FOOTHILLS MODEL FOREST GRIZZLY BEAR RESEARCH PROGRAM

2000 ANNUAL REPORT

Prepared by Gordon Stenhouse and Robin Munro January 2001

Citation: Stenhouse, G. and R. Munro. 2001. Foothills Model Forest Grizzly Bear Research Program 2000 Annual Report. 87pp. This is an interim report not to be cited without the express written consent of the senior author.

Disclaimer

This report presents preliminary findings from the first two years of a 5-year study on grizzly bears in the Yellowhead Ecosystem. It must be stressed that these data are preliminary in nature and represent data collected during the first two field seasons. All findings must be interpreted with caution. Opinions presented are those of the authors and collaborating scientists and are subject to revision based on the ongoing findings over the course of this study.

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Dedication This annual report is dedicated to the memory of Mr. John Bell, our pilot and research team member, who passed away suddenly on December 1, 2000. John was an integral part of this research program and his skills, abilities and enthusiasm has lead to the great progress that has been achieved during the first two years of this 5 year program. John was always able to maneuver the helicopter into a position to successfully conduct our aerial darting operations, could get us in to check remote snare sites, knew the areas where the weather would permit us to work, could fly directly to where the bears were likely to be during data uploads, and most of all, brought our team home safely and without incident at the end of each day. Our team and this program will miss John not only as a great pilot, but also as a co-worker and friend. We are continuing our efforts to successfully complete this program in the memory of John Bell who had a true love of grizzly bears and the lands they inhabit.

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Acknowledgements A program of this scope and magnitude would not be possible without the dedication, hard work and support of a large number of people. The program steering committee of the Foothills Model Forest provided valuable support and assistance to allow the research to proceed in order to address management needs and we thank: Jim Skrenek, Brian Wallace, and Bob Udell for this. The financial support of our many program partners allowed us to focus our attention on the delivery of the program goals within this multi-disciplinary program. A special thank you goes to the 2000 capture crew of: Bernie Goski, Martin Urquhart, John Lee, Troy Sorenson, Dave Hobson, Marc Cattet, Nigel Caulkett, Rick Ralf, Greg Slatter, Jurgen Deagle, Mike Dilon, Mike Eder, Jeff Skinner, Keith Linderman, Dennis Urban, Mike Ewald, Kim McAdam, Tony Brooks, Glen Chantal, Perry Abramenko, Jeanette Brooks, Stuart Polege, and Andy Van Imschoot. Without the dedicated hard work and perseverance of these individuals we wouldn’t have met with another very successful capture program. John Bell (helicopter pilot) deserves special recognition for always bringing us safely home and assisting in all aspects of the aerial capture program and GPS data retrieval. Mike Dupuis assisted in fixed wing flying in our relocation efforts and added greatly to the success of our fall collar retrieval efforts. A special word of thanks to Julie Dugas, one of the key members of our research team, for her expertise and enthusiasm in all areas relating to GIS and GPS. Radio room staff at Jasper National Park Dispatch assisted our aerial work by providing excellent communications between all our field crews. Lab work on all DNA hair samples was completed through Dr. Curtis Strobeck’s lab at the University of Alberta. Research support in the field, and with a variety of remote sensing needs, was provided by the program team members of the University of Calgary Geography Department under the direction of Steve Franklin. Scott Nielsen, from the University of Alberta, provided a great deal of extra field assistance in many aspects of our program this year, while he was collecting his own data for RSF modelling work. The staff at the Hinton Environmental Training Center provided a great deal of assistance in many ways this year and also provided food and lodging for the field crews. Communication efforts for this program were directed by Lisa Risvold, Patsy Vik, and Sue Wolf. A word of praise goes out to this group for keeping up with media needs and the special communication requirements associated with our program. Denise Lebel once again did an excellent job in managing the large piles of paperwork associated with the administrative details of this program.

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Table of Contents

1. Prologue 1

2. Introduction 1

3. Background 3

4. Long Term Program Objective 3 4.1 Program Goals 3 4.2 2000 Project Objectives 4 4.3 2000 Key Program Elements 4

5. Study Area and Methods 6

6. Bear Capturing and Handling 10 6.1 Field Operations 10

7. Results 13 7.1 Capture Results 13 7.2 Telemetry and Data Collection 16 7.2.1 Methods 16 7.2.2 Results 18 7.3 Home Range 21 7.3.1 Methods and Results 21

8. Analysis of the 1999 DNA Mark-Recapture Hair Data 22 8.1 Introduction 22 8.2 Methods 22 8.2.1 Population Closure 22 8.2.2 Capture Probability Variation 24 8.2.3 Population Estimates 25 8.3 Results 26 8.3.1 Population Closure 26 8.3.2 Capture Probability Variation 28 8.3.3 Population Estimates 35 8.4 Discussion 37 8.4.1 Capture Probability Analysis 37 8.4.2 The Effect of Sparse Data 38 8.4.3 Correcting for Closure 38

8.4.4. Utility of Program MARK Methods and AIC Model Selection 38

8.5 Suggestions for Future Work 39 8.5.1 Future DNA Mark-Recapture Work for Estimation of

Population Size 39 8.5.2 Ongoing Analysis of Current Population Estimate 40 8.5.3 Development of Population Monitoring Tools 41

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9. Foothills Model Forest Grizzly Bear Research Program – RSF Report, Year 2000 42

9.1 Introduction 42 9.2 RSF Models and the FMF Grizzly Bear Project 42 9.3 Current RSF Work in Progress 44 9.3.1 Spatial Autocorrelation Issues in RSF Analyses 44 9.3.2 Sampling Intensity of Availability in RSF Models 44 9.3.3 Patch-level RSF Analyses, The 1999 Dataset 45 9.3.4 Home Range Selection 46

10. Foothills Model Forest Grizzly Project – University of Calgary Remote Sensing Group 47

10.1 Field Data Collection and Analysis 47 10.1.1 Discriminant Analysis of Spectral and Field Data 47 10.2 Satellite Imagery Processing 48 10.3 Integrated Decision Tree Classification of Habitat Units 48 10.4 Graduate Student Project Progress 49 10.5 Journal Submissions 49 10.6 Conference Proceedings 50 10.7 Habitat Mapping 50

11. 1999 DNA Report – Scat Sniffing Dogs and Fecal DNA Work 51 11.1 Introduction 51

11.1.1 Biases Associated with DNA-based Mark- Recapture Estimators of Population Size 52

11.1.2 Scat Collected Using Detection Dogs 52 11.2 Study Design 53 11.3 Results 54 11.3.1 Bear Distributions Based on Collection Methods 54 11.3.2 DNA Preservation and Amplification Success 58 11.4 Stress Hormone Data 59 11.4.1 Species and Gender Differences 59 11.4.2 Within Species Differences 61 11.4.3 Hormone Preservation Studies 61 11.5 Discussion 65 11.6 Concluding Remarks 66

12. Grizzly Bear Health 68 12.1 Bear Physiology 68 12.2. Immobilizing Drugs 68

12.2.1 The Comparative Effects of Chemical Immobilizing Drug and Method of Capture on the Health of Free- Ranging Grizzly Bears 69

12.3 Body Condition 71 12.3.1 The Development and Assessment of a Body

Condition Index for Polar Bears and it’s Application to Brown Bears and Black Bears 73

12.4 Blood Analyses 75

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12.5 A Proposed Study: “Anthropogenic Stress and Health in Grizzly Bears” 75

12.5.1 Project Summary 75

13. GIS Application 78

14. Communications 79

15. Literature Cited 80 Appendix I FMF Grizzly Bear Project Publication/Technical Paper List 85

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List of Tables Table 1. Natural sub-region composition of the grizzly bear research

program area in 2000 8 Table 2. Total number of grizzly bears handled in 1999 and 2000 in

the grizzly bear research program 14 Table 3. Current status of the GPS collars and ear tag transmitters in

the grizzly bear research program 19 Table 4. Total number of locations obtained from the GPS collars in

the grizzly bear research program during 2000 19 Table 5. Annual 100% MCP home range for collared bears in the

grizzly bear research program area in 1999 and 2000 21 Table 6. Model selection results for GPS collared bear movement

analysis in the grizzly bear research program in 2000 28 Table 7. Model selection results for Pradel analysis in the grizzly

bear research program in 1999 and 2000 29 Table 8. Huggins closed model selection results in the grizzly

bear research program in 1999 and 2000 31 Table 9. Model Selection Results: Logistic home range analysis in

the grizzly bear research program in 1999 and 2000 33 Table 10. Results of 4 session Huggins closed model analysis in the

grizzly bear research program in 1999 and 2000 36 Table 11. Superpopulation estimates for the 1999 DNA hair mark-

recapture inventory in the grizzly bear research program 36 Table 12. Average N estimates using the Kenward Correction in the

grizzly bear research program in 1999 and 2000 37 Table 13. Linear discriminant analysis, independent variables and

accuracy levels in the grizzly bear research program in 1999 and 2000 47

Table 14. Black and grizzly bear scat samples found inside and

outside Jasper National Park, as a function of sex, in the grizzly bear research program in 1999 54

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Table 15. Range of hematology values for grizzly bears captured

as part of the Foothills Model Forest Grizzly Bear Research Program during 1999 and 2000 77

Table 16. Range of serum biochemistry values for grizzly bears

captured as part of the Foothills Model Forest Grizzly Bear Research Program during 1999 and 2000 78

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List of Figures Figure 1. Grizzly bear research program area boundary in 1999

and 2000 7 Figure 2. Natural sub-regions within the grizzly bear research

program area in 2000 8 Figure 3. Identified Bear Management Units within the grizzly

Bear research program area in 1999 and 2000 9 Figure 4. Distribution of sex classes of bears captured in 1999

and 2000 in the grizzly bear research program 15 Figure 5. Distribution of age-sex classes of bears captured in

1999 and 2000 in the grizzly bear research program 15 Figure 6. Distribution of collars in the grizzly bear research

program in 2000 16 Figure 7. Map of completed collar performance plots in the

grizzly bear research program in 2000 20 Figure 8. GPS collar locations for 1999 (left) and 2000 (right)

and DNA sites which captured bears in 1999 in the grizzly bear research program area 27

Figure 9. Transition probabilities per session of GPS bears

emigrating (moving out of grid area) and immigrating (moving into grid area) in the grizzly bear research program in 1999 and 2000 28

Figure 10. Pradel estimates of recapture rate of marked bears as

a function of mean capture distance from edge of the sampling grid in the grizzly bear research program in 1999 and 2000 30

Figure 11. Model averaged estimates of sex specific and bear type

from huggins model analysis in the grizzly bear research program, as presented in table 8, in 1999 and 2000 32

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Figure 12. Huggins closed model analysis estimates of GPS, DNA, male and female specific capture probabilities as a function of distance from edge of mean bear DNA capture location in the grizzly bear research program in 1999 and 2000 32

Figure 13. Predicted capture rate as a function of 50% kernel home

range size and age in the grizzly bear research program in 1999 and 2000 34

Figure 14. Proportion sessions sampled as a function of sex and

home range size for the grizzly bear research program in 1999 and 2000 35

Figure 15. Hierarchical spatial-temporal scales of selection used in

RSF analyses in the grizzly bear research program in 1999 and 2000 43

Figure 16. The influence of availability densities (number of point/

km²) on the estimated RSF parameter (access density) for G-016 in 1999 in the grizzly bear research program 45

Figure 17. Individual grizzly bear and black bear scat sample

collection sites for the grizzly bear research program in 2000. 56

Figure 18. Individual black bear hair and fecal sample collection sites for the grizzly bear research program in 2000 56

Figure 19. Individual grizzly bear hair and fecal sample collection sites for the grizzly bear research program in 2000 57

Figure 20. Grizzly bear GPS locations within the grizzly bear

research program area in 2000 57

Figure 21. Grizzly and black bear fecal corisol metabolites by sex in the grizzly bear research program in 2000 60

Figure 22. Stress hormone levels of captured grizzly and black

bears in the grizzly bear research program in 2000 62 Figure 23. Mean cortisol metabolite concentrations across

preservation groups, stored (a) frozen and (b) at room temperature in the grizzly bear research program in 2000 64

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Figure 24. Map showing the locations of grizzly and black bear scat and hair sample collections throughout the 5400 km² grizzly bear research program area in 2000 67

Figure 25. Physiological response of grizzly bears during

anasthesia with ZT and with XZT in the grizzly bear research program in 2000 70

Figure 26. Curvilinear relationship between total body mass

(TBM) and straight-line body length (SLBL) in 1229 bears represented by polar bears, grizzly bears and black bears 72

Figure 27. Nomograms for estimation of Body Condition Index

(BCI) values over straight-line body length intervals from a) 250 to 220 cm; b) 100 to 150 cm; and c) 60 to 100 cm 74

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1. Prologue This report is a compilation of materials and summaries from the research team, who are conducting specific components of this collaborative research program. Research collaborators have prepared specific program element reports and these are indicated in the following report where appropriate. Readers are also referred to Appendix A which lists the publications and papers arising from this research program to date. These technical papers present a more detailed analysis of many of the key program elements. 2. Introduction Many people, both in Canada and in other parts of the world, consider grizzly bears to represent wilderness and their presence and persistence are felt to occur in areas of the world which are viewed as wilderness regions. In addition, the grizzly bear is considered to be a species whose presence indicates a healthy ecosystem, as such it has been referred to as an “indicator species”. Grizzly bears are also considered by some to be an “umbrella” species (Paquet and Hackman 1995). These authors conclude that an additional 403 species were protected by maintaining the habitat needs of grizzly bears, wolves, and lynx. Irrespective of the number of other species involved, it is commonly felt that the presence of grizzly bears, a top carnivore, is indicative of a healthy, functioning ecosystem. We believe it is justified to use the long-term persistence of healthy grizzly bear populations as a barometer with which to measure land use practices and changing landscapes, which include grizzly bear habitat, as well as the overall “health” of an ecosystem. The grizzly bear is a wide ranging, secretive species, which often occurs at low densities and which require large areas of land in which to meet their life cycle requirements. In response to the demands of hibernation, grizzlies require high quality food in the fall, before denning, and also after den emergence in the spring. When environmental conditions dictate, grizzly bears search vast areas to meet their nutritional needs. These far ranging movements often result in bears coming into contact with humans, with an associated increase in either direct or indirect bear mortality. The low reproductive rate of grizzly bears, and the length of time it takes for bears to reach sexual maturity, has made it difficult for this species to compensate for increases in natural/human caused mortality with increased productivity. Therefore an increase in mortality, of all types, can result in a declining bear population. In North America, the historic range of the grizzly bear encompassed most of western Canada and the United States. Today, the grizzly bear population in the conterminous U.S. is estimated to be < 1000 (Servheen 1990) and is a high management priority. Even in Canada, where the grizzly bear still occurs in relative abundance, its distribution is largely restricted to remote and mountainous locations (Banci 1991). In Alberta the grizzly bear population is estimated at approximately 850 animals (AEP 2000). The reduction and fragmentation in bear distribution over the past decade has been primarily attributed to unsustainable mortality rates combined with incremental habitat loss and habitat alienation (McLellan et al. 1999). As human populations and activities expand, associated impacts will increase and result in further fragmentation of bear populations.

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West-central Alberta provides about 69% of the current primary range available to grizzly bears in Alberta, and it is thought that this area supports approximately 68% of the estimated current resident provincial grizzly bear population (Management Plan for Grizzly Bears in Alberta, 1990). This area has been considered to provide the greatest opportunity to increase grizzly bear populations in Alberta through intensive management and conservation programs. However, ongoing and increasing human activities in this region raise serious questions about the long-term conservation of grizzly bear and their habitats in this area. This increase in human activities in bear habitat is not limited to west central Alberta and in fact is occuring in all grizzly bear habitat in Alberta. It is important to also recognize that these human activities cover both industrial resource extraction activities as well as a host of recreational activities. As human activities and developments increase within this area so does the likelihood of loss of key habitats, habitat fragmentation, direct and indirect bear mortalities and a reduction in the number of secure areas for grizzly bears. Recent findings by Benn (1998) have demonstrated that a number of these factors are related to a rise in grizzly bear mortality rates over time. These factors have been shown to result in the regional extirpation of grizzly bears in certain areas of North America (Weaver et al. 1996, Caughley 1994, and Paquet and Hackman 1995). Although some human activities and development are generally considered harmful to the grizzly population (Servheen 1990, McLellan et al. 1999) they are destined to continue because of the economic and societal value associated with them. Concurrent with development, most people desire the continued existence of the remaining bear population. The challenge facing land managers is to learn how to ensure the long-term survival of this species while addressing human and societal demands on the same land base. If we are to sustain both human use activities and grizzly bears, intensive management based on detailed biological information and a greater understanding of response and interactions is required. The challenge that managers face is the determination of a balance between human needs and those of grizzly bears. There is considerable historical information about grizzly bears in portions of the current study area. Russell et al. (1979) studied population dynamics of grizzly bears in Jasper National Park (JNP). Part of their study area overlaps the JNP portion of our pilot area. These authors reported bear densities of about 10 bears/1000 km2. Wielgus and Bunnell (1994) worked in a more southern area of the Rocky Mountain east slopes and estimated grizzly bear density at 16 bears/1000 km2. Home ranges were large in JNP though females with young had much smaller home ranges and tended to confine their habitat use to upper slopes and side valleys away from adult males (Russell et al. 1979). Our study design reflects the need to achieve reasonable capture success for these females. Nagy et al. (1989) studied grizzly bears in the boreal plains north of Hinton, Alberta, an area which is also north of the present study area. This area is similar to the eastern portion of our study area. They found much lower bear densities, about 5 bears/1000km2, though they felt the population was declining during the period of study. These authors were unsure if this decline was due to habitat related factors, or other direct factors such as harvest. Based on the above research results, and predictions by Nagy and Gunson (1990), we predict about 50 resident grizzly bears in our study area.

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Nagy and Haroldson (1989) analyzed seasonal home range and movement patterns for both the JNP and boreal plains studies cited above. They report extremely large annual home ranges for both males and females (roughly 400 km2 for females without cubs), though females with cubs had smaller average home ranges (250 km2; n=4 females). They reported that movement distances for four females were reduced by about 33% during spring/early summer (15 May-21 July), with respect to their annual home ranges. This suggests spring home ranges for females with young, in the boreal plains part of the study area, may be in the 100-200 km2 range. No home ranges for females with cubs were presented for JNP, but home range sizes for other bears were similar among the two areas (Nagy and Haroldson 1989). We expect home range size for females with cubs will also be similar in the JNP portion of our study area. 3. Background In 1999, the Foothills Model Forest initiated a major 5-year grizzly bear research program in response to a number of management challenges. This research program focuses on management issues and questions by assessing grizzly bear populations, bear response to human activities, and habitat conditions to provide land managers with tools to integrate grizzly bear “needs” into the land management decision making framework. This approach is intended to allow resource managers to gain a better understanding of grizzly bear ecosystems and grizzly bear response to human activities, and to implement appropriate actions designed to conserve grizzly bears in this region. This program is directly linked to the 2000 management framework document entitled “Grizzly Bear Conservation in the Alberta Yellowhead Ecosystem – A Strategic Framework”. The research questions being pursued represent management questions for which data are needed. Results from this program will be useful for successful grizzly bear management throughout Alberta, and other areas of grizzly habitation throughout North America, as it will provide tools and techniques that address landscape level conservation issues. 4. Long Term Program Objective: To provide resource managers with the necessary knowledge and planning tools to ensure the long-term conservation of grizzly bears in the Yellowhead Ecosystem. 4.1 Program Goals: The knowledge obtained from this study will be used to:

• Provide information that will support management programs to provide stable/increasing grizzly bear populations over time,

• Identify habitat and landscape conditions that contribute to or limit viable and regionally connected grizzly bear populations,

• Develop a set of validated, user friendly, GIS based computer models for the Northern East Slopes Region, that will provide predictive capability when resource managers are making land use planning decisions in known grizzly bear range.

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4.2 2000 Project Objectives: • Utilize existing GIS based grizzly bear Cumulative Effects Models (CEM) (Purves

and Doering 1998) and existing data sets to provide an assessment of the current landscape condition as they relate to grizzly bear habitat and current human use levels. Current research will provide an assessment of the CEM default parameters and model assumptions currently in use. Data collected from radio collared bears will be used to review current CEA model input parameters.

• Use Landsat imagery to create a grizzly bear habitat classification map and compare this map product to existing grizzly bear habitat quality maps for portions of the study area.

• Use remote sensing tools to measure and quantify landscape change within the study area over time and determine if bear movement and habitat use patterns are affected by the changing landscape conditions and human activities.

• Continue with the development of a cost effective field approach to provide grizzly bear population trend information over time. Compare the results of the two DNA inventory techniques used in the 1999 field season to determine the direction for the future field work. Continue evaluation of the 1999 DNA hair data set relative to the 2000 GPS bear movement data.

• Assess and predict individual grizzly bear population resiliency within the study area. This will involve monitoring mortality and reproductive rates, as well as bear responses to human activities.

• Conduct an assessment of grizzly bear habitat selection and movement patterns in relation to current landscape conditions using univariate analysis as well as resource selection functions (RSF). RSFs will be used to develop predictive models which will enable us to predict habitat use by grizzly bears in this ecosystem and subsequent maps may be created which outline the probability of occurrence of bears on the landscape. These models are advantageous because both ecological and human use data can be incorporated into the equation. In addition, such models will provide a quantitative link between populations and landscape conditions and will incorporate both ecological and human activities occurring within this ecosystem. Results from this work will be compared to CEM results.

• Continue to communicate with and involve all interested partners and stakeholders in the ongoing progress and results of this project to ensure that management questions are answered through research efforts.

4.3 2000 Key Program Elements: The key elements of the FMF Grizzly Bear Research Program are:

• Status and Trends Provide data and develop methodologies to allow managers to assess and monitor grizzly bear population parameters over time (i.e. productivity, survival rates, mortality rates, sex ratios, etc.). These data will also form an integral component concerning model validation and ongoing development. This element will be one indicator used to assess program success. This program element is currently focusing

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on DNA inventory techniques that we see as the most promising methodology available for this task.

• Movements Gather animal data to document grizzly bear travel corridors within the research program area, document home range patterns and den sites, assess habitat use at a number of spatial and temporal scales, and document bear response to human activities at a landscape scale. This program element involves fitting a sample of the grizzly bear population with GPS radio collars in order to collect detailed movement and response data.

• Mortality Monitor and evaluate known grizzly bear mortalities within the study area of both study and non-study animals. We are monitoring our collared sample of bears to document all known losses during the study period.

• GIS Utilize existing GIS based grizzly bear cumulative effects models in selected research program areas to provide an overview of grizzly bear habitat issues at the current time. These data will be used in research program design and research program area selection. Continue to test and develop these CEA models with animal data collected through the field efforts and to integrate other resource planning tools (forestry, mining, etc.) to allow predictive capability of the effects of changing landscape conditions over time on grizzly bear populations. An array of GIS based tools is being used within all program elements and offers all collaborators the ability to link specific data sets with land conditions in a spatial and temporal manner.

• Remote Sensing We are striving to be able to effectively map grizzly bear habitat within the study area, using remote sensing tools. The mapping products will be tested to determine their degree of predictability of grizzly bear occurrence and will also be tested against other grizzly bear habitat maps that are currently available. The goal will be to have a tool that could be used to map all grizzly bear habitat in Alberta. This program element is also being used to measure and quantify landscape change through the acquisition and processing of annual satellite imagery for the study area.

• RSF modelling This program component focuses on moving our understanding of bear response to human activities and landscape conditions beyond what is possible with the existing CEA grizzly bear models. This approach will allow us to develop predictive probability equations, which can link landscape conditions to grizzly bear occurrence and also to populations. These equations provide a more rigorous approach to understanding these relationships and they are also statistically defensible.

• Communications An important element of this program will involve the continued and ongoing communication of research results and program findings to all partners and interested stakeholders. This element will be critical to maintain and enhance the partnerships formed and ultimately will be vital in achieving the program objective.

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5. Study Area and Methods In 1999 the research program area encompassed an area of 5352 km² (Figure 1). This study area was arbitrarily chosen in an attempt to define a workable study area size and one that would include a variety of land use activities. This fact would allow comparisons between portions of the landscape with varying degrees of human use and activity (i.e. inside and outside the JNP). Human presence within this area encompasses a wide variety of land use activities including, but not limited to; hunting, tourism, forestry, mining, oil and gas development and exploration, transportation corridors, trapping, commercial outfitting, and public recreational use. The original study area was bounded to the north by Highway 16 and the Athabasca River, to the east by a forestry trunk road, to the south by the Brazeau River and by a mountain range in Jasper National Park as the western boundary. It was recognized early in the planning process that these boundaries would not limit bear movement within the study area. After two years of data collection we have significant evidence to suggest that bear movements occur to a large degree to the east of the eastern study area boundary. For this reason, and in an effort to gain a better understanding of bear use and response in a diversity of habitats and landscapes, we modified the study area boundary in 2000 to include an area of approximately 9700 km². We recognize that even with this expanded study area boundary we will have, and indeed have data to support the fact that, some bear movements will still occur outside this new study area boundary. It is not our intention to change the study area boundary each year as additional movement data is collected. In fact it significantly increases the demand on our program collaborators to deal with the many data acquisition issues that arise from this study area expansion. However, we do recognize that a more in depth understanding of habitat use is possible by using all data generated by the radio collared bears. The 2000 study area (hereafter referred to as the study area) is comprised of portions of 5 distinct natural sub-regions. These are: alpine, sub-alpine, montane, upper foothills/sub-boreal spruce, and lower foothills (Figure 2). The proportional representation of these sub-regions is presented in Table 1. The study area covers a portion of both mountainous and foothills habitats. The mountainous habitat is found within the Jasper National Park region of the study area and also the Cardinal Divide and Redcap range areas. These higher elevation features (ranging to 3000 m. ASL) run in a southeast-northwest direction. Overall, the study area contains a wide variety of habitats including glaciers, mountains, alpine and sub-alpine meadows, wet meadow complexes and forests dominated by deciduous species to mixed wood forest stands as one moves further east of the mountains.

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Figure 1. Grizzly bear research program area boundary (core = red) in 1999 and

2000.

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Table 1. Natural sub-region composition of the grizzly bear research program area in 1999 and 2000.

Natural Sub-regions % Area Area (km²)

Alpine 13.28 1295 Sub-alpine 26.73 2606 Montane 1.67 163 Upper Foothills 21.42 3063 Lower Foothills 26.90 2623

Figure 2. Natural sub-regions within the grizzly bear research program area in

1999 and 2000.

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We used one component of the FMF WAM (Watershed Assessment Model) procedure to assist in the delineation of watershed units within the study area. Based on the approach used by Purves and Doering (1998) we tailored these watershed units within the research program area to approximately conform to a size similar to an adult female grizzly home range (approx. 340km²) (Figure 3). The designated watershed units are referred to as bear management units (BMU’s) within the research program area. BMU’s previously established for Jasper National Park were incorporated, merged, and in some cases, modified for incorporation into the defined BMU’s for the research program area. The sole purpose for the creation of these BMU’s was to allow us to conduct CEA model run analysis for the study area. A more formal review of the home range sizes for study animals is part of the ongoing analysis within this program.

Figure 3. Identified Bear Management Units within the grizzly bear research

program area in 1999 and 2000.

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6. Bear Capturing and Handling In order to collect detailed movement and habitat use data on grizzly bears within the study area, it was necessary to capture, immobilize, and radio collar a sample of the grizzly bear population. Since the study area presented opportunities for capturing bears in both forested and non-forested habitats, we employed two different capture techniques (aerial darting, and leg hold snaring) during the spring capture period. With an overall goal to have at least one collared grizzly bear in each bear management unit, we allocated capture effort across the 16 BMU’s defined as our core study area. In general, once a bear was captured within a BMU this unit was considered closed to further capture efforts. We then focused additional effort in the remaining BMU’s where bears had yet to be captured. The goal of this approach was to distribute radio collars within the research program area in a systematic fashion to avoid biases related to sampling effort. In the 2000 capture period we intentionally captured and collared additional bears in BMU’s along the eastern boundary of the core study area in an attempt to learn more about the issue of population closure relative to the 1999 study area boundary. In 2000 our goal was to deploy 20 GPS radio collars on grizzly bears within the research program area. This number of collars was selected based on estimated bear densities within this area, and also based on statistical requirements for data analysis. In an effort to gather data from all cohorts of the population we deployed collars on both male and female bears that were large enough for instrumentation purposes. Small subadult bears were not radio collared, however subadult bears captured as part of a family group were lip tattooed for future identification and in some instances these bears received a VHF ear tag transmitter. All capture efforts taking place in this program followed procedures currently being reviewed and revised by the Canadian Council on Animal Care for the safe handling of bears. In addition, this research program adhered to the “Protocol for the use of drugs in Wildlife Management in Alberta” (May 9, 1997) in all aspects of field work involving the capture and handling of grizzly bears. Our research protocols were also reviewed and approved by the Animal Care Committee at the Western College of Veterinary Medicine in Saskatoon, SK. Further, we obtained all necessary research permits from both Alberta Environment and Parks Canada (Jasper National Park) to allow for the capturing and handling of grizzly bears during the study period. 6.1 Field Operations As mentioned, we utilized two primary methods to capture grizzly bears within this research program: aerial darting, and snaring. • Aerial Darting

In an effort to increase capture success in open alpine and sub-alpine areas we located ungulate carcasses from road kills as bait attractants for bears. This technique was designed to attract and potentially hold bears for short time periods that would afford the opportunity to capture them using aerial darting. Our search protocol was to

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scope open areas and bait stations and look for bears and or fresh sign (tracks, scats, etc) with the aid of a Bell 206 helicopter. These search efforts were limited to open habitats where grizzly bears and their tracks can be seen from the air.

Once a grizzly bear was observed, the capture crew determined if the surrounding habitats and geography permitted a safe pursuit and capture. Chase times were limited to less than 1 minute and usually lasted for approximately 30-45 seconds. Full details on the results of bear handling (and blood chemistry/histology) are presented in a following section on bear physiology prepared by Drs. Marc Cattet and Nigel Caulkett (Section 12).

Bears were immobilized with one of the standard rifle systems for firing internally charged darts (Palmer or Pneu-dart). Bears were immobilized with either Telazol or Telazol/Xylazine according to a weight/dosage table prepared by Dr. Marc Cattet and Dr. Nigel Caulkett. Aerial darting took place from a range of approximately 10-15 m. Once a bear was darted the helicopter and crew moved away from the bear to reduce stress while ensuring that visual contact was maintained. Once the bear was showing signs of immobilization the helicopter landed a safe distance away. Further visual checks were made on the bear from the ground before the capture team approached the bear to ascertain level of immobilization. During the immobilization process and at all times during the handling procedure a person equipped with an appropriate firearm (12 gauge shotgun) stood vigil for the rest of the capture crew. Once it was safe to handle the bear, the field crew placed the bear in a comfortable sternal recumbancy position. Breathing rates and core body temperatures were monitored regularly throughout the handling procedure. Care was taken to ensure that air passages and oral cavities were free and clear to ensure there were no impediments to respiration. All bears had ophthalmic ointment applied to their eyes and blindfolds applied to reduce the risk of eye injury during immobilization. Field crews worked quickly and quietly around bears to minimize stress on the animal.

Captured grizzly bears had GPS collars applied, a VHF ear tag transmitter attached, a premolar tooth removed for aging purposes, lip tattoos applied, hair samples and fecal samples collected for DNA analysis, and blood samples collected for analysis. All bears were also inspected for any signs of previous capture, injury, and/or physical abnormality. Whenever possible bears were also weighed and a variety of standard morphological measurements taken. Topical antibiotics were administered to all bears to minimize the likelihood of infection related to handling procedures. Bears, which were immobilized with the combination of Telazol/Xylazine, received Yohimbine as an antagonist. These bears were watched from a safe distance to record recovery times. In addition, an aircraft overflight was made to check on all captured bears sometime later that same day.

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• Snaring Ground based snaring operations were conducted in portions of the research program area where aerial darting was not possible. These areas were typically those with dense forest cover occuring to the east of the front ranges of Jasper National Park. One exception to this general protocol occurred in the Lower Rocky BMU within Jasper National Park. In this BMU we had not located and captured a grizzly bear through aerial darting by the end of May. At this time the program partners agreed to proceed with some snaring operations in this BMU in an effort to have one bear collared here.

• Snare Construction: Aldrich leg snares were purchased from Margo Supplies,

Calgary, AB. The snare components consisted of a: spring, ¼" airplane cable for the foot loop and anchor cable, sliding lock, cable clamps, crimps and a swivel. Foot loops required assembly using a combination of cable clamps and crimps. Snares were constructed to lie flat and close as tightly as possible (tested by using the yo-yo technique). To reduce the chance of cable clamp nuts becoming loose, regular nuts were removed and replaced with locking nuts. We found the best snare construction technique consisted of placing a cable clamp on the locking end of the foot loop and a cable crimp on the swivel end. This allowed for easy snare removal from the leg should the cable become jammed in the sliding lock.

• Bait Collection: Baits were used to attract grizzly bears to the snare site location.

The main source of bait consisted of beaver and ungulate carcasses. Beaver carcasses (approximately 500) were purchased from registered trappers while ungulate carcasses were obtained from road-kills that had occurred in the three months prior to the capture period. Bait was kept frozen in freezers until required or transported directly to the trap sites. Large ungulate carcasses were used at sites that were easily accessible by truck. Where access to trap sites was restricted to ATV's or helicopters, beaver carcasses were an ideal bait size.

• Trap Site Selection and Construction: Trap site selection was determined from a

compilation of information obtained from trappers, hunters, Fish and Wildlife personnel, Forestry workers and aerial recognizance. Criteria used in the selection of specific trap sites was based on known bear usage, accessibility (helicopter and ground access), safe visual distance (100m minimum) to observe trap site on the ground, and environmental hazards to bears after their capture and release (water, topography). Trap site construction included the limbing of tree branches from the anchor tree, clearing trees and brush from the site, and building cubbies. A basic trap site consisted of setting 1 cubby set and 2 – 3 trail sets. The snare's anchor was attached to a live tree (30cm dbh) using the shortest anchor lead possible. All clamp nuts were checked for tightness. Barriers were set up across trails to prevent ungulates from getting caught in snares. Trap transmitters were occasionally used at sites that only had a single snare. Bait was placed in the cubby, hung in nearby trees and dragged various distances from the trap site. Dragging the bait produced a scent trail for a bear to follow to the trap site. A blended mixture of fish oil, beaver castor and blood was also used as a lure. Trap sites were re-baited as required.

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All trap sites were closed to the public. Public notices were placed in local newspapers advising of the research underway and the areas which may experience site specific area closures. Closure signs and tape were put up at all trap site access points.

• Ground Capture Procedures: Snares were checked as early as possible on a daily

basis. A team would access the site on the ground using ATV's or by helicopter. The ground team consisted of two or three experienced personnel. The trap site was observed from a safe distance to determine if a bear had been captured. Usually it was evident when a grizzly bear had been snared (vocalization, excavations, bark torn off trees). When a bear had been captured the following visual observations were made; 1) the trap site and surrounding area was observed to determine if there were any other bears present, and 2) the position of the snare on the bear’s leg was assessed. With this information the capture team would determine the best approach plan to ensure the safety of personnel and to minimize stress to the animal. With an armed team member on each side of the darter the bear was approached to a safe darting range of 10 to 15m. Once the bear was successfully darted the team retreated to a safe distance to observe the bear’s reactions to the drug.

After the bear was immobilized the animal was processed (see previous section on aerial darting for processing details). During the processing individual team members had assigned duties. When processing had been completed all other snares in the area where sprung. The team then left the trap site allowing the bear to recover. Bears captured with this technique were checked on by a helicopter over-flight within 24 hours following capture.

7. Results 7.1 Capture Results The capture period occurred between March 22 – July 17, 2000. The majority of bears, however, were captured between April 23rd – June 30th when a full complement of field staff was involved in capture activities throughout the study area. In 2000, a total of 37 bears were handled, of which 25 were grizzly bears and 12 were black bears. The grizzly bears included one handling mortality that took place within Jasper National Park during snaring operations. No non-target species was caught in snares this year. In total 21 of the captured grizzly bears were radio collared and were used to study bear movements during the second field season. We recaptured and recollared 12 bears that were collared during the first year of this program. The total number of grizzly bears handled in 1999 and 2000 is presented in Table 2. Figure 4, denotes the breakdown of males and females across years. A further breakdown of captured grizzly bears in relation to age cohort is shown in Figure 5. Findings from other grizzly bear research in both Alberta and British Columbia (Gibeau and Herrero 1998 and McLellan 1989) have shown that although the age of sexual maturity does vary among grizzly bears it is generally accepted that 0-4 years is a subadult non breeding animal. Our capture sample of bears included 3 family groups in 2000. One of these family groups (G02) was also collared and tracked in the first year of this study.

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Bears were captured in all but two of the 16 designated bear management units in our core study area. We were not successful in locating a grizzly bear in either the Upper or Lower Rocky BMU’s despite attempting both aerial darting and ground based snaring within this area. Figure 6 identifies the distribution of collared bears (and GPS collar types) in the BMU’s within the study area. This figure also shows which BMU’s had more than one bear captured and radio collared within them. No effort was made to select for specific sex cohorts during our capture efforts. Table 2. Total number of grizzly bears handled in 1999 and 2000 in the grizzly bear

research program.

Captured Bear ID Sex Age_2000 1999 2000

001 M Unknown X X 002 F 18 X X 004 F 6 X X 005 M 12 X 006 M 17 X X 007 F 4 X X 008 M 15 X 009 F 13 X 010 F 14 X X 011 F 7 X X 012 F 6 X X 013 F 5 X 014 M 10 X X 015 M Unknown X 016 F 6 X X 017 M 8 X X 018 M 4 X 019 F 6 X 020 F 5 X X 021 M 12 X 022 M Unknown X 023 F 11 X 024 M 6 X 026 F 3 X 027 F 11 X 028 F 6 X 029 M 13 X 030 M 4 X 031 M 3 X 032 F Unknown X 033 M 3 X 100 F 2 X 101 M 2 X 102 F Unknown X 104 M 2 X

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0

2

4

6

8

10

12

14

16

18

20

1999 2000 1999/2000

num

ber

of b

ears

Female

Figure 4. Distribution of sex classes of bears captured in 1999 and 2000 in the grizzly bear research program.

Male

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Males

Figure X. Number of males and females captured in 1999 and 2000.

0

1

2

3

4

5

6

7

8

0-4 yrs 5-10 yrs 11-15 yrs >15 yrs

Females

Figure 5. Distribution of age-sex classes of bears captured in 1999 and 2000 in the grizzly bear research program.

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Figure 6. Distribution of collars in the grizzly bear research program in 2000. 7.2 Telemetry and Data Collection 7.2.1 Methods • GPS Radio Collars

Some of the key research questions of this study relate to habitat use and grizzly bear response to human activities. These questions require that detailed information be collected on the movements of bears within the research program area. The approach we have taken to acquire this type of data is the utilization of GPS (global positioning systems) radio collars. These systems allow researchers the opportunity to collect detailed movement data on a 24-hour basis over a 9-10 month period. We are utilizing two different brands of GPS radio collars. By using two different brands of GPS radio telemetry equipment we felt that we would be better able to maximize data recovery. Each brand of GPS collar has different strengths and weaknesses.

• Televilt GPS radio collars: Based on the results from GPS collars during the first year of this research program, we deployed 12 Televilt Simplex GPS radio collars. Some of these collars were 1999 models (8 channel models) which were reprogrammed and attached to a new battery pack. We also acquired six 2000 models that were 12 channel units. Because of new engineering improvements

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these new units have longer battery life. Consequently, 4 collars were deployed that, theoretically, could last for 3 years with our programming schedule. Televilt collars weigh 1.26 kg and 9 were attached with two layers of cotton webbing to act as a “rot off” mechanism in the event that the animal was not recaptured in successive years. We also deployed 3 Televilt collars with new pre-programmed drop off mechanisms which allowed the collar to be automatically released from the bear following a pre-programmed number of days after the activation magnet is removed. Each collar was programmed using the SIMPSET software supplied by Televilt, Sweden. The collars had a unique GPS signal acquisition schedule, which provided coverage every four hours over a 24-hour period. However, we programmed 2 of the 2000 model year collars to collect data every 2 hours in a 24-period or a possible 12 locations each day. All collars also had a VHF beacon that was active when the GPS system was not receiving a signal. These collars also had the capability to transmit stored data at specific programmed times. We programmed the Televilt collars to transmit stored data on a monthly basis. Each collar had a unique upload time and would repeat the transmission of data on four consecutive days. If data were not collected on any of these four days (poor weather affecting flying, unable to locate bear, etc.) the collar would store the monthly data in permanent memory. This data could only then be recovered by retrieving the collar and downloading the data directly from the collar. Each collared bear also had a VHF ear tag transmitter (Advance Telemetry Systems, MN) attached to one ear. These ear tag transmitters were programmed to start transmitting in October 1999 on bears with Televilt radio collars. We programmed this schedule in order to reduce the possibility of signal interference when the GPS collars were transmitting data on the VHF frequency. Each ear tag transmitter had a VHF frequency that matched the GPS collar. This approach was taken to reduce the number of frequencies that would have to be monitored during aircraft telemetry flights.

Data files from these collars were converted to a text file which included the following: date, time, lat/long, DOP and 2D/3D.

• Advanced Telemetry Systems (ATS) GPS radio collars: We deployed 9 ATS GPS

collars which are 12 channel systems produced by ATS, Minnesota. These collars have on board systems where all GPS data is stored within the memory of the collar and the researcher must recover the collar to download the location data. These collars weighed 1.77 kg and included the Wildlink remote drop off system. This system allows the researcher to remotely trigger the collar to release from the animal and then it can be recovered to retrieve the stored GPS data. All ATS collared bears also had a VHF ear tag transmitter with a duty cycle of 15.5 hrs “ON” during the day and 8.5 hrs “OFF” during the evening. The canvass “rot off” was not required on the ATS collars because the Wildlink remote drop off system allowed us to retrieve these collars.

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The ATS collars were also programmed to collect a location every 4 hours. These collars had a pre-programmed system that allowed the GPS unit to retry getting a GPS fix every 15 minutes if a location attempt was unsuccessful. This would continue for a maximum of 3 attempts. If no locations were obtained then the unit would revert to the original acquisition schedule.

Data files from ATS collars were converted to a text file which included the following: date, time, UTM, elevation, 2D/3D, DOP and which satellites were acquired to obtain the fix. Figure 6 shows the distribution of the two types of GPS radio collars across the study area.

• Data Uploads, Collar Retrieval and Data Processing

Uploads were required for the Televilt collars only and uploads generally occurred during the last four days of every month. We utilized helicopters to upload data from the GPS Televilt collars, and on some occasions where topography dictated we were able to upload data from the ground. The helicopter uploads required the aircraft to circle the general location of the collared bear for this time period at an altitude of approximately 1500 ft above ground level. Once the uploading was completed the data was downloaded to a computer using Televilt’s RXD program and then converted to a temp file using Televilt’s Simpost program. The temp file was subsequently processed to eliminate obvious errors in the data stream using a text editor and then converted to a text file.

Collars were retrieved whenever we encountered a mortality signal during upload flights. Furthermore, in October a systematic effort was made to retrieve the ATS collars. A fixed-wing aircraft (Cessna 336) was used to locate all the bears. The collar was then retrieved using a helicopter. The data from both the retrieved Televilt and ATS collars was downloaded directly to the computer and then converted to text files. No post-processing of data was required for information taken directly from the collar.

7.2.2 Results Of the 21 collars deployed, 14 have been retrieved (Table 3). Currently, there are 7 bears collared. Three of these bears have had their original collar replaced with a new one and 4 bears continue to wear their original collars. The location of 3 other bears is unknown and the status of their collar is also unknown. Fifteen bears still have their ear tag transmitters attached and working. A total of 7699 locations have been collected from 19 bears, including 13 females and 6 males, in 2000. This includes all data both from retrieved collars and uploads (Table 4). This is an improvement from 1999, in which we obtained only 5554 locations from a total of 13 bears. When the two years are combined we have 13,353 locations for 28 different bears. In 2000, the sample size per bear ranged from 35 - 919 locations (Table 4). This large variation was primarily a result of some bears dropping their collars shortly after capture and individual collar performance. Collars remained on the bear between 22 - 210 days. When both collar types were combined the average number of fixes per day was 3.29. Considered separately, ATS averaged 3.56 fixes/day, and Televilt averaged 3.12 fixes/day. This number of fixes/day

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is reduced from 1999, in which the average number of fixes/day was 3.71 (Stenhouse and Munro 1999). The reason for this is unclear but likely resides with the manufacturing aspect of the collars. Table 3. Current status of the GPS collars and ear tag transmitters in the grizzly

bear research program.

Recovered On Bear Unknown Total ATS 7 0 2 9 Televilt 7 7 1 15 Ear Tags 3 15 4 22

Table 4. Total number of locations obtained from the GPS collars in the grizzly

bear research program during 2000.

Bear ID

Collar Type

Date of Capture

Date of drop-off

Total locations before drop-off/denning

No. of days collar on bear

Average no. locations/day

G001 Televilt 4/19/00 9/19/00 629 153 4.11 G002 Televilt 5/5/00 (den) 11/19/00 267 198 1.35 G004 Televilt 4/26/00 7/17/00 321 82 3.91 G006 Televilt 5/11/00 Unknown 35 G007 Televilt 3/22/00 7/8/00 160 108 1.48 G012 Televilt 3/27/00 7/21/00 382 116 3.29 G016 Televilt 6/17/00 (den) 11/19/00 296 155 1.91 G020 Televilt 4/11/00 6/28/00 448 78 5.74 G026 Televilt 5/10/00 9/24/00 246 137 1.80 G027 Televilt 5/17/00 (den) 11/4/00 548 171 3.20 G033 Televilt 6/19/00 (den) 11/28/00 919 162 5.67 G034 Televilt 7/17/00 10/28/00 193 103 1.87 G010 ATS 5/28/00 10/20/00 618 145 4.26 G011 ATS 5/25/00 9/11/00 206 109 1.89 G014 ATS 5/16/00 9/8/00 283 115 2.46 G017 ATS 5/23/00 6/14/00 77 22 3.50 G023 ATS 4/28/00 (den) 10/24/00 623 179 3.48 G024 ATS 5/9/00 9/24/00 586 138 4.25 G028 ATS 5/8/00 (den) 10/24/00 862 169 5.10 G029 ATS 5/18/00 Unknown G030 ATS 5/23/00 Unknown Total 7699 2340 3.29

• GPS performance testing

GPS collars provide a rich data set. However, there are two primary problems (1) inaccurate locations and (2) missing locations. Both problems may arise as a result of the physical environmental characteristics which, in turn, affect the ability of the GPS collars to obtain a location. Furthermore, such problem may lead to bias estimates of habitat selection, home range determination, and movement analysis. The objective of this component is to document vegetation and terrain induced bias on the acquisition rate and accuracy of locations obtained by the collars. Specifically, we hope to document collar bias and performance based on habitat type, terrain, and collar type. In addition, we hope to be able to correct for inaccurate locations by

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determining error polygon size and to correct for missing locations using logistic regression models where the inverse weights of the RSF probability are used for the correction term. The study area is stratified by habitat and terrain features. The habitat classes include open habitat (alpine, clear cuts), open forests (deciduous, conifer, mixed) and closed forests (deciduous, conifer, mixed). The terrain will ultimately be modelled using a sky visibility index, however, plots are initially selected based on the degree of topographical ruggedness (low, medium, high). The collars were placed 1 m above ground level, and collars were programmed to get fixes every ½ hr for 24 hrs. Several forest attributes were measured at each plot including, tree species, tree height, canopy closure, tree density, and shrub density. In addition, a sky visibility index was calculated for each plot UTM using ArcView. This index is representative of the amount of sky visible to the collar from its location. A Trimble unit was used to obtain the true UTM for the plot location.

A total of 50 plots have been completed to date (Figure 7). However, another 50 plots are required to complete our sampling design. Work is planned for the summer of 2001. Analysis of the current data has not been completed but preliminary analysis will be completed before the start of the 2001 field season.

Figure 7. Map of completed collar performance plots in the grizzly bear research

program area in 2000.

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7.3 Home Range 7.3.1 Methods and Results The annual 100% MCP home ranges were calculated using the Animal Movement Extension (Hooge and Eichenlaub 1997) for ARCVIEW GIS (ERSI Inc.). Table 5 denotes the average home range size for both sexes across years. It should be noted that some collars had fallen off bears early in the season and consequently annual home ranges for these animals will be severely underestimated. Also the multi-annual home ranges for 1999 and 2000 combined were calculated only for bears with two years of data. Table 5. Annual 100% MCP home ranges for collared bears in the grizzly bear

research program area in 1999 and 2000.

1999 2000 1999/2000 Mean (km²) Range (km²) Mean (km²) Range (km²) Mean (km²) Range (km²)

Female 671 102-2049 832 73-2980 668 471-1090 Male 962 436-1588 1611 217-3643 1733 1165-2406

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8. Analysis of the 1999 DNA Mark-Recapture Hair Data prepared by: John Boulanger, Integrated Ecological Research, Nelson, BC 8.1 Introduction The main objective of this report is to provide further insight into estimates from the 1999 Foothills DNA Grizzly Bear mark-recapture project through the use of program MARK (White and Burnham 1999) and other advanced mark-recapture analysis methods. Application of mark-recapture methods to grizzly bear populations is challenging due to sparse bear densities and potential large scale movements of bears relative to mark-recapture sampling grid size. Meeting the assumption of population closure, minimizing capture probability variation, and ensuring adequate sample sizes determine the degree of bias and precision of mark-recapture population estimates (White et al. 1982). If closure is not met then the mark-recapture estimate refers to the “superpopulation” of bears which inhabit the grid and surrounding area (Kendall 1999). For density estimates the “Average N” or average population estimate is often desired which is usually derived using estimates of movement from radio collared bears. If capture probability variation is present in mark-recapture data then more sophisticated mark-recapture models are needed to ensure reliable estimates. The cost to robust models is usually reduced precision due to larger numbers of parameters being estimated. This report provides further scrutiny into whether population closure was met, and whether the corrections provided were adequate. In addition, further analysis into biological causes of capture probability variation is given. The investigation of capture probability issues was relevant to the 1999 Foothills project due to the sparse nature of the data. Sixty four 9 by 9 km cells were sampled in which one bait site was placed in a new location for three sampling sessions. The third session was compromised by heavy snowfall and therefore only two sessions effectively captured bears. The result of this was lower power of the program CAPTURE model selection routine to pick adequate models, and low precision of estimates. Alternative estimation strategies and commentary on three session sampling designs are given in this report. Details regarding field methodology and initial analysis strategy can be found in Mowat et al. (2000). 8.2 Methods The analyses conducted can be broken down into the objectives of evaluation of population closure, exploration of capture probability variation, and estimation of population size. 8.2.1 Population Closure • GPS collar-based evaluation of closure

The correction of closure violation using the movements of radio or GPS collared bears is based upon the assumption that the radio collared bears are a random sample of the bears in the sampling grid and surrounding area (Miller et al. 1997.). This assumption was questionable with the 1999 Foothills GPS data due to the fact that

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most capture efforts were for bears on the sampling grid with little effort for bears outside of the grid. A joint mark-recapture telemetry analysis as first discussed by Powell et al. (2000) was used to estimate the degree of movement in and out of the sampling grid. This approach defines the grid and outside of grid as individual strata and then estimates the transition probabilities of movement between the two strata. More exactly, it estimates the probability that a bear that is on the grid will move off the grid and the probability that a bear that is off the grid will move on the grid.

The analysis was conducted using multi-strata models in program MARK. Unlike the analysis proposed by Powell et al. (2000), this analysis considered only movements of GPS collared bears and did not consider DNA bears. The capture probabilities, and survival of GPS bears were fixed at 1 since the survival and capture probabilities of GPS bears were not relevant for the analysis. This reduced the analysis to only the estimation of movement probabilities.

The main objective of model building in program MARK was the comparison of movement estimates for 1999 (when the DNA project was conducted) and 2000. More bears were collared in 2000 which were on the outskirts of the grid and therefore it could be argued that 2000 might be a more representative sample of bears than 1999. The GPS record were subsetted for DNA sampling occasions (May 19-June 1, June 2- June 15, and June 16-July 9) for both 1999 and 2000. Models were built which constrained movement rates to be similar and different for 1999 and 2000. In addition models were built which constrained movement in to equal movement out.

The fit of models was evaluated using the Akaike Information Criterion (AIC) index of model fit. The model with the lowest AICc score was considered the most parsimonious thus minimizing estimate bias and optimizing precision (Burnham and Anderson 1998). Delta AICc (∆AICc) values were also used to evaluate the fit of models when their AICc scores were close. In general, any model with a Delta AICc score of less than 2 was most supported by the data. Any model with a Delta AICc score of less than 4 was still worthy of consideration, but less supported by the data. Any model with a Delta AICc score of greater than 4 was not supported by the data. Given the sparseness of data, model averaging of estimates using AICc weights was used to confront model selection uncertainty (Burnham and Anderson 1998). Model averaging allows the estimates of all the models used in the analysis to be considered and therefore provides a more robust estimate than traditional estimates based on single models. In addition, evaluation of estimates and associated error provides a more realistic evaluation of parameter estimates than traditional hypothesis tests (Burnham and Anderson 1998). See http://www.cnr.colostate.edu/~anderson/quotes.pdf for more details on the perils of hypothesis testing.

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8.2.2 Capture Probability Variation • Pradel model analysis of interaction between closure and capture probability

variation. The distinction between capture probability variation and closure violation is artificial given that closure violation causes distinct forms of capture probability variation. For example, negative bias in capture probabilities is caused by closure violation which results in positive biases of population estimates (Kendall 1999). Pradel models (Pradel 1996) were used to determine the degree of closure violation, and examine the interaction of closure violation with sex specific capture probabilities. Pradel models estimate apparent survival (φ) of marked bears, the rate of recruitment or additions (f) for new bears to the data set, and the recapture rate of marked bears. Because demographic closure (i.e. no mortalities or births) can be assumed for the short duration of sampling, estimates of apparent survival reflect marked bear fidelity to the sampling grid area, and estimates of recruitment reflect additions of new bear to the sampling area (Boulanger and McLellan Submitted).

Using the general methods of Stanley and Burnham (1999), the Pradel model was constrained to various forms of open and closed mark-recapture models. More exactly apparent survival was fixed (parameter fixed at a priori value so that it is not estimated) at 1 so that the Pradel model pertained to a “No removals” open model. Additions was fixed at 0 so that the Pradel model pertained to a “No additions” open model. Both additions and apparent survival was fixed so that the Pradel model pertained to the closed model Mt used in program CAPTURE (Otis et al. 1978). Finally, a model in which all parameters were estimated was run which pertained to the fully open Jolly Seber model (Seber 1982).

In addition, bear recapture rates were modelled as function of mean individual bear distance of capture from the grid edge (as a continuous covariate). The premise behind this test is that if closure violation is occurring, then bears that were captured close to the grid edge are more likely to exhibit reduced rates of addition, fidelity, and recapture rate (Boulanger and McLellan Submitted). The logistic curve used in program MARK to model covariates is restrictive in shape and therefore distances from edge variables were log (+1) transformed, and higher order polynomial terms (i.e. distance2) were entered into the analysis to generalize the logistic curve shape (Boulanger and McLellan Submitted). As with the multi-strata analysis, a variety of likely models were introduced into the analysis and the overall fit of models was tested using AICc methods. Data was too sparse to test goodness of fit of model and therefore AICc methods were used for model selection under the assumption that overdispersion of binomial variances in the data set was not large (White et al. 1999).

• Huggins closed model analysis of sex and GPS bear based capture probability

variation. The identifiable sources of capture probability variation in this data set are sex of bear, and whether a bear was GPS collared. Bears were grouped according to these classes and the relative degree of difference due to each factor was evaluated using the Huggins closed model (Huggins 1991) in Program MARK. The Huggins model estimates bear capture probability (p), and recapture rate (c). Recapture rate in the

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case of closed model refers to the capture probability conditional on initial capture, which is used to model behavioural response to trapping. As with the Pradel analysis, distance of mean capture from the sampling grid was also modelled as a covariate since closure violation will directly affect estimates of capture probability (Kendall 1999). Models were selected using AICc methods as described for previous analyses. Model averaged population estimates were produced from this exercise and compared with other estimates as explained further in Section 8.2.3.

• Logistic regression analysis of the relationship between GPS collared bears home

range and capture probabilities One main factor that has been proposed to explain heterogeneity in capture probabilities is difference in bear capture probabilities brought upon by differential trap encounter rates. To explore this relationship, the home range areas and movement rates of GPS collared bears were estimated for locations taken during DNA sampling and logistic regression was used to estimate potential relationships with bear capture rates. Capture rates were indexed by the proportion of sampling sessions in which a bear was captured. Kernel (Worton 1989) methods of home range estimation as implemented in the Home Ranger (Hovey 1999) were used to estimate home range sizes. A grid resolution of 90 and cross validation estimation of smoothing parameters was used of kernel home range calculations as implemented in Home Ranger. Movement rates were calculated for each location and associated time interval and averaged for each bear. Sex and age of bear were also considered in the analysis. Only GPS locations with DOP values equal to or less than 5 were used in the analyses.

Logistic regression was conducted using proc GENMOD in SAS (SAS Institute 1997). A SAS macro developed by the author of this report was used to generate AICc values for model selection.

8.2.3 Population Estimates • Superpopulation estimates

Prior to DNA sampling in 1999, substantial effort was given to capture of bears for GPS collaring. This effort was approximately stratified throughout the Foothills DNA grid area by Bear Management Unit. Mowat et al. (2000) suggested that this initial effort, which identified 5 bears not captured in DNA efforts, could be used as an initial mark-recapture session to potentially boost estimate precision.

The Huggins models in Program MARK were used to further investigate the four session estimation strategy. For this exercise, captures of GPS bears which occurred before the first DNA session were considered captured in a first sampling session. In addition, GPS bear captures for collaring that occurred during the DNA sampling also were considered as captured. The actual assignment of GPS bears to capture session was undertaken by Mowat et al. (2000) based upon dates of trapping for collaring, and concurrent DNA bait site activity. These records were combined with the DNA bear captures to form a 4 session data set.

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One immediate issue was whether the DNA bears had an opportunity to be captured during GPS collaring efforts prior to the DNA inventory. A model was therefore formulated in which DNA bears had zero capture probability for the first session. The fit of this model was compared with models which considered equal capture probabilities between DNA and GPS bears for the pre-DNA sampling session. In addition, other potential factors such as distance from edge of bear capture were also considered in formulation of models. AIC model selection and associated model averaging techniques were used to obtain population estimates. Population size is a derived parameter with the Huggins model and therefore model averaging was done in Excel using the formulas of (Burnham and Anderson 1998). Confidence intervals from model averaged estimates were generated using formulas which account for the minimal number of animals present as described in White et al. (1999). Estimates from this exercise were compared to 4 session CAPTURE estimates, and 3 session Huggins and CAPTURE estimates.

• Average number of bears on grid estimates

The superpopulation estimates were scaled for bear residency using the proportion of sessions that each bear was present on the sampling grid as introduced by (Kenward et al. 1981). The scaled “Average N” estimate is simply the average proportion of sessions bears that were resident multiplied by the superpopulation estimate. Variances for the Average N estimate were estimated by combining superpopulation and Kenward proportion variances using the Delta Method (Seber 1982) under the assumption of minimal correlation between Kenward proportions and point estimates of superpopulation. As before, confidence intervals from model averaged estimates were generated using formulas which account for the minimal number of animals present as described in White et al. (1999).

8.3 Results 8.3.1 Population Closure • GPS collar based analysis.

Twelve (1999) and 14 bears (2000) with GPS collars gave enough locations to allow estimates of movement rates on and off the grid in 1999 and 2000 respectively. The distribution of radio collared bears in 1999 was much more confined to the sampling grid compared to 2000, as indicated by a plot of GPS radio collar locations sub-sampled for the time of DNA sampling (Figure 8). This result suggests that bears on the eastern edge of the sampling grid were under sampled in 1999 compared with 2000. The DNA locations roughly followed the distribution of radio collared bears with the exception of the presence of one DNA bear in the northwestern corner with no corresponding coverage of GPS bears.

AIC model selection results suggest that there are minor differences between years in terms of movement in-out and out of into the grid. The actual model selection is “tied” as indicated by Delta AICc values of less than 2 for the top three models. Given the closeness of values, we compared year specific estimates for movement probabilities (Figure 9). From Figure 8 it can be seen that the probabilities of movement from the grid area was very low, basically, few bears left (emigrated) and

26

bears that did leave immediately returned (immigrated). The estimate of immigration in this case is more of an estimate of fidelity to the grid area since no bears were outside of the grid when the project began. In 2000, there were lower probabilities of immigration and higher probabilities of emigration suggesting lesser bear fidelity to the grid areas. In both years all bears were sampled on grid area. This fact, combined with the higher rates of immigration (fidelity) suggest that the sample of GPS bears is based more upon resident than non-resident or edge bears.

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- Each color and marker type represents an individual GPS bear - Locations are only for the period of DNA sampling in 1999( May 19-July 9) - For the DNA map, the blank dots represent sites which did not capture bears, the black dots represent 1 bear, red

squares, 2 bears, and red triangles 3 bears for the entire sampling effort Figure 8. GPS collar locations for 1999 (left) and 2000 (right) and DNA sites which

captured bears in 1999 in the grizzly bear research program area

27

Table 6. Model selection results for GPS collared bear movement analysis in the grizzly bear research program in 2000.

Movement Model Parameters Model Selection Results

Rate In-Out Grid Rate Out-In Grid AICc ∆AICc Num. Par. Deviance AICc Weights

Pooled Pooled 41.10 0.00 2 10.07 0.365 Yearly Pooled 41.36 0.25 3 8.06 0.322 Pooled Yearly 42.85 1.75 3 9.56 0.152 Yearly Yearly 43.20 2.10 4 7.56 0.128 Year* session Year* session 46.58 5.48 7 3.24 1.024 Equal to Out-In Equal to In-Out 50.00 8.89 1 21.12 0.004

00.20.40.60.8

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1999 2000

Year

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ty p

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on

emigration immigration

Figure 9. Transition probabilities per session of GPS bears emigrating (moving out

of grid area) and immigrating (moving into grid area) in the grizzly bear research program in 1999 and 2000.

8.3.2 Capture Probability Variation • Pradel analysis

One general trend in the data set was the low number of captures in the third session of sampling due to snowfall reducing bait site success. Therefore, it was biologically likely that time variation in recapture rates, fidelity of marked bears, and rates of addition of marked bears to the data set existed. Therefore models were built which considered time variation in each of the Pradel parameters. The most supported model (Table 6) pooled recapture rates for sessions 1 and 2 (denote t12 in Table 7). Session 3 was modelled as a separate parameter (denoted t3). Sex specific analysis was problematic due to sparse data and poor model covergence (Table 7, model 15), and therefore sex was pooled for the majority of the analysis. This should not be interpreted to mean that sex specific differences do not exist in the data sets, instead, it means that the data set is too sparse too support sex specific estimation of the three principal parameters (fidelity, recapture rate, additions). Simplified Huggins (1991)

28

closed models are presented later which address sex specific differences in recapture rates.

Table 7. Model selection results for Pradel analysis in the grizzly bear research program in 1999 and 2000.

Model # Parameters Model Selection Results Fidelity Recapture

Rate Additions AICc ∆AICc Num. Par

1 11 t12 + d,d2 2 01 130.7 0.00 4 2 1 t12 + ld 2 0 131.0 0.28 3 3 1 t12 + ld,ld2 0 131.1 0.41 4 4 1 t12 0 131.2 0.48 2 5 1 t12 t1 t2 t3 132.2 1.48 3 6 1 t12 + d,d2 (.) + d,d2 133.1 2.42 7 7 (.)3 t12 0 133.5 2.75 3 8 (.) t12 t1 t2 t3 134.4 3.73 4 9 1 t12 (.) + d,d2 135.8 5.08 5 10 (.) (.) (.) 136.9 6.16 3 11 (.) t1 t2 t3 (.) 136.9 6.19 5 12 (.) + d,d2 t12 (.) 137.8 7.08 6 13 (.) t12 (.) + d,d2 137.8 7.09 6 14 t1 t2 t3 t1 t2 t3 t1 t2 t3 139.5 8.80 6 15 1 (sex*tt12) 0 182.6 51.88 3 1Fidelity or additions fixed at 1 or 0 2Distance from edge (d) or log(+1) distance from edge (ld) as a continuous covariate 3Parameter held constant

The most supported model was model Mt (fidelity fixed at 1 and additions fixed at 0) with recapture rate modelled as a continuous covariate. The actual covariate models were tied (i.e. Models 1-4) suggesting that there was not enough resolution in the data set to determine the most supported model. The most biologically reasonable model was model 3 which modelled distance from edge as a quadratic log transformed polynomial (Figure 10). This model suggests that recapture rates of marked bears increased as a function of distance from edge. The results of this model should be interpreted cautiously given that it is tied with other models including closed model Mt (Table 7 Model 4) which does not consider covariates and considers the population completely closed. As discussed later, this is a chronic problem associated with sparse data sets in which power or resolution to discern trends is limited. Given these limitations, the general trend from this analysis is that permanent additions is low and fidelity high to the sampling grid. Much of the movement from the sampling grid is temporary emigration as reflected in sensitivity of recapture rates to the distance of marked bear capture from sampling grid edge.

29

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ate

Figure 10. Pradel estimates of recapture rate of marked bears as a function of mean capture distance from edge of the sampling grid in the grizzly bear research program in 1999 and 2000. Estimates are from Model 3 in Table 8.

• Huggins closed model analysis of sex and GPS collar capture probabilities

The Huggins analysis considered the effects of sex, GPS capture, and distance from grid edge where individual bears were caught on capture probabilities. The main focus of model building was attempting to explain the variation within the data set with the least number of model parameters. One large source of variation was the pronounced decrease in capture probability in session 3 due to snow. This effect was experienced by all groups in the analysis. Therefore, a common session 3 capture probability parameter (denoted t3) (Table 7) was introduced into the analysis which estimated the capture probability of all the groups for session 3. Capture probabilities were pooled for sessions 1 and 2 (denoted t12) (Table 7) but estimated separately for each group in most analyses. Log transformed and standard covariates were used in the analysis. The fit of log based covariates was better than standard covariates and therefore log transformed covariates were used in all analyses.

Two models were most supported by the data set as indicated by Delta AICc values of less than approximately 2. The most supported Huggins models pooled all classes (Table 8, Model 1) suggesting that the difference between GPS and sex based classes did not warrant a more complex estimation model. Also, a model which modelled capture probability as a function of sex, presence of GPS collar, and mean distance from edge was also supported as indicated by a Delta AICc value of 2.68. In addition, models which did consider sex (Table 8, Models 4 and 5) and GPS/DNA (Table 8, Models 3 and 4) specific and distance from edge of capture specific capture probabilities (Table 8, Model 4) were still marginally supported by the data as

30

indicated by Delta AICc scores of less than 4. The reason for tied models, as discussed later, is sparse data. Model averaging of estimates by AICc weights was therefore used to incorporate model selection uncertainty into class specific capture probability estimates.

Table 8. Huggins closed model selection results in the grizzly bear research

program in 1999 and 2000. Model # Model Parameters Model Selection Results Sex GPS Covariate2 Time

variation AICc ∆AICc AICc

Weights Number

Parameters 1 Pooled

with GPS Pooled with sex

t12, t3 262.52 0.00 0.462 2

2 separate Separate (sex,gps)+ld ld2

t12, t3 265.21 2.68 0.121 7

3 separate Separate GPS+ld ld2 t12, t3 265.70 3.18 0.094 7 4 pooled Separate t12, t3 266.00 3.48 0.081 4 5 separate Pooled t12, t3 266.49 3.97 0.064 4 6 separate Separate t12, t3 266.95 4.43 0.050 5 7 pool Separate GPS+ld ld2 t12, t3 267.06 4.54 0.048 6 8 separate Separate sex+ld ld2 t12, t3 267.85 5.32 0.032 7 9 pool Separate 269.57 7.05 0.014 3 10 separate Separate 270.71 8.19 0.008 4 1Separate means that each group (male/female or GPS/DNA) had its own parameter; pooled means the groups were pooled. 2 Log based covariates

Model averaged estimates suggest reasonable differences between male, female, and GPS/ DNA bears respectively (Figure 11). Figure 12 shows estimates from Model 2 in which capture probabilities are a function of sex, GPS/DNA and distance from edge. It can be seen that all factors had significant effects of capture probability, with the largest capture probabilities seen by GPS males and the lowest capture probabilities being exhibited by DNA females. Also, it is evident that most of the GPS bears were captured at further distances from edge than DNA bears, further supporting the previous conclusion that GPS bears were more representative of resident “core” bears whereas DNA bears were composed of resident “core” bears and edge bears. However, the results of this analysis suggest that factors beyond mean location of capture caused differences in capture probabilities between DNA and GPS bears.

31

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Figure 11. Model averaged estimates of sex specific and bear type from Huggins

model analysis in the grizzly bear research program, as presented in Table 8, in 1999 and 2000.

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Figure 12. Huggins closed model analysis estimates of GPS, DNA, male, and female specific capture probabilities as a function of distance from edge of mean bear DNA capture location in the grizzly bear research program in 1999 and 2000. • Logistic regression analysis of capture probabilities and home range area.

In general, sparseness of data (10 GPS bears and 3 sample sessions) prevented solid detection of trends in the data set. The most supported model was an intercept only model suggesting that the relationship between capture rate and any of the predictor variables was poor. Models with 50% and 95% home range area, and the interaction

32

of home range area with age were also supported by the data. However, the results of this analysis should be considered exploratory given the tied nature of candidate models in the analysis.

Table 9. Model Selection Results: Logistic Home Range Analysis in the grizzly bear

research program in 1999 and 2000. Model

AICc

∆AICc

Number of parameters

Log-likelihood

1. Intercept only 39.81 0 1 -18.62 2. HRA (50%) 41.15 1.34 2 -17.57 3. HRA (95%) 41.18 1.37 2 -17.59 4. HRA (50%) X Age 41.40 1.59 3 -17.70 5. HRA (25%) 41.65 1.84 2 -17.83 6. Movement rate 42.48 2.67 2 -18.24 7. Age 43.33 3.52 2 -18.67 8. HRA (50%) X Age X sex 45.35 5.54 3 -17.28 9. Sex 45.51 5.70 2 -17.76 10. HRA (50%) X sex 45.94 6.13 3 -17.57

One interesting result was potential support for a model with the interaction of age of bear and 50% home range area as a predictor of capture probability (Table 9, Model 4) (Figure 13). This model, which is more supported than a sex and home range area interaction model (Table 9, Model 10), suggests that capture probability increases with bear age and 50% home range area. It could be speculated that this is due to bear height relative to bait station barbed wire snag height as suggested by Woods et al. (1999). It can be seen that the sample of bears is not random with few male bears with smaller home ranges and no females with larger home ranges. Therefore, age and sex specific effects cannot be completely separated by this analysis. It is emphasized that these results should be interpreted cautiously given this restriction and low sample sizes. As discussed later, hopefully more substantive data can be collected in the future to verify this interesting relationship.

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Figure 13. Predicted capture rate as a function of 50% kernel home range size and

age in the grizzly bear research program in 1999 and 2000. Sex is also shown (large squares males, small squares females).

No females with cubs of the year were collared during DNA sampling in 1999. However, a female with cubs of the year was collared in 2000 and Figure 14 compares its home range size with bears in 2000. The red line in Figure 14 is the predicted trend line from Model 2 in Table 9. Note that the position of home ranges for females with cubs is based upon extrapolation of the relationship derived from 1999 data in which there were no females with cubs. Therefore, the position of these points is based on the assumption that home range size is the only factor determining capture probabilities. As discussed later, further investigation of capture probabilities of females with cubs is needed to actually estimate capture probabilities for this class.

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0.00

0.20

0.40

0.60

0.80

1.00

1.20

0 100 200 300 400

Kernal 50% H.R.A.

Prop

ortio

n se

ssio

ns c

aptu

red

Predicted femalesmales female with cub (2000)

Figure 14. Proportion sessions sampled as a function of sex and home range size for

the grizzly bear research program in 1999 and 2000. Home range size for females with cubs for 2000 is also shown.

8.3.3 Population Estimates • Superpopulation estimates

The main objective of the Huggins analysis was to determine potential gains in precision and potential biases caused by considering the inclusion of GPS bear captures in the DNA hair snag data set. Models were built which constrained DNA bears to have 0 capture probability in the first session, therefore allowing the inclusion of extra bears identified in GPS collaring efforts without making the assumption that DNA bears had significant capture probabilities for the pre-DNA sampling session. The fit of this model was contrasted with other likely models. As with previous modelling efforts, capture probabilities for session 3 were pooled across all groups therefore accounting for time variation with minimal increase in model complexity.

A model in which DNA bears had 0 capture probability for session 1 was most supported by the data (Model 1). Competing models included models which estimated capture probability for DNA bears for session 1(Model 2) and a covariate model (Model 3). As with previous analyses, model averaging was used to obtain parameter estimates.

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Table 10. Results of 4 session Huggins closed model analysis in the grizzly bear research program in 1999 and 2000.

Model GPS DNA Session 3? Covariate Model Selection Results Main

Terms Time

Variation Main

Terms Time

Variation AICc AICc Number

Parameters 1 Group t0,t1-t2 Group t0@0, t1-2 183.17 0.00 4 2 Group t0,t1-t2 Group t0, t1-2 Pooled 185.28 2.11 5 3 Pooled t0,t1-t2 Pooled t0, t1-2 Pooled ld ld2 185.61 2.44 5 4 Group t0,t1,t2,t3, Group t0,t1,t2,t3, Pooled 190.70 7.53 8 5 Pooled t0,t1,t2,t3, Pooled t0,t1,t2,t3, Pooled 228.28 45.11 4 6 Group t0@1,t1-t2 Group t0@0, t1-2 Pooled 357.62 174.45 3

Superpopulation estimates from various models are contrasted in Table 11. In general it can be seen that models which use the primary DNA sessions showed slightly reduced precision as reflected in CV values when compared with 4 session estimates. The actual difference between estimates is not large when compared to the large confidence interval size. However, estimators such as model Mt (Table 11, Models 2,6,7) that do not model heterogeneity show reduced estimates when compared to other estimators which is most likely due to heterogeneity causing negative bias of point estimates of population size, and standard error of population size (Otis et al. 1978). One interesting result is the agreement between model averaged estimates from the 3 (Table 11, Model 3) and 4 session (Table 11, Model 5) data sets. Model averaging considers selection of all models considered in both point estimates and error estimates. By doing this, the robustness of the ultimate estimates is theoretically increased over single model estimates. Therefore, the model averaged estimates are potentially the most reliable of all estimates considered.

Table 11. Superpopulation estimates for the 1999 DNA hair mark-recapture inventory in the grizzly bear research program.

Model Sessions Model/Estimator N̂ SE Confidence Interval CV

1 3 Capture Mth 98 46.24 56 270 47.2% 2 3 Huggins Model Mt31 77 14.5 56 122 18.8% 3 3 Huggins Model Averaged2 95 38.3 65 164 40.1% 4 4 Capture Mth 107 29.8 71 198 27.9% 5 4 Huggins Model averaged3 96 30.7 71 151 31.8% 6 4 Huggins Mt 83 12.0 66 117 14.5% 7 4 Capture Mt 75 15.9 56 122 21.3%

1Model 1 in Table 3. 2Model averaged estimates from Table 3. 3Model averaged estimates from Table 5. • Average number of bears on the sampling grid estimates

The Kenward correction was applied to the model averaged Huggins superpopulation estimate (Table 11, Model 5) to obtain an estimate of the average number of bears

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present on the sampling grid at any time. Estimates are given for GPS points obtained in 1999 and 2000 to assess the impact of the potential non-random spatial sample of GPS bears.

Table 12. Average N estimates using the Kenward Correction in the grizzly bear research program in 1999 and 2000. Year1 Superpopulation2 Kenward correction Average N estimate Estimate SE % on grid SE Estimate SE Conf. interval CV 1999 96 30.7 96.97% 10.05% 93 31.29 66 147 33.6% 2000 96 30.7 83.33% 25.32% 80 35.29 53 145 44.1% 1Year in which GPS location obtained 2Huggins model averaged superpopulation estimate from 4 session data set (Table 6, Model 5).

As expected, the estimate using GPS points from 2000 was lower than the 1999 estimate (Table 9). Of these, the more reliable is probably from 2000 given the lack of coverage of GPS bears on the eastern border in 1999 as discussed earlier in the report. The Average N estimate can be converted to a density estimate of 14.9 bears/1000 km2 (SE=6.5, CI=9.9-27.1) by dividing by the grid sampling area (5352.35 km2).

8.4 Discussion 8.4.1 Capture Probability Analyses The analyses in this data set are compromised by sparse data in terms of the number of sampling sessions and the number of radio collared bears. However, some interesting trends are still apparent in the data that are unique when this project is compared to the results of other DNA projects in British Columbia. First, GPS collared bears exhibit higher capture probabilities than DNA bears (Figure 11). The only project in BC (West Slopes) which had radio collared bears exhibited the exact opposite trend with radio collared bears showing lower capture probabilities than DNA bears (Woods et al. 1999). Second, male bears exhibited much higher capture probabilities than female bears (Figure 11) which again is a unique result when compared to British Columbia projects (Boulanger, In prep). In most BC projects, there have been observed differences between male and female capture probabilities, but in all cases models which pooled sex specific capture probabilities were most supported by the data suggesting that sex-specific effects are not large, or are diluted by the wide range age classes within each sex class. Finally, an interesting potential relationship between home range size, bear age, and capture probabilities is suggested from logistic regression analysis (Figure 13). There are a variety of biological reasons factored into the observed differences between GPS and DNA bears. The difference between DNA and GPS bears can be partially explained by the fact that GPS bears were comprised mainly of “resident” bears whereas DNA bears were composed of “resident” and “edge” bears. Given this, the resident bears had more opportunity to be sampled hence the higher capture probabilities.

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Differences in capture probabilities between sexes is less clear. One speculative reason is that the sparse trap coverage (3 bait sites over 81 km2) exacerbated differences in trap encounter between males and females so that males encountered more traps and exhibited higher capture probabilities. Preliminary research into the role of trap encounter in determining capture probabilities (using GPS movement data) suggests that males encountered 1.7 times as many traps as females which supports this hypothesis (Boulanger in prep). The potential relationship between age of bear, home range size, and capture probabilities suggested by logistic regression analysis is biologically justifiable (Figure 13). The height of barbed wire is mainly targeted at adult bears and therefore it has been suspected that younger bears may exhibit reduced capture probabilities. Unfortunately, sparse data, and an uneven distribution of sex, age, and home range make it difficult to verify this finding. Hopefully, future effort will allow further verification of this model. 8.4.2 The Effect of Sparse Data Sparse data compromises the conclusiveness of findings in many of the analyses. The addition of a fourth session based on GPS captures partially improves estimate precision, however, CV estimates are still above the target 20% level set by (Pollock et al. 1990). This result, and results of the projects in British Columbia suggest that there is no substitute for substantial effort if precise DNA mark-recapture estimates are desired. For example the Huggins Population estimates from the Prophet data (Boulanger and McLellan Submitted; Poole et al. In press)(which utilized 5 sessions), and the Jumbo Data set (Strom et al. 1999) (which utilized 4 sessions) set had CV’s of 8.6% and 16% respectively. 8.4.3 Correcting for Closure The results of the multi-strata analysis highlight the challenge of correcting studies for closure using GPS bear movements. Especially difficult is the determination of how representative the sample of GPS bears is of DNA bears. The Kenward correction is simplistic in that it does not consider the effect of closure on capture probabilities. A potential alternative is to generalize the multi-strata model in program MARK to estimate population size as a derived parameter (Gary White, per comm.). This would provide a more rigorous estimate of Average N than the Kenward correction of superpopulation estimates. Recommendations for future work are given in the next section. 8.4.4 Utility of Program MARK Methods and AIC Model Selection The results of this analysis highlight the utility of the AIC method of model selection and associated model averaging methods of confronting model selection uncertainty. Any mark-recapture model is a simplification of the actual forms of capture probability variation found within a data set. Model averaging helps confront this problem by considering all models, and weighting the contribution of models to estimates by the degree in which they are supported by the data set. This new method is especially useful

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for sparse data sets such as the one in this analysis. For example, it is likely that heterogeneity of capture probabilities is present in the Foothills data set, however, the data is too sparse for most selection methods or tests to have the “power” to absolutely detect sources of variation. Using only the most supported model for the 3 session data set (Table 8, Model Mt3, Model 1) would result in an estimate of 77 which is potentially negatively biased due to heterogeneity variation. Model averaging allows consideration of heterogeneity based models even though they are less supported resulting in a more robust estimate of 95 (Table 11, Model 3). Theoretically, this process leads to a more reliable estimate of parameters and their associated standard errors (Burnham and Anderson 1998). The results of this study also illustrate the utility of the Huggins and Pradel models in program MARK. The increased flexibility of these models allows more exacting exploration of the biological roots of capture probability variation than traditional methods. One potential weakness of the Huggins model is that it can only model heterogeneity variation which is identifiable, such as sex, or presence of collar. Other non-identifiable forms such as age-specific capture probabilities cannot be modelled, and therefore, the Huggins model estimate can potentially be biased by unidentifiable forms of capture probability variation. Therefore, the use of the more general CAPTURE heterogeneity models is still warranted. In the case of this study, there is no difference between 3 session estimates from CAPTURE model Mth (Table 11, Model 1) and the Huggins model averaged estimates (Table 11 Model 3) for the three session data set, and only marginal differences for the 4 session data set. This suggests that the Huggins model is reasonably efficient in terms of modelling heterogeneity variation, or that the most relevant forms of heterogeneity variation were identified. Recent likelihood based heterogeneity models which can model both identifiable and non-identifiable forms of heterogeneity (Norris and Pollock 1996) will be incorporated in program MARK in the near future (Gary White, per comm.). As discussed in the recommendations section, in may make sense to revisit estimates as part of a meta-analysis study of mark-recapture projects. 8.5 Suggestions for Future Work 8.5.1 Future DNA Mark-Recapture Work for Estimation of Population Size The results of some of the British Columbia projects demonstrate that population estimates of reasonable precision are possible if rigorous sampling regimes are applied. Therefore, one option to get a more reliable estimate for the Foothills grizzly bear program is to do another mark-recapture project with a modified sampling design. The following general suggestions are made about this design:

• Grid cell size should be reduced and the number of sessions increased. The optimal grid cell size should be considered in unison with the number of sampling sessions, and whether sites are moved between sessions. Results from meta analyses of British Columbia (Boulanger et al., In prep) suggest the following general guidelines. If sites are to be moved then a 7x7 cell size and at least four sessions should be employed. If sites are not moved then a 5x5 cell size and at

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least 4 sessions should be employed. It is suggested that more discussion be given to design if it is decided that another population estimation project is to be conducted.

• Reduction of grid size is a viable and potentially optimal strategy. The above designs are more costly than the original 9x9 three session design due to increased spatial coverage of terrain. One method to reduce cost is to reduce overall grid size. It is suggested that this is a reasonable strategy as long as the target population of bears is above 50 (optimally about 75) and there are a sufficient number of radio collared bears in the grid area during sampling. The rationale is that the radio collared bears can be used to account for potential closure violation caused by the smaller grid size. In addition, sampling a smaller grid may result in the GPS collared bear sample being a better representation of DNA bears if the DNA grid is in a central location of the Foothills study area. Using a smaller grid potentially circumvents the problem that collaring efforts have only targeted the Foothills study area because the smaller DNA grid would be surrounded by areas in which GPS bears were collared. The total population estimate for the entire Foothills area can be obtained by extrapolating the smaller grid estimate using RSF methods.

• Optimization of collared bear numbers during the DNA efforts. Ideally at least 20 bears would be radio collared during sampling efforts. Hopefully, some of these bears would be females with cubs to allow unprecedented inference into whether this segment of the population can be captured. Placement of VHF collars, or ear tag collars on cubs or yearlings may be a reasonable strategy to temporarily inflate collar sample size for DNA sampling efforts. This would allow a better estimate of closure violation from GPS collars as well as more substantive investigation of the interaction of home range size, bear age, bear sex, and capture probability.

8.5.2 Ongoing Analysis of Current Population Estimate It is suggested that the correction for closure for the 1999 DNA estimate be re-examined each spring. This will provide valuable data on the repeatability of closure correction estimates as the GPS collar data base expands and becomes more representative of overall bear movements. In addition, it is potentially possible to generalize the multi-strata models in MARK to allow a more rigorous correction of closure bias than the residency based Kenward correction (Gary White, per comm.). Future work will focus on developing this methodology, which will allow model averaged estimates of closure correction. It is also suggested that the estimation of population size be revisited using the new heterogeneity models of Norris and Pollock (1996) when they are incorporated in program MARK. It may be possible to use a “meta-analysis” approach at this time in which capture probability heterogeneity parameters are estimated from other BC data sets which should theoretically reduce potential estimate bias and increase precision (Gary White, per comm.).

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8.5.3 Development of Population Monitoring Tools Accurate and precise estimates of large carnivore population size using DNA mark-recapture can only be obtained through rigorous and costly sample designs. It is suggested that in many circumstances, the most appropriate use of DNA based mark-recapture projects may be repeated sampling or monitoring of grid areas on an annual or bi-annual basis to allow the use of open models (Anderson et al. 1995) to estimate apparent survival and population trend, a technique that is more robust to closure violation and associated sampling problems. The development of newer mark-recapture monitoring methods is the subject of ongoing research as part of the Foothills Model Forest Grizzly Bear Research Program.

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9. Foothills Model Forest Grizzly Bear Research Program -RSF Report, Year 2000 prepared by: Scott E. Nielsen, Ph.D. Candidate. Univ. of Alberta, Dept. of Biological Sciences 9.1 Introduction To understand the habitat and resource requirements of a species, such as the grizzly bear, one typically captures a sample of animals from the population and studies the differential use or avoidance of particular features within that animal’s home range or landscape. These actual ‘use’ locations (radio-telemetry positions) are then compared against a sample of un-used locations, or more typical of mobile organisms like grizzly bears, locations that are considered available to the animal over the temporal scale of interest (i.e. random points within a home range). This process of comparing animal use to animal availability has become known as resource selection (Johnson, 1980). Although a number of different methods for analyzing resource selection are available and widely used throughout the literature (e.g. chi-square goodness-of-fit test, compositional analysis, rank based techniques, etc.) (Alldredge et al., 1998), a new approach has recently emerged that seems to be gaining steam. This new approach, called resource selection functions (RSFs) (Manly et al., 1993), is particularly desirable because it allows for the simultaneous comparison between categorical data and scalar data and also offers spatially explicit models that relate probability of animal occurrence to landscape elements. These probabilities can then be interfaced with geographic information systems (GIS), providing relatively useful and accessible maps for resource management and conservation (Boyce and McDonald, 1999). Although there are a number of general classes of RSF models, the most common approach to estimating an RSF is through the use of generalized linear modelling (GLM), where the response variable is the presence or absence of an animal and thus modelled using a binomial distribution with a logit link. This family of GLMs is often referred to as logistic regression. 9.2 RSF Models and the FMF Grizzly Bear Project Although RSF modelling has begun to gain popularity within conservation biology over the past decade (Knick and Rotenberry, 1995; Mladenoff et al., 1995; Akcakaya and Atwood, 1997; Meyer et al., 1998), including work on grizzly bears within the lower 48 (Mace et al., 1996; Waller and Mace, 1997; Mace et al., 1999; Boyce and Waller, 2001), no such modelling has been accomplished within the east slopes region of the Canadian Rocky Mountains and Foothills. To address just such a gap and further provide future management tools and GIS maps for an ecologically vulnerable grizzly bear population (McLellan and Banci, 1999), we initiated an extensive RSF modelling program in the year 2000 at the Foothills Model Forest in collaboration with the University of Alberta (Nielsen, Ph.D. project). Currently, a RSF modelling framework is being developed across a number of spatial and temporal scales (Figure 15). Spatially, there are two primary levels we are focusing on, although future micro-site work may provide finer scale details of selection. These two primary spatial scales are the home range scale (2nd order selection), or what we refer to as the landscape scale, and within home range scale (3rd order selection), or what we call the patch scale (see Johnson, 1980 for selection orders).

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Just as we can tease apart selection at different spatial scales, we also can attempt to divide time into appropriate scales of interest. Unlike most previous grizzly bear work within the region, we are fortunate in having high-resolution GPS radio-collars (6 fixes/day) providing a wealth of year-round information (many bears >600 locations/year) on grizzly bear habitat use and movements. Thus, we are able to maintain a relatively high sample size even after stratifying datasets into separate ecologically important temporal scales or seasons. Just as we divided space into two primary scales of interest, we are also focusing on two primary temporal periods or levels. These two periods are namely that of the pre-berry (den emergence to July 31st) and post-berry (August 1st to denning) seasons (2L and 2P, Figure 15). By building RSF models at these two temporal scales, we will be able to compare previous grizzly bear habitat work (e.g. Kansas and Riddell, 1995; and EIA models). Furthermore, we will also likely be able to explain more model variance since we can divide out the major foraging behaviours across the phonological year (e.g. pre-berry foraging vs. berry foraging). Finally, to address even finer levels of selection, we will explore a four-season spatial-temporal RSF model (3P, Figure 15).

Figure 15. Hierarchical spatial-temporal scales of selection used in RSF analyses in

the grizzly bear research program in 1999 and 2000.

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9.3 Current RSF Work in Progress There are currently four major RSF themes that are on going. We address each theme in the following four sections. 9.3.1 Spatial Autocorrelation Issues in RSF Analyses Before diving too deep into RSF analyses, we are first examining the influences of spatial autocorrelation within GLMs in an attempt to identify potential independence problems. With high intensity location data, such as what we currently have with the GPS collars in the FMF, we are likely to over-represent our true sample size and potentially influence our estimated coefficient standard errors within GLMs. These standard errors of the coefficient tend to be deflated with autocorrelation and hence result in increasing our probability of committing a Type I error (rejecting the null, when in fact it is true). To address such problems, we are examining and testing six separate approaches. These include:

• rarefaction of data based on Moran’s I tests (spatial autocorrelation) and Wald-Wolfowitz Run’s tests (temporal serial correlation);

• spatial patterning of standardized model residuals;

• explicitly modelling space through northing and easting covariates;

• explicitly modelling space through autologistic modelling of prior presences;

• inflation of coefficient standard errors based on spatial autocorrelation; and

• first stage, second stage model averaging techniques to determine population level coefficients and standard errors.

Results from these analyses are being compiled this spring. We will present some initial results at the regional Wildlife Society meeting in Banff in March of 2001 (Nielsen, et al.), and will likely publish final results in the near future. 9.3.2 Sampling Intensity of Availability in RSF Models Recently, I have also begun examining some issues relating to the intensity of sampling availability sites, since there is some debate over the appropriate level and intensity of such sampling. In our study, for 2nd and 3rd order selection processes of grizzly bears, we are taking the spatial positions of grizzly bears from GPS collars and querying landscape and resource data from GIS data layers (e.g. roads, habitats, elevations, etc.). In order to compare these ‘use’ locations and understand selection through RSF analyses, we correspondingly need to query data from the GIS to determine what was available to the animal (i.e. random locations within a home range). Traditionally, before such GIS techniques were used, a paired sample design was common, where use locations were compared to an equal sample size of availability sites in the field (random locations). Now, with the advent of both GPS collars and GIS, however, it is obviously quite easy to generate and query a large amount of information. We make the argument that any

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method in which we can characterize the resources that are available to the animal, the better our comparisons and resulting information will be. In highly heterogeneous landscapes with wide ranging animals like grizzly bears, a balanced approach to quantifying availability may lead to biased estimates of selection (Figure 16). Instead, placing higher densities of random available points across the landscape tends to lead to a stabilization of estimated parameters with apparently little negative effect on standard errors and confidence intervals of those coefficients. Because every variable has a different spatial structure and variability, however, we unfortunately cannot present a general ‘rule’ for availability sampling. Examination of the finest scale and most highly variable variables may be the most appropriate surrogate for determining a local ‘rule of thumb’ for availability density estimation. 1Note: density at slightly under 5 points/km2 related to a paired density design (# use (696) = # available (696)), while

the highest density represented 5000 randomly generated availability points. Notice that parameter estimates tend to stabilize at around 20 points/km2 for this variable (scale varies, however, by parameter). Figure 16. The influence of availability densities (number of points/km ²) on the

estimated RSF parameter (access density) for G-016 in the grizzly bear research program in 19991 (Nielsen, unpublished).

9.3.3 Patch-level RSF Analyses, The 1999 Dataset The third main theme of work currently underway is the development of initial RSF models (1999 data only) at the 3rd order level (patch-level) for nine separate bears (G-02, G-03, G-04, G05, G-06, G-08, G-10, G-16, and G-20). These nine bears contain a tremendous amount of data, with over 5500 individual GPS positions. We are using a first stage, second stage analysis where we use model information from the individual level to make inferences about the population. To build best approximating parsimonious

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RSF models for each bear using GLMs, we are using Akaike’s Information Criterion (AIC) (Burnham and Anderson, 1998). The following a priori variables are being considered as predictor variables within RSF analyses:

• Integrated Decision Tree (IDT) grizzly bear habitat map (Franklin et al., 2001),

• Landsat greenness index (June and August),

• Access density within 9-km2 roving windows,

• Elevation (DEM),

• Site severity index (SSI-an aspect and slope (from DEM) transformation developed by Nielsen, 1997; Nielsen and Haney, 1998), and

• Nearest distance to stream (two classes). Models are being generated for both pre-berry and post-berry seasons at the patch-level scale (level 2P in Figure 15). Mixed linear modelling of selection coefficients against levels of availability is also being examined to tease apart relationships of availability, which have restricted the interpretation and broad use of early RSF models. Results from these analyses will be presented at the IBA conference (Nielsen et al.) in Jackson Hole, WY in May of 2001 and will be submitted for review in the journal Ursus. 9.3.4 Home Range Selection The final theme of work in 2000 and early 2001 is a collaborative effort with the FMF (Munro and Stenhouse) on home range selection and home range size relationships for 1999 and 2000 data. Home ranges are being estimated through kernel estimates at two scales, the 95% and 50% level, for two periods, the pre-berry and post-berry seasons. These home range polygons are being compared to randomly located home range polygons placed within the study area. From these analyses, we are interested in not only selection of home ranges, but also whether there is a relationship between home range size and habitat quality, as has been inferred or investigated elsewhere (Nagy and Haroldson, 1990; McLoughlin, 2000). To determine these relationships, we are developing RSF models that will provide potential habitat probability maps based on habitat quality information. These maps are then being compared to actual or realized probability maps that represent the potential decrease in quality due to access density relationships. Predictor variables of interest within these RSF models include,

• mean and variance of Landsat greenness (June: pre-berry, August: post-berry),

• access densities within home ranges polygons, and

• variability of elevation within home range polygons (DEM) Results from these analyses will also be presented at the IBA conference (Munro et al.) in Jackson Hole, WY in May of 2001 and will similarly be submitted for review within the journal Ursus.

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10. Foothills Model Forest Grizzly Project, University of Calgary Remote Sensing Group

March 6, 2000, Submitted by Medina J. Hansen 10.1 Field Data Collection and Analysis The primary objective of the remote sensing field program was the collection of training sites for an Integrated Decision Tree (IDT) classification of grizzly bear habitat units. In the field season of 2000, one field crew of 2 people were successful in collecting a total of 279 sample sites over approximately 8 weeks. 165 of these sites were air calls (acquired by helicopter), 77 locations were forest plots and 37 were vegetated-non-forested sites. Charlene Popplewell entered all field data into an Access database. Medina Hansen then developed and executed a Visual Basic script to create 3 x 3 bitmaps around each field plot. These bitmaps were required for subsequent discriminant analysis of spectral and field data (see Section 10.1.1 of this report) and image classification (Section 10.3). 10.1.1 Discriminant Analysis of Spectral and Field Data A linear discriminant analysis test of variables was conducted on the assembled field dataset to determine how well the desired landcover classes were represented in the field data set. This is one of the first steps in testing the map methodology in remote sensing classifications by indicating the accuracy of the ‘ground-truth’ training areas. Linear discriminant analyses were performed using SPSS to determine the level of accuracy of the CLASS variable as predicted by independent variables collected for each plot type (AirCall, Forest, and Vegetation). Fisher’s Function Coefficient was incorporated in SPSS using CLASS as the grouping variable and the following independents for each plot type (Table 13). Table 13. Linear discriminant analysis, independent variables and accuracy levels

in the grizzly bear research program in 1999 and 2000.

Plot Type Independent Variables Accuracy Air-call elevation, slope, aspect, canopyclos, deadfall, rock, litter, soil, snags, water,

conifer, deciduous, shrub, herb, grasssedge, mosslichen 82.3%

Forest elevation, slope, aspect, deadfall, litter, water, sapwooddep, rings, coniferous, deciduous, treecount, basalarea, avecc, coniferous, deciduous

94.6%

Vegetation elevation, slope, aspect, conifer, deciduous, shrub, herb, grasssedge, mosslichen, avewater, avedeadfall, averock, avelitter, avesoil, avesnags

84.1%

Overall, there was reasonable agreement between the class label and the structural vegetation field data measured within each class (82% correct). The forest plots, measured on the ground with standard forest inventory protocol, were 95% correct. The only confusion in these classes occurred in a few plots where open conifer had been confused with treed wetlands because of similar species composition and canopy closure. The remaining vegetation (i.e. non-forest) plots were 85% correct; confusion in the field

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between shrub and open wetlands were responsible for much of this error. These tests provided a ‘heads-up’ on the likely sources of spectral confusion; it was extremely likely that if field personnel had difficulty separating field locations thought to represent two different classes, then the image would have a similar problem. 10.2 Satellite Imagery Processing A Landsat TM scene was acquired for September 8, 1999 (Track 45, Frame 23) covering the core 1999 study area. Several image pre-processing tasks were carried out on the 1999 TM imagery. First, atmospheric scattering and haze were minimized and pixel values were converted from radiance to reflectance units by applying the atmospheric correction package available in the PCI image analysis software. Second, distortions due to topographic relief were addressed by ortho-rectifying the TM imagery to a DEM and vector linear features such as roads and other access features for the study area. 68 ground control points were applied to produce any overall Root Mean Square Error value of 0.88, 0.63 in the X direction and 0.61 in the Y. 10.3 Integrated Decision Tree Classification of Habitat Units An Integrated Decision Tree Algorithm (IDTA) was then applied and incorporated the TM imagery, DEM, slope, shaded relief, GIS cut history and roads data. This type of procedure follows the methodology of Hansen et al. (2000) and Deuling (1999) and involves the recursive partitioning of a data set into smaller subdivisions or classes. “Integrated” refers to the use of different classification algorithms within a single tree structure to split the data into distinct classes. First a simple GIS attribute selection function was employed to separate cultural or anthropogenic features from natural features. Cuts, recent and from 3 - 12 years since disturbance, and major linear human use features such as roads and rail lines were separated from natural or undisturbed cover types. Pixels from the natural features class were then passed to an unsupervised K-means classification from which several non-forested, non-vegetated classes were isolated. Several general feature classes for forested and non-forested/vegetated habitat were also isolated by the K-Means and passed on to a supervised Maximum Likelihood Classification (MLC) procedure. Training areas for the MLC were derived by delineating a 3 by 3 pixel window around field sample sites collected during the 2000 field season. Input channels providing maximum class signature separability were determined by calculating and comparing battacharrya distance values and included Tasseled Cap components (Brightness, Greenness, Wetness), elevation, slope, and shaded relief. Upon comparison of signature battacharrya values, it was determined that several field classes were similar in the spectral characteristics (low separability value). Four different classification schemes involved merging class signatures with the lowest battacharrya distance values and re-calculating the accuracy of training sites. A merged “Open Conifer / Wet Treed” spectral class and a merged “Shrub / Wet Open” class output from the MLC

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was separated into different classes based on decision rules. Slope and distance from stream values were extracted for each Wet Treed and Wet Open training bitmap to construct decision rules and split the classes. Accuracy assessment has been carried out on a sample of training pixels resulting in an overall accuracy of 83% for the 1999 core study area IDTA map. A more rigorous accuracy assessment of an individual sample of pixels will be finalized after the summer 2001 field season. 10.4 Graduate Student Project Progress • Julia Linke (MSc)

Title: Change detection of seismic lines using Landsat TM and IRS imagery: effects on landscape structure and locations of grizzly bears (Ursus arctos horribilis).

Progress: Proposal was accepted on December 14, 2000. • Charlene Popplewell (MSc)

Title: Landscape Structure and Fragmentation of Grizzly Bear Management Units And Home Ranges In The Alberta Yellowhead Ecosystem

Progress: Proposal was accepted on March 31, 2000

Analyzed field plot and spectral data using linear discriminant analysis in SPSS; Scanned field plot photos; Literature review (September – November 2000)

Performed preliminary patch analysis on ER classification (October – December 2000)

• Barb Schwab (MSc)

Title: Graph Theoretic Methods for Examining Landscape Connectivity and Spatial Movement Patterns: Application to the FMF Grizzly Bear Research Program

Progress: Proposal was accepted on December 14, 2000. 10.5 Journal Submissions: Franklin, S. E., G. B. Stenhouse, M. J. Hansen, C. C. Poppelwell, J. A. Dechka, and D. R.

Peddle, Integrated Decision Tree Approach (IDTA) to classification of landcover in support of grizzly bear habitat analysis in the Alberta Yellowhead Ecosystem, Canadian Journal of Remote Sensing, submitted December 2000.

Franklin, S. E., D. R. Peddle, J. A. Dechka, and G. B. Stenhouse, Evidential reasoning using Landsat TM, DEM, and GIS data in support of grizzly bear habitat analysis, International Journal of Remote Sensing, submitted June 2000; revised December 2000.

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10.6 Conference Proceedings: Dechka, J. A., D. R. Peddle, S. E. Franklin, and G. Stenhouse, 2000, Grizzly bear habitat

mapping using evidential reasoning and maximum likelihood classifers: a comparison, Proceedings, 22nd Canadian Symposium on Remote Sensing, Victoria, BC, 393-402.

Dechka, J. A., S. E. Franklin, D. R. Peddle, and G. Stenhouse, 2000, Grizzly bear habitat mapping based on evidential reasoning, Proceedings, 6th Circumpolar Conference on Remote Sensing of Arctic Environments, Yellowknife, NWT, on CD-ROM.

Dechka, J. A., S. E. Franklin, D. R. Peddle, and G. Stenhouse, 2000, Preliminary results of a project to map grizzly bear habitat in the Alberta Yellowhead Ecosystem based on evidential reasoning, Proceedings, Geographic Information Systems and Remote Sensing for Sustainable Forest Management: Challenge and Innovation in the 21st Century, Edmonton, AB, abs., page 30.

10.7 Habitat Mapping In 1999, progress in some areas related to this project component has been made as a result of the combination of funding/support from the CCRS RSDDP contract to GeoAnalytic Inc. and the Foothills Model Forest Grizzly Bear Research Program:

• a classification structure for bear habitat mapping has been identified;

• a test of the evidential reasoning software has been completed;

• a compilation of the available satellite imagery, GIS data, and field observations related to habitat mapping has been completed;

• a number of important protocols, for example, for integration GIS and GPS bear movement data have been written and tested;

• a test of the Patch Analyst (Elke et al 1999) Arc/View software extension has been completed; and, as part of the Alberta Forest Biodiversity Monitoring Program Pilot Study,

• a preliminary example of the use and form of landscape metrics in areas of varying degrees of disturbance in the study area has been generated.

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11. 1999 DNA report – Scat Sniffing dogs and fecal DNA work prepared by: Dr. Samuel K. Wasser, Ph.D. ,Center for Wildlife Conservation and University of Washington School of Medicine Editors Note: the following report represents the final report from data collected by Dr. Wasser and his research team during the 1999 field season. Dr. Wasser focused efforts in 2000 on laboratory analysis of samples collected to date and investigating ongoing challenges concerning DNA extraction. 11.1 Introduction There is a pressing need for federal and state wildlife agencies to monitor multiple threatened and endangered species over large remote areas. Effective management requires accurate data on the number and distribution of threatened and endangered species, as well as on the degree to which they are impacted by human and other environmental disturbances. Demand for these techniques became abundantly clear in 1999 by successful lawsuits filed against the National Forest Service and Bureau of Land Management for failing to monitor their lands for endangered species. These federal agencies argued that their monitoring ability has been severely limited by a lack of cost-effective techniques for detecting multiple endangered species over large remote areas. Traditional techniques of acquiring these data for difficult to observe species have included: mark-recapture of tagged individuals; animal track or pellet counts; hidden cameras; and radio collaring. However, their implementation has been severely limited by the cost, time, invasiveness and biases associated with data acquisition. Unbiased, cost effective collection methods are clearly needed for concurrently estimating the number, distribution and degree of disturbance of multiple species at risk over large remote areas. Our project aimed to validate and implement such methods, combining noninvasive fecal DNA and hormone technology with highly trained detection dogs used to locate scat from target species. For reasons described below, scat sampling with detection dogs has the potential to be relatively free of collection biases that have plagued many of the more traditional monitoring techniques. Four K-9 teams collected grizzly and black bear scat samples over a 5,400 km² area in and around Jasper National Park (JNP), Alberta, Canada. Forty percent of the study area is within the national park, whereas the remaining 60% is in a multi-use study area to the north that is exposed to a variety of human disturbances. Scat samples are geo-referenced upon collection using a hand-held GPS unit and plotted on a Geographic Information System (GIS) that also maps disturbances in the study area. Hair sampling and radio collaring were conducted concurrently, and compared as part of the assessment of our scat methods.

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11.1.1 Biases Associated with DNA-based Mark-Recapture Estimators of Population Size

Recently, there has been a rapid growth in the use of noninvasive DNA sampling techniques using hair in a mark-recapture study design to estimate population size of species over large geographic areas. Individuals of the target species are attracted to hair snag stations using scented lures. Each individual leaves a clump of hair on the chest height barbed wire, as it crawls under the wire to access the lure. DNA obtained from the hair follicles is used to identify the individual that left the sample. The collection is then repeated two or more times to assess recapture rates. The recapture rate is used to determine the portion of the entire population that was represented by the original capture, thereby providing an estimate of population size. Most mark-recapture models require that every individual in the study area has an equal chance of being “caught” (termed equal catchability; White et al. 1982). This assumption is often violated in lure based captures, because some animals (e.g., males; more dominant individuals or species) tend to be more attracted to these lures than are others (e.g., females, and especially females with cubs; more subordinate individuals or species). Other factors such as seasonal variation in the quality and availability of natural foods may also affect temporal attractiveness of lures (Woods et al. 1999; Pierce et al. in prep). The hair snag method is additionally limited in the number of species it can target at the same time. DNA extracted from scat (Wasser et al. 1997a) can be used in the same manner as the hair snag data. However, visually searching for scat along roads, trails or transect routes can also produce “capture” biases because some individuals (e.g. females and subordinate males) may conceal their scat more than others (e.g. resident/dominant males). In an attempt to reduce capture biases associated with lures or sightability, my laboratory collaborated with the Washington State Department of Corrections to develop a new method that employs detection dogs to noninvasively acquire scat samples for DNA analysis (Wasser et al. 1999). 11.1.2 Scat Collected Using Detection Dogs We use K-9 narcotics detection methods to train dogs to locate scat of threatened and endangered species over large remote areas. The ability of these dogs to detect such samples is phenomenal and well documented. Trained dogs are able to detect odors as faint as 3 parts per million; odors can be detected from distances of 0.5 miles downwind; and up to 18 different substances (species) can be detected at once. Perhaps most importantly, these high drive, play driven dogs are motivated by their expectation of receiving a play reward upon locating a sample (Bryson 1991). They maximize their chances of receiving this reward by locating samples regardless of the subject’s sex, reproductive condition, or tendencies toward sample concealment. Thus, fecal sample detection by dogs tends to be relatively free of biases associated with the subject’s sex, reproductive condition or behaviour. DNA, as well as stress and reproductive hormones are measured in these fecal samples using techniques pioneered in my laboratory (Wasser

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1996; Wasser et al. 1988, 1997ab, in press). DNA is used to confirm species, gender and individual identities for use in mark-recapture and other analyses. The hormone measures simultaneously provide the opportunity to measure reproductive function and physiological stress associated with habitat disturbance among the suite of target species sampled in the study area. When coupled with environmental disturbance measures, these combined noninvasive methods have the potential to rapidly and concurrently assess the number, sex ratio and distribution of individuals among multiple species and the physiological impact of the disturbances each is experiencing over large remote areas. No other technique offers such potential. 11.2 Study Design We field-tested the scat detection dog (SDD) method between May 18 and July 5, 1999, during a 6 week survey of the 5,400 km² study area. Detection dog teams collected scats of grizzly bear and black bear across the entire study area for DNA and hormone analysis. Hair snags were used concurrently throughout the study area to collect hair for DNA analyses. Nineteen grizzly bears were also equipped with GPS radio collars, providing their UTM locations every 4 hrs. These combined methods enabled direct comparison of sampling efficiencies and “capture” biases associated with lure versus detection dog collection methods for hair and scat, respectively, in DNA-based mark-recapture population estimates. The entire study area was also mapped on a Geographic Information System (GIS) that included fine-grained details of habitat and disturbance types. The 5400 km2 study area was divided into 64, 9 x 9 km grids. Hair snag stations were set up consecutively at 3 separate locations within each grid, for two weeks per location. The hair snag method used lures of aged cow’s blood and other strong scent to draw bears under a chest height barbed wire . The hair collections and analyses were conducted independently by our collaborators, Mr. Garth Mowat and Dr. Curtis Strobeck of the University of Alberta. Forty of the 64 grids were also searched three times for scat by one of four dog teams. Depending on terrain and weather, each dog team walked a 5-9 km transect, extending outwards from 1 km of the hair snag. Each dog team consisted of a scenting dog, dog handler and orienteer. In all cases, the grid was searched by the dog team during the same 2-week period the hair snag was in place. All fecal samples were thoroughly mixed with a gloved hand, wrapped in a coffee filter and placed in a zip-loc bag containing silica as a preservative at a ratio of 4g silica per gram of feces. Samples were placed in a large walk-in freezer for storage at the end of each field day and remained there until transported to our laboratory for extraction and analyses. Mitochondrial, single copy nuclear and microsatellite DNA, as well as stress hormones were extracted from the scat samples. The DNA is used to determine the respective species, sex and individual identity of the animal that left the sample for mark-recapture

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models used to estimate species-specific population sizes and distributions. Stress hormones from these same samples provide an index of the amount of physiological stress each of these individuals was experiencing within 24 hours prior to leaving the sample. These GPS geo-referenced data are then mapped on a GIS along with location-specific habitat and disturbance measures, enabling assessment of species and sex-specific distributions across the landscape, as well as associated disturbance impacts. 11.3 Results 11.3.1 Bear Distributions Based on Collection Methods: The lure-based, hair snag method collected 0.47 black bear per grizzly bear hair samples, whereas the detection dog method collected the reverse, 2.4 black bear per grizzly bear scat samples. Since black bears are observed far more frequently than grizzly bears throughout the study area, these method-based differences suggest that black bears may be avoiding hair snag areas visited by grizzly bears. However, more work is needed to confirm this. Black bear fecal samples were more commonly detected inside than outside JNP, whereas grizzly bear fecal sample collections showed the reverse pattern (Table 14; F=60.26, p <0.0001 for species; F=6.85, p<0.01 for inside vs. outside JNP). The grizzly bear sample collection demonstrated this latter pattern independent of gender. By contrast, the black bear pattern was largely based on a higher representation of male fecal samples found inside JNP; female black bear fecal samples were equally represented inside and outside JNP (Table 14). Table 14. Black and grizzly bear scat samples found inside and outside Jasper

National Park as a function of sex, in the grizzly bear research program in 2000. Data include only those samples identified by DNA to species and gender levels.

Black Bears Grizzly Bears In Out Total In Out Total Male 34 17 52 11 19 30 Female 21 18 39 2 1 13 Location-specific Total 55 35 90 13 30 43 Figures 17-19 shows the distributions of black and grizzly bear hair and fecal sample collections throughout the study area, identified by mitochondrial DNA. Figure 17 shows data on black and grizzly bears, combined. Figures 18 and 19 show separate data on black and grizzly bears, respectively. (Note: No fecal sampling occurred in the yellow shaded areas in Figures 17-19; these grids were only sampled for hair.) Both hair and fecal sample collections revealed very similar distributions over the entire study area. There was minimal species overlap inside the national park (Figure 17).

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Black bear samples collected inside the national park were concentrated in the northern portion, where tourism was also most concentrated, and to a lesser degree in the central part of the park. Very few black bear samples were found in the more mountainous southern portion of the park (Figures 17 and 18). By contrast, grizzly bear samples collected inside the national park were most heavily concentrated in the more mountainous central and southern portions, where tourist densities are extremely low. A very different pattern was found outside JNP. Interspecific overlap was high, revealing very similar black and grizzly bear distributions in the multi-use area outside JNP (Figure 17). Both species were most abundant in the northern portion of the multi-use area (Figures 17-19). which is also the most highly disturbed portion of the entire study area. Grizzly bear, but not black bear, sample collections were also relatively frequent in the very southernmost part of the multi-use area (Figure 17). A map of grizzly bear distributions based on data from the 20 individuals with GPS radio collars in this study (Figure 20) is also consistent with the study-wide fecal and hair based distributions in Figures 17 and 19. (Note: a few of the GPS collars experienced failures during the study period). It is noteworthy that the most heavily avoided central-southern portion of the multi-use study area appears to be largely undisturbed forested land. Why this land appears to be avoided by both species of Ursids remains a key question of our future research in this area (see below).

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Figure 17. Individual grizzly and black bear scat sample collection sites for the

grizzly bear research program in 2000.

Figure 18. Individual black bear hair and fecal sample collection sites for the

grizzly bear research program in 2000.

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Figure 19. Individual grizzly bear hair and fecal sample collection sites for the

grizzly bear research program in 2000.

Figure 20. Grizzly bear GPS locations within the grizzly bear research program area in 2000.

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11.3.2 DNA Preservation and Amplification Success We ultimately plan to use microsatellite DNA to confirm individual identities in the samples collected. This will be used in mark-recapture analyses to estimate population sizes and sex ratios, as well as to account for inter-individual differences in analyses of the hormone data described below. Thus far, our analyses of microsatellite DNA have been restricted to the confirmed grizzly bear samples only. Unfortunately, our amplification success rate for microsatellite DNA was much lower than expected. Only 62% of all samples amplified at 1 or more loci, 33% at 2 or more, and 15% at 3 or more loci. Our low microsatellite amplification success was further demonstrated by a high number of homozygous alleles across loci; the latter indicates a problem with allelic drop-out, typical for samples of low DNA quality. The limited data we did get from these samples does, however, suggest that fecal samples collected from the same grid and session frequently were derived from multiple individuals. While our low microsatellite amplification success rate in this study was disappointing, our laboratory and others have typically averaged between 65-80% amplification success for microsatellite DNA across all loci from fresh fecal samples (e.g., Wasser et al. 1997a; Kohn et al. 1999; Murphy et al. 2000). This, coupled with study results described below, suggest that the low amplification success rate resulted from sample preservation problems that, fortunately, should be quite surmountable in future collections. Last year we purposely collected samples regardless of age (i.e., time on the ground prior to collection) as we wanted to examine how age impacts DNA amplification success. No significant effect of sample age was found on mtDNA amplification success. However, for reasons described below, we believe that the lack of an age effect occurred because storage conditions promoted DNA degradation of fresher samples whereas older dried out samples were already partially preserved (see also Kohn et al 1999). Lab and field studies suggest that moisture is among the greatest factors causing DNA degradation in feces. Samples dried under natural conditions in the field experience some degradation, but often still contain DNA of good quality as long as they don’t repeatedly re-hydrate (Wasser et al. 1997; Kohn et al. 1999; Murphy et al. 2000). This conclusion was further supported by our collaborative DNA preservation study findings that DNA degradation can be significantly reduced by increasing the speed at which samples are dried upon collection. Samples lying in open habitats also experience significantly less DNA degradation than those lying in the shade (Murphy et al., 2000). We now believe that placing our freshest samples in a closed bag under warm temperatures created an environment that rapidly degraded DNA because samples were not dried and/or frozen quickly enough (S. Wasser, personal observation). In fact, fresh scat collected directly from radio-collared grizzly bears in our Jasper study failed to amplify any microsatellite DNA, despite >90% amplification from concurrently collected blood from these individuals. The researchers who collared these bears placed the fresh scat in plastic bags, without silica, storing them in the walk-in freezer several hours later, at the end of the field day.

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The above problems were further aggravated by an inadequate freezer used to store all fecal samples during the field season. Our samples were stored in a large walk-in freezer, shared by wildlife department beaver carcasses used for trapping. The freezer was frequently being opened and apparently took too long to re-cool because of its large storage space. This undoubtedly compounded sample degradation of our freshest samples, whose high moisture content continued to overwhelm the silica used to dry them. Next year, we plan to use a higher ratio of silica to feces and oven dry samples at the end of each field day. Dried samples will then be placed in fresh silica, stored in a dedicated freezer. Several alternative storage methods recently examined by us and others will also be tested in the field using a matched sample design (see below). Since every field condition may differ, it is critical that each of these preservation techniques be field tested in the particular field site in which one is working. We also intend to continue conducting preservation experiments in our laboratory this fall and winter to further refine next year’s methodology. 11.4 Stress Hormone Data 11.4.1 Species and Gender Differences The stress hormone results on these same fecal samples revealed significant species and gender effects. Stress hormones were higher in grizzly bears than in black bears (F=39.31, p<0.0001; ANOVA) and for both species, higher for males than for females (F=5.80; p<0.02, ANOVA; Figure 21). The observed species and gender specific differences in adrenal activation could either be phylogenetic or the result of gender and species-specific differences in the levels of “disturbance” each are experiencing. To test this, we recently conducted simultaneous ACTH challenge studies on captive male and female black bears and grizzly bears at NW Trek Wildlife Park in Washington. ACTH is the hormone secreted by the pituitary gland in response to physiological stress, causing the adrenal gland to release the stress hormone, cortisol. We are measuring metabolites of cortisol in feces (Wasser et al. in press). Genetic and/or phylogenetic differences in adrenal activation would be reflected by differences in baseline levels and/or the height of the cortisol peak in response to the ACTH challenge.

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All individuals were given an identical i.m. dose of 2.2 I.U. ACTH per kg body weight, remotely delivered using a Telenject airpowered dart. Samples were collected once in the morning and late afternoon when animals were transferred between inside and outside enclosures. Defecation times, accordingly, had to be estimated in most cases based on the time of collection in relation to sample freshness. Cortisol metabolites were measured in all fecal samples collected continuously from two days prior until 96-hours post-ACTH injection. All individuals had comparable baseline cortisol concentrations, regardless of species and/or gender. Male and female grizzly bears also showed comparable levels of adrenal activation, with initial excretion peaks at ~15 hours, extending out to 75 hours post-injection. One male and one female grizzly bear sample, each collected around the time of the excretion peak, were extremely runny. Both of these samples had corticoid concentrations that were substantially lower than expected. These low values almost certainly resulted from excessive amounts of water per sample—an effect that can be removed by drying fecal samples prior to extraction, and expressing cortisol concentrations per g dry fecal weight (Wasser et al 1993). We are presently freeze-drying these samples to repeat the analyses on the dried samples. [All field samples were freeze dried prior to extractions and expressed per g dry weight; this also controls for dietary effects on steroid excretion (Wasser et al. 1993).]

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Unfortunately, the black bear ACTH challenge data were more difficult to interpret. The black bears failed to show a clear ACTH response. This was surprising because (a) their baseline concentrations were comparable to the grizzly bears in the same study; and (b) we previously demonstrated adrenal responsiveness to ACTH challenge in a closely related black bear species (Wasser et al. in press). Our failure to detect an adrenal response in the black bears is likely tied to the fact that no black bear samples were collected between 15 and 39 hours for the male and between 15 and 63 hours for the female black bear, which also corresponded to the time of the expected excretion peak (15-36 hours). Thus, either the zoo keepers failed to collect the samples defecated by the black bears around the time of the peak; or, the black bear failed to defecate during the second day of sample collections, when the peak was expected, and may have resorbed the cortisol subjecting it to enterohepatic recycling. It is also possible that the remotely delivered dart failed to provide a complete injection of ACTH to the black bears; We plan to repeat the ACTH challenge study on the black bears in order to address these questions. However, greater care will be taken to assure that all samples are collected. The planned replicate study will also use a pole syringe rather than a remotely delivered dart for these injections to assure that ACTH delivery is complete. 11.4.2 Within Species Differences Within species differences in stress hormone levels were also found in fecal samples collected in our field study, depending on the collection location. Among grizzly bears, stress levels appeared to be highest in the northern portion of the multi-use area (grids 6, 14, Figure 22). Those grids are also in some of the most heavily disturbed parts of the study area, where the combined grizzly and black bear densities were also highest (Figure 17). Stress hormone levels in at least one black bear sample was also relatively high in neighboring grid 15 of the multi-use area. However, stress levels in black bears were notably highest in grids 4 and 12 within the national park, where tourist densities are highest (Figure 22). Very few grizzly bears were detected in these areas (Figures 17-19). These results are suggestive of disturbance impacts. However, more data are needed before arriving at any definitive conclusions. 11.4.3 Hormone Preservation Studies To facilitate interpretation and quality of the stress hormone data, we also tested preservation of stress hormones in captive grizzly bear feces across 12 months. Approximately, 10 kg of fresh feces from two male grizzly bears was thoroughly mixed for two hours. Twenty subsamples of this fecal mass were then immediately freeze-dried and assayed for immunoreactive cortisol, providing a "time zero" value of ng immunoreactive hormone per g of dried feces. The remaining fecal mass was then divided into several hundred 20g samples, divided equally among the following five groups:

• Control (samples exposed to air at room temperature for one week);

• Freeze-dried (at –20°C for one week);

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Figure 22. Stress hormone levels of captured grizzly and black bears in the grizzly bear research program in 2000.

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• Oven-dried (at 45°C for one week);

• Silica-dried (stored in ziploc bags with indicating silica beads; fresh silica added at one week); and

• Ethanol (feces mixed with 90% ethanol and stored in vapor-proof vials). Half of each group was stored at room temperature and the other half at –20°C, creating a total of ten groups. At 1, 2, 3, 4, 9, and 12 months after excretion, ten samples from each of the ten groups (i.e., 100 samples at each timepoint) were freeze-dried, enabling all hormone concentrations across groups to be expressed per g dry weight and analyzed for immunoreactive cortisol metabolites. When stored frozen (Figure 23a), control and freeze-dried samples maintained steady values of immunoreactive glucocorticoid levels across time, while ethanol, and especially silica-dried, and oven-dried groups showed an ~25% decline over the first 60 days and then maintained relatively steady levels thereafter. When stored at room temperature (Figure 23b), the freeze-dried group maintained steady values, while other groups showed various changes over the first 100 days: A gradual ~40% decline occurred in the control group, a gradual ~50% increase occurred in the ethanol group, while erratic increases and decreases occurred in the oven-dried and silica-dried groups;. After the first 100 days most groups maintained steady hormone levels except for the ethanol group, which declined dramatically at 270-365 days. The ethanol group also showed high variation at 4 months and 9 months. Unpreserved samples were minimally impacted for the first week and did not begin to significantly decline until ~1-2 months. Thus, most samples collected within 1 month after excretion and stored frozen or freeze-dried for up to one year retain levels of immunoreactive stress hormones comparable to fresh samples. The erratic pattern of samples stored in ethanol at room temperature was particularly noteworthy. The increase in apparent hormone level indicates that after 2 months, hormone metabolites were being altered to a form with greater affinity for the assay antibody. This effect has also been noted in an identical study conducted concurrently on African elephant feces stored at room temperature in ethanol (Hunt & Wasser, unpublished data). Interestingly, ethanol preserved samples did not undergo these changes when stored frozen. We plan to further test ethanol stored samples in the field next summer, particularly since ethanol appears to work well as a fecal DNA preservative (Wasser et al. 1997a, Murphy et al. 2000).

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Figure 23. Mean cortisol metabolite concentrations across preservation groups,

stored (a) frozen and (b) at room temperature in the grizzly bear research program in 2000.

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11.5 Discussion Novel studies of the nature described here, rarely occur without problems and our first attempt was no exception. Nevertheless, results suggest that the problems we did face are quire surmountable and that overall our fecal method has great potential. Both hair snag and scat collection methods obtained comparable numbers of samples (~400 samples each), despite hair snags occurring in over 20% more of the study area (i.e., 64 versus 40 grids). In addition, a single hair snag station often obtained up to 30 separate hair snags in this study, all of which tended to be from a single individual (G. Mowat, personal communication). By contrast, the microsatellite DNA that amplified from fecal samples collected in the same grid and session more often appeared to represent multiple individuals. Hopefully, these trends will be maintained as our microsatellite amplification success rate improves. The stress data also showed considerable promise that, when coupled with DNA analyses, could prove quite powerful. Preliminary data demonstrated gender and species differences, with stress levels being higher among grizzly versus black bears, and among males versus females within each species. Results of our ACTH challenge suggest that these differences most likely result from gender- and species-specific environmental disturbances each are experiencing; however, a definitive conclusion regarding causality of species-specific differences must await replication of the black bear ACTH challenge. From preliminary efforts, it does appear that the potential for coupling scat-derived stress (Figure 22), distribution and density data (Figure 17) with GIS-based human use data has enormous potential for efficiently unraveling impacts of human disturbances on these populations. Again, what remains is to enhance our microsatellite amplification success rates. The biggest problem we faced in this first study year was a higher rate than expected of microsatellite DNA degradation in scat. However, for reasons detailed above, we believe these problems are readily surmountable and will also test this next year. We have also made several improvements in our sample preservation methods that will be tested next year in addition to acquiring a dedicated freezer for our fecal samples. All fecal samples will be split into quarters at the time of collection, placing one part in 90% ETOH, one part in silica, and one part in buffer containing 20% DMSO, Tris and EDTA saturated with NaCl as preservatives; the final quarter will remain unpreserved. At the end of each field day, the silica and unpreserved samples will each be cut in half; one of each half will be oven dried at 40°C and then returned to its original storage conditions (i.e., with fresh silica or unpreserved). At the end of each week, DNA will be extracted from a portion of each sample in the various preservation groups. The remainder of those samples will be extracted in our laboratory after the close of the field season.

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Several variations on our DNA extraction and amplification methods will also be examined, including increasing the initial quantity of feces extracted, higher incubation temperatures and extended incubation during extraction; longer and hotter initial denaturations, heat activated Taq polymerase, a touchdown cycling protocol to increase annealing temperatures, increased cycles, and thin-walled PCR tubes to reach temperatures quickly during amplification. Matched samples from all groups will be analyzed and compared for microsatellite DNA amplifications success rates and concentrations of cortisol metabolites. 11.6 Concluding Remarks We believe that study results to-date already demonstrate the tremendous potential of the SDD method (Figure 24). When coupled with the sampling, preservation and extraction improvements outlined above, we expect that our overall monitoring approach will prove to be ideally suited for addressing a wide variety of conservation and management related questions in the immediate and long-term future.

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- Scat collections are represented by circles; hair collections are represented by triangles - Orange colored circles and triangles represent grizzly bears - green circles and triangles represent black bears - Gray and yellow circles and triangles represent samples that did not amplify for mtDNA used to determine

species identities Note: The study area is divided into the 64 9X9 km grid cells. All of these cells were sampled for hair. Those cells

sampled for scat are unshaded; the yellow shaded cells were not systematically sampled for scat. Roads and National Park boundaries are also shown.

Figure 24. Map showing the locations of grizzly and black bear scat and hair

sample collections throughout the 5,400 km2 grizzly bear research program area in 2000.

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12. Grizzly Bear Health prepared by Dr. Marc Cattet (Canadian Cooperative Wildlife Health Centre) and Dr. Nigel Caulkett (Western College of Veterinary Medicine, University of Saskatchewan). 12.1 Bear Physiology The gathering of physiological data from captured bears was not identified as a key element in the 1999 and 2000 annual workplans of the FMF Grizzly Bear Research Program. Nonetheless, the importance of these data have become increasingly apparent over the first two years of the project. Through the routine measurement of heart and respiratory rates, and body temperature, and through the collection and analysis of blood, it has been possible to critically evaluate a new immobilizing drug for use in bears, and to evaluate the physiological effects of different methods of capture, i.e., aerial darting and leg-hold snares. Through the measurement of the total body weight and length of captured bears, it has been possible to adopt a practical and reliable body condition index that was originally developed for use with polar bears, to also be used with grizzly bears. The body condition data, in conjunction with the results of blood analyses, have allowed comprehensive assessment of the health of captured bears. The health of the individual bears is now recognized as a critical factor to understanding the response of grizzly bear populations to human activities, and has become the focus of a proposed study on anthropogenic (human-caused) stress and health in grizzly bears. This study will be linked to the FMF Grizzly Bear Research Program and will enhance greatly the interdisciplinary perspective of the project over the next three years, as well as help to provide a biological information base that is unprecedented among studies of grizzly bears. 12.2 Immobilizing Drugs Investigators: Marc Cattet, Nigel Caulkett, and Gordon Stenhouse. A sub-project was initiated in 1999 to determine and compare the efficacy, and behavioural and physiological effects, of two drug combinations for the use in free-ranging immobilizing grizzly bears. One drug combination, a 1:1 mixture of zolazepam and tiletamine (ZT), is sold under the tradename Telazol and has been long regarded as the drug-of-choice for the immobilization of bears. Despite its widespread use, however, ZT may produce prolonged recoveries over many hours in some bears, it has relatively poor painkilling effect, and its anesthetic effects cannot be reversed effectively with any available antagonist drug. The other drug, a 1.3:1:1 mixture of xylazine, zolazepam, and tiletamine (XZT), is a new drug combination that was developed initially through research with captive polar bears in 1998. Relative to ZT, the XZT combination was found to have superior painkilling effect and its anesthetic effects could be reversed readily with the antagonist drug yohimbine. In addition, because of its greater potency, XZT could be administered to effect at a significantly lower volume and, therefore, cause less trauma to muscle tissue at the site of injection.

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The promising results of XZT with captive polar bears prompted further investigation into the efficacy and safety of this drug combination with free-ranging grizzly bears. Over the past two years, XZT has proven effective and safe for use in immobilizing grizzly bears on the FMF Grizzly Bear Research Program, and will be used more routinely over the next three years. Nevertheless, the effort to obtain more detailed measurements of the physiological responses (e.g., blood gases) of grizzly bears to the two drugs, ZT and XZT, will continue. The results-to-date from this investigation, as well as the results from a comparison of the physiological responses of bears to different methods of capture, are summarized below in an abstract that has been accepted for presentation at the 13th International Conference on Bear Research and Management in Jackson Hole, Wyoming, from May 21st to 25th, 2001. 12.2.1 The Comparative Effects of Chemical Immobilizing Drug and Method of Capture on the Health of Free-ranging Grizzly Bears This investigation had two objectives. The first was to compare the immobilizing characteristics and physiological effects of two chemical immobilizing drugs, zolazepam-tiletamine (ZT) and xylazine-zolazepam-tiletamine (XZT), on free-ranging grizzly bears. The second was to compare the hematological and serum biochemical effects of two methods of capture, leg-hold snare and helicopter darting, on free-ranging grizzly bears. Fifty-eight grizzly bears were captured in west-central Alberta during 1999 and 2000. Chemical immobilization was induced with ZT in 29 of the bears, and with XZT in the other 29 bears. The drug dosage required for complete immobilization was significantly higher with ZT than with XZT (mean ± standard deviation: 10.2 ± 4.0 mg/kg versus 6.3 ± 1.8 mg/kg). Similarly, the volume of drug required for immobilization with ZT was approximately 2.5 times greater than the required volume of XZT. Mean induction times were similar between the two drugs (6.5 ± 3.6 min), as was the mean duration of immobilization effect (63.6 ± 29.8 min). During 75 minutes of immobilization, heart rates were consistently greater in bears immobilized with ZT (Figure 25), whereas mean arterial blood pressures were greater in bears immobilized with XZT. Immobilization with XZT also resulted in lower respiratory rates and percent hemoglobin oxygen saturation during the initial 15 minutes following drug administration, but these values increased afterward. Rectal temperature remained stable during immobilization with XZT, but tended to decrease during immobilization with ZT (Figure 25). There were no differences between drugs in their effects on the hematological and serum biochemical values of bears with the single exception that serum glucose values were significantly greater in bears immobilized with XZT than with ZT (8.1 ± 2.5 mmol/L versus 6.2 ± 1.6 mmol/L) (Tables 15 and 16). A single intramuscular injection of yohimbine at 0.15 mg/kg was generally effective in reversing the immobilization with XZT, with complete reversal occurring in 21.1 ± 16.6 min. Together, these results indicate that both drugs are effective for immobilizing grizzly bears, but their potential to impact on the health of bears is different. Because ZT is less potent than XZT, it must be administered in greater volume and has the potential to cause more extensive tissue injury at the site of injection when delivered by darts that contain explosive charges. In addition, immobilization with ZT cannot be reversed effectively with an antagonist drug. In contrast, immobilization with XZT can be reversed, but XZT has greater potential to produce transient hypoxemia,

69

110 *

*

*

***

*

Hea

rt ra

te

(bea

ts /

min

) 90

70

50 30

Res

pira

tory

rate

(b

reat

hs /

min

) 24

18

12

6 39.5

39.0

Rec

tal t

empe

ratu

re (o

C)

38.5

38.0

37.5

37.0 0 15 30 45 60 75

Time Following Drug Administration (min)

Figure 25. Physiological response of grizzly bears during anesthesia with ZT (red, n = 29) and XZT (blue, n = 29) in the grizzly bear research program in 2000. Values are presented as the mean + the standard error of values recorded within 15 minute intervals following drug administration. Significant differences (p < 0.05) between drug groups, within specific time intervals, is indicated by *.

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hypertension, and hyperglycemia. The effects of method of capture also were compared for 55 grizzly bears, independent of the effects of immobilizing drugs. Thirty-three bears were captured at bait sites using leg-hold snares and 22 bears were immobilized from a helicopter using remote injection equipment. When comparing hematological values, grizzly bears captured by snare had a significantly higher concentration of white blood cells and neutrophils, and a significantly lower concentration of lymphocytes and eosinophils, than did grizzly bears that were darted from a helicopter (Table 15). When comparing serum biochemical values, grizzly bears captured by snare had significantly higher concentrations of sodium, chloride, glucose, total cortisol, alanine aminotransferase, aspartate aminotransferase, creatine kinase, and total bilirubin than did grizzly bears that were darted from a helicopter (Table 16). Together, the hematological and biochemical data indicate that capture by leg-hold snare is associated generally with a higher degree of stress and soft tissue injury in grizzly bears than is capture by helicopter darting. 12.3 Body Condition Investigators: Marc Cattet, Nigel Caulkett, and Gordon Stenhouse. Body condition can be defined as a measure of the abundance of potential energy stored in body tissues (primarily fat and skeletal muscle) of an animal relative to its body size. As a biological variable, body condition is important for monitoring long-term trends in the fluctuation of food availability in wild populations, for addressing ecological issues, and for assessing the health of individual animals. A body condition index (BCI) has been developed for use in polar bears and is based on two measurements, straight-line body length (the straight-line distance from the tip of the nose to the end of the last tail vertebra) and total body weight, that are measured routinely during the handling of captured bears. The BCI can be used to compare body condition among individual polar bears regardless of sex, age, reproductive class, geographical population, or date-of-capture. The desire to have a similar index for use in grizzly bears captured as part of the FMF Grizzly Bear Research Program prompted an effort over the past two years to record length and weight data for as many captured grizzly bears, as possible. These data, as well as additional length and weight data for grizzly bears and black bears that has been provided through other research programs, has enabled a statistically-sound comparison by species of the relationship between straight-line body length and total body weight. These comparisons have indicated that the relationship between these two measurements is the same across all three species (polar, grizzly, and black), despite their significant differences in overall body size. The key importance of this finding is that the length and weight data from all three species could be combined to derive a BCI that is applicable to all North American bears, regardless of species (Figure 26). The results from this research effort are summarized below in an abstract that has been accepted for presentation at the 13th International Conference on Bear Research and Management in Jackson Hole, Wyoming, from May 21st to 25th, 2001.

71

700

60 130 200 270

Straight-Line Body Length (cm)

Polar bears (1072) -

Grizzly bears (106) -

Black bears (51) -

TBM = e-11.1.SLBL3.13

n = 1229 600

500

400

Tota

l Bod

y M

ass (

kg)

300

200

100

0

Figure 26. Curvilinear relationship between total body mass (TBM) and straight-line body length (SLBL) in 1,229 bears represented by polar bears, grizzly bears, and black bears. The logarithmic equation provides the mean value for the TBM at a given value for the SLBL.

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12.3.1 The Development and Assessment of a Body Condition Index for Polar Bears and its Application to Brown Bears and Black Bears Body condition is a measure of the abundance of potential energy stored in the body tissues (primarily fat and skeletal muscle) of an animal relative to its body size. This investigation had three objectives. The first objective was to develop a body condition index (BCI) for free-ranging polar bears which could be measured easily and used to compare individual animals regardless of sex, age, reproductive class, geographical population, or date-of-capture. The second objective was to evaluate the use of the BCI by comparing it to two other indices, the Quetelet Index (QI) and the Fatness Index (FIdd), that have been used in recent years to measure body condition in free-ranging polar bears. The third objective was to determine if the application of the BCI also could be extended to assess body condition in brown (or grizzly) bears and black bears. The BCI was developed from the measurements of total body mass (TBM) and straight-line body length (SLBL) recorded during the handling of 1072 captured polar bears. The BCI is the standardized residual determined from the regression of TBM on SLBL, and it ranges in value continuously from –3.0 to +3.0. The BCI values of the sample population were distributed normally and independent of body size. The variation in BCI values of 31 killed polar bears accounted for 25% of the variation in the mass of potential energy tissue (dissectable fat + skeletal muscle), but only 1% of the variation in the mass of structural tissue (skin, fur, bone, and viscera). These results indicate the BCI is sensitive to the abundance of potential energy stored in the body tissues of a polar bear independent of its body size. When comparing the body condition of 420 adult bears captured during different months (i.e., cross-sectional comparison), the differences in mean body condition values among months within different adult classes were similar for the BCI and the QI (equal to TBM ÷ SLBL2 in kg/m2). There were differences between the BCI and the QI, however, when comparing mean body condition values among adult classes within some months. These differences were explained by the finding that the QI was affected strongly by body size. When comparing the change in body condition of 20 bears captured at two different times (i.e., longitudinal comparison), there was no agreement between the BCI and the FIdd

(equal to the proportion of fat to lean body mass in kg/kg based on body composition estimated using deuterium dilution). There was, however, positive and significant correlation between the BCI and FIdissect (FI based on dissected tissue mass) in 31 killed polar bears, and between the BCI and FIchemical (FI based on whole body chemical composition) in 11 killed polar bears. Together, these findings caution against the use of an isotope dilution model developed in black bears and brown bears to estimate body lipid content in polar bears. Regression relationships between TBM and SLBL were determined for 106 brown bears and 51 black bears and compared against the relationship calculated for 1072 polar bears. The slopes and intercepts of the species-specific equations did not differ among species and, therefore, enabled the pooling of all data (n = 1229). BCI values were then re-calculated for all bears and nomograms were constructed to allow estimates of the BCI to be made at the points of intersection between paired values of SLBL and TBM. The use of nomograms provides rapid determination of BCI values without complex calculations (Figure 27).

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a)

+2.5+2.0+1.5+1.0+0.5

0-0.5-1.0-1.5-2.0-2.5

1501401301201100100

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500

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b)

BCI values

Tota

l Bod

y M

ass

c)

Tota

l Bod

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ass

65 70 75 80 85 90 95 100

Straight-line Body Length (cm)

Figure 27. Nomograms for estimation of Body Condition Index (BCI) values over straight-line body length intervals from: a) 150 to 220 cm; b) 100 to 150 cm; and c) 60 to 100 cm. The estimate of the BCI value is found at the point of intersection for paired values of straight-line body length and total body mass.

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12.4 Blood Analyses Investigators: Marc Cattet, Nigel Caulkett, and Gordon Stenhouse Blood is collected routinely from all bears captured during the FMF Grizzly Bear Research Program. The blood is subsequently analyzed to determine the types and numbers of different blood cells (hematology), and the concentrations of different analytes found in the blood serum (serum biochemistry). The results of these analyses have been critical for assessing the health of the individual bears, as well as for determining the effects of different immobilizing drugs and the effects of different methods of capture (Table 15 and 16). In the case of a 6 year-old female bear (G20), captured during October 2000, analyses also were completed to determine the serum concentrations of two reproductive hormones, estrogen (0 pg/ml) and progesterone (3.0 ng/ml). These blood results are strongly suggestive that G20 was pregnant at the time of her capture, and it is anticipated that one or more cubs will be accompanying her this spring. The spectrum of blood analyses will likely broaden over the next three years to include other analytes that are indicative of stress, reproductive function, immune function, and energy metabolism. The justification for this proposed expansion of blood analyses is provided in the following section on the study of “anthropogenic (human-caused) stress and health in grizzly bears.” 12.5 A Proposed Study: “Anthropogenic Stress and Health in Grizzly Bears” Investigators: Marc Cattet, Nigel Caulkett, Gordon Stenhouse, and Ralph Nelson In its first two years, the focus of the FMF Grizzly Bear Research Program has been directed primarily at grizzly bears as a population of organisms. However, it has increasingly become evident that, if changing ecological conditions can affect the movement patterns and population status of grizzly bears, then population level perturbations could be preceded by increased stress, or distress, and reduced health at the level of the individual grizzly bears. This possibility has prompted a major effort to expand the focus of the FMF Grizzly Bear Research Program over the next three years. The following is the project summary provided in a research proposal that was submitted to the United States National Science Foundation in January 2001. 12.5.1 Project Summary The physiological response to different stressors is recognized to have the potential to increase metabolic energy demand in humans and many domestic species. If the stress response is extreme or prolonged, the shift of energy from other biological functions to the stress response can lead to a gradual reduction in overall health. There has been little investigation to date of the links between the stress response, health, and population dynamics of wild animals that are ranging free within their natural environments and exposed to a wide variety of stressors. Nevertheless, human-caused or anthropogenic stressors have been implicated as a serious threat to the long-term viability of some species. An example is the grizzly bear (Ursus arctos). The population of 1,000 or more

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that exists currently in the lower forty-eight states of the USA has declined from an estimated 50,000 to 100,000 before the American West was settled. The destruction and degradation of grizzly bear habitat and human-caused mortality have been identified as the greatest threats to the persistence of this species bear in the lower forty-eight states. The proposed project will investigate the physiological response of wild grizzly bears to anthropogenic stressors of varying duration. It also will seek evidence for a “stress link” in the relationships between the health and population dynamics of grizzly bears and the ecological conditions of their home range under varying degrees of human activity. The specific research objectives are to:

• Characterize the physiological response of grizzly bears to different anthropogenic stressors.

• Identify health consequences of stress in grizzly bears by determining effects of stress on reproductive function, immune function, and energy metabolism.

• Seek evidence to determine if the stress response and health of grizzly bears is associated significantly with existing levels of human activity and landscape change within their home range.

The proposed project will be linked to the Foothills Model Forest (FMF) Grizzly Bear Research Program, a cooperative, inter-disciplinary, 5-year research effort to assess grizzly bear populations and their response to changing ecological conditions in an area of 9,752 km2 in west-central Alberta, Canada. Since 1999, forty grizzly bears have been captured and fitted with Global Positioning System radio collars that have provided detailed movement data used to define the home ranges and determine the rates of movement of individual bears. The movement data in combination with remote sensing data, and a large spectrum of Geographical Information Systems (GIS) data sets, is being used to compare grizzly bear movements and response to human activities among portions of the study area with differing degrees of human activity. During the next three years, the proposed research will integrate field research and detailed laboratory analyses to expand greatly upon the physiological information that is currently being collected. The stress response of captured grizzly bears to anthropogenic stressors of varying duration (acute, sub-acute, and chronic) will be assessed through the measurement of tissue levels of stress-related proteins (cortisol binding protein, heat shock proteins). The health of captured grizzly bears also will be assessed through an evaluation of reproductive function (reproductive endocrinology combined with ovarian ultrasonography and semen evaluation), immune function (humoral and cell-mediated immunity), and energy metabolism (body condition, metabolic hormones, and infrared thermographic calorimetry). The linkage of the proposed project to the FMF Grizzly Bear Research Program will enhance greatly the interdisciplinary perspective of the research, while saving a substantial amount of research money. The potential benefits to be gained from the proposed research will be that:

• it will provide research training for three new scientists, as well as the exposure of a minimum of five students to aspects of field and laboratory research;

76

• it will lead to the technological development of a set of sensitive and practical biochemical probes to be used to assess stress and health in grizzly bears; and

• it will provide new information regarding the potential effects of anthropogenic stressors on the health and population dynamics of grizzly bears.

Table 15. Range of hematology values for grizzly bears captured as part of the

Foothills Model Forest Grizzly Bear Research Program during 1999 and 2000.

Variable and Units Rangea Drug

Effectsb Capture Effectsc

Red blood cells (× 1012/L) 2.5 – 8.1 ns ns Hemoglobin (g/L) 72 – 245 ns ns Packed cell volume (L/L) 17 – 65 ns ns Mean corpuscular volume (fL) 65 – 87 ns ns Mean corpuscular hemoglobin (pg) 25.0 – 33.8 ns ns Mean corpuscular hemoglobin concentration (g/L) 304 – 422 ns ns Platelets (× 109/L) 93 – 460 ns ns White blood cells (× 109/L) 3.3 – 25.5 ns *** Neutrophils (%)

(× 109/L) 45 – 99

1.5 – 17.9 ns ns

*** ***

Lymphocytes (%) (× 109/L)

0 – 25 0.0 – 2.3 ns

*** ***

Monocytes (%) (× 109/L)

0 – 31 0.0 – 2.7

ns ns

ns ns

Eosinophils (%) (× 109/L)

0 – 9 0.00 – 0.65

ns ns

** ***

Basophils (%) (× 109/L)

0 – 1 0.00 – 0.08

ns ns

ns ns

ns

a Range represents the minimum and maximum values for 34 grizzly bears for which hematology was completed. b Results of two-factor ANOVA to determine if there were any significant differences in hematology values between

bears immobilized with ZT (n = 15) and bears immobilized with XZT (n = 19). Significance indicated by ‘ns’ for non-significant, ‘**’ for p ≤ 0.01, and ‘***’ for p ≤ 0.001.

c Results of two-factor ANOVA to determine if there were any significant differences in hematology values between bears captured by aerial darting (n = 14) and bears captured by leg-hold snare (n = 20). Grizzly bears captured by leg-hold snare had significantly higher values for white blood cells and neutrophils, and significantly lower values for lymphocytes and eosinophils.

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Table 16. Range of serum biochemistry values for grizzly bears captured as part of the Foothills Model Forest Grizzly Bear Research Program during 1999 and 2000.

Variable and Units Rangea Drug

Effectsb Capture Effectsc

Sodium (mmol/L) 131 – 152 Ns *** Potassium (mmol/L) 3.0 – 6.8 Ns Ns Chloride (mmol/L) 95 – 121 Ns *** Bicarbonate (mmol/L) 10 – 28 Ns Ns Anion gap (mmol/L) 13 – 30 Ns Ns Calcium (mmol/L) 1.96 – 2.77 Ns ns Phosphorus (mmol/L) 0.81 – 2.22 Ns ns Urea (mmol/L) 1.7 – 23.9 Ns ns Creatinine (µmol/L) 47 – 249 Ns ns Glucose (mmol/L) 3.1 – 13.6 ** * Cholesterol (mmol/L) 3.48 – 8.81 Ns ns Total bilirubin (µmol/L) 2 – 13 Ns ** Amylase (U/L) 7 – 378 Ns ns Lipase (U/L) 108 – 769 Ns ns Alkaline phosphatase (U/L) 10 – 130 Ns ns Alanine aminotransferase (U/L) 11 – 195 Ns ** Aspartate aminotransferase (U/L) 55 – 702 Ns *** γ-Glutamyltransferase (U/L) 2 – 108 Ns ns Creatine kinase (U/L) 49 – 26,020 Ns ** Total protein (g/L) 58 – 86 Ns ns Albumin (g/L) 34 – 50 Ns ns Globulin (g/L) 21 – 41 Ns ns Albumin:globulin ratio 0.85 – 1.89 Ns ns Total cortisol (nmol/L) 42 – 1,227 Ns * Selenium (ppm) 0.045 – 0.370 Ns ns a Range represents the minimum and maximum values for 44 grizzly bears for which serum biochemistry was

completed. b Results of two-factor ANOVA to determine if there were any significant differences in serum biochemistry values

between bears immobilized with ZT (n = 22) and bears immobilized with XZT (n = 22). Grizzly bears immobilized with XZT had significantly higher values for glucose concentration. Significance indicated by ‘ns’ for non-significant, ‘*’ for p ≤ 0.05, ‘**’ for p ≤ 0.01, and ‘***’ for p ≤ 0.001.

c Results of two-factor ANOVA to determine if there were any significant differences in serum biochemistry values between bears captured by aerial darting (n = 18) and bears captured by leg-hold snare (n = 26). Grizzly bears captured by leg-hold snare had significantly higher concentrations of sodium, chloride, glucose, total cortisol, alanine aminotransferase, aspartate aminotransferase, creatine kinase, and total bilirubin.

13. GIS Applications The GIS component of the program has been successful in integrating field data, such as the grizzly bear GPS-radio collar locations, into the GIS system. Detailed procedures and data-checking steps are now in place and will ensure consistency and efficiency in the upcoming field seasons. The digital data has been used for data and spatial analyses including grizzly bear road crossings, distances from roads, etc. New data sets have been or will be acquired. These include:

• recent Landsat TM imagery, 1998 IRS imagery (5m, panchromatic),

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• Weldwood and crown land Alberta Vegetation Inventory (AVI),

• Weldwood’s Ecological Land Classification (ELC) and

• updated provincial digital base data. The digital data will aid in new analyses including quantifying landscape change over time in terms of grizzly bear habitat suitability and potential. Two students from the British Columbia Institute of Technology (BCIT) are working on a project to create a realistic 3-D landscape visualization tool. This tool will incorporate satellite imagery and spatial data to demonstrate to land managers the movement patterns of radio-collared grizzly bears within the study area. This project is supervised by the FMF GIS staff and, if successful, will provide an effective communication tool for the program. 14. Communications Communication and understanding as critical is industry, government, and the public are to play a role in sustainable forest management and a healthy grizzly bear population. Research and stewardship often goes unrecognized by the public, often because it is done quietly and without fanfare. Foothills Model Forest believes that communication builds understanding and support, and is dedicated to delivering a well-rounded program to that end. A long-term communications strategy is developed for Foothills Model Forest. Annual communication work plans are developed for Foothills Model Forest as well as the grizzly bear research program. Foothills Model Forest understands and is committed to communicating a sustainable forest management message. The key messages of the Foothills Model Forest Grizzly Bear Program are as follows:

• Sustainable Grizzly Population The goal of the research program is to provide land and resource managers with common and consistent data and analysis, and planning and management tools to help ensure the long-term conservation of grizzly bears in the Alberta Yellowhead Ecosystem. • Partnerships Successful conservation of the Grizzly Bear requires a cooperative, integrated approach by government, industry, scientists, associations and key stakeholders. • Grizzly Bears are an Indicator Species Grizzly bears are regarded as an “indicator” and “umbrella” species. They are widely regarded as a species with very poor resiliency to stress, therefore provide a reliable indicator of ecosystem health. They are an “umbrella” species since the maintenance of landscape conditions favourable for grizzly bears result in conditions beneficial to a wide range of other wildlife.

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• Stewardship The conservation of grizzly bear populations is an ongoing process and will require the long-term commitment and participation of land and resource managers along with the overall support of the public. During the first two years of this program efforts were made to communicate the above messages to partners, general public and key stakeholders. Communication activities have been successful with considerable interest coming from a number of media outlets. In February 2000 a second (first series aired in the fall of 1999) major feature on this program was presented on the Discovery Channel. 15. Literature Cited Akcakaya, H.R. and J.L. Atwood. 1997. A habitat-based metapopulation model of the

California Gnatcatcher. Conservation Biology 11:422-434.

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Alldredge, J.R., D.A. Thomas and L.L. McDonald. 1998. Survey and comparison of methods for study of resource selection. Journal of Agricultural, Biological and Environmental Statistics 3:237-253.

Anderson, D.R., G.C. White and K.P. Burnham. 1995. Some specialized risk assessment methodologies for vertebrate populations. Environmental and Ecological Statistics 2:91-115.

Banci, V. 1991. The status of the grizzly bear in Canada in 1990. Commission on the Status of Endangered Wildlife in Canada. Report. British Columbia Ministry of Environment. Victoria. 131pp.

Benn, B. 1998. Grizzly bear mortality in the Central Rockies Ecosystem. M.Sc. Faculty of Environmental Design. University of Calgary.

Boulanger, J. and B. McLellan. Submitted. Closure violation bias in DNA based mark-recapture population estimates of grizzly bears. Canadian Journal of Zoology.

Boyce, M.S. and L.L. McDonald. 1999. Relating populations to habitats using resource selection functions. Trends in Ecology and Evolution 14:268-272.

Boyce, M.S. and J.S. Waller. 2001. Predicting the number of grizzly bears in the proposed recovery area of the Bitterroot ecosystem. Manuscript.

Bryson, S. 1991. Search dog training. Howell Book House. Chicago, IL.

Bryson, S. 1996. Police dog tactics. McGraw Hill, New York.

Burnham, K.P. and D.R. Anderson. 1998. Model selection and inference: A practical information-theoretic approach. Springer. New York, U.S.A. 353pp.

Deuling, M.J. 1999. Quantification and assessment of caribou habitat fragmentation: An integrated GIS, remote sensing, and landscape ecology method. Unpublished Master of Science Thesis. University of Calgary, Calgary, Alberta.

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Gibeau, M.L. and S. Herrero. 1998. Managing for grizzly bear security areas in Banff National Park and the central Canadian Rocky Mountains. 10pp.

Hansen, M.J., S.E. Franklin, C.G. Woudsma and M. Peterson. 2000. Caribou habitat mapping and fragmentation analysis using Landsat MSS, TM and GIS data in the North Columbia Mountains, British Columbia, Canada. Remote Sensing of the Environment. In press.

Hovey, F.W. 1999. The Home Ranger. Version 1.5. Revelstoke, B.C. Ursus Software.

Huggins, R.M. 1991. Some practical aspects of a conditional likelihood approach to capture experiments. Biometrics 47:725-732.

Johnson, D.H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65-71.

Kansas, J.L. and R.N. Riddell. 1995. Grizzly bear habitat model for the four contiguous mountain parks. 2nd Iteration. Riddell Environmental Research Ltd. and Ursus Ecosystem Management Ltd. for Parks Canada. 95pp and appendices.

Kendall, W.L. 1999. Robustness of closed capture-recapture methods to violations of the closure assumption. Ecology 80(8):2517-2525.

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Appendix I FMF Grizzly Bear Project Publication/Technical Paper List 1. Stenhouse, G.B. 1999. The Foothills Model Forest Grizzly Bear Research Project. A

research initiative in support of “A Framework for the Integrated Conservation of Grizzly Bears”. Work plan for 1998-1999. 120pp.

2. J.L. Lee and G.B. Stenhouse. 1999. Comparison of Grizzly Bear telemetry location data with a grizzly bear habitat model. Foothills Model Forest Report. 29pp.

3. Dugas, J. and Stenhouse, G.B. 1999. Grizzly Bear Management: Validating Existing Cumulative Effects Models. Thirteenth annual conference on geographic information systems. Vancouver, B.C. 1999.

4. GeoAnalytic Inc. Application of Evidential Reasoning to the Classification of Grizzly Bear Habitat using Landsat TM and Ancillary Data, Milestone Report 1. December 31, 1999. For: Canada Centre for Remote Sensing, Contract number: 23413-9-D220-01/SQ

5. GeoAnalytic Inc. Application of Evidential Reasoning to the Classification of Grizzly Bear Habitat using Landsat TM and Ancillary Data, Milestone Report 2. January 31, 2000 For: Canada Centre for Remote Sensing, Contract number: 23413-9-D220-01/SQ

6. Dechka, J. Franklin S., Peddle D., and G. Stenhouse. Land cover mapping and landscape fragmentation analysis in support of grizzly bear habitat management.

7. Presented at Geographic Information Systems and Remote Sensing for Sustainable Forest Management: Challenge and Innovation in the 21st Century, Workshop, February 23-25, 2000, Edmonton, AB.

8. Stenhouse, G.B. and R. Munro. 1999. Foothills Model Forest Grizzly Bear Research Program 2000 Annual Workplan (year 2)

9. Stenhouse, G.B. and R. Munro. 2000. Foothills Model Forest Grizzly Bear Research Program 1999 Annual Report. 110 pp

10. S.E. Franklin, D.R. Peddle, J.A. Dechka, and G. B. Stenhouse. Grizzly bear habitat mapping in the Alberta Yellowhead Ecosystem using evidential reasoning with Landsat TM, DEM and GIS data. Paper presented at the Sixth Circumpolar Conference on Remote Sensing of Arctic Environments, Yellowknife, NWT , June 2000.

11. S.E. Franklin, D.R. Peddle, J.A. Dechka, and G.B. Stenhouse. Evidential reasoning with Landsat TM, DEM, and GIS data for landcover classification in support of grizzly bear habitat mapping. International Journal of Remote Sensing. 2001 (in press).

12. Stenhouse, G.B. and G. Mowat. Grizzly Bear DNA Hair Inventory Project Results. Paper presented at the Managing for Bears in Forested Environments Conference, October 17-19, 2000, Revelstoke, B.C.

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13. Wasser, S. and G.B. Stenhouse. Grizzly Bear Inventory using trained dogs and DNA scat analysis. Paper presented at the Managing for Bears in Forested Environments Conference, October 17-19, 2000, Revelstoke, B.C.

14. Skrenek, J. D. Hodgins and G.B. Stenhouse. Managing cumulative effects on Grizzly Bears: An inter agency and multi-stakeholder strategy in the Alberta Yellowhead Ecosystem. Paper presented at “Environmental Cumulative Effects Management Conference" November 1-3, 2000, Calgary, Alberta.

15. S.E. Franklin, G.B. Stenhouse, M.J. Hansen, C.C. Popplewell, J.A. Dechka, and D.R. Peddle. An Integrated Decision Tree Approach (IDTA) to mapping landcover using satellite remote sensing in support of grizzly bear habitat analysis in the Alberta Yellowhead Ecosystem. 2000. Canadian Journal of Remote Sensing. (submitted).

16. Mucha, D.M., G.B. Stenhouse and J. Dugas. A 3D landscape visualization tool using satellite imagery for grizzly bear management in the Alberta Yellowhead Ecosystem. Paper submitted to the 2001 Alberta Chapter of the Wildlife Society Annual Meeting, March 3-5, Banff, Alberta.

17. Stenhouse, G.B. 2001. Grizzly Bear Conservation in the Northern East Slopes of Alberta: the integration of land management direction and grizzly bear research. Paper presented at the 13th International Conference on Bear Research and Management. May 21-25, 2001, Jackson, WY. Manuscript submitted to Ursus for publication.

18. Popplewell, C. Stenhouse, G.B., Hall-Beyer M. and S.E. Franklin. 2001. Using remote sensing and GIS to quantify the Landscape structure and habitat fragmentation within grizzly bear management units in the Yellowhead Ecosystem, Alberta. Poster presented at the 13th International Conference on Bear Research and Management. May 21-25, 2001, Jackson, WY. Manuscript submitted to Ursus for publication.

19. Nielsen, S.E., Boyce, M., Stenhouse, G.B. and R. Munro. 2001.Resource selection of grizzly bears in the Yellowhead Ecosystem of Alberta, Canada. Poster presented at the 13th International Conference on Bear Research and Management. May 21-25, 2001, Jackson, WY. Manuscript submitted to Ursus for publication.

20. Cattet, M.R.L., Caulkett, N.A. and G.B. Stenhouse. 2001. The comparative effects of chemical immobilizing drug and method of capture on the health of free-ranging grizzly bears. 2001. Paper presented at the 13th International Conference on Bear Research and Management. May 21-25, 2001, Jackson, WY. Manuscript submitted to Ursus for publication.

21. Cattet, M.R.L., Caulkett, N.A. and G.B. Stenhouse. 2001. The development and assessment of a body condition index for polar bears and its application to brown bears and black bears. Paper presented at the 13th International Conference on Bear Research and Management. May 21-25, 2001, Jackson, WY. Manuscript submitted to Ursus for publication.

22. Munro, R.H.M., Nielsen, S.E., Stenhouse, G.B. and M.S. Boyce. 2001. The influence of habitat quality and human activity on grizzly bear home range size. . Paper presented at the 13th International Conference on Bear Research and Management. May 21-25, 2001, Jackson, WY. Manuscript submitted to Ursus for publication.

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23. Stenhouse, G.B. 2001. The Foothills Model Forest Grizzly Bear Research Program: Building on Partnerships. Invited paper presented at the Alberta Conservation Association “Partners in Conservation Conference”, February 10, 2001, Nisku, Alberta.

24. Nielsen, S.E., Boyce, M.S., Stenhouse, G.B., Munro, R. 2001. Using Resource Selection Functions in Population Viability Analysis of Yellowhead Grizzly Bears. Paper presented at Alberta Conservation Association “Partners in Conservation Conference”, February 10, 2001, Nisku, Alberta.

25. Stenhouse, G.B. 2001. Foothills Model Forest Grizzly Bear Research Program. Invited paper presented at the 2001 Environmental Research and Technology Development Forum for the Upstream Oil and Gas Industry. Sponsored by Petroleum Technology Alliance Canada (PTAC). January 31, 2001, Calgary, Alberta.

Last modified February 1, 2001.