linear features, forestry and wolf predation of caribou and other prey ...

1 PTACT Final Report 

LINEAR FEATURES, FORESTRY AND WOLF 

PREDATION OF CARIBOU AND OTHER PREY 

IN WEST CENTRAL ALBERTA 

Final Report 

Covering Activities from January 1, 2007 to December 31, 2009 

Prepared by: 

Mark Hebblewhite, Principal Investigator, Wildlife Biology Program, University of Montana 

Marco Musiani, Principal Investigator, Faculty of Environmental Design, University of Calgary 

Nick DeCesare, PhD student, University of Montana 

Saakje Hazenberg, Field Coordinator 

Wibke Peters, MS student, University of Montana 

Hugh Robinson, Post‐doctoral Fellow, University of Montana 

Byron Weckworth, PhD Student, University of Calgary 

Prepared For: 

Petroleum Technology Alliance of Canada 

Canadian Association of Petroleum Producers 

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ACKNOWLEGEMENTS 

Primary funding was provided through the Canadian Association of Petroleum Producers (CAPP) 

through the Petroleum Technology Alliance of Canada (PTAC)’s environmental research program under 

the Broad Industry Initiatives Caribou Fund. 

Additional funding was provided by Shell Canada Ltd., Weyerhaeuser Company, Parks Canada, Alberta 

Fish and Wildlife Division, British Columbia Ministry of Environment, and the Universities of Montana, 

Alberta, and Calgary. 

Funding for research activities in 2008/09 was also provided by WWF‐Canada through the Endangered 

Species Recovery Fund (ESRF) and the Alberta Conservation Association. 

Numerous individuals and organizations made this research possible. We thank Gary Sargent at CAPP for 

his support and administrative help to make our project successful. Many people within Alberta Fish and 

Wildlife Division run the ongoing caribou monitoring program within west‐central Alberta, most notably 

Dave Hervieux, Kirby Smith, Dave Stepnisky, Dave Hobson, Jan Ficht, and Mike Russell. We also thank 

Alberta Fish and Wildlife wardens for their assistance with spring/summer 2008 wolf trapping activities. 

Within the Alberta Caribou Committee we thank Anne Hubbs and Nicole McCutchen for facilitating data 

sharing agreements, and Stan Boutin for ongoing discussion. Within Parks Canada, we thank Mark 

Bradley, Layla Neufeld, Alan Dibb, Cliff White, Geoff Skinner, Landon Shepherd, Brenda Shepherd, Wes 

Bradford, Jesse Whittington, Dave Dalman, Jacqui Syroteuk, and the staff at Pallisades for their 

contributions to the caribou research program. For outstanding contributions to ground and aerial elk 

telemetry in Jasper National Park, we thank Heidi Fengler and Lucas Habib especially. Within the 

Foothills Facility for GIS in the McDermid Lab at University of Calgary, we thank Adam McLane and David 

Laskin for their assistance with remote sensing lab and field work throughout the project. We thank 

Bighorn Helicopters, especially Clay and Janice Wilson, and Brad Culling for enabling safe and humane 

animal capture and handling services. Mike Dupuis and Silvertip aviation provided aerial telemetry 

services, and Pacific Western, Precision, Highland and Yellowhead helicopters for safe air travel services. 

Mark Sherrington and Roger Creasey provided administrative and field assistance that was invaluable. 

On the British Columbia side of the border, we thank project partner Dale Seip from BC Ministry of 

Forests especially for his ongoing collaboration, as well as Doug Heard and Conrad Thiessen from BC 

Ministry of Environment, and Rick Roos from BC Parks for his facilitation of our field work in Kakwa 

Provincial Park BC. Matthew Wheatley and others within Alberta Parks, Tourism and Recreation ably 

facilitated our research in Alberta provincial parks. We also thank the administrative assistance from 

PTAC’s Tannis Such, the University of Montana’s Jeanne Franz, Jodi Todd, Jim Adams, Laura Plute, 

ReNeea Gordon, and the University of Calgary’s EVDS administration. Simon Slater at the University of 

Alberta continues to provide excellent monitoring of the Redrock‐Prairie Creek and Narraway herds in 

conjunction with Weyerhaueser. Nathan Webb (University of Alberta) provided expert advice regarding 

application of the StatScan software to GPS cluster identification for field work. We thank genetic 

collaborators Drs. Stefano Mariani and Allan McDevitt from the University of Dublin for their productive 

collaboration on our genetics research. Veterinary advice and assistance for safe and human animal 

capture protocols was provided by Drs. Todd Shury, Helen Schwantje, MaryAnne McCrackin, Geoff 

Skinner, and Mark Cattet. Fiona Schmiegelow was extremely helpful during this first year of our project – 

we couldn’t have made as much progress without her visionary leadership in the west‐central area over 

the last 10 years. Finally, we thank the caribou and wolves for the privilege of a glimpse into their lives in 

our effort to contribute to their long‐term persistence. 

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FUNDING 

DISCLAIMER 

This progress report contains preliminary data from ongoing academic research directed by the 

University of Montana and Calgary that will form portions of graduate student theses and 

scientific publications. Results and opinions presented herein are therefore considered 

preliminary and to be interpreted with caution, and are subject to revision. 

COVER PHOTO CREDITS 

Cover photographs are credited to Mark Bradley (top‐left), Byron Weckworth (bottom‐right) 

and Marco Musiani (2 remaining photos) 

SUGGESTED CITATION 

Hebblewhite, M., Musiani, M., N. DeCesare, S. Hazenberg, W. Peters, H. Robinson, and B. 

Weckworth. 2010. Linear Features, Forestry and Wolf Predation of Caribou and Other Prey in 

West Central Alberta. Final report to the Petroleum Technology Alliance of Canada (PTAC). 84 

pages. 

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TABLE OF CONTENTS PAGE 

1.0 INTRODUCTION 6 

1.1 Project Overview 6 

2.0 STUDY AREA 8 

3.0 GENERAL FIELD METHODS 10 

3.1 Introduction 10 

3.2 Animal Capture and Radio‐collaring 10 

3.3 Data Sharing and Compilation 12 

4.0 APPARENT COMPETITION REVIEW 14 

4.1 Scope 14 

4.2 Abstract 14 

4.3 Summary of Findings 15 

5.0 CARIBOU POPULATION GENETICS IN WESTERN ALBERTA AND EASTERN 

BRITISH COLUMBIA 18 

5.1 Scope 18 

5.2 Abstract 18 

5.3 Summary of Findings 18 

6.0 VEGETATION AND HUMAN DISTURBANCE MAPPING IN THE 

TRANSBOUNDARY STUDY AREA 26 


6.2 Methods 26 

6.2.1 Land cover and vegetation data 26 

6.2.2 Anthropogenic data 27 

7.0 PREDATOR EFFICIENCY OVER A REGIONAL GRADIENT OF HUMAN 

DEVELOPMENT 31 

7.1 Scope 31 

7.2 Schedule 31 



7.4.1 Resource Selection Functions 31 

7.4.2 Predation Efficiency 33 

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7.5 Results 35 

7.5.1 Resource Selection Functions 35 

7.5.2 Predation Efficiency 38 

8.0 HUMAN ACTIVITIES AND PRIMARY PREY PRODUCTIVITY IN 

WOLF‐CARIBOU SYSTEMS 52 

8.1 Scope 52 

8.2 Schedule 52 


8.4 Objectives 53 

8.4.1 Moose Resource Selection 53 

8.4.2 Moose Abundance Models 53 




8.6 Progress and Results to Date 56 



8.7 Outlook: Combining Resource Selection and Abundance 

Estimation 62 

9.0 CARIBOU MIGRATORY AND SURVIVAL PATTERNS OVER REGIONAL 

GRADIENTS IN HUMAN DEVELOPMENT 63 



9.3 Results 66 

10.0 IMPLICATIONS AND CONSERVATION STRATEGIES 69 

10.1 Management Implications of Genetic Findings 69 

10.1.1 Allowing for survival of the partially migratory 

Canadian Rockies caribou 69 

10.1.2 Allowing for natural levels of migrants among 

caribou populations 70 

10.1.3 Implications 72 

10.2 Caribou‐wolf overlap: minimizing impacts in risk areas 72 

10.3 Future Research: Spatial population viability analysis 73 

PRESENTATIONS, PUBLICATIONS, AND WORKSHOPS 77 

LITERATURE CITED 79 

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SECTION 1. INTRODUCTION 

Woodland caribou (Rangifer tarandus caribou) are classified as threatened in Alberta (under the Alberta 

Wildlife Act) and nationally (under the Species at Risk Act)(COSEWIC2002), with many local populations 

in decline throughout their range. This decline is largely attributed to anthropogenic activities that are 

altering predator‐prey dynamics (Alberta Caribou Recovery Team 2005). In Alberta, woodland caribou 

are divided into the Boreal and Mountain caribou ecotypes (Alberta Woodland Caribou Recovery 

Team2005). Our study supports this division and demonstrates its significance in the greater ecological 

and evolutionary dynamics of caribou (McDevitt et al. 2009). In the Fall of 2006, an interdisciplinary and 

multi‐university research project led by Mark Hebblewhite and Marco Musiani was initiated to broadly 

determine causes for declines in threatened woodland caribou populations in west‐central Alberta (the 

mountain ecotype) and east‐central British Columbia. Core funding was obtained in late fall 2007 

provided by the Petroleum Technology Alliance of Canada in association with the Canadian Association 

of Petroleum Producers. Additional project funding has included the University of Montana, the 

University of Calgary, Shell Canada, Weyerhaeuser Company, and Parks Canada Agency. Collaborating 

government agencies include Alberta Sustainable Resource Development – Fish and Wildlife Division, 

Alberta Community Development and Parks (Willmore Wilderness and Kakwa Wildland Provincial Parks), 

British Columbia Ministry of Environment, BC Provincial Parks, BC Ministry of Forests, and the Foothills 

Research Institute (formerly Foothills Model Forest). Collaborating researchers include Dr. Fiona 

Schmiegelow, Dr. Greg McDermid and his lab at the University of Calgary, and Dr. Stefano Mariani at the 

University of Dublin. This report describes the main objectives of the research project, and reports on 

progress in field activities and research over the period from January 1 st , 2007 to December 31 st , 2009. 

1.1 PROJECT OVERVIEW 

The primary goal of this research was to determine how human activities affect caribou population 

dynamics through modification of predator‐prey relationships. This knowledge can then be used to 

develop appropriate conservation strategies across the range of caribou in west‐central Alberta and 

east‐central British Columbia (Figure 1). We investigated the genetic, demographic, and ecological (e.g. 

predator‐prey) dynamics of caribou hypothesizing two primary mechanisms for caribou declines: 

1. Conversion by logging of old forests to early seral habitats results in high primary prey densities. 

Because of the strong numeric response of wolves (Canis lupus) to ungulate prey, logging 

increases wolf density and thus predation rates on caribou (Weclaw and Hudson 2004, Lessard 

2005, Sorenson et al. 2008). 

2. Seismic exploration lines and access roads increase predator efficiency by increasing the rate at 

which wolves kill prey because wolves select for, and move faster on, such linear features (James 

and Stuart‐Smith 2000, Dyer et al. 2002, Neufeld 2006). 

Our research focused on the impacts of these two mechanisms on demography, population genetics, 

and landscape ecology of caribou populations in west‐central Alberta. Landscape genetics approaches 

allowed us to evaluate natural vs. anthropogenic causes for population structuring and habitat 

fragmentation. These techniques also helped us identify ecologically relevant herd designations and 

compare with migratory behavioral data for subsequent herd‐level ecological analyses. We applied new 

6


statistical approaches to examine the effects of the two primary causes for caribou declines (predator 

efficiency, prey density increases) in caribou populations across a large human development gradient. 

Because of the huge spatial scale of predator‐prey relationships, few studies have empirically linked 

linear features to increased wolf predation rates. Recognition of the importance of altered primary prey 

density is likewise hindered by few quantitative studies on this relationship. Furthermore, human‐ 

triggered changes to predator efficiency and primary prey density are often confounded in space and 

time because forestry and oil & gas development co‐occur. Thus, managers face a problem of trying to 

understand the relative roles of increases in predator efficiency (primarily associated with oil & gas) vs. 

the production of primary prey habitat (predominantly associated with forestry). By comparing caribou‐ 

human relationships across a large gradient of caribou herds in west‐central Alberta, our overall 

objectives was to determine the effects of the different types of development on caribou and to develop 

caribou recovery strategies in west‐central Alberta. 

The strength of our methods was to compare caribou dynamics across the entire mountain caribou 

range in Alberta, contrasting protected areas to those in highly developed landscapes (Figure 1). While 

many caribou populations are declining (7 of 10 know local herds), several are stable or potentially 

increasing (Table 2.1). We used the gradients in human activity and development that occur across 

these caribou ranges as the basis for our experimental design. Human disturbance occurs predominantly 

in eastern foothills forests. The South Jasper population(s) in Jasper National Park (JNP) exists in lands 

largely undeveloped by either forestry or human access, and act as a crucial baseline control area 

(Arcese and Sinclair 1997). Moving north, the A La Peche (ALP) herd has extensive forestry and oil & gas 

development on its winter range, but not on its summer range. The Little Smoky herd (LSM) is the most 

developed on winter and summer range and presently the subject of intensive management recovery 

efforts including wolf and moose control. The Redrock‐Prairie Creek (RPC) has the highest intensity of 

human activity and development on its winter range, with little development on its summer range. The 

Narraway (NAR) herd has moderate human development on its winter range, but even less development 

on its summer range. Finally, the newly identified caribou herd, the Redwillow herd (which may be an 

extension of the Narraway) is similar to the Narraway herd, with less development on its winter range. 

By comparing caribou dynamics across this gradient, we are able to test for thresholds in responses of 

wolves to human development. Our research makes use of the substantial research and monitoring 

effort invested in these caribou herds in cooperation with project partners Weyerhaeuser, Alberta Fish 

and Wildlife, BC Ministry of Environment, and Jasper National Park. 

We also collaborated with Dr. Dale Seip, BC – Ministry of Forests through his research on the 4‐5 

caribou herds immediately North West of the Red Willow herd (Quintette, Moberly, Parsnip, Kennedy, 

and Burnt Pine herds), and through joint monitoring of both wolves and caribou in the Red Willow herd. 

Dr. Seip also received funding from PTAC and we actively collaborated on population genetic and 

predation‐related questions to enable our research to be applicable over an even larger geographic area. 

7


2.0 STUDY AREA 

Our study area encompasses a 40,000 km 2 portion of the Canadian Rockies of western Alberta 

and eastern British Columbia containing seven populations of woodland caribou of two Threatened 

ecotypes (Table 2.1, Figure 2.1). Six populations are federally classified as the southern mountain 

ecotype (A la Peche, Banff, Narraway, Redrock‐Prairie Creek, Redwillow, and South Jasper) and the 

seventh as the boreal ecotype (Little Smoky). The Banff population was recently extirpated by an 

avalanche event in April 2009, which included the mortality of 2 radio‐collared individuals, and the South 

Jasper population is composed of three sub‐populations (Brazeau, Maligne, and Tonquin) as identified in 

Figure 2.1. 

The Canadian Rockies contain rugged mountainous terrain which gives way to rolling boreal 

foothills along their eastern front. Caribou in this region exhibit genetic evidence of mixed lineages of 

diverged Beringian‐Eurasian migratory (R. t. groenlandicus) and North American sedentary (R. t. caribou) 

subspecies (McDevitt et al. 2009). These two subspecies were largely separated by glacial ice during the 

late Pleistocene (Dueck 1998, Flagstad and Roed 2003), but an ice‐free corridor at the end of the last 

glaciations appears to have allowed their mixing along the eastern Rockies (Catto et al. 1996, McDevitt et 

al. 2009). Barren‐ground caribou typically reside in the open tundra of North America and Asia, and are 

known for aggregated, seasonal migrations between winter ranges and calving grounds (Fancy et al. 

1989), whereas woodland caribou populations are typically more sparse and sedentary. Individuals in 

our study area are partially migratory, where some winter in the foothills and migrate 60–90 km west to 

alpine summer ranges, and other individuals are sedentary in either the foothills or mountains year‐ 

round. 

The predator community includes wolves, grizzly bears, black bears (Ursus americanus), cougars 

(Puma concolor), wolverine (Gulo gulo), lynx (Lynx canadensis), and coyotes (Canis latrans). Other 

ungulate prey include moose (Alces alces), elk (Cervus elaphus), mule deer (Odocoileus hemionus), white‐ 

tailed deer (Odocoileus virginianus), bighorn sheep (Ovis canadensis), and mountain goats (Oreamnos 

americanus). Wolves rely primarily on elk as prey in the southern end of this ecosystem (Hebblewhite et 

al. 2004), but shift towards more common moose along a north‐south gradient (Parks Canada pers. 

comm., Franke et al. 2006). 

Table 2.1. Caribou herd status and approximate population size. Estimates and status provided by 

Alberta Fish and Wildlife unpublished data, Parks Canada unpublished data, Hebblewhite et al. (2007), 

and BC Ministry of Forests (D.R. Seip) unpublished data, for the northern BC herds. 

Caribou Population Status Approximate Population Size 

Banff Immediate Risk of Extirpation 5‐7 

Jasper (all three herds combined) Stable ~100 

A La Peche Declining 150 

Little Smoky Immediate Risk of Extirpation 80 

Redrock‐Prairie Creek Declining ~300 

Narraway Declining ~100 

Redwillow – Bearhole Declining ~50 

Quintette Stable 173‐218 

Moberly Stable ~42 (minimum count) 

Burnt Pine Stable ~13 (minimum count) 

Parsnip Unknown Unknown 

Kennedy Unknown Unknown 

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Figure 2.1. Canadian Rockies study area along the Alberta – British Columbia divide, including 100% 

minimum convex polygons (MCPs) of woodland caribou populations, 1998 – 2009. 

9


3.0 GENERAL FIELD METHODS 

3.1 INTRODUCTION 

We used the capture and radio‐collaring of adult caribou, wolves, and moose to monitor movements and 

survival of individuals across the range of conditions present in the study area. Specifically, we used 

GPS‐enabled collars (Lotek‐2200, 3300S, 3300M, 4400S, 4400M, Lotek Wireless, Inc., Newmarket, 

Ontario, Canada; ATS‐G2000, Advanced Telemetry Systems, Inc., Isanti, Minnesota, USA) to collect 

location data of high quality and quantity for fine‐scale analysis of resource selection, predation, and 

movement patterns. 

3.2 ANIMAL CAPTURE AND RADIOCOLLARING 

Winter helicopter net‐gunning was used to capture caribou, wolves, and moose (Andryk et al. 1983), and 

we supplemented these efforts with additional summer foot‐hold trapping for wolves (Frame and Meier 

2007). All animal capture procedures were approved by government and university animal care 

protocols and permitting processes (Table 3.1). Full details of animal capture protocols are available 

upon request from any project personnel. During 2007–2009 we supervised the capture of 36 woodland 

caribou, 40 wolves, and 37 moose across our study area (Figure 3.1), collectively deploying 72 GPS collars 

and 40 VHF collars. We incurred three capture‐related moose mortalities during the study (1 neck 

fracture & 2 capture myopathy). This prompted a thorough internal review and amendment to moose 

capture protocols, with adjustments to conservative working temperatures (< ‐5°C) and chase times (< 1 

minute). 

Table 3.1. Research and collection permits, Canadian Rockies, 2007‐2009. 

Alberta Sustainable Resource Development: Fish and Wildlife Division 

Collection Licenses: #21803, #27086, #27088, #27090 

Research Permit: #27085, #27809, #27812 

Alberta Tourism, Parks, and Recreation 

Research and Collection Permits: RC08WC014 & Wilka101‐07 

______________________________________________________________________________ 

British Columbia Ministry of Environment: Permit and Authorization Service 

Wildlife Act Permit VI08‐31411 

Park Use Permits: 101964 

______________________________________________________________________________ 

Parks Canada Agency 

Research and Collection Permit: JNP‐2007‐952 

______________________________________________________________________________ 

University of Montana 

Animal Use Protocol: 056‐06MHECS‐010207 

Animal Use Protocol: 059‐09MHWB‐122109 

______________________________________________________________________________ 

University of Calgary 

Animal Use Protocol: BI‐2007‐57 

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Figure 3.1. Animals captured for research in the Canadian Rockies, January 2007 – March 2009. 

11


3.3. DATA SHARING AND COMPILATION 

Through data sharing agreements with Alberta SRD, Weyerhaeuser Company, Shell Canada, Parks 

Canada, and the University of Alberta, we have assembled a database of ~500,000 caribou GPS fix 

attempts and >100,000 wolf GPS fix attempts (Figures 3.2, 3.3). 

Figure 3.2. GPS locations of collared caribou in all study area populations, Canadian Rockies, 

1998–2007. 

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Figure 3.3. GPS locations of collared wolves (showing only data from This Study and those 

collected in Jasper NP) and overlap with caribou population home ranges, Canadian Rockies, 2003– 

2007. 

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4.0 APPARENT COMPETITION REVIEW 

4.1 SCOPE 

Ecosystem change is occurring globally via habitat alteration, introduced species, climate change, and 

disease, though the community effects of each can be complex and indirect. The last decade has seen an 

explosion of papers on the effects of inter‐specific interactions as factors causing species endangerment. 

Presumably caribou is a species endangered due to such mechanisms. Predation is a common 

component of species declines, yet ecologists lack a review integrating the role of apparent competition, 

or shared predation in multiple‐prey systems, on predator‐mediated endangerment of prey, such as 

caribou. 

There is a rich theoretical literature concerning coexistence among prey species that exhibit exploitative 

and apparent competition, yet this exists with some disconnect to the growing body of empirical studies 

demonstrating the role of these dynamics in species decline. In this review we use the theoretical 

literature to provide an integrated discussion of parameters driving asymmetry among competing prey. 

We then incorporated several recent studies linking apparent competition to the endangerment of prey 

species. Thus, our review of the theory and empirical evidence demonstrated the potentially destructive 

effects of apparent competition on species such as caribou. Lastly we discuss research and conservation 

options for demonstrating and addressing apparent competition to best guide endangered species 

recovery. 

Our findings on this particular objective are fully described in the paper: 

DeCesare, N. J., Hebblewhite, M., Robinson, H. and M. Musiani (2010). Endangered, apparently: the role 

of apparent competition in endangered species conservation. Animal Conservation, In Press. 

A summary of findings is also provided below. 

4.2 ABSTRACT 

Conservation biologists have recently reported growing evidence of food‐web interactions as causes of 

species endangerment –a phenomenon also suggested for caribou decline. Apparent competition is an 

indirect interaction among prey species mediated by a shared predator, and has been increasingly linked 

to declines of prey species across taxa. We reviewed theoretical and empirical studies of apparent 

competition, with specific attention to the mechanisms of asymmetry among apparently competing prey 

species. Asymmetry is theoretically driven by niche overlap, species fitness traits, spatial heterogeneity 

and generalist predator behaviour. In real‐world systems, human induced changes to ecosystems such as 

habitat alteration (e.g. caribou habitat) and introduced species may be ultimate sources of species 

endangerment. However, apparent competition is shown to be a proximate mechanism when resultant 

changes introduce or subsidize abundant primary prey for predator populations (e.g. wolves). 

Demonstration of apparent competition is difficult due to the indirect relationships between prey and 

predator species and the potential for concurrent exploitative competition or other community effects. 

However, general conclusions are drawn concerning the characteristics of prey and predator species 

likely to exhibit asymmetric apparent competition, and the options for recovering endangered species 

such as caribou. While short‐term management (e.g. wolf control) may be required to avoid imminent 

extinction in systems demonstrating apparent competition, we propose adaptive conservation efforts 

directed at long‐term recovery. 

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4.3 SUMMARY OF FINDINGS 

Similar to exploitative competition, apparent competition can be defined as a reciprocal negative 

interaction (‐, ‐), theoretically promoting coexistence among prey. However, asymmetrical (‐, 0) 

interactions may be more common in nature, and could cause declines in one prey species (Fig. 4.1). It is 

precisely this asymmetry that may put some species, such as caribou, at risk while others flourish under 

predation by a shared predator. 

Figure 4.1. Food web schematic depicting direct (solid) and indirect (dashed) interactions characteristic 

of apparent competition dynamics between primary (1) and secondary (2) prey under a shared predator. 

In exploitative competition models, the species fitness ratio (K1/K2) has been used to compare average 

fitness among consumer species, according to the maintenance requirements of prey species per unit 

resource, and their maximum rate of resource harvest. This ratio compares the theoretical competitive 

ability of prey species such as comparing potential population growth allowed by inherent life history. 

In systems with shared predation, the species fitness ratio is expanded to include both resource driven 

growth rates and sensitivity to predation among apparently competing prey. Coexistence can then be 

theoretically represented as a function of both the species fitness ratio and niche overlap among prey 

species, where the degree of niche overlap constrains allowable fitness differences (Fig. 4.2). For 

example, when niche overlap among two species is high, the difference between low and high species 

fitness ratios might represent the difference between persistence and extinction for a species (Fig. 4.2). 

15


Figure 4.2. Theoretical implications for extinction of the relative species fitness ratio and niche overlap 

between a primary (1) and secondary (2) prey species. 

Endangered species (such as caribou) are often secondary prey to predators subsisting on an abundant 

primary prey with higher average fitness, and prey species would be expected to contribute disparately 

to the predator numerical response. Contrary to single prey systems (rho P =0) with regulatory predation, 

asymmetric apparent competition among multiple prey (rho P higher than 0) produces a positive y‐ 

intercept in the numeric response to secondary prey, depensatory predation, and thus a mechanism of 

extinction via apparent competition. The lack of numeric response to secondary prey is a hypothesized 

link to declines of threatened species in several multiple‐prey systems. 

Our overview indicated the critical relationships existing between apparent competition, predation rates 

and dynamics of prey species. Asymmetry in apparent competition has theoretical implications for 

endangered species decline, though we have shown potential mechanisms for relaxed predation at low 

prey density. Here we use examples in the literature to identify the empirical conditions associated with 

asymmetry in apparent competition. Typical of all examples is human‐induced change to resource, prey 

or predator communities (Table 4.1). 

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Table 4.1. Hypothesized cases of species decline induced by asymmetric apparent competition among 

prey, including parameters such as their role of declining species as primary (1) or secondary (2) prey to 

the predator, resource niche overlap (rho R ), relative species fitness ratio (k1=fitness of alternate prey, 

k2=fitness of declining prey; all values assumed higher than 1 except when noted as such), and the 

suspected ultimate cause of asymmetry among sympatric prey. Note caribou as species in decline. 

Review of the many species and systems studied revealed practical patterns linking theoretical 

mechanisms to both the occurrence and strength of apparent competition in natural systems (Table 4.1). 

First, shared predation among prey species inherently implies some level of realized apparent 

competition just as shared resources imply exploitative competition for food. Many examples of 

asymmetric apparent competition occur in the absence of exploitative competition. Thus, increased 

consideration of predation as a crucial component of the niche of species and niche overlap among 

species is warranted. Given predation niche overlap among prey, theory predicts that primary prey 

species should experience regulatory predation, but secondary prey such as caribou should be more 

susceptible to dispensatory predation. 

In our review of asymmetry in apparent competition this prediction is well supported, with rare or 

endangered species (e.g. caribou) often succumbing to a predator population (e.g. wolf) that is 

otherwise sustained by an abundant primary prey (Table 1). 

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5.0 CARIBOU POPULATION GENETICS IN WESTERN ALBERTA AND EASTERN 

BRITISH COLUMBIA 

5.1 SCOPE 

We examined the genetic diversity of caribou at the individual, population, and meta‐population levels. 

We tested genetic relationships and diversity within and among caribou populations characterized as 

migratory or resident, or including both migratory and resident individuals. Our genetic analyses are 

assisting the Provincial Governments, Parks Canada and all stakeholders to evaluate their caribou 

population management and conservation programs. 

Our findings on this particular objective are fully described in the paper: 

McDevitt, A. D., Mariani, S., Hebblewhite, M., DeCesare, N. J., Morgantini, L., Seip, D., Weckworth, B. V. 

and M. Musiani (2009). Survival in the Rockies of an endangered hybrid swarm from diverged 

caribou (Rangifer tarandus) lineages. Molecular Ecology 18: 665–679. 

A summary of findings is also provided below. 

5.2 ABSTRACT 

In North America, caribou (Rangifer tarandus) experienced diversification in separate refugia before the 

last glacial maximum. Geographical isolation produced the barren‐ground caribou (Rangifer tarandus 

groenlandicus) with its distinctive migratory habits, and the woodland caribou (Rangifer tarandus 

caribou), which has sedentary behaviour and is now in danger of extinction. Herein we report on the 

phylogenetics, population structure, and migratory habits of caribou in the Canadian Rockies, utilizing 

molecular and spatial data for 223 individuals. Mitochondrial DNA analyses show the occurrence of two 

highly diverged lineages; the Beringian–Eurasian and North American lineages, while microsatellite data 

reveal that present‐day Rockies’ caribou populations have resulted from interbreeding between these 

diverged lineages. An ice‐free corridor at the end of the last glaciation likely allowed, for the first time, 

for barren‐ground caribou to migrate from the North and overlap with woodland caribou expanding 

from the South. The lack of correlation between nuclear and mitochondrial data may indicate that 

different environmental forces, which might also include human‐caused habitat loss and fragmentation, 

are currently reshaping the population structure of this postglacial hybrid swarm. Furthermore, spatial 

ecological data show evidence of pronounced migratory behaviour within the study area, and suggest 

that the probability of being migratory may be higher in individual caribou carrying a Beringian– Eurasian 

haplotype which is mainly associated with the barren‐ground subspecies. Overall, our analyses reveal an 

intriguing example of postglacial mixing of diverged lineages. In a landscape that is changing due to 

climatic and human‐mediated factors, an understanding of these dynamics, both past and present, is 

essential for management and conservation of these populations. 

5.3 SUMMARY OF FINDINGS 

We investigated 12 different caribou herds as part of our study, where herd was defined usually by 

provincial wildlife agencies as a ‘local population’, typically according to the watershed frequented (Fig. 

5.1). In total, our study area likely contains approximately 1000 caribou distributed in 12 local herds 

(Thomas & Gray 2002). At least three of these herds are known to be in decline (Table 5.1). 

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Figure 5.1. Ranges of caribou herds analysed in this study (minimum convex polygons; straight lines on 

map) and core areas used by monitored individuals (kernel 95% probability polygons; curved lines) in the 

Canadian Rocky Mountains, Alberta (AB) and British Columbia (BC) provinces. Dotted lines delineate the 

boundaries between federal (marked as Federal) and provincial ecotype designations of herds (AB or 

BC). Green shading depicts national parks and provincial protected areas; topography and major roads 

are also indicated. 

19


Ecotype Designations 

Herd Code Federal Alberta 

British 

Columbia 

20 

Status N HE AR 

% Partially 

Migratory 

% Migratory % BGH 

Redrock- 

Prairie Creek RPC Southern Mountain Mountain Northern Stable 52 0.793 5.762 11 100 56 

Narraway NAR Southern Mountain Mountain Northern Unknown 45 0.789 6.051 0 100 8 

Little Smoky LSM Boreal Boreal - Risk of Extirpation 39 0.669 5.074 60 68 38 

A La Peche ALP Southern Mountain Mountain Northern Stable 28 0.799 5.993 57 68 30 

Parsnip PAR Southern Mountain - Mountain Increasing 18 0.804 6.141 18 18 6 

Kennedy KEN Southern Mountain - Northern Stable 17 0.795 5.972 55 73 7 

Jasper JNP Southern Mountain Mountain Northern In Decline 13 0.751 5.198 20 70 42 

Quintette QUI Southern Mountain - Northern Stable 11 0.837 6.545 55 64 45 

Moberly MOB Southern Mountain - Northern Stable 3 – – 67 67 0 

Pine PIN Southern Mountain - Northern Stable 2 – – 0 0 0 

Red Willow RWR Southern Mountain - Northern Unknown 2 – – 0 100 0 

Banff BAN Southern Mountain Mountain Northern Risk of Extirpation 2 – – 0 50 100 

Table 5.1. Caribou herds, federal and provincial ecotype designation, and conservation status.


Levels of genetic diversity (HE and AR) were moderately high among herds (Table 5.1) and comparable to 

other population level studies of caribou herds (McLoughlin et al. 2004; Zittlau 2004; Boulet et al. 2007). 

However, the Little Smoky herd had lower levels of diversity compared to the other herds (Table 5.1). 

We obtained > 500 000 VHF and GPS locations from 231 adult female caribou from 2001 to 2007 

throughout the 12 caribou herds. A full suite of migratory behaviours from sedentary, to partially or fully 

migratory characterized each population (Table 5.1; Fig. 9.1 [below]) The proportion of migratory 

individuals in caribou herds ranged from 0.18 (in the Parsnip herd) to 1.00 in the Narraway, Red Rock 

Prairie Creek, and Red Willow herds (Table 5.1). 

Twenty‐four polymorphic sites were identified in the control region of mitochondrial DNA in the study 

area which resulted in 17 unique haplotypes, neatly joined into the two lineages identified previously as 

the North American lineage (NAL) and the Beringian–Eurasian lineage (BEL). Both lineages were found 

throughout the study area (Fig. 5.2). 

Figure 5.2. Median‐joining networks of (a) the Beringian–Eurasian and (b) the North American lineages. 

Numbers on branches indicate number of mutations if greater than one. The geographical distribution of 

both haplogroups is shown in (c) (red circles: NAL; yellow circles: BEL). 

Caribou in our study area have long been considered to belong to the woodland subspecies, with the 

barren‐ground subspecies occurring in the tundra, hundreds of kilometres further north. Here we show 

21


unambiguously that the BEL associated with the barren‐ground caribou is present in the Canadian Rocky 

Mountains (Fig. 4c). Even though the BEL and NAL diverged before the onset of the last glacial maximum 

(23 000–19 000 bp), nuclear DNA and spatial data clearly demonstrate for the first time that the two 

lineages have extensively interbred, possibly since the end of the Wisconsin glaciations (~14 000 bp), 

producing a unique, mixed gene pool. This type of mixture is not unprecedented. Isolation and expansion 

from refugia during Pleistocene glacial cycles has left a genetic legacy across many taxa, particularly in 

western North America (Fontanella et al. 2008, Latch et al. 2009, O’Neill et al. 2005, Soltis et al 1997). 

The signatures of this legacy are often apparent in the morphological and phylogenetic discontinuities 

observed for many species in the region, which are sometimes reflected in subspecific taxonomic 

classifications. These patterns are even evident in large, vagile mammals, including wolves (Weckworth 

et al. 2005, 2010), black bears (Peacock et al. 2007, Stone and Cook 2000, Wooding and Ward 1997), and 

mule deer (Latch et al. 2008, 2009). Similar patterns may also describe the phylogeographic history of 

caribou 

When caribou were grouped into herds, several pairwise comparisons were nonsignificant (Table 2). The 

Quintette herd was not significantly differentiated from the herds in Narraway, Red Rock Prairie Creek 

and Parsnip. The Parsnip herd was also not significantly differentiated from the herd in Kennedy (Table 

5.2). This mirrors, to a good extent, the GPS telemetry data on range overlap (Fig. 5.1). 

Table 5.2. Pairwise FST values between herds using mtDNA (upper diagonal) and microsatellites (lower 

diagonal). 

Clustering analysis in the program Structure revealed the presence of four distinct genetic clusters. 

Cluster 2 largely corresponded to the herd in the Little Smoky area, cluster 1 was mostly confined to 

Narraway and Red Rock Prairie Creek, cluster 3 had a more southerly distribution but also had individuals 

present far north, while cluster 4 had a mainly northerly distribution (Fig. 5.3a). When spatial data were 

incorporated, the program TESS also revealed that the most likely number of clusters was K = 4, yielding 

a picture that is largely in agreement with that obtained with Structure (Fig. 5.3b), yet providing a higher 

degree of detail, which the nonspatially explicit model of Structure cannot fully capture. 

22


Figure 5.3. Geographical distribution of inferred genetic clusters identified by the programs Structure (a) 

and TESS (b). In both graphs, the red circles largely represent individuals from the Red Rock Prairie Creek 

and Narraway areas (RPC/NAR/RDW), individual caribou from the distinctive Little Smoky (LSM) area are 

identified by yellow circles, the green cluster essentially includes A la Pace and Jasper (JNP/ALP/BNP) 

area caribou and the blue cluster mainly contains northern individuals (north) which corresponds to five 

herds (QUI, MOB, PIN, PAR and KEN; see Fig. 5.1 and Table 5.1). 

At the herd level, there was no association between the two mitochondrial lineages and proportions of 

‘migratory’ vs. ‘sedentary’ caribou (χ2 = 0.04; P = 0.83). However, at the individual level, there was a 

positive association between whether or not an individual caribou migrated and belonged to the BEL 

(Fig. 5.4). The best‐supported model was a simplified function that related the probability of belonging to 

the BEL to whether or not a caribou was migratory (‘yes’ or ‘no’, where ‘possible’ = yes) accounting for 

differences in the function between migration and BEL for different TESS four herd units (Fig. 5.3b). 

Although the variance explained by this model was relatively low (pseudo R2 = 0.11), the biological 

effects were still significant indicating a range of 4–25% increase in migratory probability for individual 

caribou if they belonged to the BEL lineage (Fig. 5.4). 

23


Figure 5.4. Relationship between migratory tendency (classified as sedentary, 0, or partially migratory 

and migratory, 1) and the probability of an individual caribou to belong to the Beringian– Eurasian 

lineage, conditional on each of the main TESS genetic clusters we identified (K = 4, Fig. 5.3b). Genetic 

clusters are colour coded as in Fig. 5.3(b). Conditional probabilities were derived from a generalized 

linear mixed model run on individual caribou (n = 223). 

Compared to typical woodland populations elsewhere in Canada, caribou in the Canadian Rockies exhibit 

a more pronounced tendency to migrate (Bergerud et al. 1990; Terry et al. 2000; Apps et al. 2001; Boulet 

et al. 2007). Our modelling revealed that within the mountain park and the Northern caribou 

populations, the probability of being migratory increased if an individual caribou belonged to the 

Beringian–Eurasian lineage. In these environments, migratory ability may represent an important 

adaptive trait of these genetically unique ‘hybrid populations’, whose habitat presents the challenge of 

spatial and seasonal changes in forage quality. In fact, migratory caribou frequent the tundra‐like alpine 

areas during the summer only (Fig. 9.1 [below]), that is, the period of high plant productivity (Shackleton 

1999; Apps et al. 2001). 

Clearly the nonadaptive nature of the genetic markers employed here does not allow for the detection of 

any causal relationship between genetic and phenotypic (behavioural) traits. Although we cannot fully 

comprehend the actual significance of the detected association, it is important to stress that migration 

has been an adaptive response to climate change in the past and is predicted to reduce the risk of 

extirpation given future climate change (Austin & Rehfisch 2005; Broenniman et al. 2006; Levinsky et al. 

2007). Research confirms that climatic impacts will be reduced for species with a behavioural 

predisposition for migration (Austin & Rehfisch 2005; Broenniman et al. 2006). Further investigations 

should evaluate the relationships between the presence of the two mitochondrial DNA lineages in our 

caribou populations, and the implications for individual fitness and for population survival of the 

interrelated sedentary and migratory life strategies. 

24


Our data also revealed that populations along the northwest– southeast axis of the Rocky Mountains are 

less divergent than, for instance, the Little Smoky herd, east of Highway 40 (Fig. 5.1). This herd is spatially 

contiguous to the A la Peche herd (Fig. 5.1), yet from a genetic point of view it constitutes a unique and 

separate cluster detectable using both mtDNA and microsatellite data and with or without the 

incorporation of spatially explicit data into analytical models (Table 5.2, Fig. 5.5a and 5.5b). The Little 

Smoky herd also happens to be the only of ‘boreal’ ecotype, according to both Federal and Provincial 

(Alberta) caribou designations, which was alarming in some respects because of its relative isolation from 

other boreal woodland populations (the closest other one being > 100 km away and also at immediate 

risk of extirpation). Yet, the Little Smoky herd also shows a high proportion of barren‐ground haplotypes, 

suggesting that despite having originated from the same postglacial event as the other populations, 

other factors — possibly anthropogenic — have contributed to its recent independent evolution. 

25


6.0 VEGETATION AND HUMAN DISTURBANCE MAPPING IN THE 

TRANSBOUNDARY STUDY AREA 


To achieve our research objectives, a consistent spatial database of vegetation related (land cover, 

canopy closure, disturbance density, stand age) and anthropogenic (anthropogenic and natural features) 

data is required for the entire Alberta and BC portions of the study area (Figure 2.1). The transboundary 

nature of our study area results in over 7 different jurisdictions (Figure 1) and at least 3 different 

ecological or vegetation inventory methods between provinces and national Parks (ELC in Parks Canada, 

AVI in Alberta, TRIM data in British Columbia). Furthermore, many key portions of our study area were 

not inventoried during provincial vegetation/timber inventories (e.g., the Willmore). To navigate this 

significant barrier to achieving our research objectives, we set out in this first year of the project to 

develop remotely‐sensed GIS databases for land cover and other vegetative components for our study 

area. We worked in collaboration with the Foothills Research Institute Grizzly Bear Research Program 

(FRIGBRP) land cover mapping initiative, led by Dr. Greg McDermid at the University of Calgary’s Foothills 

Facility for GIS in partnership with Gordon Stenhouse of the FRIGBRP. Over the last 8 years, the FRIGBRP 

has expended millions of dollars developing land cover and human disturbance layers for the Alberta 

portion of the study area, and our goals were to extend this land cover mapping to the unmapped BC 

portion of the study area (e.g., Figure 2.1; Table 6.1). 

Since its launch in 1999, the FRIGBRP has conducted seven phases of landscape mapping in western 

Alberta, with our updated extension to lands in British Columbia representing the seventh phase. During 

this time they have identified several key lessons to producing effective and reliable remotely‐sensed 

mapping products for wildlife research, including: 1) an emphasis on continuous rather than categorical 

mapping, 2) a reduction of spatial inconsistencies, and 3) a wariness of over‐simplication (McDermid et 

al. In prep). The combination of these mapping philosophies and an application framework founded on 

ecology led to a remote‐sensing strategy of producing multiple spatial data products to capture the 

multi‐attribute nature of complex landscapes (McDermid et al. 2005, McDermid et al. 2009, McDermid et 

al. In prep). 

6.2 METHODS 

6.2.1 Landcover & Vegetation Data. 

This includes assembled landcover and other anthropogenic and biophysical datasets managed under the 

auspices of the FRIGBRP by Dr. Greg McDermid in collaboration with Gordon Stenhouse under the data 

sharing agreement signed 09/07/2007. 

FRIGBRP personnel conducted ground‐truthing vegetation sampling data at >3800 field plots across 

the study area, generally within


Raster and vector GIS products produced from this work included (Table 6.1): 

Vegetation 

* Forest crown closure: 0–100% (Figure 6.2) 

* Landcover: Categories included: upland trees, 

wetland trees, upland herbs, wetland herbs, 

shrubs, water, barren land, and snow/ice 

* Tree species composition: 0% coniferous – 

100% coniferous 

* Normalized Difference Vegetation Index 

(NDVI): index of green vegetation 

* Burned cover types, including year of fire 

* Stand age (in Alberta only) 

6.2.2 Anthropogenic data. 

27 

Topographic/Hydrologic 

* Elevation 

* Slope 

* Aspect 

* Curvature 

* Hillshade (solar insolation) 

* Topographic position index: from drainages (‐) 

to ridges (+) 

* Streams 

* Lakes 

* Snow cover (Figure 6.3) 

Through cooperation with Alberta SRD our database of linear features (roads, pipelines, and seismic 

lines) within Alberta is provided by the Alberta SRD Access database. To create a similar database in BC, 

we acquired SPOT imagery (5‐meter resolution) and orthorectified airphotos through BC collaborators 

Dale Seip, BC‐MOF, and Malcom Gray 

and Barbara Gray‐Wiksten, Integrated 

Land Management Bureau) for manual 

digitizing of remaining linear features 

throughout the BC portion of the study 

area. Vector GIS products produced 

from this work include spatial point, 

polyline, and polygon representation of 

roads, seismic lines, pipelines, well pads, 

and cut‐blocks across the study area. 

Figure 6.1. Locations of vegetation 

sampling plots collected in British 

Columbia for the purposes of ground‐ 

truthing a new remotely‐sensed land 

cover raster for the study area. Plots 

were visited in front country areas 

(McGregor and Redwillow River 

drainages) and along an 11‐day 

backcountry hike during summer, 2007.


Table 6.1. Terrain and landcover GIS layers used in predictive RSF models for caribou, wolves, and 

moose within the Banff, Jasper, and A la Peche study area, 2001–2009. 

Variable Variable Range of Description 

Terrain 

Type Values 

North Categorical 0,1 North aspects from 315° to 45° 

South Categorical 0,1 South aspects from 135° to 225° 

East Categorical 0,1 East aspects from 45° to 135° 

West Categorical 0,1 West aspects from 225° to 315° 

Flat Categorical 0,1 No aspect (slope = 0) 

Slope Continuous 0–6827% Percent slope (equivalent to 0 – 90°) 

Elevation 

Landcover 

Continuous 553–3955m Elevation in meters 

Alpine Barren Categorical 0,1 Barren ground between 2200 and 2700m. 

Alpine Herb Categorical 0,1 Alpine meadows above 2200m. 

Alpine Shrub Categorical 0,1 Shrub communities above 2200m. 

Burn Categorical 0,1 Areas burned 1950 to present. 

Closed Conifer Categorical 0,1 Coniferous forest with >50% canopy closure and >70% 

conifer composition. 

Deciduous 

Forest 

Categorical 0,1 Deciduous dominated forests 30% and


Figure 6.2. Tree crown closure raster (30 meter pixel resolution) derived using remote‐sensing 

methodology and ground‐truthing across study area. 

29


Figure 6.3. Percent snow cover raster (November‐May, 2007; 500 meter pixel resolution) derived 

from MODIS remote‐sensing imagery across study area. 

30


7.0 PREDATOR EFFICIENCY OVER A REGIONAL GRADIENT OF HUMAN 

DEVELOPMENT 

7.1 SCOPE 

We are using GPS‐based wolf movement data to address how anthropogenic changes to forest structure 

and linear features affect wolf predation efficiency and ultimately kill rates in a multi‐prey ecosystem. 

7.2 SCHEDULE 

� 2007 – GPS collaring of wolves 

� 2008 – GPS collaring of wolves and kill‐site inspections 

� 2009 – GPS collaring of wolves and kill‐site inspections & Resource Selection & Overlap Analysis 

� 2010 – Cox Proportional Hazards “Time‐to‐Kill” Analysis and manuscript submission 


Habitat fragmentation in the form of linear corridors (roads, pipelines, and seismic lines) may promote 

wolf travel (Thurber et al. 1994, James and Stuart‐Smith 2000), increasing predation efficiency (search 

rate) and thus the wolf functional response (number of prey killed per wolf per day) to all prey; James 

and Stuart‐Smith 2000, Neufeld 2006, McKenzie et al. 2009). This hypothesized relationship between 

wolf predation efficiency and kill‐rates assumes that both: 1) wolves disproportionately select linear 

features or areas containing them when foraging, and 2) that these features provide increased predation 

efficiency through increases in speed, detection rates, and/or attack success. Consequently, we 

approach this question using two analysis frameworks: analysis of resource selection using resource 

selection functions (RSFs) to assess wolf patterns of selection with regard to habitat and linear features, 

and Cox Proportional Hazards (CPH) time‐to‐event modeling of the effect of linear features upon the 

time‐to‐kill as wolves forage. 

7.4 METHODS 

7.4.1 Resource Selection Functions 

Resource or habitat selection by animals is an important determinant of fitness and is a focus of many 

wildlife studies where the goal is to estimate impacts of human activity on wildlife and design mitigation 

strategies (Johnson et al. 2005). A common approach for examining resource selection in the wildlife 

literature is the use of Resource Selection Functions (RSF; Compton et al. 2002, Manly et al. 2002, Jones 

and Tonn 2004, Johnson et al. 2006). RSFs are attractive to ecologists because they provide quantitative, 

spatially‐explicit, predictive models for animal occurrence (Mladenoff et al. 1995, Manly et al. 2002). 

Identification of important habitat with RSF models rather than raw data like telemetry locations is 

advised because of problems with sampling a small number of individuals, making inferences based on 

short‐temporal windows of sampling, and on theoretical grounds that predicted potential habitat can be 

identified with the use of statistical approaches like RSF models. 

RSF models have also been extremely useful in understanding the cumulative effects of human 

development on sensitive species such as caribou (Johnson and Boyce 2005, Johnson et al. 2005), 

31


delineating habitat for conservation and protection (Seip et al. 2007), identifying legally defined critical 

habitat for endangered species (Saher and Schmiegelow 2005), identifying mitigation strategies for 

development, and developing population‐linked habitat management models for grizzly bears (Nielsen et 

al. 2006) in Alberta. RSF models are also useful for understanding predator‐prey dynamics, for example, 

between wolves and elk (Hebblewhite et al. 2005, Hebblewhite and Merrill 2007), and have recently 

been applied to understanding wolf‐caribou dynamics (Stotyn 2008) and overlap between wolves, 

caribou and moose in the Little Smoky herd in Alberta (Neufeld 2006). For these reasons, we developed 

Resource Selection Functions for both wolves and caribou in multiple winter ranges across our study area 

to assess both the within‐species patterns of selection relative to habitat and anthropogenic features and 

the among‐species patterns of spatial overlap. 

We conducted these analyses in caribou winter range areas in both protected (Banff & Jasper NPs) and 

managed (A la Peche & Redwillow) landscapes. In the Banff, Jasper, and A la Peche study area, we 

collected 33,439 GPS locations from 40 caribou, 14,406 GPS locations from 34 wolves in 12 packs, and 

1,529 VHF and GPS locations from 28 moose during 2001–2009 for model training. We also withheld 

additional VHF location data from 117 caribou, 23 wolves, and 15 moose for model validation. In the 

Redwillow study area we collected 5,778 GPS locations from 6 caribou, and 4,764 GPS locations from two 

wolves in two packs for model training. Though we defined these as “winter range” analyses, many 

areas did receive year‐round use by caribou; thus we categorized all data within the winter range 

according to summer (May–October) and winter (November–April) seasons and focused analyses on 

winter data. 

To assess resource selection, we compared locations “used” by animals to those “available” to them by 

comparing the proportionate use of resources relative to their proportionate availability in a mixed‐ 

effects modeling framework (Manly et al. 2002). Following Manly et al. (2002: p100) we use logistic 

regression to derive the coefficients for a typical fixed‐effects RSF using: 

32 

(equation 7.1) 

where is the estimated RSF as a function of covariates xn, and is the coefficient estimate for 

each covariate estimated from logistic regression (Manly et al. 2002). We considered fixed‐ and mixed‐ 

effects RSF models and evaluated the top model using AIC (see below): for more detail on mixed‐effects 

RSF models, see Gillies et al. (2006). 

RSF analysis models patterns of preference and avoidance by comparing the habitat components used by 

animals to those generated by random selection. We assessed selection at multiple scales (Johnson 

1980) by varying the zone of availability for each caribou and wolf according to both individual‐ and 

population‐level 100% minimum convex polygons (Mohr 1947; Figure 1). Within each polygon of 

availability we generated a random sample of available points equal in number to the set of used caribou 

GPS locations. 

Resource variables: We overlaid used and available points on a suite of raster layers (30 meter 

resolution) in a geographic information system (GIS) to quantify the underlying habitat associated with 

each point. We measured a suite of habitat variables characterizing topography, vegetation, 

hydrography, and human footprint. We used a digital elevation model to derive layers of elevation, 

slope, aspect, and a topographic position index, which characterized the gradient between drainages and 

ridges according to three user‐defined spatial scales (1, 2 and 5 kilometer radius scales; Jenness 2006).


To characterize vegetation across our study area, we worked in cooperation with remote sensing 

specialist Dr. Greg McDermid at the University of Calgary. We used continuous layers of percent conifer 

species, and percent canopy closure to characterize vegetation at each location. These products were 

developed using Landsat 5 Thematic Mapper (TM) or Landsat 7 TM sensors as described by McDermid 

(2006). We selected this remote sensing approach in recognition that the composition and structure of 

vegetation across the landscape exist at a multitude of scales and that inclusion of continuous remotely 

sensed products avoids the hazards of oversimplifying with categorical maps (McDermid et al. 2005). 

That said, we also included a simple categorical coverage of landcover types, which included upland 

trees, wetland trees, upland herbs, wetland herbs, shrubs, water, barren, and snow/ice. We used 

polygons of recent wildfires to add burns as a cover type, and we also estimated the distance of locations 

to the nearest burn polygon. We used a continuous layer of forest canopy closure (0‐100%) to 

characterize open areas, and estimated a vector layer of forest “edge” by dichotomizing this layer into 

open (0%) and forested (>0%) areas. We then estimated the distance of each location to forest edges. 

We used vector geodatabases of roads, seismic lines and trails to estimate the distance of each location 

to the nearest of each type of human‐use linear feature. 

We used mixed‐effects logistic regression in Stata 10.0 to assess relationships between resource 

variables and the probability of use by caribou and wolves. We used a mixed approach to developing top 

models. First, we screened all habitat covariates for collinearity (P>0.5), and removed collinear variables 

based on deciding which variable was the most relevant to management (for example, in the wolf RSF 

model, distance to water and distance to road were collinear, so we retained distance to road because of 

its management relevance). Second, we conducted univariate analysis to examine individual variable 

relationships with the relative probability of caribou selection. For categorical variables (landcover, cover 

classes) we selected a reference category that was either the most frequent (upland forests). For 

continuous variables, we explored linear and non‐linear relationships using quadratic terms and the 

fractional polynomial command in Stata that fits higher‐order non‐linear functions relating the 

continuous covariate to the relative probability of caribou use in the RSF model. Once the final suite of 

covariates and non‐linear forms were selected, we used we used manual forward stepping model 

selection with AIC to select the most parsimonious model (Stephens et al. 2005). We compared top fixed‐ 

effects models with the same fixed effects and a random effect for individual caribou in a mixed effect 

RSF model (see Gillies et al. 2006). 

Finally, once the final model selected, we examined model goodness of fit using the Hosmer and 

Lemeshow (2000) goodness of fit test, and classification diagnostics such as % classification success, the 

Receiver Operating Characteristic (ROC) curve of the logistic regression model, and examined residual 

diagnostics to screen for outliers and influential data points. Finally, we examined the predictive capacity 

of RSF models using k‐folds cross validation across the entire dataset, and then, across individual animals 

as k=n sets. Model validation across individual animals addresses the more appropriate biological 

question of how well the population‐averaged RSF model predicts individual animals. 

7.4.2 Predation Efficiency 

Data collection has only recently been completed for this portion of our study (Sep, 2009). In this report 

we present preliminary descriptive results, but modeling of the effects of linear features of predation 

efficiency (described below) is not yet completed. 

We have collected wolf GPS data to estimate parameters of the functional response for testing the 

effects of linear features on predator efficiency. We identify putative kill‐sites within GPS‐based 

33


movement paths using software originally developed for detection of disease outbreaks in 

spatiotemporal health data (Program SaTScan; Kulldorff et al. 2005) but adapted for identifying wolf kill‐ 

sites using GPS data (Webb et al. 2008). We have field‐validated a sample (N>500) of putative kill‐sites 

to assess characteristics associated with kill and non‐kill point clusters (Webb et al. 2008). When 

conducting time‐to‐kill analyses, we will quantify the number of locations, time spent, step length, turn‐ 

angles, and nearest‐neighbor distances associated with each point cluster. We will then use logistic 

regression to assess which cluster parameters are most predictive of kill‐sites (Hosmer & Lemeshow 

2000). Methods for identifying clusters during nomadic (fall/winter) seasons for wolves are previously 

reported (Webb et al. 2008), but during denning and rendezvous seasons the dispersed, central‐place 

foraging of wolves and potential for juvenile prey present new challenges (Sand et al. 2008). We focused 

initial sampling in the winter, when wolf‐caribou overlap is high, but have recently (Summer 2009) 

extended this approach to the summer to develop season‐specific models. We will use cross validation 

of training and withheld testing data to estimate models’ predictive fit (Boyce et al. 2002). 

Kill‐site models will allow us to categorize wolf GPS data into behavioral sequences corresponding to 

parameters of wolf predation efficiency or functional response. The functional response, or kill‐rate of 

wolves (kills/predator/time), is a function of search rate (a), handling time (h), and prey density (N) and 

time (T; Holling 1959b). A type II form of the functional response (though I will consider other functional 

forms), or kill‐rate f(N), to these parameters is quantified with Holling’s Disc Equation (Holling 1959a,b): 

f(N) = aNT /( 1 + ahN) (equation 7.2) 

Simulation models show that search rate component, a, is the most sensitive to human development and 

has the largest impact on functional response, whereas the effect of h, handling time, is relatively 

constant (Lessard 2005). With GPS data, handling time (h) can be estimated by the duration of time 

spent at each kill‐site (Figure 7). The inter‐kill foraging time (Tk), or time‐to‐kill, is proportional to the 

inverse of Holling’s (1959b) kill‐rate, and is a function of prey density (N) and instantaneous search rate 

(a). We use predictive models of wolf kills to divide movement paths into behavioral sequences of 

handling (kill‐sites) and inter‐kill foraging events. We then use Cox Proportional Hazards (CoxPH) 

modeling in a repeated‐event, time‐to‐event modeling framework to test the effects of linear features on 

wolf time‐to‐kill, while controlling for prey density, topographic and vegetation covariates (Cox 1972, 

Anderson and Gill 1982, Therneau and Grambsch 2000, Hosmer et al. 2008). 

Spatial RSFs developed for caribou (see above, Resource Selection Functions) and moose (developed in 

Section 8 below) provide a relative index of prey density (N) associated with wolf movements (Boyce and 

McDonald 1999, Ciarniello et al. 2007, Seip et al. 2007). We also control for the effects of other 

vegetation and topographic covariates on Tk (see Appendix B for a complete list). Lastly, we will include 

several measures of linear feature effects (mean distance to linear feature, number of linear feature 

crossings, linear feature density) to assess correlations between linear features and wolf predation 

efficiency. To test a related assumption of Holling’s disc equation as it applies to wolf predation, we will 

do a complimentary analysis of factors influencing handling time (Holling 1959b). An alternative 

hypothesis about the effects of linear features on predation efficiency might be that linear features do 

increase efficiency, but that this increase is compensated for by additional handling or resting time, 

resulting in no net increase in per‐capita kill‐rate (sensu Fortin et al. 2004). Thus, increased efficiency 

could decrease time‐to‐kill (decrease a), or increase handling time (h). We will use linear regression and 

similar model development procedures as above to assess the relative effects of the same covariates on 

handling time at each kill‐site. 

34


7.5 RESULTS 

7.5.1 Resource Selection Functions 

Caribou Resource Selection. Caribou selected for higher elevations, avoided steep slopes, generally 

selected north‐facing aspects, and preferred forested habitats across a range of canopy closure values 

(Tables 7.1, 7.2). In terms of distance variables, caribou avoided areas near roads and seismic lines 

(positive coefficient suggests increased use with increasing distance from linear features), though 

quadratic terms suggested they may have preferred areas of intermediate distances from roads in both 

study areas (Figures 7.1, 7.2, 7.5, 7.6). Caribou in the Banff, Jasper and A la Peche study area also 

showed a preference for areas close to trails during winter. 

The top Redwillow model satisfied the Hosmer Lemeshow Goodness of Fit test (P=0.08, failed to 

reject hypothesis that model fit was incorrect), and had adequate, but not exceptional discriminatory 

ability to distinguish between used and unused (available) caribou habitat with a ROC score of 0.742. 

While use availability designs make the use of ROC scores potentially invalid (Boyce et al. 2002), an ROC 

score of 0.5 means essentially random predictive capacity, whereas a ROC score of 1.0 means perfect 

classification. Therefore, models with ROCs of 0.7–0.08, 0.8–0.9, and 0.9–1.0 would indicate acceptable, 

excellent, and outstanding discrimination of used/available data, respectively (Hosmer and Lemeshow 

2000). Finally, k‐folds cross validation across the Redwillow data set suggested models predicted well 

(Spearman rank correlation, rho = 0.96, SE = 0.01). But k‐folds cross validation across individual caribou 

revealed substantial within‐individual variation when compared to the naïve population‐averaged model 

with a predictive capacity much below that across the entire dataset (rs =+0.61, SE = 0.02). In the Banff, 

Jasper and A la Peche study area, the top winter caribou model had an improved ROC of 0.883, 

demonstrating excellent to outstanding discrimination. K‐folds cross validation revealed excellent 

predictive capacity to both model training data (Spearman rank correlation rho=0.996) and withheld 

model validation VHF data (Spearman rank correlation rho=0.985). 

a) 

Figure 7.1. In the Redwillow study area, a) Caribou avoided areas close to linear features (e.g., 

pipelines, seismic lines – not including roads) in resource selection analyses. b) Caribou avoided areas 

close to and extremely far from active roads in resource selection analyses. Selectivity for distance to 

active roads and linear features are shown in blue from the resource selection function (RSF). Also 

shown are the percent availability of the landscape in different distance to road/linear feature classes 

(tan) compared to the percent used by caribou (gold), which shows a) more use by caribou at farther 

distances than available in the landscape and b) highest caribou use at intermediate distances from 

35 

b)


a) 

Figure 7.2. In the Banff, Jasper, and A la Peche study area, a) Caribou selected for areas close to trails in 

winter resource selection analyses. b) Caribou avoided areas close to secondary roads in resource 

selection analyses. Selectivity for trails and secondary roads are shown in green from the resource 

selection function (RSF). Also shown are the percent availability of the landscape in different classes 

(white) compared to the percent used by caribou (gold). 

Wolf Resource Selection. Wolves avoided steep slopes and had mixed selection of aspects between 

study areas during winter, selected for shrub habitats and low and high canopy forests (Tables 7.3, 7.4). 

Compared to caribou, wolves selected to be relatively close to roads with negative coefficients at close 

distances for distance to primary roads in both study areas (Figures 7.3, 7.4, 7.7, 7.8). However, in the 

Redwillow the quadratic term for distance to roads suggested that they selected for areas both close to 

and far from roads, and avoided intermediate areas. Selection in the Redwillow for seismic lines was also 

fit in a non‐linear fashion, with use peaking about 600m from seismic lines. They preferred areas close to 

water, and also preferred to be closer to trails in the Banff, Jasper, and A la Peche study area. They also 

selected for burns and other open habitats and avoided high elevation/alpine areas. 

Model performance for the Redwillow wolf RSF built with only n=2 wolves was quite poor. ROC 

scores confirmed weak discriminatory power with an ROC = 0.71. Classification success was also quite 

poor, with only 64% of locations being classified correctly. Finally, similar to the caribou model validation, 

the model validated well across the entire set at random (rs = +0.95, SE = 0.03), but poorly comparing 

one wolf to the other (rs = +0.3, SE = 0.10), highlighting the importance of increasing sampling across a 

greater number of packs. In the Banff, Jasper and A la Peche study area, the top winter wolf model had a 

strong ROC of 0.893, demonstrating excellent to outstanding discrimination. K‐folds cross validation 

revealed excellent predictive capacity to both model training data (Spearman rank correlation 

rho=0.997) and withheld model validation VHF data (Spearman rank correlation rho=0.985). 

36 

b)


a) 

Figure 7.3. In the Redwillow study area, a) Wolves selected areas about 600m from linear features, 

avoiding them slightly closer than this distance, but strongly avoiding distances greater than about 

1000m from roads. However, wolves b) strongly avoided areas close to roads. Selectivity for distance to 

active roads and linear features are shown in blue from the resource selection function (RSF). Also 

shown are the percent availability of the landscape in different distance to road/linear feature classes 

(tan) compared to the percent used by wolves (gold), which shows a) more use by wolves at farther 

distances than available in the landscape and b) highest caribou use at intermediate distances from 

roads than available in the landscape. 

a) b) 

Figure 7.4. In the Banff, Jasper and A la Peche study area, a) Wolves strongly selected areas close to 

trails, and b) avoided areas close to secondary roads. Selectivity for distance to trails and secondary 

roads are shown in green from the resource selection function (RSF). Also shown are the percent 

availability of the landscape in different classes (white) compared to the percent used by wolves (gold). 

37 

b)


Moose Resource Selection. Moose in the Banff, Jasper, and A la Peche study area preferred shrublands, 

grasslands, open conifer forests, and areas close to openings, and avoided alpine habitats (Table 7.5, 

Figure 7.9). They selected for intermediate elevations, reduced slopes, areas close to water, and 

southern and eastern aspects. Moose also preferred areas close to primary roads and trails, while being 

farther from secondary roads within the study area. The top winter moose model had a strong ROC of 

0.902, demonstrating outstanding discrimination. K‐folds cross validation revealed good predictive 

capacity to both model training data (Spearman rank correlation rho=0.912) and withheld model 

validation VHF data (Spearman rank correlation rho=0.988). 

7.5.2 Predation Efficiency 

During 2008–2009, we visited >500 GPS‐based spatio‐temporal clusters, documenting kill‐sites of 9 

mammalian prey species, including caribou, moose, elk, white‐tailed deer, mule deer, bighorn sheep, 

mountain goats, beaver, and marmots. Winter data were collected during winters 2007‐2008 and 2008‐ 

2009, and a single summer session was conducted during May‐September 2009. GPS‐based kill sampling 

methods were developed in winter studies (Webb et al. 2008), but both wolf behavior (central‐place 

denning activity) and prey age structure (neonates born in June) are different in summer. To 

conservatively assess the effectiveness of these methods during summer, we reduced our GPS fix interval 

to 30 minutes and visited every single location for a subset of wolves‐months to census kill‐rates (Figure 

7.10). 

Analyses are ongoing for this component of our research. Preliminary results suggest a variety of kill and 

non‐kill related behaviors at cluster sites (Table 7.6) as well as some degree of prey specialization within 

packs (Table 7.7). DeCesare et al. (2010) suggested that a generalist predator can still facilitate apparent 

competition among prey even if predator individuals exhibit some degree of specialization. As seen in 

our preliminary data, some packs appear to concentrate predation effort on alpine ungulates such as 

bighorn sheep and mountain goats (Glacier Pass pack), some on deer species (Two Lakes pack), some on 

moose (Sunwapta), though every pack exhibited some degree of variability and generalist foraging (Table 

7.7). However, body size of prey should be taken into consideration when viewing these data when the 

overall biomass gained from each prey species is discussed. 

The PhD student (Nick DeCesare) is scheduled to complete this work as part of his dissertation at the 

University of Montana during fall 2011, with manuscripts prepared and submitted at or before the time 

of defense. 

38


Table 7.1. Redwillow winter caribou resource selection function model coefficients for the top model 

showing variable, description, beta coefficient (for linear effects positive means selected for, negative 

means selected against) and SE. For Non‐linear effects, quadratic terms include a squared term. 

Variable Description Beta SE P > |z| 

LANDCOVER 

wetland_herb 1.974074 0.13534


Table 7.2. Banff, Jasper and A la Peche winter caribou resource selection function model 

coefficients for the top model showing variable, description, beta coefficient (for linear effects 

positive means selected for, negative means selected against) and SE. For Non‐linear effects, 

quadratic terms include a squared term. 

Variable Description Beta SE P > |z| 

LANDCOVER 

closed_conifer closed canopy conifer 0.3377307 0.024716


Table 7.3. Redwillow winter wolf resource selection function model coefficients for the top model 

showing variable, description, beta coefficient (for linear effects positive means selected for, negative 

means selected against) and SE. For Non‐linear effects, quadratic terms include a squared term. 

Covariate Description Coef. SE P>|z| 

LANDCOVER 

shrub shrub landcover 1.4549 0.2300 0 

c40to60 Canopy coverage 40 ‐ 60% 1.2562 0.2316 0 

c60to80 Canopy coverage 60 ‐ 80% 1.1736 0.2273 0 

c80to100 Canopy coverage 80 ‐ 100% 2.7169 1.1970 0.023 

edge_dist distance to forest edge ‐0.0017 0.00019 0 

TOPOGRAPHY 

slope slope ‐0.01610 0.00806 0.046 

south aspect: 135° to 225° ‐0.28570 0.0819 0 

west aspect: 225° to 315° ‐0.3968 0.0850 0 

water_dist distance to water ‐0.00068 0.00006 0 

HUMAN USE 

roads_dist distance to roads ‐0.00026 0.00007 0 

roads_dist 2 Quadratic (var^2) 1.01E‐07 1.29E‐08 0 

linear_dist distance to seismic lines 0.00100 0.00024 0 

linear_dist 2 distance to seismic lines ‐7.38E‐07 1.81E‐07 0 

_constant (model intercept) ‐0.6663 0.2429 0.006 

41


Table 7.4. Banff, Jasper, and A la Peche winter wolf resource selection function model coefficients for 

the top model showing variable, description, beta coefficient (for linear effects positive means selected 

for, negative means selected against) and SE. For Non‐linear effects, quadratic terms include a squared 

term. 


LANDCOVER 

open_conifer open canopy conifer 0.74633 0.037201 >0.0001 

low_elev_shrub low elevation shrubs 0.416908 0.041873 >0.0001 

low_elev_herbaceous low elevation herbaceous 0.552483 0.05211 >0.0001 

alpine_barren alpine barren ‐0.59719 0.105068 >0.0001 

open_dist distance to open landcover ‐0.00131 6.18E‐05 >0.0001 

ice_rock rock & ice ‐2.11983 0.271491 >0.0001 

burn burn landcover 0.471044 0.056505 >0.0001 

burn_dist distance to burn ‐0.00014 2.82E‐06 >0.0001 

TOPOGRAPHY 

elevation elevation 0.014748 0.000408 >0.0001 

elevation 2 Quadratic (var^2) ‐4.06E‐06 1.23E‐07 >0.0001 

slope Slope ‐0.03786 0.000889 >0.0001 

water_dist distance to water ‐0.00068 0.000034 >0.0001 

east aspect: 45° to 135° 0.18822 0.030046 >0.0001 

south aspect: 135° to 225° 0.244078 0.03146 >0.0001 

HUMAN USE 

1°_roads_dist distance to primary roads ‐5E‐05 1.97E‐06 >0.0001 

2°_roads_dist distance to secondary roads 0.000054 1.06E‐06 >0.0001 

trail_dist distance to trails ‐0.00023 8.00E‐06 >0.0001 

_constant (model intercept) ‐11.8734 0.329183 >0.0001 

42


Table 7.5. Banff, Jasper, and A la Peche winter moose resource selection function model coefficients for 

the top model showing variable, description, beta coefficient (for linear effects positive means selected 

for, negative means selected against) and SE. For Non‐linear effects, quadratic terms include a squared 

term. 


LANDCOVER 

open_conifer open canopy conifer 0.74633 0.037201 >0.0001 

low_elev_shrub low elevation shrubs 0.416908 0.041873 >0.0001 

low_elev_herbaceous low elevation herbaceous 0.552483 0.05211 >0.0001 

alpine_barren alpine barren ‐0.59719 0.105068 >0.0001 

open_dist distance to open landcover ‐0.00131 6.18E‐05 >0.0001 

ice_rock rock & ice ‐2.11983 0.271491 >0.0001 

burn burn landcover 0.471044 0.056505 >0.0001 

burn_dist distance to burn ‐0.00014 2.82E‐06 >0.0001 

TOPOGRAPHY 

elevation elevation 0.014748 0.000408 >0.0001 

elevation 2 Quadratic (var^2) ‐4.06E‐06 1.23E‐07 >0.0001 

slope Slope ‐0.03786 0.000889 >0.0001 

water_dist distance to water ‐0.00068 0.000034 >0.0001 

east aspect: 45° to 135° 0.18822 0.030046 >0.0001 

south aspect: 135° to 225° 0.244078 0.03146 >0.0001 

HUMAN USE 

1°_roads_dist distance to primary roads ‐5E‐05 1.97E‐06 >0.0001 

2°_roads_dist distance to secondary roads 0.000054 1.06E‐06 >0.0001 

trail_dist distance to trails ‐0.00023 8.00E‐06 >0.0001 

_constant (model intercept) ‐11.8734 0.329183 >0.0001 

43


Table 7.6. Preliminary field validation results of visiting putative wolf kill‐sites, as delineated by wolf GPS‐ 

based location clusters identified from program SaTScan, in west‐central Alberta, for a portion of the 

study period covering a single winter and summer sampling session each, 2008–2009. 

Season Pack Wolf Kill Non‐kill Scavenge Total 

Glacier Pass W135 12 33 0 45 

Kakwa W134 11 24 0 35 

Signal W110 6 47 1 54 

Summer Sunwapta W056 2 0 0 2 

Sunwapta W112 13 141 1 155 

Winter 

Total 

Subtotal 

44 245 2 291 

A la Peche W122 1 9 0 10 

Brazeau W107 3 14 0 17 

Glacier Pass W129 13 21 0 34 

Kakwa W124 17 23 0 40 

Sheep Creek W131 0 1 0 1 

Signal W110 16 27 1 44 

Sunwapta W056 17 35 5 57 

Two Lakes W127 29 50 1 80 

Subtotal 

96 180 7 283 

140 425 9 574 

44


Table 7.7. Species composition of kill‐sites detected using field validation of visiting putative wolf kill‐sites, as delineated by wolf GPS‐based location 

clusters identified from program SaTScan, in west‐central Alberta, for a portion of the study period covering a single winter and summer sampling 

session each, 2008–2009. 

Ungulate prey Other 

Season Pack Wolf 

Caribou 

Deer 

spp. 

Elk 

Mountain 

goat 

Moose 

Bighorn 

sheep 

Beaver Marmot Unknown 

Glacier Pass W135 1 0 0 4 2 3 1 2 0 

Kakwa W134 0 5 1 0 5 0 0 0 2 

Signal W110 0 2 1 0 1 1 0 1 2 

Summer Sunwapta W056 1 0 0 1 0 0 0 0 0 

Sunwapta W112 2 1 0 2 8 0 1 1 0 

Winter 

Total 

Subtotal 

4 8 2 7 16 4 2 4 4 

A la Peche W122 0 1 0 0 0 0 0 0 0 

Brazeau W107 0 1 0 0 0 2 0 0 0 

Glacier Pass W129 0 1 0 4 1 4 0 0 2 

Kakwa W124 0 12 3 0 3 0 0 0 0 

Signal W110 0 10 3 0 0 2 0 0 1 

Sunwapta W056 2 8 5 0 6 1 0 0 0 

Two Lakes W127 0 27 0 0 3 0 0 0 1 

Subtotal 

2 60 11 4 13 9 0 0 4 

6 68 13 11 29 13 2 4 8 

45


Figure 7.5.Winter resource selection function (RSF) for caribou for the Redwillow winter range, 2006 to 

2009. 

46


Figure 7.6. Winter resource selection function (RSF) for caribou for the Banff, Jasper, and A la Peche 

study area, 2001 to 2009. 

47


Figure 7.7. Winter resource selection function (RSF) for wolves for the Redwillow winter range, 2006 to 

2009. 

48


Figure 7.8. Winter resource selection function (RSF) for wolves for the Banff, Jasper and A la Peche study 

area, 2001 to 2009. 

49


Figure 7.9. Winter resource selection function (RSF) for moose for the Banff, Jasper and A la Peche study 

area, 2001 to 2009. 

50


Figure 7.10. Example kill‐site validation data showing the GPS‐based movements of a single wolf, and 

the spatio‐temporal clusters determined to be actual kill‐sites, Canadian Rockies, 2009. 

51


8.0 HUMAN ACTIVITIES AND PRIMARY PREY PRODUCTIVITY IN WOLF‐CARIBOU 

SYSTEMS 

8.1 SCOPE 

Moose populations in west‐central Alberta have not been 

studied, despite their key role in caribou conservation via 

wolf‐mediated apparent competition and in Alberta’s 

provincial moose harvest. We are integrating aerial moose 

surveys, GPS collar technology, and resource selection 

modelling to 1) understand the habitat relationships of 

moose relative to forest characteristics and human 

disturbance, and 2) estimate moose population densities 

across our study area to better guide management of moose 

harvest and the conservation of woodland caribou. 

8.2 SCHEDULE 

Figure 8.1. Moose detected during aerial 

� 2008 – GPS collaring of 16 moose (ATS store on board) 

surveys winter 2008/2009. 

� 2009 – GPS and VHF collaring of 22 moose(ATS store on 

� 

board and Lotek); Distance sampling surveys in WMU 440 and sightability trials 

2010 – Distance surveys in WMU 353 and sightability trials; GPS collar retrieval March 2010; analysis 

and MS defense (fall) 


While there is sufficient knowledge of moose habitat use patterns to understand general selection in 

most regions, local habitat selection can vary substantially as habitat composition changes (Peek 2007). 

Consequently, some researchers caution against generalizing observations of moose habitat preferences 

and applying them to other geographical areas (e.g., Timmermann and McNicol 1988, Osko et al. 2004). 

In general, moose often prosper in early successional vegetation communities following disturbances 

due to a higher quantity and quality of forage (e.g., Timmermann and McNicol 1988, Forbes and 

Theberge 1993). Hence, landscapes with increased young seral stands are hypothesized to support larger 

moose populations and thereby support more wolves (McNicol and Gilbert 1980, Kunkel and Pletscher 

2000, Wittmer et al. 2005, Stotyn 2008). Increased food availability is often coupled with increased 

exposure to unfavourable factors such as higher predation risk and weather impacts due to a lack of 

shelter and cover (Dussault et al. 2004, Dussault et al. 2005). For example, forestry practices often 

associated with higher forage abundance add linear features such as roads or seismic lines to the 

landscape (Poole and Serrouya 2003). This in turn promotes wolf travel and thereby increases wolf 

predation efficiency (Bergerud 1974, James and Stuart‐Smith 2000, James et al. 2004, Neufeld 2006). 

However, logging has been shown to not affect predation by wolves on moose during winter seasons 

(Kunkel and Pletscher 2000). Thus, moose must make trade‐offs between food availability and exposure 

to other limiting factors, such as predation (Dussault et al. 2006). 

While habitat selection is generally assumed to be related to fitness (Boyce and McDonald 1999, Pearce 

and Ferrier 2001, Railsback et al. 2003, Nielsen et al. 2005, McLoughlin et al. 2006), the occurrence of a 

species may not always be a good predictor of habitat quality (Vanhorne 1983). In some situations 

52


animals will select habitat that can decrease survival (Gates and Gysel 1978, Nielsen et al. 2006). Despite 

increased mortality risk in these attractive sinks (Pulliam 1988) animals may immigrate from source 

populations (Schlaepfer et al. 2002). These ecological traps are particularly common in human‐modified 

environments (Delibes et al. 2001, Donovan and Thompson 2001, Schlaepfer et al. 2002). The 

assumption that there is a positive correlation between abundance and selection in an ecosystem with 

predation and human development can be tested by comparing results of RSF modeling with abundance 

estimates, this has yet to be done for moose. 

Furthermore, knowledge of moose density itself is important for caribou conservation and recovery 

planning if caribou declines are related to apparent competition dynamics with moose. For example, 

modeling studies by Weclaw and Hudson (2004), Lessard et al. (2005), and Courtois and Quellet (2007) 

show that wolf reduction without concurrent moose reduction will fail to recover caribou as wolf 

populations will quickly recuperate, unless moose density is also reduced (Hayes et al. 2003). Therefore, 

the Alberta Woodland Caribou Recovery Plan recommends active management of predators in 

combination with controlling moose densities in caribou ranges, while also monitoring moose population 

size and trend in caribou ranges to gauge potential for negative effects of apparent competition (Alberta 

Woodland Caribou Recovery Team 2005). Despite these aggressive recovery strategies, there have been 

no formal tests of the assumption that there is a direct relationship between increases in moose 

densities and the rate of forest harvest in caribou ranges in west‐central Alberta. Little is known about 

moose population abundance or habitat selection. Furthermore, monitoring efforts by AB F&W are 

impeded in the presence of limited financial resources. 

8.4 OBJECTIVES 

8.4.1 Moose Resource Selection 

Understanding seasonal changes in moose resource selection is important in order for managers to 

better evaluate habitat overlap of moose and caribou during critical time periods (e.g. times of food 

scarcity or during calving time). We aim to understand moose resource selection as a function of 

anthropogenic disturbance, abiotic and biotic factors, as well as overlap with caribou by developing a 

regional resource selection function (RSF) to address two key questions about moose management in 

west‐central Alberta. 

1. Moose resource selection as a function of forest condition and human disturbance. By examining 

patterns of seasonal resource selection, we will contribute to the identification and mapping of 

critical winter range for moose. 

2. The spatial separation hypothesis predicts that the survival and population dynamics of the 

threatened woodland caribou are driven, indirectly, by their spatial separation from moose, such 

that separation from moose is equivalent to separation from their primary predator, wolves. Insights 

into the mechanisms of caribou‐moose overlap will help guide future forest and on‐going moose 

harvest management to best conserve moose and caribou populations. 

8.4.2 Moose Abundance Models 

1. We aim to assess the relative abundance of moose across west‐central Alberta by combining 

population estimates obtained from Gasaway surveys flown by AB F&W in recent years (reaching 

back until 2004/2005) and Distance sampling data collected in winters 2008/2009 and 2009/2010. 

2. Compare Gasaway method and Distance sampling in terms of precision, cost and time efficiency. 

53


3. Both aforementioned aerial survey methods will lead to biased population estimates when not 

correcting for decreased detectability. While moose abundances are assumed to be lower in denser 

habitats, moose will also be missed more in these habitats with decreased sightability. We aim to 

correct both of the aforementioned methods for decreased sightability. 

4. Combine results from RSF modeling and abundance estimation by using reference areas with 

estimated moose populations and extrapolating those estimates in combination with habitat 

selection models across a broader range. 

8.5 METHODS 


We used GPS collar data of 4 moose to model preliminary moose winter Resource Selection Functions 

(RSFs) within the Little Smoky, Redrock‐Prairie Creek, Narraway and A La Peche caribou home ranges 

(Figure 8.3). Collars were deployed for about one year, collected location data every four hours and were 

retrieved in spring 2009. We used mixed effects RSFs to assess habitat use by moose. 


1. Gasaway method: Aerial surveys have become an invaluable tool to estimate population size of 

wildlife (e.g., Caughley 1974, Samuel et al. 1987, Anderson and Lindzey 1996). In North America, 

moose populations are commonly estimated using the Gasaway‐method (Gasaway et al. 1986), a 

stratified random block sampling design. It is usually assumed that 100% of all moose are detected 

within intensively surveyed blocks (400m line spacing; Gasaway et al. 1986). Due to high time and 

cost requirements (Ward et al. 2000), this block‐based method is favorable for small survey areas 

and dense moose populations in preferably open habitats (Buckland et al. 2001, Nielson et al. 2006). 

High costs of the Gasaway method in west‐central Alberta often preclude moose surveys, especially 

in caribou ranges. AB F&W conducted Gasaway surveys in a number of Wildlife Management Units 

(WMUs) within and around caribou ranges in recent years. We aim to use the survey data obtained 

since ~2005 (Figure 8.4) to build large scale (5 min latitude x 5 min longitude grids – size of the 

intensive resampled survey blocks) habitat suitability models for moose based on abundance 

estimates. 

2. Distance Sampling: Due to limited financial resources, aerial surveys are currently limited to a small 

area of caribou range. While many of the non‐surveyed caribou ranges are thought to contain lower 

moose densities, they are also experiencing rapid landscape alterations due to forestry and oil and 

gas development, which may lead to a range expansion by moose. Therefore, we aimed to apply an 

aerial survey technique that is more efficient in terms of cost and time than the Gasaway method 

currently used by AB F&W, while providing population estimates of moose with a high level of 

precision (95% confidence interval [CI] of ± 20% or less of the estimate, following aerial survey 

recommendations by ABFW). Such an alternative to Gasaway surveys are line transect surveys that 

estimate sightability bias using distance sampling methodology and can be more useful in low 

density populations in varying cover types (Samuel et al. 1987, Buckland et al. 2001). Distance 

sampling uses transect lines along which the observer records the perpendicular distance of detected 

moose and later a detection function of the decreasing detection probability with increasing distance 

is fitted to the data (Buckland et al. 2001). Distance sampling trials conducted in two WMUs by AB 

F&W, ACA and the University of Montana indicate that distance sampling may be an alternative to 

54


the Gasaway method due to its similar accuracy and precision, while being more cost‐ and time‐ 

effective (M. Russel, AB F&W, unpublished data). 

The line spacing for Distance surveys conducted varied between 3 – 5 minutes of longitude. Surveys 

were flown in a Bell 206 Jet Ranger Helicopter. Perpendicular distances were measured by flying to 

the location where the moose was initially detected and taking a waypoint. The difference of the 

UTM‐East value of the transect line from the UTM‐East value of the waypoint equals the distance 

from the line to the moose. Additional variables were recorded at each moose location, such as 

vegetation cover, landcover type, terrain, snowcover, group size, etc. to allow analysis including 

multiple covariates later. For further information on Distance sampling methodology see Buckland et 

al. 2001 and Buckland et al. 2004. 

3. Sightability: The Gasaway method makes the assumption of 100% sightability within the 200m strips 

of the resurveyed blocks and does not directly correct for animals missed during flights (Borchers et 

al. 2002). For distance sampling the most critical assumption is that animals on the center line are 

detected with 100% certainty (g(0)=1.0 assumption; Buckland et al. 2001, Borchers et al. 2002, 

Nielson et al. 2006). Research has shown that, detection probability of animals near the apex is often 

much less than 1.0 due to visibility bias and will decrease with distance from the transect line 

(Anderson and Lindzey 1996, Borchers et al. 2002, Nielson et al. 2006). This results in population 

estimates that are biased low (Buckland et al. 2001, Borchers et al. 2002, Buckland et al. 2004). The 

magnitude of visibility bias can be highly underestimated by researchers and can be especially severe 

when estimating populations in heterogeneous landscapes (Pollock et al. 2006), such as west‐central 

Alberta. 

To test for the violation of the g(0)=1.0 assumption in Distance sampling, the assumption of 

complete detectability within the 200m survey strips of the Gasaway method and potentially 

correcting for this bias, we conducted sightability trails with radio‐collared moose in cooperation 

with ACA and AB F&W in winter 2008/2009 and 2009/2010. Radio‐collared moose were initially 

located from a Cessna 337 and a randomly chosen survey block of 1.6km by 1.6km was projected 

over the animal. These coordinates were reported to a second pilot in a helicopter, which then flew 

in regular, parallel paths across the sampling block aiming to fly directly over the collared moose 

(Figure 8.2). Every moose detected was recorded to simulate aerial survey conditions. Missed target 

moose were relocated immediately and variables for that location recorded. 

Figure 8.2. Example of a survey 

plot for the sightability surveys 

that was flown in February 

2009. Green lines indicate the 

flight path, and red stars show 

waypoints taken to measure 

the perpendicular distance 

between the line and the 

detected moose. Survey lines 

were spaced about 400 meters 

apart and the altitude above 

ground and flight speed was 

kept approximately constant 

for all blocks surveyed. 

55


8.6 PROGRESS AND RESULTS TO DATE 


Currently we are early in year two of the moose research component, final results are pending. To collect 

data for RSF modeling within caribou home ranges, we captured 35 moose of which 4 animals were 

captured twice (recaptured to retrieve the GPS collar and/or replace it with a VHF collar). Antenna 

failures of the first 11 moose collars, deployed in March 2008, highly increased the cost of this project 

component although we warranty replaced them with the collar manufacturer, ATS. Additionally, we put 

an extensive amount of aerial survey effort into relocating these failed GPS collars, surveying each initial 

capture location of the moose. We were able to retrieve 4 (each stored data of about one year, collecting 

over 12,000 moose GPS locations) of these 11 GPS collars, but could not retrieve more GPS collars from 

the ground during our field work season. We deployed five GPS collars in December 2008 that were 

purchased by AB F&W, 11 VHF collars in February 2009 (provided by the University of Alberta) and 11 

GPS collars in March 2009 (warranty replaced). Moose collars were distributed across an elevation 

gradient from the continental divide to the foothills, and across a human disturbance gradient including 

moose in protected areas as the Kakwa Wildland Park, Willmore Wilderness Park, and Jasper National 

Park, mostly within caribou home ranges. 

From four retrieved moose collars we have thus far developed a preliminary RSF for moose across the 

Little Smokey, Redrock‐Prairie Creek, Narraway and A La Peche caribou home range. Moose preferred 

mixed forests, close distances to water, intermediate distances to openings and seismic lines and 

selected against closed canopy cover types (selected open cover types). The sampled moose also 

preferred areas further away from roads (no differentiation between primary and secondary roads for 

this analysis) at lower elevations (Table 8.2 and Figure 8.3). The top winter moose model had a ROC of 

0.72. K‐folds cross validation had an average Spearman’s rank correlation of rho=0.96 when withholding 

1/5 th of the GPS data from each training model set and using the withheld data as evaluation data set 

(within sample model evaluation, Boyce et al. 2002). 

Table 8.1. Redrock‐Prairie Creek, Little Smokey, Narraway and A La Peche winter moose resource 

selection function model coefficients for the top model showing variable, description, beta coefficient 

(for linear effects positive means selected for, negative means selected against) and SE. For Non‐linear 

effects, quadratic terms include a squared term. 


Landcover >0.0001 

Mixed Forest Forests >30% and 0.0001 

Canopy Closure Percent canopy cover ‐0.01414 0.00102 >0.0001 

Water Distance Distance to water ‐0.00023 0.00006 >0.0001 

West West aspects from 225° to 315° ‐0.28483 0.06482 >0.0001 

Elevation Elevation in meters. ‐0.00287 0.00024 >0.0001 

Slope 

Human Impact 

Slope ‐0.03591 0.00337 >0.0001 

Seismic Distance Distance to Seismic Line 0.00067 0.00022 0.002 

Seismic Distance 2 Quadratic (var 2 ) ‐1.3E‐06 1.8E‐07 >0.0001 

Road Distance Distance to paved and gravel road 0.00018 1.6E‐05 >0.0001 

Open Distance Distance to open landcover 0.00039 2.8E‐05 >0.0001 

Open Distance 2 

Quadratic (var 2 ) ‐1.3E‐08 3E‐09 >0.0001 

Constant (model intercept) 2.74351 0.24701 >0.0001 

56


Figure 8.3. Preliminary winter resource selection function (RSF) for moose for the Redrock‐Prairie Creek, 

Little Smokey, Narraway and A La Peche Caribou home range based on winter data of four GPS moose 

collars (about 4,000 used locations), February 2008 to February 2009. 

With the moose GPS data currently available, we have only initial indication for a potential functional 

response of moose habitat selection and human development in west‐central Alberta. However, even 

though the four collared moose inhabited the same ecosystem, they also occupied regions that contain 

different relative intensities of human impact (e.g. human impact is much higher for moose sampled 

further east) and varying abundances of habitat types. Osko et al.(2004) showed that habitat preferences 

for two moose groups were different depending on availability of habitat types, showing that habitat 

preferences are often not fixed and will change as availabilities change (see also: Arthur et al. 1996, 

Mysterud and Ims 1998, Boyce and McDonald 1999). Still, differences in availability are often ignored in 

habitat selection studies. To account for unbalanced sample sizes as well as temporal and spatial 

autocorrelation we used a mixed model RSF with a random intercept (see Chapter 7; Hebblewhite and 

Merrill 2008, Bolker et al. 2009) to assess moose habitat selection for these four GPS collars. Following 

moose GPS collar retrieval of 14 more collars (scheduled for March 2010) we will evaluate moose 

resource selection additionally with a random coefficient (γnj xn) to account for individual variation in 

selection or avoidance of landscapes altered by humans among moose: 

w*(x)ij = β0 + γ0j + β1x1ij + … + βnxnij + γnj xnj + єij (equation 8.1) 

57


where β0 is the fixed‐effect intercept, γ0j is the random variation in the intercept for individual moose, 

and γnj xnj is the variance around β1 among individual moose for covariate xnj (Hebblewhite and Merrill 

2008). 

Additionally, animals have to make decisions at different scales in order to avoid predation and 

maximize forage (Johnson 1980). Courtois et al. (2002) estimated moose habitat selection at 2 scales. 

Selection at the coarser scale (home range selection) was not affected by clear‐cuts and also clear‐cuts 

were not related to fitness (mortality and productivity). Therefore, on a finer scale, moose had to make a 

tradeoff between maximization of food intake and avoidance of negative impacts. Within their home‐ 

ranges moose tended to avoid clear‐cuts during most seasons, showed selection for mature over young 

stands and selection was strongest for mixed stands. This research shows that a comparison between 

different scales of selection is essential to understand how moose trade‐off predation risk and forage 

availability. Thus far, we only assessed 3 rd order selection (within home‐range; Johnson 1980), but will 

include analysis on the 2 nd order (home‐range selection; Johnson 1980) in our final analysis following 

collar retrieval. 


1. Gasaway Survey data: AB F&W conducted Gasaway surveys in a number of WMUs within and 

around caribou ranges in recent years. Most WMUs are surveyed every five years, with exception to 

mountain WMUs were the Gasaway method is not applicable. Through data sharing agreements we 

received the survey data obtained since ~2005 in west central Alberta (Figure 8.4). This data consists 

of detailed stratification data, abundance estimates and GPS waypoints for each moose detected 

during the survey, which with we aim to build large scale (5 min latitude x 5 min longitude grids – size 

of the intensive sample survey blocks) habitat suitability models for moose based on abundance 

estimates and compare those to RSF models based on GPS collar data. 

2. Distance Sampling: We flew Distance surveys in WMU 440 in February 2009 and are currently in the 

process of conducting one Distance survey in WMU 353 ( to be completed March 2010: Figure 8.4). 

To estimate moose density in the foothills region of WMU 440 we fitted a global detection function 

to our combined data set (WMU 440 Distance data and Sightability data (see below); Figure 8.5) 

considering moose clusters using the Mark Recapture Distance Sampling (MRDS) engine in program 

DISTANCE 6.0 (Thomas et al. 2009). We then used this fitted detection function to estimate moose 

density by multiplying cluster density by mean cluster size, as there seemed to be no size bias 

present (Buckland et al. 2001) in program DISTANCE (Thomas et al. 2009). We estimated a moose 

density of 0.27 moose/km2 (CV=0.4 with a 95%CI, a relatively imprecise estimate) with an average 

cluster size of 1.26, assuming100% sightability (g(0) = 1.0) along the transect line (the default in 

DISTANCE). 

We surveyed the mountain region of WMU 440 using a different approach by ‘cluster’ sampling 

along valley bottoms. Analysis for the mountain stratum is pending, but potential analysis methods 

may be using the program Aerial Survey (Unsworth et al. 1994) or cluster sampling techniques 

developed for Distance sampling (Thomas et al. 2007). 

To model a global detection function by pooling distance sampling data is possible as long as 

different habitat types are represented approximately equally (Buckland et al. 2001). Data from 

Distance sampling surveys that are currently conducted in WMU 353 will be used to model a 

detection function with higher precision. This global detection function can also be applied to data 

from WMU 440, most likely decreasing the coefficient of variation. 

58


Figure 8.4. Map of study area including GPS and VHF collars deployed as of January 2010 and 

caribou home ranges (Kernels). WMUs that have been surveyed by AB F&W using the Gasaway 

technique and data is available are displayed in orange and WMUs that already were surveyed or 

are planned to be surveyed with Distance sampling are displayed in yellow (WMU 440 in AB and 

WMU 7‐19 in BC). WMU 353 will be surveyed with Distance sampling in spring 2010 and has 

been surveyed using the Gasaway technique in recent years (orange stripes). Unsurveyed WMUs 

are marked blue. Each surveyed WMU is labeled by the survey year. 

59


Detection probability 

0.0 0.2 0.4 0.6 0.8 

100 150 200 250 300 350 

Distance 

In order to compare efficiency of Distance sampling and Gasaway surveys we calculated the effort 

necessary to achieve a coefficient of variation of 20% (maximum variation AB F&W aims for with 

Gasaway surveys). This can be easily done, because precision in Distance is a function of density and 

survey effort (km) and we therefore simulated the transect length we would have required to 

achieve a CV of 20% at 95% in WMU 400 following formula Buckland et al. (2001, pg. 242). We used 

data from past Gasaway surveys and compared the average rotary wing flight effort (neglecting fixed 

wing effort necessary for stratification for Gasaway surveys) to our Distance sampling pilot study in 

WMU 440 corrected for a CV of 0.2. We assessed flight effort as a function of moose density to 

control for any potential effects of moose density. Figure 8.7 shows that Distance surveys have the 

potential to require less effort than comparable Gasaway surveys, even ignoring the additional fixed‐ 

wing costs of moose stratification flights for Gasaway. Also, at higher moose densities, Distance 

sampling should get cheaper, per unit effort, compared to Gasaway, where effort remains constant 

given its sampling design. While further tests are required across a broader range of moose densities 

and under different conditions, Distance sampling shows great potential. 

60 

Figure 8.5. Detection function plot 

(Half‐normal model, chosen based 

on AICc) of the combined data set. 

We left truncated the data to 66m, 

which corresponds to the 

obstructed‐view area beneath the 

helicopter; we right truncated the 

largest 5%. This function is based on 

100% sightability (g(0) = 1.0) along 

the transect line.


These promising results encouraged us to conduct further distance sampling surveys in WMU 353 in 

Alberta and WMU 7‐19 in British Columbia (depending on availability of funding; Figure 8.4) in winter 

2009/2010 to receive very recent data on moose abundance with Little Smokey and the Narraway 

caribou home ranges for moose abundance modeling. 

3. Sightability: Because further sightability trials are currently in progress (to be completed March 

2010), we can only draw preliminary conclusions from data available to date. In 23 sightability blocks 

we conducted, we only detected 14 radio‐collared moose. Therefore, our tentative results indicate 

sightability conditions vary highly in west‐central AB and sightability at g(0) may be as low as 60%, 

with decreasing probability of detecting a moose with increasing distance from the transact path. 

This confirms the need to develop a sightability adjustment along the transect line for Distance and 

also for Gasaway sampling. A decreased sightability will decrease abundance estimates substantially 

(Table 8.2 and Figure 8.8) and will also lead to flawed conclusions on moose habitat selection (for 

models based on aerial survey data where moose are missed especially in denser habitats, while 

more frequently detected in open habitats). 

Table 8.2. Simulating sightability at g(0) for 0.8, 0.6 and 0.4 

sightability would approximate the following density 

estimates/km 2 (also see Figure 8.8). 

Detectability at g(0) Denity estimate (km 2 )% Density increase 

1 0.27 / 

0.8 0.34 25 

0.6 0.45 66 

0.4 0.72 166 

61 

Figure 8.6. Flight effort 

(hrs) in relation to moose 

density in the surveyed 

WMUs (data from 2000‐ 

2009). We estimated the 

required amount of flight 

effort (hrs/100km2) for 

potential Distance surveys 

in WMU 440 (aiming for CV 

~0.2).


Detection probability 

0.0 0.2 0.4 0.6 0.8 

Detection function plot 

100 150 200 250 300 350 

Distance 

8.7 OUTLOOK: COMBINING RESOURCE SELECTION AND ABUNDANCE ESTIMATION 

After assessing variables that influence moose habitat selection, analyzing how human development 

changes these habitat selection patterns on multiple scales, and pooling moose abundance estimates 

corrected for sightability; we will be able to calibrate the GPS and aerial survey data through model 

cross‐validation against each other (e.g., Saher, 2005; Ciarniello et al., 2007; Boyce et al., 2002). We will 

then estimate the potential density of moose in unsurveyed regions using the calibration of moose 

densities from surveyed regions and predicted probability of use by RSF models (e.g., Boyce and 

McDonald 1999, Boyce and Waller 2003) and thereby develop a moose habitat‐based abundance model 

across caribou home ranges within our study region. This will provide broad scale density estimates and 

habitat suitability models for west central Alberta. 

62 

Figure 8.7. The black, solid curve shows 

the global detection function for 

Distance survey data from winter 

2008/2009. It is a half‐normal model 

based on 100% sightability (g(0) = 1.0) 

along the transect line. The red, dotted 

lines indicate an approximation of the 

detection functions with forced 

intercepts when simulating decreased 

sightability scenarios for 0.8, 0.6 and 0.4 

sightability at g(0).


9.0 CARIBOU MIGRATORY AND SURVIVAL PATTERNS OVER REGIONAL 

GRADIENTS IN HUMAN DEVELOPMENT 


The evolution of partial migration is a topic rich with hypotheses and theoretical discussion (Cox 1985, 

Lundberg 1988, Kaitala et al 1993). One might expect that selection and speciation processes would 

eventually fix constant sedentary or migratory behavior within a single species or lead to speciation 

among behavioral groups given differential costs and benefits of migration. However, partial migratory 

strategies among individuals in a population and within individual decision‐making are common in birds 

(Adriaensen and Dhondt 1990, Gillis et al. 2008), fishes (Brodersen et al. 2008), insects (Dingle 1996), and 

mammals (Ball et al. 2001, Hebblewhite and Merrill 2007, White et al. 2007). 

Partial migration is particularly common in ungulates (Fryxell et al. 1988). However, the selective 

balance between sedentary and migratory strategies may be altered by global climate change (Berthold 

1999, 2001 but see Nilsson et al. 2006), anthropogenic alteration (Hebblewhite et al. 2006) or 

fragmentation (Berger 2004) of habitat, or changes in predator communities (Hebblewhite et al. 2006, 

White et al. 2007). This might create disparate selection pressures between strategies (Berthold et al. 

1990), in the form of altered survival and fecundity rates, or could cause a behavioral shift away from a 

strategy. Bolger et al. (2008) found that disruption of migratory routes caused rapid population collapses 

among obligate migratory ungulate populations, though they did not discuss how selection pressures or 

behavioral plasticity within partially migratory populations might affect the response to such changes. 

Studies linking migration strategy and demographic outcomes are needed to fully understand the 

evolutionary history and adaptive future of partial migration in the conservation of species. 

Behavioral shifts towards sedentary behavior have been detected among partially migratory elk 

(Cervus elaphus) populations in the Canadian Rocky Mountains (Hebblewhite et al. 2006), and have been 

shown to occur among woodland caribou (Rangifer tarandus caribou) populations in west‐central Alberta 

(Figure 9.1; McDevitt et al. 2009), where habitat fragmentation from oil, natural gas, and forestry 

industries has occurred disproportionately on caribou winter ranges. In these populations migratory 

behaviors among populations have been correlated to genetic haplotype frequencies (McDevitt et al. 

2009). However, it is unclear whether migratory or sedentary strategies are associated with different 

survival rates, or if habitat fragmentation exhibits direct impacts on caribou survival. We studied the 

relative roles of migration behavior and habitat fragmentation in predicting survival among adult female 

caribou. 

63


64 

Figure 9.1. Sample case 

of summer and winter 

Global Positioning 

System locations and 

home ranges (95% 

probability kernels) 

depicting migratory and 

sedentary behaviors of 

caribou (Panels A and B, 

respectively) in the 

Canadian Rockies, 

Canada. 

The annual home 

ranges for other caribou 

monitored in each herd 

are also indicated. 

Sedentary behaviors 

were not observed in 

the Redrock‐Prairie 

Creek population, which 

included migratory or 

partially migratory 

individuals only. 

Figure adapted from 

McDevitt et al. (2009).


9.2 METHODS 

We used a combination of VHF‐ and GPS‐based telemetry data from 288 radio‐collared adult females to 

correlate survival times with a number of independent covariates in a Cox proportional hazards (CPH) 

modeling framework (Cox 1972, Hosmer et al. 2008). Incorporation of counting process theory into CPH 

modeling in recent decades provided a framework suitable with left truncated, staggered‐entry data 

such as these (Anderson and Gill 1982, Therneau and Grambsch 2000). 

We assessed the role of behavioral (migratory vs. sedentary strategy), fragmentation (cutblocks and 

linear features), and habitat (alpine habitats and topographic position) covariates in predicting survival 

probability, as well as potentially confounding covariates such as population and season. We considered 

two possible definitions of “migration” when categorizing individuals, based on (1) distance‐threshold: a 

20 km distance between geometric mean locations in summer and winter seasons, and (2) overlap: 

whether or not the migration distance was greater than a 95% confidence radius about geometric mean 

locations. Because AIC model selection criteria were more supportive of the 20km distance‐threshold as 

a predictive categorization of migratory behavior, we selected this as the method of categorization for 

this analysis. Thus, we categorized all individuals into two categories: Sedentary (0) and Migratory (1) 

using a threshold minimum distance of 20 km between seasonal mean locations to delineate migration. 

We also considered a three‐class categorization where in Sedentary individuals were further sub‐ 

categorized into Low‐ and High‐Elevation Sedentary, to isolate individuals spending the summer season 

in foothill and mountain ecoregions, respectively. 

We then buffered daily locations for each caribou by the mean daily step length estimated from GPS 

data (850m) and estimated a suite of time‐varying covariates which would correlate to each caribou’s 

daily survival probability. To quantify fragmentation, we quantified the proportionate area of cutblocks 

and linear features within each 850m buffer. We used Alberta Vegetation Inventory data to identify 

stands originating in 1960 or later as cutblocks, and identified cutblocks in the British Columbia portion 

of our study area using a combination of a Shrub landcover type classified from 30 m LANDSAT data 

(McDermid et al. 2009) and manual digitizing to remove alpine/natural shrub fields. Similarly, we used 

Alberta’s Access database of linear features (roads, pipelines, and seismic lines) and estimated a 250m 

buffer surrounding each feature to estimate the proportionate area affected by linear features (Dyer et 

al. 2001, Sorensen et al. 2008). We also estimate the proportionate area of an alpine landcover type, 

and the mean topographic position within the buffer. The topographic position index (TPI) was 

estimated from 30m digital elevation model and was an index of topographic curvature at the 5 km scale, 

where negative values indicated drainages or valleys and positive values indicated ridges. 

In CPH modeling, we used a start‐of‐study origin for analysis time (4 October 1998; Fieberg and 

DelGiudice 2009) wherein each individual entered the analysis at this point or upon capture afterwards, 

and exited analysis upon death or right censoring. We used plots of Kaplan‐Meier survival curves and 

smoothed hazards among categorical levels to assess initial model assumptions and differences among 

categories. We followed steps Hosmer et al. (2008) with respect to covariates, and completed all 

analyses in Stata 10 (StataCorp 2007; Cleaves et al. 2008). We built seasonal models using univariate 

analysis of each covariate, assessment of collinearity and proportional hazards among covariates, 

consideration of confounding and interacting covariates, and the use of Martingale residuals to 

determine functional form (Cleaves et al. 2008, Hosmer et al. 2008). We used Akaike Information 

Criteria (AIC) to assess the evidence for support of each model as the best candidate, and assessed the 

overall fit of models using the partial likelihood ratio test and analysis of Cox‐Snell residuals (Cleaves et 

al. 2008). 

65


9.3 RESULTS 

Based on a two‐level categorization (migratory‐sedentary), summer survival was lower for migratory 

caribou compared to sedentary caribou. With a three‐level categorization (migratory, sedentary‐ 

mountains, sedentary‐foothills), migratory individuals had lowest survival, followed by those in lower‐ 

elevation foothills, with highest summer survival for caribou in high elevation mountain habitats (Figure 

9.2). However, the two‐level categorization was most supported by AIC model selection criteria (Table 

9.1). These results suggest that current patterns of adult survival are exhibiting selection against 

migratory woodland caribou, which is further supported by a declining trend in the proportion of 

collared individuals that exhibited migration in two herds monitored since 1981 (Figure 9.3). 

Table 9.1. Comparison of sample size (N=number of deaths), number of parameters (k), information 

criteria (ΔAICc), and model weights (w) and relative rank, for models of summer female woodland 

caribou survival as classified into one, two, and three groups according to migratory and sedentary 

behavior, Canadian Rockies, 1998–2009. 

Model N k ΔAICc w Rank 

one‐class (no effect of migration) 56 1 2.33 0.18 3 

two‐class (migratory, sedentary) 56 2 0 0.58 1 

three‐stage (migratory, sedentary‐mountain, sedentary‐foothills) 56 3 1.74 0.24 2 

Figure 9.2. Survival probability by partial migration behavioral strategy for 

adult female woodland caribou in the Canadian Rockies, 1998–2009. 

66


Figure 9.3. The proportion of radio‐collared adult females exhibiting migration over time in two 

populations of woodland caribou in the Canadian Rockies, 1981–2009. 

Spatial, time‐varying, CPH models revealed significant effects of spatial covariates on daily 

caribou survival probability. During winter, the most parsimonious model contained negative effect s of 

cutblocks (β=2.458, P=0.043) and valley‐like topography (β =‐0.0044, P=0.030) on daily caribou survival 

(Table 9.2). During summer, the most parsimonious models (ΔAICc


Table 9.2. Comparison of sample size (N=number of deaths), number of parameters (k), information 

criteria (ΔAICc), and model weights (w) and relative rank, for spatial, time‐varying Cox proportional 

hazards models of winter and summer female woodland caribou survival as classified into one, two, and 

three groups according to migratory and sedentary behavior, Canadian Rockies, 1998–2009. 

Winter Summer 

Model N k ΔAICc w Rank N k ΔAICc w Rank 

null 38 1 2.798 0.06 7 56 1 12.936 0.00 7 

migration 38 2 3.846 0.04 10 56 2 12.207 0.00 6 

alpine 38 2 3.564 0.04 9 56 2 0.000 0.38 1 

TPI 38 2 0.668 0.19 2 56 2 14.496 0.00 12 

cutblocks 38 2 2.206 0.09 3 56 2 14.467 0.00 11 

linear 38 2 4.767 0.02 15 56 2 15.087 0.00 15 

cutblocks linear 38 3 4.569 0.03 14 56 3 16.609 0.00 19 

tpi alpine 38 3 2.698 0.07 6 56 3 2.202 0.13 4 

tpi forestry 38 3 0.000 0.26 1 56 3 16.133 0.00 17 

migration alpine 38 3 4.207 0.03 12 56 3 0.003 0.38 2 

migration tpi 38 3 2.652 0.07 5 56 3 13.438 0.00 8 

migration forestry 38 3 3.411 0.05 8 56 3 13.975 0.00 9 

migration linear 38 3 5.867 0.01 16 56 3 14.085 0.00 10 

migration alpine tpi 38 4 4.566 0.03 13 56 4 2.324 0.12 5 

migration forestry linear 38 4 5.915 0.01 18 56 4 15.537 0.00 16 

68


10.0 IMPLICATIONS AND CONSERVATION STRATEGIES 

10.1 MANAGEMENT IMPLICATIONS OF GENETIC FINDINGS 

10.1.1 Allowing for survival of the partially‐migratory Rockies caribou 

Caribou in our study area have long been considered to belong to the woodland subspecies, with the 

barren‐ground subspecies occurring in the tundra, hundreds of kilometres further north. Here we show 

unambiguously that the Beringian–Eurasian mitochondrial lineage associated with the barren‐ground 

caribou is present in the Canadian Rocky Mountains (Fig. 10.1, right panel). 

Our study highlighted that caribou of the Canadian Rockies illustrate an intriguing example of glacial 

vicariance creating diverged lineages, followed by localized postglacial sympatry, which promoted the 

mixing of gene pools that had been evolving independently for several thousand years. New adaptive 

genetic diversity may have been generated by ‘hybrid swarming’ in response to new habitat availabitliy 

over a relatively short period of time. 

Fig. 10.1 An ice‐free corridor at the end of the last glaciation likely allowed, for the first time, for barren‐ 

ground caribou to migrate from the North and overlap with woodland caribou expanding from the South 

(left panel, from Dueck 1998). Here, through the integrated use of nuclear and mitochondrial markers, 

we showed that the previously diverged lineages are present in the study area (right panel) and have 

interbred in recent times. 

The Canadian Rockies caribou exhibit the genetic and phenotypic traits of an intraspecific hybrid swarm 

that has proved very successful in adapting to the forests and alpine tundra of the postglacial Rocky 

Mountains environment. In a landscape that is changing due to climatic and human‐mediated factors, an 

69


understanding of these dynamics, both past and present, is essential for management and conservation 

of these populations. 

In the Rockies, alpine areas and forest areas occur at different altitudes — whereas at the subcontinental 

scale tundra and forest occur at different latitudes. The spatial and altitudinal range of caribou migration 

in the Rockies is unique when compared to northern caribou (Musiani et al. 2007). Finally, the fact that 

lineage mixing in our study area occurs relatively far from the obvious ecotone zones between the 

barren‐ground and woodland ranges also suggests uniqueness of these populations. 

Conservation planners should take into consideration the need to allow for long‐term persistence of the 

Rockies caribou –i.e. an additional level of concern for a species known to be threatened. The most 

obvious application of our new findings is that of allowing for survival of the caribou individuals that 

select the migratory strategy. Migration likely results in more frequent encounters with predators as well 

as with habitats that are altered by human development. Our maps highlight areas characterized by 

higher encounter rates with important predators such as wolves. Other maps indicate areas with human 

features such as forestry and linear developments that have known impacts on caribou. These should be 

considered sensitive areas for caribou mortality, especially considering that our modeling indicates lower 

survival for migratory individuals (see above section 9). 

10.1.2 Allowing for Natural Levels of Migrants among Caribou Populations 

Methods: 

The effective number of females exchanged per generation was estimated from mtDNA Fst‐values 

according to the approximation Nfm = ((1/Fst)–1)/2. The extent of gene flow (Nm) among the different 

herds was evaluated from overall FST estimates of microsatellites by the equation: Nm = ((1/FST) – 1)/4 

(Slatkin 1995; Michalakis & Excoffier 1996). Although the veracity of the absolute gene flow estimates 

depends on several assumptions that may not be met in the present situation (population in equilibrium 

with respect to genetic drift and migration, island model of population structure), they nevertheless 

provide a basis for estimating natural levels of gene flow among typical Rockies populations. To calculate 

generation time for caribou we took into consideration that a female can be sexually mature as early as 

16 months of age, but more commonly at 28 months. With good nutrition females give birth to calves 

each year, but may skip years in poor ranges. 

Results: 

Estimates of fixation indices (FST) 

Nuclear Microsatellites: Global FST values were similar and significant for all groupings tested (average 

among herds: 0.044; among Structure clusters: 0.048; among TESS clusters: 0.042; P < 0.0001). All 

pairwise comparisons of FST between clusters identified by the program Structure (pairwise FST values 

ranges: 0.020–0.091) and the program TESS (pairwise FST values ranges: 0.016–0.067) were significant. 

Mitochondrial DNA: Population subdivision was evaluated based on mitochondrial DNA using spatial 

analysis of molecular variance (SAMOVA). The analysis showed that with the organization in seven 

groups, there was a tendency for the Narraway and Parsnip herds to form one cluster, whereas all other 

herds clustered separately (average FST = 0.189, P < 0.000001; FCT = 0.208, P < 0.038). 

70


Table 10.1. Average, minimum and maximum numbers of caribou migrants per generation (Nm) values 

between populations using microsatellites (with alternative methods, representing all individuals) and 

using mtDNA (with SAMOVA approach, representing females). 

method Fst Nm 

herds 0.04 5.43 

structure 

average 0.048 4.96 

min 0.091 2.50 

max 0.02 12.25 

tess 

average 0.042 5.70 

min 0.067 3.48 

max 0.016 15.38 

samova (females) 0.189 2.15 

Table 10.2. Pairwise and average numbers of caribou migrants per generation (Nm) values between all 

herds using mtDNA (upper diagonal, representing females) and microsatellites (lower diagonal, 

representing all individuals). 

Natural Levels of Migrants Among Caribou Herds (Nm) 

Herd RPC NAR LSM ALP PAR KEN JNP QUI Female Average (Nfm) 

RPC 0.87 1.11 2.75 3.22 2.14 10.39 3.48 4.49 

NAR 10.25 0.39 4.52 1.96 0.48 0.96 2.21 

LSM 3.86 3.30 1.00 1.90 0.76 0.83 1.08 

ALP 13.12 6.26 4.72 9.12 1.07 5.92 19.99 

PAR 12.84 7.12 3.09 10.53 2.95 3.38 27.28 

KEN 8.84 5.56 4.71 7.95 19.75 1.35 1.45 

JNP 4.23 3.04 2.37 4.23 5.41 3.74 13.09 

QUI 9.11 9.67 2.74 7.28 3.58 6.13 3.58 

Average (Nm) 6.68 

Table 10.3. Pairwise and average numbers of caribou migrants per generation (Nm) values between 

populations using mtDNA (upper diagonal, representing females) and microsatellites (lower diagonal, 

representing all individuals). Significance for populations units was assessed after Bonferroni correction 

(initial alpha = 0.0018). 

Natural Levels of Migrants Among Caribou Populations (Nm) 

Significant Populations RPC NAR LSM ALP PAR KEN JNP QUI Female Average (Nfm) 

RPC 0.87 1.11 2.75 3.22 2.14 1.37 

NAR 10.25 0.39 1.96 0.48 0.96 

LSM 3.86 3.30 1.00 1.90 0.76 0.83 1.08 

ALP 13.12 6.26 4.72 1.07 

PAR 12.84 7.12 3.09 10.53 

KEN 8.84 5.56 4.71 7.95 1.35 1.45 

JNP 4.23 3.04 2.37 4.23 5.41 3.74 

QUI 2.74 7.28 6.13 3.58 

Average (Nm) 6.04 

71


10.1.3 Implications 

These results suggest that caribou herds or populations in the Rockies exchange a minimum of 1 female 

migrant and higher numbers of male migrants every generation. As generation time is 2‐3 years, it is 

advised that a comparable number of caribou should be exchanged with neighboring populations every 3 

years, as a minimum. In particular, managers should make sure that a minimum of one female every 3 

years immigrates in a given herd or population, survives and reproduces naturally in that population. This 

figure could also be interpreted as 2 females every 6 years, or 3 every nine years. 

Addressing habitat fragmentation demonstrated by mismatch between genetic and telemetry data 

Concepts: 

1. We have telemetry data over more than 10 years showing no dispersal between caribou herds in 

the study area. This encompasses around 3 generations’ time. 

2. We have genetic data indication that there are migrants every generation (Nm) between all 

herds or populations (Tables 10.1, 10.2 and 10.3). 

3. This mismatch between genetic and telemetry data is not a paradox. The paradox can be 

reconciled as follows: 

a. Telemetry data indicates what has happened in the last 10 years (and there is no reason 

to think that this will change). Habitat fragmentation has likely impeded dispersal. 

b. Genetic data shows what happed in the past. Microsatellites indicate levels of dispersal 

experienced until approximately 50 years ago, and mitochondrial DNA until more years 

ago. Thus, it could be argued that in the past there was dispersal among herds and 

reproduction of dispersing individuals. Caribou herds likely were more connected than 

nowadays. 

4. We can use this information to address ongoing problems of fragmentation. We should aim at 

restoring caribou habitat also in areas between ranges occupied by herds. We should not focus 

only on maintaining habitat in existing, fragmented herd ranges. 

10.2 CARIBOU‐WOLF OVERLAP: MINIMIZING IMPACTS IN RISK AREAS 

Identifying areas of overlap between wolves and caribou is a key step to minimizing risks to caribou. In 

fact, risks might be higher for caribou when development results in more high quality wolf habitat in 

overlap areas (forestry) or it increases wolf travel efficiency in such areas (e.g., by providing wolves with 

seismic lines as travel routes). 

Identification of high overlap areas could be potentially useful to mitigate effects of development by 

avoiding high overlap areas. Recent advances in RSF applications to predator‐prey theory confirms that 

RSF models can be used to estimate overlap using the product estimator of two independent RSF models 

(Kristan and Boarman 2003, Hebblewhite et al. 2005). Because wolf predation is primarily driven by 

species like moose, elk and deer in caribou systems (Hebblewhite et al. 2007), the assumption of 

independence seems reasonable for wolves and caribou. 

We treated RSF models for caribou and wolves as habitat ranking models, and used them to assess 

caribou‐wolf overlap by subtracting inter‐species RSFs. Specifically, we subtracted the binned wolf RSF 

model from the binned caribou RSF model. This generated a caribou‐wolf overlap index from ‐10 to +10, 

72


where high values indicate high quality caribou habitat and low quality wolf habitat, and low values 

indicate low quality caribou habitat and high quality wolf habitat. We liken this index to a spatial 

prediction of caribou “safe zones” (Figures 10.1, 10.2), wherein high values are those likely to be both 

preferred by caribou and avoided by wolves. 

Our maps can be used by environmental managers, industry and other stakeholders. Decision makers 

will evaluate the risk for caribou posed by human‐induced habitat alterations happening in areas with a 

low value for the “safe zones” index. 

10.3 FUTURE RESEARCH: SPATIAL POPULATION VIABILITY ANALYSIS 

Conservation of woodland caribou will depend on our ability to effectively monitor population trends 

and population dynamics (and the mechanisms acting upon them) within and among subpopulations 

across the species range. We are using existing monitoring data collected in Alberta to assess the 

relationships between vital rates and population growth to provide a case study for using spatially‐ 

explicit population viability analyses in guiding conservation efforts. This analysis is ongoing, and 

discussion herein preliminary. 

We are using spatially‐explicit population viability analysis (PVA) techniques to assess: 1) the relationship 

between vital rates (adult survival and recruitment) and population growth, 2) the power in our ability to 

monitor trends or changes in population growth rates using estimates of these vital rates from currently 

established protocols, 3) the effects of misclassification errors in calf‐cow ratio data, and 4) the long‐ 

term viability of woodland caribou in Alberta as a case‐study in using spatially‐explicit PVAs to predict 

changes in meta‐population dynamics. We are also considering threats and population growth rates 

specific to each local population. 

Preliminary results include a literature review of over 40 woodland caribou populations and studies to 

develop a population model to assess the relationship between adult and calf survival rates and 

population growth rate (Figure 10.3). 

73


Figure 10.1. Caribou‐wolf overlap model developed to illustrate “safe zones” for caribou. 

High (green) areas indicate zones where both the probability of use by caribou is high and the 

probability of use by wolves is low, while Low (brown) areas indicate places where the 

probability of use by caribou is low and the probability of use by wolves is high within the 

Redwillow study area, British Columbia, 2006–2009. 

74


Figure 10.2. Caribou‐wolf overlap model developed to illustrate “safe zones” for 

caribou. High (green) areas indicate zones where both the probability of use by caribou 

is high and the probability of use by wolves is low, while Low (brown) areas indicate 

places where the probability of use by caribou is low and the probability of use by 

wolves is high within the Banff, Jasper and A la Peche study area, 2001–2009. 

75


Figure 10.3. Preliminary simulation results estimating the 2‐dimensional threshold between positive 

and negative population growth, as estimated by adult female survival and March aerial surveys of 

calf:cow ratios. 

76


PRESENTATIONS, PUBLICATIONS AND WORKSHOPS 

Publications 

DeCesare, N. J., Hebblewhite, M., Robinson, H. and M. Musiani (2010). Endangered, apparently: the role of 

apparent competition in endangered species conservation. Animal Conservation, In Press. 

Hebblewhite, M., White, C.A. and M. Musiani (2009) Revisiting extinction in protected areas: Is it 

acceptable for mountain caribou to go extinct in a National Park? Conservation Biology 00: 000‐000. 

McDevitt, A. D., ., Mariani, S., Hebblewhite, M., DeCesare, N. J., Morgantini, L., Seip, D., Weckworth, B. V. 

and M. Musiani (2009). Survival in the Rockies of an endangered hybrid swarm from diverged caribou 

(Rangifer tarandus) lineages. Molecular Ecology 18: 665–679. 

Post, E., Brodie, J., Wilmers, C.C., Hebblewhite, M., & Anders, A.D. (2009). Global population dynamics and 

hotpots of climate change. Bioscience, 59: 489‐499. 

Webb, N., Hebblewhite, M. & Merrill, E. H. (2008) Statistical methods for identifying wolf kill sites in a 

multiple prey system using GPS collar locations. Journal of Wildlife Management. 

Merrill, E. H., Sand, H., Zimmerman, B., McPhee, H., Hebblewhite, M., Webb, N., Wabakkan, P. & Frair, J. L. 

(2010) Opportunities and challenges for using predator movements to assess kill sites and attack rates. 

Philosophical Transactions of the Royal Society B‐Biological Sciences In Review. 

Presentations at Scientific Conferences 

Weckworth, B. V., McDevitt, A. D., Mariani, S., Hebblewhite, M., DeCesare, N. J., Morgantini, L., Seip, D., 

Musiani, M. (2009) Mixing it up after the Ice Age: post‐Pleistocene genetic and behavioral dynamics of 

partially migratory caribou in the Canadian Rockies. 94th Annual Conference of the Ecological Society of 

America, Albuquerque, NM, USA (Invited Special Sessions, Oral) 

DeCesare, N. J., Hebblewhite, M., Smith, K. G., Weckworth, B. V., and Musiani, M. (2009) The demographic 

consequences of partial migration among woodland caribou in fragmented landscapes. 94th Annual 

Conference of the Ecological Society of America, Albuquerque, NM, USA (Invited Special Sessions, Oral) 

McDevitt, A. D., Mariani, S., Hebblewhite, M., DeCesare, N. J., Morgantini, L., Seip, D., Weckworth, B. V. and 

M. Musiani (2009). Survival of a unique and sensitive type of caribou in the Canadian Rockies. 19th 

Annual Meeting of the Alberta Chapter of the Wildlife Society, Edmonton, AB. (Paper Presented, Oral) 

Peters, W., Webb, N., Hebblewhite, M., Smith, K. G. & Stepnisky, D. (2009) A preliminary evaluation of 

distance sampling to estimate moose density in west‐central Alberta. In The Wildlife Society 16th 

Annual Conference. Monterey, CA. 

Polfus, J. L., Hebblewhite, M. & Heinemeyer, K. (2009) Northern woodland caribou habitat modeling and 

cumulative effects assessment. In The Wildlife Society 16th Annual Conference. Monterey, CA. 

Hebblewhite, M. and Musiani, M. (2008). The challenge of woodland caribou conservation in the Canadian 

Rockies: are more parks the answer? BC Protected Areas Research Forum, Prince George, BC. (Paper 

Presented, Oral) 

Hebblewhite, M. and Musiani, M. (2008). Y2Y Peace‐Breaks steering committee: The Challenge of 

Woodland Caribou Conservation in the Canadian Rockies. BC Protected Areas Research Forum, Prince 

George, BC. (Paper Presented, Oral) 

Weckworth, B. V., Hebblewhite, M. and Musiani, M. (2008). Heavy wolf harvesting results in sociological 

and genetic degradation. The Wildlife Society Annual Conference, Miami, FL. (Paper Presented, Oral) 


M. Musiani (2008). A new challenge for caribou (Rangifer tarandus) management in the Canadian 

Rockies: reconciling present–day demographics with Quaternary evolutionary history. Annual Meeting 

77


of the Canadian Society for Ecology and Evolution, University of British Columbia, Vancouver, BC. (Paper 


Robinson, H.S., Hebblewhite, M. and M. Musiani (2008). Modeling relationships between fire, caribou, 

wolves, elk and moose to aid prescribed fire and caribou recovery in the Canadian rocky mountain 

national parks. International Association of Wildland Fire Conference, Jackson, WY, U. S. (Paper 



wolves, elk and moose to aid prescribed fire and caribou recovery in the Canadian Rocky Mountain 

National Parks. Canadian Parks for Tomorrow, University of Calgary, Calgary, AB. (Paper Presented, 

Oral) 

Hebblewhite, M. (2008) Disentangling bottom‐up and top‐down effects of fires on montane ungulates. In 

The '88 fires: Yellowstone and beyond. International Association for Wildland Fire, vol. 16 (ed. R. E. 

Masters, K. E. M. Galley & D. G. Despain), pp. 33‐35. Jackson Hole, WY: Tall Timbers Research Station, 

Tallahassee Florida, USA. 

Hebblewhite, M. (2008) Invited Plenary speaker: Integrating fires and wildlife conservation in National 

Parks: challenges for generation X. In The '88 fires: Yellowstone and beyond conference, International 

Association for Wildland Fire. Jackson Hole, WY. 

Workshops / Project Partner Presentations 

McDevitt, A. D. (2010) The role of landscape genetics in conserving and managing Canadian cervids. Calgary 

Zoo, Calgary, AB (Invited Talk, Oral) 

Musiani, M. and Hebblewhite, M. (2009). Linear Features, Forestry and Wolf Predation of Caribou and 

Other Prey in West Central Alberta. Resource Access and Ecological Issues Forum, Calgary, AB. (Paper 


Musiani, M. and Hebblewhite, M. (2008). A Unique and Sensitive Type of Caribou in the Rockies. Resource 

Access and Ecological Issues Forum, Calgary, AB. (Paper Presented, Oral) 


wolves, elk and moose to aid prescribed fire and caribou recovery in the Canadian Rocky Mountain 

National Parks. Montane Ecosystem Science Workshop, Banff, AB. (Paper Presented, Oral) 


M. Musiani (2009). Conservation genetics of caribou (Rangifer tarandus) in the Canadian Rockies. 

Montane Research Program Information Session., Calgary, AB, 2009 March. (Poster) 

78


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