University of Alberta

Linear feature effects on small mammal abundance and resources in the boreal forest

by

Amy F. Darling /&*S

A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of:

Master of Science in Ecology

Department of Biological Sciences

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•+• Canada DEDICATION In loving memory of Becky Lynn Shank (1982-2008) who told me to "eat more cake" whenever she got the chance. I am grateful to have called her friend. ABSTRACT

Human activities are increasingly shaping northern boreal forest processes through creation of anthropogenic open habitat and edge. I found that linear features (e.g., pipelines) had vegetation similar to meadows and caused changes in resource distributions that predicted small mammal abundance responses to linear features and edge. Linear features had higher abundance of meadow voles {Microtus pennsylvanicus) compared to forest. Deer mouse (Peromyscus maniculatus) numbers peaked in forest edge whereas red-backed voles (Myodes [Clethrionomys] spp.) decreased on linear features. Principle diet sources of meadow voles (plants), red-backed voles (maybe fungi) and deer mice (arthropods) responded to linear features in ways that effectively predicted small mammal response to disturbance. Only deer mice manifested a linear feature isotopic signature and might have been the only species foraging in all habitats.

Small mammals differed isotopically and biotopically such that stable isotopes have potential to trace predator consumption of linear feature prey. ACKNOWLEDGEMENTS

Many people helped bring this project to completion. I thank my supervisor Erin Bayne for his guidance and insightful advice. I thank my committee members Peter Kershaw, Stan Boutin, and Craig Machtans for their input and helpful suggestions. I gratefully acknowledge Liidlii Kue First Nation (LKFN), Fort Simpson Metis Local 52, Dehcho First Nation, Environment and Natural Resources, GNWT, and Enbridge Inc. (Mark Gerlock) for permitting me to work in the Fort Simpson, NT study area. I recognize funding and(or) logistical support from the following groups: Alberta Sport, Recreation, Parks and Wildlife Foundation, Canadian Circumpolar Institute, Northern Scientific Training Program, Integrated Landscape Management, Environment Canada, and Alberta Conservation Association Challenge Grants in Biodiversity. The following organizations provided me with personal funding: Natural Sciences and Engineering Research Council (USRA), Department of Biological Sciences (Teaching Assistantships and supplemental scholarships), the Government of Alberta, Faculty of Graduate Studies and Research, and the Graduate Students Association.

I thank Jeff Ball for his help from start to finish - from study design, to carrying heavy loads of traps, to help with statistics, and sharing field supplies, data and ideas. The following field assistants provided invaluable support and made 'smammaling' a lot more fun: Trish Fontaine, Martin and Hedwig Lankau, Sara Hartfeil, Sheila Holmes, Ashley Hillman, Amanda Laycock, and Megan Whidden. I thank the WISEST (Women in Scholarship, Engineering, Science, and Technology) program and students Chantal Diggs-Avery and Kelsey Gottfried. Thank you to Cris Gray for assistance, training, and keeping us organized. Thanks to Maily Huynh and Calvin Cheung for going above and beyond the call of duty in diet analysis and other lab work. I am also grateful to lab assistants Vanessa Eckert, Katie-Lynn Russell, Courtney Spelliscy, Andrea Tarrant, Kaylee Byers, Wesley Nishi, and AnneLiese Smylie for their long hours cooped up in a small room working and conversing with me. Thanks to Vanessa Waugh (LKFN) for data entry. Thanks to Christine Robichaud for sharing isotope analysis work and results.

Thank you to Rich Moses for first-rate 'smammaler' training. Thank you to Wayne Roberts, Ray Poulin and Amy Runck for small mammal identification assistance. I am grateful to Randy Currah, Steven Karafit, Randal Mindell, Danny Shpeley, and Dorothy Fabijan for assistance with diet item identification.

Thank you to Department of Biological Sciences support staff who keep everything running smoothly, especially Barry McCashin, GIS technicians (Charlene Nielsen, Joy Manalo Stevens, and Erin Cameron), and Holly Bigelow (Biogeochemical Analytical Laboratory technician). I am grateful to Myles Stocki (Stable Isotope Facilities, Department of Soil Science, University of Saskatchewan) who was accommodating, and did an excellent job analysing my isotope samples. Thank you to Boutin and Bayne lab members for thought-provoking Friday discussions and everyday dialogue.

Thank you to my family, friends and especially my husband Chris Quilley for listening to me talk about the intricacies of the lives of rodents for the better part of three years. TABLE OF CONTENTS

Chapter 1: General Introduction 1 1.1 Anthropogenic edges 1 1.2 Habitat conversion and edge creation in the boreal forest 2 1.3 What is known about small mammals, disturbance type, and edge effects in forest ecosystems? 5 1.4 Resources to measure to understand small mammal linear feature responses 10 1.5 Food resource use in disturbed habitats and the use of stable isotopes 12 1.6 Thesis overview 14 Chapter 2: Linear feature effects on abundance of northern boreal forest small mammals and their resources 16 2.1 Introduction 16 2.2 Methods 18 2.2.1 Study areas and study design 18 2.2.2 Small mammal sampling methods 23 2.2.3 Resource distribution assessment 24 2.2.4 Data analysis 27 2.2.4.1 Small mammals 27 2.2.4.2 Resource distribution assessment 29 2.3 Results 30 2.3.1 Small mammal abundance relative to pipeline habitat and edge proximity 30 2.3.2 Resource distribution assessment 41 2.4 Discussion 46 2.4.1 Small mammal and resource responses to linear features 46 2.4.2 Implications for boreal forest ecology 50 Chapter 3: Stable isotope analysis potential for identifying linear feature resource value 54 3.1 Introduction 54 3.2 Methods 57 3.2.1 Study areas and study design 57 3.2.2 Stable isotope sample collection 57 3.2.3 Stable isotope procedures 58 3.2.4 Data analysis 59 3.2.4.1 Linear feature isotopic signatures 59 3.2.4.2 Distribution of legumes 60 3.3 Results 60 3.3.1 Sample sizes 60 3.3.2 Linear feature isotopic signatures 62 3.3.2.1 Carbon 62 3.3.2.2 Nitrogen 66 3.3.3 Forest edge isotopic signatures 66 3.3.4 Distribution of legumes 68 3.4 Discussion 68 3.4.1 Linear feature isotope ratios 68 3.4.2 Potential to trace use of disturbed habitats using stable isotopes of linear features 72 Chapter 4: Small mammal diet in relation to linear features 75 4.1 Introduction 75 4.2 Methods 77 4.2.1 Study areas and study design 77 4.2.2 Stable isotope sample collection and stable isotope procedures 77 4.2.3 Gut content estimates of deer mouse and red-backed vole diet 77 4.2.4 Data analysis 78 4.2.4.1 Gut content estimates of deer mouse and red-backed vole diet 78 4.2.4.2 Stable isotope analysis of diet 79 4.3 Results 82 4.3.1 Gut content analysis of deer mouse and red-backed vole diet 82 4.3.2 Stable isotope analysis of diet 86 4.4 Discussion 94 4.4.1 Small mammal diet in relation to linear features 94 4.4.2 Potential to trace use of disturbed habitats using stable isotopes of small mammals 99 Chapter 5: Synthesis 101 5.1 Small mammal and resource responses to linear features and edge 101 5.2 Small mammal food resource use: Stable isotope and conventional analysis 102 5.3 Tracing wildlife use of linear feature food resources 103 5.4 Limitations and recommendations for future research 104 5.5 Conclusion 106 Literature Cited 107 Appendix A 133 A.l Citations used to generate Table A. 1 and Table 1.1 134 Appendix B 137 B.l Red-backed voles (Myodes spp.) and shrews (Sorex spp.) in the northern boreal forest 137 B.2 Methods 138 B.3 Results and Discussion 138 Appendix C 140 C.l Tracking small mammal movements on linear features 140 C.2 Methods 141 C.3 Results and Discussion 141 Appendix D 144 D.l Distribution of airborne seeds in the boreal forest 144 D.2 Methods 144 D.3 Results and Discussion 145 Appendix E 147 E.l Generating isotopic discrimination factors for small mammal muscle tissue 147 E.2 Methods .....148 E.3. Results and Discussion 148 Appendix F 154 F.l Detailed descriptions of study plots 154 F.l.l Northwest Territories study area 154 F.l.2 Alberta study area 155 LIST OF TABLES

Table 1.1: Small mammal count responses to created open habitat ('habitat response') and distance from edge within forest ('edge response'). Displayed are the percent of responses in each category with number of studies in parentheses. One study often had more than one response, thus percentages within a species and disturbance type can sum to greater than 100. See Appendix A for citations.

Table 2.1. Parameter coefficients, standard errors, and/?-values refer to the effect of linear feature versus forest habitat on counts of red-backed voles, deer mice, meadow voles and all rodents with a random effect (intercept) for plot (negative binomial model, xtnbreg). 'All rodents' sums counts for red-backed voles, deer mice, meadow voles, heather voles, shrews, chipmunks, jumping mice, and taiga voles. 'Habitat response' refers to counts in linear feature habitat. P-values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance.

Table 2.2. Parameter coefficients, standard errors, and/?-values refer to the effect of proximity to linear feature edge on counts of red-backed voles, deer mice and all rodents in the forest with a random effect (intercept) for plot (negative binomial model, xtnbreg, except where noted). 'All rodents' are summed counts of red-backed voles, deer mice, meadow voles, heather voles, shrews, chipmunks, jumping mice, and taiga voles. P- values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance. Edge responses are relative to forest edge when in the forest.

Table 2.3. Parameter coefficients, standard errors, and/?-values refer to the effect of linear feature versus forest habitat on vegetation with a random effect for plot. Random effects were intercepts only for random effects regression (xtreg or xtnbreg), or intercepts and slopes for mixed effects Poisson regression, xtmepois (StataCorp 2007). Vegetation was measured in 2006 (NT) and 2007 (AB). 'Habitat response' refers to amount in linear feature habitat. P-values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance.

Table 2.4. Parameter coefficients, standard errors, and/?-values refer to the effect of proximity to linear feature edge on vegetation with a random effect for plot. Random effects were intercepts only for random effects regression (xtreg or xtnbreg), or intercepts and slopes for mixed effects normal (xtmixed) or Poisson regression (xtmepois, StataCorp 2007). Vegetation was measured in 2006 (NT) and 2007 (AB). Edge responses are relative to forest edge when in the forest. P-values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance.

Table 2.5. Parameter coefficients, standard errors, and/?-values refer to the effect of a) linear feature versus forest habitat, and b) distance from edge when in forest (exception: fungal biomass compared 3 m from edge to 250 m from edge) on presence/absence of vegetation with a random effect (intercept) for plot (xtlogit). Vegetation was measured in 2006 (NT) and 2007 (AB). 'Habitat response' refers to presence in linear feature habitat. Edge responses are relative to forest edge when in the forest. NA indicates "not applicable" because variable caused failure of logistic regression. P-values considered significant at a = 0.025 (Bonferroni correction for two tests) and neutral responses indicate non-significance.

Table 3.1. Sample sizes of arthropods, fungi, plants, and small mammals collected in Northwest Territories and Alberta in each habitat (forest interior, forest edge, and linear features) and each plot.

Table 3.2. Summary statistics, p-values, and enrichment factors for stable carbon isotope ratios (813C) of soil, arthropods, plants, fungi, and small mammals on linear features and in forest. P- values refer to the effect of linear feature versus forest habitat with a fixed effect for plot and were considered significant at a = 0.025 (Bonferroni correction for two tests). Analyses of groups (e.g., "all plants") had species as a fixed effect. 'Habitat response' refers to linear feature isotope ratios with forest as a reference. "Enriched" signifies an increase in the heavier isotope (higher 813C), "depleted" a decrease, and "neutral" is a non-significant response. Enrichment factor is the difference between 13 13 13 m mean 8 C on linear features and in forest (8 Ciinear feature - 8 Cforest) per mil (%o).

Table 3.3. Summary statistics, p-values, and enrichment factors for stable nitrogen isotope ratios (815N) of soil, arthropods, plants, fungi, and small mammals on linear features and in forest. P-values refer to the effect of linear feature versus forest habitat with a fixed effect for plot and were considered significant at a = 0.05. Analyses of groups (e.g., "all plants") also had species as a fixed effect. 'Habitat response' refers to linear feature isotope ratios with forest as a reference. "Enriched" signifies an increase in the heavier isotope (higher 815N), "depleted" a decrease, and "neutral" is a non­ significant response. Enrichment factor is the difference between mean 815N on linear 15 15 features and in forest (8 Niinearfeature- 8 Nforest) in per mil (%o).

Table 4.1. Isotopic values (813C and 815N) and elemental composition (% nitrogen (%N) and C:N ratios) of small mammals (muscle tissue) collected near Fort Simpson, NT and overall;?- values and i?-squared values from regression of isotope ratios against species with fixed effects for plot and habitat. P-values considered significant at a = 0.05. Isotope means and standard deviations (sd) are in per mil (%o). Table 4.2. Prediction reliability (resubstitution summary) of small mammal stable isotope signatures determined using k-nearest neighbour discriminant analysis where k (nearest neighbours) = 4. Classification is in % with sample size (n) in parentheses.

Table 4.3. Isotopic values (813C and 815N) and elemental composition (% nitrogen (%N) and C:N ratios) of food sources (plants, arthropods, and fungi) collected near Fort Simpson, NT and overall/?-values and i?-squared values from regression of isotope ratios against species with fixed effects for plot and habitat. P-values considered significant at a = 0.05. Isotope means and standard deviations (sd) are in per mil (%o).

Table 4.4. Proportion of plants (PLNT), arthropods (ARTH), and fungi (FNGI) in small mammal diets based on standard mixing models (IsoError) and concentration-dependent mixing models (IsoConc). A513C and A815N derivation: TL=trophic level and TF=transformation (see Appendix E). Proportion in diet is mean percentage of diet (± standard error for IsoError). LIST OF FIGURES

Figure 2.1. Map depicting locations of six plots near Fort Simpson, NT (2005 to 2007), and five plots northwest of Manning, AB (2007). In the NT map the Mackenzie River runs east to west near the top, and the Liard River runs south to north through the centre. The pipeline starts in the northwest corner, crosses the Mackenzie River, and continues south. In AB numerous linear features are visible.

Figure 2.2. Study plot design a) NT and b) AB. "X" denotes trap location, with one trap on the transect (Tincat®) and another 10 m to the right (Longworth®). In NT in 2006 transects 5,10 and 15 (from left) were not trapped.

Figure 2.3. Vegetation transect at one trap location. Transects were 20 m long with 1-m x 1-m vegetation quadrats placed every 10 m anterior to trap locations.

Figure 2.4. Mean capture rates (counts per 1000 trap nights) of the three most common small mammal species on linear features and in the forest in a) NT (2005 and 2006 combined), and b) AB (2007). Error bars are 95% confidence intervals from the averages and standard deviations of plot capture rates by habitat. MEDV = meadow vole, DERM = deer mouse, REBV = red-backed vole.

Figure 2.5. Mean capture rates (counts per 1000 trap nights) of less common small mammal species on linear features and in the forest in a) NT (2005 and 2006 combined), and b) AB (2007). Error bars are 95% confidence intervals from the averages and standard deviations of plot capture rates by habitat. MJMP = meadow jumping mouse, STWL = short-tailed weasel, CHIP = least chipmunk, SHRW = shrew, HTHV = eastern heather vole, YCHV = taiga vole, and RESQ = red squirrel.

Figure 2.6. Influence of distance from linear features on predicted counts of a) red- backed voles, b) deer mice, and c) all rodents (summed counts of red-backed voles, deer mice, meadow voles, heather voles, shrews, chipmunks, jumping mice, and taiga voles) in NT (2005,2006) and AB (2007). Predicted counts (number of events) were derived from negative binomial regression models with random intercepts for plot.

Figure 3.1. Influence of habitat (linear feature, forest edge, and forest interior) on predicted mean stable isotope ratios of carbon (8 C) and nitrogen (8 N) of all plants, nitrogen fixing peavine, soil, arthropods, fungi, deer mice, red-backed voles, and meadow voles in NT and AB. Error bars are confidence intervals for predicted means based on regression of the stable isotope ratios on habitat. Raw 81 C and 815N values are given for AB deer mice.

Figure 4.1. Cumulative scores for occurence of food types in alimentary tracts of a) red- backed voles, and b) deer mice. Error bars are 1 standard deviation. Figure 4.2. Percent occurence of food types in stomachs of a) red-backed voles, and b) deer mice.

n i e Figure 4.3. Mean 8 CandS N values of small mammals and their food sources. Food sources are a) not corrected for dietary discrimination, b) corrected for dietary discrimination based on trophic level values and c) corrected for dietary discrimination based on trophic level and transformed to encompass most consumers(+0.45%o for A813C and-1.0%o forA815N, see Appendix E). Error bars are 1 standard deviation. CHAPTER 1: GENERAL INTRODUCTION

1.1 Anthropogenic edges

One of the most widely studied phenomena in landscape ecology is the response of wildlife to anthropogenic edge (Yarrow and Marin 2007, Ries et al. 2004). Anthropogenic edges are the transition zones between lands disturbed by human activity and remaining native habitat. Early in the history of wildlife management, creation of anthropogenic edges was viewed as a positive management strategy because of the benefits that edge habitat had for game species such as white-tailed deer {Odocoileus virginianus), rabbits, wild turkeys (Meleagris gallopavd), and other game birds (Leopold 1933, Lidicker 1999, Menzel et al. 1999). More recently, landscape ecologists have emphasized the negative impacts that anthropogenic edges create for habitat specialists (Lidicker 1999). How many species have, and in what situations a particular species has, "positive", "neutral", or "negative" responses to edges remains a key uncertainty in landscape ecology and wildlife conservation. In an attempt to provide a generalized framework for understanding edge effects, Ries and Sisk (2004) argued that the nature of the human disturbance, often open, treeless habitat, was a key determinant of what type of edge effect would occur. They hypothesized that edge responses in the focal habitat (often forest) can be predicted by resource availability in the adjacent habitat (often cleared, open habitat). When non- forest areas provide unique and (or) concentrated resources, generalists and open-habitat specialists should increase in abundance and prefer cleared habitat (Ries et al. 2004). Positive edge effects can occur when such species reach high enough densities in preferred open habitat that they spill over into adjacent non-habitat edges (Tattersall et al. 2002). Habitat enhancement can also occur when forest edge near open areas has an abiotic environment more similar to the cleared habitat than the forest interior, favouring open habitat species in the forest edge (Murcia 1995, Chen et al. 1999, Ries and Sisk 2004). Species attracted to complementary resources (i.e., different resources are available in each habitat), also can show positive edge effects as inhabiting an edge allows a species access to both resources (Heske 1995, Ries and Sisk 2004). Edges may also attract organisms if they contain resources either rare or not available in either

1 anthropogenic or native habitat, and thus represent a third, enhanced habitat type (Ries and Sisk 2004). Negative edge effects occur either when individuals make behavioural decisions to avoid transition areas due to lack of key resources (e.g., particular foods, Mills 1995), predation risk (e.g., lack of cover, Ries et al. 2004), inappropriate microclimate (Murcia 1995, Ries and Sisk 2004), or when there is a negative fitness consequence of living near an edge (Bowers and Dooley 1993, Lidicker 1999, Pulliam 1988). Negative fitness consequences can occur when edges have high resource abundance but also high predation risk or mortality, and thus are ecological traps (Bowers and Dooley 1993). Animals might be attracted to poor quality edge habitat where reproductive success is low and (or) mortality is high (sinks) from good habitat (sources) further from edge (Pulliam 1988). Neutral edge responses often occur because a study has insufficient statistical power to detect edge effects. Alternatively, species might perceive no difference in resource quality or availability, predation risk, or microclimate between created open habitat and adjacent focal habitat such that they use both habitats equally (Ries and Sisk 2004). In this case, resources are supplementary between the two habitats and neither habitat offers anything different (Ries et al. 2004). Neutral edge responses could also arise if there is no difference in habitat suitability between edge and interior of the focal habitat and a large difference in habitat suitability between cleared habitat and focal habitat resulting in a sharp transition whereby a species uses their preferred habitat exclusively to the edge, but does not use the adjacent habitat at all (Lidicker 1999).

1.2 Habitat conversion and edge creation in the boreal forest

The boreal forest is a dynamic mosaic of forest patches of varying age (Parisien et al. 2006) and this heterogeneity is driven largely by fire (Schmiegelow et al. 2006). Natural disturbance such as fire creates a wide variety of forest types and serai stages which in turn create a high diversity of natural edges that vary in resource levels (Bayne 2000). In recent years human activities have increased to become a predominant process shaping landscape patterns in the boreal forest, by creating new anthropogenic edges with no natural analogue (Bayne 2000, Primack 2002, Schneider 2002). It is unclear how

2 increases in anthropogenic edges will affect plants and wildlife that might be adapted to natural edges (Bayne 2000). Having evolved in a fire-driven ecosystem, boreal forest plants and animals might be adapted to patchy landscapes with a wide variety of edges, and might be robust to human disturbance, particularly when it emulates natural disturbance (Corkum 1999, Schmiegelow et al. 2006). However, several studies have found plants and animals respond differently to fire-induced and human-induced edges (Weyenberg et al. 2004, Schmiegelow et al. 2006, Larrivee et al. 2008), with post-harvest and post-fire forests having significantly different plant, invertebrate, bird, and mammal communities (Schieck and Song 2006). One possible reason is that human-induced edges tend to be abrupt, "hard" edges, whereas fire tends to produce complex or irregular shaped patches with "soft" edges (Eberhart and Woodard 1987, Larrivee et al. 2008). Furthermore, burned areas often regenerate quickly (Nowak et al. 2002), whereas anthropogenic edges persist for long periods of time whether due to maintenance (e.g., re-clearing of powerline right-of-ways), continued use (e.g., crops or roads), or negative impacts of the disturbance on abiotic or biotic conditions (Lee and Boutin 2006). Anthropogenically created open areas and the resulting edge could impact boreal forest differently than do natural disturbances, and thus have high potential to alter ecosystem dynamics. The three major sources of anthropogenic disturbance in the boreal forest of western Canada are agriculture, forestry, and energy sector development. Agricultural development in the boreal forest typically results in the loss of forest habitat, creates a landscape dominated by food crops or grasses, and results in sharp edges of managed woodland which are rarely allowed to regenerate to original forest condition (Bayne and Hobson 1997, Huhta et al. 1998, Moore et al. 2003). In contrast, forest harvesting initially results in short-lived open habitat with no or low canopy cover, increased early successional habitat (herbs, grasses, shrubs and young trees) and other open habitat characteristics (Hanski et al. 1996, Schweiger et al. 2000, Schieck and Song 2006). Before long, succession erases sharp clearcut edges and replaces them with broader "soft" edge transitions that blur the line between open disturbed habitat and intact canopy (Schweiger et al. 2000). The effects of these kinds of cleared habitat and edge types have been studied on a wide variety of boreal species and in general the effects of agriculture

3 seem to be more severe than those of forestry (Hanski et al. 1996, Bayne and Hobson 1997, Huhta et al. 1998). The softening of forestry edges over time can be seen in the attenuation of negative effects on many wildlife species (Corkum 1999, Tallmon and Mills 2004, Schieck and Song 2006). Far less is known about the effects of the energy sector on boreal wildlife. The dominant type of disturbance created by the energy sector is linear features, such as pipeline or powerline rights-of-way (ROW), roadside verges, and seismic lines (Schneider et al. 2003). Linear clearings are proliferating in many areas of the world, particularly in the boreal forest of western Canada (Forman and Alexander 1998, Schneider 2002). Linear features create unique open habitat that often has a sharp edge with abrupt shifts to forest vegetation. They are narrow and long with a high amount of edge per unit area (Coffin 2007, Bayne et al. 2005). When pipelines are built, soil is typically turned over to a depth of 2 m or more, and vegetation significantly altered to clear a 25-m-wide right-of-way (Burgess and Harry 1990). Creation of other linear features like seismic lines often results in soil compaction due to vehicle traffic (Revel et al. 1984, Lee and Boutin 2006). Like harvested forest, these types of linear disturbances can regenerate, albeit at a much slower rate. Slow regeneration is due to a number of reasons, such as degree of initial disturbance to soil, nutrients, moisture regimes and tree roots, or due to subsequent effects on understory plant species recruitment, growth and survival (Revel et al. 1984, MacFarlane 2003). For these various reasons, especially periodic grading to keep shrubby vegetation low (MacFarlane 2003), linear features are relatively permanent compared to forestry disturbances, and maintain sharp edges more like what occurs in agricultural landscapes. Linear features thus possess a number of the characteristics common to landscapes in which the magnitude and extent of the edge influence is large, i.e., the edge is abrupt, open, and maintained (Harper et al. 2005), and could act more like agricultural edges than forest clearcut edges. Unlike agriculture, linear features can provide suitable food or cover resources for open-habitat or generalist species, but like agriculture might be too disturbed to be suitable for forest-adapted species (Bayne et al. 2005, Dyer et al. 2002, Schneider 2003).

4 1.3 What is known about small mammals, disturbance type, and edge effects in forest ecosystems? Small mammals are a key component of forest ecosystems as predators of insects, birds and seeds, (Langley 1994, Bradley and Marzluff 2003, Hoffmann et al. 1995), in nutrient cycling (Clark et al. 2005, Bakker et al. 2004, Sirotnak and Huntly 2000), in plant seed and fungal spore dispersal (Terwilliger and Pastor 1999, Maser et al. 1978, Longland et al. 2001), and as prey for top-level mammalian and avian predators (Reed et al. 2007, Naylor and Bendell 1983, Banfield 1974). Given their pivotal roles in forest ecosystem processes, understanding how small mammals respond to different types of human disturbance and resultant edges is a key element of effective ecosystem management in the northern boreal forest. However, changes in presence and abundance of small mammals via habitat loss, degradation, and fragmentation caused by forestry and agriculture have been more extensively studied than changes caused by linear features (Table 1.1). Whether or not resource variation in and adjacent to created open areas is a good determinant of the magnitude of edge effects has not been explicitly tested for most small mammal species. Few studies have quantified changes in small mammal resources near compared to far from edge. However, if resource levels in the cleared habitat are good predictors of the magnitude of edge effects then I hypothesize that negative cleared habitat and edge effects on forest species (e.g., red-backed voles (Myodes [formerly Clethrionomys see Musser et al. 2005] spp.) and shrews (Sorex spp.)), would be most pronounced in agricultural landscapes, less severe in forestry, with linear features intermediate. Generalists, such as Peromyscus spp. (white-footed mice and deer mice {P. leucopus and P. maniculatus, Witt and Huntly 2001)) and chipmunks (Tamias spp., Hamilton and Whitaker 1979) are likely to respond neutrally to all three cleared habitat types. As opportunistic species, they likely will increase near edges in general, with greatest increases near forestry edges, lesser increases near linear feature edges, and lesser still at agricultural edges. Grassland species such as meadow voles (Microtus pennsylvanicus) are likely to increase the most in highly disturbed agricultural areas, increase intermediately on linear features, and increase least in harvested forest. Edge effects are unlikely to differ between habitat types for meadow voles, because they are

5 proximity or "mass" responses (animals spill over into edges of adjacent habitat but do not penetrate further) rather than a functional change near edges. To test these hypotheses I used the approach of Ries and Sisk (2004) who applied a vote-counting approach to evaluate the predictability of edge effects based on resource distribution for red-backed voles, Peromyscus spp., meadow voles, chipmunks and shrews {Sorex spp. and Blarina spp.). A total of 40 studies of small mammal abundance or density responses to edges and (or) cleared habitats in forest, mainly in North America, have been published in the peer-review literature. 34 of these have been done in landscapes altered by agriculture or forest harvesting but only 10 were conducted in boreal forests. Studies reporting statistically significant cleared habitat or edge responses were scored as positive or negative and studies reporting no response were scored as neutral. Studies reporting non-significant trends were scored once for the direction of the trend (positive or negative) and once for neutral. Mixed responses were recorded in each appropriate category (positive, negative, or neutral), though no studies had all three responses (Appendix A). For example, Cummings and Vessey (1994) found that meadow voles responded positively to grassy margins of agriculture fields (positive response), but were absent from the crop fields (negative response) and thus for this species were entered twice. More often, the responses were mixed between neutral and one direction of response. For example, red-backed vole abundance did not differ between partially harvested and uncut forest (neutral response) but decreased in regenerating clearcuts relative to uncut forest (negative response, Fuller et al. 2004). Table 1.1 (citations in Appendix A) synthesizes how the abundance of small mammals changes in response to cleared habitat and edge conditions for red-backed voles, Peromyscus spp., meadow voles, chipmunks and shrews in relation to the three main types of disturbance. As a rule, small mammals responded in predictable ways given their resource needs. In general, created open habitat in agricultural areas negatively impacted red-backed voles (one study in the United Kingdom) and shrews (one study with a neutral and a negative impact, a second study with a negative impact). In contrast, variable habitat responses to agriculture were found for Peromyscus spp. (40% positive responses, 60% neutral, and 40% negative) and eastern chipmunks {Tamias striatus, 50% positive responses and 50% negative), possibly due to differences

6 in food and (or) cover value of the crop or habitat fragment. Meadow voles responded positively to cleared habitat in agricultural areas with the exception of intensively farmed areas (Cummings and Vessey 1994, Basquill and Bondrup-Nielsen 1999). Edge responses in forest remnants in agricultural landscapes were largely positive for Peromyscus spp. (75% positive responses in eight studies), and neutral for red-backed voles (67% neutral responses in nine studies), Tamias spp. (one study) and shrews (one study). When examined within forest remnants, meadow voles were higher near the edge, suggesting spillover from the preferred open habitat (two studies). The harvested open habitat in areas dominated by forestry (e.g., clearcuts) generally has a negative impact on red-backed voles (88% negative responses in 17 studies) whereas meadow voles respond positively (75% positive responses in four studies). Peromyscus spp. and Tamias spp. respond variably to cleared habitat, though they tend to respond positively (50% positive responses in 10 studies for deer mice and 57% positive responses in seven studies for chipmunks). Shrews generally had no response to forestry-created open habitat with a tendency to respond negatively (all seven studies reported neutral responses (100%), and two studies (29%) reported negative responses). Clearcut-forest edges appear to positively impact Tamias spp. (67% positive responses) and possibly Peromyscus spp. (40% positive responses), though neutral responses were just as common in the former and more common in the latter. Red- backed voles do not respond to clearcut-forest edges generally (67% neutral responses). Shrews exhibit mostly neutral responses to clearcut-forest edge, with one study out of three reporting positive effects, and another reporting both neutral and negative responses. See Table 1.1 for more detail. Far fewer studies (n=5) have been conducted on small mammal response to linear features (see Table 1.1 for more detail), and two were on natural linear features (grassy margin along a river and a lakeshore). Red-backed voles respond negatively to the linear feature itself (two studies). Peromyscus spp. and Tamias spp. also responded negatively to linear features (two studies and one study, respectively). Meadow voles and shrews increased in abundance on linear features, particularly if the linear features were well- vegetated (100% positive responses). Red-backed vole abundance in the forest decreased with proximity to linear feature edges (100% negative responses), though only studies on

7 natural edges currently exist. Similar to effects of other disturbance types, abundance of Peromyscus spp. in forest tends to peak at the interface between linear feature and forest, though neutral responses were also common (50% positive responses). Macdonald et al. (2006) captured few meadow voles in forest near rivers, nevertheless meadow vole counts in forest appeared to be independent of distance from edge. McGregor (2008) found increased Tamias spp. near linear features. Macdonald et al. (2006) suggested shrews do not respond to natural linear features. Though none have examined abundance, several authors have found that roads impede shrew and other small mammal movements, with the possible exception of deer mice (Oxley et al. 1974, Schreiber and Graves 1977, Goosem 2001, Rico et al. 2007). This review demonstrates that although there is considerable variability in cleared habitat and edge responses within and between species of small mammals in North America, general patterns are predictable based on small mammal resource needs. It also demonstrates that the nature of the disturbed open habitat does have an impact on edge response by small mammals thus requiring more studies in order to understand linear feature impacts. Generalists like deer mice and chipmunks seem to be more responsive to disturbance and appear to thrive at edges. This suggests that they benefit from the complementary or enhanced nature of resources in edge environments. Grassland species like meadow voles (Thompson 1965, Banfield 1974, Peles and Barrett 1996) thrive in created non-forest habitat, and seem to spill over into remnant forest patches in landscapes dominated by agriculture and forestry. Only one study has previously evaluated edge response of meadow voles to linear features and found no response although the study lacked statistical power (Macdonald et al. 2006). In contrast, forest specialists such as red-backed voles (Banfield 1974, Witt and Huntly 2001) and shrews (Bowers et al. 2004) are generally negatively impacted by cleared habitat. However, red- backed voles and shrews rarely respond to distance fromedge . In the case of shrews this might reflect the mixture of species that have been considered (Bowers et al. 2004). The neutral edge response by red-backed voles is not entirely intuitive given their strong association with forests, but suggests that the resources required by this species are generally not negatively impacted by, and are found near, edges.

8 Table 1.1. Small mammal count responses to created open habitat ('habitat response') and distance from edge within forest ('edge response'). Displayed are the percent of responses in each category with number of studies in parentheses. One study often had more than one response, thus percentages within a species and disturbance type can sum to greater than 100. See Appendix A for citations.

Species Habitat Response Edge Response (No. Habitat type Positive Neutral Negative studies) Positive Neutral Negative (No. studies) Myodes spp. (red-backed voles) Agriculture 100 0) 100 (1) Forestry 12 24 88 (17) 22 67 22 (9) Linear features 100a (2) 50a 50a (2) Peromyscus spp. (deer mice and white-footed mice) Agriculture 40 60 40 (5) 75 50 13 (8) Forestry 50 50 20 (10) 40 60 (5) Linear features 100a (2) 50a 50a (4) Microtus pennsylvanicus (meadow voles) Agriculture 100 33 (3) 100 (2) Forestry 75b 25 (4) 100 a a 0) Linear features 100 (3) 100 (1) Tamias spp. (chipmunks) Agriculture 50 50 (2) 100 (1) Forestry 57 29 29 (7) 67 67 (3) Linear features 100 (1) 100 (1) Sorex spp. and Blarina spp. (shrews) Agriculture 50 100 (2) 100 (1) Forestry 14 100 29 (7) 33 67 33 (3) a a Linear features 100 (1) 100 (1) : One linear feature was a river or a lakeshore. b: Review of Microtus studies, 6/11 were onM pennsylvanicus.

vo 1.4 Resources to measure to understand small mammal linear feature responses Many studies have attempted to link habitat features or resources to small mammal abundance. Through these studies, small mammal biologists have a good understanding of what resource factors influence small mammal distribution. However, there has been little quantification of how these factors differ in created open, forest edge, and forest interior habitats. The three factors that are the best determinants of small mammal distribution are microclimate, food, and structural resources (Miller and Getz 1977, Lin and Batzli 2001, Ries and Sisk 2004, Getz et al. 2005, Silva et al. 2005). Given that the same species' response to edge can be positive, negative or neutral depending on the distribution of resources between adjacent patches and the edge (Ries et al. 2004), it is important to understand how these factors are impacted by all types of human disturbance if we are to develop a general understanding of edge effects. Microclimatic variation can influence distribution of small mammal resources or of the small mammals themselves (Maser et al. 1978, Monthey and Soutiere 1985, Orrock et al. 2000, Fuller et al. 2004). For instance, most small mammal species seem to prefer mesic environments, with the exception of deer mice which have less restrictive water requirements and thus occur in xeric areas (Banfield 1974, Miller and Getz 1977, Monthey and Soutiere 1985, Fuller et al. 2004). Edges of forests are often hotter and drier than the forest interior due to increased light, and wind levels following removal of the canopy (Chen et al. 1999, Ries et al. 2004). Changes in microclimate also influence the structure and composition of understory plant communities (Ries et al. 2004, Larrivee et al. 2008). As understory plants provide lateral cover for small mammals, any changes in plant type (e.g., grass versus herbaceous versus shrubs versus trees) due to edge or disturbed habitat effects can alter protection from predators, heat and risk of desiccation (Ostfeld et al. 1999, Fuller et al. 2004, Nordyke and Buskirk 1991, Carey and Johnson 1995). Plant parts are also important foods; as rodents consume bark, roots, chlorophyllous matter (leaves, stems, buds, petioles), and reproductive structures such as seeds and berries (Dyke 1971, Banfield 1974, Burt and Grossenheider 1976, West 1982, Carey and Johnson 1995).

10 Plant cover, and seed and berry production can increase in cleared areas and adjacent forest edges due to increased light levels (Geiger 1965, Murcia 1995, Greene et al. 2002). However, such increases might be counteracted by concomitant decreases in shade-tolerant plants (Saunders et al. 1991, Schweiger et al. 2000, Harper et al. 2005, MacFarlane 2003), or plants such as red currant (Ribes triste) or Canada buffaloberry (Sheperdia canadensis) which are negatively impacted by decreased humidity, increased temperatures or increased competition in disturbed areas (Sanders et al. 1991, MacFarlane 2003). Coarse woody debris (CWD) and downed woody material (DWM) are important for forest small mammals directly as structural resources or indirectly as substrate for food resources (Keinath and Hayward 2003). DWM such as fallen logs provide cover from predators and travel routes, but are also cool, moist substrates for growth of fungal food (Carey and Johnson 1995, Fuller et al. 2004, Pearce and Venier 2005). DWM often decreases or is of lower quality (size or decay state) in disturbed habitat (Sippola et al. 2001). However, increased wind speeds and higher temperatures can dry out soil at edges (Snail and Jonsson 2001, Kristan et al. 2003) which can result in greater blow down of trees (Primack 2002). Fungi, important food for red-backed voles (Dyke 1971, Maser et al. 1978, Mills 1995), often decrease in disturbed habitat (Clarkson and Mills 1994, North et al. 1997). Despite possible increases in DWM substrate used by fungi at edge, fungi usually also decline near edges due to the drier, windier, and hotter conditions in disturbed habitat which extend into forest edges (Murcia 1995, Sippola et al. 2001, Snail and Jonsson 2001). Other food items such as insects, important for deer mice and shrews (Dyke 1971, Banfield 1974, Carey and Johnson 1995, Bowers et al. 2004), also have been found to respond to open habitat and distance from edge (Van Wilgenburg 2001, Ferguson 2004, Blake 2006, unpublished). Insect responses to cleared habitat and edge often depend on what substrates are sampled; shrub insects often respond positively (Peng et al. 1992, Jokimaki et al. 1998, Marenholtz 2007, unpublished), while ground dwelling insects decrease (Schowalter et al. 1981, Parry et al. 1998, Richardson et al. 2002, Harmon et al. 1986, Carey and Johnson 1995). Foraging sites for small mammals could decrease, as bare ground increases (Schweiger et al. 2000) and moss (Odor and

11 Standovar 2001, Hylander 2005) and leaf litter often decrease in cleared habitat (Matlack 1993, Burke and Nol 1998, Watson et al. 2004). Edges, through a poorly understood mechanism, often have decreased leaf litter depth (Matlack 1993, Burke and Nol 1998, Watson et al. 2004) which could reduce foraging or predator refuge sites. There are many food and structural resources that might be critical for determining whether a small mammal can occupy a habitat and quantifying how those resources differ in non-forest, edge, and interior habitats in relation to linear features is important in helping us understand edge effects.

1.5 Food resource use in disturbed habitats and the use of stable isotopes

Ries et al. (2004) stated that far too many studies explain organism responses to edge based on supposition about vegetation or resources without independently testing which resources actually influence the quality of the organism's habitat. In particular, there has been little effort to understand how structural versus food resources vary in response to edge and the relative importance of each. The importance and distribution of structural resources that provide cover and protection from predators has been well established by many wildlife studies (e.g., Carey and Johnson 1995, Peles and Barrett 1996, Fuller et al. 2004, Elliott and Root 2006). Structural resources seem to vary less spatially and temporally than food resources (Dyke 1971, Banfield 1974, Maser et al. 1978). However, measuring resources in the field means that food and cover resources are often confounded, as the two types are often correlated (Getz et al. 2005, Peles and Barrett 1996, Monthey and Soutiere 1985). Thus, our knowledge of structural resources important to small mammals is generally quite good, though possibly confounded with food resources. Cleared habitat- and edge-induced variability in small mammal food resources warrants further study. Some small mammals have specialized food requirements. Red-backed voles often depend on fungi for food and possibly water (Dyke 1971, Maser et al. 1978, Mills 1995). Deer mice tend to consume large amounts of insects and conifer seeds (Dyke 1971, Banfield 1974, Carey and Johnson 1995). Meadow voles are herbivores and typically are confined to areas with grass and other herbaceous plants (Zimmerman 1965,

12 Banfield 1974, Ostfeld et al. 1999). However, whether or not changes in food resources are a good explanation for patterns of habitat use remains unclear. Numerous studies have examined the diet of small mammals (e.g., Maser et al. 1978, Norrie and Millar 1990, Peles and Barrett 1996), and changes in small mammal food resource distribution (Miller and Getz 1977, Mills 1995, Marenholtz 2007, unpublished), but few have ever taken a comparative approach and compared small mammal diets among different habitat types. We also need to understand interactions between changes in food resources and small mammal distributions, i.e., whether food resources that respond to disturbance are "critical" resources or if small mammals will simply switch their diets to accommodate locally available food. One reason for the lack of information on how small mammal food resources vary in response to habitat types is the difficulty in quantifying food availability and consumption (Vickery 1981). Information on food selection is often based on the analysis of partially digested food remains such as feces or boli (Pinnegar and Polunin 1999, Thompson et al. 1999, Michener and Kaufman 2007, Caut et al. 2008b) which demonstrates what the animal ate in the short-term, with no information about where the food came from (Pinnegar and Polunin 1999, Thompson et al. 1999). If we are to understand the role of food in driving organism responses to habitat disturbance, a better method is needed. One of the tools available to ecologists is stable isotope analysis (SIA). SIA is a method used increasingly in ecological studies to determine plant and animal nutrient sources (Post 2002), and impacts of habitat disturbance (Evans and Belnap 1999). Diet analysis using stable isotopes is a viable alternative to gut content analysis that uses one sample (e.g., muscle, fur, or blood) to provide a view of diet that is integrated over long time periods depending on the metabolic rate of the tissue (Gannes et al. 1997). For example, liver and kidney tissues turn over quickly and reflect elements incorporated into tissue from diet over a period of several days, whereas blood and muscle turnover slowly and reflect diet over the past several weeks of the organism's life (Gannes et al. 1997, Arneson et al. 2006). Isotopes are atoms of the same element, such as carbon (C) or nitrogen (N), differing in their mass number only (e.g., heavy 13C and light 12C and heavy 15N and light 14N). Isotopes exist in natural ratios in the

13 environment, in which the heavier isotope is often rare (Sulzman 2007, Nier and Gulbransen 1939). Isotope fractionation or discrimination occurs when physical and chemical processes cause a change in the ratio of heavy to light isotopes between a source substrate and a product (Dawson et al. 2002). Nitrogen isotope ratios provide information about trophic level and change as isotopes move up the food chain (McCutchan et al. 2003). Carbon isotope ratios typically reflect diet source and do not change much between diet and the consumer (McCutchan et al. 2003). Consumers' tissues reflect and enrich the isotopic signature of their food (DeNiro and Epstein 1978, DeNiro and Epstein 1981, Gannes et al. 1997) allowing use of isotope ratios to assess the relative contribution of different foods to diet. Soil disturbance and deforestation can cause a shift in stable isotope ratios by altering inputs and outputs of carbon and nitrogen (Evans and Belnap 1999, France 1996). For example, the removal of the canopy layer results in enrichment of carbon isotope ratios (higher C, France 1996). Many disturbances to the nitrogen cycle cause 15N enrichment (Sah et al. 2006), though heavy 15N accumulates in older and more decomposed soil (Dijkstra et al. 2006) thus disturbed habitats could also be depleted in 15N. Animals obtaining resources originating from disturbed soils could therefore have unique isotopic signatures relative to forest-dwelling animals even if they had identical diets. Thus, SIA has the potential to provide a link between resource availability on linear features and animal use of those resources.

1.6 Thesis overview

My main objective was to evaluate whether small mammals respond numerically to linear feature habitat and whether or not they exhibit any edge responses within native forest habitat as predicted by my review (Chapter 2). To understand the mechanisms that influenced small mammal patterns I also quantified how resources required by small mammals changed in response to linear feature habitat and edges (Chapter 2). A major failing of many studies attempting to demonstrate that resources drive cleared habitat and edge responses is that they do not independently test whether the resources actually influence habitat quality for the animal (Ries et al. 2004). Thus, my third objective was to directly examine food resource use of small mammals inhabiting linear feature and

14 edge habitat using stable isotope analysis and gut content analysis. In Chapter 3,1 did this by searching for a unique chemical signature of linear features that would allow tracing assimilation of linear feature resources by small mammals. In Chapter 4,1 used mixing models and gut content analysis to determine the major components of small mammal diets, then compared isotope signatures to the aforementioned changes in food resources on linear features and with distance from edge.

15 CHAPTER 2: LINEAR FEATURE EFFECTS ON ABUNDANCE OF NORTHERN BOREAL FOREST SMALL MAMMALS AND THEIR RESOURCES

2.1 Introduction Despite an exhaustive literature examining the effects of edges on wildlife, our understanding of when a species is likely to show an edge effect is limited. How many species have, and in what situations a particular species has, a "positive," "neutral," or "negative" response to edges remains a key uncertainty in ecology (Ries et al. 2004). The dynamic nature of boreal forest patches creates a high diversity of natural edges which some have hypothesized makes boreal forest species better adapted to deal with edge environments (Bayne 2000, Parisien et al. 2006, Schmiegelow et al. 2006). On the other hand, human activities have increased to become a predominant process shaping the structure of boreal forest landscapes and creating new anthropogenic edges with no natural analogue (Bayne 2000, Primack 2002, Schneider 2002). In particular, the energy sector produces vegetated linear features such as pipeline and powerline rights-of-way (ROWs), roadside verges, and seismic lines. Unlike other natural and human disturbances, linear features create unique open-habitat with straight edges, abrupt shifts to forest vegetation, and highly disturbed or compacted soil (Revel et al. 1984, Burgess and Harry 1990, Lee and Boutin 2006). Although linear features are long, most are narrow making it questionable whether they are perceived as edges by many species of wildlife (Coffin 2007, Bayne et al. 2005). Small mammals are vital components of the boreal forest ecosystem (Banfield 1974, Langley 1994, Terwilliger and Pastor 1999, Longland et al. 2001, Clark et al. 2005), thus understanding small mammal responses to linear feature edges is a key component of effective ecosystem management. A synthesis of the small mammal literature suggests that the direction and magnitude of cleared habitat and edge effects are relatively consistent for a given species of small mammal (Table 1.1). Also, the nature of the human disturbance is a key determinant of the type of edge effect that will occur (Ries and Sisk 2004). Differences in distribution of food and structural resources within open habitat and edge types seem to be important factors for predicting cleared 16 habitat and edge effects for small mammals (Ries and Sisk 2004). Agricultural edges, for instance, can seasonally attract game species because of a complementary mixture of food resources from the agricultural landscape, and cover resources from adjacent forest (Leopold 1933). Forestry edges on the other hand, especially as they regenerate and the edge softens (Schweiger et al. 2000), might go unnoticed and elicit no response if animals perceive harvested and original forest to have equivalent (supplementary) resources (Hayward et al. 1999, Ries et al. 2004). Linear features are less well-studied and it is unclear whether they provide complementary (unique), supplementary, or enhanced resources for small mammals on the clearings or in adjacent forest edges. Availability and abundance of resources important to small mammals can change in response to linear feature habitat and edges (Matlack 1993, Murcia 1995, Davies- Colley et al. 2000, Van Wilgenburg 2001). This could subsequently drive changes in small mammal communities via resource mapping whereby an organism's distribution mirrors that of its resources (Ries et al. 2004). Linear features in the boreal forest are open-canopy clearings with high light levels, and are hot (Blake 2006, unpublished) and dry (Geiger 1965, Saunders et al. 1991, Murcia 1995, MacFarlane 2003), thus linear features have a meadow-like microclimate that favours productivity for many plants (Wender 2004). Open areas near forest often regenerate to grass and other herbaceous plants which benefits grassland small mammals such as meadow voles. If left undisturbed, succession leads to woody plants and shrubs (Revel et al. 1984, Schweiger et al. 2000, Li et al. 2007). As linear features are often re-cleared, succession to shrubby habitat might not occur and instead can stagnate at the stage of light- and disturbance- adapted plant communities with a lack of shade-tolerant herbs (MacFarlane 2003). Linear features thus can act as enhanced habitat for grassland species through increased foraging opportunities on herbs or grasses (e.g., meadow voles, Zimmerman 1965, Banfield 1974, Ostfeld et al. 1999). The abiotic and biotic environment on linear features can also influence the environment in the forest portion of edge in ways that can benefit opportunistic deer mice and chipmunks (Sullivan et al. 2008), or could negatively impact specialized forest species, such as red-backed voles. For example, increases in insects (Peng et al. 1992, Jokimaki et al. 1998, Marenholtz 2007, unpublished), and plant production of seeds and berries (Geiger 1965, Saunders et al. 1991, Murcia 1995,

17 MacFarlane 2003, Wender et al. 2004) on linear feature and in forest edge could enhance habitat quality for generalist deer mice and chipmunks (Dyke 1971, Carey and Johnson 1995, Greene et al. 2002, Bowers et al. 2004, Hamilton and Whitaker 1979). Foraging opportunities might be decreased for forest species (e.g., red-backed voles) on linear features and near forest edges, however, with the lack of canopy leading to low leaf litter (Schweiger et al. 2000), and high heat reducing the cover of moss (Odor and Standovar 2001, Hylander 2005) and availability of fungal food (Clarkson and Mills 1994, North et al. 1997, Sippola et al. 2001, Snail and Jonsson 2001). The objective of this chapter was to determine patterns of small mammal abundance on linear features (mainly pipeline ROWs) and with increasing distance from edge. I predicted that linear feature habitat would be exploited by open-habitat specialists, avoided by forest specialists, and forest generalists would be found in equal numbers on linear features and in forest (i.e., neutral or no response). Linear features could also cause a functional change in habitat for some distance into forest edge. I predicted a positive edge response for generalists and open habitat species, and negative or neutral edge effects for forest species such as red-backed voles. Simultaneously, I tested which small mammal resources were related to small mammal abundance patterns with respect to linear feature habitat and proximity to forest edge.

2.2 Methods

2.2.1 Study areas and study design

To determine how small mammals respond to edges, I conducted research from May to August in 2005 and 2006 in the Mackenzie and Liard River Valleys east of Fort Simpson, Northwest Territories (61° 46' N, 121° 15' W) along the Norman Wells Pipeline ROW (Fig. 2.1). In the summer of 2007 (July and August) I also trapped in the Chinchaga Forestry Region (57° 18' N, 118° 23' W) approximately 65km northwest of Manning, Alberta (Fig. 2.1). The NT study area was within the Hay River Lowland Ecoregion (Environment Canada 2005a). There were few anthropogenic disturbances in the NT study area aside from the pipeline (operating since 1985). The pipeline is buried leaving a 25-mwide

18 ROW in various stages of regeneration. Forestry has been minimal to non-existent, and there have been no forest fires since 1965 or earlier (K. Mindus pers. comm.). The mean summer temperature of the Hay River Lowland Ecoregion is 13 °C, with cold winters (mean temperature -19 °C) and annual mean precipitation of 350-450 mm. Study plot forest stands were 77 to 117 year-old mixedwood comprised of dry sites with trembling aspen {Populus tremuloides), balsam poplar {Populus balsamifera), white spruce {Picea glauca) and some black spruce {Picea mariana), dry upland pockets of jack pine (Pinus banksiana), and wet lowland patches of black spruce and tamarack {Larix laricina) (Environment Canada 2005a, K. Mindus pers. comm.). After construction in 1985 and prior to 1989, the pipeline ROW was fertilized and seeded with slender wheatgrass {Elymus trachycaulus), boreal creeping red fescue {Festuca rubra), meadow foxtail {Alopecurus pratensis), sheep's fescue {Festuca ovina), Richmond timothy {Phleum pratense), Kentucky bluegrass {Poa pratensis), and reed canary grass {Phalaris arundinacea, M. Gerlock, pers. comm.). The AB study area in the Chinchaga Forestry Region is located within the Clear Hills Upland Ecoregion of the Boreal Plains Ecozone (Environment Canada 2005b). This ecoregion has many more linear features than the NT study area (Environment Canada 2005b, Fig. 2.1). Linear features in this area were created as early as 1985 and as recently as 2000. Summers are short and cool (mean summer temperature is 13 °C) and winters cold (mean winter temperature is -17.5 °C). Mean annual precipitation ranges from 400-600 mm. Environment Canada (2005b) describes the region as dissected upland 550-1050 m above sea level with rolling plateaus and broad, gently undulating valleys. Common linear feature vegetation was grasses and legumes such as clover (likely the exotic Trifolium hybridum (Alsike clover), MacFarlane 2003), peavine and vetch. Study plot forest stands in AB were 77 to 137 years old and were comprised primarily of trembling aspen and white spruce mixedwood with pockets of balsam poplar, lodgepole pine {Pinus contorta), which can dominate at higher elevations, and black spruce in poorly drained lowland. Balsam fir {Abies balsamea) can also be present in uplands, but was not common (Environment Canada 2005b, Manning Diversified 2005).

19 0 2 4 8 12 16

Figure 2.1. Map depicting locations of six plots near Fort Simpson, NT (2005 to 2006), and five plots northwest of Manning, AB (2007). In the NT map the Mackenzie River runs east to west near the top, and the Liard River runs south to north through the centre. The pipeline starts in the northwest corner, crosses the Mackenzie River, and continues south. In AB numerous linear features are visible. It is important to note that the mesic, mixedwood forest characterising the NT study plots was one habitat type among many in the region. Much of the surrounding landscape in the NT study area was conifer forest, or composed of tall and low shrubs (Dehcho Land Use Planning Committee 2006). In an area of about 6938 km surrounding my NT plots, 64% of the landscape was wet habitat, with tamarack, black spruce, shrubs, or open water, whereas 35% was mesic habitat similar to my study plots, with aspen, white spruce, pine, poplar or birch (Forest Management Division 2008). In AB, mesic mixedwood forest was more predominant in the landscape (about 75% of the forest in the Chinchaga region) than it was in the NT landscape (Manning Diversified). In both study areas I measured edge effects relative to a zero meter mark that was placed at the point of edge creation, i.e., where canopy trees of the original forest still stood on the edge of linear features (Murcia 1995). This was usually the point of edge maintenance (limit of the undergrowth, Murcia 1995). In the Northwest Territories, six 29 ha study plots 410 m long (perpendicular to linear feature) and 700 m wide (parallel to linear feature) were established 1 to 2 km apart in 2005 and 2006 (FERR, HOOK, LIAR, MANN, MART and PORC, Fig. 2.1). Combined areal extent of NT study sites was approximately 172 ha. In the Alberta study area, five 29 ha study plots 413 m long (perpendicular to linear feature) and 700 m wide (parallel to linear feature) were established at least 1.6 km apart in 2007 (GRIZ, HILL, POWL, SLIP, and STOW, Fig. 2.1). Combined areal extent of the AB study plots was approximately 145 ha. AB plots bordered a pipeline only (STOW, GRIZ) or a pipeline and a seismic line or road (SLIP, POWL, and HILL). Transects were laid out in six (NT, Fig 2.2a) to nine (AB, Fig. 2.2b) groups per site perpendicular to a linear feature. The distance between trap locations in the forest was 37.5-m along transects, and 50-m between the groups of forest transects (measured perpendicular to the linear feature). Transects were established every 50m along the linear feature in NT and every 100m along the linear feature in AB. Transects were staggered to gather data on small mammals from more distance classes with a given level of effort than would have been possible with a standard square grid. In each site I established one transect (15 of each type of trap alternating) roughly along the centre of the linear feature.

21 a)

310 m

Linear "»M feature

700 m

b)

413 m

x . .x x x- x jfc.-.x.-- x f x x-J, x •: •#•••;:;,;x.-! • x :' :;x ?:X Linear feature

700 m

Figure 2.2. Study plot design a) NT and b) AB. "X" denotes trap location, with one trap on the transect (Tincat®) and another 10 m to the right (Longworth®). In NT in 2006 transects 5,10 and 15 (from left) were not trapped. 2.2.2 Small mammal sampling methods

Two types of traps were used for live-capture of small mammals: multiple- capture Tincat® traps countered "trap-happiness", and Longworth® traps enabled capture of larger rodents, such as pregnant voles. Multiple-capture Tincat traps were located on the transect, while single-capture Longworth® traps were located 10-m away (to the right in Fig. 2.2) on a bearing parallel to the pipeline. In 2006 at a subset of interior trap locations, kill-traps were set to augment collection of small mammals for stable isotope analysis. Each plot was surveyed for three consecutive nights, twice each year in NT (late May to mid July and late July to early August) except for the plot FERR, and once in AB (July and early August). Alternate transects were trapped in each session. In NT odd transects (1 to 15) were trapped in the first session, as well the 15-trap linear feature transect (Fig. 2.2). Even numbered transects (2 to 14) and the linear feature transect were trapped in the second session in NT. In AB odd transects (1 to 13) were trapped once, on one side of the linear feature. Traps were set overnight (earliest 1300h) and checked each morning (between 0700h and 1200h). Small mammal traps were baited with peanut butter with 2-3 cm3 slices of carrots added for moisture. Handfuls of cotton batting were added to alleviate small mammal mortality due to exposure. Once set, traps were covered with leaf litter and where possible wedged under a heavy item such as a log to prevent disturbance by larger animals such as weasels, corvids, or bears. When an animal was captured I recorded: trap set date and time, time of day, trap location, species, weight, sex, age (juvenile or adult), reproductive condition and whether or not multiple individuals were captured. I also marked each animal by cutting a triangle out of the pinnae or with a single Monel (No. 1) metal ear tag (National Band and Tag, Newport, Kentucky, USA). This enabled me to use counts of unique individuals as an index of small mammal abundance (Slade and Blair 2000, McKelvey and Pearson 2001). I classified shrews as "Sorex spp." and collected specimens for identification by upper incisors (Appendix B). At the end of each field season traps were cleaned with a 10% solution of chlorine bleach to prevent the spread of disease.

23 2.2.3 Resource distribution assessment

Vegetation, fungi, and downed woody material characteristics were collected for pipeline and forest trap locations in July and August of 2006 and 2007. Habitat characteristics were estimated along 20-m transects placed with "zero" five meters to the left of the Tincat®, and ending five meters beyond the Longworth® (Fig. 2.3). Three 1-m x 1-m quadrats were examined at the 0 m, 10 m, and 20 m marks of each 20-m habitat transect (Fig. 2.3). Measurements taken in each quadrat included percent composition of ground cover less than 50 cm in height (e.g., shrubs, forbs, legumes, graminoids, moss, leaf litter, bare ground, and fungi), and counts of stems of plants over 50 cm in height. Tall vegetation was used to create two variables of lateral cover. The first variable was count of "food stems" composed of rose (Rosa acicularis), Canada buffaloberry, gooseberry (Ribes lacustre or Ribes oxyacanthoides), red-osier dogwood (Cornus stolonifera), red currant, saskatoon (Amelanchier alnifolia), wild red raspberry (Rubus idaeus), low-bush cranberry (Viburnum edule), and legumes. The second variable was count of "live stems" composed of food stems plus stems of alder (Alnus spp.), conifer, aspen, balsam poplar, willow (Salix spp.), birch (Betula spp.), fireweed (Chamerion angustifolium), tall forbs, and Labrador tea (Ledum groenlandicum).

24 + To forest interior Tincat^ | ^Longworth

Legend o = upper left corner of 1 m x 1 m veg quadrat X = trap location = small mammal trapping transect

Figure 2.3. Vegetation transect at one trap location. Transects were 20 m long with 1-m x 1-m vegetation quadrats placed every 10m anterior to trap locations.

25 To obtain volume of downed woody material (DWM), DWM below 50cm from ground with length greater than lm (Keinath and Hayward 2003) was counted along the length of the 20-m transects and recorded in one of four categories: A) < 5 cm diameter, B) 5 cm to 15 cm, C) 16 cm to 25 cm, and D) > 25 cm. In Alberta, the DWM categories were increased to better encompass the larger trees in the more southern location: A) < 7 cm, B) 7 cm to 20 cm, C) 21 cm to 40 cm, and D) > 40 cm. DWM was converted to volume (m3/ha) per category using a modification of Van Wagner's (1968) formula to account for the number of logs in each diameter class, then summed for total DWM volume,

V = [w2 x V(42 X N) /8I where P"is volume per unit area (m /ha), di is log diameter (cm) of the i category (taken to be the lower value of the range), iVis the number of logs of the ith diameter class, and L is the length of the transect (20m). Concurrent with small mammal trapping in 2006, counts of fungal sporocarps (fruiting bodies or mushrooms) and berries were conducted along 50-m transects on linear features, in forest interior (between 250 m and 295 m fromth e forest edge) and 3- m from the forest edge parallel to the linear feature. All berries were counted regardless of age (overwintered, new unripe, or new ripe). Berries of some species were ripeb y end of June to early July (e.g., dewberry (Rubus pubescens) and raspberry) whereas others did not ripen until late July or early August (e.g., buffaloberry, rose). Approximately 10 transects were conducted in each of the three categories in each plot (62 linear feature transects, 64 edge transects, and 62 forest interior transects. Fungi were counted in three diameter size classes: small: 0-4 cm, medium: 5 cm-8 cm, large: >8 cm. To better define the largest size class, large fungi diameters were measured to the nearest millimetre. Berry counts were recorded separately by species and counted as "ground berries" if they were found within 50 cm of the ground, and as "shrub berries" if they were found above 50 cm in height. Fungal counts were converted to fungal biomass using a predictive formula developed in the northern boreal forest to convert cap area of mushrooms to fresh biomass (Carrier and Krebs 2002, Kluane Ecological Monitoring Project 2004, unpublished). Mushroom cap area is given by the area of a circle in cm : 26 Cap area= s-(O.S X diameter)" The cap area of each fungal sporocarp was determined using the average diameter of the size class: 2 cm for small, 6.5 cm for medium, and 11.1 cm for large (derived from 14 large sporocarps). Constants to derive biomass were calculated based on linear regression of cap area on measured biomass in northern boreal forests with correlation coefficient between 0.93 (Carrier and Krebs 2002) and 0.94 (Kluane Monitoring Program 2004, unpublished) to give the following formula with cap area in cm2 and weight in grams: Wet weight = 0.74803 X (Cap area) -I- 0.0006126 X (Cap area)2 I calculated wet weight for each size class, multiplied by the number of sporocarps in each size class, and summed the size class wet weights to yield fungal biomass.

2.2.4 Data analysis

2.2.4.1 Small mammals

Analyses were done independently for each study area (Northwest Territories and Alberta) and year (2005, 2006 and 2007). Separation in this way allowed me to demonstrate the generality and replicability of linear feature and edge effects in the northern boreal forest. I ran regressions on count response variables, i.e., the number of unique individuals of red-backed voles, deer mice, meadow voles, and "all rodents." "All rodents" was a summation of counts for all small mammals excluding red squirrels and short-tailed weasels. Plots containing less than three mammals of a given species were excluded. In instances where small mammals were too rare for statistical analyses I examined differences in capture rates (counts per 1000 trap nights) between habitats (linear features, forest edge under 50 m, and forest beyond 50 m). One trap night was one trap open on one night. To avoid over-estimating the number of trap nights, a trap that was visited without capture was designated as half a trap night (0.5, Beauvais and Buskirk 1999). I considered three types of count regression models for assessing the statistical significance of edge effects. Count data is often best fit by a Poisson model due to the positive and discrete nature of counts (Cameron and Trivedi 1998). My sample variance

27 was often more than twice the sample mean (data was overdispersed) which indicated that a Poisson maximum likelihood estimator might not be appropriate (Cameron and Trivedi 1998). One modification is negative binomial regression. The negative binomial distribution is a Poisson and gamma distribution mixture which has a more general distribution, does not require equidispersion, and the variance is a quadratic function of the mean (Cameron and Trivedi 1998). Other possible modifications are the zero- inflated Poisson or negative binomial models are often appropriate when models have an overabundance of zeros (Long and Freese 2006). Zero-inflated models generate zeros by two different processes: 1) binomial probability governs the binary outcome of whether a count is zero or has a positive outcome, and 2) a second probability governs whether the rest of it (the positive potentials) will be zero or positive. I used "countfit" in ST ATA to compare performance of regular and zero-inflated Poisson and regular and zero-inflated negative binomial models based on Akaike (AIC) and Bayesian (BIC) information criteria, likelihood ratio tests, and Vuong tests (Long and Freese 2006, StataCorp 2007). Direction and slope of small mammal response to linear features varied by plot, so I used mixed effects models to account for autocorrelation within study plots due to unobserved heterogeneity (Cameron and Trivedi 1998). Mixed effects models included a fixed effect of habitat (forest or pipeline) or distance fromlinea r feature edge (in meters) as well as a random effect for plot. Random effects models either fit random intercepts for each plot, keeping the slopes of responses equal, or they allowed intercepts and coefficients (slopes) to vary randomly (StataCorp 2007). I could not model overdispersion and the random slopes in the same model; mixed effects Poisson models fit random intercepts and random coefficients (slopes) for each plot, whereas negative binomial random effects models fit random intercepts only (StataCorp 2007). Thus, I compared results from random coefficients Poisson models to results of negative binomial random intercept models to test the robustness of response patterns and statistical significance. To assess significance of habitat and edge effects on small mammals I used a = 0.025 (Bonferroni correction) because I used the same abundance data to test for edge effects that I used to test for habitat effects.

28 2.2.4.2 Resource distribution assessment

I examined environmental variable responses to linear feature habitat and proximity to edge. Similar to the small mammal analyses, I employed mixed effects regression models to assess the response of small mammal resources to edge proximity and the presence of linear features while accounting for autocorrelation within plots (Cameron and Trivedi 1998). I analysed linear feature effects on shrub stem counts (food stems and all live stems together), volume of downed woody material, and percent ground cover of forbs, moss, live graminoids (grasses and sedges), graminoid litter, leaf litter, shrubs, fungus and bare ground. Analyses of the effects of distance to edge on resources included canopy variables to control for canopy influence on understory characteristics (McKenzie et al. 2000, MacFarlane 2003) using percent canopy closure and type class of trees. Linear features had no canopy, thus these covariates were not included when examining cleared habitat effects. Canopy variables used as covariates were derived from Geographical Information System (GIS) map layers for the Hay River Lowland Ecoregion in the vicinity of Fort Simpson, NT (Forest Management Division 2008) and for the Chinchaga Forestry Region northwest of Manning, Alberta (Manning Diversified 2005). In order to use cross-sectional linear regression models with plot as a random effect (xtreg), vegetation variables were normalised for each study area separately with InskewO or bcskewO in ST ATA, which selects the best normalisation out of a range of possibilities (StataCorp 2007). The InskewO command took the natural log of each variable plus a constant to produce a new variable with skewness nearly zero. The bcskewO command returned an exponential transformation of the variable plus a constant. Normalised variables were then standardised by their mean and variance, such that the mean was zero and the standard deviation was one (StataCorp 2007). Percent ground cover of live and litter graminoids in NT, and fungi, bare ground, and moss in both study areas had too many zeroes for normalisation. Thus, I examined occurrence of those characteristics with respect to linear features and proximity to edge, followed by regression of normalised non-zero values for each variable. I also analysed linear feature effects on counts of ground berries, shrub berries, and total berries, and biomass of epigeous fungal sporocarps. I compared linear feature, edge (<50 m) and interior

29 (between 250 m and 295 m fromedge ) berry counts and fungal biomass using Poisson or negative binomial and normal regression, respectively. To assess significance of habitat and edge effects on vegetation I used a = 0.025 (Bonferroni correction) because I used the vegetation data to test for edge effects that I used to test for habitat effects.

2.3 Results

2.3.1 Small mammal abundance relative to pipeline habitat and edge proximity

In NT over 6331.5 trap nights in 2005 I captured 659 individuals of nine species of small mammal (Fig. 2.4, Fig. 2.5). Red-backed voles were the most numerous (58% of individuals captured, n = 383), followed by meadow voles (21% of captures, n=141) and deer mice (12% of captures, n=81). I also captured shrews (Sorex spp., n=21), least chipmunks (Eutamias minmus, n=17), meadow jumping mice {Zapus hudsonicus, n=13), one eastern heather vole {Phenacomys ungava), one taiga vole {Microtus xanthognathus), and one short-tailed weasel {Mustela erminea). In NT in 2006 transects 5,10 and 15 were not trapped, nor were transects 2,4, 6, 8, 12, and 14 trapped in FERR, resulting in lower number of trap nights than in 2005. In NT in 2006 over 4843 trap nights I captured 777 individuals of eight species of small mammal (Fig. 2.4a, Fig. 2.5a). Counts of red-backed voles were again highest (63% of captures, n=486), followed by deer mice (17% of all captures, n=145) and meadow voles (10% of captures, n=77). I also captured shrews (n=26), least chipmunks (n=20), eastern heather voles (n=6), red squirrels {Tamiasciurus hudsonicus, n=14) and one short-tailed weasel. Skull analysis of NT red-backed voles indicated that most were northern red-backed voles, Myodes rutilus (50 to 55 of 60 skulls), as only one had the complete post-palatal bridge of a southern red-backed vole {Myodes gapperi) and two had upper molar patterns similar to southern red-backed voles (Appendix B). Based on tooth analysis of 42 NT shrews (Appendix B), I caught largely masked shrews {Sorex cinereus, n=38) with few arctic and montane shrews {Sorex arcticus, n=3 and Sorex monticolus, n=l, respectively). In northern Alberta over 1332 trap nights in 2007 I captured 598 small mammals of five species (Fig. 2.4b, Fig. 2.5b). Most of these were southern red-backed voles (60% of captures,

30 n=361), followed in abundance by deer mice (32% of captures, n=192), shrews (3% of captures, n=20), meadow voles (3% of captures, n=17) and eastern heather voles (1% of captures, n=7). Tooth analysis of 12 AB shrews revealed that most were S. cinereus (n=6), followed by S. monticolus (n=4) and S. arcticus (n=2). Independent small mammal surveys in the Fort Simpson, NT area showed a similar pattern of increasing captures from 2005 to 2006 (Government of the Northwest Territories 2008) and capture rates were lower than mine. The independent survey captured less than 8 animals per 100 trap nights overall, compared to my capture rates of 10 animals or more per 100 trap nights overall. In the Fort Simpson area in 2007, the year I conducted my study in AB, NT small mammal abundance declined from its peak in 2006 (Government of the Northwest Territories 2008). Thus the overall higher capture rate in my AB study area in 2007 (similar to 44 small mammals per 100 trap nights) was unrelated to temporal changes in NT small mammal populations, and may have been influenced by local habitat, climate, or landscape effects in AB. Negative binomial models were generally more appropriate than Poisson and zero-inflated models for rodent, shrub stem, and berry counts, thus for simplicity I do not report zero-inflated models. Mixed effects Poisson models (random intercepts and slopes for plot effects) yielded similar response directions and significance to negative binomial models (random intercepts only). Thus, results are normally reported only for the negative binomial models with few exceptions (see tables 2.1 to 2.4 for more detail). I also compared random intercept regression to mixed effects random coefficient

31 MEDV DERM REBV Species

MEDV DERM REBV Species

Figure 2.4. Mean capture rates (counts per 1000 trap nights) of the three most common small mammal species on linear features and in the forest in a) NT (2005 and 2006 combined), and b) AB (2007). Error bars are 95% confidence intervals from the averages and standard deviations of plot capture rates by habitat. MEDV = meadow vole, DERM = deer mouse, REBV = red-backed vole.

32 Linear feature Forest

I" a) o> 10 I sH I

Q. to 6 c § 5 _o^ a) 4 2 £ 3H 3 -«QJ . (0 24 O C (0

190 180 b) f 170 160 f 150 140 § 130 120 110 100 90 80 70 I 60 50 40 30 I 20 I 10 0 J±L iSHR W HTHV Species

Figure 2.5. Mean capture rates (counts per 1000 trap nights) of less common small mammal species on linear features and in the forest in a) NT (2005 and 2006 combined), and b) AB (2007). Error bars are 95% confidence intervals from the averages and standard deviations of plot capture rates by habitat. MJMP = meadow jumping mouse, STWL = short-tailed weasel, CHIP = least chipmunk, SHRW = shrew, HTHV = eastern heather vole, YCHV = taiga vole, and RESQ = red squirrel.

33 regression for percent ground cover vegetation variables, volume of DWM and fungal biomass. With one exception (AB ground cover of graminoids) the type of regression did not affect the direction or significance of responses, so I report the intercept-only regressions. Regression analysis was not feasible for many small mammal species due to low sample size, so I examined mean counts per trap night (mean capture rates) on linear features and in forest (Fig. 2.5). Mean capture rates in linear features, forest edge, and forest interior, were also compared for common small mammal species to demonstrate the magnitude of difference in small mammal numbers between linear features and forest (Fig. 2.4). Small mammal responses to linear feature habitat that are discussed below are shown in detail in Table 2.1, Fig. 2.4 and Fig. 2.5. Cleared linear features had significantly higher numbers of meadow voles than did forest in NT and in AB. Mean capture rate of meadow voles on linear features was more than 35 times that of forest in NT and 10 times more in AB. Deer mice did not respond significantly to linear feature habitat. Red-backed vole counts were significantly higher in forest than on linear features in both study areas and their capture rate was six times higher in forest than on linear feature in 2005, four times higher in forest in 2006 and five times higher in forest in 2007. Least chipmunks had variable responses to linear features with capture rate higher on linear features than in forest in NT in 2005, but roughly equivalent between habitats in 2006 mostly due to a negative response in one plot (MANN) with high grass cover. Mean shrew capture rate was higher on linear features than in forest in AB in 2007 and possibly in NT in 2005, but roughly equivalent in NT in 2006. Arctic shrews were captured only on linear features in AB (n=2) and mostly on linear features in NT (2/3 specimens). Masked shrews were captured mainly in forest in NT (24/31 specimens), and mainly on linear features in AB (4/5 specimens). One montane shrew was captured in NT forest, and most AB montane shrews were also in forest (3/4 specimens). In NT, only one meadow jumping mouse out of 14 was caught in forest and short-tailed weasels were captured only on the linear feature (n = 2). The taiga vole and red squirrels were captured only in forest. Only one heather vole out of six individuals was captured on linear features (total for NT and AB). "All rodents" had variable

34 responses to linear features. In 2005 NT "all rodents" responded significantly positively to linear features.

35 Table 2.1. Parameter coefficients, standard errors, and/7-values refer to the effect of linear feature versus forest habitat on counts of red-backed voles, deer mice, meadow voles and all rodents with a random effect (intercept) for plot (negative binomial model, xtnbreg). 'All rodents' sums counts of red-backed voles, deer mice, meadow voles, heather voles, shrews, chipmunks, jumping mice, and taiga voles. 'Habitat response' refers to counts in linear feature habitat. P-values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance.

Study area Year Coefficient Standard Error F-value Habitat Response

Red-backed voles NT 2005 1.43 0.291 0.000 Negative 2006 0.980 0.207 0.000 Negative AB 2007 1.53 0.274 0.000 Negative Deer mice NT 2005 -0.171 0.347 0.622 Neutral 2006 -0.0786 0.252 0.755 Neutral AB 2007 0.154 0.246 0.533 Neutral Meadow voles NT 2005 -3.86 0.343 0.000 Positive 2006 -4.05 0.531 0.000 Positive AB 2007 -2.05 0.570 0.000 Positive All rodents NT 2005 -0.753 0.105 0.000 Positive 2006 -0.106 0.114 0.355 Neutral AB 2007 0.524 0.149 0.000 Negative

36 In 2006 NT "all rodents" were equivalent for forest and linear features. In 2007 AB "all rodents" were nearly twice as high in forest as on linear features based on mean capture rates (significant difference based on regression, Table 2.1). Red-backed vole counts in NT were independent of distance to edge though they tended to be higher near edges (Table 2.2 and Fig. 2.6). Both AB 2007 edge proximity responses were non-significant, and the direction of response was opposite depending on the analysis. Negative binomial regression for AB 2007 indicated lower red-backed vole counts near edges, likely due to negative edge responses in plots HILL, STOW and GRIZ, whereas Poisson regression indicated higher red-backed vole counts with edge proximity perhaps due to positive edge responses in POWL and SLIP. Overall, these weak and opposite responses showed that AB red-backed voles responded neutrally to distance from linear features. Deer mouse counts were consistently higher closer to forest edge. Deer mice responded significantly negatively to increasing distance from edge in both study areas in all years. Meadow vole capture rates in forest edge were more than twice that of forest interior in NT and AB. Least chipmunk capture rates were higher in edge in 2005, but roughly equivalent in 2006. Shrew capture rates in forest interior were more than twice that in forest edge in 2006 in NT, but roughly equivalent in NT in 2005 and in AB. 81% (25/31) of the S. cinereus NT forest specimens, and the single AB forest specimen, were captured beyond 50 m from edge. The single arctic shrew found in forest was within 30 m of edge in an open, moist area of moss and spruce in the NT (A. Darling pers. observ.). In NT the S. monticolus specimen was captured 12 m from edge, and in AB three S. monticolus were captured > 65m from edge. The taiga vole was captured 300m from edge in NT. Only one of six heather voles in NT and one of four heather voles in forest in AB were captured within 50 m of edge. Red squirrel distribution in NT in 2006 seemed unrelated to distance from edge. In both study areas "all rodents" abundance was negatively related to distance from edge. This positive edge effect was significant in NT, but not in AB.

37 Table 2.2. Parameter coefficients, standard errors, andp-values refer to the effect of proximity to linear feature edge on counts of red-backed voles, deer mice and all rodents in the forest with a random effect (intercept) for plot (negative binomial model, xtnbreg, except where noted). 'All rodents' are summed counts of red-backed voles, deer mice, meadow voles, heather voles, shrews, chipmunks, jumping mice, and taiga voles. P-values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance. Edge responses are relative to forest edge when in the forest.

Study area Year Coefficient Standard Error P-value Edge Response

Red-backed voles NT 2005 -0.000744 0.000650 0.252 Neutral 2006 -0.00697 0.000618 0.259 Neutral AB 2007 0.00101 0.000525 0.055 Neutral 2007a -0.00103 0.00212 0.626 Neutral

Deer mice NT 2005 -0.00983 0.00219 0.000 Positive 2006 -0.00546 0.00138 0.000 Positive AB 2007 -0.00460 0.000947 0.000 Positive All rodents NT 2005 -0.00226 0.000586 0.000 Positive 2006 -0.00141 0.000542 0.000 Positive AB 2007 -0.000699 0.000448 0.119 Neutral a: Mixed effect Poisson model used to account for variations in slope of response between plots.

38 Predicted count of deer mice Predicted count of red-backed voles .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2

o o

O O Ol

w o

O 8 O

o o

N3 N3 O O O O

o o models withrandominterceptforplot. heather voles,shrewschipmunksjumpingmiceandtaiga)inNT(20052006AB b) deermice,andcallrodents(summecountofred-backevolesmeadow Figure 2.6.Influencofdistancfromlinearfeaturesnpredictedcounta)red-backevoles, (2007). Predictedcounts(numberofevents)werederivefromnegativbinomialregression

Predicted count of ail rodent s O -

Resource responses to linear feature habitat are summarized in Tables 2.3 and 2.5a. Linear features in NT and AB had higher percent ground cover of leaf litter, graminoid litter, live graminoids, bare ground, and forbs than did forest. AB and NT linear features also had higher counts of live shrub stems than forest, though in AB this was non-significant. Linear features in NT and AB had low percent ground cover of downed woody material and moss. Percent ground cover of non-zero fungi was not influenced by linear feature habitat. Counts of berries showed a mixed response depending on the analysis, but tended to be higher in forest than on linear features (xtnbreg). Stem counts of preferred food shrub species did not change significantly on linear features, nor did non-zero biomass of fungi. Linear features in AB had lower percent ground cover of shrubs, whereas NT linear features were shrubbier than forest. Occurrence (presence/absence) of all six rare habitat variables varied significantly between linear feature and forest. In contrast to percent ground cover, occurrence of moss and fungi was significantly lower on linear features, whereas graminoid litter, live graminoids, and bare ground were present on linear features significantly more often than in forest.

41 Table 2.3. Parameter coefficients, standard errors, and/7-values refer to the effect of linear feature versus forest habitat on vegetation with a random effect for plot. Random effects were intercepts only for random effects regression (xtreg or xtnbreg), or intercepts and slopes for mixed effects Poisson regression, xtmepois (StataCorp 2007). Vegetation was measured in 2006 (NT) and 2007 (AB). 'Habitat response' refers to amount in linear feature habitat. P-values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance.

Study area Model Coefficient Standard Error P-value Habitat Response

Downed woody material volume NT xtreg 1.63 0.0751 0.000 Negative AB xtreg 1.59 0.0939 0.000 Negative % Ground cover of shrubs NT xtreg -0.430 0.0951 0.000 Positive AB xtreg 0.865 0.111 0.000 Negative % Ground cover of leaf litter NT xtreg -1.27 0.0827 0.000 Positive AB xtreg -1.11 0.104 0.000 Positive % Ground cover of moss NT xtreg 0.764 0.141 0.000 Negative8 AB xtreg 1.48 0.0927 0.000 Negative8 % Ground cover of fungi NT xtreg -0.929 0.582 0.111 Neutral8 AB xtreg -0.0215 0.120 0.858 Neutral8 % Ground cover of graminoid (grass and sedge) litter NT xtreg -1.75 0.0733 0.000 Positive8 AB xtreg -1.69 0.0835 0.000 Positive % Ground cover of live graminoids (grass and sedge) NT xtreg -1.54 0.0802 0.000 Positive8 AB xtreg -1.40 0.0826 0.000 Positive % Ground cover bare NT xtreg -0.719 0.238 0.003 Positive8 AB xtreg -1.292 0.102 0.000 Positive8 % Ground cover of forbs NT xtreg -0.491 0.0899 0.000 Positive AB xtreg -0.809 0.102 0.000 Positive Count of food shrub stems NT xtnbreg -0.0150 0.0978 0.878 Neutral AB xtnbreg 0.0712 0.114 0.534 Neutral Count of live shrub stems (total lateral cover) NT xtnbreg -0.294 .0678 0.000 Positive AB xtnbreg -0.315 0.0791 0.000 Positive AB xtmepois -0.177 0.464 0.702 Neutral Fungal Biomass (All NT) NT xtreg 0.0973 0.245 0.691 Neutral8 Counts of berries (All NT) Berrv Class Ground xtnbreg 0.536 0.222 0.016 Negative Ground xtmepois 0.439 0.778 0.573 Neutral >50cm xtnbreg 0.781 0.192 0.000 Negative >50cm xtmepois 1.27 1.06 0.230 Neutral Total xtnbreg 0.859 0.178 0.000 Negative Total xtmepois 0.684 0.900 0.447 Neutral a: Percent ground cover or biomass when present (non-zero). See Table 2.5 for presence/absence analysis. Resource responses to distance fromedg e in the forest are summarized in Tables 2.4 and 2.5b. Edge habitat in NT had significantly higher counts of berries growing 50 cm or more above ground and a trend for higher counts of total berries. Volume of downed woody material decreased towards the edge in NT. In contrast, DWM in AB did not respond to distance from edge, whereas percent cover of leaf litter, and moss were negatively influenced (the latter was not always significant) and percent cover of bare ground increased near AB forest edge. In NT and AB, edge proximity had no influence on counts of shrub stems, ground berries (those below 50 cm fromth e ground), nor on percent ground cover of shrubs, live and litter graminoids, or fungi. Non-zero fungal biomass and occurrence (presence/absence) of fungal biomass in NT did not respond to edge proximity. Occurrence of fungi by percent cover also did not respond to edge proximity. Live and litter grass and sedges were too rare in forest for logistic regression to converge on a solution.

43 Table 2.4. Parameter coefficients, standard errors, and/?-values refer to the effect of proximity to linear feature edge on vegetation with a random effect for plot. Random effects were intercepts only for random effects regression (xtreg or xtnbreg), or intercepts and slopes for mixed effects normal (xtmixed) or Poisson regression (xtmepois, StataCorp 2007). Vegetation was measured in 2006 (NT) and 2007 (AB). Edge responses are relative to forest edge when in the forest. P-values considered significant at a = 0.025 (Bonferroni correction for two tests), neutral responses indicate non-significance.

Study area Model Coefficient Standard Error P-value Edge Response

Downed woody material volume NT xtreg 0.00124 0.000383 0.001 Negative AB xtreg -0.000098 0.000360 0.785 Neutral % Ground cover of shrubs NT xtreg 0.000293 0.000444 0.509 Neutral AB xtreg 0.0000927 0.000376 0.806 Neutral % Ground cover of leaf litter NT xtreg -0.000358 0.000408 0.380 Neutral AB xtreg 0.000816 0.000355 0.021 Negative % Ground cover of moss NT xtreg -0.000159 0.000450 0.724 Neutral8 AB xtreg 0.000918 0.000339 0.007 Negative8 % Ground cover of fungi NT xtreg -0.000196 0.00149 0.895 Neutral8 AB xtreg 0.0000300 0.000480 0.950 Neutral8 % Ground cover of graminoid (grass and sedge) litter NT xtreg -0.0000726 0.000566 0.898 Neutral8 AB xtreg -0.000445 0.000332 0.181 Neutral % Ground cover of live graminoids (grass and sedge) NT xtreg -0.000583 0.000690 0.398 Neutral8 AB xtreg 0.000177 0.000334 0.595 Neutral % Ground cover bare NT xtreg -0.00628 0.00400 0.116 Neutral8 AB xtreg -0.0000788 0.000221 0.000 Positive8 % Ground cover of forbs NT xtreg 0.000249 0.000438 0.570 Neutral AB xtreg 0.000758 0.000368 0.039 Neutral AB xtmixed 0.000433 0.000547 0.428 Neutral Count of food shrub stems NT xtnbreg -0.000110 0.000458 0.809 Neutral AB xtnbreg -0.000328 0.000367 0.372 Neutral 50cm xtnbreg -0.828 0.185 0.000 Positive Total xtnbreg -0.600 0.166 0.000 Positive Total xtmepois -0.680 0.361 0.060 Neutral a: Percent ground cover or biomass when present (non-zero). See Table 2.5 for presence/absence analysis.

44 Table 2.5. Parameter coefficients, standard errors, and/?-values refer to the effect of a) linear feature versus forest habitat, and b) distance from edge when in forest (exception: fungal biomass compared 3 m from edge to 250 m from edge) on presence/absence of vegetation with a random effect (intercept) for plot (xtlogit). Vegetation was measured in 2006 (NT) and 2007 (AB). 'Habitat response' refers to presence in linear feature habitat. Edge responses are relative to forest edge when in the forest. NA indicates "not applicable" because variable caused failure of logistic regression. P-values considered significant at a = 0.025 (Bonferroni correction for two tests) and neutral responses indicate non-significance. sL Study area Coefficient Standard Error P-value Habitat Response

% Ground cover of moss NT 3.07 0.245 0.000 Negative AB 3.66 0.477 0.000 Negative % Ground cover of fungi NT 1.72 0.598 0.004 Negative AB 2.56 0.476 0.000 Negative % Ground cover of litter graminoid (grass and sedge) NT -5.66 0.541 0.000 Positive % Ground cover of live graminoids (grass and sedge) NT -5.57 0.610 0.000 Positive % Ground cover bare NT -4.25 0.316 0.000 Positive AB -3.27 0.361 0.000 Positive Fungal Biomass NT 0.866 0.329 0.008 Negative

b) Study area Coefficient Standard Error P-value Edge Response

% Ground cover of moss NT 0.00211 0.00176 0.231 Neutral AB 0.0158 0.00734 0.031 Neutral % Ground cover of fungi NT 0.00134 0.00155 0.388 Neutral AB 0.000291 0.000963 0.763 Neutral % Ground cover of litter graminoid (grass and sedge) NT NA % Ground cover of live graminoids (grass and sedge) NT NA % Ground cover bare NT NA AB NA Fungal Biomass NT 0.566 0.370 0.126 Neutral

45 2.4 Discussion

2.4.1 Small mammal and resource responses to linear features

My results demonstrate that linear features alter small mammal communities in the boreal forest. In most cases, linear feature habitat was used by generalists and open- habitat specialists and avoided by forest specialists. Positive edge effects for open- habitat specialists suggest a "spill over" effect while forest species generally had no response to edge. Generalist deer mice appeared to exhibit an emergent positive edge effect. Response of total small mammal abundance varied between regions with positive or neutral linear feature habitat and edge effects in the Northwest Territories and a negative habitat effect and no edge effect in Alberta. Meadow voles had a clear preference for pipeline ROWs and other features in both regions. Similar to other disturbed habitat (Kaminski et al. 2007) linear features had more understory cover due to higher cover of grass and sedges, leaf litter, forbs, and, on some NT plots, more shrubs. While I only measured a limited subset of resources required by small mammals, there was little evidence that structural or food resources used by meadow voles were enhanced near edges compared to further into the forest. Thus, increased abundance of meadow voles in the edge seems to result from spillover which is further supported by the fact that meadow voles in the forest were captured exclusively within 50 m of the edge. If I had only trapped in forest, it would have seemed this was an emergent positive edge effect, thus data collected from both adjacent habitats is essential in understanding edge effects for meadow voles. Meadow voles were not the only grassland-associated small mammals found on pipelines; linear features seem to be highly suitable habitat for many species. Virtually all meadow jumping mice were captured on linear features while shrews, least chipmunks, and weasels also tended to be more abundant on the linear feature than in forest. In the case of least chipmunks, linear features were avoided if they lacked shrubs and were covered mostly by grass (plot MANN). Similar to my results, Boonstra and Krebs (2006) caught weasels mostly in open areas in Kluane, Yukon, and short-tailed weasels can be found in boundaries of meadows (Banfield 1974). Within shrews, most

46 S. arcticus, an open habitat specialist (Bowers et al. 2004), were collected from linear features. Open grassland habitat in the boreal is relatively rare. Normally, species like the meadow vole have to rely on natural graminoid patches created by fire, drying creek beds, or beaver ponds for habitat (Douglass 1976,1977, Wright et al. 2002, Pohlman et al. 2007). The common element of the natural habitats and linear features is that they have high herb and grass cover (Douglass 1976, Terwilliger and Pastor 1999). Unlike linear features, however, most natural meadows in the boreal are periodically flooded (Douglass 1976, 1977, Pohlman et al. 2007). Linear features thus represent new, preferred, and relatively permanent open habitat in the boreal forest that has no natural analogue (Schneider 2002). The consequences of such changes in habitat permanency are unknown. Deer mice were captured in the forest and on linear features in near equal proportion, consistent with their reputation of opportunistically exploiting a wide variety of habitats (Moses and Boutin 2001, Witt and Huntly 2001). Deer mice also increased significantly and consistently with proximity to forest edge in both regions. Edge proximity also appeared to positively influence least chipmunks in 2005, but not in 2006. The latter neutral response might be a case where statistical power to detect an edge effect is low, as least chipmunks inhabit forests and are thought to prefer edge or open areas (Hamilton and Whitaker 1979) with shallow leaf litter (Kaminski et al. 2007). Why deer mice had such a consistent edge response across their entire range is unclear. Spillover effects from the pipeline seem unlikely as deer mice are less abundant but still reasonably common in the forest interior. Based on the resources examined, there was little evidence that edges supported enhanced resources required by deer mice, i.e., only shrub berries were consistently higher at edges. Concurrent studies on arthropods showed that biomass of some orders of shrub-associated insects was higher near forest edges than in the forest interior in both study areas, but total biomass in NT was not (Marenholtz 2007, unpublished, Bayne et al. 2008, unpublished). As deer mice are considered somewhat arboreal (Miller and Getz 1977, Barry et al. 1984), and capable insect predators (Dyke 1971, Langley 1994) they could have responded positively to edges because of these increases in shrub-level food resources. Another concurrent study on my plots found elevated temperatures near edges (Blake 2006, unpublished)

47 which could produce the xeric habitat preferred by deer mice (Dyke 1971, Banfield 1974, Miller and Getz 1977, Morris 2003). However, in light of the more numerous neutral resource responses to edge proximity (e.g., DWM in AB, understory cover, forbs in NT, some berries), and decreases in some forms of cover (leaf litter and moss in AB, and DWM in NT) edges cannot be a more resource rich habitat for deer mice per se. Instead, some resources used by deer mice were unevenly distributed between forest and linear features. The forest had high protective cover (trees and DWM), as well as higher amounts of some food sources (berries and fungi), whereas foraging opportunities were high on linear features due to increased leaf litter, forbs, grass, and biomass of shrub associated arthropods (biomass of shrub insects was on average 39% higher on linear features than in the forest edge or interior, Marenholtz 2007, unpublished). Thus, deer mice might be occupying the edge to access the complementary resources that exist between the linear features and forest (Ries and Sisk 2004). Of the deer mice I captured on or within 50 m of the linear feature, 80 were recaptured at some other location. Of these, 41 % were captured in a habitat other than that of their initial capture suggesting movement between linear feature and forest might be common and that highly mobile deer mice have the ability to access spatially segregated resources (Dyke 1971). Red-backed voles were far less abundant on linear features than in the forest, with the greatest magnitude of difference occurring in AB. The paucity of red-backed voles on linear features can be due to the decrease in suitable cover (DWM, shrubs in AB, and canopy trees) and food (fungi and berries) on linear features as compared to forest. Ground cover of shrubs was also lower on linear features in AB. The abundance of red-backed voles in NT was not significantly correlated with distance from edge while in AB there was a trend for less red-backed voles near edge. However, the edge effect was dependent on the statistical model used to represent the data suggesting strong site to site variation. Red-backed voles in NT appear unaffected by decreases in DWM near edge, despite strong associations with DWM in other studies (Carey and Johnson 1995, Fuller et al. 2004, Pearce and Venier 2005). In AB and NT, fungi, a supposed important food source (Dyke 1971, Maser et al. 1978, Mills 1995), and many other resources did not respond to distance from edge. That said, moss and leaf litter cover were significantly lower near edges in AB, and bare ground was more prevalent suggesting a

48 drier, more open, and hotter edge environment less hospitable to forest species (Dyke 1971, Miller and Getz 1977, Orrock et al. 2000, Odor and Standovar 2001, Fuller et al. 2004, Hylander 2005). In general, linear features appear to be extremely poor habitat for red-backed voles as compared to forest, but there was little evidence of a decline in resource availability near edges. Other forest species also responded negatively to linear features possibly indicating that habitat quality is reduced on linear features for forest species due to reductions in cover and food resources. Red squirrels were captured only in forest. Heather voles were present on linear features in low numbers suggesting brief movements from preferred forest habitat into cleared areas (Tattersall et al. 2002). Shrew capture rates in forest interior were higher than those in forest edge in NT in 2006 and in AB, despite higher capture rates on linear features. The negative edge response for shrews could be due to higher temperatures and lower humidity (Blake 2006, unpublished, Murcia 1995, Clayton 2003), lower leaf litter (Greenberg et al. 2007), and (or) lower ground arthropod biomass (Blake 2006, unpublished, McCay and Storm 1997) in forest edge, all of which can reduce habitat suitability. Species differences in resource and habitat preferences could explain the contrasting trend of higher shrew capture rate on linear features yet lower numbers at edges. Shrew captures in forest seemed to be mostly S. cinereus and S. monticolus, which prefer dense low cover and moist forests, in contrast to shrew captures on linear features which also included S. arcticus, known to inhabit open areas (Bowers et al. 2004, Burt and Grossenheider 1976). Alternatively, increased arthropod biomass (Marenholtz 2007, unpublished) and leaf litter cover on linear features could have compensated for reductions in microclimate quality on linear features, whereas no compensation occurred to counter microclimate degradation in forest edge. The greater negative impact of linear features on small mammals in AB relative to NT could be due to the fact that AB linear features were younger disturbances with lower cover of shrubs and critical resources in general as compared to the older, more regenerated linear features in NT. Cumulative impacts in AB could also play a role due to the greater abundance of linear features in that area. Another possible explanation relates to red-backed voles, the most numerous species in both study areas. M. gapperi

49 was the only species captured in AB, and M. rutilus dominated NT captures based on skull analysis (Appendix B). Whitney (1976) classified M. rutilus as having a broader niche than M. gapperi, and able to exploit a wider variety of different habitats like a "Peromyscus of the north" in terms of dietary habits, although both species seem to be negatively affected by linear feature development and grass-dominated habitat (Douglass 1976, Macdonald et al. 2006). M. rutilus in NT might have been better able to deal with reduced food and cover resources on linear features than could more specialised M. gapperi of AB, leading to more negative impacts in AB.

2.4.2 Implications for boreal forest ecology

Changes in small mammal distributions could lead to novel or intensified species interactions (e.g., predation) with effects possibly cascading throughout the forest community to other trophic levels (Ries et al. 2004). For example, predators of small mammals such as weasels, owls and foxes (Banfield 1974) could be attracted to linear features because they had higher local abundance of small mammals in NT, and consistently higher numbers of meadow voles across the northern boreal forest. Meadow voles might be preferred prey because they use open habitat, are palatable, and are highly colonial, which leaves them in high-payoff clumps (Banfield 1974, Day 1968, Boonstra and Krebs 2006). For example, in the Kluane region of the Yukon, weasels tended to use open habitat and their use of open habitat was strongest in years when Microtus spp. were plentiful (Boonstra and Krebs 2006). Red-backed voles, by contrast, are non- colonial, tend to have low local densities, and are spread out over the landscape (Hanski and Henttonnen 1996). Red-backed voles and deer mice are likely more difficult to catch than meadow voles for most predators (Miller and Getz 1977, Barry et al. 1984, Wilson and Ruff 1999, Boonstra and Krebs 2006) possibly due to less effective predator avoidance behaviour of meadow voles (Peles and Barrett 1996). Predators subsidized by easy meadow vole prey on linear features could spill over into forest edges (Lidicker 1999, Cantrell et al. 2001, Ries and Sisk 2004) and have negative impacts on forest specialists such as red-backed voles, or on sensitive species such as songbirds (Batary and Baldi 2004, Boonstra and Krebs 2006, Cain et al. 2006). This can occur in particular after vole numbers reach peaks, and then crash, forcing

50 predator populations "fattened" (i.e., reached high numbers) on small mammals to enter adjacent forest patches to search for alternate prey (Huhta et al. 1998) when small mammals are scarce (Drost and McCluskey 1992, Redpath 1995). Negative spill over effects on alternate prey likely occur in NT mixedwood forest where small mammals reached their highest relative abundance on linear features as compared to forest. Whether this has a large landscape effect is less clear because much of the landscape impacted by linear features is an entirely different habitat. Other more predominant habitats, such as bog with black spruce and a marshy understory, might have a reduced contrast between native and disturbed habitats and thus weaker linear feature and edge effects. Alternatively, if predators have strict dietary or habitat requirements, forest disturbance could lead to decreased predator populations. Habitat disturbance could reduce forest prey populations to densities that are unable to support forest specialist predators like marten predators (Carey et al. 1992, Taylor and Buskirk 1994). Negative impacts of linear features on predators of small mammals are a possibility in highly fragmented northern AB, where total small mammal counts on linear features were significantly lower than those in the forest, and red-backed voles tended to be low near edges. Small mammals in disturbed habitat can also act as predators and negatively impact nesting birds directly through predation (Bradley and Marzluff 2003). In particular, deer mice are agile climbers (Barry et al. 1984) and common nest predators (Bayne and Hobson 1997, Bradley and Marzluff 2003, Cain et al. 2006), thus increased deer mice in edge habitat could lead to increased nest predation. The increase in small mammals on linear features is mostly due to herbivorous meadow voles (Zimmerman 1965, Banfield 1974, Ostfeld et al. 1999). Thus indirect effects of small mammals on songbird predation are more likely than direct effects, through predator attraction to linear features or edges. Small mammal species differ in food resource use, and could thus have different impacts on forest recovery. Meadow voles have been shown to have significant negative impacts on herbaceous plant, seedling, and tree regeneration (Ostfeld et al. 1999, Cadenasso and Pickett 2000) to the point of creating plant communities of unpalatable species (Howe et al. 2006). The permanency of linear features might be facilitated by

51 open-habitat species like the meadow vole that can prevent forest recovery. Deer mice are also significant seed predators (Sullivan 1979) and can thus negatively impact plant establishment. On the other hand, deer mouse seed caches can be important for plant dispersal and can place seeds in favourable growth environments (Vander Wall et al. 2005). Red-backed voles are important for timber production because they disperse ectomycorrhizal fungi spores needed by conifers for vigorous growth much more often than do other boreal forest small mammals (Terwilliger and Pastor 1999). Red-backed vole avoidance of cleared areas such as linear features could therefore result in slower tree regeneration if fungal spores were limiting (Terwilliger and Pastor 1999). However, red-backed vole avoidance of linear features might be a summer phenomenon due to interspecific competition with meadow voles, and could disappear in the non-breeding season (Iverson and Turner 1972), at which time spores could be dispersed into cleared habitat. It appears that linear features can support small mammal communities in high abundance as long as critical food and cover resources remain readily accessible, which validates the predictive framework developed by Ries et al. (2004). However, the permanency of these small mammal communities is less clear. Fitness measures are required to test whether or not abundance of different species is high because linear features represent high quality habitat with high fitness for individuals, or if abundance is high due to linear features acting as sinks, and attracting ultimately doomed small mammals or (Pulliam 1988, Johnson 2007). Deer mice appeared to prefer edge habitat, but many studies have found that areas with high densities corresponded with lower fitness for Peromyscus spp. and other species (Morris 1989, Menzel et al. 1999, Ries and Fagan 2003, Wolf and Batzli 2004). On the other hand, linear features are probably high quality habitat for meadow voles and meadow jumping mice, as the treed portions of forest are not suitable habitat and have lower fitness(Banfiel d 1974, Douglass 1976, Terwilliger and Pastor 1999). The permanency of habitat effects is somewhat unclear, as edge effects can dissipate as cleared areas and edges change via aging (Tallmon and Mills 2004). However, linear features allow access to forest and this access promotes continued disturbance such that age of linear features is reset periodically, much like agriculture

52 and unlike forestry. For instance, up to 30% of the length of forest cut in a given year in Alberta can be on existing lines (Schneider 2002, MacFarlane 2003). Furthermore, Matlack (1993) found that climatic alteration in edge decreased over time but was still distinguishable from continuous forest even after side canopy regrew (which can take 20 to 40 years). Linear features appear to affect small mammals in similar ways as other anthropogenic features (e.g., clearcuts or agriculture), with increases in grassland species and generalists and decreases in forest specialists in cleared habitat and in edge. However, key differences (i.e., longevity, periodic disturbance, high availability of resources, long and narrow shape) could make linear features more suitable for permanent 'invasion' by typically rare species than other types of clearings (Getz et al. 1978). Ries and Sisk's (2004) mechanistic framework of edge effects based on resource distribution was effective in predicting small mammal responses to linear features and proximity to edges. Linear features caused a change in small mammal communities from forest dominated by red-backed voles to open habitat and edge dominated by meadow voles and deer mice. Linear feature increasing pervasiveness in the boreal forest (Schneider 2002) makes it important to understand the long term effects of such shifts in the small mammal community. Changes in small mammal communities have ramifications for linear feature regeneration, predator dynamics, and predation on alternate prey such as boreal forest songbirds.

53 CHAPTER 3: STABLE ISOTOPE ANALYSIS POTENTIAL FOR IDENTIFYING LINEAR FEATURE RESOURCE VALUE

3.1 Introduction Knowing where in a landscape a species chooses to forage is a critical component of understanding habitat selection (Arthur et al. 1989, Burke andNol 1998, Mysterud et al. 1999). While conventional methods of mapping habitat use such as radio-telemetry or trapping tell us where animals spend the greatest amount of time, they provide little information on the resources gained in these habitats. In contrast, stable isotope analysis (SIA) is a tool used increasingly by animal ecologists to provide information on long- term diet, as well as indicate where in a landscape an animal is obtaining resources (McCutchan et al. 2003, Bearhop et al. 2004, Marra et al. 1998). Stable isotope analysis can be used to trace animal use of disturbed habitats because human disturbance can create unique chemical identifiers that can be traced up the food chain. Habitat disturbance can alter processes involved in nutrient cycling and isotope fractionation (change in isotope composition) and lead to lasting changes in stable isotope ratios (Nadelhoffer and Fry 1988, France 1996, Evans and Belnap 1999, Boeckx et al. 2006). Human settlements and agriculture create very distinct isotope signatures that in some cases can last for centuries, or even millennia (Commisso and Nelson 2008, Shahack-Gross et al. 2007, Boeckx et al. 2006, Kendall et al. 2007). Agriculture effects on carbon and nitrogen isotope ratios arise from intense, prolonged disturbance (Commisso and Nelson 2008), inputs such as manure or fertilizer (Shahack- Gross et al. 2007, Kendall et al. 2007), or the type of crop grown (Boeckx et al. 2006). Harvesting forests for timber can also create unique isotopic signatures, though these signatures are often less pronounced than in agro-ecosystems likely because forestry creates short-lived disturbances that regenerate quickly (France 1996, Hanski et al. 1996, Schweiger et al. 2000, Schieck and Song 2006). Forestry also does not disturb the soil to the same extent as urbanization or agriculture. Forestry effects on carbon and nitrogen isotopes relate more to removal of canopy trees (France 1996), reduction in leaf

54 litter (Nadelhoffer and Fry 1988), and soil microbial process changes brought about in disturbed, dry, windy, and hot soils of open areas (Pardo et al. 2002, Burton et al. 2006). Foods with unique isotopic signatures could differ in abundance among habitats, leading to changes in isotopic signatures of opportunistic consumers capable of switching their diets to accommodate locally available foods (Codron et al. 2006, Nakagawa et al. 2007). For example, Nakagawa et al. (2007) found that omnivorous rodents occupied higher trophic levels (had elevated 515N values) in highly degraded forests where insect prey (a higher trophic level than their usual plant diet) were more abundant compared to primary and less degraded forest. Linear features are being created in many areas of the world, particularly in the boreal forest of western Canada (Forman and Alexander 1998, Schneider 2002). Similar to agriculture or urban development, linear feature soil is highly disturbed. When pipelines are built, soil is tilled to a depth of approximately 2m, and vegetation removed to bare soil or significantly altered to clear a 25-m-wide ROW (Burgess and Harry 1990). Soil compaction often occurs due to the weight of equipment used on such features (Revel et al. 1984, Lee and Boutin 2006). Unlike agriculture and more like clearcuts, however, linear disturbances are allowed to regenerate slowly (Revel et al. 1984, MacFarlane 2003). Linear features thus are an intermediate disturbance between forestry and agriculture and potentially have stronger unique chemical isotope signatures than clearcuts. The possible existence of a stable isotope 'linear feature signature' has broad implications for tracing the impacts of linear features on boreal forest food webs. For example, with a few non-invasive sampling events like hair snares or feather clippings (Gannes et al. 1997), a linear feature signature could enable tracing of resource acquisition by animals from pipeline habitat. This could greatly enhance our ability to trace the extent of the ecological footprint caused by linear features and provide fundamental insights into terrestrial food webs in disturbed ecosystems. The two isotopes typically used in understanding animal foraging and habitat use at smaller spatial scales are heavy carbon (13C) and heavy nitrogen (15N). Stable isotope results are expressed with delta notation depicting the difference (8) between the ratios of

55 the sample and ratios of a standard expressed in parts per thousand (%o, per mil), as shown by the equation:

& /JWL.^ 1000 XR standard ; where X is 13C or 15N and R is an isotopic ratio: 13C:12C or 15N:14N. To predict linear feature effects on stable isotope ratios of carbon and nitrogen I examined previous work in areas disturbed by agriculture and forestry. Enrichment in C tends to occur in open areas compared to closed canopy forest due to understory re-use of respired ( C depleted) CO2 fromdecompositio n of litter, photosynthetic discrimination against C at low light levels (France 1996, Buchmann et al. 1997), and less C discrimination under conditions of water stress (Hogberg et al. 1995) such as occurs in dry open areas. The effect of forest disturbance on 15N enrichment is more variable; some studies have found enrichment of 15N, others no change, and still others depletion, depending on the form of nitrogen used by the plant (nitrate or ammonium) and on changes induced in the soil nitrogen cycle (Pardo et al. 2002, Sah and Ilvesniemi 2006, Sah et al. 2006, Jerabkova and Prescott 2007). In general, natural processes that lead to nitrogen loss (e.g., ammonia volatilization, nitrification followed by leaching and denitrification, or denitrification) discriminate against 15N, leaving N remaining in the system enriched in 15N (Hogberg et al. 1995, Dawson et al. 2002). Often forest disturbance leads to enrichment of ,5N through such losses. Though 15N enrichment seems common in disturbed habitats, nitrogen cycles are complex (Hogberg 1997), and several factors could lead to 15N depletion on linear features such as addition of fertilizer (Hogberg et al. 1995, Kendall et al. 2005), early age and decay state of linear feature soil as compared to forest (Nadelhoffer and Fry 1988, Hobbie et al. 2005, Sah and Ilvesniemi 2006), or soil compaction (Tan and Chang 2007). Nitrogen fixing legumes such as clover {Trifolium spp.), peavine (Lathyrus spp.) and vetch (Vicia spp.) could potentially lead to depletion in 15N (Stock et al. 1995, France and Schlaepfer 2000, Marshall et al. 2007) as well, thus changes in distribution of legumes in response to habitat disturbance could influence isotope values. My objective was to determine if distinct stable isotope signatures of carbon and nitrogen existed on linear features as compared to undisturbed boreal forest. I also

56 assessed the potential of such signatures for tracing wildlife use of linear feature food resources. I examined whether or not small mammals potentially obtaining resources from linear features had unique isotopic signatures relative to forest-dwelling small mammals regardless of food types consumed. My main focus was on red-backed voles, meadow voles, and deer mice. Stable isotope analysis at the baseline of boreal forest food webs was needed to determine whether any observed differences in small mammals were due to changes in diet or changes in isotope signatures between habitats. I predicted that plant, soil, fungal, and small mammal tissues collected from linear features would be enriched in 13C (high 513C), and possibly enriched in 15N (high 515N), relative to specimens from forest.

3.2 Methods

3.2.1 Study areas and study design

See Chapter 2 for detailed methods. Additional red-backed vole, deer mouse, and meadow vole samples were collected from the NT study area in June 2007.

3.2.2 Stable isotope sample collection

I collected specimens of plants, fungi, soil, insects and small mammals from linear feature, edge and forest interior locations. I defined edge as forest within 50 m of the interface between forest and linear feature, and forest interior as forest 250 m or more from the forest-linear feature interface. Plants, arthropods, and fungi selected for SIA were widespread in the study areas, and were believed to be preferred rodent food items (Norrie and Millar 1990). Arthropod larvae were collected for a concurrent study (Marenholtz 2007, unpublished) and from this collection I selected specimens falling into the aforementioned habitat categories. Small mammals that died in traps were collected opportunistically during abundance studies (see methods in Chapter 2). Sample sizes of the three numerically dominant species (red-backed voles, meadow voles, and deer mice) were augmented in the NT study area by euthanizing live-trapped animals in a knock-down canister filled

57 with gas (halothane in mineral oil). In 2006 and 2007 snap-traps were set at a subset of interior NT locations to augment collection. Leaves and petioles from seven plant species were analyzed to search for a baseline signature difference between forest, edge and linear feature habitats. All seven species were collected in AB: aspen, bunchberry (Cornus canadensis), prickly rose, creamy peavine (Lathyrus ochroleucus), dewberry, assorted grasses, and low-bush cranberry. Only aspen, bunchberry, rose, and peavine were collected in NT. Peavine is also a nitrogen fixer (Johnson et al 1995), and was examined as a possible confounding factor in interpreting isotope differences between linear feature and forest locations. For fungi I used mostly Leccinum spp. because it was common and a preferred small mammal food item (Norrie and Millar 1990). Soil was collected in NT by digging and removing the layer from 0 to 10 cm, and in AB by taking a soil core from 0 to 15 cm. I attempted to collect two samples of fungi, soil, and each plant species from the three habitat categories in each plot. I also prepared two whole-body samples of insect larvae in each of two orders (Lepidoptera and Coleoptera) for each habitat category and plot combination. To avoid contamination of samples I wore gloves and collected leaves, invertebrates and fungi a few meters away fromtra p locations. Samples were sealed in Ziploc® bags, paper coin envelopes, or Whirl-Paks®. Small mammals, fungi, and plants were preserved by freezing at - 4°C within 8 hours of collection, then at -20°C at the University of Alberta. Arthropods were preserved in 70% ethanol. Small mammal samples were also frozen at -80°C for four days to destroy the viability of Echinococcus multilocularis eggs (Hildreth et al. 2004).

3.2.3 Stable isotope procedures

Biopsied small mammal hind leg muscle was soaked in a 2:1 chloroform:methanol solution for 10 to 12 hours, then rinsed three times with fresh solution to remove 13C-depleted lipids and obtain unbiased C isotope values with reduced intersample variability (Beaudoin et al. 1999, Pinnegar and Polunin 1999, Post 2002). All samples were dried in preparation for stable isotope analysis. Small mammal muscle was freeze-dried for 24 hours or less, and fungi, soil and plant tissues were oven- dried for seven days at 70 °C. Arthropods were oven-dried at 40 °C until they reached a constant weight (Marenholtz 2007, unpublished). Soil was sifted through a 1-mm sifter after drying to remove large organic matter and stones. Tissues were ground into fine, homogenous powder in a bearing-ball mechanical grinder, and loaded into 6x4 mm sterile tin capsules which were folded and compressed to seal in the sample. Small mammal, insect, fungal and soil samples were analyzed by Stable Isotope Facilities, Department of Soil Science, University of Saskatchewan (Saskatoon, Saskatchewan, Canada) and combusted at ~ 1800°C using an ANCA g/s/1 sample preparation unit coupled to a Tracer/20 mass spectrometer (Europa Scientific of Crewe, U.K.). Plant samples were combusted by the Biogeochemical Analytical Laboratory in the Department of Biological Science at the University of Alberta using a Model 440 Elemental Analyzer (Control Equipment Corporation). Stable isotope results are depicted as the difference (8) between the ratios of the sample and ratios of a standard. PeeDee Belemnite limestone was the standard for 813C thus carbon stable isotope ratios of terrestrial organisms are usually negative (Coleman and Fry 1987). Atmospheric nitrogen gas was used as the standard for 815N, thus nitrogen isotope ratios are usually near zero for plants, and above zero for animals (Paszkowski et al. 2004). Measurement error of stable isotope analysis equipment was quite small: ± 0.1 per mil precision for 813C and ± 0.3 per mil precision for S15N.

3.2.4 Data analysis

3.2.4.1 Linear feature isotopic signatures

Stable isotope ratios of carbon (813C) and nitrogen (815N) of plants, fungi, soil, small mammals, and arthropods were compared between habitats using fixed effects regression analyses in STATA (StataCorp 2007). Sample sizes were too low to allow use of plot as a random effect in mixed effects models. Analyses were conducted separately for each study area rather than with study area as a fixed effect to 1) test the generality of the linear feature signature, and 2) because some sample groups were unevenly distributed among habitats in both study areas. Analyses were conducted separately for each species, genus or order, or with 'species' as a covariate when examining groups (i.e., all plants, arthropods, and small mammals).

59 Carbon and nitrogen isotope ratios were examined for normality prior to running regressions and normalised with InskweO (StataCorp 2007) as needed. When samples were available in only one forest habitat (edge or interior) I compared forest to linear feature only, and did not examine edge influences. Results are reported as enrichment factors based on the difference in mean isotope ratio values between linear features and forest, with forest as the reference condition. Normalised variables were back- transformed prior to calculating enrichment factors. Mean predicted isotope ratios in forest interior, edge and on linear features were used to generate Fig. 3.1.

3.2.4.2 Distribution of legumes

To determine whether legumes affected linear feature nitrogen isotope signatures, I followed procedures similar to those in Chapter 2 to analyse the influence of linear feature versus edge versus forest interior habitat on percent ground cover of legumes and stem counts of alder and tall legumes (clover, vetch, and peavine). Percent ground cover of legumes was normalized and standardized, and I compared results of random intercept only and random slopes mixed effects regression. Counts of alder and legume stems were low, so I used countfit in STATA (StataCorp 2007) to determine when to use Poisson and negative binomial models. I compared regular regression and mixed effects regression. All analyses included canopy type and canopy closure as covariates.

3.3 Results

3.3.1 Sample sizes

Sample sizes in each plot and habitat (linear feature, forest edge, and forest interior) are summarized in Table 3.1 for small mammals, fungi, plants, and insects. In addition, 12 soil samples were collected from each of three NT plots (MANN, PORC, and FERR) with four samples in each habitat category within a plot. In AB I collected two samples of soil from each habitat category in each plot. In the secondary study area in AB I did not kill small mammals for stable isotope analysis and opportunistically collected three deer mice, 13 red-backed voles, and one eastern heather vole.

60 Table 3.1. Sample sizes of arthropods, fungi, plants, and small mammals collected in Northwest Territories and Alberta in each habitat (forest interior, forest edge, and linear features) and each plot.

Northwest Territories Plots Alberta Plots Habitat FERR HOOK LIAR MANN MART PORC GRIZ HILL POWL iSLI P STOW Fungi Fungi Linear feature 2 1 3 2 2 2 2 Forest edge 1 1 1 1 1 2 2 2 2 3 Forest interior 2 2 2 3 2 3 3 Aspen Aspen Linear feature 2 2 2 2 2 2 2 2 2 2 2 Forest edge 2 2 2 2 2 2 2 2 2 2 2 Forest interior 2 2 2 2 2 2 2 2 2 2 2 Bunchberry Bunchberry Linear feature 2 2 1 2 2 2 2 2 2 1 2 Forest edge 2 2 2 2 2 2 2 2 2 2 2 Forest interior 2 2 2 2 2 2 2 2 2 2 2 Rose Rose Linear feature 2 2 2 2 2 2 2 2 2 2 2 Forest edge 2 2 2 2 2 2 2 2 2 2 2 Forest interior 2 2 2 2 2 2 2 2 2 2 2 Peavine Peavine Linear feature 2 2 2 2 2 2 2 2 2 2 Forest edge 1 1 1 2 1 2 2 2 2 2 2 Forest interior 1 2 2 2 1 1 2 2 2 2 2 Coleoptera Cranberrj Linear feature 1 3 2 2 2 2 l 2 Forest edge 1 1 5 2 2 2 2 l 2 Forest interior 1 4 1 3 2 1 2 2 2 1 Lepidoptera Grass Linear feature 1 3 2 2 2 2 2 2 2 Forest edge 2 4 1 2 1 2 2 2 2 2 Forest interior 4 1 3 1 1 2 2 2 2 2 Meadow voles Dewberry Linear feature 1 4 8 3 8 2 2 2 2 2 Forest edge 1 3 2 2 2 2 2 Forest interior 2 2 2 2 2 Red-backed voles Red-backed voles Linear feature 1 3 6 3 4 Forest edge 8 3 4 1 6 13 1 2 Forest interior 3 9 6 4 8 11 2 3 2 3 Deer mice Deer mice Linear feature 3 5 3 2 2 Forest edge 1 3 4 3 6 Forest interior 1 4 4 5 4 4 1 Eastern heather voles Eastern heather voles Forest interior 1 1 2 1 1

61 3.3.2 Linear feature isotopic signatures

Though the effects of habitat on stable isotope ratios were statistically significant about half the time, model i?-squared values were low (usually lower than 0.60), and the magnitude of mean predicted S13C difference between habitats was small (Table 3.2, Table 3.3). When species was a covariate in combined analyses (e.g., "all plants"), R- squared values jumped to near or above 0.80, whether or not other predictor variables (plot and habitat) were included. Based on Wald's tests (testparm in STATA, StataCorp 2007) the overall effect of species was always significant (p = 0.000). This shows the greater influence of species on 513C and 8I5N values than the influence of habitat (Fig. 3.1, Table 3.2, Table 3.3).

3.3.2.1 Carbon

Samples were significantly enriched in 13C on linear features about half the time (see Table 3.2 and Fig. 3.1). Soil and individual species, genus, or order 8 C enrichment factors ranged from -1.13 to +1.97%o with amean of+0.337%o for NT samples, and from -0.174 to +2.59 %o with a mean of+1.05%o for AB. Pooling all plant species together, plants on linear feature were significantly enriched in carbon. Individual plant species were enriched on linear features (enrichment ranging from +0.061 to +2.59%o) although trembling aspen and prickly rose in NT and bunchberry in AB were not significantly different between habitats. The increase in 813C on linear features averaged for individual plant species was 1.04%o in NT and 1.32%o in AB. Deer

1 "X mouse 8 C was also higher on linear features. Fungi on linear features were significantly 1 -> depleted in C compared to forest but non-significantly in AB. Pooled small mammal species, red-backed vole, and meadow vole 813C were not significantly different on linear features as compared to forest in NT. Sample sizes of rodents were too low to evaluate in AB. Soil and arthropod 813C values were not related to habitat.

62 Table 3.2. Summary statistics, /^-values, and enrichment factors for stable carbon isotope ratios (513C) of soil, arthropods, plants, fungi, and small mammals on linear features and in forest. P-values refer to the effect of linear feature versus forest habitat with a fixed effect for plot and were considered significant at a = 0.05. Analyses of groups (e.g., "all plants") had species as a fixed effect. 'Habitat response' refers to linear feature isotope ratios with forest as a reference. "Enriched" signifies an increase in the heavier isotope (higher 813C), "depleted" a decrease, and "neutral" is a non-significant response. Enrichment 13 13 13 factor is the difference between mean 5 C on linear features and in forest (5 0jnear feature - 8 Cforest) in per mil (%0).

Study area Linear feature 8 C Forest 813C Coefficient P-value Habitat Response / Enrichment factor (mean ± sd) (mean ± sd) R-squared (mean and SE)

Soil NT -27.2 ±1.06 -27.6 ± 0.646 -0.506 0.122 Neutral/0.117 + 0.431 AB -27.2 ±0.561 -27.0 ± 0.642 0.174 0.386 Neutral / 0.427 -0.174 All plants NT -28.9 ±1.24 -29.9 ± 1.28 -0.937 0.000 Enriched / 0.295 + 0.985 AB -29.1 ± 1.34 -30.5 ± 1.18 -1.38 0.000 Enriched/0.421 + 133 Trembling aspen NT -29.8 ± 0.938 -30.2 ±1.26 -0.4102 0.329 Neutral/0.172 + 0.411 AB -29.0 ± 0.507 -30.2 ±1.15 -1.28 0.002 Enriched/0.381 + 1.23 Bunchberry NT -28.6 ± 1.21 -29.5 ±1.09 -0.836 0.025 Enriched / 0.432 + 0.930 AB -29.9 ±1.39 -30.0 ± 0.708 0.0350 0.6773 Neutral / 0.250 + 0.061 Low-bush cranberry AB -28.9 ± 1.05 -30.4 ±1.16 -1.49 0.002 Enriched / 0.482 + 1.50 Dewberry AB -30.3 ± 1.30 -30.8 ± 0.741 -0.602 0.046 Enriched/0.491 + 0.539 Grass AB -28.2 ±1.56 -30.8 ±1.20 -2.65 0.000 Enriched/0.691 + 2.59 Creamy peavine NT -28.4 ± 0.591 -30.4 ± 1.54 -1.53 0.002 Enriched / 0.696 + 1.97 AB -29.2 ± 1.139 -31.4± 1.15 -2.19 0.000 Enriched / 0.580 + 2.14 Prickly rose NT -28.8 ±1.55 -29.6 ± 1.13 -0.851 0.059 Neutral / 0.298 + 0.850 AB -28.5 ±1.28 -29.7 ± 1.26 -1.20 0.008 Enriched / 0.474 + 1.20 Fungi (mostly Leccinum SDD.) NT -25.9 ± 0.879 -24.7 ±1.30 1.90 0.029 Depleted/0.712 -1.13 AB -25.0 ±1.60 -25.4 ± 0.804 0.0346 0.666a Neutral/0.731 + 0.324 All arthroDods NT -26.5 ±1.27 -26.2 ±1.76 0.0899 0.821 Neutral / 0.544 -0.314 Coleoptera NT -25.7 ± .9265 -24.95 ±1.24 0.930 0.061 Neutral/ 0.551 - 0.780 Lepidoptera NT -27.2 ±1.18 -27.4 ±1.27 -0.240 0.306a Neutral/0.113 + 0.263 AH small mammals NT -25.2 ±1.09 -24.5 ± 0.950 0.00963 0.777a Neutral / 0.644 - 0.732 Red-backed voles NT -24.3 ±0.614 -24.0 ± 0.596 0.298 0.073 Neutral/0.134 - 0.333 Deer mice NT -24.4 ± 0.476 -25.0 ± 0.498 -0.430 0.005 Enriched / 0.470 + 0.523 Meadow voles NT -26.3 ±0.451 -26.9 ±1.09 -0.341 0.244 Neutral / 0.487 + 0.568 a: Normalised using InskewO (StataCorp 2007). Coefficient signs can be reversed due to the normalisation procedure.

63 Soil oLinearfeature i 4 O i—^-* 1 -fcEdge

Clnterior

i—&—i

I——®—i ©Northwest I Territories

cAlberta

Predicted mean 513Carbon

All plants

-33 -32 -31 -30 -29 -28 Predicted mean 6"Carbon 15

14 Fungi

13 -

12 - 11 - HM 10 -

9 - 8 Ff 7 •o e

5

4 -

-28 -27 -26 -25 -24 -23 Predicted mean 6"Carbon

64 Arthropods

« O I ' to S 2 e m E T> • 1 _o »

-29 -28 -27 -26 -25 Predicted mean 61sCarbon

9 - Small mammals ' • 8 • «C

Red-backed voles 5> # \ a i f i c . i % ^ « s • « 1 o V B 4 Meadow "2 voles U *0°. 3 - \*Y 2 • \jDeepr mice

1 - — • • ' T .....—.-J -26 -2S -24 -2} Predicted mean ("Carbon

Figure 3.1. Influence of habitat (linear feature, forest edge, and forest interior) on predicted mean stable isotope ratios of carbon (813C) and nitrogen (515N) of all plants, nitrogen fixing peavine, soil, arthropods, fungi, deer mice, red-backed voles, and meadow voles in NT and AB. Error bars are confidence intervals for predicted means based on regression of the stable isotope ratios on habitat. Raw 813C and 815N values are given for AB deer mice.

65 3.3.2.2 Nitrogen

Overall, nitrogen stable isotope ratios were lower (depleted in N) on linear features than in forest (see Table 3.3 and Fig. 3.1). Changes in 815N on linear features based on enrichment factors for soil and individual species, genera, or orders ranged from -2.41 to +1.42%o (average -0.698%0) for NT and -3.58 to +1.33%o (average - 0.855%o) for AB. All plant species pooled together were significantly depleted in 15N on linear features. Most individual plant species were depleted in 15N (significant depletion for bunchberry, cranberry, grass and NT rose, non-significant for AB rose and peavine) though S15N was higher on linear features for aspen (significant enrichment in AB, non­ significant in NT), NT peavine (non-significant), and AB dewberry (non-significant). Depletion in 815N on linear features averaged for individual plant species was -0.717%o in NT and-0.456%o in AB. The magnitude of depletion was greater for "all small mammals", meadow voles, and red-backed voles than for deer mice, yet only deer mouse 815N was significantly lower on linear features. Linear feature soil and fungi were significantly depleted in 15N in AB and non-significantly depleted in 15N in NT. Arthropod 815N was not significantly affected by habitat.

3.3.3 Forest edge isotopic signatures

"All plants" in NT were the only group to exhibit an edge effect in 8 C and were significantly enriched in edge by 0.59l%o (coefficient = -0.568,/? = 0.021, R-squared = 0.325, Fig. 3.1). For nitrogen, only AB bunchberry 815N was significantly lower in edge than in forest interior by 1.32%o (coefficient = 1.52, p = 0.013, R-squared^ 0.508). In AB, red-backed voles were captured only in forest edge and interior and stable isotopes did not differ between the two habitats (Fig. 3.1).

66 Table 3.3. Summary statistics, p-values, and enrichment factors for stable nitrogen isotope ratios (515N) of soil, arthropods, plants, fungi, and small mammals on linear features and in forest. P-values refer to the effect of linear feature versus forest habitat with a fixed effect for plot and were considered significant at a = 0.05. Analyses of groups (e.g., "all plants") also had species as a fixed effect. 'Habitat response' refers to linear feature isotope ratios with forest as a reference. "Enriched" signifies an increase in the heavier isotope (higher 515N), "depleted" a decrease, and "neutral" is a non-significant response. Enrichment 15 15 factor is the difference between mean 8 N on linear features and in forest (S^Nunearfcature - 6 Nforest) in per mil (%o).

Linear feature 815N Forest 815N Habitat Response / Enrichment factor Study area Coefficient P-value (mean ± sd) (mean ± sd) R-squared (mean and SE) Soil NT 1.60 ±0.929 1.74 ±1.65 0.0459 0.933 Neutral / 0.238 -0.134 AB 2.94 ± 0.554 3.87±1.15 0.925 0.008 Depleted / 0.507 - 0.925 All Dlants NT -2.12 ±1.85 -1.41 ±2.10 -0.147 0.006a Depleted / 0.383 - 0.713 AB -1.77 ±1.62 -1.30±2.15 -0.0453 0.023a Depleted/0.441 - 0.466 Trembling aspen NT -2.50 ±1.798 -3.92 ± 2.03 -1.42 0.059 Neutral/ 0.177 + 1.42 AB -2.63 ±1.61 -3.96 ±1.83 -1.38 0.029 Enriched / 0.363 + 133 Bunchberry NT -3.30 ±1.87 -0.889 ±1.31 2.42 0.000 Depleted/0.491 -2.41 AB -1.79 ±1.20 -0.0622 ±1.65 1.73 0.007 Depleted / 0.367 -1.72 Low-bush cranberry AB -3.68 ±1.46 -2.00 ± 2.07 1.82 0.025 Depleted / 0.340 -1.68 Dewberry AB -2.09 ±1.01 -2.96 ±1.40 -0.837 0.087 Neutral / 0.292 + 0.873 Grass AB -1.07 ±0.872 -0.000909 ±1.39 1.07 0.048 Depleted/ 0.151 -1.07 Creamy peavine NT -0.263 ± 0.248 -0.286 ±0.326 -0.0904 0.469 Neutral / 0.239 + 0.0234 AB 0.0210 ±0.351 0.0532 ± 0.349 0.0102 0.929 Neutral / 0.379 - 0.0322 Prickly rose NT -2.13 ±1.56 -0.227 ±1.31 1.90 0.000 Depleted/ 0.459 -1.90 AB -1.24 ±1.629 -0.352 ±1.56 0.890 0.163 Neutral/0.152 - 0.890 Fungi (mostly Leccinum SDD.1 NT 9.16 ±1.94 9.81 ±2.27 1.20 0.261 Neutral / 0.782 -0.651 AB 8.46 ± 2.30 12.0 ±1.58 3.69 0.000 Depleted/ 0.592 -3.58 All arthropods NT 1.45 ±2.66 2.26 ±3.38 0.504 0.463 Neutral / 0.6452 - 0.808 Coleoptera NT 2.92 ± 2.76 4.86 ± 2.45 1.95 0.125 Neutral / 0.345 -1.94 Lepidoptera NT 0.196 ±1.95 -0.336 ±1.80 -0.518 0.526 Neutral / 0.420 + 0.532 All small mammals NT 4.34 ±1.61 5.33 ± 2.54 0.0696 0.0873 Neutral/0.681 - 0.990 Red-backed voles NT 5.68 ±1.78 6.642± 2.30 0.0410 0.674a Neutral / 0.439 - 0.959 Deer mice NT 2.87 ± 0.768 3.35 ± 0.865 0.0673 0.044 Depleted / 0.461 - 0.477 Meadow voles NT 4.18 ±0.954 5.36 ±1.44 0.996 0.077 Neutral / 0.466 -1.18 : Normalised using InskewO (StataCorp 2007). Coefficient signs can be reversed due to the normalisation procedure.

67 3.3.4 Distribution of legumes

Results of random slopes regression of nitrogen fixers by habitat did not differ from regression with a random intercept for plot, thus only results for the latter are presented. Legumes were more common on linear features than in forest, but alder had the opposite response. Linear features had higher percent ground cover of legumes (NT coefficient = -0.576,/? = 0.000; AB coefficient - -1.13,/? = 0.000), and higher counts of tall peavine, vetch, and clover than did forest (NT coefficient = - 2.78, p = 0.000, Poisson; AB coefficient = -1.74, p - 0.000, negative binomial). Alder was more abundant in forest than on linear features in NT (coefficient= 1.180, p - 0.000, negative binomial), and more common in forest interior than at edge (coefficient^ 0.481,/? = 0.008, negative binomial). In AB, alder was significantly more common in forest edge than in forest interiors or on linear features (coefficient= -1.466,/? = 0.033 and coefficient^ -0.805,/? = 0.023, respectively, negative binomial), though non-zero counts were rare. Percent ground cover of legumes and count of nitrogen fixing stems, again rare in forest, were not significantly different between forest edge and interiors in either study area.

3.4 Discussion

3.4.1 Linear feature isotope ratios

As predicted, linear feature stable isotope signatures of 8I3C and 815N exist in some northern boreal forest food resources of small mammals (plants and fungi) and in deer mice. Significant edge effects were rare (one for each stable isotope) but were as predicted being intermediate between stable isotopic signatures of forest interior and linear feature. Contrary to prediction, linear features did not have a significant effect on 813C and 815N signatures of soil (with the exception of 815N in AB), some plants, nor on most consumers (two orders of arthropods, red-backed voles, and meadow voles). In general, the magnitude of AB enrichment factors was greater than the magnitude of NT enrichment factors. This could reflect a longer time since initial disturbance for NT linear features as compared to AB linear features (Bukata and Kyser 2005) or lesser initial disturbance to the NT linear features due to a shallower pipeline trench intended to

68 prevent damage to permafrost (Burgess and Harry 1990). The ultimate baseline samples, soil, did not appear responsible for the linear feature signatures of 8 C and 8 N which could relate to the difficulty of adequately representing the net effect of complex nitrogen cycle processes on 815N using total soil (Dawson et al. 2002). The magnitude of observed increases in 813C of linear feature plants (similar to +l%o) were lower than the 2%o increase found by France (1996) in under- and mid-story plants from clear-cut in Ontario boreal forest, lower than a 3%o increase found in Wisconsin pine trees in open areas, and close to the approximately l%o increase in maple trees in Wisconsin open areas (Leavitt 1993). C enrichment on linear features likely relates to the open-canopy effect described by France (1996) and others (e.g., Hogberg et al. 1995, Buchmann et al. 1997), whereby plants in clearings are enriched relative to those in adjacent forest due to higher light conditions, water stress, and(or) mixing of respired air with open enriched air. To our knowledge this effect has never been demonstrated for linear features which are relatively narrow forest gaps. Subtle effects of open areas on 813C have been noted before; Hannam et al. (2005) found a 0.3%o 1 'X increase in C of forest floor residue in clear-cut trembling aspen stands compared to uncut boreal forest. Unexpected decreases or lack of pattern in 8 C in some baseline samples (aspen and rose in NT, bunchberry in AB, soil, and fungi) could relate to unmeasured factors that affect isotope ratios. For example, decreases in overstory leaf area cover can lead to enrichment of 8 C in understory leaves (Buchmann et al. 1997). Forest samples were collected in relatively low-density northern boreal forest, thus they might be closer in value to linear feature samples than expected. In the case of decomposers it is possible that fungal obtainment or fractionation of stable carbon isotopes differs from plants such that depletion in carbon is the actual "linear feature signature" for fungi (seen in NT). Overall, the open-canopy effect on 813C seems to be fairly consistent, albeit small, in primary producers of linear features and other clearings in North American forests. Contrary to previous studies, forest clearing did not lead to enrichment in 15N of primary producers, soil, and fungi on linear features, but rather to depletion or no response. Linear feature 815N was lower than forest 815N, for example linear feature plants were on average 0.717%o (NT) and 0.456%o (AB) lower, soil was 0.925%o (AB)

69 lower, and fungi was 3.58%o (AB) lower than in forest. The depletion in fungi in AB was equal to a typical trophic level shift (Post 2002, Michener and Kaufman 2007), suggesting the possible existence of a strong "canopy effect" on 815N. Several factors could have contributed to the observed depletion in soil, plant, and fungal 15N on linear features, including addition of fertilizer (Hogberg et al. 1995, Kendall et al. 2005, M. Gerlock pers. comm. 2007), and changes in nitrogen cycling or nitrogen inputs (Hogberg et al. 1995, Dawson et al. 2002). Depletion in 15N on linear features could be brought about by normal soil processes that lead to 15N enrichment in the forest, while disruption on linear features could prevent normal enrichment. For example, as organic matter advances in age and decays, 8l5N increases to become high in mature and (or) deeper soils (Nadelhoffer and Fry 1988, Sah and Ilvesniemi 2006). Hobbie et al. (2005) found that mature forest soil and plants were about 4%o enriched in 15N compared to those in areas recently exposed by retreating glaciers. Huygens et al. (2008) showed that the enrichment of 15N with soil depth is due to high abundance of fungal mycorrhizae and bacteria, increased microbial activity, build up of 15N enriched microbial compounds, and turnover of the microbial community. Disruption to those soil processes and microbial communities, and subsequent suppression of microbiota recovery due to compaction (Tan and Chang 2007, Revel et al. 1984, Lee and Boutin 2006) could have led to the observed depletion in 15N on linear features as compared to forest. Soil compaction from linear feature creation and continued use of linear features also leads to decreased nitrification (Tan and Chang 2007) and thus decreased 815N (similar to l%o depletion, Tan et al. 2006). Ostrom et al. (1998) found that soil tilling, as occurs with digging trenches for pipelines, can increase mineralisation which also leads to depletion in 15N in soil nitrate. Hogbom et al. (2002) found that plant shoots were depleted in 15N by about 3%o for the first few years following clear cutting, then returned to normal levels. Thus, the long time since disturbance for most linear features studied (7 to 20 years) could explain the small magnitude of depletion in most samples. Another possible cause of 15N depletion that I explored was the effect of nitrogen fixers. Nitrogen fixation could potentially have caused the depletion in 15N on linear features, since percent cover and stem counts of legumes were higher on linear features. Legumes could have input near-zero 815N into the soil (Marshall et al. 2007) and counteracted the N enrichment that occurs via normal soil microbial processes (Hogberg et al. 1995, Dawson et al. 2002). Stock et al. (1995) found that nitrogen fixing terrestrial plants caused depletion of 15N in soil, and France and Schlaepfer (2000) found that nitrogen fixing cyanoprokaryotes significantly reduced 15N of arthropod larvae in lakes. My results for decreased soil 515N on linear features were consistent with this hypothesis, especially in AB. In my study areas, however, stable nitrogen isotope ratios of most plants were depleted relative to atmospheric nitrogen, and relative to creamy peavine, a nitrogen fixer. It appeared therefore that non-fixing plant leaf litter had a greater likelihood of causing the depleted values on linear features than did nitrogen fixing plant leaf litter. It is possible that contribution of inorganic nitrogen depleted in 15N on linear features from live nitrogen fixers, or contribution of 15N-enriched material in forests from other sources (e.g., fungi) is important, however. The roles nitrogen fixers and high-15N sources play in influencing stable nitrogen isotope signatures on linear feature and in forest, respectively, merit further study. In other studies, forest clearing resulted in I5N enrichment compared to undisturbed areas due to increased nitrification and subsequent leaching of 14N-rich compounds (Sah and Ilvesniemi 2006), or due to decreased input of 15N-depleted plant litter (Pardo et al. 2002). Tan et al. (2006) found that forest floor removal led to increased 815N in aspen leaves, and Bukata and Kyser (2005) found that oak tree 815N increased in response to clearcuts, similar to my result for trembling aspen. Aspen was the only tree sampled, and deeper rooting species might tap into soil layers that respond differently to forest disturbance, i.e., 15N-enriched soil, compared to the shallow depth of my samples of soil (15 cm maximum) and roots of other plants. Local ecosystem effects could also be a factor, as Handley and Scrimgeour (1997) reported depletion in disturbed tropical habitat and enrichment in disturbed temperate habitat. Different plant parts can have opposite isotopic responses to open habitat (e.g., leaves and roots in Dijkstra 2003), thus analysing other plant parts of aspen or whole plants might result in different findings. Lack of response of 815N to habitat in some cases could indicate that forest nitrogen dynamics are not significantly altered by linear features, and thus do not lead to nitrogen losses and enrichment as was predicted (Hogberg et al. 1995, Dawson et al. 2002). This is consistent with findings of Jerabkova and Prescott (2007) that forest

71 harvesting does not change dominant soil processes (no increase in nitrification) nor compounds available for use by plants (NH4 remained dominant over NO3, Hope et al. 2003, Westbrook et al. 2004).

3.4.2 Potential to trace use of disturbed habitats using stable isotopes of linear features My main objective was to determine whether or not stable isotopes would be useful in tracking wildlife use of linear feature food resources. Unique 513C and 515N linear feature isotope signatures exist in primary producers, and sometimes in soil and decomposers in the northern boreal forest. These unique signals appeared to transfer up the food chain to deer mice but not to other species. Linear features did not influence stable isotope ratios of most consumers (two orders of arthropods, red-backed voles, and meadow voles), and the ability of habitat to explain the data was often poor based on R- squared values. Overall, directly tracing use of linear feature resources (primary producers and decomposers) to small mammal consumers using 8 C and 8 N does not seem to be feasible in northern boreal forests. Though significant, the small increase in deer mouse 8 C (0.523%o) on linear features was less than that observed for moose in relation to old burn sites (l%o, Bada et al. 1990), and less than the observed canopy effect in plants (similar to +l%o). The baseline linear feature 8 C signature in plants also did not transfer strongly to most consumers. Linear feature meadow voles had a trend for increased 813C like that of their food (plants), but it was non-significant and smaller than the shift seen in plants. As acknowledged by France (1996), the 813C canopy effect in northern forest (similar to 2%o) is smaller than that reported in neotropical forests (van der Merwe and Medina 1989, Cerling et al. 2004). Neotropical open areas and even sub-canopy gaps are typically 4 to 5%o enriched in C compared to closed forest (van der Merwe and Medina 1989), which corresponds well with the 4 to 6%o enrichment of animals foraging in neotropical open forests as compared to closed forest (Ambrose and DeNiro 1986, Cerling et al. 2004). In this study, the trend of lower 813C in NT linear feature fungi (depletion of 1.13%o) might have been reflected in a slight trend for lower 8 C in linear feature red-backed voles (depletion of 0.333%o), thought to consume large amounts of fungi (Dyke 1971, Maser et al. 1978, Mills 1995, Chapter 4). The small canopy effect seen in plants and fungi thus might be ecologically significant, but will make tracing use of open canopy resources by wildlife difficult in northern forests which have relatively open canopies and are slow growing. Depletion in nitrogen stable isotopes of NT plants (similar to -0.698), fungi (similar to -0.651), and insects (similar to -0.808) on linear features appeared to be reflected in 815N of most NT consumers, though only significantly for deer mice. Furthermore, deer mouse depletion was less than that seen in food sources. On the other hand, linear feature meadow voles, red-backed voles, and Coleopteran larvae had trends for decreased 815N that was greater than that of plants and fungi collected in the NT. The influence of the unique linear feature 815N signature might be of little use in ecological studies, as the direction of change is relatively consistent, but the magnitude of change is not and is small. An alternate explanation for the lack of habitat effects on consumer stable isotope ratios is that presence of consumers on narrow linear features did not reflect where they gained their resources, because they might have foraged in forest. In support of this idea, food types consumed by small mammals in the short-term, based on gut content analysis, did not differ between linear features and forest for deer mice (Huynh 2008, unpublished) and red-backed voles (Cheung 2008, unpublished), despite changes in distribution of potential food sources (Chapter 2). Captures and collections of small mammal individuals in non-preferred habitat might have been brief exploratory movements, and might not have reflected the long term habitat use of those individuals. Long-term diet as represented by vole muscle tissue (Cherel and Hobson 2005) might have included items consumed mostly or exclusively in preferred habitat, which could explain the lack of transfer of the linear feature isotope effect to voles. I attempted to determine if red-backed voles and meadow voles might be foraging in their non- preferred habitat by tracking them (Appendix C). Sample sizes were too low to reliably determine where they were likely foraging. However, each species tended to stick to their preferred habitat (Appendix C). Furthermore, deer mice, which exhibited little habitat preference, peaked in abundance in forest edge, and could have been using linear feature resources (Chapter 2). Deer mice were the only small mammal to significantly

73 1 'X 1 ^ exhibit the isotopic response (enrichment in C and depletion in N on linear features) which could indicate only deer mice were feeding on food resources from all habitats. However, one important food item for deer mice, arthropods (Dyke 1971, Banfield 1974, Bowers et al. 2004), did not have a linear feature isotope signature, which might reflect where the insect prey was foraging, and not necessarily where deer mice would capture them. 8 C and 8 N isotopic signatures of linear features in the northern boreal forest, though unique, are not large like those in other disturbed ecosystems (France 1996, van der Merwe and Medina 1989). Usefulness of 813C and 815N signatures in tracing resource use in disturbed boreal forest might be limited due to overriding influence of larger natural sources of variation. Species differences in 813C and 815N, for instance, were much larger than habitat differences in isotopes within a species. Temporal or microclimatic shifts in isotope values could also override small changes on narrow linear features (France 1996). Unique isotope signatures of linear features might be of limited utility in determining disturbed habitat use in boreal forests, particularly at larger scales. Detailed knowledge of critical food resources (Chapter 4), distribution of critical resources, and foraging behaviour are likely to be more useful than linear feature signatures per se, in determining small mammal use of disturbed habitat. Chapter 4 examines distinctiveness of stable isotope signatures of small mammal species, and explores the possibility of a linear feature signature starting with small mammal species as the baseline, because the species differed strongly in habitat preferences (Chapter 2).

74 CHAPTER 4: SMALL MAMMAL DIET IN RELATION TO LINEAR FEATURES

4.1 Introduction Many studies cite changes in resources as an explanation for why organisms react to edge. However, relatively few studies have independently tested which resources influence the quality of edge habitat for an organism (Ries et al. 2004). Logically, when cleared habitat provides unique or concentrated resources, positive edge effects should occur (Ries et al. 2004). When key resources (e.g., particular foods, Mills 1995) are lacking, negative edge effects can occur, and when resources do not differ appreciably between cleared and native habitat, species might use habitats equally (neutral edge effect, Ries et al. 2004). To accurately predict when different types of edge effects will occur we need to understand what resources are important for wildlife. Considerable effort has been made to determine which structural resources influence the distribution of small mammals (Carey and Johnson 1995, Peles and Barrett 1996, Fuller et al. 2004, Elliott and Root 2006). However, often food resources vary more spatially and temporally than do structural resources (Dyke 1971, Banfield 1974, Maser et al. 1978, Ure and Maser 1982). As foods such as fungi, plants, and arthropods change in response to human disturbance, small mammals might change their distributions to take advantage of increased food resources, or because they are forced out of habitat with decreased food resources (Chapter 2, Marenholtz 2007, unpublished, Blake 2006, unpublished). To determine the importance of food as a factor influencing small mammal response to edge, a better understanding of what small mammals consume is required. Conventional methodologies of diet analysis such as fecal or gut content analysis and behavioural observations (Vickery 1981, Pinnegar and Polunin 1999, Thompson et al. 1999, Michener and Kaufman 2007, Caut et al. 2008b) are difficult to interpret because dietary components differ in retention times and (or) digestibility. Food items also differ in available nutrients and in dietary contribution depending on the ability of the organism to assimilate the nutrients (Claridge et al. 1999, Levey and del Rio 2001). Conventional approaches also are restrictive because they

75 reflect short-term diet, or the meal the animal was seen eating (Norrie and Millar 1990, Claridge et al. 1999, Levey and Karasov 1994). Stable isotope analysis could be a better method to determine whether food resources that respond to disturbance and are thought to be important are in fact crucial for small mammals because it can assess long-term food resource use quantitatively (Marra et al. 1998, McCutchan et al. 2003, Bearhop et al. 2004). Stable isotopes of carbon and nitrogen reflect the isotopic signatures of food items in a predictable manner. Carbon isotopes, for instance, normally resemble the signature of food items with al% or less increase in 813C (l%o enrichment in 13C), and stable nitrogen isotopes reflect the isotopes of food items with a 3-5%o increase in 15N (3-5%o enrichment in 815N, Peterson and Fry 1987). My objective was to directly examine food resource use by small mammals inhabiting linear feature and forest habitat via stable isotope analysis. I determined whether or not differences in diet could explain patterns in small mammal habitat use, based on observed patterns of arthropod, fungus, and plant material. In Chapter 3 I established that within a species, small mammals inhabiting different habitats did not differ greatly in stable isotope ratios, suggesting individuals of a species consumed similar foods no matter where they were caught. If small mammal species specialize on different food resources that in turn respond differentially to habitat disturbance, this could provide a good mechanistic understanding of small mammal habitat and edge responses demonstrated in Chapter 2. I predicted that small mammal species would differ significantly from each other in stable isotope ratios of carbon (813C) and nitrogen (515N). I predicted that the difference would be due to dietary preference, with red- backed voles consuming mainly fungi, meadow voles and heather voles consuming mainly plants, and omnivorous deer mice consuming a mixture of plants, arthropods and fungi, with an emphasis on arthropods.

76 4.2 Methods

4.2.1 Study areas and study design

See Chapter 2 for detailed methods. This chapter uses NT study area data only, and excludes AB data due to low small mammal sample sizes (Table 3.1).

4.2.2 Stable isotope sample collection and stable isotope procedures

See Chapter 3 for detailed methods.

4.2.3 Gut content estimates of deer mouse and red-backed vole diet

To compare the importance of food sources using stable isotope analysis, I needed to narrow diet item possibilities down to those actually consumed (Phillips and Koch 2002, Caut et al. 2008b). Though conventional methods such as gut content analysis are not particularly useful for quantitatively demonstrating the relative importance of food items, they are useful in identifying the major food items consumed which then limits the number of food items that need to be considered in isotopic analysis (Pyare and Longland 2001, Caut et al. 2008b). Gut contents often red-backed voles and 12 deer mice from linear features were compared to contents of 10 red-backed voles and 13 deer mice from forest interior; gut contents of 20 red-backed voles and 25 deer mice were examined in total (Cheung 2008, unpublished, Huynh 2008, unpublished). Meadow voles and heather voles were excluded from gut content analysis because they are mainly herbivorous unlike omnivorous deer mice and red-backed voles (Dyke 1971, Forsyth 1985, Pastor et al. 1996, Wilson and Ruff 1999). Alimentary tracts of red-backed voles and deer mice were removed and stored frozen at -20°C. Stomach contents were thawed then placed in Petri dishes containing transparent 1 mm x 1 mm grids for macroscopic food item analysis under a dissecting scope. Occurrence (presence/absence) of vascular plant, seeds, berries, fungi, lichen, moss, and arthropod items in stomachs was recorded. Intestinal contents were squeezed into a clean vial containing 70% ethanol, and items too large for examination under a compound microscope were removed for macroscopic examination. After occurrence analysis, stomach contents were added to the intestinal contents, mixed to achieve a homogenous solution (hereafter gut contents or alimentary tract contents). Slides were made using one drop of the mixed suspension with 2 drops of a mounting medium, polyvinyl alcohol in lactic acid with acid fuchsin (APVA). Slides were made permanent by drying at 60 °C for 2-6 hours. Individual small mammals each had three slides examined to make about 30 and 36 slides per habitat type for red-backed voles and deer mice, respectively. Each slide was divided into quadrats formed by 11 rows (A to K) intersecting 11 columns (1 to 11). Each quadrat served as a potential field of view (FOV). For each slide, 15 fields of view (FOV) at lOOx magnification were randomly selected for examination, resulting in 45 FOV per individual. Forty-five FOV was likely adequate to observe all food types consumed by an individual as cumulative food items observed hit a plateau at around 40 FOV. Occurrence of food items was rated using a scale of 0 to 2: 0 indicated the absence of the item, 1 indicated present once in a FOV, 2 indicated that the food item appeared more than once in a FOV. Each FOV was also scanned at 400x magnification to facilitate detailed identification and scoring of vascular plant, lichen, moss, fungal, arthropod, and unidentified food items. Food type scores for an individual small mammal were derived by summing scores (0,1, or 2) in each FOV per individual (45 FOV total) to obtain a score out of a maximum of 90. The maximum would only be reached if scores were all 2, i.e., if all FOV had two or more of a food type present. Masticated bait (peanut butter, sunflower seeds, carrot) and non-food items (hair, cotton) were excluded from the analysis.

4.2.4 Data analysis

4.2.4.1 Gut content estimates of deer mouse and red-backed vole diet

Total alimentary tract (stomach and intestinal) contents of red-backed voles and deer mice were analysed using Kruskal-Wallis tests in ST ATA (StataCorp 2007), also known as analyses of variance by ranks, due to non-normality of scores for most food types (Zar 1999). I used one-tailed Dunnett's tests (a = 0.01) to test for significant differences between pairs of food type ranks after designating one food type as a "control" (Dunnett 1955, Zar 1999). For stomach-only contents, I used multivariate

78 logistic regression to examine the influence of species (red-backed vole or deer mouse) on major food types in the diet (StataCorp 2007).

4.2.4.2 Stable isotope analysis of diet

To determine if small mammal species differed significantly in diet based on stable isotopes I used fixed effects regression to compare stable isotope ratios of carbon (5,JC) and nitrogen (813N) of deer mice, red-backed voles, meadow voles, and heather voles in NT, and red-backed voles and deer mice in AB using STATA (StataCorp 2007) with plot and habitat as covariates. Regression analysis only analysed differences between species for one isotope at a time, thus I applied K nearest neighbour discriminant analysis (StataCorp 2007) to examine prediction reliability of species membership for small mammal individuals using both isotopes simultaneously. Rosing et al. (1997) recommended using k (nearest neighbours) equal to one less than the smallest true group size. Sample size for heather voles was lowest (n=5), thus I chose k=4. I designated prior probabilities of belonging to each species as proportional to the

Species grOUp Size (tllUS/^-backed vole = 0.5170, #ieer mouse = 0.2955, ^meadow vole = 0.1591, andpheather vole= 0.0284). Ties in classification were broken using the nearest group (StataCorp 2007). Agreement between true species group and classified species group was compared to expected agreement between groups by chance using a kappa-statistic measure (StataCorp 2007) of inter-classifier agreement which has been shown to work well for discriminant analysis classification tables (Titus et al. 1984). In order to use mixing models to determine proportional contribution of difference food sources to small mammal diets, food source signatures must be distinct from one another. Phillips and Gregg (2001a) showed that if standard deviations (sd) are low (about 0.25%o) in the sources (foods) and the mixtures (small mammals) then a difference of 2%o between sources is adequately distinct to allow use of mixing models and be within 10% (with a 95% confidence interval and sample size of 10) of the true proportion of the mixture that can be contributed to each source. Within groups of food items (plants, fungi, and arthropods), I tested whether isotope signatures of carbon and nitrogen differed significantly between species, plots, or habitats using fixed effects

79 regression with covariates of plot, species, or habitat as needed. I also compared the means between groups to see if they differed by the 2 ± 0.25%o criteria or more. Relative contribution of food types to small mammal diets was determined using three types of mixing models (see Appendix E for further explanation) executed by the programs IsoError (Phillips and Gregg 2001a, 2001b) and IsoConc (Phillips and Koch 2002) available from http://www.epa.gov/wed/pages/models.htm. A common problem with mixing models is that consumer values fall outside the mixing polygon (triangular space enclosed by lines connecting the three food sources) which could indicate that (1) an important food source is missing, (2) discrimination values are wrong, or (3) an assumption of the mixing model was violated (Phillips and Koch 2002). I addressed the first possibility with gut content analysis to determine possible food items. To address the second issue, food source isotopic values were corrected for discrimination (difference or change) between isotopic ratios of food sources and isotopic ratios of consumer tissues (Phillips and Koch 2002). Discrimination is denoted as A813C for carbon and A815N for nitrogen (Caut et al. 2008a) and was based on literature reviews specific to small mammal muscle tissue, and on factors that affect A8I3C and A815N (see Appendix E). Discrimination factors based on trophic level calculations were: 13 13 A8 Carthropod = 1.95%o, AS^Cfongus = 0.400%o, and A8 Cpiant= 0.699%o for carbon, and 15 15 15 A8 NarthK,pod = 2.70%o, A8 Nfimgus = 2.50%o, and A8 Npia„t = 3.1696o. To evaluate two- isotope, three-endpoint mixing models for most species, I transformed trophic-level- derived A813C and A815N values by adding 0.45%o to A813C values and subtracting 1.0%o 15 13 13 13 from A8 N values, thus A8 Cpiant_t = 1-15, A8 CarthroPod_t = 2.40%o, and A8 Cfungus_t = 15 15 0.850%o, and A8 Npia„tj = 2.1696o, A8 NarthropodJ = 1.7096o, and AS^N^ngusj = 1.5096o. For the third issue, a key assumption of mixing models is that elemental composition of food sources is roughly equivalent, i.e., all food sources have similar concentrations of C and N and thus each diet item contributes similar proportions of C and N to consumer tissues (Phillips and Koch 2002). This assumption could be violated for omnivores, which might draw more N from N-rich sources (e.g., invertebrates), than C from the same N-rich source (Phillips and Koch 2002). To address this assumption I used concentration-dependent mixing models (IsoConc) developed by Phillips and Koch (2002) which increases the proportion of consumer N or C coming from high %N or high

80 %C sources, and decreases the proportion coming from low %N or %C sources (Phillips and Koch 2002). These models do not incorporate measures of variance, so I also used IsoError (Phillips 2001) which estimates the proportion for each source (0-1), standard errors for these estimates, and approximate 95% confidence intervals. Standard dual isotope linear mixing models (IsoError) for three distinct food sources (plants, arthropods and fungi) were used when consumer (mixture) values fell within the triangular space formed by straight lines between the food sources in a bivariate plot of carbon and nitrogen isotopes (Phillips and Gregg 2001a, 2001b, Phillips 2001):

x (Ac ~ ~h¥JM - Ss) - (Sc - SS){JM - 1B)

(Ac - AB)(8A - 8B) - (Sc - 8S)(JLA - AB)

_(SM-8C)-(8A-SC) fs =

where 8 and I represent mean isotopic ratios for two elements (e.g., 813C and 815N), and the subscripts refer to the sources A, B, and C, and Mthe mixture. Standard single isotope two-end member (two food source) mixing models (IsoError, Phillips and Gregg 2001a, 2001b) were used when food source stable isotope ratios of only one element (C or N) encompassed small mammal values with symbol and subscript definitions as above:

J A f ?; « x s K°A - s) = - fs 1 TA

Concentration-dependent dual-isotope three end-member mixing models (IsoConc) were used to adjust the proportions contributed by each source to the 8 C and 815N values of the consumer by %N and %C of each food source (Phillips and Koch 2002, Appendix E). For the two-isotope, three source mixing model IsoConc uses a system of three equations in three unknowns incorporating elemental composition of food sources (e.g., concentrations of carbon [C] and nitrogen [N]) and matrix algebra to

81 solve for the source proportions given assimilated carbon, nitrogen and program- estimated biomass in the mixture (Phillips and Koch 2002). For the dual isotope mixing models of red-backed voles and deer mice an additional adjustment to food source isotopic values was made (added 0.45%o to A813C and subtracted 1.0%o from A815N) to shift the mixing polygon such that it encompassed small mammal isotopic values (Appendix E, Fig. 4.3c).

4.3 Results

4.3.1 Gut content analysis of deer mouse and red-backed vole diet Based on alimentary tract (stomach plus intestine) slide analysis, deer mice and red-backed voles consumed mostly vascular plants, fungi, and arthropods. Lichen and moss were excluded from statistical comparisons of dietary proportions due to rarity in the gut contents. The ranks of red-backed vole food type scores differed significantly (Kruskal-Wallis x2 = 37.4, p = 0.0001). Fungi was the prevailing food item observed in red-backed vole alimentary tracts (rank = 993.5), followed by plants (rank = 479.5), then arthropods (rank = 357.0). Red-backed vole alimentary tracts contained significantly more fungi than plants (one-tailed Dunnett's test statistic = 4.65 > 2.56, a = 0.01), significantly more fungi than arthropods (one-tailed Dunnett's test statistic = 5.76 > 2.56), and non-significantly more plants than arthropods (one-tailed Dunnett's test statistic = 1.11 < 2.56). Deer mouse food type scores differed significantly in ranks (Kruskal-Wallis x2 = 19.9, p = 0.0001). Fungi was the dominant food item observed in deer mouse alimentary tracts (rank = 1309.0), followed by arthropods (rank = 917.5), and plants (rank = 623.5, Fig. 4.1b). Deer mouse alimentary tracts contained significantly more fungi than plants (one-tailed Dunnett's test statistic = 4.45 > 2.56), non-significantly more fungi than arthropods (one-tailed Dunnett's test statistic = 2.54 < 2.56), and non-significantly more arthropods than plants (one-tailed Dunnett's test statistic = 1.91< 2.56). a) 100 • Linear feature

90 D Forest Interior

O 80

•2- 70

o 60 o iS 40

O

111 Plant Lichen Moss Fungus Arthropod Food Types

b) 100 - a Linear feature 90 - • Forest interior o 80 - at X 70 - 60 - Scor e 50 -

lativ e 40 -

30 - Cum u 20 - 10 - 11 0 JT ar.in.1 -X r=3k: Plant Lichen Moss Fungus Arthropod Food Types Figure 4.1. Cumulative scores for occurence of food types in alimentary tracts of a) red- backed voles, and b) deer mice. Error bars are 1 standard deviation.

83 Stomach content results were possibly less reliable than alimentary tract analysis, as magnification was lower and the main focus was on microscopic examination of slides. Vascular plant tissue was found in 65% of red-backed vole stomachs (13/20), and fungi in 60% of stomachs (12/20). Moss, seeds, and arthropods were rarely present in red-backed vole stomachs (15% (3/20), 10% (2/20), and 5% (1/20), respectively, Fig. 4.2a). Stomach content presence/absence results for deer mice revealed that 80% of deer mouse stomachs had arthropods present (14/25), 68% (17/25) had berries, 56% (14/25) had vascular plant tissue, 16% (4/25) had lichen and moss while seeds, and fungi were each in only 12% of stomachs (3/25, Fig. 4.2b). No food types occurred significantly more than any others (logistic regression all/? > 0.05) within a species. Arthropods occurred in deer mouse stomachs 60 times more often (odds ratio) than in red-backed vole stomachs (multivariate logistic regression coefficient = 4.10, SE = 1.46,/? = 0.005) and only deer mouse stomachs contained berries and lichens. Red-backed vole stomachs contained fungi 10 times more often (odds ratio) than did deer mouse stomachs, though the difference was not significant (multivariate logistic regression coefficient = 2.30, SE = 1.51,/? = 0.126).

84 100

90 a)

80 at o c 70 - a* 60 8 50 40 uat aat. 30 20

10

0 Plant Lichen Moss Seed Berry Fungus Arthropod Food Types

100

90 b)

80

70 -

60 urrenc e 50 - Oc c 40

30 Percen t 20

10 -

0 . „. . .. Plant Lichen Moss Seed Berry Fungus Arthropod Food Types

Figure 4.2. Percent occurence of food types in stomachs of a) red-backed voles, and b) deer mice.

85 4.3.2 Stable isotope analysis of diet

Small mammal species differed significantly in stable isotope ratios of carbon and nitrogen (Table 4.1, Fig. 4.3). The one exception was heather voles which were not

1 T significantly different from meadow voles in 5 C (coefficient = 0.0115,/? = 0.899, a = 0.0167 with Bonferroni correction for three comparisons). Small mammal species clustered reliably into their true species groups based on K nearest neighbour discriminant analysis (Table 4.2). The Kappa-statistic measure of inter-classifier agreement indicated that the number of correct classifications was significantly more than would be expected by chance (agreement = 93.8%, expected agreement = 37.9%, Kappa-statistic ± standard error = 0.899 ± 0.0533,/? = 0.000). Deer mice were correctly classified as deer mice 96.2% of the time (50/52 deer mice), 94.5% of red-backed voles were correctly classified (86/91), 85.71% of meadow voles were correctly classified (24/28) and all heather voles were correctly classified (5/5, Table 4.2). Three meadow voles were incorrectly classified as deer mice (3.57%), five red-backed voles were incorrectly classified as deer mice (5.49%), one deer mouse was incorrectly classified as a meadow vole (1.92%), and one deer mouse was incorrectly classified as a red-backed vole (1.92%). Food sources (plants, fungi, and arthropods) were distinct isotopically and 1-1 i O differed by 2%o or more with the exception of arthropod and fungal 5 C (A8 Canhropods- fungi = 1.2, coefficient = -0.0782,/? = 0.033, a = 0.025 with Bonferroni correction for three comparisons, Table 4.3). Fungi and arthropods differed by more than 2%o in mean 815N, however, and thus were considered distinct food sources for use in two-isotope mixing models. Species effects on 813C within a food group (applies to plants and arthropods) were often significant in analyses in chapter 3. Plants species mean 513C differed by less than 2%o and standard deviations were typically close to l%o, however. Plant species mean 815N differed by 2%o or more between aspen and several species, but given their high SD (l-2%0) aspen samples were not different enough to be distinct food sources. 813C and 815N of insect orders differed significantly by more than 2%o, but SD was higher than l%o and caused the two orders to be too close in isotope ratios to be considered distinct food sources. For each species or group, plots could also influence isotope values. Arthropods were not significantly different in 813C in any plot. Arthropod 815N differed significantly in some plots, but by less than 2%o and with high SD. Mean plant 8 C differed significantly between several plots, but always by less than 2%o. Plant 815N did not differ significantly between any plots. Fungi mean 813C and 815N did not differ by more than 2 %o between any plots.

87 Table 4.1. Isotopic values (513C and 815N) and elemental composition (% nitrogen (%N) and C:N ratios) of small mammals (muscle tissue) collected near Fort Simpson, NT and overall p-values and /{-squared values from regression of isotope ratios against species with fixed effects for plot and habitat. .P-values considered significant at a = 0.05. Isotope means and standard deviations (sd) are in per mil (%o).

8»C 8,5N %N C:N Species (n) Mean ± sd P-value R-squared Mean ± sd P-value R-squared Mean ± sd Mean ± sd Red-backed voles (91) -24.1± 0.610 0.000 0.6438 6.46 ±2.24 0.000 0.6814 14.9 ±1.38 3.32 ±0.135 Deer mice (52) -24.8 ± 0.539 3.23 ± 0.860 14.7 ± 1.55 3.35 ±0.101 Meadow voles (28) -26.4 ± 0.589 4.35 ±1.09 15.3 ±1.24 3.27 ±0.110 Heather voles (5) -26.5 ± 0.423 1.35 ±0.596 14.9 ±1.38 3.34 ±0.112

Table 4.2. Prediction reliability (resubstitution summary) of small mammal stable isotope signatures determined using k-nearest neighbour discriminant analysis where k (nearest neighbours) = 4. Classification is in % with sample size (n) in parentheses.

Classified species % (n) True species (n) Red-backed voles Deer mice Meadow voles Heather voles Red-backed voles (91) 94.5(86) 5.49(5) 0.00(0) 0.00(0) Deer mice (52) 1.92(1) 96.2(50) 1.92(1) 0.00(0) Meadow voles (28) 3.57(1) 10.71(3) 85.7(24) 0.00(0) Heather voles (5) 0.00(0) 0.00(0) 0.00(0) 100.0(5)

Table 4.3. Isotopic values (813C and 815N) and elemental composition (% nitrogen (%N) and C:N ratios) of food sources (plants, arthropods, and fungi) collected near Fort Simpson, NT and overall p-values and /{-squared values from regression of isotope ratios against species with fixed effects for plot and habitat. P-values considered significant at a = 0.05. Isotope means and standard deviations (sd) are in per mil (%o).

815C S1SN %N C:N Food source (n) Mean ± sd /"-value R-squared Mean ± sd P-value R-squared Mean ± sd Mean ± sd Plants (135) -29.6 ±1.34 0.000 0.609 -1.65 ±2.04 0.000 0.4860 2.69±0.965a 18.1±6.75a Arthropods (53) -26.2 ± 1.65 2.06 ±3.21 11.2 ±2.11 4.66 ±0.953 Fungi (12) -25.0 ±1.28 9.65 ±2.13 4.30 ±1.12 11.4 ±4.21 a: Plant % N and C:N ratio exclude graminoids, mosses, and ferns and were based on the following references, details in text: Michelsen et al. 1998, Dijkstra et al. 2003, Robbins et al. 2005, Caut et al. 2008.

88 • Plants a) 4 Arthropods • Fungi O Meadow voles A Deer mice D Red-backed voles O Heather voles

-#-

-32 -31 -30 -29 -28 -27 -26 -25 -24 -23 Mean 513Carbon

89 16 • Plants 15 b) 14 A Arthropods

13 • Fungi

12 O Meadow votes 11 A Deer mice 10 D Red-backed votes 9 O Heather voles 8 7 6 5 4

3 2 1 0 -1 -2 -3 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 Mean 613Carbon

90 15 • Plants c) 14 A Arthropods 13

12 • Fungi

11 O Meadow voles

10 A Deer mice

9 D Red-backed voles 8 o Heather voles 7

6

5

4

3

2

1

0

-1

-2

-3 t y r < r r t i f i i i 32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 Mean 513Carbon

Figure 4.3. Mean 813C and 815N values of small mammals and their food sources. Food sources are a) not corrected for dietary discrimination, b) corrected for dietary discrimination based on trophic level values and c) corrected for dietary discrimination based on trophic level and transformed to encompass most consumers(+0.45%o for A813C and-1.0%o forA515N, see Appendix E). Error bars are 1 standard deviation.

91 Small mammal species differed in the major proportion of diet consumed based on mixing models (see Table 4.4). All mixing models indicated that meadow voles ate chiefly plants, followed twice by fungus and twice by arthropods. Average percentage of plant in meadow vole diet ranged from 44.7% (trophic level discrimination factors in an IsoError model) to 56.2% (trophic level discrimination factors in IsoError) depending on the model. Percentage of fungus consumed by meadow voles ranged from 4.80 to 31.3%, and percentage of arthropods consumed ranged from 15.2 to 46.2%. Deer mice consumed mainly arthropods (78.5 and 86.4%), followed by plants (19.9 and 23.6%), and little fungi (-9.90 and 1.58%) based on models with discrimination factors from transformed trophic level values, though IsoConc results are questionable as deer mice fell outside the concentration-adjusted mixing polygon, resulting in a negative proportion for fungi. Trophic-level-based mixing models indicated the same pattern of high consumption of arthropods (52.9 and 89.1%). Plant versus fungi carbon stable isotopes indicated deer mice ate more fungi than plants, likely due to the absence of arthropods in the one isotope, two-endpoint model, because arthropods and fungi had nearly equivalent carbon isotope signatures. Arthropods were the only food source with confidence intervals that never included zero percent for deer mice. Two out of three mixing models including arthropods indicated that red-backed voles consumed mainly arthropods (60.9% and 77.2%) while the third indicated red-backed voles consumed mainly fungus (59.5%). Average proportion of fungi in the diet was always higher than 20%, and fungus confidence intervals never included zero, although plant confidence intervals did. Both two-endpoint mixing models for heather voles indicated that they consumed mainly plants compared to arthropods (66.7±6.60% versus 33.3 ± 6.60%) and fungus (63.3 ± 8.13% versus 36.7 ± 8.13%).

92 Table 4.4. Proportion of plants (PLNT), arthropods (ARTH), and fungi (FNGI) in small mammal diets based on standard mixing models (IsoError) and concentration-dependent mixing models (IsoConc). A513C and A815N derivation: TL=trophic level and TF=transformation (see Appendix E). Proportion in diet is mean percentage of diet (± standard error for IsoError).

A813C and A815N Proportion in diet (%) 95% Confidence intervals (%) derivation Model Food sources Isotope(s) Plant Arthropod Fungus Plant Arthropod Fungus Meadow voles TL IsoError ALL 813C, 815N 44.7 ± 3.45 41.2 ±6.14 14.0 ±4.18 37.9-51.6 29.1-53.4 5.70 - 22.3 TL IsoConc ALL 813C, 815N 49.0 46.2 4.80 TL and TF IsoError ALL 813C, S15N 53.4 ±3.80 15.2 ±6.59 31.3 ±4.31 45.8-61.1 2.09 - 28.3 22.7 - 39.9 TLandTF IsoConc ALL 813C, S15N 56.2 19.6 24.1 Deer mice TL IsoError PLNT, ARTH S15N 47.1 ±7.09 52.9 ± 7.09 33.1-61.1 38.9 - 66.9 TL IsoError PLNT, ARTH 813C 10.9 ± 4.69 89.1 ±4.69 1.50-20.2 79.8 - 98.5 TL IsoError PLNT, FNGI 815N 83.9 ±2.00 16.1 ±2.00 80.0 - 87.9 12.1-20.0 TL IsoError PLNT, FNGI S13C 4.65 ±8.38 95.3 ± 8.38 0.000 - 22.9 77.1 -100 TLandTF IsoError ALL 8I3C, 815N 19.9 ±4.21 78.5 ±7.52 1.58 ±5.15 11.5-28.3 63.6 - 93.4 0.00-11.8 TL and TF IsoConc ALL 813C, 815N 23.6 86.4 -9.90 Red-backed voles TL IsoError FNGI, ARTH 815N 77.2 ±5.87 22.8 ± 5.87 65.5 - 88.8 11.2-34.5 TL IsoError FNGI, PLNT S15N 53.7 ±3.56 46.3 ±3.56 46.4 - 60.4 39.1-53.6 TL and TF IsoError ALL 813C, 815N 1.97 ±4.44 60.9 ±8.19 37.2 ± 5.86 0.00 -10.9 44.6-77.1 25.5-48.8 TL and TF IsoConc ALL 813C, 815N 0.700 39.7 59.5 Heather voles TL IsoError PLNT, ARTH 813C 47.8 ± 5.00 52.2 ± 5.00 36.3 - 59.3 40.7 - 63.7 TL IsoError PLNT, FNGI 813C 44.2 ± 6.62 55.8 ± 6.62 29.9 - 58.5 41.5-70.1 TLandTF IsoError PLNT, ARTH 8,5N 74.2 ± 9.30 25.8 ± 9.30 51.4-96.9 30.8-48.6 TL and TF IsoError PLNT, ARTH 813C 57.8 ± 4.94 42.2 ± 4.94 46.1-69.5 30.5 - 53.9 0.879 - TL and TF IsoError PLNT, FNGI S15N 92.1 ±2.95 7.86 ± 2.95 85.2-99.1 14.8 TLandTF IsoError PLNT, FNGI 813C 54.8 ± 6.20 45.2 ± 6.20 41.1-68.4 31.6-58.9 4.4 Discussion

4.4.1 Small mammal diet in relation to linear features

A correlation between food availability and small mammal counts is not enough evidence to show that food resource distribution can cause small mammal community shifts; actual evidence of the importance of food resources is required. Stable isotopes, mixing models, and gut content analysis suggested that the three dominant small mammal species in the northern boreal forest have significantly different diets. Deer mice consumed mainly arthropods, meadow voles and heather voles consumed mainly plants, and red-backed voles consumed mostly arthropods or fungi, depending on the analysis type and model used. Gut content analysis provided useful qualitative information on what diet items to include as end-members in mixing models, but diet was difficult to quantify reliably with gut content analysis. Mixing models are reliant on adjusting food sources by the correct discrimination factors to function properly (Phillips and Koch 2002, Caut et al. 2008b), which proved difficult to obtain for boreal small mammals (Appendix E). Gut content analysis on a subset of red-backed voles and deer mice showed that of several food items consumed (vascular plant, fungus, seed, berry, moss, lichen, and arthropod), fungi was usually the most frequently detected. Alimentary tract (stomach plus intestinal tract) contents suggested that deer mice and red-backed voles both consumed mainly fungi, with arthropods a higher proportion in deer mouse diet. Deer mouse stomachs contained arthropods more often than did red-backed vole stomachs, and contained arthropods and berries more often than they contained fungus. Stomach contents of both species had high amounts of vascular plant tissue, however some of this was likely carrot bait recently consumed in the traps. The prevalence of fungi in alimentary tracts and red-backed vole stomachs could be misleading because fungal spores are resistant to digestion (Claridge et al. 1999), and are easily dispersed in homogenous liquid mixtures. Tissues of arthropods, berries, and plants might be more easily digested (Finke 2007) and are often in larger pieces less easily mixed in suspension, thus easier to miss in microscopic analysis especially when diet composition is recorded on the basis of presence/absence (Hansson 1970). Digestive enzymes, starch, lipids, and non-food items (e.g.,

94 cotton bedding) can also obscure food items and future gut content analyses should attempt to clear the gut, though the best method to do so is unclear (Hansson 1970). Stable isotope mixing models showed that small mammals differing in habitat use (Chapter 2) also differed in isotopic signatures, and thus likely in diet. Meadow voles and heather voles consumed mainly plants, but also some fungi and arthropods. This is consistent with literature that suggests meadow voles augment their herbivorous diets with arthropods or fungi (Burt and Grossenheider 1976, Reich 1981) but often eat only plant matter (Thompson 1965). Furthermore, meadow voles are often confined to areas with grasses and other herbaceous plants (Zimmerman 1965, Banfield 1974, Ostfeld et al. 1999) and tend to prefer less fibrous, more easily digested plants or plant parts such as legumes and growing leaves and buds in general (Keys and Soest 1970, Norrie and Millar 1990). This is consistent with their preference for linear feature habitat which had higher cover of forbs, grasses (Chapter 2), and legumes (Chapter 3). The relative paucity of forbs and legumes in the forest could have contributed to the sharp decline in meadow voles with distance away from forest edge (Chapter 2). Heather voles eat forbs but rarely grasses or sedges, and also eat fungi, and perhaps because of this were found in forest rather than on linear features (Forsyth 1985, Wilson and Ruff 1999, Chapter 2). Stable isotope analysis suggested that red-backed voles consumed mainly arthropods, with fungi as a second choice, which conflicted with gut content analysis which suggested they ate more fungus than anything else. Gut content analysis might simply be biased towards fungi because of the prevalence of resistant fungal spores (Claridge et al. 1999) that are easily suspended in mixtures (Hansson 1970). Forsyth (1985) reported that southern red-backed voles consumed large quantities of arthropods, although they preferred growing, vegetative parts of plants. High variability of red-backed vole isotope ratios (Table 4.1, Fig. 4.3) suggests that red- backed voles can be opportunistic and shift their diet in response to availability of foods (Merritt 1981, Dyke 1971). For example, Dyke (1971) found that red-backed voles were able to switch the dominant food item in their diet temporally to match what was seasonally or annually available (berries versus fungi), so it is possible that my populations switched their diet in response to spatial variation of arthropod and fungal food. Some northern populations of red-

95 backed voles seemed insensitive to changes in their presumed food and structural resources (Douglass 1976, Keinath and Hayward 2003) but if insects are a more important food source than previously assumed, such a lack of response might simply indicate misinterpretation of what resources are critical (Carrier and Krebs 2002). Most previous studies strongly suggest plants and fungi are more prevalent in red-backed vole diet than are arthropods (Dyke 1971, Maser et al. 1978, Burt and Grossenheider 1976, Mills 1995, Cheung 2008, unpublished), and it is possible that mixing models are implying incorrectly high proportions of arthropods in the diet, due to mixing model tendency to overestimate the proportion consumed of rare food items and underestimate proportion consumed of common items (Rosing et al. 1997). Many red-backed vole populations appear dependent on fungi for food and possibly for water (Dyke 1971, Norrie and Millar 1990, Maser et al. 1978, Mills 1995, Moses and Boutin 2001). In Chapter 2 I found that red-backed vole numbers were low in linear features, where fungus was also low and that neither red-backed voles nor fungus responded to distance from edge. Results of the concentration-dependent mixing model (IsoConc) which suggested red-backed voles consume mainly fungi, might be the only valid three-source model (Phillips and Koch 2002) because other mixing models assume that elemental composition of food sources is approximately equivalent, i.e., all food sources have similar concentrations of C and N (Phillips and Koch 2002). In the case of a mixed diet of plants, arthropods, and fungi, this assumption was violated (Phillips and Koch 2002), which might be why I found enrichment in red-backed vole 13C compared to fungi, but depletion in 15N. Similarly, Mcilwee and Johnson (1998) reported that a mid-size marsupial was depleted in heavy nitrogen, but not carbon stable isotopes compared to fungi, which might relate to C and N concentrations in the fungi, and how the elements were taken up by the marsupial. Other reasons for mixing models unexpectedly attributing high importance to arthropods could relate to missing food sources. At any rate, red- backed voles consumed a large amount of fungi, and alimentary tracts of red-backed voles collected in the forest contained a higher diversity of fungal genera (Cheung 2008, unpublished). As predicted, deer mice consumed mainly arthropods along with plants and little fungi based on mixing models. This contrasted with gut content analysis which suggested deer mice consumed mainly fungi, followed by arthropods. Deer mice are notoriously opportunistic (Witt

96 and Huntly 2001, Moses and Boutin 2001) and could have widely varying diets that include more fungi in some regions, but again conventional gut content analysis can be biased towards widely dispersed resistant fungal spores, and mixing models could be showing the true proportion. In this case, unlike for red-backed voles, most previous studies agree with the mixing model findings that deer mice eat mainly insects (Dyke 1971, Banfield 1974, Carey and Johnson 1995, Bowers et al. 2004) and that fungi is usually a less important food for deer mice (Pyare and Longland 2001, Banfield 1974, Forsyth 1985). Furthermore, in Chapter 2 I found that deer mice were equally abundant on linear features as in forest and were higher in edges, which correlated well with shrub-dwelling arthropod biomass and numbers that were higher on linear features, and sometimes higher in forest edge versus forest interior habitat (Marenholtz 2007, unpublished). Deer mice are also morphologically and behaviourally more capable insect predators than red-backed voles (Dyke 1971, Langley 1994). If deer mice do eat mainly insects, they are likely to continue to thrive at edges where insect numbers and biomass often increase (Peng et al. 1992, Jokimaki et al. 1998, Marenholtz 2007, unpublished). I expected omnivorous deer mice to occupy a higher trophic level than herbivores, and indeed deer mouse isotopic values were higher than those of herbivorous heather voles (Forsyth 1985). Deer mice were trophically enriched compared to meadow voles for carbon but not for nitrogen isotope ratios. Possible causes of lower nitrogen isotope ratios in deer mice range from differing physiologies of deer mice and meadow voles (Ambrose 1991), to metabolic routing, to possible omitting of a food source in the mixing model (Phillips 2001). Appropriate food sources to use in mixing models, based on gut content analysis, would have been vascular plants, arthropods, fungi, and berries. Berries were the only major food source that was unavailable. Berry species recorded in my study area included low-bush cranberry, Canada buffaloberry, bunchberry, rose, wild red raspberry, red-osier dogwood, and saskatoon (Chapter 2). Recent studies examined berry stable isotopes of the same species in Saskatchewan boreal forest (Bennett and Hobson 2008 in press) and average 813C and 815N values for the berry species listed above was -26.5%o and 1.07%o, respectively. If berry carbon and nitrogen isotope ratios in my study area were similar, this would place berries close to arthropods in isotopic signature. Assuming discrimination similar to that in plants, trophic-level-

97 corrected berry signatures would be similar to 8 C = -25.8%o and 8 N = 4.23%o (Appendix E, Robbins et al. 2005), which falls within the mixing polygon in Fig. 4.3b close to arthropods. Thus, the prevalence of arthropod in small mammal diets could have been confounded with the similar but unmeasured berry values. In the case of voles, this is important because reports of voles consuming berries (Banfield 1974, Carey and Johnson 1995, Hanski and Hentonnen 1996, Maser et al. 1978, McDonough and Rexstad 2005, Miller and Getz 1977) are more numerous than those reporting consumption of arthropods (Burt and Grossenheider 1976, West 1982, Forsyth 1985). Deer mice are thought to consume large amounts of insects (Dyke 1971, Banfield 1974, Carey and Johnson 1995, Bowers et al. 2004), though berries can be important, particularly over winter (Dyke 1971, Banfield 1974, Burt and Grossenheider 1976). Furthermore, lack of berries in red-backed vole and deer mouse gut contents in this study could be due to the relative difficultly of identifying amorphous fragments in stomach contents. Low 815N of deer mice relative to meadow voles could relate to consumption of old, over-wintered berries (Dyke 1971) as Handley and Scrimgeour (1997) found that rose (Rosa canina) fruits became 3%o depleted in 15N with age. In NT approximately 10% of berries counted in June and less than 1% of berries counted in July were overwintered berries (see Chapter 2 for methods). Either overwintered berries were unlikely food sources for my specimens (also see Smith 1973) or they were removed by foraging animals prior to sampling. Deer mice are highly mobile and Dyke (1971) found that deer mice compensated for declines in berries by increasing movements to find old berries from the previous year's crop. Further research into the importance of overwintered and new berries to deer mice and voles is thus required. Another possible missing food source is passerine chicks or eggs, because deer mice were captured on video preying on songbird nests, as were red-backed voles to a lesser extent (Bayne et al. 2007, unpublished, Bayne et al. 2008, unpublished). Consumption of mammal or bird tissues by our specimens could not be confirmed via gut contents without special staining techniques. Wrong discrimination values could also have caused consumer values to fall outside the mixing polygon. I used a literature review specific to my system and applied previously tested influences of trophic level on discrimination to obtain the best possible estimates of discrimination factors (Appendix E). There might be no ideal substitute to conducting feeding trials with the species and foods of interest to obtain system-specific discrimination factors (Caut et al. 2008b), however. Wild small mammals feeding on natural diets could differ greatly from lab-reared animals fed mostly manufactured or plant-based diets. For instance, the only study of wild small mammals feeding on natural diets in my review of muscle isotopic discrimination produced the highest values for both A813C and A815N, though marine nutrient sources might have had a large influence on their system and it was not a controlled feeding experiment (Drever et al. 2000). Lab rodents were often fed C4-plant or manufactured diets (Table E.l), and were likely under less physiological stress than animals in the wild, and diet and stress both affect stable isotope values and discrimination factors (Arneson and MacAvoy 2005, Sare et al. 2005). Future ecological studies using isotopes to delineate food webs and trophic relationships would benefit from controlled feeding studies on a subset of wild specimens fed natural diets, though in some cases this might not be feasible (e.g., endangered or rare species, Caut et al. 2008b). Specific discrimination factors, along with measurement and reporting of %N, %C, and C:N would greatly improve diet studies that often rely on inaccurate and potentially misleading discrimination estimates. Error could also have been introduced by removing lipids from small mammal muscle tissue only and not from diet item tissues, as lipid removal has been found to increase 8 C and 815N in fish tissues by +1.07%o and +1.59%o, respectively (Murry et al. 2006). Post et al. (2007) demonstrated that in some cases lipids should be extracted from plants or plant 8 C values should be normalised using % carbon before comparison to consumers to reduce error due to variable lipid content in plants. Neither approach was an option for this study, and effects of lipid correction in food sources on small mammal diet proportions results merits further research.

4.4.2 Potential to trace use of disturbed habitats using stable isotopes of small mammals

Direct tracing of linear feature resource use by animals based on unique isotopic signatures inherent to linear features does not appear to be feasible (Chapter 3). For predators of small mammals, however, stable isotope analysis has great potential for tracing use of linear feature, edge, and forest habitat based on the relative importance of different small mammal

99 species in the diet. Unique stable isotope signatures of the three most common small mammal species in the boreal forest were highly predictable and reliably distinctive, often approaching differences seen between trophic levels (l%o increase in 6 C and a 3%o increase in 815N, Arneson and MacAvoy 2005, Caut et al. 2008a). These small mammals also had strong and contrasting habitat preferences (Chapter 2). Meadow voles were captured almost exclusively on linear features. Deer mice, the other small mammal caught in high numbers on linear features, were also nearly a trophic level's worth different from red-backed voles for carbon and nitrogen isotope ratios (0.7%o lower in 813C and 3.23%o lower in 815N). Together, meadow voles and deer mice constitute a 'linear feature signature' at the level of the vertebrate food chain that could be traced up to raptors and furbearers, and determine predator use of linear features. As deer mouse contribution to predator diet increased relative to use of meadow voles or other species, isotope signatures could also indicate use of 'edge' resources. Red-backed voles were more common in the forest than any other species (Chapter 2) and thus represent a 'forest signature' than can trace predator use of native forest food resources.

100 CHAPTER 5: SYNTHESIS

5.1 Small mammal and resource responses to linear features and edge The small mammal community on linear features differed markedly from that in intact boreal forest, with overall positive influence on small mammal abundance in NT, but a negative effect on overall abundance in AB. Resource differences between linear features and forest could have driven observed small mammal linear feature and edge responses, which differed for deer mice, red-backed voles and meadow voles, the numerically dominant small mammals in the northern boreal forest. Linear features had adverse effects on abundance of some small mammal species, and positive effects on others. Linear features were grassy, herbaceous, and sometimes shrubby habitat that attracted open habitat specialists and generalists. Meadow vole abundance was highest on linear features, and linear features hosted species not captured as much, if at all, in forest: least chipmunks, shrews (possibly due to S. arcticus), short-tailed weasels, and meadow jumping mice. Deer mouse abundance was equivalent between linear features and in forest. Red-backed vole abundance was lowest on linear features. Linear features also had low, or no, captures of other species found in forest (red squirrels, heather voles, taiga voles, and possibly S. montanus). Due to the relative permanency of linear features compared to other grassy habitats in the boreal (Douglass 1976,1977, Pohlman et al. 2007) linear features could promote the establishment of unique small mammal communities with unknown consequences for forest dynamics. Edge effects near linear features were rare, likely reflecting the fact that few resources thought to be important for small mammals responded to distance from edge. Red-backed voles were evenly distributed throughout the forest without regard to distance from edge in NT, with possibly lower numbers near edge in AB. Deer mice and possibly chipmunks increased near edges. Edges are likely not a more resource-rich habitat for deer mice and chipmunks per se because only shrub berries and some insects (Marenholtz 2007, unpublished) were higher near edges and some forms of cover decreased. Instead, deer mice and chipmunks could be occupying the edge to access complementary resources that are differentially distributed between

101 the linear features and forest (Ries and Sisk 2004). Higher numbers of meadow voles at edges likely reflected spillover from linear features combined with their unwillingness to venture further into non-preferred habitat (Ries and Sisk 2004). Lack of edge effects for red-backed voles was similar to the majority of previous studies which reported largely neutral edge effects (Chapter 1). The lack of response is predictable given the lack of edge response of most small mammal resources, including fungi, further supporting the notion that edge effects relevant to small mammals are dependent on resource availability (Ries and Sisk 2004). Shifts in available resources and subsequent changes in small mammal communities on linear features could have negative implications for boreal forest ecosystem functioning. Increased abundance of herbivorous meadow voles and granivorous deer mice on linear features and decreased abundance of mycophagous red-backed voles could lead to decreased recruitment of trees and slower regeneration (Sullivan 1979, Ostfeld et al. 1999, Terwilliger and Pastor 1999, Cadenasso and Pickett 2000, Howe et al. 2006). Small mammals in disturbed habitat can negatively impact nesting birds directly through predation (Bradley and Marzluff 2003). Of more concern, however, is the potential for small mammals to attract predators to linear features or edges due to high local abundance of colonial meadow voles, and reasonable numbers of other small mammals and increase predation indirectly. Once in the area, predators could prey on more sensitive boreal forest species, such as songbirds, especially given the tendency for small mammal populations to crash periodically, forcing predators to seek, and potentially decimate, alternate prey (Drost and McCluskey 1992, Redpath 1995, Huhta et al. 1998, Michelat and Giradoux 2000, Cain et al. 2006).

5.2 Small mammal food resource use: Stable isotope and conventional analysis To predict linear feature habitat and edge effects based on resource availability, it is important to understand what resources are important to animals, which is often difficult to determine for food resources in particular (Dyke 1971, Ure and Maser 1982, Elliott and Root 2006). Deer mice, red-backed voles, and meadow voles each appeared to specialize on different food resources and those food resources responded to linear features in ways corresponding to

102 small mammal responses. Deer mice consumed mainly arthropods, which could have been higher near linear feature edges (Peng et al. 1992, Jokimaki et al. 1998, Marenholtz 2007, unpublished, Bayne et al. 2008). Thus, food resources on and near linear features could have compensated for lack of other resources such as DWM. Red-backed voles consumed relatively more fungi than deer mice, and, like red-backed voles, fungal sporocarps were less abundant on linear features with no response to distance from forest edge. Stable isotope mixing models unexpectedly estimated arthropods as the most important food for red-backed voles, but this could have been due to not meeting an assumption of mixing models (see discussion on discrimination factors below). On the other hand, red-backed voles might simply be more omnivorous than is typically assumed (West 1982, Forsyth 1985). Meadow voles consumed mainly plant matter, and high light and temperature regimes (Geiger 1965, Saunders et al. 1991, Murcia 1995, MacFarlane 2003, Wender 2004, Blake 2006, unpublished) led to prolific development of herbaceous plants on linear features. Stable isotope analysis successfully quantified dietary patterns seen in gut contents, though further investigation of the role of berries is warranted, and was less labour intensive. Results frombot h methods of diet analysis further supported the hypothesis that changes in resources on and near linear features could lead to observed changes in small mammal abundance and edge responses (Ries and Sisk 2004).

5.3 Tracing wildlife use of linear feature food resources Linear features possessed unique stable isotopic signatures of carbon and nitrogen. Plant 813C was approximately l%o higher on linear features than in forest, slightly lower than the canopy effect elsewhere in the boreal forest (France 1996), whereas plant 815N decreased on linear features (less than l%o depletion) similar to findings of Tan et al. (2006) for soil compaction, but less than the depletion found by Hobbie et al. (2005) in recently de-glaciated areas. Because the linear feature stable isotope signatures transferred only to deer mice, and not to most consumers, and habitat had a small effect on stable isotope ratios relative to other influences, linear feature isotope signatures per se did not seem useful for tracing wildlife use of linear feature resources.

103 Tracing use of linear feature food resources by higher level predators of small mammals such as owls, marten, or foxes (Reed et al. 2007, Naylor and Bendell 1983, Banfield 1974) using stable isotope ratios might be possible. Small mammal species differed strongly in isotopic signatures and in habitat preference based on differing abundance in different habitats. Many predators hunt the highest density prey (Hanski and Henttonnen 1996, Boonstra and Krebs 2006). Thus, a predator hunting small mammals on linear features would likely consume mainly meadow voles and some deer mice, whereas predators hunting in forest would consume mainly red-backed voles. Predators hunting on linear features would thus pick up the 'meadow vole' signature and predator feeding habits could be easily quantified using non-invasive samples such as hair, blood or feathers, without resorting to labour-intensive sorting through pellets, feces, or gut contents. Discrimination factors between food sources and consumers can vary between tissue types (DeNiro and Epstein 1978,1981, Hobson et al. 1993) and non-invasive or non-lethal sampling is desirable. Thus, small mammal species differences in isotope ratios should be confirmed in blood or fur and the resulting stable isotope ratios can then be used in mixing models.

5.4 Limitations and recommendations for future research

The permanency of linear feature small mammal communities is unknown for two reasons that merit further research: 1) regeneration of linear features could erase changes in small mammal communities, and 2) small mammals in edges and on linear features could have low fitness and thus could decline over time (Pulliam 1988, Morris 1989, Menzel et al. 1999, Ries and Fagan 2003, Wolf and Batzli 2004, Johnson 2007). The first issue could be examined by comparing resource availability and small mammal communities on linear features of various ages that have been (or can be) blocked off from further access and allowed to regenerate. The second issue could be addressed by narrowing the focus and extent of the study to get better estimates of true densities, and collecting demographic information on survival and reproduction (Douglass 1976, Smith 1973, Morris 1989). Trapping can be used to indirectly infer habitat use based on capture location, and stable isotope analysis can often determine where animals are obtaining food sources. Both methods of

104 determining habitat use have their limitations, however. The former is indirect and does not distinguish dispersal from habitat use, and the latter only informs about food resources in fortunate cases where disturbed habitat is isotopically distinctive in some way (either intrinsically or in the trophic level of foods available). Tracking small mammal movements could be a powerful aid to interpreting the importance of habitats and resources to small mammals (Keinath and Hayward 2003, Cunjak et al. 2005, Appendix C). Tracking small mammal movements could identify individuals that move predominantly in linear feature versus forest interior versus forest edge that inhabit or that frequently cross the edge to access more than one habitat. A pilot project revealed that small mammals tended to stay in the habitats in which they were captured. Improvements to the spool and line technique (Appendix C) or use of fluorescent powder (McDonald and St. Clair 2004b, Lemen and Freemanl985) can help increase sample sizes. To be more effective in determining use of non-preferred habitat, future tracking studies should focus on contrasting movement in different habitats, perhaps by translocation of individuals to non-preferred habitat prior to release (McDonald and St. Clair 2004b, Lemen and Freemanl985). Some aspects of the use of stable isotope analysis in ecological research are still rudimentary. In particular, the use of fixed isotopic discrimination factors obtained from literature is quite common in diet and trophic level studies, yet this can lead to inaccuracies in isotope models used to estimate contribution of food sources to a consumer (Robbins et al. 2005, Hwang et al. 2007, Caut et al. 2008a, 2008b, Appendix E). This limitation can be partially overcome by using discrimination factors specific to the tissue, diet, and organism types of interest to the researcher (Appendix E). Even with such specificity used for this study, however, incorrect discrimination factors could lead to erroneous allocation of the importance of arthropods versus fungi for red-backed voles among other issues. The best solution, therefore, appears to be to use species-specific, diet-specific, and tissue-specific discrimination factors which can only be obtained through controlled feeding experiments using pure diets and diet switching (Caut et al. 2008b, Appendix E). This is difficult for wild animals, and rarely attempted (but see Haramis et al. 2001) especially in the case of rare or endangered animals (Felicetti et al. 2003, Caut et al. 2008b). Small mammals, however, reproduce quickly (Banfield

105 1974), and populations are robust even to permanent removal of individuals from the population (Sullivan et al. 2003). Future research should focus on controlled feeding experiments utilizing wild small mammals and diet items collected from the habitat(s) of interest (e.g., Norrie and Millar 1990) to determine accurate discrimination factors for various tissues and diet types. This study found strong evidence to support the hypothesis that resources influence small mammal responses to linear features and edges. However, interactions within and between small mammal species can also affect small mammal distributions. Red-backed voles and deer mice can be excluded from habitat by larger and competitively superior meadow voles (Darveau et al. 2001); however the reverse has been found with red-backed voles and deer mice excluding meadow voles from woodland on islands (Grant 1971), and several studies report little to no effect of competition on small mammal distributions (Galindo and Krebs 1985, Wolff and Dueser 1986, Davidson and Morris 2001). Some interaction of competition, predation, and resource distribution could ultimately determine exclusion or lack thereof of one or more of the three species (Grant 1971, Merritt 1981) and species interactions warrant further research.

5.5 Conclusion

Managing cumulative impacts of ever-increasing numbers of linear features in the boreal forest is a primary concern, especially as development increases in the Canadian north (Schneider 2002). My research demonstrates that linear features have a role as habitat and can cause small mammal community shifts that are predictable on the basis of resource availability. Changes in distributions of small mammal species that differ in feeding habits, social behaviour, and possibly in attractiveness to predators (Day 1968), can have far-reaching consequences in the boreal forest for forest regeneration, nutrient cycling, and predator-prey dynamics, and could also warn of changes to come for other taxa. Integrating changes in small mammal species abundance in mixedwood forest into models of cumulative impacts, and investigating possible small mammal changes in other boreal habitats, is essential for developing effective management strategies for the northern boreal forest.

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132 APPENDIX A Table A.l: Citations for small mammal abundance responses to non-forest cleared habitat and distance from edge within forest (Table 1.1). Displayed are numbers corresponding to citations that follow. Note one study could have more than one response (positive, neutral, and (or) negative), thus can be found in more than one category per species and disturbance type.

Habitat Response Edge Response Species Habitat type Positive Neutral Negative Positive Neutral Negative My odes [Clethrionomys] spp. (red-backed voles) Agriculture 36,u 2 u M. gapperi unless has 5f , 8, 10,12c, 13, u subscripts: g = M. u 14,16f , 19,27, 2,8,14,19,21, Forestry 18,22 10, 12, 14, 16f 14,27 28c,29c glareolus, c= M 28c,29c,31,34, 35c californicus, f= M a a a rufocanus, r= M. rutilus Linear clearings 9r, 23 23 8

Peromyscus spp. (mice) P. Agriculture 7,32, 7,, 24, 30, 24,39, l,,2,4m,7,7,,26,, l,,4m, 15,, 38, 39, maniculatus unless has Forestry 12,13,22,29,33 10,18,22,27,31 8,10 29 33 2, 8,27 subscripts: 1= P. leucopus, or m=both P. leucopus and P. Linear clearings 23a, 25, 8a, 17, 23a, 25, maniculatus mixed population

Microtus pennsylvanicus Agriculture 3. 7,24 7 3,7 (meadow voles) Forestry 8,22b,31 18 8 Linear clearings 9,11, 23a 23a

Agriculture 32 40 15 Tamias spp. (chipmunks) Forestry 13,22,33,34 18,22 20,29 20,33 20,29 Linear clearings 25 25 Sorex spp. (shrews) Agriculture 36 24b, 36 15 Marina spp. (short-tailed b 6,10b, 12, 18,22, shrews) denoted by Forestry 22 6,10b 27 29, 33 33 subscript "b" 27,29 Linear clearings 23a 23a a:Linear feature was a river or a lakeshore. : Review of 12 studies, 6 were on M. pennsylvanicus, 5 were on other Microtus spp. u: Study was in the United Kingdom. A.l Citations used to generate Table A.l and Table 1.1. 1. Anderson, C.S., A.B. Cady, and D.B. Meikle. 2003. Effects of vegetation structure and edge habitat on the density and distribution of white-footed mice (Peromyscus leucopus) in small and large forest patches. Canadian Journal of Zoology 81: 897-904. 2. Bayne, E.M., and K.A. Hobson. 1998. The effects of habitat fragmentation by forestry and agriculture on the abundance of small mammals in the southern boreal mixedwood forest. Canadian Journal of Zoology 76: 62- 69. 3. Cadenasso, M.L., and S.T.A. Pickett. 2000. Linking forest edge structure to edge function: mediation of herbivore damage. Journal of Ecology 88: 31-44. 4. Chalfoun, A.D., M.J. Ratnaswamy, and F.R. Thompson. 2002. Songbird nest predators in forest-pasture edge and forest interior in a fragmented landscape. Ecological Applications 12: 858-867. 5. Christensen, P., and B. Hornfeldt. 2006. Habitat preferences of Clethrionomys rufocanus in boreal Sweden. Landscape Ecology 21: 185-194. 6. Clayton, J.C. 2003. Effects of clearcutting and wildfire on shrews (Soricidae: Sorex) in a Utah coniferous forest. Western North American Naturalist 63: 264- 267. 7. Cummings, J.R., and S.H. Vessey. 1994. Agricultural influences on movement patterns of white-footed mice (Peromyscus leucopus). American Midland Naturalist 132: 209-218. 8. Darveau M., P. Labbe, P. Beauchesne, L. Belanger, and J. Huot. 2001. The use of riparian forest strips by small mammals in a boreal balsam fir forest. Forest Ecology and Management 143: 95-104. 9. Douglass, R.J. 1977. Effects of a winter road on small mammals. The Journal of Applied Ecology 14: 827-834. 10. Fuller, A.K., D.J. Harrison, and H.J. Lachowski. 2004. Stand scale effects of partial harvesting and clearcutting on small mammals and forest structure. Forest Ecology and Management 191: 373-386. 11. Getz L.L., F.R. Cole; and D.L. Gates. 1978. Interstate roadsides as dispersal routes for Microtus pennsyhanicus. Journal of Mammalogy 59: 208-212. 12. Gitzen R.A., S.D. West, C.C. Maguire, T. Manning, and C.B. Halpern. 2007. Response of terrestrial small mammals to varying amounts and patterns of green- tree retention in Pacific Northwest forests. Forest Ecology and Management 251: 142-155. 13. Hadley, G.L., and K.R. Wilson. 2004. Patterns of density and survival in small mammals in ski runs and adjacent forest patches. Journal of Wildlife Management 68:288-298. 14. Hayward G.D., S.H. Henry, and L,F. Ruggiero. 1999. Response of red-backed voles to recent patch cutting in subalpine forest. Conservation Biology 13: 168- 176. 15. Heske, E.J. 1995. Mammalian abundances on forest-farm edges versus forest interiors in southern Illinois: is there an edge effect? Journal of Mammalogy 76: 562-568.

134 16. Hornfeldt, B„ P. Christensen, P. Sandstrom, and F. Ecke. 2006. Long-term decline and local extinction of Clethrionomys rufocanus in boreal Sweden. Landscape Ecology 21: 1135-1150. 17. Johnson, W.C., R.K. Schreiber, and R.L. Burgess. 1979. Diversity of small mammals in a powerline right-of-way and adjacent forest in East Tennessee. American Midland Naturalist 101: 231-235. 18. Kaminski, J.A., M.L. Davis, M. Kelly, and P.D Keyser. 2007. Disturbance effects on small mammal species in a managed Appalachian forest. American Midland Naturalist 157(2): 385-397. 19. Keinath D.A., and G.D. Hayward. 2003. Red-Backed Vole {Clethrionomys gapperi) response to disturbance in subalpine forests: Use of regenerating patches. Journal of Mammalogy 84: 956-966. 20. King D.I., C.R. Griffin, and R.M. DeGraaf. 1998. Nest predator distribution among clearcut forest, forest edge and forest interior in an extensively forested landscape. Forest Ecology and Management 104: 151-156. 21. Kingston S.R., and D.W. Morris. 2000. Voles looking for an edge: habitat selection across forest ecotones. Canadian Journal of Zoology 78: 2174-2183. 22. Kirkland G.L., Jr. 1990. Patterns of initial small mammal community change after clearcutting of temperate North American forests. Oikos 59(3): 313-320. 23. Macdonald, S.E., B. Eaton, C.S. Machtans, C. Paszkowski, S. Hannon, and S. Boutin. 2006. Is forest close to lakes ecologically unique? Analysis of vegetation, small mammals, amphibians, and songbirds. Forest Ecology and Management 223: 1-17. 24. Manson R.H., and E.W. Stiles. 1998. Links between microhabitat preferences and seed predation by small mammals in old fields. Oikos 82: 37-50. 25. McGregor, R.L., D.J. Bender and L. Fahrig. 2008. Do small mammals avoid roads because of the traffic? Journal of Applied Ecology 45: 117-123. 26. Meiners, S.J., and K. LoGiudice. 2003. Temporal consistency in the spatial pattern of seed predation across a forest-old field edge. Plant Ecology 168: 45-55. 27. Menzel, M.A., W.M. Ford, J. Laerm, and D. Krishon. 1999. Forest to wildlife opening: habitat gradient analysis among small mammals in the southern Appalachians. Forest Ecology and Management 114: 227-232. 28. Mills, L.S. 1995. Edge effects and isolation: red-backed voles on forest remnants. Conservation Biology 9: 395-402. 29. Mills, L.S. 1996. Fragmentation of a natural area: dynamics of isolation for small mammals on forest remnants. Pages 199-218 in R.G. Wright, editor. National Parks and Protected Areas: Their Role in Environmental Protection. Blackwell Science, Cambridge, Massachusetts, USA. 30. Morris, D.W. 1989. Density-dependent habitat selection - testing the theory with fitness data. Evolutionary Ecology 3: 80-94. 31. Moses, R.A., and S. Boutin. 2001. The influence of clear-cut logging and residual leave material on small mammal populations in aspen-dominated boreal mixedwoods. Canadian Journal of Forest Research 31: 483-495. 32. Nupp, T.E. and R.K. Swihart. 2000. Landscape-level correlates of small-mammal assemblages in forest fragments of farmland. Journal of Mammalogy 81: 512-526.

135 33. Rosenberg K.V., and M.G. Raphael. 1986. Effects of forest fragmentation on vertebrates in douglas-fir forests. Pages 263-272 in J. Verner, M.L. Morrison, and C.J. Ralph, editors. Wildlife 2000: Modeling Habitat Relationships of Terrestrial Vertebrates. University of Wisconsin Press, Madison, WI, USA: 34. Sullivan, T.P., D.S. Sullivan, and P.M.F. Lindgren. 2008. Influence of variable retention harvests on forest ecosystems: Plant and mammal responses up to 8 years post-harvest. Forest Ecology and Management 254: 239-254. 35. Tallmon, D.A. and L.S. Mills 2004. Edge effects and isolation: Red-backed voles revisited. Conservation Biology 18: 1658-1664. 36. Tattersall, F.H., D.W. Macdonald, B.J. Hart, P. Johnson, W. Manley and R. Feber. 2002. Is habitat linearity important for small mammal communities on farmland? Journal of Applied Ecology39: 643-652. 37. West S.D., R.G. Ford, and J.C. Zasada. 1980. Population response of the Northern Red-Backed Vole (Clethrionomys rutilus) to differentially cut white spruce forest. USDA, Forest Service, Pacific Northwest Forest and Range Experiment Station, Research Note PNW-362. 38. Wilder S.M., and D.B. Meikle. 2006 Variation in effects of fragmentation on the White-Footed Mouse (Peromyscus leucopus) during the breeding season. Journal of Mammalogy 87: 117-123. 39. Wolf M., and G.O. Batzli. 2002. Effects of forest edge on populations of white- footed mice Peromyscus leucopus. Ecography 25: 193-199. 40. Bennett, A.F., K. Henein, and G. Merriam. 1994. Corridor use and the elements of corridor quality: chipmunks and fencerows in a farmland mosaic. Biological Conservation 68:155-165.

136 APPENDIX B

B.l Red-backed voles {Myodes spp.) and shrews {Sorex spp.) in the northern boreal forest Closely related species of small mammals can differ in behaviour and habitat preference in ways that affect their responses to anthropogenic disturbance. Though similar in their general preference for forest habitat (Banfield 1974), southern red-backed voles (Myodes [formerly Clethrionomys see Musser et al. 2005] gapperi) and northern red-backed voles (Myodes rutilus) might behave differently in smaller-scale resource use (Banfield 1974, Maser et al. 1978, Merritt 1981, Carrier and Krebs 2002). M. rutilus, for instance, have been called the "Peromyscus of the north" due to their tendency to be opportunistic like deer mice (Whitney 1976, Douglas 1976), whereas M. gapperi seem to be sensitive to anthropogenic disturbance and negatively affected (Mills 1995, Darveau et al. 2001, McDonough and Rexstad 2005, Appendix A). Similarly, despite shrews (Sorex spp.) sharing a preference for moist habitats and a dependence on insects for food, each species prefers slightly different habitats ranging from meadows, swamps, and marshes to shrubby areas or forests with dense understory (Burt and Grossenheider 1976, Clayton 2003, Bowers et al. 2004, Elliott and Root 2006). Species of red-backed vole or shrew could thus strongly affect small mammal responses to linear features and edge. Based on range maps (Banfield 1974, Merritt 1981), the study area in southern Northwest Territories fell near the southern limit of M. rutilus and about 100km north of the northern limit of M. gapperi). The study area in northwest Alberta was in the range of M. gapperi only (Banfield 1974), which have been noted to have a gray colour morph lacking the distinctive reddish band down the head and back (Burt and Grossenheider 1976, Bowers et al. 2004). In boreal mixedwood forest in south-western NT and north­ western AB, shrews were likely a mixture of four species: Sorex hoyi, Sorex monticolus, Sorex cinereus, and Sorex arcticus (Burt and Grossenheider 1976, Bowers et al. 2004). Due to potential interactions between red-backed vole or shrew species and linear feature responses I used skull analysis to determine the proportion of voles that were M. rutilus in the NT study area, and to determine proportion of shrews in NT and AB that were Sorex hoyi, Sorex monticolus, Sorex cinereus, and Sorex arcticus. Skull analysis

137 was necessary because external morphology is not a good indicator of North American red-backed vole species (i.e., species are cryptic, Cook et al. 2004, Jung et al. 2006) nor diminutive species such as shrews (van Zyll de Jong 1983, Carey and Johnson 1995).

B.2 Methods

See Chapter 2 for details of study areas and design, and Chapter 3 for details of small mammal collection. Red-backed vole skulls were separated from the body, then pre-cleaned (skin and fur peeled away from the bone, eyes and brain removed) and cleared (remaining tissues dissolved) using a solution of 1.0 N KOH (potassium hydroxide). Skulls were soaked in 1.0 N KOH for six to ten hours in individual compartments. Cleared skulls were then soaked in water for four hours, and air dried in a fumehood. Tissues remaining on the skull were peeled off with forceps. Species of red-backed vole was determined based on the completeness of the post-palatal bridge, which is a primary diagnostic characteristic for distinguishing M. rutilus from M. gapperi (Merritt 1981, Jung et al. 2006). M. gapperi had completely fused posterior edges of their palates (complete post-palatal bridges). An incompletely fused posterior edge of the palate indicated M. rutilus. When captured, gray-phase M. gapperi were recorded as such. Shrews were identified by Wayne Roberts (University of Alberta Zoology Museum) and Ray Poulin (Royal Saskatchewan Museum) on the basis of dentition (van Zyll de Jong 1983).

B.3 Results and Discussion

Though rare, southern red-backed voles (M. gapperi) were present in boreal forest near Fort Simpson, NT. Skull analysis of NT red-backed voles indicated that most were M. rutilus. Out of 60 voles examined one had the complete post-palatal bridge of M. gapperi, whereas 50 had the incomplete post-palatal bridges of M. rutilus. Nine were inconclusive due to broken post-palatal bridges, though two appeared to have upper molar patterns characteristic of M. gapperi and five appeared to have upper molar patterns characteristic of M. rutilus (Banfield 1974). All four AB vole skulls examined as reference samples were M. gapperi with complete post-palatal bridges. Of 361 red- backed voles captured in AB, eight lacked reddish-brown pigment and were considered

138 gray-phase M. gapperi. Five gray-phase red-backed voles were caught in forest interior, three were caught in forest edge, and none were caught on linear features. No gray-phase red-backed voles were captured in the NT. Measurements were taken on two gray-phase vole: tail lengths were 34 mm and 36 mm, hind feet were 12 mm and 16 mm, ear lengths were 13 mm and 14 mm and body length was 100 mm and 91 mm. For comparison, average tail length of 10 AB red-phase voles was 34 mm, average hind foot length was 16 mm, average ear length was 14 mm, and average body length was 88 mm. The similar dimensions in addition to overall appearance confirm the gray and red striped voles were the same species. Based on tooth analysis of 42 NT shrews, S. cinereus (masked shrews, n=38) dominated the shrew community in NT, accompanied by few arctic shrews {S. arcticus, n=3) and one montane shrew (S. monticolous). Though sample sizes were low, S. monticolous (n=4) and S. cinereus (n=6) appeared co-dominant in AB and S. arcticus was again relatively rare (n=2). Red-backed voles were actively collected in NT for stable isotope and diet analysis, thus proportion of specimens identified as M. gapperi likely reflects the proportion of the population that was M. gapperi (1/51 or less than 2%). Gray phase red- backed voles in AB were unlikely to differ from regular red-backed voles in capture probabilities, and thus proportion captured should be equivalent to the proportion present in the population (8/361 or 2%). Shrews, on the other hand, were collected opportunistically following trap mortality, thus proportions could be skewed to shrew species less able to survive in captivity and might not represent true shrew species proportions in the study areas. The single confirmed M. gapperi captured near Fort Simpson, NT (61 46' N, 121° 15' W) represents the northernmost capture of M. gapperi in western Canada to date, as previous records of M. gapperi in the Northwest Territories were more than 100 km south of my study area near Fort Liard (Jung et al. 2006), or south of the Kakisa River (Dyke 1971), and captures in the Yukon were also further south (La Biche River Valley, 60° 8' N, 124° 4' W). Genetic tests could be used to explore possible hybridization between red-backed vole species in this area.

139 APPENDIX C

C.l Tracking small mammal movements on linear features

Corroboration between observed locations of resources, and actual locations of use is often lacking from studies on small mammals (Wolf and Batzli 2004, Keinath and Hayward 2003). Small mammals are difficult to observe because they are small, cautious, cryptic, and often nocturnal (Lemen and Freeman 1985). Trapping is often used to indirectly infer habitat use based on capture location, but does not distinguish whether the animal was using habitat resources where it was captured, or if it was simply passing through the area. Stable isotope analysis can also be used to determine where animals are obtaining food sources, although this relies on either a) animals assimilating site-specific isotopic signatures via diet (Chapter 3, Cunjak et al. 2005) or b) species having dietary preferences for isotopically distinct food items differentially distributed between habitats (Chapter 2 and Chapter 4, Nakagawa et al. 2007). In either case, there is potential for ecological misinterpretation due to various additional influences on stable isotopes such as season or microclimate (France 1996). Tracking small mammal movements can aid in interpreting the importance of habitats and resources to small mammals (Keinath and Hayward 2003, Cunjak et al. 2005). In conjunction with analysis of small mammal and resource distribution in linear feature and forest habitats (Chapter 2), and stable isotope analysis (Chapters 3 and 4), tracking small mammal movements could identify the relative importance of linear features versus forest as foraging habitat. I was interested in examining three types of behaviour in red-backed voles, deer mice, and meadow voles: movement exclusively on linear features, movement exclusively in forest and movement in both habitats that involved crossing the edge. If small mammals preferred to forage in a particular habitat their movement paths should meander through preferred habitat with few movements into alternate habitats (McDonald and St. Clair 2004a). I predicted that meadow vole movements would be constrained to the linear features if they perceived linear features as valuable habitat, whereas red-backed vole movements would be constrained to forest if linear features are poor habitat. Linear features and forest contained suitable resources

140 for deer mice, thus I predicted they would use both habitats equally, or would prefer the edge between the two habitats (Chapter 2).

C.2 Methods See Chapter 2 for details of study areas and design, and small mammal capture techniques. Small mammals were tracked using the spool and line technique (Boonstra and Craine 1986). Spools of thread were obtained from Imperial Threads Inc. (Northbrook, Illinois) and either left white or dyed red or blue to distinguish individuals tracked in close proximity. Spools were shrink-wrapped in saran wrap over a candle and thread was pulled from the centre of the spool until the bobbin reached 5% of the projected body weight of subjects (1 g spool for 20 g small mammals), resulting in removal of about 194 m of thread which left approximately 80 to 90 m for tracking use. Shrink-wrapped spools were then glued to the back fur of small mammals using wood- and-leather glue. Spools were held against each small mammal for 30 seconds to one minute. The subject was observed in a 5 gallon bucket for 10 minutes while the glue dried further. While the glue dried, the loose end of the thread was tied to an object near the trap the animal was first captured in. To reduce overlap of tracked individuals, small mammal home range sizes were used to determine where to trap and track. Meadow voles have the smallest reported home range radius of 10 to 17 m (Burt and Grossenheider 1976). Red-backed voles have larger home range radii of 40 to 70 m and deer mice also tend to range widely with average home range radii of 22 to 82 m (Witt and Huntly 2001, Banfield 1974). Sample sizes were low (17 animals tracked in total) and spool failure rates were high (i.e., the spool often fell off or was chewed off close to the release site; only five specimens appeared to reach the end of their spool thread) thus results were analysed qualitatively.

C.3 Results and Discussion Each small mammal tended to remain in the habitat they were captured in which was usually their preferred habitats. That is, most red-backed voles were captured and tracked in forest, most meadow voles were captured and tracked on linear features, and deer mice were captured within 10 m of forest edge and tended to remain in edge. Spools

141 often fell off before the end of the thread was reached, resulting in path lengths that varied from 4 m to 97 m (average 41.6 m). Deer mice were captured and released either in forest (n=4) or at the point of edge creation (0 m from edge, n=l). The deer mouse released at the point of edge creation travelled directly along the edge for 84 m, at which point the thread ran out. Three deer mice released near the edge (5 to 8 m from edge) moved exclusively within the forest away from the edge (to maximum distances between 16 and 21m from edge). One of those deer mice doubled back towards the edge after reaching its maximum distance from edge of 16 m (total path length of 20 m). One deer mouse tracked in forest interior as a control remained there with no obvious movement towards edge, and no obvious differences in the path or direction taken compared to small mammals released closer to edge. I tracked five meadow voles, the majority of which travelled on linear features rather than in forest. One meadow vole's spool fell off within five meters of its release on the linear feature, though it moved towards the centre of the linear feature. Two other meadow voles were also released on linear features. One stayed on the linear feature until its spool fell off (total path length = 37 m), and the other stayed on the linear feature for 53 m (75% of its total path), then crossed into forest for the remaining 25% of its path. One of the two meadow voles released right at the forest edge ran immediately onto the pipeline though it quickly lost its spool (14 m total path length). The other meadow vole released at the edge travelled directly along the edge (0 m into forest) for 77.5 m. The latter individual might have been fleeing from noise and vibrations caused by a gas generator turned on next to the release site shortly after the release and its path might not have reflected preferred habitat or foraging. Seven red-backed voles were tracked; five were captured, released, and tracked only in forest (n=5). One red-backed vole was released right at the edge, four were released in forest between 5.0 m and 27.5 m from edge, and two were released on linear features. The vole released right at the edge (0 m) travelled into the forest to a maximum distance from edge of 14 m (total path length of 52 m). Two of the four forest-released voles moved further into the forest (from 8 m to 37 m, total path length = 72 m and from 27 m to 44m, total path length = 28 m), whereas the other two travelled parallel to the

142 pipeline from their release points (3 m fromedge , total path length = 35 m and 1 m from edge, total path length = 6 m). One red-backed vole released on the linear feature travelled to the edge (linear feature path length = 13 m) and crossed into forest where it remained for the rest of the thread length (forest path length = 84 m) ending up 29 m from the forest edge. The other red-backed vole released onto a linear feature lost its thread spool after 4.5 m preventing accurate assessment of its habitat choice, though the thread pointed towards tall shrubs in the centre of the linear feature. Overall, failure to capture and track animals in their non-preferred habitat and small sample sizes limit my ability to interpret how small mammal movements relate to habitat preference or use. Furthermore, spools often brushed off on vegetation, or in burrows, limiting much of the paths to short distances that could have represented escape behaviour rather than habitat use. In future deployment of this method it would be better to reduce spool size along with weight by removing excess thread from the outside of the spools as done by Boonstra and Craine (1986) rather than the inside. Small mammals could also be held longer to ensure the glue dried more thoroughly. Despite these limitations, the fact that small mammals tended to remain in the habitat they were captured in or move to the habitat with highest captures, i.e., linear feature for meadow voles, forest for red-backed voles, and forest edge for deer mice (Chapter 2), suggests that trapping is a good index of habitat use in this study. Future tracking studies should focus on contrasting movement in different habitats, perhaps by translocation of individuals to non-preferred habitat prior to release (McDonald and St. Clair 2004b).

143 APPENDIX D

D.l Distribution of airborne seeds in the boreal forest

Seeds are often important foods for deer mice and chipmunks (Smith 1973, Sullivan 1979, Vander Wall et al. 2005) and occasionally for red-backed voles and meadow voles (Banfield 1974, Burt and Grossenheider 1976). Understanding how destruction of boreal forest to create linear features will affect seed production and availability as food resources is thus important for understanding small mammal responses to linear features. Seeds are potentially important sources of regeneration (Chabrerie and Alard 2005), thus it is important to understand seed response to linear features for remediation objectives also. Clearing trees in boreal forest to create linear features could lead to increases in availability of seeds either through stimulation of plant production in remaining plants exposed to light and higher temperatures at edges and in cleared areas (Geiger 1965, Murcia 1995, Greene et al. 2002) or through introduction of novel seed-producing species (Cadenasso and Pickett 2001, Wolf and Batzli 2004). On the other hand, clearing forest can lead to decreases in availability of seeds if predominant seed-producing plants are large trees, shade-tolerant (Saunders et al. 1991, Schweiger et al. 2000, Harper et al. 2005), or negatively impacted by decreased humidity, increased temperatures or increased competition at edges and in clearings (Saunders et al. 1991, MacFarlane 2003). I collected wind-dispersed seeds to determine if there was a difference in wind-dispersed seed availability between forest, forest edge and linear feature habitat.

D.2 Methods See Chapter 2 for details of study areas and design. Seed rain traps were placed between Tincat® and Longworth® trap locations but offset towards forest interior (by 1 m) to avoid interference with other vegetation sampling. Seed rain traps were placed when study plots that were established in May and June 2006 and were collected by August 11,2006. Seed rain traps were constructed of nylon socks attached to a 12.7 cm diameter plastic funnel via elastic bands, similar to funnel traps in previous studies

144 (Chabrerie and Alard 2005, Cottrell 2004). Funnels were set on top of PVC piping to surround and suspend the nylon sock. Traps were placed into the ground such that PVC pipe rims were level with the ground and runnel rims were 5 cm off the ground. Seed rain traps were set at 15 forest interior, 15 forest edge, and 15 linear feature small mammal trap locations. Forest interior trap locations ranged from 280 m from edge to 345 m from edge. Edge traps were placed between 5 m from edge and 28 m from edge. Linear feature traps were placed along the centre of the linear feature. At the end of seed rain exposure period, nylon socks and contents were collected in paper coin envelopes and oven-dried for seven days at 70 °C. Seed rain contents were emptied into clean Petri dishes lined with a 50 mm x 50 mm grid and examined under a dissecting scope. I recorded number, length, and width of seeds and cones and placed contents into a Whirl-Pak® for future identification. Number of seeds collected in each habitat (forest interior, forest edge, and linear feature) was compared using a Kruskal-Wallis test (analysis of variance by ranks) in STATA (StataCorp 2007) due to non-normality of seed numbers in each habitat type (Zar 1999). I also combined forest edge and forest interior seed counts and compared them to linear feature seed counts using the Mann-Whitney two-sample statistic (Zar 1999, StataCorp 2007).

D.3 Results and Discussion

Seed count ranks did not differ significantly between habitats (Kruskal-Wallis x2 - 3.809,p = 0.1489). Linear features had the highest rank (8098.5), followed by edges (6336.0), and forest interior had the lowest rank of seed count (5665.5). Raw seed counts were 40 seeds (average ± standard deviation = 3.32 ± 8.04 seeds) on linear features (n=88), 37 seeds (2.82 ± 5.59 seeds) in forest edge (n=60), and 36 seeds (2.88 ± 5.59 seeds) in forest interior (n=52). Seed counts for forest habitats combined were also not significantly different from linear features (Mann-Whitney z = -1.925,/) = 0.0542) though they indicated a trend for more seeds in forest. Overall seed numbers collected were very low because I sampled in low seed dispersal months (June and July) when overwinter and spring seed rain had ended (Urbanska and Fattorini 2000, T.R. Cottrell pers. comm.) and fall seed dispersal had

145 likely not yet begun (Cadenasso and Pickett 2001, Greene et al. 2002, Cottrell 2004). Sampling overwinter between August and May would likely result in a more accurate depiction of availability of wind-dispersed seeds in different habitats. Not all seeds dispersed over the winter will be available as food in the summer and a method that targets the summer seed bank (post-dispersal seeds on the ground) might be more useful. The seed bank could be examined through methods such as sifting leaf litter and soil to selectively retain seeds of various sizes (Yin et al. 2007), or collecting leaf litter and soil and allowing seeds to germinate in a greenhouse (i.e., the emergence method, Cubiiia and Aide 2001, Ghorbani et al. 2006). Forest disturbance by linear features does not appear to impact overall seed counts. Further research into species of seeds consumed by small mammals and seed responses to linear features is needed, however, as responses can be species-specific (Cubina and Aide 2001, MacFarlane 2003). Modifications of methods as suggested above could also lead to significant differences among habitats.

146 APPENDIX E

E.l Generating isotopic discrimination factors for small mammal muscle tissue In order to accurately estimate importance of diet items using stable isotope mixing models, food source isotopic signatures must be corrected for food source- consumer tissue discrimination that occurs during digestion and assimilation (Hobson and Clark 1992, Robbins et al. 2005). Discrimination refers to the difference between isotopic ratios of food sources and ratios of consumer tissues, often denoted as AS C for carbon and A815N for nitrogen (Caut et al. 2008a). Applying discrimination factors to food source ratios are intended to adjust a given food source's isotopic values such that they equal the isotope ratios the consumers would have if they ate exclusively that food source. The commonly accepted discrimination factors between animal muscle tissue

n if and diet are a l%o increase in 8 C and a 3%o increase in 8 N (Arneson and MacAvoy 2005, Caut et al. 2008a). Generalised, fixed discrimination factors can be inaccurate, however, because discrimination varies widely by species, tissue and diet types, diet quality, and other factors (Robbins et al. 2005, Hwang et al. 2007, and Caut et al. 2008a, 2008b). In short, the magnitude of discrimination factors to use in isotope analysis remains an unsolved problem for ecologists (Tsahar et al. 2008) that can greatly affect the outcome of mixing models (Phillips and Koch 2002, Gaye-Siessegger et al. 2004). A common recommendation is to obtain exact discrimination factors for each new system by raising the consumers of interest on pure diets of the potential food items for a length of time suitable for the tissue of interest (e.g., several weeks for blood or muscle, Gannes et al. 1997, Caut et al. 2008b). Raising wild animals on pure natural diets is not feasible in many ecological studies because such field studies are intended to study natural diet and behaviour (e.g., Ben-David et al. 1997, Mcilwee and Johnson 1998, Szepanski et al. 1999, Drever et al. 2000, Newsome et al. 2004, but see Haramis et al. 2001) or might focus on rare or endangered animals (Felicetti et al. 2003, Caut et al. 2008b). My focus was on response of small mammals to linear disturbance in a natural environment as measured by several variables. Rather than capturing and raising animals on supposed 'natural diets' and potentially compromising other aspects of my study, I 147 instead conducted two literature reviews to generate applicable discrimination factors for this study. In the first, I reviewed the influence of food source isotopic values, elemental composition, protein quality, and trophic level on discrimination factors for carbon and nitrogen stable isotopes in various animals (invertebrates and vertebrates). In the second literature review (summarized in Table E.l) I compiled information from studies comparable to my own, i.e., studies on small mammals and muscle tissue discrimination factors. I then applied general patterns, or "rules" of discrimination from the first review to the small mammal discrimination literature as a test of the concepts and to help organize the small mammal studies to generate the best discrimination factors for my study.

E.2 Methods See Chapter 2 for detailed methods on study areas, design, and stable isotope sample collection. To complete my literature review of small mammal discrimination factors and influences on them (Table E.l) to the fullest possible extent, I generated percent nitrogen (% N) in the diet for some studies (DeNiro and Epstein 1978, DeNiro and Epstein 1981, Arneson and MacAvoy 2005, MacAvoy et al. 2005, and Miller et al. 2008) as 16% of protein contents (Bell 1990, Robbins et al. 2005) and generated percent carbon (% C) as 52% carbon from protein, 75% from fats, and 44% from carbohydrates (Robbins et al. 2005). Protein quality was approximately based on Robbins et al.'s (2005) ranking of milk > fish > other animals > plant > fruit protein quality, with 1 = highest quality and 4 = lowest quality. Overall trophic level of small mammal food sources was estimated based on the trophic levels suggested by food source isotope values (mainly 815N), elemental composition (mainly %N and carbon to nitrogen (C:N) ratios), protein quality, and diet type (plant, fungus, arthropod, etc.). To better apply the literature review results to my system I examined the above indicators of trophic levels for the three food sources in this study: plants, fungal sporocarps, and arthropods.

£.3. Results and Discussion Based on a literature review, some factors appeared to have a consistent influence on discrimination values. This can be useful for applying lab- or literature-derived discrimination values to field situations. Protein content, elemental composition (%N, %C, and C:N ratios), diet type, and isotopic composition of the diet can all influence nitrogen and carbon stable isotope discrimination (Robbins et al. 2005). Overall, increasing trophic level, which correlates with higher protein content, high %N, low C:N, protein-rich diets (e.g., invertebrates), and higher 813C and 815N, seems to cause increases in A8 C and decreases in A8 N. High protein content diets and high quality protein diets, which typically correspond to high trophic levels, consistently have lower discrimination factors of A815N (about 1 to 2%o lower) than low protein diets (Robbins et al. 2005, Sare et al. 2005, Tsahar et al. 2008, Waddington and MacArthur 2008). Robbins et al. (2005) examined the food items fruit, plants, other animals (arthropods and birds), fish, and milk and found that A815N decreased by about 0.5 to 1.0%o with increase in protein or diet quality. High protein diets might have higher A8I3C, though only by about 0.5%o (Waddington and MacArthur 2008) and Sare et al. (2005) found that low (14% protein), in addition to high (26% protein) protein diets had A8 C about 3%o higher than a medium (17% protein) protein diet. Podlesak and McWilliams (2006) found that waxworms (Galleria mellonella) in the diet of yellow-rumped warblers (Dendroica coronata) increased A813C by 2.1%o with a slight decrease (similar to 0.2%o) or no change in A815N, because invertebrate 813C and 815N were higher than those of C3 plant foods. Similarly, McCutchan et al. (2003) found that invertebrates had lower A815N compared to plants, and also compared to other high protein diet items (vertebrate, microbial, and animal-based prepared diets). Pearson et al. (2003) found a strong positive influence of %C on A813C (R-squared values of 0.50 or higher) but only a weak positive influence of %N on A815N. Robbins et al. (2005) found a weak negative influence of %N on A815N, and Caut et al. (2008a) found no effect of %N on A815N. Vanderklift and Ponsard (2003) and Robbins et al. (2005) both found increasing A815N with increasing C:N ratios, though in the latter case protein quality was more important. Caut et al. (2008a) found that as 813C and 815N values of the diet increased, given a particular diet quality, discrimination factors decreased, consistent with other findings for A815N, and contrary to those for A813C. I used my literature review of discrimination in small mammal muscle tissue as a test of the concepts above and to develop discrimination factors for Chapter 4 (literature

149 review is summarized in Table E.l). As proposed by Caut et al. (2008a), A515N decreased as 815N increased in small mammal diet studies though Drever et al. (2000) and Minagawa and Wada (1984) results were outliers with high nitrogen discrimination values at relatively high isotope values. A813C also decreased as 813C increased (Caut et al. 2008a) in the small mammal literature. When 813C values were restricted to be -20%o or more negative to be similar to the upper range in this study (-22%o), however, A813C appeared to increase as 8 C increased. Similar to other findings, increasing % N had little (weak positive) effect on AS15N, and no effect if %N values back-calculated from diet composition (% protein, fat, etc.) were excluded. Decreasing %C likewise had no obvious effect, but possibly resulted in decreasing A813C. Decreasing C:N ratios (increasing trophic level) resulted in increasing A813C values. Decreasing C:N ratios appeared to be unrelated to A815N for small mammals. For all studies together, increasing protein quality resulted in decreasing A813C. Studies with 813C of-20%o or lower, however, showed the opposite pattern with the highest A813C value for the highest protein quality diet and lowest AS C values for the medium to low protein quality diets. The average of the two highest protein quality categories (1 and 2) had about l%o lower A515N than the average of the two lowest protein quality categories (3 and 4). The lowest trophic level was about 0.50 %o higher than the other trophic levels for A815N. Trophic level had no effect on overall A813C as all trophic levels were within 0.1 %o on average. Based on studies with 8 C of -20%o or lower, however, the middle trophic level had the lowest AS C, and the highest trophic level changed the most. Taken all together, as trophic level increases for small mammal food sources, discrimination factors for S15N tend to decrease or remain the same. Small mammal A8l3C for food source 513C values of -20%o and lower (similar to this study) tend to remain unchanged for different trophic levels, but might be highest for the highest trophic level.

150 Table E.1: Muscle tissue discrimination values for rodents from published studies of controlled and wild diets. Protein quality ranks based on Robbins et al. (2005) with l=highest quality and 4=lowest quality. Approximate trophic level estimated from isotopic signatures, elemental composition (%N, %C, and C:N), protein quality, and diet type. "NA" signifies not applicable.

Author Species Diet (primary components) 8I5C 5ISN %N %C C:N Protein Trophic level A8"C (%>) A515N («o) («•) («•) quality Arneson and Mus musculus Beet sucrose, casein, soybean oil (experiment 1) -26.5 6.1 4.83 59.2 12.3 1 High 1.5 3.1 MacAvoy 2005 Arneson and Mus muscultis Cane sucrose, fish meal, soybean oil (experiment 2) -14.8 9.1 3.53 54.2 15.3 2 High -2.1" 2 MacAvoy 2005

Arneson and Mus muscultis Harlan Teklad diet 2018" -20.5 2.8 3.00c 36.3d 12.1 4 Low 1.2 3.1 MacAvoy 2005 Caut et al. 2008a Rattus norvegicus Corn, fish (diet A) -15.7 10.9 2.4 38.9 16.2 2 Medium -1.69 1.39 Cautetal. 2008a Raltus norvegicus Corn, fish, alfalfa (diet E) -18.7 7.9 2.6 38.4 14.8 2 Medium -0.88 1.5

Caut et al. 2008a Rattus norvegicus Corn, casein (diet B) -12.6 4.5 1.4 39.8 28.4 1 Low -5.12 1.73

Caut et al. 2008a Rattus norvegicus Alfalfa, corn (diet C) -22.6 -0.8 1.6 41.1 25.7 4 Low -0.404 4.59 DeNiro and Mus musculus Wayne Lab-Blox F6 dief -19.3 NA 3.84c NA NA 2 High -1.89 NA Epstein 1978 DeNiro and Mus musculus Wayne Lab-Blox F6 diet' NA 5.1 3.84° NA NA 2 High NA 3.8 Epstein 1981 Dreveretal. 2000 Peromyscus keeni 23% invertebrates, 51% bird eggs, 13% plants, 13% intertidal -21.4 14.3 NA NA NA 3 High- 2.4" 1.7" organisms medium Drever et al. 2000 Microtus 32% plants, 30% invertebrates, 28% bird eggs, 10% intertidal -23.3 15.1 NA NA NA 3 Medium -1.2" 5.0" townsendii organisms MacAvoy et al. Mus musculus Casein, soybean, cane sugar sucrose -18.6 5.9 NA NA NA 1 High -2.5 2.7 2005 MacAvoy et al. Mus musculus Harlan Teklad diet 2018" -20.5 2.8 3.00c 36.3d 12.1 4 Low 1.5 3.2 2005 Miller etal. 2008 Peromyscus LabDiet® 5001 (corn, soy, beet, fish, yeast, alfalfa: 23% -19.4 3.6 3.68c NA NA 2 Medium -0.7 2.5 maniculattts crude protein, 4.5% fat, 6.0% fiber) Minagawa and Mus musculus Oriental yeast NA 16.1 3.79° NA NA 3 High- NA 2.9 Wada 1984 medium Teiszen 1983 Meriones Corn -12.2 NA NA NA NA 4 Low -0.3 NA unguienlatus Teiszen 1983 Meriones Wheat -21.8 NA NA NA NA 4 Low 0.5 NA unguienlatus *: Compared to bulk diet. 75% of consumer 8I3C likely came from the protein source which would result in muscle AI3C of 0.175 b: Estimated using tissue-diet discrimination relationships in Drever et al. (2000) c: Nitrogen content (%N) calculated as 16% of crude protein content and carbon content (%C) is 52% of carbon in protein, 75% in fats, and 44% in carbohydrates (Robbins et al. 2005) d: Harlan Teklad diet 2018 is composed of 18.8% crude protein, (6% crude fat, 3.8% crude fiber, 50% available carbohydrate from wheat, corn, soybean and yeast. ": Wayne Lab-Blox F6 diet is composed of 24% crude protein, 4% crude fat and 4% crude fiber, primarily from corn, wheat, soybeans, fish, whey, yeast (Robinson et al. 1994). Plants represented the lowest trophic level of food collected for this study based on 513C and 815N (Table 4.3). Plants also typically have low %N (2.69 ± 0.965 %, Michelsen et al. 1998, Dijkstra et al. 2003, Robbins et al. 2005, Caut et al. 2008a), high C:N ratios (18.1 ± 6.75%, Robbins et al. 2005, Caut et al. 2008a, Table 4.3), and low protein content (e.g., 12±7% in forbs and 11±4% in graminoids, Redburn et al. 2008) placing them at a low trophic level. Fungal sporocarps (fruiting bodies) occupied the highest trophic level based on 8 C and 8 N but were likely mid-quality for protein content overall as % N was 4.30 ± 1.12%, and their C:N ratio was 11.4 ± 4.21 (Table 4.3). In the literature, similar species (Boletus spp.) were comprised of 15% crude protein and crude protein averaged for fungi was 15.4% in the USA (Claridge et al. 1999) and 21.5% in boreal forest in Sweden (Gronwall and Pehrson 1984). Leccinum spp. fungi contained 3.60 to 3.96% N in Michelsen et al. (1998) and 4.30 to 5.63% N in Taylor et al. (1997). In Sweden, fungi provided 3.8 to 5.2 kcal/g of energy to small mammals (Gronwall and Pehrson 1984), though only about 65.4% of this energy was likely digestible (Claridge et al. 1999). Plant (monocot and sedges) energy, by comparison, was 34 to 58% digestible (Claridge et al. 1999, Karasov 1982), and arthropod energy was 84.4% assimilable (Bell 1990). Much of the nitrogen in fungal sporocarps exists in inaccessible forms, such as in cell walls and spores, or as non­ protein nitrogen, resulting in 74.1% digestibility in Californian red-backed voles (Myodes californicus, Claridge et al. 1999). Arthropod larvae had % N of 11.2 ± 2.11% and a C:N ratio of 4.66 ± 0.953 which suggested they occupied the highest trophic level of the three food types, though they occupied the middle trophic level based on 8 C and 815N (Table 4.3). In the literature, arthropod %N is also high, around 9.13% for Coleoptera and Lepidoptera (Bell 1990). Arthropods also represent the highest protein quality due to higher levels of digestible protein and energy than plants and fungi. Coleoptera and Lepidoptera specimens (mainly adults) provided 5.59 kcal/g of energy on average for birds and mammals, and protein content of arthropod larvae ranges from 18.1% (Finke 2007) to 24.0% (Banjo et al. 2006) to 59.5% (Bell 1990). Based on isotopic values, %N, C:N ratios, and typical protein content, the trophic level order from lowest trophic level to highest for my food sources is plant < fungi < arthropod, though fungi and arthropods could have similar levels of energy and protein.

152 To estimate possible discrimination factors for small mammals I used the overall trophic level designation which combined isotopic signatures, elemental composition, protein quality, and diet type of food sources (Table E.l). I assigned trophic levels of high and high-medium to arthropods, trophic levels of medium and high-medium to fungi, and the trophic level of low to plants. Many studies used C4 plants (not present in my study area) in diets, so I used A813C of studies with 813C of -20%o or lower to reduce influence of C4 plants on discrimination factors (Tieszen 1978, Podlesak and McWilliams 2006). Of the studies with 8I3C of-20%o or lower, two were available to represent arthropods, and for this study A813Caithropod = 1 -95. Two studies represented fungus for carbon discrimination factors (AS^Cfungus = 0.400), and four studies represented plants (A813Cpiant = 0.699). For nitrogen stable isotopes, the average of high 15 = trophic levels (n=6) resulted in A8 Narthropod 2.70%o, the average of medium trophic levels (n=6) resulted in A815Nfungus = 2.50%o, and the average of low trophic levels (n=4) 15 resulted in A8 Npiant = 3.16%o." A limitation of mixing models is that consumer values must fall within the geometric space defined by the food sources (mixing polygon, Phillips 2001). I was able to use the above A813C and A815N values to create diet item endpoints for two-isotope, three-endpoint mixing models for meadow voles, but all other species fell outside the mixing polygon (Fig 4. lb). I also used the above values in one isotope, two-endpoint mixing models (standard and concentration-dependent) for meadow voles, deer mice, red-backed voles, and heather voles. To evaluate two-isotope, three-endpoint mixing models for other species, however, I transformed trophic-level-derived A8 C and AS N values such that the mixing polygon encompassed all species except heather voles. Transformations were a compromise between retaining A813C and A815N close to those determined by trophic level, creating a mixing polygon that included most consumers, and not exceeding the range of A813C and A815N values reported in the literature (Table E.l). Thus, I added 0.45%o to A813C values and subtracted 1.0%o from A815N values to obtain transformed discrimination factors: AS Cpiantj = 1.15, AS Carthropod_t = 2.40, and 15 15 15 AS^Qungusj = 0.850, and AS NplantJ = 2.16, A8 Narthropod_t = 1.70, and A8 NfilllgII8_, = 1.5. The resulting mixing polygon encompassed red-backed voles, deer mice, and meadow voles, allowing use of three-endpoint mixing models (Chapter 4, Fig. 4.3c).

153 APPENDIX F

F.l Detailed descriptions of study plots

F.l.l Northwest Territories study area

There were few anthropogenic disturbances in the Northwest Territories (NT) study area aside from the Norman Wells Gas Pipeline (operating since 1985) that stretches for 869 km from Norman Wells, NT to Zama City in north-western Alberta (Natural Resources Canada 2008). The pipeline itself was completely buried leaving a 25 m wide ROW in various stages of regeneration. Five plots were established on the pipeline ROW and one plot (LIAR) was established on an abandoned winter road south of Liard River ferry crossing (Fig. 2.1). There were very few seismic lines in the area and all were inactive, with all but the upper canopy re-grown, as pipeline ROW access in NT was restricted. Two of the six study sites were north of the Mackenzie River (MART and HOOK), and the other four were located to the south of the river. PORC was roughly 400 m from the southern shore of the Mackenzie River and the two southernmost plots were within 400 m of the Liard River (MANN and LIAR). MANN was situated close to a long-standing First Nations fur-bearer trapping area. The sixth plot, FERR was located approximately 700 m from the Liard River ferry crossing and LIAR and FERR were both situated approximately 400 m from a highway. A 20-year- old seismic line ran through the centre of MART, and did not have a complete canopy. Some plots had portions with steep inclines, though plots were selected to avoid this. PORC, FERR, and MART were all fairly flat. The 'back' two thirds of MANN had steep inclines, with transects running up the incline. LIAR had 20% of transects traversing across a slope on the west side of the plot (the right side of Fig. 2.2). HOOK had 10% of transects traversing across a slope, again to the west of the plot (the right side of Fig. 2). FERR was flanked by a ROW access road approximately 13m wide, reducing distance to edge for three forest trap locations.

154 All plots in NT were mixedwood forest, usually dominated by white spruce and trembling aspen. LIAR generally had a thick alder understory, and the LIAR linear feature was a grassy, 12 m wide winding winter road. In 2005 traps were placed on the side of the winter road (lm from forest edge) to prevent potential damage from vehicles. By 2006 it was clear the road was not in use by local people and traps were placed in the centre of the road without incident. FERR had a thick conifer understory with some alder and was wet in the interior. The pipeline ROW at FERR was relatively bare, but had a vegetated strip near the middle with tall aspen saplings, and abundant shrubs of various species including rose. PORC had patches of open moss and lichen understory under pine trees, and also patches of heavy alder. The PORC pipeline was fairly bare, although at least half of the pipeline area had heavy forb and grass re-growth. HOOK understory in forest was dominated by buffaloberry with some aspen and spruce. HOOK had heavy re-growth on the pipeline, with tall aspen saplings and abundant shrubs, along with a moderate amount of forbs, and a lesser amount of grass. MART was mixedwood dominated by aspen. MART understory was short and open, and mostly dominated by shrubs such as buffaloberry. The pipeline section at MART was also short, relatively open, and dominated by shrubs. The pipeline also had moderate cover of grass and forbs, and the soil was interspersed with rocks. MANN was on the side of a hill with a creek running through part of it, and a pure aspen stand near the crest of the hill. The understory was often mossy. About half the understory was dense alder and spruce, and the other half was more open, with aspen and buffaloberry. The pipeline ROW at MANN was very grassy and shrubs were rare.

F.1.2 Alberta study area

In the Alberta study area there were many more linear features. One of the five plots bordered one pipeline (STOW), one plot bordered one road (GRIZ), and three plots bordered both a pipeline and a seismic line or road (SLIP, POWL, and HILL). Each plot also had one or more additional seismic lines near to or within its boundaries, and each was in various stages of regeneration from herbaceous cover only to substantial understory regrowth up to approximately 3 m (Bayne et al. 2008, unpublished). All of these seismic lines had a distinct game trail (Bayne et al. 2008, unpublished). Again all

155 plots were mixedwood forest dominated by white spruce and trembling aspen. The understory of most AB plots was more open than NT plots, with a mixture of young trees, shrubs and often abundant forbs interspersed in moss. GRIZ was situated on a 26 m wide road underlain by a pipeline with well-vegetated ditches (mostly grass and forbs, also shrubs and young trees), thus the zero meter mark was within about 1 m of undergrowth at the point of edge creation. A closed-canopy 10 m wide seismic line crossed the road and ran diagonally through GRIZ. SLIP transects were situated perpendicular to a 13 m wide seismic line which was also an ATV trail and winter road. Vegetation was sparse and consisted mainly of grass and forbs such as clover. A 17 m wide dirt road established in 1985 by Norcana (based on a tree tag) ran along the south edge of the plot (left in Fig. 2.2). A 6-m wide open-canopy seismic line developed by Fastway in 1993 and Norcana in 2000 (tree tags) cut across SLIP transects at a 55 degree angle to the main seismic line. An 8 m wide open-canopy seismic line ran alongside and eventually crossed transect 7 in Fig. 2.2. Another 6-m wide open-canopy line cut across the bottom of the plot, entering the forest at the zero mark of transect 10 and exiting the forest about halfway up transect 1 at the road (Fig. 2.2). POWL was on an 80 m wide power-line and pipeline ROW. The POWL ROW was covered in tall grass with significant re-growth of shrubs and young trees from the point of edge creation to about 10 m into the linear feature. Approximately 15 m into the linear feature there was a strip of tall shrubs and trees (e.g., willow) running parallel to the forest edge on the POWL linear feature. Beyond that there were scattered trees and shrubs across to the other side of the ROW. Two 5 m wide closed-canopy linear features ran through POWL: a cutline across the back of the plot, and a winding trail roughly perpendicular to edge in the back (top in Fig. 2.2) third of the plot. An open-canopy 7 m wide ATV trail ran at 80 degrees to the powerline and crossed transect 15 (to the right in Fig 2.2). A nearby seismic line to the east of POWL was built by Enertec in 1997(tree tag). STOW was on a 45 m wide pipeline ROW. The ROW at STOW was covered by grass, as well as an extensive undergrowth of forbs and shrubs. Tall (2 m or more), young trees grew on the linear feature about 5 m from, and parallel to, the forest edge. An old, indistinct seismic line ran through a marsh north of the plot (left in Fig 2.2). Several other linear features existed near, but not in, STOW including a 7 m wide line cleared by Norcana in 1987,

156 and a 6 m wide clearing by Enertec in 1997. HILL was on narrow 8 m wide pipeline ROW that was also an ATV trail, with a 25 m wide road 20 to 100 m west of transect 1 (left in Fig. 2.2). The ROW at HILL was bare for the 4 m closest to the forest edge we studied ("above" the line in Fig. 2.2b) and thickly vegetated with grass, forbs, and shrubs (usually no greater than 1 m in height) for the remaining 4 m.

157