screened for outliers which may be a result of errors in the

mapping. A final set of 11 footprint variables and four non-footprint variables representing natural land cover were used in the analysis (Table 2.1). Data processing was completed in ArcMap (version 2.0) and R Software version 3.2.2 (R Core Team, 2015).

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Table 2.1. Footprint and landcover features used as explanatory variables in species abundance models, from the ABMI human footprint map (2007, 2010 and 2012; ABMI HFP), the ABMI Wall-to-wall Landcover map (2000 and 2010; ABMI LC), or from the Alberta

Environment and Sustainable Resource Development historical wildfire layer (AESRD HW). Variable Code Source Description

Total

footprint TFP ABMI HFP

Combined footprint of all human features listed below. Also includes less common features such as railway tracks, urban residential, the vegetated verges associated with roads and rails, mine sites (open ground, consistent or

expanding over years), other industrial development (factories, parking lots, airports, buildings), gravel pits and sumps.

Roads Rd ABMI HFP

Paved or gravel roads, not including the vegetated margins. Does not distinguish between road type, size or use; however, widths reflect road type, with each buffered by a width reflective of that features type (5-15m, from one lane gravel roads to four lane highways).

Cutblocks

<10 years CB<10 ABMI HFP

Cutblocks harvested within 10 years before the snowtrack survey date. All cutblock categories include areas >5ha used by the forestry industry, in which <20% of live trees were retained during harvest (includes clearcut, salvage logging, selective logging), and which have not since been disturbed. Cutblocks are derived from the Alberta Vegetation Index and individual company information, and updated with SPOT imagery (ABMI, 2010).

Cutblocks

>10 years CB>10 ABMI HFP

Cutblocks harvested greater than 10 years before the snowtrack survey date. Cutblocks

10-40 years

CB10-40 ABMI HFP

Cutblocks harvested between 10 and 40 years before the snowtrack survey date. Cutblocks

>40 years CB>40 ABMI HFP

Cutblocks harvested greater than 40 years before the snowtrack survey. Vegetated

roads, trails VRT ABMI HFP

Vegetated or dirt roads and ATV trails; includes all roads, trails and pathways lacking gravel or paved surfaces (up to 7m wide). Derived as linear feature and buffered by 6m.

Seismic lines SL ABMI HFP

Cleared linear corridors (soil, rock or low vegetation) 2-10m wide, used for oil and gas exploration. Based on samples to be representative of these features, the linear features were buffered by 5m for seismic lines cleared pre-2005, and 3m for those after 2005.

Transmission

lines TL ABMI HFP

Electrical transmission lines (poles and wires) and associated cleared utility corridor (>10m wide). Derived as linear features and buffered by 19m.

Pipelines PL ABMI HFP Linear underground oil and gas pipeline structures, used for transporting petrochemicals, and associated cleared linear corridors (>10m wide). Derived as linear features and buffered by 12m.

Well sites or

pads WS ABMI HFP

Oil and gas well pads; sites cleared of vegetation for oil and gas drilling and extraction. Does not distinguish between active, abandoned or capped sites. Denoted as a 1ha square.

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Coniferous

forest Con ABMI LC

Forested land with >10% tree cover, in which coniferous species compose >75% of the tree cover. All forest layers also include regenerating cutblocks and treed wetlands if they meet the forest criteria. Despite potential overlap with cutblock categories, correlation coefficients are low at all scales (Appendix).

Mixed forest Mix ABMI LC Forested land with >10% tree cover, in which neither coniferous nor deciduous species compose >75% of tree cover. Deciduous

forest Dec ABMI LC

Forested land with >10% tree cover, in which deciduous (broadleaf) species compose >75% of the tree cover. Recent

wildfires Wf

AESRD HW

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Relating species response to footprint and landcover explanatory variables

The ABMI snowtrack surveys spanned the 2001 to 2013 sampling seasons, and thus multiple years of human footprint and landcover data were used to provide the closest temporal match between the survey date and the landscape data. Snowtrack surveys conducted from 2001 to 2007 were modelled against the 2007 human footprint version, surveys from 2008-2010 were modelled against the 2010 human footprint version, and surveys from 2011-2013 were modelled against the 2012 footprint version. Similarly, surveys conducted between 2001 and 2005 were modelled against the 2000 version of wall-to-wall landcover, while surveys from 2006-2013 were modelled against the 2010 version. The 2012 version of human footprint contains data on the year of harvest for forestry cutblocks, thus providing more temporally explicit information than the 2007 and 2010 versions. Therefore, forestry cutblock data from the 2012 human

footprint map were used for all snowtrack survey sites, but only the cutblocks which would have existed at the time that the snowtrack survey was conducted were included.

In order to assess the variability in responses across spatial grains, I use three different grain sizes to characterize footprint and landcover variables, defined as buffer radii around the snowtrack transects. The transect buffers used were 250m (corresponding to an area of

approximately 0.7 km2), 1500m (approximately 33 km2), and 5000m (approximately 170 km2). This method has also been used as a means to interpret habitat selection at different orders of selection (Johnson, 1980), with the finest scale (250m buffer) indicating site level habitat selection, and the largest scales representing home range habitat selection (e.g. Beasley et al., 2007; DeCesare et al., 2012). Based on average home range sizes for all species, the larger scales may approximate home range size for coyote, lynx, wolf and moose (see A2 for details on home range estimates).

Models

I related species relative abundance to human footprint and tested variation across spatial extent and grain using binomial (logit link) generalized linear mixed effects models (R package ‘lme4’; Bates et al., 2015). For each species, I compared a set of models with various combinations of human footprint as explanatory variables to a null model which did not include footprint, using a

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quasi-binomial Aikaike Information Criterion to account for mild overdispersion in all models (QAIC; Burnham & Anderson 2002). In addition to human footprint, certain non-footprint variables (e.g. climate, biotic interactions, other habitat) were accounted for in all models. In order to simplify our methods, I followed Dawe et al. (2014) in taking a multi-stage approach, with the first stage determining which ‘non-footprint’ variables to include, and the second stage competing models of human footprint to the null model. The first stage was completed only at the 1500m grain, and the second stage was completed at each of the three spatial grains (250, 1500m, and 50000m buffers).

The first stage determined which non-footprint variables were necessary to include in all

subsequent models for a given species, to account for other sources of variation besides footprint. There were three categories of non-footprint variables: ‘non-footprint habitat’, ‘biotic

interactions’ and ‘climate’, and the goals for this stage were to a) reduce the number of variables within each category and b) reduce the number of categories. The same human footprint

variables were included in all of these first-stage models (using global models from Table 2.2). To reduce the number of variables in the ‘non-footprint habitat’ and ‘biotic interactions’ categories, I first compared models relating species abundance to human footprint, with each model containing a different combination of variables within that category, and selected the most parsimonious model within 2QAIC for each category. To narrow down which categories

(climate, biotic interactions, and non-footprint habitat) were important to account for, I compared models relating species abundance to human footprint, with each model containing a different combination of categories (using the reduced set of variables from the above step), and selected the most parsimonious model within 2 QAIC. This analysis was completed only at the 1500m spatial grain, since during preliminary exploration the variables and categories selected did not vary widely between grains, and thus I simplified the approach. The final categories with their ‘top’ variables were included in all subsequent models at all spatial grains. The analysis of non-footprint variables was secondary to the questions addressed in this study, and thus a full explanation is found in A3.

In both stages, two sampling variables were included in all models. Days Since Snow was included to control for the accumulation of tracks with time after a fresh, track-obliterating snowfall. The calendar year of the start of that winter sampling season (Year) was included as a

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random intercept to account for potential temporal variation in species relative abundances

unrelated to the spatial predictor variables (e.g. population fluctuations such as lynx-hare cycles). The second stage of my modelling approach focused on my key question of testing footprint responses across scales. For each species, I constructed a set of competing hypotheses represented by a null (no footprint) model, a total footprint model, and a set of individual

footprint models with various combinations of human footprint features (see Table 2.2 for model sets). The individual footprint models represented competing hypotheses based on particular species-footprint relationships reported by previous smaller scale studies (described above). The total footprint model includes one composite measure of all footprint features available in the ABMI Human Footprint Map, excluding man-made water features such as canals and reservoirs (ABMI, 2012a). This model provides a single measure which I define here as the ‘cumulative effects’ of all human footprints, thus representing the hypothesis that at a regional scale, species respond most strongly to the combined effect of human footprint, as opposed to independent effects of specific types of footprint features. The null model includes only the non-footprint variables (determined in the first stage; see details in A3), and represents the hypothesis that large mammals do not respond to human footprint at the regional scale.

The set of variables included in each global model was screened for collinearity; any with correlation >0.7 (Spearman or Pearson) or variance inflation factor >3 (Zuur, Ieno, & Elphick, 2010) were not included in the same models (completed for each scale; Spearman correlation table for the 5000m scale found in A4). Based on high collinearity between roads and agriculture (>0.7), these were not included in the same models. The footprint associated with rural

infrastructure and residences was highly collinear with both roads and agriculture (>0.7), and may be less important at a regional scale due to its concentration in small areas, and was thus omitted from candidate models (but included in total footprint). The global models were assessed for overdispersion (ĉ>1.5; Zuur, Ieno, Walker, Saveliev, & Smith, 2009), patterns in residuals, spatial autocorrelation (variograms) and outliers (defined as Cook’s distance >1; Montgomery and Peck, 1992, cited in Zuur, Ieno, & Smith, 2007).

In model selection, unless one model had overwhelming support (>90% weight), the models within 2-6 QAIC were used for inference, with models within 6QAIC considered well-supported. Models within 2 QAIC have essentially equal support, while models within 6 QAIC have some

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support (Burnham & Anderson, 2002; Richards, 2008). Model-averaging was not used as it is less suitable for identifying only the top parameters and may not be mathematically logical unless different scaling methods are used (Cade, 2015).

The standard errors for the parameter estimates were adjusted for overdispersion using quasi-binomial standard errors (𝑞𝑆𝐸 = 𝑆𝐸 × √ĉ ; Zuur et al., 2009), and corresponding 95%

confidence intervals were calculated as ±1.96 × 𝑞𝑆𝐸. I calculated conditional and marginal R2 as measures of variance explained by each model using methods outlined by Nakagawa & Schielzeth (2013). I use the term ‘strong’ to describe a result wherein the 95% confidence intervals did not overlap each other when comparing effects between two species or scales, or when confidence intervals did not include zero when discussing magnitude of response.

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Table 2.2. Explanatory variables in model sets. For each species, a set of additional non-footprint variables were included based on preliminary model selection (see Models). Days Since Snow and Year as a random effect were also included in all models.

W

OL

F

Global: Rd + CB<10 + CB>10 + SL + VRT+ PL + TL + WS

Null: (Non-footprint variables only) Total: TFP 1: Rd + CB<10 + CB>10 + SL + VRT+ PL + TL 2: Rd + CB<10 + CB>10 + SL + PL + TL + WS 3: Rd + CB<10 + CB>10 + SL + PL + TL 4: Rd + CB<10 + CB>10 + SL + VRT+ WS 5: Rd + CB<10 + CB>10 + SL + VRT 6: Rd + CB<10 + CB>10 + SL + WS 7: Rd + CB<10 + CB>10 + SL 8: Rd + CB<10 + SL + WS 9: Rd + CB>10 + SL 10: Rd + SL + VRT+ PL + TL 11: CB<10 + CB>10 + SL + VRT+ PL + TL 12: CB<10 + CB>10 + WS 13: CB<10 + CB>10 14: Rd + SL L YNX Global1: Rd + CB<10 + CB10-40 + CB>40 Global2: Ag + CB<10 + CB10-40 + CB>40 Null: (Non-footprint variables only) Total: TFP 1: Rd + CB<10 + CB10-40 2: CB<10 + CB10-40 + Ag 3: CB>40 + Rd 4: CB>40 + Ag 5: CB<10 + CB10-40 + CB>10 6: Rd 7: Ag C OYOT E Global1: Rd + CB<10 + CB10-40 + CB>40 + SL + WS + PL Global2: Ag + CB<10 + CB10-40 + CB>40 + SL + WS + PL

Null: (Non-footprint variables only) Total: TFP 1: Rd + CB<10 + CB10-40 + CB>40 + SL 2: Ag + CB<10 + CB10-40 + CB>40 + SL 3: Rd + CB<10 + CB>40 4: Ag + CB<10 + CB>40 5: Rd + CB10-40 6: Ag + CB10-40 7: Rd + CB10-40 + SL + PL 8: Ag + CB10-40 + SL + PL 9: Rd + CB<10 + CB10-40 + CB>40 10: Ag + CB<10 + CB10-40 + CB>40 11: Rd + SL + WS + PL 12: Ag + SL + WS + PL 13: CB<10 + CB10-40 + CB>40 + Ag + WS 14: CB<10 + CB10-40 + CB>40 + WS 15: CB<10 + CB10-40 + CB>40 MO OSE Global: Rd + SL + CB<10 + CB10-40 + CB>40 Null: (Non-footprint variables only)

Total: TFP 1: Rd + SL + CB<10 + CB10-40 2: Rd + SL + CB<10 + CB>40 3: Rd + SL + CB10-40 + CB>40 4: Rd + SL + CB<10 5: Rd + SL + CB10-40 6: Rd + CB10-40 7: Rd + CB>40 8: Rd + CB<10 + CB10-40 9: Rd + CB<10 + CB>40 10: Rd + CB10-40 + CB>40 11: Rd + CB<10 12: Rd + CB<10 + CB10-40 + CB>40 13: SL + CB<10 + CB10-40 + CB>40 14: SL + CB<10 15: CB<10 + CB10-40 + CB>40 16: Rd + SL DE E R Global: Rd + CB<10 + CB10-40 + CB>40 + SL + WS

Null: (Non-footprint variables only) Total: TFP 1: Rd + CB<10 + CB10-40 + CB>40 + SL 2: Rd + CB<10 + CB10-40 + CB>40 + WS 3: Rd + CB<10 + CB10-40 + CB>40 4: Rd + CB<10 + CB>40 + SL + WS 5: Rd + CB<10 + CB>40 + WS 6: Rd + CB<10 + CB>40 + SL 7: Rd + CB<10 + SL + WS 8: Rd + CB<10 + WS 9: Rd + CB<10 + SL 10: CB10-40 + CB>40 11: CB<10 + CB10-40 + CB>40 + SL + WS 12: CB<10 + SL + WS 13: CB<10 + CB10-40 + CB>40 + WS 14: CB<10 + CB10-40 + CB>40 15: CB<10 + CB>40 16: Rd + SL

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Results

Previous studies

I found 63 studies in my literature review and used these results to make predictions regarding the overall direction (positive or negative) of species-footprint relationships (37 species-footprint pairings; Table 2.3). In most cases, there were one or more combined studies which were used to compile my predicted responses (for more detail on the contribution of each study, see A5). From this review, I expected wolf responses to roads, pipelines, well sites and young cutblocks to be positive at small spatial grains and negative at larger ones, reflecting that an increase in these features over broad areas can negatively influence abundance. Furthermore, I expected lynx to respond positively to intermediately-aged and older cutblocks, and negatively to agriculture, roads, young cutblocks and total footprint. I expected coyote and deer to respond positively to most features, with the exception of agriculture at small grains, and young or old cutblocks for coyote, and of well sites and intermediately-aged cutblocks for deer. Finally, I expected moose responses to roads, seismic lines and young cutblocks to vary with grain, and expect moose to have a positive association with intermediately-aged and older cutblocks and total footprint (see Table 2.3 and A4 for details on expected responses).

In the reviewed literature for which measures of study area extent were available, I found that the majority of studies were completed at small extents (median 2400 km2, range <1 to 385,100 km2), with very few at regional scales (Figure 2.2 and A6). I was able to provide a measure of the study area extent for 41 of the 63 studies. The remaining ones either did not provide sufficient information to have a precise or estimated study area, or are sources which compiled multiple studies (e.g. review articles or book chapters).

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Table 2.3. Summary of hypothesized species-footprint responses, based on previous studies. Symbols indicate the expected direction of response (positive or negative), and whether this might change with grain (small | large). For an expanded version of this table see Appendix S5, and for detailed references and spatial extents of sources, see Appendix S6.

Variable Hyp. Summary of previous research Sources

W

OL

F

Rd + | – Associated with prey and used for transport, but may avoid over large areas because of human activity. 1,2,3,4,5,6,7,8 PL, TL + | – May use as transport if nearby, but avoid over large areas because of human activity. 4,10

SL, VRT + Associated with prey and used for transport, although use not as pronounced in snow season. 4,11,12,10,9 WS + | – Are associated with increased prey, but poorer hunting success, so over large areas may not be beneficial. 11,13 CB<10 + | – Within smaller areas, more young seral associated with more prey, but fewer prey and more human activity

when prevalent over large areas.

11,12,6, 14

CB>10 – Not associated with primary prey, and may be poor hunting habitat. 4,15

TFP – Although wolves are adaptive, overall avoid human activity, associated with footprint. 16,17

L

YNX

Ag – Associated with lower hare abundance and higher human activity, may contribute to range contraction. 18,19,20 Rd – May avoid roads because fewer prey, more human activity, and more competition with coyote. 21,18,22

CB<10 – Youngest stands may be too dense for hunting. 23,19,24,22,25,20

CB10-40 + Mid-aged stands provide ideal balance between abundant prey and good hunting habitat. CB>40 + Oldest stands generally have few prey, but more open for hunting.

TFP – Lynx occurrence is associated with higher levels of intact habitat, and generally avoid areas of high human activity.

26,19,20

C

OYOT

E

Ag – | + May provide stable source of prey, and escape from competitors (which avoid agricultural habitats), although prefer habitat with less human activity and exploitation.

27,28,29,30

Rd + Used for travel and/or hunting, and may provide competitive advantage over lynx. 31,32,33,34,35,32,36

SL, PL + Used for travel, may have other benefits. 30,31,32,37

WS + Small openings created may provide ideal mixture of hunting habitat and refuge from snow. 38 CB<10 – Although in summer provides forage and prey, in winter may be avoided due to snowpack/exposure. 39,40,38

CB10-40 + May be ideal trade-off between snow cover and prey availability. 27,38,39,40

CB>40 – Associated with lower fitness, possibly due to fewer and less diverse food sources.

TFP + Coyotes are generalists, and can adapt and have competitive advantage in disturbed habitats. 27,30,34,41,42,43,44

MO

O

SE

Rd – | + Avoid roads because of human activity, but over large areas may benefit from increased forage and salt _{along roads.} 46,47,15,48,8 SL – | + Avoid seismic lines (possibly due to predation), but over large areas benefit from increased forage. 15

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CB<10 + | – More young cuts within smaller areas provide more forage, but over wide areas high prevalence means less cover from predators and snow.

49,50,50,6

CB10-40 + May provide balance between forage and cover. 51

CB>40 + Needed for cover when heavy snow, but does not provide as much forage. 51,50 TFP + Although footprint may provide some benefits (forage and predator refuge), need sufficient thermal and

predation cover, so expected to prefer areas with low human activity.

52,53,6,55,54

DE

E

R

Rd + Use verges for habitat and salt, less sensitive to human activity. 8,36,60,48,57

SL + Found to use these, possibly because intersperse forage and cover. 58,15,55,11,9,4,10

WS – May avoid higher densities of well sites, possibly due to predation or human activity. 59

CB<10 + Provides forage, associated with higher deer densities. 60,11,12

CB10-40 – Provide less forage and cover, may be avoided in winter. 11,61,62,51,8

CB >40 + Use for cover from snow and predators, and thermal cover. 62,63

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Figure 2.2. Spatial extents of previous studies which examined response to footprint for any of the five focal large mammal species (from studies used to complete Table 2.3). The first panel shows only studies at scales <40,000 km2_{, while the second shows all studies. Both only} include the publications for which an area for spatial extent was available (n=41); of these, the range was 0.32 to 385,100 km2_{, the median} was 2,400 km2_{, and the mean was 46,994 km}2_{(standard deviation 102,500 km}2_{). For a list of studies and corresponding spatial scales, see} A6.

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Variation in responses across spatial extents

Out of the 37 species-footprint relationships for which I hypothesized responses based on

previous smaller-scale studies (Table 2.3), 20 also had strong responses at the regional extent (at one or more grains; Table 2.4). Of these strong responses, only nine were found to be the

expected direction at all spatial grains (Table 2.5, Figure 2.3). These expected responses were also some of the strongest observed responses, including the negative response of wolf and lynx to total footprint, the positive response of coyote and deer to total footprint, the negative

response of lynx to roads and agriculture, and the positive response of deer to roads and recent cuts. Seven of the 20 strong responses were not consistent with expectations across all spatial grains (Table 2.5, Figure 2.3).

Four species-footprint relationships had responses in the opposite direction than expected based on the smaller-extent studies (for one or more spatial grains). These were the positive response to pipelines at larger spatial grains for wolves, the negative response to vegetated roads and trails for wolves, the negative response to roads at the regional scale for moose, and the negative response to seismic lines for deer (Table 2.4, Figure 2.5). Overall, 17 of the 37 responses previously documented at smaller spatial extents were not found to be strong predictors of large mammal abundance at the regional scale (Table 2.4).

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Table 2.4. Responses summarized from previous small-extent studies (from Table 2.3) are shown for each species (“Small” column). A change in response with grain is indicated by two responses separated by a line (e.g. S|L, or S|M|L; as in Table 2.3). For the responses found at the regional extent of my study (“Large” column), a “w” denotes a weak response (confidence intervals overlap zero). Light shading indicates that the response is as expected, medium shading indicates responses which were expected for only some grains, and dark shading indicates response which were

opposite from expected. Unshaded cells are those which were not tested based on a lack of previous studies (blank cell) or which had weak responses at the regional extent 1_.

WOLF LYNX COYOTE MOOSE DEER

Small Large Small Large Small Large Small Large Small Large

Agriculture – – – | + + Roads + | – – – (–) + (+) – | + w | – + + Pipelines + | – w | + + w| + |w Transmission line + | – w Seismic lines + + | w + w – | + w + w | – Veg. roads/trails + – Well sites + | – w + w | + – w Cutblocks <10 yrs + | – w – w + w + | – w + + Cutblocks 10-40 yr – w + w + w + w | + – w Cutblocks >40 yrs + w | + – w + w + w Total Footprint – – – – + + – w + +

1_{In Table 2.4, a response in parenthesis (-) indicates that models with that variable were weakly}

supported (>6QAIC), however there is a strong response. This occurred for species which respond strongly to both roads and agriculture, but much more strongly to one (separate models due to collinearity).