FOOTHILLS MODEL FOREST GRIZZLY BEAR RESEARCH PROGRAM

2001 ANNUAL REPORT (year 3 of a 5 year study)

Prepared and edited by Gordon B. Stenhouse and Robin Munro March 2002

Citation: Stenhouse, G. and R. Munro. 2001. Foothills Model Forest Grizzly Bear Research Program 2001 Annual Report. 127 pp. 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 three 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 three 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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The Foothills Model Forest Grizzly Bear Research Program 2001

Executive Summary

The FMF Grizzly Bear 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. The study area is approximately 9900 km2 and covers a portion of both mountainous and foothills habitats. A strong gradient in land-use activities and human disturbance exists across the study area. Currently, oil and gas exploration and development, forestry, mining, hunting, settlement, tourism, and recreation dominate the human land use practices and activities. In 2001, a total of 29 grizzly bears were handled, of which 23 were fitted with GPS radio collars. We recaptured and recollared 17 bears that were collared during the first two years of this program. From the cementum analysis conducted, our capture sample of bears was found to include 21 adult and eight subadult bears. 1. The influence of habitat quality and human activity on grizzly bear home range selection and size

RSF models and multiple regression analysis were used to explore the effects of habitat quality, topography and human access densities on home range selection and size of 10 female grizzly bears. High values of greenness derived from Landsat TM imagery corresponded well with grizzly bear home range selection during spring and fall seasons. This supports the general belief that bears seek out the most productive habitats. The proportion of high greenness was also inversely related to home range size in the fall but not in spring. Topography and human access density did not influence either home range selection or home range size. Removal and extensive fragmentation of higher-quality habitats could lead to greater patchiness of critical habitats and thus, longer inter-patch travelling sessions between forage events. 2. Resource selection functions and population viability analyses

Spatially explicit RSF models and maps that describe grizzly bear habitat (relative probability of occurrence) have been developed. Specific research objectives are: 1) describe habitat selection; 2) identify key grizzly bear habitats; 3) examine impacts of human development; 4) search for mechanistic links and appropriate scales of selection; and finally 5) develop a habitat-based population viability (PVA) model for the Yellowhead Ecosystem.

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Accounting for GPS Collar Bias

To overcome biases within habitat-selection models, we evaluated the use of weighted logistic regression and multiple-imputation on a known (simulated ‘truth’) animal. Results indicate that weighted logistic regression was more efficient than multiple-imputation (a more stochastic approach) for correctly detecting selection and making appropriate inferences. RSF Modeling for Individuals and Populations

We focused on 3rd order resource selection during two seasons (pre-berry and post-berry) of 1999 using variables previously identified as important for predicting grizzly bear occurrence.

• Individual-level RSF Models Results indicated that selection for habitats was variable, depending on the bear and the season. In the spring (pre-berry), individuals tended to select for areas of high greenness, near streams, and in alpine habitats. Avoidance during this season was evident for non-vegetated areas and young regenerating forests. The influence of forest management (cut-blocks) varied considerably. Model strength and prediction was greater during the post-berry season. During this period, the importance of greenness for predicting bears was evident, as all bears responded significantly.

• Population -level RSF Models During the pre-berry season, grizzly bears selected for areas of high greenness. Perennial streams were selected, while both major streams and intermittent streams were used in proportion to availability. Alpine habitats were selected, while both non-vegetated areas and young (3 to 44 years old) regenerating forests were strongly avoided when compared to the reference category of closed forest stands. No significant pattern of selection was detected for cut-blocks, although there was a tendency for recent cut-block classes (0 to 12 years old) to be avoided. Access density, elevation and hillshade all failed to affect distributions of grizzly bears during this season. During the post-berry season, variables contributing to the grizzly bear model included alpine, recent burn, cut-blocks 22 to 44 years old, open forests, young regenerating forests, shrub/wetlands, greenness, major streams, perennial streams, and both high and moderate impact access density. High values of greenness were again strong predictors of grizzly bear occurrence. Furthermore, bears tended to be found along major streams, with only a slight preference for habitats along perennial streams. Habitat classes where selection occurred included alpine, recent burn, cut-blocks 22 to 44 years old, herbaceous, open forests, and shrub/wetland habitats.

• Habitat Index Models and RSFs We tested the ability of current habitat index models (index 1-10 in grizzly bear habitat quality) to predict grizzly bear occurrence, using an RSF approach (e.g., using the habitat index model as an independent variable). Based on these tests and further analyses that have examined food index models in more detail (Nielsen et al. 2002b, see

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part 9.4), we caution the use of current habitat index models for assessing grizzly bear habitats and cumulative effects assessments. 3. Food phenology models for grizzly bear predictions

We compared the use of three habitat models for estimating the relative probability of occurrence for grizzly bears in eastern Jasper National Park (JNP). These models included, 1) the IDTA habitat map (Franklin et al. 2001); 2) food index models generated from the predicted occurrence of plant foods and assigned monthly importance values; and 3) probabilistic food models representing the occurrence of each plant bear food. Grizzly bear food resources in Jasper National Park were principally related to elevation, hillshade, age of stand, soil drainage, and the interaction of vegetation and age. Food index maps produced from the predicted presence of each species and monthly food values (Kansas and Riddell 1995) proved poor predictors of grizzly bear occurrence. The use of habitat-effectiveness models that base habitat potential from qualitative food models in the western four contiguous parks of Canada (Banff, Jasper, Kootenay, and Yoho) should be cautioned, since grizzly bear predictive performance was so poor. We found substantial improvement in the use of a remote sensing classification (Franklin et al. 2001) and empirically based food probability models. 4. Pre-berry and Post-berry RSF models for 1999-2001

Using similar methods as those outlined in previous modelling, we describe here RSF models developed at the population-level across all three years of GPS radiotelemetry data (1999 to 2001). In comparison with the reference habitat category (closed conifer), model 2 pre-berry estimates for habitat selection were positive for alpine, cuts >12 years old, deciduous forests, non-vegetated areas, open conifer, recent burn, regenerating forests, and shrub-bog-wetlands. Negative selection (avoidance) occurred for cuts 0-12 years old, herbaceous areas, and mixed forests (Table 8). Changes in selection during the post-berry season included avoidance of old cut-blocks and selection for young cut-blocks. Open conifer, non-vegetated habitats, and regenerating forests switched from positive to negative selection. Interpretations of such results are purely based on the reference to selection for closed conifer stands. Both greenness and habitat diversity were strongly significant and positive during the pre- and post-berry seasons. Access density varied by type, with only motorized low use linear features being negative. The overall cumulative effect on selection however, was negative, as this class made up the majority of the linear features with the other classes being uncommon to rare. 5. Microsite Habitat Selection

In total, 231 use plots were completed on four subadult and adult female grizzly bears. From May to June it appears that Hedysarum spp. digging dominated the feeding activity. In early July through to early August, the grizzly bears began to key in on herbaceous species, such as clover, cow parsnip and equisetum. By August 1 and through till the end of September, the bears were primarily feeding on Sherperdia

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canadensis. Hedysarum digging continued into September, although this type of activity diminished considerably from July onwards. It appears that bears in our study area did not significantly utilize any of the Vaccinium species. Anting behavior remained fairly constant throughout the active season. 6. Animal Health (1999 to 2001)

Assessment and monitoring of the health of individual grizzly bears has been a major focus of the FMF grizzly bear research program since beginning in 1999. Over the past years, the comparison of health data among individual animals has allowed us: (1) to evaluate and improve the safety of different drug combinations used to anesthetize grizzly bears; and (2) to evaluate the stress and potential health consequences of different methods of capturing grizzly bears. Further, 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 and black bears, to also be used with grizzly bears. Overall, capture and physical restraint by leg-hold snare caused a greater degree of physiological disturbance than did chemical immobilization of free-ranging bears by remote injection from a helicopter. 7. Scat Detection Dog Studies, 1999-2001

Specially trained scenting dogs were used to detect fecal samples over large, remote geographic areas. Stress and reproductive hormones extracted from feces will be used to indicate physiological condition of the animal. DNA extracted from feces is used to confirm the species (mitochondrial DNA; mtDNA), gender (single copy nuclear DNA; scnDNA) and individual identity (microsatellite DNA; µsatDNA) of the animal that left each sample used in these analyses. The genetic data are also used to estimate species-specific abundance and distributions in relation to location-specific environmental disturbances. 8. Graph Theoretic Methods for Examining Landscape Connectivity and Spatial Movement Patterns: Application to the FMF Grizzly Bear Research Program New approaches to measuring and understanding connectivity based on Graph Theory (a branch of mathematics) have been introduced, and while promising, have yet to be thoroughly tested and established. The graph theoretic model provides empirical measurements for landscape connectivity and may aid in understanding the movement patterns of associated grizzly bear populations. Specific research objectives are: 1) To modify and apply a graph theoretic model for the analysis of movement and connectivity patterns associated with female grizzly bear populations. 2) To validate the graph theory model with real movement data. 3) To compare the graph theoretic based model to existing approaches modeling connectivity (e.g. Linkage Zone Model).

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9. Grizzly Bear Foothills Habitat Fragmentation by Seismic Cutlines Mapped from Indian Remote Sensing (IRS) Imagery

The purpose of this study was to identify and map seismic lines on the landscape using 5 m resolution panchromatic Indian Remote Sensing (IRS) satellite imagery. As well, the relationship between landscape structure and grizzly bear landscape use will be explored. Mapping seismic lines from IRS images solely proves to be a successful method, which hardly overestimates but slightly underestimates cutlines. This initial assessment of landscape metrics leads to conclude that seismic cutlines of both types, explorative and exploitative, are a major fragmentation causes for the grizzly habitat in the year 1999, when investigating from a strictly landscape structural perspective. It is to be the objective of the year 2002, to explore further the meaning of these fragmentation levels, in relation to grizzly bear landscape use. 10. Mapping and Quantification of Change in Landscape Structure in Grizzly Bear Habitat

The research to be completed and described here will aid resource managers in their ability to understand landscape changes in the FMF grizzly bear research program area over the past 50 years. Deliverables include (1) a methodology for determining past landscapes based on historical aerial photos and which then resemble satellite image products of more recent vintage, (2) a series of map products documenting previous landscape and showing landscape change up to the present including greenness maps and classification maps, and (3) an estimation of landscape use for alternative landscapes by grizzly bears. The data compiled will also allow for the modeling of future scenarios of landscape change in addition to being inputs in RSF modeling activities. The projected completion date for this research is February 2003.

11. Habitat Structure and Fragmentation of Grizzly Bear Management Units and Home Ranges in the Alberta Yellowhead Ecosystem

This thesis research looked at the degree of grizzly bear habitat fragmentation present in 1999 in the Foothills Model Forest (FMF) Grizzly bear research program (GBRP) study area in the Alberta Yellowhead Ecosystem. A baseline of landscape structure was established that will be useful for evaluating change and making future land management decisions. Structural differences among Bear Management Units (BMUs) and Minimum Convex Polygon (MCP) home ranges were assessed.

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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 2001 capture crew of: Martin Urquhart, Bernie Goski, John Lee, Dave Hobson, Marc Cattet, Nigel Caulkett, Katina Christison, Perry Abramenko, Neil Brad, Jeanette Brooks, Tony Brooks, Andy Davidson, Jurgen Deagle, Mike Dilon, Mike Eder, Mike Ewald, Randy Flath, Matt Garnett, Rocky Hornung, Randy Kadatz, Keith Linderman, Kim McAdam, Dennis Palkin, Stuart Polege, Joe Pollock, Todd Ponich, Rick Ralf, Shane Ramstead, Dave Robertson, Ken Schmidt, Greg Slatter, Dennis Urban, Andy Van Imschoot, and Terry Winkler. Without the dedicated hard work and perseverance of these individuals we wouldn’t have met with another very successful capture program. A special thank you goes to John Saunders who, despite having arrived on the scene under difficult circumstances, exceeded our expectations. He adjusted smoothly and effectively to his new role with the grizzly bear research team, a testament to his skill and knowledge as a pilot. Thanks also to Mike Dupuis who 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 for her expertise, enthusiasm in all areas relating to GIS and GPS goes to Julie Dugas who is one of the key members of our research team. A thank you to the vegetation plot crew members Kristen Kolar, Terry Larsen, Erin Moore and Stephanie Woelk. Their hard work and keen attitudes ensured a successful summer field season. 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.

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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. The staff at the Hinton Environmental Training Centre 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 Anna Kaufman, Fiona Ragan, Lisa Risvold, and Patsy Vik. 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. FOREWORD.......................................................................................................................... 1 2. INTRODUCTION................................................................................................................. 1 3. PROGRAM BACKGROUND ............................................................................................. 2 4. LONG TERM PROGRAM OBJECTIVE............................................................................. 3 5. GRIZZLY BEAR RESEARCH PROGRAM AREA AND METHODS ........................... 3 6. BEAR CAPTURING AND HANDLING .......................................................................... 7

6.1 Field Operations........................................................................................................... 7 7. RESULTS - 2001 GRIZZLY BEAR CAPTURE AND GPS MOVEMENT DATA Gordon Stenhouse and Robin Munro, Foothills Model Forest.................................... 11

7.1 Capture Results .......................................................................................................... 11 8. THE INFLUENCE OF HABITAT QUALITY AND HUMAN ACTIVITY ON

GRIZZLY BEAR HOME RANGE SELECTION AND SIZE. Robin Munro (Foothills Model Forest), Scott Nielsen (University of Alberta), Julie Dugas (Foothills Model Forest), Gordon Stenhouse (Foothills Model Forest), and Mark Boyce (University of Alberta)........................................................... 14 9. RESOURCE SELECTION FUNCTIONS AND POPULATION VIABILITY

ANALYSES Scott E. Nielsen and Mark S. Boyce, Department of Biological Sciences, University of Alberta, scottn@ualberta.ca....................................................................... 17

9.1 Introduction ................................................................................................................ 17

9.2 Accounting for GPS Collar Bias ............................................................................... 18

9.3 RSF Modeling for Individuals and Populations (Nielsen et al. 2002a). ............. 20 9.3.1 Methods............................................................................................................... 21 9.3.2 Results.................................................................................................................. 23 9.3.3 Discussion ........................................................................................................... 26

9.4 Food phenology models for grizzly bear predictions (Nielsen et al. 2002b)..... 26 9.4.1 Methods............................................................................................................... 27 9.4.2 Results.................................................................................................................. 29

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9.4.3 Discussion ........................................................................................................... 32

9.5. Pre-berry and Post-berry RSF models for 1999-2001 ............................................ 33 9.5.1 Methods............................................................................................................... 33 9.5.2 Results.................................................................................................................. 36 9.5.3 Discussion ........................................................................................................... 40

9.6 Field Programs and Future Directions.................................................................... 40 10. MICROSITE HABITAT SELECTION BY FEMALE GRIZZLY BEARS R. Munro (Foothills Model Forest), S. Nielsen (University of Alberta), G. Stenhouse (Foothills Model Forest), and M. Boyce (University of Alberta) .............. 43

10.1 Introduction ................................................................................................................ 43

10.2 Methods....................................................................................................................... 43

10.3 Results.......................................................................................................................... 44 10.3.1 Seasonal Changes in Feeding Activity............................................................ 45 10.3.2 Bear Activity by IDT classification .................................................................. 46 10.3.3 Scat analysis ........................................................................................................ 49 10.3.4 Animal Health (1999 to 2001) ........................................................................... 49

11. SCAT DETECTION DOG STUDIES, 1999-2001

...................Samuel K. Wasser, Ph.D. (Center for Conservation Biology, University of Washington Department of Zoology), Gordon Stenhouse (Foothills Model Forest)................................................................................................................................... 53

11.1 Introduction ................................................................................................................ 53

11.2 Methods....................................................................................................................... 54 11.2.1 Why Test This Method on the Yellowhead Grizzly Bears? ......................... 54 11.2.2 Utility of Fecal Hormone and DNA Measures .............................................. 54 11.2.3 Degradation and preservation of fecal hormones......................................... 56 11.2.4 Factors Impacting DNA Amplification Success ............................................ 57 11.2.5 Sample Collection Biases .................................................................................. 58 11.2.6 Use of detection dogs for scat collection......................................................... 59 11.2.7 Scat detection dog and handler training......................................................... 59 11.2.8 Field Studies of the Proposed Methodology in the Yellowhead................. 60

11.3 Continued Studies...................................................................................................... 66 11.3.1 Disturbance Measures: Habitat and Human Use Data Sets ........................ 66 11.3.2 DNA Extraction and Amplification ................................................................ 67 11.3.3 Fecal hormone extractions ................................................................................ 68

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11.3.4 Population monitoring using scat detection dogs ........................................ 69 12. GRAPH THEORETIC METHODS FOR EXAMINING LANDSCAPE

CONNECTIVITY AND SPATIAL MOVEMENT PATTERNS: APPLICATION TO THE FMF GRIZZLY BEAR RESEARCH PROGRAM Barb Schwab (M. Sc. Student, Department of Geography, University of Calgary, blschwab@telusplanet.net) and Clarence Woudsma (Associate

.........................................Professor, Department of Geography, University of Calgary, cwoudsma@ucalgary.ca) ................................................................................................... 74

12.1 Executive Summary................................................................................................... 74

12.2 Introduction ................................................................................................................ 75 12.2.1 Connectivity........................................................................................................ 75 12.2.2 Graph Theory Models ....................................................................................... 75

12.3 Methodology............................................................................................................... 76 12.3.1 Cost Surface Development and Validation.................................................... 76 12.3.2 Node and Edge Creation................................................................................... 78 12.3.3 Graph Calculations and Comparisons............................................................ 79

12.4 Research Progress ...................................................................................................... 80 13. GRIZZLY BEAR FOOTHILLS HABITAT FRAGMENTATION BY SEISMIC

CUTLINES MAPPED FROM INDIAN REMOTE SENSING (IRS) IMAGERY Julia Linke (Masters of Science Project Progress Report, University of Calgary) ..... 82

13.1 Introduction ................................................................................................................ 82

13.2 Methods....................................................................................................................... 83 13.2.1 Mapping and Validating Seismic Cutlines..................................................... 83 13.2.2 Landscape Metric Assessment ......................................................................... 86

13.3 RESULTS ..................................................................................................................... 86 13.3.1 Seismic Maps ...................................................................................................... 86 13.3.2 Seismic Cutlines as Fragmentation Features ................................................. 87

14. MAPPING AND QUANTIFICATION OF CHANGE IN LANDSCAPE

STRUCTURE IN GRIZZLY BEAR HABITAT P. Kirk Montgomery, (Master of Science Project Report, University of Calgary)..... 90

14.1 Introduction ................................................................................................................ 90

14.2 Purpose and Background.......................................................................................... 90

14.3 Data and Methods...................................................................................................... 91 14.3.1 Spatial Data ......................................................................................................... 92

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14.3.2 Ground Data ....................................................................................................... 93

14.4 Summary ..................................................................................................................... 93 15. HABITAT STRUCTURE AND FRAGMENTATION OF GRIZZLY BEAR

MANAGEMENT UNITS AND HOME RANGES IN THE ALBERTA YELLOWHEAD ECOSYSTEM

Charlene Popplewell M.Sc. Thesis, University Of Calgary, September 2001............ 94

15.1 Introduction ................................................................................................................ 94

15.2 Research Objectives and Methods........................................................................... 94

15.3 Discussion and Conclusions..................................................................................... 98

15.4 Implications for Management ................................................................................ 102

15.5 Recommendations for Future Research................................................................ 103 16. LITERATURE CITED....................................................................................................... 105 APPENDIX I

Publication/Technical Paper List .................................................................................. 121 APPENDIX II

Foothills Model Forest Grizzly Bear Research Partners ............................................. 126

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List of Tables Table 1. Natural sub-region composition of the grizzly bear research program area. ..... 5 Table 2. Collared and handled grizzly bears between 1999 and 2001............................... 11 Table 3. GPS location data between 1999 and 2001.............................................................. 11 Table 4. Reproductive status of females from 1999 to 2001. ............................................... 12 Table 5. Variables and estimated coefficients for the spring 95% and 50% kernel home range selection analysis. ................................................................................ 15 Table 6. Independent predictor variables used for a priori RSF models.. ......................... 33 Table 7. Comparison of seasonal a priori RSF models predicting the relative occurrence of grizzly bears in the Yellowhead study area.................................. 35 Table 8. Variables and estimated parameters for seasonal (pre-berry and post- berry) resource selection function (RSF) models of the Yellowhead

Ecosystem, Alberta. ................................................................................................... 36 Table 9. Total number of vegetation micro-site plots for female grizzly bears 2001. ..... 45 Table 10. Mean concentrations of reproductive hormonesa measured in the blood

serum of grizzly bears captured as part of the FMF grizzly bear research program from 1999 to 2001. .................................................................................... 52 Table 11. Measurements taken on scat at time of collection to assess sample age.

Average weather and exposure conditions over past week are also recorded, including amount of shade, temperature, and precipitation........... 65

Table 12. Description of Cost Surface Types......................................................................... 77 Table 13. Accuracy Assessment of IRS-mapped and of ACCESS program Seismic

layers. ......................................................................................................................... 86 Table 14. Densities of Explorative Cutlines and combined Explorative and Exploitative Cutlines, stratified by BMU.............................................................. 88 Table 15. Selected landscape metrics that were focused on in the research..................... 95 Table 16. Major conclusions of this research in summary. ............................................... 100

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List of Figures Figure 1. Grizzly bear research program area boundary (red = original 1999 boundary; blue shaded = 2000/2001 new study area boundary). .................... 4 Figure 2. Natural sub-regions within the grizzly bear research program area. ............... 5 Figure 3. Identified Bear Management Units within the grizzly bear research program area in 1999, 2000 and 2001..................................................................... 6 Figure 4. Age distribution of GPS radio collared grizzly bears (1999-2001). .................. 12 Figure 5. Relationship between Landsat TM greenness (proportion of home range in high greenness classes) and kernel home range sizes (95% and 50%)

for the spring and fall seasons of 1999 and 2000 in west-central Alberta. ..... 16 Figure 6. Probability of detection for Televilt GPS collars in the 2001 FMF grizzly bear research program area based on GLM models that incorporated

habitat and terrain bias. ......................................................................................... 19 Figure 7. Probability of detection for ATS GPS collars in the 2001 FMF grizzly bear research program area based on GLM models that incorporated

habitat and terrain bias. ......................................................................................... 20 Figure 8. Relative probability of occurrence for G20 during 1999. ................................... 24 Figure 9. Hierarchical polar clustering diagram for 32 grizzly bear food resources

(Genus-species codes).. .......................................................................................... 29 Figure 10. Predicted occurrence of Shepherdia canadensis in Jasper National Park based on variables hillshade, elevation (non-linear), stand age, and soil drainage (non-linear). ............................................................................................ 30 Figure 11. Example final AIC selected resource selection function (RSF) models for eastern Jasper National Park, Alberta. .......................................................... 31 Figure 12. Pre-berry RSF model based on model 2 (minimum SBIC model) variables.. ................................................................................................................. 37 Figure 13. Post-berry RSF model based on model 2 (minimum SBIC model) variables. .................................................................................................................. 38

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Figure 14. Cumulative impact of six classified linear access features on the selection of habitats during the post-berry period by grizzly bears

in the FMF grizzly bear research program area................................................. 39 Figure 15. Map of Shepherdia canadensis locations within a research grid in the Foothills Model Forest..................................................................................... 41 Figure 16. Seasonal changes in female grizzly bear feeding activities.............................. 45 Figure 17. Hedysarum feeding sites by IDT habitat class. .................................................... 46 Figure 18. Herbaceous feeding sites by IDT habitat class. .................................................. 47 Figure 19. Sherperdia canadensis feeding sites by IDT habitat class. ................................... 48 Figure 20. Anting activity sites by IDT habitat class............................................................ 48 Figure 21. 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.............................................................................................. 62 Figure 22. Grizzly bear GPS locations within the grizzly bear research program area (in red) in 1999 and 2000. .............................................................................. 63 Figure 23. Node Creation using G016 1999 Kernel Home Range. ..................................... 78 Figure 24. Edge Creation using G016 1999 Kernel Homerange. ........................................ 78 Figure 25. Dispersal Distance for G016 1999 Kernel Homerange. ..................................... 79 Figure 26. Image tile stratification of FMF grizzly bear research program area and sampling lines used for field verification.................................................... 84 Figure 27. Sampling details of the systematic 300 m field plot data collection. .............. 85 Figure 28. Seismic Lines in the FMF grizzly bear research program Area stratified into nine BMUs. ..................................................................................... 88 Figure 29. Landscape Metric Changes in Nine Foothills BMUs in 1999 by a) explorative seismic lines and b) by explorative and exploitative

seismic lines combined. ......................................................................................... 89

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Figure 30. Methods flow chart. ............................................................................................... 97 Figure 31. Landscape-level metrics for the CT classification: (a) Number of Patches, (b) Mean Patch Size in hectares, (c) Edge Density in

meters/hectare, (d) Mean Nearest Neighbor in meters.................................. 101

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1. FOREWORD This report is a summary of accomplishments and research findings for the third year of this five year research program. It also provides the reader with a summary of what each research program element has achieved over the first three years of this program and provides insights into the work remaining during the final two years of the program. This report has been prepared and compiled by Gordon Stenhouse and Robin Munro, however each of the chapters have been written by our program collaborators and a number of graduate students working on this research program. This annual report has not gone into the full details of methodology and analysis of each program element as this detail is provided in journal publications and would make this volume too large. It has always been a priority for our research team to publish the results of completed work in peer reviewed scientific journals and a listing of publications that have resulted from this program to date are presented in this annual report as an appendix. 2. INTRODUCTION 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, where the historic range of the grizzly bear once covered the entire province, the population is estimated at approximately 850 animals (AEP 2000). The current status of the provincial grizzly bear population is currently under review by the Alberta Endangered Species Conservation Committee. 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 may result in further fragmentation of bear habitats and populations. 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 to be an “umbrella” species (Paquet and Hackman 1995) whose presence, as a top carnivore, is indicative of a healthy ecosystem. The low reproductive rate of grizzly bears and the length of time it takes for bears to reach sexual maturity have made it difficult for this species to compensate for increases in natural/human caused mortality with increased productivity. From an ecological perspective it is

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justified to use the long-term persistence of healthy grizzly bear populations as a barometer with which to measure current and historic land use practices and sustainable resource management practices. 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 is occurring in all grizzly bear habitat in Alberta. It is important to also recognise that these human activities cover both industrial resource extraction 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 security areas for grizzly bears. 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 sound biological information and a greater understanding of response and interactions is required. The challenge that resource managers face is the determination of a balance between human needs and those of grizzly bears. 3. PROGRAM BACKGROUND In 1999, the Foothills Model Forest (FMF) initiated a co-operative, international, multidisciplinary, five year grizzly bear research program in the Yellowhead Ecosystem of west-central Alberta. 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 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

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other areas of grizzly habitation throughout North America, as it will provide tools and techniques that address landscape level conservation issues. A key focus of this program is to collect sound scientific data, which will form the basis for defensible management decisions and actions. 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. 5. GRIZZLY BEAR RESEARCH PROGRAM AREA AND METHODS In 1999 the research program 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, public recreational use urban/rural settlements. 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 recognised early in the planning process that these boundaries would not limit bear movement within the study area. After two years of data collection we had significant evidence to suggest that bear movements occurred 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 (Figure 1). We recognise 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 recognise 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 five distinct natural sub-regions. These are: alpine, sub-alpine, montane, upper

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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 mixedwood forest stands as one moves further east of the mountains.

Figure 1. Grizzly bear research program area boundary (red = original 1999

boundary; blue shaded = 2000/2001 new study area boundary).

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Table 1. Natural sub-region composition of the grizzly bear research program area.

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.

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We used one component of the FMF Watershed Assessment Model (WAM) 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. 340 km²) (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 a Cumulative Effects Assessment (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, 2000 and 2001.

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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. However there were times when more than one bear was collared within any BMU. In the 2001 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 2001 our goal was to deploy 20 Global Positioning System (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 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 large enough for instrumentation purposes. Small subadult bears were not radio collared, however subadult bears captured as part of a family group were tattooed for future identification and in some instances these bears received a Very High Frequency (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 fieldwork 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, Saskatchewan. 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.

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• 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. During the 2001 capture period we used fewer bait attractants in alpine areas than was the case in the previous two field seasons. We felt that less effort was necessary to capture “new bears” and our efforts were focused on recapturing bears from previous seasons. Our search protocol was to survey 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 could be seen from the air.

Once a grizzly bear was observed, the capture crew determined if the surrounding habitat and geography permitted a safe pursuit and capture. Chase times were limited to less than one minute and usually lasted for approximately 30-45 seconds.

Bears were immobilized with one of the standard rifle systems for firing internally charged darts (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, 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

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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.

• 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 occurring 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 (Figure 3). 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 there. • 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. Ground based capture crews also used pail snare sets at selected sites. Often these were placed in conjunction with cubby or trail sets but often they were placed in areas where a grizzly bear sighting had been reported.

• 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

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(helicopter and ground access), safe visual distance (100 m 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 one cubby set and two – three trail sets. The snare's anchor was attached to a live tree (30 cm 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. 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 15 meters. 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, page 8, 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 were sprung. The team then left the trap site allowing the bear to recover. Bears captured with this technique were checked by a helicopter over-flight within 24 hours following capture.

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7. RESULTS - 2001 GRIZZLY BEAR CAPTURE AND GPS MOVEMENT DATA Gordon Stenhouse and Robin Munro, Foothills Model Forest

7.1 Capture Results The capture period occurred between April 19 – July 6, 2001. 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 2001, a total of 29 grizzly bears were handled, of which 23 were fitted with GPS radio collars. One capture mortality (a black bear) took place within Jasper National Park during snaring operations. No non-target species were caught in snares this year. No effort was made to select for specific sex cohorts during our capture effort. We recaptured and recollared 17 bears that were collared during the first two years of this program. The total number of grizzly bears handled during the first three years of this research program is presented in Table 2. The number of GPS locations from these collared bears is shown in Table 3. It is important to recognise that the number of data points for 2001 will likely increase when collars are retrieved from dens sites in the spring (2002). Table 2. Collared and handled grizzly bears between 1999 and 2001. Year Number Handled Number Collared Recaptures 1999 23 19; 8M/11F n/a 2000 23 21; 9M/12F 12; 4M/8F 2001 29 23; 7M/16F 17; 6M/11F Table 3. GPS location data between 1999 and 2001. Year Number of Bears Number of Locations 1999 13 6051 2000 20 9000 2001 21 9437 Total 29 unique individuals 24,487 locations From the cementum analysis conducted, our capture sample of bears was found to include 21 adult and eight subadult bears (Figure 4). 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.

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Distribution of age-sex class of captured grizzly bears in 1999-2001

0

5

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15

20

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

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Figure 4. Age distribution of GPS radio collared grizzly bears (1999-2001). Bears were captured in all of the 16 designated bear management units in our core study area. After three years of attempting to collar a bear in the Lower Rocky BMU’s, we managed to capture a female and cub (>1 year) in June 2001 using ground based snaring. Location information from this bear will be important in the preparation of Resource Selection Function (RSF) models that deal with habitats and human use features within Jasper National Park. In 2001 we observed the largest number of family groups within the study area (Table 4). Many of our radio collared females (7) had cubs of the year in 2001. Table 4. Reproductive status of females from 1999 to 2001. Year # of Family Groups COY Yearlings > 1 Year Unknown 1999 2 - - - 42000 6 5 2 3 -2001 12 12 - 5 1 • Den Entry and Emergence

Over the first three years of this research project we have made a concerted effort to locate the den sites of collared grizzly bears and to document den entry and emergence dates. This effort has allowed us to not only recover “dropped GPS collars” but also to gain further insights into differences in denning behavior between bears who inhabit different habitats within our study area. The following summary is not meant to be complete but rather to provide an overview of denning behavior over the past three years. The study team has prepared den site location

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maps of the data presented below, however due to the sensitive nature of den site locations these maps are not included within this report.

• 1999

In 1999, the majority of females had denned by between October 31/99 and November 5/99. Denning dates for males in 1999 were not obtained. In the spring, females emerged from their dens between April 17/00 and May 12/00. Only one female, G016, emerged with cubs of year (coys) and had emerged by May 12/00. The one known male, G017, emerged by April 17/00.

• 2000

In general, bears entered dens later in 2000 than in 1999. Nearly all females had denned by November 20/00 with the exception of G007 who denned by November 28/00. G016 with 2 coys denned by November 19/00 while G002 with two year olds and G027 with yearlings had denned by November 19/00 and November 4/00, respectively. Although the exact denning dates were not established for any of the males in 2000, G033 and G024, had still not denned by November 28/00.

Males tended to emerge from their dens earlier than females. In addition, females without cubs or with older cubs emerged before females with coys. Males and females without cubs or with older cubs emerged between April 15/01 and April 28/01 while females with coys emerged between late April and early May.

• 2001

In 2001, all known bears were denned by mid November. Females denned between October 17/01 and November 17/01. Two males, G033 and G029, were both denned by November 15/01.

• Den Site Selection

In general, bears whose home range was primarily located in the mountains tended to den at higher elevations in the mountains. Interestingly, bears who primarily resided in the foothills denned either along the front ranges of the Rocky Mountains or out in the foothills. No bears reused den sites from previous years, however, seven female bears showed fidelity to the same general location across years (< 1 km between den sites), including G002, G004, G010, G012, G016, G020, and G027. Only G016 is known to have located all three of her dens within one km of previous sites. Russell et al. 1979 concluded that for the bears they observed within Jasper National Park, males usually emerged in early April and most females did not leave the den until at least mid-April to early May. Females with newborn cubs were the latest to emerge. The data we have collected generally supports Russell’s conclusions, however our data was also gathered from bears that den in the foothills outside Jasper National Park.

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8. THE INFLUENCE OF HABITAT QUALITY AND HUMAN ACTIVITY ON GRIZZLY BEAR HOME RANGE SELECTION AND SIZE.

Robin Munro (Foothills Model Forest), Scott Nielsen (University of Alberta), Julie Dugas (Foothills Model Forest), Gordon Stenhouse (Foothills Model Forest), and Mark Boyce (University of Alberta)

We have used RSF models and multiple regression analysis to explore the effects of habitat quality, topography and human access densities on home range selection and size of 10 female grizzly bears in west central Alberta. We hypothesize that grizzly bears will select for areas of higher quality habitat and topographical relief, while avoiding areas associated with high road density. Furthermore, we hypothesize that home range size will be inversely related to habitat quality and topographic variability. As habitat quality increases, home range sizes should decrease. Similarly, rugged terrain (i.e., highly variable topography) is thought to limit the size of home ranges (McLoughlin and Ferguson 2000). In addition, we expect that, as levels of human use increase or expand, habitats will become unavailable or be avoided and home range sizes will correspondingly expand. We used two approaches to test whether bears were reacting to their available spatial resources and human activities. The first approach explored home range selection to determine whether bears selected certain landscape features within the study area (i.e., 2nd order selection). RSF analysis was used to describe home range selection (Manly et al. 1993). The proportion of greenness, terrain (standard deviation of elevation), and total road density within home ranges were compared to that which was available. The second approach addressed the question of home range size as a function of habitat quality and human activity using multiple linear regression analysis. We describe the relationships between home range size and that of habitat quality (greenness values), road density and terrain (standard deviation of elevation). We assumed that high greenness values represented important habitat resources and as such, should be a strong incentive for selection and influence on home range size. Those that have assessed habitat selection at the scale of the home range have either used points (Mace et al. 1996), instead of home range polygons, or home range composition against study area composition (McLoughlin 2000). Here we focus our analyses to the scale of the home range, using polygon comparisons, to compliment analyses completed for the area at the third-order level (Nielsen et al. 2002a). To quantify availability of resources, random home ranges were generated using ArcInfo. Home ranges for each research bear were copied, maintaining shape and size, and then randomly rotated and moved to a new area within the study area. High values of greenness derived from Landsat TM imagery corresponded well with grizzly bear home range selection during spring and fall seasons (Table 5). This supports the general belief that bears seek out the most productive habitats. This also supports previous findings, where greenness has been shown to be important at scales

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finer than the home range (Waller and Mace 1997, Nielsen et al. 2002b). The fact that resource selection analyses have shown greenness to be a consistent variable across scales of selection suggests that the greenness index may be a good surrogate for habitat quality. Although the association between greenness and bear foods is poorly understood, the greenness index may provide managers with a tool to map grizzly bear habitats across large multi-ecosystem scales. The proportion of high greenness was also inversely related to home range size in the fall but not in spring (Figure 5). Topography and human access density did not influence either home range selection or home range size. Removal and extensive fragmentation of higher-quality habitats could lead to greater patchiness of critical habitats and thus, longer inter-patch travelling sessions between forage events. It has already been well documented that increasing home range size, as a result of variability in food resources, can result in increased risk of human-induced mortality (Blanchard and Knight 1991, Knight et al. 1988). We hypothesize that females expend as much energy for reproduction as food supplies allow, and thus a decline in the quantity of higher-quality habitats may result in lower levels of female reproductive fitness, which in turn has potential ramifications for the population. Future research will include re-running this analysis with a larger sample size, and exploring the relationship between patchiness and distribution of greenness across the landscape and its effect of home range selection and size. Furthermore, we will explore the relationship of roads further by categorizing them based on traffic volume. Table 5. Variables and estimated coefficients for the spring 95% and 50% kernel

home range selection analysis. 95% Kernel 50% Kernel Variable β SE p β SE P High Greenness 0.245 0.112 0.029 0.136 0.055 0.013Elevation SD -0.011 0.006 0.054 -0.005 0.005 0.349Road Density -0.041 0.265 0.878 2.015 1.396 0.149Constant -0.927 1.714 0.589 -3.206 1.252 0.010 Log Likelihood Null Model (-2LL) 59.474 73.722 Full Model (-2LL) 46.658 58.672 Psuedo- r2 0.215 0.204 Goodness-of-fit Model χ2 12.817 15.050 Model P 0.005 0.002

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a. Spring, 95%

log(

hom

e ra

nge

kern

el s

ize)

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3b. Spring, 50%

c. Fall, 95%

0 5 10 15 20 250

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3d. Fall, 50%

Proportion of Home Range with High Greenness

0 10 20 30 40

Figure 5. Relationship between Landsat TM greenness (proportion of home range in

high greenness classes) and kernel home range sizes (95% and 50%) for the spring and fall seasons of 1999 and 2000 in west-central Alberta.

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9. RESOURCE SELECTION FUNCTIONS AND POPULATION VIABILITY ANALYSES

Scott E. Nielsen and Mark S. Boyce, Department of Biological Sciences, University of Alberta, scottn@ualberta.ca

9.1 Introduction Habitat loss threatens the persistence of grizzly bears (Ursus arctos L.) in the Rocky Mountains of Canada and the United States (Clark et al. 1996, McLellan and Banci 1999). Under current social-environmental pressures, land managers are being asked for innovative strategies for the management and conservation of sensitive species, such as grizzly bears. Critical to any such strategy, however, is the local understanding of important resources (Leopold 1936) and the potential impact human development and recreation may have on those resources. Although habitat effectiveness models for grizzly bears already exist for portions of west-central Alberta (i.e., Jasper National Park Cumulative Effects Assessment (CEA)), their applicability in resource planning outside the Parks Region has been limited. Furthermore, the habitat module used within the existing CEA model structure is largely qualitative and recently shown to be a poor predictor of grizzly bear occurrence in eastern Jasper National Park (JNP) and the adjacent Foothills (Nielsen et al. 2002a, Nielsen et al. 2002b). Resource selection functions (RSF) have been suggested as one alternative approach (Manly et al. 1993). The most common method used to derive an RSF is through the parameterization of telemetry data (3rd order selection, Johnson 1980) using logistic regression (i.e., binomial generalized linear models (GLM) with a logit link). The principal advantage of RSF models over habitat effectiveness or habitat suitability models is that they use empirical data to estimate models of the responses of the animals to resources. RSFs have been particularly useful for conservation and land management planning, because they can provide spatially explicit predictions of occurrence (typically relative) across the landscape (i.e., GIS map). Given these habitat-based predictions, RSF models can further be used to infer population size and density (Boyce and McDonald 1999). Prior to the FMF grizzly bear research program, RSF models for grizzly bears existed only for populations within the Rocky Mountain Region of the United States (Mace et al. 1996, Mace et al. 1999, Boyce and Waller 2002). These populations, however, are not subjected to the land resource pressures currently exhibited within the Foothills of Alberta. Models from the United States further differ with regard to the habitats and food resources available for grizzly bears. We therefore begin our research by attempting to provide spatially explicit RSF models and maps that describe grizzly bear habitat (relative probability of occurrence). Our specific research objectives are: 1) describe habitat selection; 2) identify key grizzly bear habitats; 3) examine impacts of human development; 4) search for mechanistic links and appropriate scales of selection; and finally 5) develop a habitat-based population viability (PVA) model for the Yellowhead Ecosystem. In this report, we describe recent

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progress in RSF modeling and field studies that are aimed at shedding some light on processes of habitat selection in local grizzly bear populations. The organization of this summary is as follows:

1. Section 9.2. Review and progress on incorporating GPS collar bias in RSF models.

2. Section 9.3. RSF modeling for individuals to populations using 1999 GPS data. We specifically address how autocorrelation and pseudoreplication can be overcome (Nielsen et al. 2002a).

3. Section 9.4. Developing grizzly bear food models (Nielsen et al. 2002b).

4. Section 9.5. Pre-berry and Post-berry RSF models for 1999-2001 data: Pooled bears and years.

5. Section 9.6. Field programs and future directions. 9.2 Accounting for GPS Collar Bias Estimates and inferences of habitat selection using GPS telemetry data can be questionable in the presence of GPS collar bias. If, for example, we were less likely to obtain a GPS fix in closed forest, steep terrain stand, we would bias selection towards open, flat habitats. To date, there has been considerable evidence for non-random GPS collar error in the presence of canopy and complex terrain (Rempel et al. 1995, Obbard et al. 1998, Dussault et al. 1999, Rettie and McLoughlin 1999). Researchers, however, have really only documented the error without any proposed methodologies for dealing with the problem or overcoming it when modeling habitat selection. To address these shortfalls, the FMF grizzly bear research program established a number of GPS collar (Televilt and ATS) testing plots across orthogonally stratified habitat and terrain conditions (see FMF grizzly report 2000). With the collaboration of the Central East Slopes Elk Project (Jacqueline Frair, Univ. of Alberta), we developed spatially explicit models predicting potential collar bias for Lotek (J. Frair), ATS, and Televilt collar systems (Frair et al. 2002). A probability of fix (Pfix) model was developed for the three collar types using a binomial logit GLM. Here, we modeled whether collars obtained a successful fix (by plot) based on the following explanatory variables: 1) collar brand (ATS, Lotek, Televilt); 2) habitat type (open, open conifer, closed conifer, deciduous, and mixed forest); 3) season (leaf-on vs. leaf-off); and 4) terrain slope from a DEM. Since multiple acquisition attempts were obtained for each plot over a 24 hour period, we clustered our model on plot to account for pseudoreplication and autocorrelation problems inherent with such a repeated sampling design. We felt it was important, however, to cover the entire 24 hour window at a site to assure that non-random errors in satellite geometry (e.g. consistently poor acquisition windows) weren’t affecting results. Results indicated that the model was significance (χ2 = 61.94, P < 0.001) overall, with reasonable

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classification accuracy (ROC = 0.72) (Frair et al. 2002). GIS maps depicting Pfix for Televilt and ATS collars in the 2001 study area are depicted in Figures 6 and 7.

Figure 6. Probability of detection for Televilt GPS collars in the 2001 FMF grizzly

bear research program area based on GLM models that incorporated habitat and terrain bias. Note that the scale of detection ranges from 55% to 100% probability. The receiver operating characteristic (ROC) for the model (classification accuracy across all probabilities) was 0.72 indicating useful application.

To overcome biases within habitat-selection models, we evaluated the use of weighted logistic regression and multiple-imputation on a known (simulated ‘truth’) animal. Results indicate that weighted logistic regression was more efficient than multiple-imputation (a more stochastic approach) for correctly detecting selection and making appropriate inferences (Frair et al. 2002). Some additional work on GPS collar-bias is planned for the spring of 2002. Confidence in these models will be important, because most aspects of the current study rely upon GPS data. We will need to pay particular attention to modeling the influence of terrain, since it did not show the degree of effect expected. This may simply be an artifact of the scale of DEM (100m) we used. An

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increase in sample size for Televilt and ATS collars in sparsely sampled orthogonal factors will add some certainty to our p-fix models.

Figure 7. Probability of detection for ATS GPS collars in the 2001 FMF grizzly bear

research program area based on GLM models that incorporated habitat and terrain bias. Note that the scale of detection ranges between 77.5% and 100% probability. The receiver operating characteristic (ROC) for the model (classification accuracy across all probabilities) was 0.72 indicating useful application.

9.3 RSF Modeling for Individuals and Populations (Nielsen et al. 2002a). Here, we describe RSF modeling techniques for individual and population-level habitat modeling. We focus on patch-level resource selection (3rd order processes; Johnson 1980) during two seasons (pre-berry and post-berry) of 1999 using variables previously identified as important for predicting grizzly bear occurrence (Mace et al. 1996, Mace et al. 1999, Boyce and Waller 2002). To adjust for autocorrelation and pseudoreplication,

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we used variance-inflation factors (individual-level) and clustered logistic regression (population-level). We further compare seasonal models to understand inter-seasonal dynamics and contrast these RSF models with a grizzly bear habitat index model (Kansas and Riddell 1995) previously developed for the region. This work represents a summary from the paper entitled “Modeling grizzly bear habitats in the Yellowhead Ecosystem of Alberta: Taking autocorrelation seriously” in the journal Ursus (Nielsen et al. 2002a). For more specifics, please refer to this work. 9.3.1 Methods • Use and Availability Sampling

Analyses were based on nine individual bears, each with ≥346 locations over ≥100 day periods. Locations were imported into a GIS and used to delineate 100% minimum home range convex polygons (MCP). Data were stratified by season (pre-berry or spring: den emergence to July 31; post-berry or summer/autumn: August 1 to denning). Resource use, estimated from telemetry locations, was compared to available resources using logistic regression (Manly et al. 1993). We defined availability as all areas within 100% annual MCP home ranges. To quantify availability, we randomly generated 1,000 points within each MCP.

• Remote Sensing and GIS Data

The Integrated Decision Tree Approach (IDTA) habitat classification (Franklin et al. 2001) was used for representing habitat types. Eight principal habitat classes were used from this classifier, lumping together a number of similar ecological or spectral classes. These classes included: alpine; recent burn; cut 0 to 12 years old; herbaceous; closed forest; open forest; non-vegetated; and shrub-wetlands. Three additional habitats (from GIS data) were overlaid on the IDTA map to characterize additional habitat features not described in the IDTA map. These three habitats included cuts 13 to 22 years old, cuts 23 to 44 years old, and young regenerating forests (3 to 44 years old). A tasselled-cap transformation of the September 1999 Landsat TM image was used to calculate greenness values across the study area (Crist and Cicone 1984; Manley et al. 1992). Greenness scores were divided into 10 classes similar to those used by Mace et al. (1999). High greenness values typically indicated areas of high vegetative reflectance and leaf area index (LAI), while low values indicated non-vegetated areas of rock, snow and ice (White et al. 1997, Waring and Running 1998). Distances to three types of streams were evaluated to represent riparian habitats; major, perennial, and intermittent. Major streams were defined as hydrographic features that averaged ≥20m between banks and were hydrologically active year round. Perennial streams were identified as hydrographic features with shorelines <20m in width and continually flowing, except in drought conditions. Intermittent

21

streams were those hydrographic features averaging <20m in width and often dry or obscured by vegetation during photo interpretation. We used a 100m DEM to determine elevation for each location. We further used a quadratic term for elevation, since a priori we suspected non-linear selection of elevation zones (Waller and Mace 1997). A hillshade grid model was derived from the DEM using Spatial Analyst in ArcView. Aspect and slope were set within the hillshade model at 225° and 45° respectively. High hillshade values corresponded to xeric southwest slopes, while low hillshade values depicted cool, mesic northeast slopes.

Linear features that could provide human access were placed into three categories (low, moderate, and high) based on potential impacts to bears, travel volume, and other characteristics. High-impact features included undivided paved and two lane gravel roads. Moderate-impact features included one lane gravel roads, unimproved roads and truck trails. Low-impact features included seismic lines and utility lines (pipelines and transmission lines). The density of these features (km/km2) was calculated within nine km² scale moving windows around each 50m grid pixel. Previous work has identified nine km² as the daily area used by adult female grizzly bears in the area (Gibeau 2000).

• RSF Modeling Strategies

We partitioned our data into model training (90%) and model testing (10%) datasets. Model significance was determined through log likelihood χ2 ratios, while evaluation of model fit was determined with a Hosmer and Lemeshow (1980, 1989) goodness-of-fit statistic, Ĉ, on both the training and testing (validation) datasets. Because we are not confident of our classification of random locations (e.g., availability, not absence), these goodness-of-fit statistics should be viewed as conservative measures.

Autocorrelation between telemetry locations for individual-level models was considered by using a Newey-West (1987) estimator of variance within a binomial logit generalized linear model (GLM) (McCullagh and Nelder 1989). This variance estimator accounts for autocorrelation between observations (see Lennon 1999) by inflating estimated standard errors. One would likely detect significance more frequently than one should (Type I error) without such adjustments (Lennon 1999, 2000). We determined the autocorrelation structure within our dependent variable using partial autocorrelation functions. From these examinations, we found evidence of autocorrelation occurring out to a six lag distance (e.g. 24 hours). We used this truncation lag for all Newey-West variance estimations. At the population level, we adjusted variance estimates to account for repeated measurements on a single animal (our unit of replication) by using robust clustering methods, replicating a conditional fixed effect logistic regression model.

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9.3.2 Results • Individual-level RSF Models

Results indicated that selection for habitats was variable, depending on the bear and the season. In the spring (pre-berry), individuals tended to select for areas of high greenness, near streams, and in alpine habitats. Avoidance during this season was evident for non-vegetated areas and young regenerating forests. The influence of forest management (cut-blocks) varied considerably. Two bears (G5 and G8) avoided cut-blocks of all age classes, although only recent cut-block avoidance by G5 was significant. G20, on the other hand, selected for both mid-aged (13 to 22 year old) and older (22 to 44 year old) cut-blocks. Not surprising, G20 also occurred in areas associated with high levels of high and low-impact access features. In contract to G20, G16 avoided habitats with high levels of high and low-impact access features.

Model strength and prediction was greater during the post-berry season. During this period, the importance of greenness for predicting bears was evident, as all bears responded significantly. Alpine and shrub/wetland classes maintained the most consistency for selection of IDTA habitats. In comparison, young regenerating forests and non-vegetated classes were consistently avoided. Selection for streamside habitats persisted. G20 continued to occur within high density areas of both high and low impact access features. However, during this season, G20 avoided cut-blocks >12 years old. G8 and G5 (other bears with cut-blocks in home ranges) showed similar avoidance behaviors. Four bears (G4, G6, G8, G16) avoided high density areas of high impact access features. Selection and avoidance of low impact features was quite variable, depending upon the bear.

Significant inter-seasonal dynamics occurred for cut-block, stream, and access density use. Cut-blocks 13 to 22 years of age were selected during the pre-berry season by G20, but avoided during the post-berry period. We assume such resource switching behaviors relate to forage selection of Hedysarum spp. roots during the spring, as compared to foraging of berries during the late summer and early fall period. G20 further showed strong selection for areas of high greenness during the post-berry season, but no such selection during the pre-berry period. Two bears, G10 and G5 selected for areas near intermittent and perennial streams during the pre-berry period, but either avoided (G10) or used as available (G5) during the post-berry season. G4 and G8 avoided high-impact access features during the pre-berry season, but did not avoid those areas during the post-berry period. Bears G2, G3, G6, and G16 failed to show significant inter-seasonal dynamics, at least at the temporal seasonal scale examined here. In Figure 8, we present examples of habitat selection for an individual (G20) during the pre-berry (a.) and post-berry (b.) seasons.

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Figure 8. Relative probability of occurrence for G20 during 1999. Models for pre-

berry (a.) and post-berry (b.) seasons depicted. Validation points are K-fold partitioned testing data (GPS use locations) withheld for model cross-validation.

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• Population -level RSF Models During the pre-berry season, alpine, non-vegetated habitats, young regenerating forests, greenness, and perennial streams all contributed in the final RSF model. Grizzly bears selected for areas of high greenness. Perennial streams were selected, while both major streams and intermittent streams were used in proportion to availability. Alpine habitats were selected, while both non-vegetated areas and young (3 to 44 years old) regenerating forests were strongly avoided when compared to the reference category of closed forest stands. No significant pattern of selection was detected for cut-blocks, although there was a tendency for recent cut-block classes (0 to 12 years old) to be avoided. Access density, elevation and hillshade all failed to affect distributions of grizzly bears during this season. During the post-berry season, variables contributing to the grizzly bear model included alpine, recent burn, cut-blocks 22 to 44 years old, open forests, young regenerating forests, shrub/wetlands, greenness, major streams, perennial streams, and both high and moderate impact access density. High values of greenness were again strong predictors of grizzly bear occurrence. Furthermore, bears tended to be found along major streams, with only a slight preference for habitats along perennial streams. Habitat classes where selection occurred included alpine, recent burn, cut-blocks 22 to 44 years old, herbaceous, open forests, and shrub/wetland habitats. Young regenerating forest stands were strongly avoided. Bears tended to use areas of high-impact access density, although this appears to be mostly due to selections made by two individuals, G20 and G5. The reverse relationship occurred for moderate impact access density areas, as bears tended to avoid truck trails, unimproved roads and one -lane gravel roads. Intermittent streams, hillshade, and elevation did not significantly contribute to explaining patterns of use during this season. Inter-seasonal differences at the population level appeared for alpine habitats and moderate impact access density. Selection of alpine habitats, significant in both seasons, increased substantially in the post-berry period. Moderate impact access density was non-significant during pre-berry, but strongly significant and negative (avoidance) in the post-berry model.

• Habitat Index Models and RSFs

We tested the ability of current habitat index models (index 1-10 in grizzly bear habitat quality) to predict grizzly bear occurrence, using an RSF approach (e.g., using the habitat index model as an independent variable). The pre- and post-berry models were significantly different from null models, but failed to maintain fit in Hosmer-Lemeshow goodness-of-fit tests on both training and testing data sets. Although goodness-of-fit tests can be highly conservative, there was further evidence of poor fit based on validation points and comparisons with models using simple predictors like greenness. Based on these tests and further analyses that have

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examined food index models in more detail (Nielsen et al. 2002b, see part 9.4), we caution the use of current habitat index models for assessing grizzly bear habitats and cumulative effects assessments.

9.3.3 Discussion Our individual-based RSF models revealed a high degree of variability in selection of resources. High variability among individual grizzly bears makes population-level models quite general. Differences in inter-seasonal selection were observed for only two variables at the population-level, alpine habitats and habitats associated with moderate impact access density features. Selection behaviors during the spring season were substantially less predictable than summer/autumn models indicating a potential lack of necessary remote sensing and GIS data appropriate for that season. Significance levels for variables were frequently under-estimated in unadjusted logistic regression models. Without careful adjustments, inferences from model parameters can be biased (increased type I error rate). Because these models were developed from only one year of data (1999), they may not reflect the long-term patterns of selection by grizzly bears within the study area. Instead, we view this examination as developmental RSF modeling approaches. 9.4 Food phenology models for grizzly bear predictions (Nielsen et al. 2002b). We compared the use of three habitat models for estimating the relative probability of occurrence for grizzly bears in eastern Jasper National Park (JNP). These models included, 1) the IDTA habitat map (Franklin et al. 2001); 2) food index models generated from the predicted occurrence of plant foods and assigned monthly importance values; and 3) probabilistic food models representing the occurrence of each plant bear food. This work represents a summary from a paper submitted to EcoScience entitled “Incorporating food phenology models for grizzly bear predictions” (Nielsen et al. 2002b). For more specifics, please refer to this work. We make four principal assumptions in the development and testing of food models in this section, (1) the most relevant factor influencing grizzly bear habitat selection is food; (2) the vast majority of a grizzly bear’s nutritional demand in the northern Rockies is met through herbivorous feeding activities (Jacoby et al. 1999); (3) presence/absence (or predicted presence) of foods are sufficient to predict grizzly bears, even though energetic or productivity characteristics may be more reasonable and/or predictive for bears (e.g., Mattson et al. 1999, Mattson 2000); and (4) monthly time steps are at a sufficient temporal scale to cover phenological developments and use of bear plant foods. Due to these assumptions and further limitations associated with GPS grizzly bear location data (e.g. GPS collar bias), we consider this paper a methodological test of the usefulness of different GIS and remote sensing data for predicting grizzly bears and identifying grizzly bear habitats.

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9.4.1 Methods • Remote Sensing Habitat Map

The Integrated Decision Tree Approach (IDTA) habitat classification (Franklin et al. 2001) was used for deriving basic habitats. The map was re-classified from the original 23 classes (many of which are not present in JNP) into five principal vegetative cover types. These include alpine, closed forests, open conifer, shrub/wetland complexes, and non-vegetated habitats (e.g., snow, rock, shadow, and water).

• Food Index Models

We used 1,343 field vegetation plots established between 1977 to 1979 in JNP to predict bear plant foods considered important for local grizzly bear populations (Kansas and Riddell 1995). In total, we recognised 32 species of plant foods in JNP. With logistic regression, we developed predictive models for each species using 11 GIS environmental covariates from the Yellowhead Ecosystem Working Group (YEWG) ecological land classification (Gordon et al. 1998) and other existing digital GIS data. These environmental layers include habitat, terrain, soil, and disturbance history data. The 1,343 plots were partitioned into a model training (90%) and model testing (10%) dataset, allowing for within sample validation. Model selection procedures for each species followed a forward minimum-AIC selection method, where environmental parameters were added, based on AIC scores, until parsimony was achieved (Burnham and Anderson 1998, Anderson et al. 2000). Hosmer and Lemeshow (1980) goodness-of-fit statistic (Ĉ) and area under the curve estimates from receiver operating characteristic (ROC) curves (Swets 1988) were used for assessing model fit and performance for both model training and model testing datasets. Following model development, optimal probability cut-off points for prediction of species presence was determined through the optimization of sensitivity and specificity curves from ROC plots (Zweig and Campbell 1993). Given these cut-off values, maps were generated for each species across JNP, where species were either predicted present or absent in each pixel. Using Kansas and Riddell (1995) food values, by month, food indices were developed representing the summed value of foods present within each pixel for each month between April and October. Final food indices were scaled (based on maximum monthly value) to range between 0 (low importance) and 10 (high importance) to match existing habitat index models for the Park. Taxonomy of bear plant foods follows that of Moss (1994).

• Food Probability Models

As an alternative to the food index models that assume a particular importance value for each species in each month and that the appropriate probability cut-off value for prediction was chosen correctly, we maintained the original probability

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function (0 to 1) for each species. We then used species-specific food probability models in each month as potential predictors of grizzly bear occurrence. Because logistic regression is sensitive to collinearities among explanatory variables (Hosmer and Lemeshow 1989), we excluded species that were highly correlated (r > 0.75) with higher ordered AIC species (e.g., those species with a low AIC contribution were removed when correlated with species having higher AIC values) in RSF bear models. A number of phytosociological relationships exist between the 32 species of interest (see Figure 9). We verified that final model structures were unaffected by collinearity through the examination of variance inflation factors (VIF) on a randomly generated continuous variable. Final model selection procedures followed a forward minimum AIC selection method as in the food index models.

• Grizzly Bear Radiotelemetry and Modeling Strategy

We used radiotelemetry data from 1999 and 2000 on 10 individual bears within the 2,300 km² secondary study area of eastern JNP that overlapped the IDTA habitat map. In total, 3,924 GPS radiolocations from the secondary study area were retrieved from the 10 bears (eight female and two male) between April and October of 1999 and 2000. Locations used for monthly model development varied from 77 locations in April to 936 in June. Data were not corrected for GPS collar bias and therefore parameters should be interpreted with caution. Minimum home range convex polygons (100% MCP) were generated for each bear for all data between 1999 and 2000. From these home range polygons, available resources were generated using an equal area based (one random point per home range ha.) random sampling of GIS environmental data (habitat models). Third-order (Johnson 1980) resource selection, by month, was evaluated for grizzly bears in JNP using the above three habitat models (IDTA habitat map, food index model, and food probability model). Used resources, estimated from GPS telemetry locations, were compared with random GIS samples of available resources to obtain a resource selection function (RSF) using logistic regression. For the categorical IDTA map, we used the most common habitat cover class, non-vegetated areas, as the reference category. Models were developed at the population level, pooling sexes. We used the robust cluster method to calculate variances around each parameter (Nielsen et al. 2002a). Using such a model, we assume the unit of replication to be the individual, not the telemetry location. Three models (IDTA map, food probability, and food index) for each month (one for each habitat model) were generated and compared against each other using an AIC information theoretic approach (Anderson et al. 2000). Models were AIC ranked (∆i) within each month and the likelihood of the model being the best, given the data, were estimated using Akaike weights (wi).

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0.00.10.20.30.40.5

Distances

achmil

astespp

equarvherlan

latoch

sentri

thaspp

valsit

veresc

vicame

astrspp

clalan

hedspp

junspp

amealnarcuva

corstoempnig

fravir

lonspp

ribspp

rosaci

rubspp

rubida

sorspp

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vaccae

vacmem

vacmyr

vacsco

vacvit

vibedu

Figure 9. Hierarchical polar clustering diagram for 32 grizzly bear food resources

(Genus-species codes). Clustering method is based on the Euclidian distance (centriod link) of phytosociological species scores determined through Detrended Correspondence Analysis (DCA) of 1,352 vegetation plots from Jasper National Park, Alberta. Distances are in measures of R2 values.

9.4.2 Results • Food Index and Food Probability Models

Goodness-of-fit tests (Ĉ) for model training data, confirmed agreement between the model and data for 25 of 32 species, while ROC values >0.7 (representing reasonable/good model performance) occurred in 28 of 32 species. Six of these 28 ROC values were >0.9, indicating high model accuracy. Validation of training models using withheld testing data, dropped the total number of fit species to 22, while validated ROC estimates pointed to reasonable classification accuracy for 25 of the 32 species. Five of these species had high model accuracy. Common predictor variables chosen for final AIC selected models included elevation (linear and

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quadratic responses), hillshade, age of stand, soil drainage (linear and quadratic), and the interaction of vegetation and age. Using estimated coefficients from each model, the probability of occurrence for each food species was calculated across JNP using a GIS. Resulting probability maps were subsequently used for monthly grizzly bear occurrence models (food probability grizzly bear RSF models). Using these same food models, optimal probability cutoff values for each species were estimated from sensitivity and specificity graphs. Optimal cutoff values ranged from 1.76 % for Rubus idaeus (L.) to 35.77 % for Juniperus spp. Based on these cutoff values, the presence/absence of each species was estimated across JNP using a re-classification of the original probability levels in a GIS. An example model for Shepherdia canadensis is shown in Figure 10. Qualitative food values were assigned for each species in each month (Kansas and Riddell 1995) for predicted (presence) grid cells resulting in monthly food index maps that were used for estimating grizzly bear occurrence (food index grizzly bear RSF models).

Figure 10. Predicted occurrence of Shepherdia canadensis in Jasper National Park

based on variables hillshade, elevation (non-linear), stand age, and soil drainage (non-linear).

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• Grizzly Bear RSF Habitat Model Comparisons Model assessments using AIC weights (wi) indicated that food index models developed for all seven months inadequately described grizzly bear occurrence in Jasper National Park. In contrast, the food probability models and the IDT model (remote sensing classification) both performed substantially better than food index models. In April and July (Figure 11), the IDT model had the greatest model support within the candidate sets at likelihood’s of 80.4 % and 98.7 % respectively. Selection of classified IDTA habitat classes varied substantially among months. For instance, bears were 14 times more likely (odds ratio) to use alpine habitats in July then in May, while a substantial increase in selection of shrub/wetland habitats was evident in September followed by a dramatic reduction in October.

Figure 11. Example final AIC selected resource selection function (RSF) models for

eastern Jasper National Park, Alberta. Food probability models were selected in May (a.) and August (c.), while the IDTA (Integrated Decision Tree Approach) habitat map (Franklin et al. 2001) most supported in July (b.).

For five months (May, June, August, September, and October), the food probability models out-performed the IDTA model (Figure 11). The likelihood of support for these models were 100%, given the data and models tested. The number of species, composition of species, and the direction and magnitude of responses varied among months. The simplest model (June) contained the species Achillea millefolium,

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Claytonia lanceolata, Heracleum lanatum, Thalictrum spp., while the most complex model (August) included Astragalus spp., Claytonia lanceolata, Cornus stolonifera, Fragaria virginiana, Hedysarum spp., Heracleum lanatum, Rubus idaeus, Vaccinium scoparium, V. vitis-idaea, Valeriana sitchensis, and Verartrum eschscholtzii. Foods that consistently contributed to monthly grizzly bear RSF models included Claytonia lanceolata, Astragalus spp., Hedysarum spp., Rubus idaeus, and Thalictrum spp. A number of food probability RSF models contained species with negative coefficients indicating apparent avoidance or under-sampling of areas associated with those foods.

9.4.3 Discussion Grizzly bear food resources in Jasper National Park were principally related to elevation, hillshade, age of stand, soil drainage, and the interaction of vegetation and age. Non-linear responses were common for the variables elevation and soil drainage. Food index maps produced from the predicted presence of each species and monthly food values (Kansas and Riddell 1995) proved poor predictors of grizzly bear occurrence. Monthly food values generated by Kansas and Riddell (1995) are perhaps too generalized or inappropriate for food prediction maps used here. In either case, the use of habitat-effectiveness models that base habitat potential from qualitative food models in the western four contiguous parks of Canada (Banff, Jasper, Kootenay, and Yoho) should be cautioned, since grizzly bear predictive performance was so poor. We found substantial improvement in the use of a remote sensing classification (Franklin et al. 2001) and empirically based food probability models. Food probability models proved to be the most successful predictor of grizzly bears in five of seven months, with the IDT map favored in the months of April and July. Selection of resources and habitats were temporally variable. Major differences existed in the selection of habitat cover classes and foods among months, providing evidence for resource switching at the temporal scale examined here. Analyzing selection of habitats and/or resources for longer periods (i.e., two or four season models) likely will mask important selection processes operating at finer scales (Schooley 1994). Fundamental food resources used by grizzly bears appear to be phenologically driven at temporal scales rarely addressed in previous habitat selection studies (see however, Mattson 2000). Important bear plant foods in probability models tended to correspond well with feeding observations and feces examinations for regional grizzly bear populations (Hamer and Herrero 1987, Hamer et al. 1991, Hamer 1996). Incorporation of ant and ungulate resources within these models, however, may be a necessary future step. This is likely to be particularly important for male grizzly bears, as their diets tend to contain larger contribution of meat (Jacoby et al. 1999).

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9.5. Pre-berry and Post-berry RSF models for 1999-2001 Using similar methods as those outlined in section 9.3, we describe here RSF models developed at the population-level across all three years of GPS radiotelemetry data (1999 to 2001). We have modified here, however, our measures of access density, using separate definitions and scales. Future analyses are on going at the individual and population levels using years individually to tease apart variation in more detail. 9.5.1 Methods • Use and Availability Sampling

We based use of resources on 20,502 GPS radiotelemetry locations gathered between 1999 and 2001, falling within our current mapping boundaries (e.g. greenness map and habitat map). Use data were stratified into pre-berry (n = 11,559) and post-berry (n = 8,943) seasons. We partitioned each season’s data into a model training (83%) and model testing (17%) data set, basing the proportion on the approximate number of parameters potentially used in RSF models (Huberty 1994). Annual 100% minimum convex polygon (MCP) home ranges were used to define the extent in which availability of resources for individual bears were measured. A sampling intensity of five random locations/1 km² was used, thus maintaining an unbiased spatial sampling intensity of available resources. At the population-level, we combined random locations from individual bears. The extent of our availability sampling and analysis is therefore equivalent to the merged annual MCP home ranges.

Table 6. Independent predictor variables used for a priori RSF models. Note that

two spatial scales are used for density of linear features and vegetation diversity. All parameters were checked for collinearity within a scale.

Code Variable Unit Of Measureg greenness Transformation v vegetation (11 habitats, not including one reference habitat [closed conifer]) Category r1 vegetation diversity within a 2.25 ha window Richness r2 vegetation diversity within a 10.89 ha window Richness m1m density of motorized linear features in 1km2 window medium use km/km2 m1l density of motorized linear features in 1km2 window, low use km/km2 m1h density of motorized linear features in 1km2 window, high use km/km2 n1m density of non-motorized linear features in 1km2 window, medium use km/km2 n1l density of non-motorized linear features in 1km2 window, low use km/km2 n1h density of non-motorized linear features in 1km2 window, high use km/km2 m5m density of motorized linear features in 4.7km2 window, medium use km/km2 m5l density of motorized linear features in 4.7km2 window, medium use km/km2 m5h density of motorized linear features in 4.7km2 window, high use km/km2 n5m density of non-motorized linear features in 4.7km2 window, medium use km/km2 n5l density of non-motorized linear features in 4.7km2 window, low use km/km2

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Code Variable Unit Of Measuren5h density of non-motorized linear features in 4.7km2 window, high use km/km2 s1 distance to major stream Km s2 distance to perennial stream Km s3 distance to intermittent stream Km • RSF Variables

We used the integrated decision tree approach (IDTA) classification (Franklin et al. 2001) for characterizing habitats, but further reclassified habitats into spectrally or ecologically similar classes to reduce complexity and uncertainty between confused classes. In addition, we added a young regenerating forest class from fire history digital data, defined as stands burned between 1955 and 1996. The remaining 11 habitat classes include alpine-subalpine, closed conifer forest, recent cut (0-12 years old), old cut ( >12 years old), deciduous forest (closed and open), herbaceous, mixed forest, non-vegetated (snow, rock, shadow, water, etc.), open conifer forest, recent burn (1997 fire event), and shrub-bog-wetland. To determine whether habitat diversity affected selection, we calculated IDTA habitat variety for 2.25-ha (5 by 5 cell) and 10.89-ha (11 by 11 cell) windows. We hypothesize that areas with higher habitat variety will be selected, because they would provide more potential local resources for various behavioral activities (e.g. feeding, bedding, etc.). Greenness from a tasseled cap transformation of a Landsat image’s (September 1999) spectral frequencies was used as a separate surrogate for habitat quality. We used distance to stream (km) in three separate classes (major, perennial, and intermittent) as a surrogate for riparian habitats, as the IDTA classification did not include a riparian habitat within its classification. Previous literature indicates that riparian habitats can be important for grizzly bears. As a measure of human impact, we calculated density of human linear features in moving windows at two scales (1 km² and 4.7 km²), stratified by motorized and non-motorized categories, and placed into low, medium or high human use intensity. Low was defined as categories with a 0 to 100 events/month, medium as 100 to 1000 events/month and high as >1000 events/month. The scale of 4.7 km² represents the average movement radius of a female grizzly bear over a four hour period (GPS acquisition interval), while the scale of 1 km² represents that which has been widely used in the literature previously. Impact was based on use of features determined through counts and expert opinion. Table 6 provides a complete list of independent variables used for modeling grizzly bear occurrence and codes used for representing those variables.

• RSF Modeling Strategies

We follow an information theoretic design (Burnham and Anderson 1998, Anderson et al. 2000), where 11 a priori models per season were compared using Schwartz’s

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Bayesian information criteria (SBIC). SBIC attempts to select parsimonious models, with greater parameter penalty than the more liberal Akaike information criteria (AIC). SBIC is recommended in situations where one is data rich (Hastie et al. 2001), as in our case. All models were developed using training data. Models were validated for their predictive capability using testing data (Boyce et al. 2002).

To overcome non-random errors typical of GPS radiotelemetry data (Obbard et al. 1998, Dussault et al. 1999, Rettie and McLoughlin 1999), we used sampling (probability) weights to adjust for biased fix rates (Frair et al. 2002). Sample weights represent the inverse of the probability that the observation was included due to sampling error (see section 9.2). To address the potential for non-independence between GPS locations within an individual, we further used modified sandwich variance estimators that cluster observations on the individual (unit of replication). Independence was thus assumed across individuals, but not necessarily within an individual (see section 9.3).

Table 7. Comparison of seasonal a priori RSF models predicting the relative

occurrence of grizzly bears in the Yellowhead study area. Models were assessed by ranking (∆i) Schwartz’s Bayesian information criteria (SBIC) and describing the weights (wi) of the model given the data. See Table 6 for description of variables used in the model structure.

Season & Model #

Model Structure KI SBIC ∆i wi

Pre-berry 2 g+v+r1+n1m+n1l+m1m+m1l+m1h+s1+s2+s3 21 71039.6 0 1

11 g+v+r1+m1m+m1l+m1h+s2 17 71254.6 215.0 <0.001 7 g+n1m+n1l+m1m+m1l+m1h 6 72594.8 1555.2 <0.001

10 g+m1m+m1l+m1h 4 72701.8 1662.2 <0.001 1 g+v+r2+n5m+n5l+m5m+m5l+m5h+s1+s2+s3 21 74949.5 3909.9 <0.001 5 g+v+r1+s2 14 75597.1 4557.5 <0.001 8 g+n5m+n5l+m5m+m5l+m5h 6 77086.5 6046.9 <0.001 9 g+m5m+m5l+m5h 4 77500.8 6461.2 <0.001 6 v+r1 12 77501.4 6461.8 <0.001 4 G 1 78220.2 7180.6 <0.001 3 null model 0 81423.9 10384 <0.001

Post–berry 2 g+v+r1+n1m+n1l+n1h+m1m+m1l+m1h+s1+s2+s3 22 54750.7 0 1

11 g+v+r1+m1m+m1l+m1h+s2 17 55857.3 1106.6 <0.001 7 g+n1m+n1l+n1h+m1m+m1l+m1h 7 56588.0 1837.3 <0.001

10 g+m1m+m1l+m1h 4 57388.3 2637.6 <0.001 1 g+v+r2+n5m+n5l+n5h+m5m+m5l+m5h+s1+s2+s3 22 59931.4 5180.7 <0.001 8 g+n5m+n5l+n5h+m5m+m5l+m5h 7 62194.9 7444.2 <0.001 5 g+v+r1+s2 14 62243.9 7493.2 <0.001 6 v+r1 12 63786.7 9036.0 <0.001 9 g+m5m+m5l+m5h 4 64238.0 9487.3 <0.001 4 g 1 64857.0 10106 <0.001 3 null model 0 67734.2 12984 <0.001

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9.5.2 Results • RSF Models

During both the pre- and post-berry seasons, SBIC weights indicated strong support for full models (model 2) using small scale variables (1-km² linear feature densities and 2.25 ha. IDTA variety). All other models were largely unsupported (Table 7). Models using equivalent structure, but at larger scales (e.g. model 1), were ranked much lower than small-scale models. In fact, models using greenness and the three motorized linear access features alone (model 10) out-performed full models based at large-scale measurements. This indicates that habitat selection is dramatically influenced at processes occurring at patch scales and that linear features at smaller scales are directly influencing the selection of habitats by animals. In comparison with the reference habitat category (closed conifer), model 2 pre-berry estimates for habitat selection were positive for alpine, cuts >12 years old, deciduous forests, non-vegetated areas, open conifer, recent burn, regenerating forests, and shrub-bog-wetlands. Negative selection (avoidance) occurred for cuts 0-12 years old, herbaceous areas, and mixed forests (Table 8). Changes in selection during the post-berry season included avoidance of old cut-blocks and selection for young cut-blocks. Open conifer, non-vegetated habitats, and regenerating forests switched from positive to negative selection. Interpretations of such results are purely based on the reference to selection for closed conifer stands.

Table 8. Variables and estimated parameters for seasonal (pre-berry and post-berry)

resource selection function (RSF) models of the Yellowhead Ecosystem, Alberta. Models are based on GPS radiotelemetry data between 1999 and 2001. Robust variances are based at the unit of replication of the individual and estimates have been adjusted for GPS collar bias.

Pre-Berry Post-Berry Robust Robust Variable βi S.E. P βi S.E. P Greenness 0.016 0.002 <0.001 0.018 0.002 <0.001Alpine 0.606 0.272 0.026 0.487 0.308 0.114Cut 0-12-years-old -0.206 0.233 0.378 0.385 0.218 0.076Cut >12-years-old 0.298 0.216 0.167 -0.287 0.286 0.315Deciduous forest 0.042 0.116 0.718 0.438 0.190 0.021Herbaceous -0.021 0.158 0.892 -0.241 0.246 0.327Mixed forest -0.309 0.180 0.085 -0.128 0.226 0.572Non-vegetated 0.118 0.224 0.598 -0.060 0.186 0.746Open conifer forest 0.035 0.158 0.825 -0.172 0.166 0.302Recent burn 1.574 0.645 0.015 0.121 0.705 0.863Regenerating forest 0.276 0.305 0.365 -0.201 0.502 0.689Shrub-bog-wetland 0.033 0.123 0.785 0.051 0.119 0.666Habitat diversity-2.25 ha. 0.225 0.025 <0.001 0.267 0.032 <0.001Non-motorized density 1-km2, medium 0.052 0.311 0.867 0.079 0.354 0.824

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Pre-Berry Post-Berry Robust Robust Variable βi S.E. P βi S.E. P Non-motorized density 1-km2, low 0.191 0.167 0.253 0.650 0.165 <0.001Non-motorized density 1-km2, high 0.345 0.568 0.544Motorized density 1-km2, medium 0.223 0.082 0.006 0.296 0.082 <0.001Motorized density 1-km2, low -0.482 0.105 <0.001 -0.813 0.225 <0.001Motorized density 1-km2, high 0.664 0.337 0.049 0.491 0.282 0.081Major stream distance (km) -0.002 0.022 0.948 -0.036 0.029 0.222Perennial stream distance (km) -0.195 0.052 <0.001 -0.143 0.060 0.017Intermittent stream distance (km) -0.234 0.174 0.179 -0.439 0.205 0.033

Figure 12. Pre-berry RSF model based on model 2 (minimum SBIC model) variables.

Alpine and old cut-blocks (>12-years-old) areas here appear quite attractive, while the high mountainous terrain is avoided.

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Figure 13. Post-berry RSF model based on model 2 (minimum SBIC model)

variables. The Foothills east of Jasper National Park are highly influenced by access density as large areas of grizzly bear habitat are compromised with motorized low-impact linear features.

Both greenness and habitat diversity were strongly significant and positive during the pre- and post-berry seasons. Our hypothesis that habitat diversity at the patch level was important was supported with these results. Access density varied by type, with only motorized low use linear features being negative. The overall cumulative effect on selection however, was negative, as this class made up the majority of the linear features with the other classes being uncommon to rare. All stream variables positively influenced habitat selection, as bears were less likely to be further from streams (i.e., negative coefficients). Only perennial streams, however, were significant in the pre-berry season, while both perennial and intermittent streams were significant in the post-berry season.

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Inspections of spatially explicit maps of seasonal RSF models (Figures 12 and 13) reveal substantial cumulative effects of access density on selection during the post-berry season and less so during the pre-berry season. With the eastern study area’s high density of low use motorized features, blocks of habitats become less available during the post-berry season. The cumulative impact of all the access features is spatially depicted in Figure 14 for the post-berry season. Note the substantial decrease/negative impact for the Foothills region, with Jasper enjoying relatively little impact to even positive impacts along backcountry trails. It should be noted, however, that although these models control for effects of habitats, they do not address interactions between habitat and human features (interactions were not fit within these models).

Figure 14. Cumulative impact of six classified linear access features on the selection

of habitats during the post-berry period by grizzly bears in the FMF grizzly bear research program area.

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Following RSF map binning, validation of seasonal RSF models revealed strong indication of predictive fit with validated rank correlations being positive (rs = 0.903 pre- and post-berry) and highly significant (P<0.001) for both seasons.

9.5.3 Discussion RSF models revealed seasonal changes in selection between the pre- and post-berry periods. As uncovered with food phenology models, the appropriate temporal scale is likely even finer than modeled here. Regardless of the inter-seasonal variation observed at finer scales, selections of resources were observed and models were successful in predicting bear occurrence. During the post-berry period, avoidance of human linear features was evident, as the Foothills east of Jasper National Park saw drastic reductions in habitat effectiveness. Coefficients for individual access features were often misleading in resulting models, as many were positive, yet observed effects were small as their densities were either localized or low. Thus, the cumulative impact of access density was overwhelmingly driven by low impact motorized features in our study area. This category comprised the majority of features on the landscape and because they were strongly avoided, contributed most to the observed impact. This is not to say that bears did not use areas with high human disturbance. For instance, selection for cut-blocks occurred in both seasons, while habitat diversity (a strong predictor of bear occurrence) was frequently related to anthropogenic activities in the Foothills. Although such areas might be attractive from a habitat perspective, long-term fitness gains are likely compromised by selection of such habitats within high access, high risk areas (i.e., attractive sinks). 9.6 Field Programs and Future Directions • Field Programs

In 2001, we established 215 random microsite plots within four female (G03, G12, G20, and G36) grizzly bear MCP home ranges. On these plots, we measured forest, habitat, and food attributes that will be used for comparisons with equivalent GPS use locations. Moreover, these plots are further being used for development of predictive food models similar to those described in section 9.4, but outside of Jasper National Park. Such food models will be used for RSF modeling across the study area. To specifically quantify russet buffaloberry dynamics we further established four 0.09 ha (30m x 30m) research grids. Within these grids, we mapped and characterized 317 russet buffaloberry (Shepherdia canadensis) shrubs and measured general environmental parameters (elevation, slope, aspect, soil temperature, etc.) along with tree and sapling stem mapping. The overall russet buffaloberry production within these grids for 2001 was 18,887 berries. At these grids, we are examining the spatial and temporal dynamics of berry production, which includes spatial analyses of male and female plants and individual variation in berry production between years. An example map of russet buffaloberry shrubs near the

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Gregg River is provided in Figure 15. This site represents an open conifer stand lying at the interface of the 1956 Gregg River burn, where only partial mortality occurred. Shrub densities and berry productivity here were quite low compared to the three other research grids.

Relative Easting (m)

0 5 10 15 20 25 30

Rel

ativ

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orth

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(m)

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5

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Figure 15. Map of Shepherdia canadensis locations within a research grid in the

Foothills Model Forest. Shrub, environmental and forest attributes were quantified across the 0.09 ha grid.

• Future Directions

In 2002, we plan to produce patch-scale RSF models for GPS grizzly bear radiotelemetry data from 1999 to 2001. Models will be developed at the individual and population levels for each year for three to four seasons. Such models will describe the relative probability of occurrence for grizzly bears within the extended 2001 study area (~10,000 km²) as a function of multiple habitat, environmental, and human disturbance explanatory variables. Results will advance our understanding of habitat requirements for local grizzly bear populations within the Foothills and further provide local managers with a statistically robust grizzly bear habitat map for resource management planning. RSF models will further be examined for the cumulative impacts of access density on use of resources within the Foothills. To tease apart the variation in habitat selection, we will be examining selection across years and seasons and among individuals, thus revealing differences between

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sex, age and environmental conditions. Of principal importance will be the understanding of underlying mechanisms relating to habitat selection. To this end, we plan to continue measuring habitat and food microsite characteristics at selected GPS locations (random and grizzly bear use sites). Spatial and temporal patterns of grizzly bear foods will be evaluated at each site using spatially explicit analyses and repeated measures designs. Finally, in 2002 we plan to begin habitat based population viability analyses for Yellowhead grizzly bears. Such analyses will consider future landscape scenarios within the Foothills during the next Century. The likelihood of grizzly bear persistence, by scenario, will be assessed, providing resource managers with estimates of various human impacts.

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10. MICROSITE HABITAT SELECTION BY FEMALE GRIZZLY BEARS R. Munro (Foothills Model Forest), S. Nielsen (University of Alberta), G. Stenhouse (Foothills Model Forest), and M. Boyce (University of Alberta)

10.1 Introduction Although habitat can be analysed at many scales, it is often broadly classed into two levels: (a) macrohabitat selection, i.e. the selection of general broad scale vegetation classes (IDT or greenness maps) and (b) microhabitat selection, which tends to focus on uncovering specific understory vegetation structures within the broader habitat classes. Currently, Scott Nielsen and Mark Boyce are examining selection at the broader scale. As of May 2001, we initiated a new component within the FMF grizzly bear research program designed to examine bear selection at the microsite level. The importance of this selection level is two fold. Firstly, microhabitat work is important for understanding the mechanism of selection at the broader scale and the seasonal importance of different habitats. A clear understanding of why specific broader habitat classes may or may not be important to grizzly bears is essential for the effective management and conservation of the species. The analysis at the broader habitat classifications cannot provide us with this type of information. It is hoped that we will be able to link bear use of broad habitat classes (e.g. greenness) to within habitat class selection to better understand selection at the mechanistic level. Secondly, it enables researchers to collect valuable data on the diet of grizzly bears in our area. Knowledge of their diet is critical if we are to understand the ecology and behavior of grizzly bears (Herrero 1978). Bears must satisfy their nutritional requirements for the entire year in approximately seven months. It is, therefore, not surprising that there is a strong relationship between food quantity and quality and reproductive rates (Jonkel and Cowan 1971, Rogers 1987, Bunnell and Tait 1981). Some populations may ultimately be regulated by food (McLellan and Hovey 1995). Here we present a preliminary summary of the 2001 microsite data, complete analysis of the data set is ongoing. 10.2 Methods Our sampling protocol was restricted to four female grizzly bears because of the high costs associated with vegetation sampling in such a large and remote study area. We attempted to select females whose home range best represented the natural sub-regions and various levels of human activity within the study area. G003 was a seven year old female with no cubs whose home range primarily encompassed the alpine/subalpine sub-regions within Jasper National Park and consequently, there was a limited amount of human activity within her home range. Conversely, both G020 and G036 had home ranges located in upper foothills, an area that has one of the highest concentrations of human activity within the entire study area. G020 was a six year old female with two cubs of the year and G036 was a three year old female with no cubs. Finally, G012 was seven year old female with two cubs of the year who had a home range that incorporated both the upper and lower foothills sub-region. Consequently, wetlands,

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conifer stands, and mixedwood stands dominated her home range area. This female also had a fairly high level of human activity within her home range. Data was retrieved from all females, with the exception of G020, every two weeks. G020 had collars which uploaded on a monthly basis. Data was sub-sampled such that only one location per day was randomly selected for each bear. We attempted to maintain similar sample sizes for all four females across the active season. To match the pixel size of the IDT map (30m), our plots were 20 m². The centre of the plot was situated at the GPS UTM location. To account for GPS error, however, if bear sign was detected within 20 m of the UTM, the centre of the plot was positioned over the bear sign. Percent forb and shrub cover for all plant species were recorded within five 0.5 m² quadrats located at five meter increments along a 20 m N-S transect running through the centre of the plot. Slope, aspect, canopy cover (measured in all four cardinal directions), soil temperature and soil depth were recorded at each 0.5 m² quadrat. Food shrub density was also measured within a 1 x 20 m (central transect) area. If Sherperdia canadensis was present within this same 1x 20 m plot, then berry productivity for that species was also determined. Tree density within the 20 m² plot was also measured using X2 prism standing at the centre of the plot. The amount of hiding cover was estimated using a visual index stick set a 10 m distances in all four cardinal directions from the centre of the plot. Bear activity at each site was also recorded and a photo taken. Overall aspect, slope and elevation for the plot was taken at the centre point. 10.3 Results In 2001, a total of 231 use plots were completed. Table 9 indicates the total number of sample plots for each bear across the active season. Sampling efforts were consistent across all four bear and both seasons with the exception of G020 who, unfortunately, dropped her collar in late August and was not re-collared until late October. Consequently, we were only able to obtain a limited amount of fall data (n=12) for this bear (Table 9). Plots were divided into five classes of bear activity, including herbaceous feeding (i.e. grasses, Trifolium spp., Heracleum lanatum, Equisetum spp, Taraxacum officinale), Hedysarum spp. digging, Sherperdia canadensis feeding, anting, and bedding. Often times, more than one activity was detected at a site. In total, 34 plots had signs of herbaceous feeding, 58 Sherperdia spp. feeding, 59 Hedysarum spp. feeding, 67 bedding, and 85 anting activity. Only one ungulate (Odocoileus spp.) kill site was found. No rodent diggings were found at any of the sites. Frequently, both bedding and anting activity were associated with other types of activity. Of the 67 bedding sites, 47 were associated with all forms of feeding activity. 52 of the anting sites were also associated with the other types of feeding activity.

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Table 9. Total number of vegetation micro-site plots for female grizzly bears 2001. Sample Size (N) Bear ID Springa Fallb G003 27 34 G012 36 36 G020 36 12 G036 22 28 Total 121 110 a. Spring season was den emergence to July 31 b. Fall season was August 1 to den entry 10.3.1 Seasonal Changes in Feeding Activity Not surprisingly, there were seasonal differences in grizzly bear feeding activity across the active season (Figure 16). From May to June it appears that Hedysarum spp. digging dominated the feeding activity. In early July through to early August, the grizzly bears began to key in on herbaceous species, such as clover, cow parsnip and equisetum. By August 1 and through till the end of September, the bears were primarily feeding on Sherperdia canadensis. Hedysarum digging continued into September, although this type of activity diminished considerably from July onwards. It appears for 2001, at least, that bears in our study area did not significantly utilize any of the Vaccinium species. Anting behavior remained fairly constant throughout the active season.

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Figure 16. Seasonal changes in female grizzly bear feeding activities.

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10.3.2 Bear Activity by IDT classification • Hedysarum spp Feeding

Although all four female grizzly bears used Hedysarum spp. in the early spring, there was variation in terms of the habitat types where this plant species grows and consequently variation in the types of habitats each bear utilized at this time (Figure 17). Although sample size is small, it tentatively appears there may be three groups of bears. G003, whose home range is located predominately within the mountains and Jasper National Park, used primarily alpine habitats to dig for Hedysarum spp. Interestingly, both G036 and G029, whose home range fall within the high human activity area of the foothills, used Hedysarum spp. in cut-blocks, with cut-blocks that are greater than 20 years of age dominating this habitat class. Finally, G012, whose home range is the most eastern portion of the study area where wet lands and mixedwood stands are more common, used Hedysarum spp. in a wide variety of habitat types, including: mixedwoods and closed conifer stands as well as wet open and wet treed areas.

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Figure 17. Hedysarum feeding sites by IDT habitat class.

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• Herbaceous Feeding The most common herbaceous species identified at these plots were Trifolium spp., followed by Equisetum spp. and Heracleum spp. When all bear plots were combined anthropogenic habitat types (i.e. cut-blocks, roads, well sites, and pipelines) accounted for 53% of all herbaceous feeding plots (Figure 18). Closed conifer stands were the next most commonly used habitat. It accounted for nearly 17% of all herbaceous feedings. On an individual basis there was great diversity in the habitat selected.

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Figure 18. Herbaceous feeding sites by IDT habitat class. • Sherperdia canadensis Feeding

In general, bears foraged for Sherperdia spp. in open conifer, mixedwood, and closed conifer stands as well as cut-blocks (Figure 19). Again, however, there appears to be behavioral differences among bears. G003 foraged on Sherperdia spp. primarily in open and closed conifer stands. While G036 and G020 both utilized older cut-blocks and open conifer stands, G012 used both mixedwood and open conifer stands. Although there are differences in habitat selection with regards to Sherperdia spp., open conifer stands were utilized by all four bears. Furthermore, 85% of all Sherperdia spp. plots had canopy closure of less than 65%. It appears that the slightly open canopy may provide suitable growing conditions for this plant species.

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Figure 19. Sherperdia canadensis feeding sites by IDT habitat class. • Anting Activity

Only three of the four females showed any significant amount of anting behavior (Figure 20). Only one of G003’s locations had any sign of anting activity. The majority of anting was exhibited by G012, G020 and G036. Although anting appears to take place in a large variety of habitat types, the majority of the sign occurred in cut-blocks.

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Figure 20. Anting activity sites by IDT habitat class.

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• Bedding Sites Since most bedding sites were associated with other forms of activity I did not examine the IDT habitat classification to which these sites belonged. However, there were 19 plots where only bed sites were found. Of these latter sites, 47% and 32% were located in closed conifer stands and wet treed areas, respectively. Only three bed sites were located in more open habitat including one in a cut-block, one in the alpine and one in an open conifer stand.

10.3.3 Scat analysis Over 220 scat samples were collected at use sites. Often multiple scat samples were collected at each site. Due to limited financial resources scats were sub-sampled such that only one scat per site was selected for diet analysis. Rick Riddell of Wildlands Consulting Company is currently carrying out the diet analysis. It is estimated that the food analysis will be completed by March 31, 2002. 10.3.4 Animal Health (1999 to 2001) The persistence of a population of animals over time is, in large part, a function of the health of the individual animals comprising the population. If animals are in poor health as a result of disease, inadequate nutrition, prolonged stress, or a combination of these and other factors, the long-term persistence of the population is threatened. Conversely, if most animals are in good health, the population is likely to remain stable or grow with time. Assessment and monitoring of the health of individual grizzly bears has been a major focus of the FMF grizzly bear research program since beginning in 1999. Because no one single measure can provide a reliable picture of health, the combination of many measurements including physiological function (heart and respiratory rates, body temperature), body condition, and a broad array of blood analyses have been used to assess health. Over the past years, the comparison of health data among individual animals has allowed us: (1) to evaluate and improve the safety of different drug combinations used to anesthetize grizzly bears; and (2) to evaluate the stress and potential health consequences of different methods of capturing grizzly bears. Further, 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 and black bears, to also be used with grizzly bears. Many of these research findings were presented at the 13th International Conference on Bear Research and Management, at Jackson Hole, Wyoming, in May 2001, and have since been submitted for publication in the peer-reviewed scientific literature. The abstracts from these submitted manuscripts are presented in the following three bullets.

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• Anesthesia of grizzly bears using xylazine-zolazepam-tiletamine or zolazepam-tiletamine (submitted to Ursus in November 2001)

Investigators: Marc Cattet, Nigel Caulkett, and Gordon Stenhouse.

The immobilization features and physiological effects of combinations of xylazine-zolazepam-tiletamine (XZT) and zolazepam-tiletamine (ZT) were compared in 46 wild grizzly bears handled during 90 captures. Although induction time was similar between drugs, induction dosage and volume were less with XZT than with ZT. Induction of immobilization with XZT was predictable and smooth, and muscle relaxation was good during the period of immobilization. XZT was safely tolerated at 2-3 times the recommended dosage of 6-7 mg/kg (or xylazine at 2.4-2.8 mg/kg + ZT in a 1:1 ratio at 3.6-4.2 mg/kg). Bears immobilized with XZT had slower pulse rates and higher rectal temperatures than bears immobilized with ZT. The risk of hyperthermia at higher ambient temperatures (≥ 25°C) was of potential concern with XZT. Although transient hypoxemia (SpO2 ≤ 85 %) developed immediately following induction in some bears, it was not severe enough to pose significant health risk. The provision of supplementary oxygen during hypoxemia resulted in increased hemoglobin oxygen saturation (SpO2) and decreased pulse rate. Although the time to full reversal of effects was highly variable, the effects of XZT immobilization could be reversed with the α2-antagonist drug yohimbine.

• Effects of method of capture on chemical immobilization features and physiological

values in grizzly bears (submitted to the Journal of Wildlife Diseases in December 2001)

Investigators: Marc Cattet, Katina Christison, Nigel Caulkett, and Gordon Stenhouse.

The chemical immobilization features and physiological effects of two methods of capture, immobilization of free-ranging (FR) bears by remote injection from a helicopter or physical restraint (PR) by leg-hold snare prior to immobilization, were compared in 46 wild grizzly bears handled during 90 captures, between 1999 and 2001. Induction dosages and times were greater for FR bears than PR bears, a finding consistent with depletion of, or decreased sensitivity to, catecholamines. FR bears also had higher rectal temperatures at 15 minutes following immobilization, presumably a result of intense muscular exertion during capture. PR bears had higher white blood cell counts, with greater proportions of neutrophils and lesser proportions of lymphocytes and eosinophils, than did FR bears. This white blood cell profile was a stress response affected by elevated levels of serum cortisol. Serum concentrations of alanine aminotransferase, aspartate aminotransferase, and creatine kinase were higher in PR bears, and indicative of muscle injury. Serum concentrations of sodium and chloride also were higher in PR bears and attributed

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to dehydration through water deprivation and increased insensible water loss. Overall, capture and physical restraint by leg-hold snare caused a greater degree of physiological disturbance than did chemical immobilization of free-ranging bears by remote injection from a helicopter. Efforts to minimize the time spent in a snare prior to chemical immobilization should reduce the magnitude of physiological disturbance experienced by captured grizzly bears.

• The development of a body condition index for ursids (submitted to Canadian

Journal of Zoology in November 2001)

Investigators: Marc Cattet, Nigel Caulkett, Martyn Obbard, and Gordon Stenhouse.

The objective of this study was to develop a body condition index (BCI) for polar bears, black, and grizzly bears which could be measured easily and used to compare among individual animals regardless of sex, age, reproductive state, geographical population, or date-of-capture. The BCI was developed as the standardized residual of the regression of two ln-transformed measurements, total body mass (TBM) against straight-line body length (SLBL), that were recorded routinely during the handling of 1,784 captured bears. The transformation of mass-length data to natural logarithms resulted in a linear relationship between mass and length, but the relationship in polar bears differed from that in black bears and grizzly bears. As an indicator of body size, SLBL correlated directly with structural mass (skin and fur + bone + viscera) and skeletal mass (bone only) in 31 killed polar bears and 33 killed black bears. As an indicator of body condition, the BCI correlated directly with potential energy tissue (fat + skeletal muscle) mass in 31 killed polar bears and 33 killed black bears. There was no association between SLBL and the BCI in any of the three species, indicating the BCI was independent of body size. The use of mass-length nomograms that are specific for either black bears and grizzly bears, or for polar bears, allows rapid estimation of BCI values in the field without complex calculations. The BCI was compared against two other indices (Quetelet Index and Fatness Index) used in recent years to estimate body condition in bears, and was found to be a more reliable and accurate index.

• Animal Health (2001 and Onward)

Investigators: Marc Cattet, Nigel Caulkett, Janice Bahr, Matt Vijayan, and Gordon Stenhouse.

In 2001, the animal health component of the project was expanded to look at potential health consequences to grizzly bears as a result of alteration or loss of their habitat. In this regard, the key aspects of health are stress and reproduction. Evidence from other species, including humans, has shown that reproductive function is reduced, or fails, in animals suffering from long-term or chronic stress. If

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habitat alteration or loss is perceived as a significant stress by individual grizzly bears, it follows that reproduction may be comprised – a finding that could have serious implications for the long-term persistence of the population.

The investigation of stress, reproduction, and the environment will be a major focus of the project over the next few years, and will be conducted along three lines. The first will be to evaluate reproductive function in bears by measuring the blood concentrations of several key hormones (testosterone, estrogen, progesterone, prolactin, follicular stimulating hormone, and luteinizing hormone) involved in the control of reproduction. This work is being done in collaboration with Dr. Janice Bahr, a reproductive endocrinologist and leading authority on reproductive function in bears who is based at the University of Illinois in Champaign-Urbana. Preliminary results from these ongoing analyses are presented in Table 10. The second will be to develop laboratory assays for biochemical indicators of chronic stress that can be measured in the blood and tissues of grizzly bears. This work is being done in collaboration with Dr. Matt Vijayan, a renowned biochemical physiologist and director of a state-of-the-art stress laboratory at the University of Waterloo in Ontario. The third line of approach will be to develop linkages and seek significant associations between the animal health data and the environmental data that is being collected concurrently by investigators from the Universities of Calgary and Alberta. The success of this task will rely upon the skills of Julie Dugas, a GIS expert and data manager with the Foothills Model Forest in Hinton, Alberta.

Table 10. Mean concentrations of reproductive hormonesa measured in the blood serum of grizzly bears captured as part of the FMF grizzly bear research program from 1999 to 2001.

Sex and Reproductive

Classb Testosterone

(ng/ml) Estradiol (pg/ml)

Progesterone (ng/ml)

Solitary adult female - 15.8 ± 1.0 (17)

3.02 ± 0.50 (17)

Adult female with offspring - 14.5 ± 0.7 (10)

2.13 ± 0.41 (10)

Juvenile female - 14.3 ± 0.9 (15)

2.48 ± 0.34 (15)

Adult male 3.06 ± 0.85 (12)

- -

Juvenile male 0.44 ± 0.16 (5)

- -

a Values presented as mean ± standard error with sample size in parentheses. Up to January 2002, the analyses that were completed were testosterone in males, and estradiol and progesterone in females. Pending analyses include testosterone in females, and prolactin, follicular stimulating hormone (FSH), and luteinizing hormone (LH) in bears of both gender.

b Adult bears are ≥5 years and juveniles are <5 years.

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11. SCAT DETECTION DOG STUDIES, 1999-2001 Samuel K. Wasser, Ph.D. (Center for Conservation Biology, University of Washington Department of Zoology), Gordon Stenhouse (Foothills Model Forest)

11.1 Introduction Interest in non-invasive endocrine and genetic technologies has grown rapidly over the past several years. This is largely due to the needs of scientists and managers for rapid, cost-effective methods to monitor how wildlife abundance, distribution, and physiological health change in response to environmental pressures. However, studies of this nature are likely to face many scientific and legal challenges because of the considerable political and economic management implications of the results. This makes it essential that the methods used to acquire such data be thoroughly validated. My laboratory has consistently been at the forefront of the development and validation of these non-invasive methods (Wasser et al. 1988, Wasser et al. 1991, Wasser et al. 1993, Wasser et al. 1994, Wasser et al. 1996, Wasser et al. 1997, Wasser et al. 2000, Wasser et al. 2001) and our continued studies are a direct result of these experiences. A key strength of the methodology we are using is its ability to cost-effectively acquire hormone and DNA data through low bias methods that maximize the number of individuals sampled, as well as the number of samples per individual, over the landscape. This ability is vital for population studies given the high variability in (a) habitat conditions and disturbances over the landscape and (b) the type and intensity of stressors within and between individuals on any given day. Specially trained scenting dogs are used to detect fecal samples over large, remote geographic areas with minimal capture heterogeneity or subject disturbance (Wasser et al. 2001). Stress and reproductive hormones extracted from feces are used to indicate physiological condition of the animal (Wasser et al. 1988, Wasser et al. 1993, Wasser et al. 2000, Wasser et al. 1997, Wasser 1996). DNA extracted from feces (Wasser, et al. 1997) is used to confirm the species (mitochondrial DNA; mtDNA), gender (single copy nuclear DNA; scnDNA) and individual identity (microsatellite DNA; µsatDNA) of the animal that left each sample used in these analyses. The genetic data are also used to estimate species-specific abundance and distributions in relation to location-specific environmental disturbances. Data are then layered onto a geographic information system (GIS) that includes detailed indices of habitat and human disturbances (Stenhouse and Munro, 1999). Collection of genetic and endocrine data in the field has historically been constrained by the invasiveness of sample acquisition, low sample accessibility, and bias associated with capture heterogeneity (unequal capture rates across subjects). Acquiring such data from feces can significantly enhance sample acquisition rates while eliminating biases

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associated with physiological disturbances from invasive sample collection methods (e.g., blood withdrawal or tissue collection). The use of scat detection dogs for sample collection makes this approach even more powerful. It permits a rapid, relatively unbiased sampling of a large number of individuals over broad geographic areas, enabling investigators to cost effectively address a scale of ecological, physiological and conservation related questions never before possible. 11.2 Methods 11.2.1 Why Test This Method on the Yellowhead Grizzly Bears? We currently have an excellent opportunity to field test the proposed methodology as part of the Yellowhead grizzly bear research program. This multi-national program is one of the most comprehensive grizzly bear studies ever undertaken, driven by the conflicting need to preserve grizzly bear habitat in an area where the pressures to maximize resources utilization are growing rapidly. Collaborating with government and industry on this project enables us to take advantage of their radio telemetry and associated serological data to evaluate sampling biases in our scat detection dog method. It provides extensive remote sensing and GIS data used to map the habitat and human disturbances in the study area. And, it helps to assure that our results will influence future resource management policies. 11.2.2 Utility of Fecal Hormone and DNA Measures The stress response of vertebrates is a complex set of physiologic reactions that involves virtually every physiologic system. Glucocorticoids (GCs) are involved in nearly all of these responses, inducing some, suppressing others to prevent them from overshooting, or playing a permissive role by setting the stage for other responses (Sapolsky et al. 2000). The intimate role played by GCs in these responses is what makes their measurement such an excellent peripheral index of the overall stress response (Sapolsky et al. 2000, Moburg and Mench, 2000). Reproductive hormones (estradiol, progesterone and testosterone) are also useful indirect indices of the stress response because suppression of reproductive processes can be an important part of this process (Wasser 1996, Sapolsky et al. 2000, Wingfield et al. 1998, Wasser and Barash 1983), directly impacting population viability. Fecal steroid measures have become a powerful tool for measurement of stress and reproductive hormones. Initial work focused on reproductive steroids, characterizing female reproductive condition and tying this to behavior (Wasser et al. 1988, Wasser 1996, Czekala et al. 1994, Brockman et al. 1995, Lasley and Kirkpatrick 1991, Strier and Ziegler 1997, Wasser et al. 1995, Monfort et al. 1993, Brown et al. 1996, Schwarzenberger et al. 1996, Möstl et al. 1987, Mostl 1992). The PI’s lab also demonstrated that sample storage in ethanol preserved fecal hormones without freezing for up to 20 days as well

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as blocked the inflation of hormones in feces from urine contamination. Most steroids in urine are conjugated, while unconjugated in feces. Ethanol blocked the deconjugation of urine-contributed steroids, either by denaturing the deconjugating enzymes and/or by killing the bacteria that secrete them (Wasser et al. 1993). We additionally examined dietary impacts on steroid excretion. Dietary effects result primarily from change in dietary fiber (Goldin et al. 1981, Goldin et al. 1982), increasing fecal bulk, largely in the form of water (Wasser et al. 1993). The overall rate of steroid excretion increases slightly with increased dietary fiber, but this is overcompensated by an increase in fecal bulk, causing a net decrease in steroid concentration per gram wet weight in feces. Freeze drying samples to remove water content, and expressing hormone concentrations per gram dry weight, satisfactorily controlled for dietary impacts on steroid excretion, providing a tight correspondence between serum and fecal hormone measures across diets of five, 10 and 20% fiber over 13 months (Wasser et al. 1993). Freeze drying also allows a more uniform subsampling of feces for the otherwise unevenly distributed hormones and DNA in the sample (Wasser et al. 1988, Wasser et al. 1996, Wasser et al. 1997). Our lab and others (Wasser et al. 2000, Graham and Brown 1997, Palme et al. 1999, Goymann et al. 1999) also tested several antibodies for the measurement of glucocorticoid (GC) metabolites in feces. The ICN corticosterone antibody (ICN kit #07-120102) provided the best measure of cortisol metabolites in feces, reflecting adrenal activity across a broad range of vertebrates (Wasser et al. 2000). That assay has since been used to show that fecal GCs are positively correlated with more aggressive timber harvesting practices in northern spotted owls (Wasser et al. 1997), tourist activities in Rocky Mountain elk (Millspaugh et al. 2001), lack of rainfall and low dominance rank in free-ranging African elephants (Foley et al. 2001), high dominance rank in free-ranging African wild dogs (Creel et al. 1997), and captivity stressors in spotted hyenas (Goymann et al. 1999). The utility of fecal steroid measures also instigated attempts to measure DNA in feces. Low molecular weight DNA (≤700 bp) can now be accurately isolated and amplified from scat (Wasser et al. 1997, Taberlet et al. 1996, Kohn and Wayne 1997, Constable et al. 1995, Gerloff et al. 1999). The genetic data can be applied to studies of relatedness and paternity (Constable et al. 1995, Gerloff et al. 1999), as well as mark-recapture and other population-based models used to estimate the abundance and distribution of wildlife in remote areas (Kohn et al. 1999). It also enables investigators to tie hormone and other data from the sample to the individual that left it. A primary focus of our continued efforts is to address sample degradation and preservation questions that remain critical to the reliable application of fecal hormone and DNA measures in the field.

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11.2.3 Degradation and preservation of fecal hormones We recently conducted degradation and preservation studies of fecal cortisol metabolites in captive grizzly bears over a 12 month period (Hunt and Wasser, in prep.). Approximately 10 kg of fresh feces, pooled from two captive male grizzly bears, were thoroughly mixed for two hours and divided into "time zero" samples (n=20, immediately freeze-dried and assayed for immunoreactive cortisol) and five treatment groups: (1) Untreated controls (exposed to air at room temperature); (2) Freeze-dried at –20°C for ~one week; (3) Oven-dried at 45°C for one week; (4) Silica-dried (ziploc bags with indicating silica beads, replaced with fresh silica as needed); and (5) Ethanol (feces mixed with 90% ethanol and stored in vapor-proof vials). Freeze-drying (group 2) provides the “gold standard” for long-term sample preservation in the lab (see below). Group treatments 3-5 were chosen because they have been the most commonly used by field researchers (Wasser et al. 1988, Wasser 1996, Millspaugh et al. 2001, Foley et al. 2001, Creel et al. 1997, Whitten et al. 1998, Ziegler et al. 1997). Half of each group was stored at room temperature and the other half at –20°C. At one, two, three, four, nine, 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 gram dry weight and analysed for immunoreactive cortisol metabolites. The concentration of GC’s remained constant in freeze dried samples out to one year, whether stored frozen or at room temperature. Untreated samples stored frozen also retained constant GC concentrations for one year. However, GC concentrations in untreated samples stored at room temperature, as well as silica and oven-dried samples stored frozen or at room temperature, declined progressively by 30% (silica and oven-dried) to 40% (untreated) in the first 90 days, remaining stable thereafter. GC concentrations in the ethanol preserved sample, stored at –20°C also declined by 15% in the first 90 days, but then returned to baseline levels by one year. The most surprising result was from the ethanol preserved samples, stored at room temperature. GC concentrations remained fairly constant in that group for the first 60 days, but then increased by 50% over the next 60 days. All groups had remarkably small error bars, reflecting the effectiveness of thoroughly mixing samples prior to extraction. High Pressure Liquid Chromatography (HPLC) analyses of the cortisol metabolites in the time 0 and the 60 day freeze dried samples stored at room temperature each revealed a single peak at fraction 80, slightly less polar than corticosterone (fraction 75), and consistent with the predominant cortisol metabolite peak reported in baboon feces (Wasser et al. 2000). Study results raised several questions worthy of further pursuit.

i. Did silica and oven drying methods unsatisfactorily preserve GCs because samples were dried too slowly and/or at too warm a

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temperature? This question arose because: GC concentrations in the freeze dried samples stored at room temperature did not degrade over time. Many of the silica dried samples became moldy after 48 hours. Oven dried and silica dried samples took a full week to dry and became rock-hard in the process. This question is important because of logistic advantages of drying over ethanol as a preservation method. The latter is a controlled and flammable substance that is difficult to acquire in many countries and even more difficult to transport on commercial airlines.

ii. Did the increase in GC concentration in the room temperature ethanol preserved samples occur because ethanol metabolized the GC’s into a form that had a higher affinity to our antibody; or, did the ethanol simply liberate additional GCs that were bound to lipids or binding globulins? If the latter, ethanol sample storage at room temperature could still provide adequate preservation in the field, with the addition of extraction procedures to liberate bound GCs uniformly across all samples. This question is important because ethanol potentially provides an immediate fixative, stopping all bacterial and enzymatic action at the time of collection when the bacterial load and activity are highest. Ethanol also appears to be an excellent preservative of fecal DNA (Wasser et al. 1997, Murphy et al. In review).

iii. Do some preservation methods cause samples that vary in initial GC concentrations to degrade proportionately, preserving their relative profiles?

11.2.4 Factors Impacting DNA Amplification Success Two major causes of DNA amplification failure in scat are degraded DNA and the presence of PCR inhibitors. DNA degradation tends to be highest under moist, warm conditions. Preserving fecal samples in ethanol, DMSO or by drying helps guard against DNA degradation (Wasser et al. 1997, Murphy et al. In review, Frantzen et al. 1998). PCR inhibitors in feces are most commonly diet-ingested (see Monteiro et al. 2001). Numerous methods have been reported in the literature to target low molecular weight (degraded) DNA and eliminate PCR inhibitors (Whittington et al. 1998, Da-Silva et al. 1997, Moreira 1998). Our lab showed that the Qiagen DNA extraction kit (Qiagen Inc., Valencia, CA) performed better at targeting low molecular weight DNA than did several other published methods (Wasser et al. 1997). Qiagen subsequently modified this kit to facilitate removal of PCR inhibitors in scat. The modified Qiagen stool kit was prototyped in the PI’s laboratory. The stool kit worked very well for many different inhibitors in grizzly bear feces and significantly enhanced PCR amplification success. Using fecal samples collected in 2001, we further improved upon this protocol by increasing incubation time in lysis buffer and proteinase-K to one hour each, and adding a GeneClean III (Bio101, Carlsbad, CA) step. However, since

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PCR inhibitors may be both diet and species-specific, it may be important to employ other methods for removal of inhibitors in some samples. 11.2.5 Sample Collection Biases Models for estimating population size assume that the genetic identification of captures and recaptures of individuals is done without error. The sources of error related to our genetic data fall into two categories. First, errors in genotyping, scoring and amplification (PCR) can create false individuals and thus positively bias estimates. Second, separate individuals with identical genotypes may be erroneously considered as one, negatively biasing estimates (Mills et al. 2000, Waits and Leberg 2000). Sample acquisition problems can also introduce bias. Most mark-recapture models typically assume minimal capture heterogeneity—i.e., every individual in the population has an equal probability of getting caught (Burnham and Overton 1979, White et al. 1982, Lynch 1988, Weir 1990), and ‘population closure’—i.e., individuals are not entering or leaving the study area during the sampling period (Kendall 1999). Violations of these assumptions have posed problems for non-invasive DNA collection methods using lures to draw subjects into sample collection stations (Mills et al. 2000, Mowat and Strobeck 2000, Clarke et al. 2001, Boulanger and McLellan 2001). For example, the attractiveness of lures has been reported to vary with the sex and reproductive condition of individuals in the population (Clarke et al. 2001, Lopez et al. 1998, Taberlet and Luikart 1999, Ballard et al. 2000, Towns and Ferreira 2001), avoidance of dominant species in proximity to the lure (Kendall et al. 2001), or prior handling (e.g. for radio collaring (Kohn et al. 1999, Koehler 1998, Powell et al. 2000). These effects can cause the models to under or over estimate the number of individuals of particular age/sex classes (Moreira 1998, Pollock 1982, Rosenbert et al. 1995, Woods et al. 1999). While some mark/recapture models do account for differences in subject catchability (Powell et al. 2000, Pollock 1982, Otis et al. 1978), these require intensive sampling to acquire reasonable precision. This may not be obtainable for low density populations. It is, accordingly, advantageous to minimize sample collection bias in the field whenever possible. Capture heterogeneity can also result from collection of scat samples across a landscape by visual detection. Some individuals (e.g. females) may tend to conceal their feces whereas others (e.g., males) may defecate in attempt to make their feces more conspicuous as a territorial mark. The latter may result in scat deposited in very specific, non-random locations on the landscape (Lopez et al. 1998, Brashares and Arcese 1999, Ben-David et al. 1988). Vegetation density, substrate conditions and observer skills and experiences can compound these problems of fecal sample detectability in different habitats. Depending on the methods used to estimate population size, population closure may also pose a problem for scat collections

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because the age of scat is unknown. For reasons described below, capture heterogeneity should be significantly reduced by sample collections using scat detection dogs. 11.2.6 Use of detection dogs for scat collection In cooperation with the Washington Department of Corrections, we have developed methods to train detection dogs to find and alert to scat from selected wildlife species (Wasser et al. In review 2001). Scat detection dogs are trained using scenting techniques similar to those for narcotics, bomb and arson detection, as well as search and rescue work. Scat detection dogs are trained to alert to species-specific fecal odors through immediate rewards upon finding feces of target species. These detection dogs are selected for trainability and evaluated for this work based on their temperament, strong object orientation and play drive. Detection of a target sample is motivated by the anticipated reward of a play object. This makes it unlikely that these highly reward-driven dogs will alter their capture probabilities as a function of the species, sex, reproductive status, age, sample concealment tendencies or other characteristics of the subject. Sample acquisition is enhanced by the sensitive sense of smell in canids (3 ppm), which enables them to detect specific multiple (18+) odors (species) at distances as far as 0.5 miles away (Bryson 1991, Syrotuck 1972). One detection dog trained in our program located scat from kit fox with 100% accuracy at four times the rate of trained observers (Smith et al. 2001). These advantages are particularly valuable for collecting samples to be used in mark-recapture and other population studies. 11.2.7 Scat detection dog and handler training Scat detection dogs and their handlers begin their training as part of the Canine Narcotics Training Program at McNeil Island. Dogs are initially introduced to marijuana odor utilizing a scent box. The scent box contains five compartments, each open to the outside by a 5 cm hole. Marijuana scented paraphernalia is placed in one of the five compartments. Initiated by the verbal command “find it”, the dog is guided to investigate each compartment of the scent box and encouraged to smell at the hole openings. Upon sniffing the correct hole, the dog is immediately rewarded with a well-timed toss of a tennis ball across its visual field, followed by verbal praise and ~90 seconds of play. The dog quickly learns to associate sample detection with the play reward. The reward expectation maintains a high motivation to locate the source of target odors throughout the day. Samples are next hidden at multiple indoor locations, varying height and degrees of detection difficulty. We next use the scent box to introduce the dogs to grizzly and black bear scat. The pairing of reward and species-specific scat scent occurs almost immediately. Scat samples are then hidden over a defined area in the field. Samples are set out in the field one or more days prior to any given training session to allow the human scent trail to dissipate. Dogs are introduced to scat from many different individuals of each target

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species, that fed on a variety of natural diets, allowing them to generalize across individuals, sex and diets of the target species. Scats for dog training will be obtained from multiple wild grizzly and black bear, collected across seasons in Glacier National Park and the study area in Alberta, Canada. Dog handlers are initially aware of sample locations so they can observe how the dog’s detection of a scent, relative to its source, varies with air speed and direction in relation to topography, vegetation and weather. This information allows handlers to help the dog relocate lost scent by compensating for any environment and micro-climate conditions (e.g., moving the dog down wind; noticing where scent may have pooled, risen or caught; or tightening the search lines within a grid). Samples are next hidden with the handler unaware of their number or location. This forces the handler to rely on the dog’s behavior in relation to the environment, providing the final skills necessary for the handler to guide the dog to source under any condition. Detection dogs are also conditioned to avoid scat from non-targeted species by giving them verbal and leash corrections if they indicate on feces of a non-targeted species. Dogs unable to sustain a high motivation for the work may be rejected from the program at any time during the training process. Rejection rates of pre-screened dogs average ~20%. By the end of training, sample detection rates average ~90% for target species while 0% for non-target species. 11.2.8 Field Studies of the Proposed Methodology in the Yellowhead We field tested the scat detection dog method in the study area during a six week survey for grizzly and black bear scat in 1999, and an eight week study in 2001. The 1999 data have been fully analysed (Wasser et al. In review 2001) and are detailed below. The 5400 km² study area was divided into 64 9 X 9 km grid cells. For comparison purposes, the date and location of scat collection transects within any cell were coordinated with the presence of hair snag stations for DNA collection also in that cell (see Mowat and Strobeck 2000, Woods et al. 1999 for details). Hair snag stations were in place for 10 consecutive days within each of the 64 cells and then moved to a new location within its cell, for a total of three session/cell. Snag stations were placed in the highest quality bear habitats/cell. Forty of the 64 cells were searched for scat three times at two week intervals by one of four dog teams. Each cell was searched during the same 10 day period the hair snag was in place. All transects were conducted with the dog off leash, always remaining in-sight of its handler, maximizing the area covered by each dog. Each dog team walked for ~ seven hours, covering a 5-9 km transect extending outward from one km of the hair snag in the higher quality grizzly bear habitat per cell. At the time of

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collection, all fecal samples were placed in ziploc bags with a 4:1 ratio of silica per gram feces, and stored frozen (Wasser et al. 1997). We compared the respective distributions over the landscape of grizzly and black bear samples collected by scat detection dogs versus hair snags. The distributions of grizzly bear hair and feces were also compared to concurrent GPS radio collar data from 19 grizzly bears, distributed throughout the 5,400 km² study area, providing their UTM locations every 4 hours. • Bear distributions based on sample collection methods

Scat detection dog and hair snag methods collected comparable numbers of samples (~400 each), despite 50% more cells being sampled for hair than were sampled for scat by dog teams (i.e., 64 versus 40 grid cells, respectively). Preliminary microsatellite DNA results also suggest that multiple scat samples collected during a 5-7 km transect are more likely to represent multiple individuals (Wasser unpublished) compared to hair samples collected at a single sampling station in that same grid cell (Mowat 2000).

The lure-based, hair snag method collected 0.47 black bear per grizzly bear hair samples. The detection dog method collected the reverse, 2.2 black bear per grizzly bear scat samples. Since black bears are thought to be more common in this habitat (black bears are observed daily, whereas grizzly bears are almost never observed), the above comparison could suggest that black bears were avoiding hair snag sites previously visited by grizzly bears.

Despite the above differences, hair and fecal sample collections analysed for mtDNA produced highly comparable distributions of grizzly and black bears over the entire study area (Figure 21). The distributions of the 19 individual grizzly bears equipped with GPS radio collars in the study (Figure 22) were also quite similar to those obtained from hair and feces, over the study area (Figure 21). Both scat and hair collections revealed minimal species overlap inside Jasper National Park. Black bear samples collected inside the national park were concentrated in the lower elevation, northern portion and, to a lesser degree, the central portion of the park (Figure 21). By contrast, very few grizzly bears were found in this high tourist density, northern most part of the park (Figure 21), despite the area having exceedingly low habitat fragmentation (Popplewell et al. In press). Grizzly bear hair and fecal samples (Figure 21) as well as telemetry-based locations (Figure 22) acquired inside the national park were most heavily concentrated in the more mountainous central and southern portions of the park.

A very different pattern was found in the multi-use area outside the park (Figure 21). Densities of both grizzly and black bears were highest in the northern portion

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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 21. 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.

of the multi-use area, which also has the highest level of human use. That area is characterized by clearcut logging, high density of all-weather roads, and two large coal mines located at the north end of the town of Cadomin (Nielsen et al. In press). The heaviest concentration of bear scat in that area was found along primary roads (Figure 21). Nielsen et al. (in press) reported similar results in an examination of RSF models for grizzly bears in the study area; radio collared grizzly bears selectively used areas defined as high human access (based on road and seismic line densities) while avoiding areas of moderate human access. Very few black or

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grizzly bear samples were collected in the old growth lodgepole pine forest of the central to southern portion of the multi-use area, south of Cadomin, with the exception of the area close to the primary road on the eastern border of the study area (Figure 21). Grizzly bear, but not black bear, sample collections were also relatively frequent in the very southernmost part of the multi-use area (Figure 21).

Figure 22. Grizzly bear GPS locations within the grizzly bear research program area

(in red) in 1999 and 2000. • DNA amplification success and sample age

Our initial field trials on bears in 1999 were hampered by low amplification success of DNA from fecal samples: ~65% for mtDNA and scn DNA and only 40% for µsatDNA, compared to ~98% from hair. DNA amplification success from fecal samples appeared to be compromised by three factors: (1) Improper freezing in a multi-user, walk-in freezer. The freezer was frequently opened and took a long time to re-cool. (2) Suboptimal preservation methods. Recent studies (Murphy et al. In review, Frantzen et al. 1998) suggest that 90% ethanol, or 20% DMSO in Tris/EDTA/NaCl buffer, may provide better preservation of DNA in fecal samples than the silica based method we used (Wasser et al. 1997) perhaps because the latter method dried samples too slowly. (3) High concentration of PCR inhibitors in scat collected in the study area. The impact of PCR inhibitors became apparent from our 2001 sampling efforts. We compared the amplification success of scat samples collected in the study area between July and September 2001 to samples collected by

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the same dog teams in the Northern Cascades of Washington during May and June of 2001. All 2001 scats were preserved in 90% ethanol and analysed using our revised methods (see also below). To date, DNA amplification success of the 2001 samples has been comparable across these two areas (77% Jasper, 83% Northern Cascades) and significantly higher than in the 1999 study. However, while optimizing our new protocol, most modifications were needed to improve amplification success of the Jasper samples relative to that of the Northern Cascades. The Jasper habitat is much drier than the Cascade habitat (good conditions for preserving DNA). Thus, the most likely explanation for these habitat differences in amplification success is diet-ingested PCR inhibitors in scat. The age of all fecal samples were also estimated at the time of collection in the 2001 sampling period, based on the criteria in Table 11. Sixty-two percent of transect samples were estimated to be ≤ 2 weeks old and 85% to be ≤ 1 month old. (Future studies will assess the validity of the sample age indices in Table 11).

• Species and sex-differences in GC concentrations

We also measured glucocorticoid metabolites in the grizzly and black bear feces collected by scat detection dogs area during 1999. The species and gender of individuals that left the samples were confirmed by mtDNA and scnDNA. Significant species and gender differences were found in fecal GC metabolite concentrations. Fecal GC metabolites 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).

To examine whether the sex- and species-specific differences in GC concentrations are environmental or phylogenetic/gender-based in origin, we conducted concurrent adrenocorticotropic hormone (ACTH) challenges on a captive male and female grizzly bear and black bear. ACTH administration in vertebrates mimics the adrenal stress response, causing a rapid rise in GC secretion, followed by a return to baseline within a few hours (Norris 1996). The same pattern should occur in feces, with the onset of the peak delayed by 24 hours — the GC excretion lag time in ursids (Wasser et al. 2000). Each individual was given 2IU ACTH/kg i.m. No sex-difference in GC concentrations was detected for grizzly bears at time 0 (female = 40 ng/g; male = 30 ng/g) or at the 24 hour excretion peak (female = 107 ng/g; male = 110 ng/g). These results suggest that the grizzly bear sex-differences observed in the field study were probably due to environmentally based differences in physiological stress. Unfortunately, management complications prevented us from collecting fecal samples from the black bears between 16 and 60 hours, causing us to miss the expected GC excretion peak at 24 hours. Baseline GC concentrations of the male and female black bear were, however, the same (female = 35 ng/g; male = 48 ng/g), and comparable to those for the grizzly bears. This lends further support to the notion that the species and sex-differences were environmentally mediated.

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These comparisons will be repeated in 2002, increasing sample size, investigating black bear adrenal function in more detail, and generating a varied stress hormone profile to be used in our degradation and preservation studies.

Table 11. Measurements taken on scat at time of collection to assess sample age.

Average weather and exposure conditions over past week are also recorded, including amount of shade, temperature, and precipitation.

1o Contents: 1 - Veg, leaves,

stems 2 – root, tubers,

corms 3 – berries,

seeds 4 – meat, hair,

bone 5 – moths 4 – orange/red 6 – ants 7 – not

discernable 2o Contents: 0 - homogenis 1 – veg, leaves,

stems 2 – root, tubers,

corms 3 – berries,

seeds 4 – meat, hair,

bone 5 – moths

6 - ants 7 – not

discernable Exterior Color: 1 – fresh, veg.

green 2 – exposure

darkened 3 – pale, leached

5 – black/ purple

6 – other __________ Interior Color: 1 – dark green 2 – brown/dark 3 – white,

leached 4 – orange/red 5 – black/

purple Veg beneath:

1 – green, not discolored

2 – yellow; slightly discolored

3 – dark due to scat

4 – no veg. beneath

5 – sample disturbed

Moisture: 1 - moist/fresh 2 - ext. dry, int.

moist creamy

3 - ext. dry, int. moist firm

4 - dry throughout

5 - moist bottom 6 - moist from

rain

Odor: 1 – vegetation,

grassy 2 – sweet, berry-

like 3 – meat 4 – none,

earthy, duff Odor Strength: 1 – Very Strong 2 - Strong 3 - Moderate 4 - Weak 5 - None Mold: 1 - None 2 - Moderate 3 - Heavy Inverts: * 1 - None 2 - Moderate 3 - Heavy *non-parasitic

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• Between-dog comparisons The overall number of fecal samples detected/hour varied markedly across dogs in 1999, ranging from 0.34-1.12 samples/hour. The number of sample patches detected/hour was more equivalent across dogs, ranging from 0.27-0.43. These two measures differ because each patch often included multiple samples (range: 1-34 samples/patch). Dogs also varied in the percentages of black versus grizzly bear scats detected, as well as in the sex ratios of the detected bear scat samples (based on samples that amplified for mtDNA and scnDNA). Three out of four dogs detected a ratio of 70:30 black to grizzly bear scats (range =60-79, n=181) and 67:33 males to females (range = 63-70, n=112). The fourth dog detected a black bear:grizzly bear ratio of 39:61 (n=13) and a male:female sex ratio of 56:44 (n=9). However, the overall number of samples collected and amplified was relatively low for the latter dog, making its calculations less reliable. We will continue to control for such dog-based differences in sampling efficiencies by randomly assigning dogs, without replacement, to the cells they are sampling.

• Overview of DNA, Endocrine and Detection Dog Methods

The methodologies we are investigating hold considerable promise as short or long term monitoring tools. The fecal DNA and hormone analyses provide reliable information about the identities, distributions and physiologic conditions of subjects over the landscape. And, the detection dog method appears to provide an effective means of maximizing sample collections for such analyses with minimal capture biases. Some validation questions remain to be answered in the proposed studies. These include: reliability of sample age estimates and impacts of sample age on DNA and hormone degradation and preservation in the field. More precise estimates of capture biases will also be made following confirmation of individual identities using µsatDNA.

11.3 Continued Studies 11.3.1 Disturbance Measures: Habitat and Human Use Data Sets The remote sensing/GIS component of the FMF grizzly bear research program includes an extensive digital data set with the following data layers: hydrology, digital elevation model, Alberta Vegetation Inventory (AVI), eco-classification habitat data, forest age data, grizzly bear habitat classification layer, roads and linear features data (railway, pipelines, seismic lines, etc.), forest harvesting history layer, human use intensity data layer, landsat TM classified grizzly bear habitat map, Indian Remote Satellite Imagery (IRS) one meter resolution, fire history data layer, and annual satellite imagery for the study area (1996, 1998, 1999 and 2000). This team is defining landscape metrics (percent fragmentation, measures of edge density, road densities, connectivity, etc.) for each of the currently defined 16 Bear Management Units (350 km2), grid cells (25 km2) and individual bear home ranges (based on radiotelemetry). The remote sensing/GIS team

is also measuring and quantifying annual landscape change using satellite imagery and software they are currently developing. These combined metrics will be used to define the high, medium and low disturbance study areas, and the location-specific disturbances, for the physiologic stress hormone comparisons discussed below. 11.3.2 DNA Extraction and Amplification Freeze-dried samples are ground, sifted through a steel mesh, and thoroughly mixed. Fecal DNA is extracted based on modifications of Wasser et al. 1997. Briefly, 1600 µl of Qiagen ASL Buffer (provided in the QIAmp stool kit) is added to 200 mg of well-mixed, freeze-dried feces. Samples are vortexed (10 seconds), incubated (one hour at 70°C) and then centrifuged (13,000 rpm for 3 minutes). All supernatant is transferred to a new tube containing inhibitex tablets for removal of PCR inhibitors (provided in kit), vortexed briefly, incubated (one minute at room temperature) and centrifuged (13,000 rpm for three minutes). All supernatant is transferred to a clean tube and centrifuged. Six hundred µl of the supernatant is transferred to a fresh tube containing 25 µl of Proteinase K, 600 µl AL buffer (provided in kit) are added, vortexed briefly and incubated (one hour at 70°C). After adding 600 µl of ethanol, all lysates are vortexed briefly and loaded in 2-3 centrifugations onto the spin columns, washed with 500 µl AW1 (provided in kit), centrifuged (20 seconds), washed with 500 µl AW2 (provided), centrifuged (one minute) and the DNA eluted in 200 µl of Qiagen AE buffer after a one minute incubation. Samples are then purified using glass milk (Geneclean III®non-spin Kit, Bio 101, Inc., Vista, CA) at 55°C. A portion of the control region of mtDNA is PCR amplified using fluorescent primers, analysed on an ABI 3100 Genetic Analyser (Perkins Elmer Applied Biosystems, Foster City, CA), and compared to known black and brown bear control samples, interspersed throughout the same run, to ascertain the taxonomic identity of the unknown fecal samples (Clarke et al. 2001, Woods, et al. 1999, Paetkau and Strobeck 1996). The samples are set up in 20 ul volumes containing 1.5mM MgCl2, 0.2uM dNTPs, 0.13uM 6-FAM labeled LTRPROB13, 0.13uM HSF21 (Wasser et al. 1997), 0.5U Taq DNA Polymerase (Promega, Madison, WI) and 2.0ul of template DNA. The reactions are cycled in a 9700 Thermocycler (Perkins Elmer Applied Biosystems) once for denaturation at 94°C for two minutes, annealing at 53°C for 30 seconds and extension at 72°C for 30 seconds, then repeated 34 times with the denaturation time shortened to 30 seconds at 92°C. A final elongation for five minutes at 72°C completes the amplification. Aliquots (1.5ul) of the amplified products are added to 13.5ul of deionized formamide containing 0.029% GeneScan TAMRA-500, denatured for five minutes at 95°C, subjected to capillary electrophoresis with a five second injection time on an ABI3100 using GeneScan parameters, analysed by GENOTYPER software (Perkin Elmer-Applied Biosystems) and confirmed by visual inspection.

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Upon confirmation of species identity, the fecal samples are profiled for individual identities using 6 µsatDNA loci, G01A, G10B, G10C, G01D, G10L, G10X (Paetkau and Strobeck 1994, Paetkau et al. 1995), and gender using primers SRY41F, SRY121R (Taberlet et al. 1993), and ZFX/ZFY (Woods et al. 1999). Microsatellite primers are modified with 5’ fluorescent tags and amplified as described by Paetkau et al. 1998 and Clarke et al. 2001. As a cost-saving, locus G10X is multiplexed with the gender primer set using an annealing temperature of 51°C. Amplified products are analysed on the ABI3100 (Paetkau et al. 1998). Known grizzly bear serum samples (n=19) from radio collared individuals are interspersed throughout the runs, along with mock extraction blanks and negative DNA controls, to ensure correct identification of alleles and the detection of any contamination, respectively. All samples are extracted in duplicate with each extract PCR amplified twice for nuclear DNA to increase the likelihood of detecting alleles subject to allelic drop-out. The multiple PCR strategy is similar to that recommended for hair by Tablerlet et al. 1996. However, the additional extraction is better suited to the uneven distribution of DNA in feces ( Wasser et al. 1997, Wasser unpublished). Microsatellite loci will also be analysed to assure that they conform to Hardy-Weinberg Equilibrium and hence that there is not an excess of homozygotes at each locus (Taberlet, et al. 1999). Similar analyses will be conducted to detect potential null alleles (Paetkau and Strobeck 1995). 11.3.3 Fecal hormone extractions Two ml of 90% methanol is added to exactly 200 mg of sample. The vial is capped and vortexed in a pulsing vortexer (Glas-Col Multipulse Vortexer) at high speed for 30 minutes. After centrifugation (~2200 rpm for 20 minutes), the methanol supernatant is transferred to labeled cryovials with air-tight screw-top caps and stored at –20°C until analysed (Wasser et al. 2000). The above extraction technique consistently recovers ~90% of the steroids in the sample based on recoveries of 3-H estradiol, testosterone, progesterone and cortisol added to the grizzly bear feces in validation studies (Wasser unpublished). The reproductive and stress hormone extractions and assays used in this study have each been validated in the PI’s lab. Assays include: the ICN 125I total estrogen kit, our in-house 3H progesterone (Wasser et al. 1993, Wasser et al. 1994, Wasser et al. 1996) and 3H testosterone assays (unpublished data) and the ICN 125I corticosterone assay kit (Wasser et al. 2000). Mean intra- and inter-assay coefficients of variation for each of these assays are ~7%. All hormone measures are expressed per gram dry weight to control for any dietary variation in steroid excretion rate (Wasser et al. 1993).

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11.3.4 Population monitoring using scat detection dogs • Field Sampling Procedures

Two field sampling procedures, tested in 2001, will be continued in 2002. The first approach maximizes the number of individuals sampled, the second maximizes the number of samples per individual. For the first approach, four detection dog teams will sample thirty-six 5 X 5 km grid cells over the 5400 km² grizzly bear research program area in and around Jasper National Park, Alberta, CANADA. Grid cell sizes were reduced to 5 X 5 km and the number of sampling sessions increased to five because of the low grizzly bear population density (n=65), determined by the 1999 hair DNA mark/recapture study (Mowat and Strobeck 2000). The smaller grid size is also better suited to the smaller home ranges of black bears.

Twelve contiguous cells will be located in each of three disturbance zones, pre-determined as high, medium, low, based on the above mentioned disturbance measures. Each of the 36 cells will be sampled by dog teams five times (once every two weeks) between July-September, for years one-three. Dog teams will walk a seven hour transect/cell, on average, during any given sampling period. A new transect route will be chosen within a given cell for each sampling period. Dogs will be randomly assigned to each cell (without replacement for the first four sessions). All transect routes will be habitat-based in attempt to maximize coverage of areas where bears are most likely to range within each cell. Bias resulting from this non-random placement of transect routes within a given cell is minimized by collectively spacing the five transects per grid cell to provide reasonable coverage of that cell. This approach produced minimal capture heterogeneity bias in the robust models used to estimate population size from hair (Woods et al. 1999, J Boulanger, pers. comm.). The second procedure employs a back tracking sampling method to acquire scat samples from individually radio collared bears. The previous two weeks of four hour interval telemetry points from an individual grizzly bear’s GPS collar are up-loaded into our hand-held GPS units . A dog team is then positioned at each end of a portion of that bear’s route over the two week period. These two teams walk from one consecutive way point to the next until they meet in the middle, collecting all fecal samples encountered along the way. Previous trials in 2001 averaged 20±1 samples per follow. Backtracking follows will be conducted on two male:two female grizzly bears in each of the high, medium and low disturbance areas (six males and six female bears total), in each of years one-three. For both types of sampling, dogs will be off-lead, in visual range of handlers, to maximize the area covered. The location of each scat sample will be recorded using a hand-held GPS unit (Garmin Pro II, Garmin, Lenenxa, KS). The condition of the sample (age, exposure) will also be recorded using the age-estimators described in

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Table 11. Inter-observer reliability in assessing sample age will be maintained weekly by having each team member independently rate the same samples during non-field trials and compare and discuss their determinations.

• Population size estimation

Individual bears will be declared using the psib equation (Woods et al. 1999, Waits et al. 2001) for the probability of an individual sharing the same genotype as a sibling (Boulanger and McLellan 2001, Poole et al. 2001). The detailed genetic database from previous radio collaring and hair-based DNA efforts on this project will be included in estimation of psib probabilities. Previous simulation work has suggested that the effect of genetic error rates on population estimates is not large if proper filtering procedures are used (Roon in prep). However, this is based upon genetic error rates estimated from hair-based sampling (Paetkau in prep). Potential biases in estimates based upon scat sampling will be assessed using simulation methods and compared to those from the hair-based sampling, as well as with published results of other simulation studies (Waits and Leberg 2000, Roon in prep). We will evaluate four separate models for estimating population size: program CAPTURE (Otis et al. 1978), program MARK (White and Burnham 1999), rarefaction (Kohn et al. 1999) and jackknife methods (Burnham and Overton 1979). Depending on the accuracy of our sample aging estimates, the traditional "sampling period" time frame associated with hair snag stations (e.g. Woods et al. 1999) may not necessarily be used with certainty to obtain population size estimates. Instead, each visit to an area would be considered to be a sampling session. Population size would then be estimated by observing the change in new individuals detected per visit using both rarefaction and mark-recapture methods in a fashion similar to the estimation of species richness (Kohn et al. 1999, Burnham and Overton 1979, Boulinier et al. 1998). Because the exact age of scats and associated sampling period time frame would be uncertain, the population size will correspond to the “superpopulation” of bears that inhabit the sampling grid and surrounding area (Kendall 1999) whose scats were detectable by the dogs. Point estimates of population size will be made using scats estimated to be <2week and compared to those using scats estimated to be ≤1 month to evaluate the impacts of sample age on each method. Any inflation of population estimates caused by using older scats (>2week but ≤ 1 month) when compared to newer scats (<2 week) should be evident by the percentage difference and standard errors associated with the two respective estimates. Furthermore, if sample sizes permit, apparent fidelity of bears to the sampling areas (reflecting population closure) can be estimated using Pradel (Pradel 1996) models, as demonstrated by Boulanger and McLellan 2001 in program MARK (White and Burnham 1999), to determine if bears are more prone to show lower fidelity in samples collected over longer time periods.

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Problems associated with closure violation and movements off the grizzly bear research program area will also be investigated using collared bears and the methods of Powell et al. 2000. The method of Powell et al. 2000 will allow estimation of probabilities of movement onto and off of sampling grid cell areas using multi-strata models (in which one strata is the grid and the other is the area off the grid). This analysis will be further constrained to determine the impact of sample grid area size, and other factors on the fidelity of bears to sampling areas. Finally, the Powell et al. 2000 analysis will be used to compare “scat” capture probabilities between different GPS collared bears (confirmed by µsatDNA) while accounting for the proportion of time that each individual spent on the sampling area.

• Trend estimation

Another promising application of the scat data is in the estimation of population trend. For trend monitoring, each nine week sampling period/year pertains to a single sampling session, with the three annual sampling sessions occurring at the same time each year. The trend analyses will determine the relative efficiency of transects in terms of number of unique bears identified each year. Annual transects will provide a relative estimate of recapture rate of bears identified in previous years. Analyses of these data will again be conducted separately for scats estimated to be <2 week and ≤1 month old. The use of mark-recapture models that estimate population rate of change (Pradel 1996) and apparent survival (Lebreton et al. 1992, Seber 1986) as incorporated in program MARK (White and Burnham 1999) will be investigated for use with the resulting data. Preliminary results (J. Boulanger, unpublished data) and published studies (Schwarz 2001) suggest that the Pradel model (Pradel 1996) is reasonably robust to heterogeneity variation and closure violation problems that complicate estimation of population size. The method of Powell et al. 2000 will also be used to integrate the mark-recapture data and radio telemetry data to potentially refine survival rate estimates. Program MARK allows a variety of biologically based hypotheses to be addressed using mark-recapture data. For example, differences in apparent survival rates based upon disturbance or other perturbations can be explicitly modeled and tested (Lebreton et al. 1992, Anderson et al. 1995, Burnham 1996). Further refinement of models will also be investigated. The three year funding period will only allow general estimates of apparent survival and trends from these models. Therefore, a simulation study will be conducted to determine relative power and precision of this technique to detect changes in trend. Individual based simulation models that consider the demography of bear populations, heterogeneity in capture probabilities, and age specific vulnerability of bears to sampling will be used to evaluate the Pradel 1996 and other monitoring models in program MARK. Parameter values for simulations

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will be based upon the preliminary estimates from the three years of field data. Radio telemetry data will be used to estimate true survival rates and yearly fidelity of bears to sampling areas to aid in simulation model formulation. Sampling protocols based upon less costly opportunistic and stratified sampling schemes of scats will also be considered.

• Stress Hormone Disturbance Comparisons

Physiological impacts of environmental disturbance will be examined by comparing metabolites of the adrenal stress hormone, cortisol, as well as the reproductive hormones (estradiol and progesterone for females; testosterone for males) in fecal samples collected in the low, medium and high disturbance areas. Samples will be separated by species, gender and individual based on DNA analyses. The between-individual mean and variance around each reproductive steroid should provide a measure of disturbance that nicely complements the fecal cortisol metabolite stress measure. Cycling females should be relatively synchronous in their timing of reproduction. Thus, reproductive steroids of all adult females should be uniformly low at time of emergence. Mean progesterone and to some degree estradiol levels should then begin to rise with the number of conceptions among non-lactating adult females. Although bears exhibit delayed implantation, the extent of rise in these hormones should still reflect the occurrence and health of the pre-implantation conception (Wasser 1996). Persistently low means, and/or high variances around the means, for reproductive hormones over time should reflect low and asynchronous conception rates as well as relatively unhealthy pre-implantation stages of pregnancy. A relatively low mean and high variance in progestins and estrogens over time should also correspond to elevated cortisol concentrations in the same samples and be found in areas having high disturbance indices. (Similar analyses will occur for testosterone in males.) Cortisol and reproductive hormone metabolites will also be examined in the backtracking samples (females with cubs will be excluded from the reproductive hormone analyses). Disturbance estimates will be determined from the habitat and human use measures for each of the 12 bears followed annually in the backtracking study. In this case, we will average disturbance indices associated with each 5 X 5 km area surrounding every GPS way-point used to define that individual’s backtracking. Popplewell et al. (in press) addressed large-scale human disturbance classes for grizzly bears in the Alberta Yellowhead Ecosystem based on landscape structure and fragmentation, using a combination of remote sensing, GIS and landscape metrics. Nielsen et al. (in press) developed more fine-grained resource selection functions (RSF) for these bears, based on elevation and hillshade, cover type including vegetation and extent and age of cut-blocks, greenness (based on Landsat image analysis), rivers and streams, and human access density based on densities of

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primary and secondary roads and seismic lines. These two models were used to predict grizzly bear densities, and relative probabilities of grizzly bear use of the area, respectively. These two approaches will be used to predict physiological stress levels and reproductive function of black and grizzly bears in these various habitat types. Species- and sex-specific bear densities in each area will also be included as independent variables in these analyses. In this way, we will also examine how habitat characteristics, including human and natural disturbances, as well as the densities of conspecifics and competitor species correlate with physiologic stress and reproductive function of these two ursids. These models will be applied at population (using grid-cell data) and individual levels (using backtracking data). Popplewell et al. (in press) found that grizzly bear density was relatively low in one of the least fragmented areas of Jasper National Park—a finding consistent with our 1999 scat data (Figure 21). Tourist densities were highest in this area, suggesting that low grizzly bear density in this low habitat fragmentation area may result from avoidance of the relatively high tourist activity in this area. Interestingly, black bears preferred this same area in our 1999 study (Figure 21). This hypothesis predicts that same-sex grizzly bear samples in this area will have relatively high GC concentrations compared to other areas in the park, and be significantly higher than those in same-sex black bear samples in that area. In the multi-use area outside the park, Nielsen et al. (in press) found that grizzly bears frequented high human access areas (based on road and seismic line densities) while avoiding those of moderate human access. This too was consistent with the high grizzly and black bear use of the heavy disturbance north-central part of the multi-use study area compared to the old growth lodgepole pine area to the south in our 1999 study (Figure 21). The high use of black bears for the former area was particularly striking given their tendency to avoid areas heavily utilized by grizzly bears. Food availability may be lower in the old growth lodgepole pine area compared to the heavily disturbed area to the north. We predict GC’s in samples from both species will be relatively high in northern-central portion of the multi-use area, and that GC’s in same-sex black bear samples will be greater than those in same-sex grizzly bear samples from that area.

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12. GRAPH THEORETIC METHODS FOR EXAMINING LANDSCAPE CONNECTIVITY AND SPATIAL MOVEMENT PATTERNS: APPLICATION TO THE FMF GRIZZLY BEAR RESEARCH PROGRAM

Barb Schwab (M. Sc. Student, Department of Geography, University of Calgary, blschwab@telusplanet.net) and Clarence Woudsma (Associate Professor, Department of Geography, University of Calgary, cwoudsma@ucalgary.ca)

12.1 Executive Summary Analysis of habitat fragmentation has become a common method of understanding the environmental impacts of anthropogenic activities. For example, forestry cut-blocks fragment an otherwise contiguous natural environment thereby impacting species and their habitat. While current grizzly bear habitat research initiatives focus on human impact and the resulting fragmentation, studies of this kind fail to adequately address the issue of connectivity within these landscapes. Habitat connectivity is recognised as a landscape condition necessary to maintain animal populations in fragmented environments. New approaches to measuring and understanding connectivity based on Graph Theory (a branch of mathematics) have been introduced, and while promising, have yet to be thoroughly tested and established. The graph theoretic methodology will lead to improved measurement of connectivity and increase our understanding of the role of connectivity.

Specific research objectives are: 1) To modify and apply a graph theoretic model for the analysis of movement and connectivity patterns associated with female grizzly bear populations. 2) To validate the graph theory model with real movement data. 3) To compare the graph theoretic based model to existing approaches modeling connectivity (e.g. Linkage Zone Model).

Graph theory is a heuristic methodology, which quantifies landscape connectivity at multiple temporal and spatial scales. It utilizes the basic elements of nodes (centroids of habitat patches), edges (connections between patches) and paths (connections between numerous centroids). By representing habitat mosaics as a mathematical ‘graph’, the spatial configurations of patches, connections, and dispersal (movement) matrices can be analysed. The graph theory framework employs algorithms to generate indices and measures, which portray connectivity quantities, patterns and relationships within the landscape. The major data inputs include Remote Sensing based digital habitat maps (the basis of nodes), Geographic Information Systems (GIS) landscape and human data layers, and GPS (Global Positioning System) bear collar movement data.

The major benefit of this project is its ability to utilize existing data and provide a new methodology and tools to allow for long-term habitat assessment. The graph theoretic model provides empirical measurements for landscape connectivity and aids in understanding the movement patterns of associated grizzly bear populations. It will also identify critical changes in landscape structure demonstrating the function of

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habitat fragmentation and decreased levels of connectivity. Finally, the graph theoretic model provides management with a validated analytical tool for monitoring the impacts of human activity. 12.2 Introduction Assessing the effects of human disturbance on landscape habitat has long been a focus of ecologists, biologists and land use managers. The destruction of habitat leads to the consequent loss of animal populations and landscape biodiversity. The effective management of landscape habitat therefore is reliant on our understanding the environmental impacts of anthropogenic activities. As a result, much of conservation biology and landscape ecology deals directly with issues of landscape degradation and habitat loss. According to Rosenberg et al. (1997) and Beier and Noss (1998), habitat loss and fragmentation are among the most pervasive threats to population viability. In an attempt to reduce the isolation of habitat fragments, numerous studies (Walker and Craighead 1997, Beier and Noss 1998; Bunn et al. 2000) have recommended preserving landscape ‘connectivity’ for the movement of species between habitat patches. This project takes advantage of recent developments in the application of graph theoretic approaches to understand this key issue. 12.2.1 Connectivity Connectivity is recognised by Northern East Slopes Environmental Resources Committee (2000) as a landscape condition necessary to maintain the continuous distribution of grizzly bears in fragmented environments, specifically adult females. Habitat connectivity refers to the functional linkage among habitat patches, either because habitats are physically adjacent or because the dispersal range of the species effectively connects patches across the landscape (With et al. 1997). For large carnivores such as grizzly bears, habitat connectivity is imperative for safe movement within home ranges (Noss et al. 1996). 12.2.2 Graph Theory Models Graph Theory is a heuristic approach allowing researchers to examine connectivity at multiple temporal and spatial scales. By representing habitat mosaics as a mathematical ‘graph’, the configurations of patches, connections and dispersal matrices can be analysed. The framework utilizes the basic elements of nodes or vertices (which represent centroids or core areas of habitat patches), edges (indicating connections between patches) and paths (connections between numerous patches or nodes). Algorithms generate indices and measures to address and quantify landscape connectivity, patterns and relationships.

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The focus of this research is on applying graph methods to study habitat connectivity associated with female grizzly bears in the FMF grizzly bear research program area. Specific study objectives are:

1. To modify and apply a graph theoretic model for the analysis of movement and connectivity patterns associated with grizzly bear populations.

2. To validate the graph theory model with real movement data.

3. To compare the graph theoretic based model to existing approaches modeling connectivity (e.g. Linkage Zone Model).

Combining the graph theoretic approach with the capabilities offered by remote sensing, GIS and GPS telemetry data provides a unique opportunity to explore, quantify, and validate connectivity in the context of habitat use and human influence. The graph theoretic model will provide empirical information concerning the connectivity between identified habitat patches, specifically addressing the movement of female grizzly bears within landscape corridors. Additionally, the approach provides an opportunity to test landscape sensitivity and explore landscape thresholds within both natural and developed landscapes of the FMF grizzly bear research program area. 12.3 Methodology Spatial data analysis is conducted using ESRI’s ArcInfo and ArcView Geographic Information System (GIS) software packages. ArcInfo provides a working environment that is capable of performing both vector and raster/grid analysis. The graph theory algorithms are run using a combination of AML, Awk, Fortran, C, and Unix modules linked within ArcInfo to create graph edges and calculate connectivity measures. Additional efforts are currently ongoing to improve the capabilities and delivery of the modeling procedure and graph code. Spatial and statistical analysis also remains ongoing with validation of the model occurring throughout. The methodological approach is segmented into three main sections: 1) cost surface development and validation 2) node and edge creation 3) graph calculations and comparisons. 12.3.1 Cost Surface Development and Validation The proposed methodological approach begins with the creation and comparison of three primary cost surfaces (Table 12). Each cost surface represents a different base layer or friction surface where the variation in friction is related to the variation in land cover, terrain, and human influence as well as other factors. Suitable grizzly bear habitat will have lower friction values allowing for movement, while human influence elements are represented with higher friction values thus restricting movement.

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Table 12. Description of Cost Surface Types.

Model Type Input Layers Weighting Scheme Output Linkage Zone Model Cost Surface – developed in collaboration with Julie Dugas, GIS Specialist, Foothills Model Forest and Helen Purves, GIS Specialist, Jasper National Park

• Study area • Bear management units • Human use features • Vegetation cover • Riparian Note: These layers are used to develop four criteria: access route density, intensity of developed human sites, presence or lack of hiding cover, and proximity to riparian areas.

The sum of the four input layers provides a single combined danger score: • 7-10 minimal danger • 11-12 low danger • 13-14 moderate

danger • 15-18 high danger

Scored map identifying areas of varying levels of danger from human influence where the higher the value, the greater the danger to grizzly bears.

Subjective Cost Surface – developed using scientific literature previously studying grizzly bear populations, Barb Schwab, University of Calgary

• Integrated Decision Tree (IDT) habitat map

• DEM for elevation and slope • Road layer • Streams and lakes • Seismic lines • Utility lines • Facility polygons and points • Access polygons

The sum of each pixel’s friction from the individual layer provides a single combined score indicating the overall cost of traversing that pixel: • 1 lowest cost • 500 highest cost

Cost surface developed indicating the potential cost or friction value associated with grizzly movement across the landscape.

Resource Selection Function (RSF) Cost Surface – developed in collaboration with Scott Nielson, University of Alberta

• Integrated Decision Tree (IDT) habitat map

• DEM for elevation and slope • Road layers • Streams and lakes • Seismic lines • Utility lines • Facility polygons and points • Access polygons • 1999 and 2000 female bear

points • Greenness (tasseled-cap

transformation of Landsat TM)

The sum of each individual layer provides a single combined score indicating the biological significance of those features to female grizzly bear populations: • ‘-‘ avoidance • ‘+’ selection

Cost surface developed with RSF coefficients indicating the selection or avoidance of features based on the occurrence of GPS bear points.

Validation using 2001 GPS telemetry data will determine which surface best represents grizzly bear movement on the landscape. The validation will be based on a comparison between the least-cost path produced by the model and actual grizzly bear GPS points occurring on the landscape. Completion of the validation procedure is forecasted for mid February. It is speculated that the RSF based cost surface will most accurately represent the movement paths or connections of female grizzly bears between habitat patches due to the biological background inherent within RSF models.

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12.3.2 Node and Edge Creation The basis of graph construction is the creation of nodes derived from the selection of habitat patches, where each centroid or node represents the centre or core area of a habitat patch. Currently, 1999 GPS data points are used to identify the land cover types most associated with the ‘presence’ of bear locations. These land cover types are then selected from the 2000 Integrated Decision Tree (IDT) map to establish all potential habitat patches within each bears home range (Figure 23). The collection of all nodes represents the basis of the graph for analysis. Habitat patches are further selected based on size where every patch less than one hectare is eliminated from the procedure.

Figure 23. Node Creation using G016 1999 Kernel Home Range. The second element in graph construction is the creation of edges that represent the connections between patches or nodes. Edges are expressed as a distance matrix D, whose elements dij are the functional distances between patches i and j. Edge creation is facilitated using the costdistance and costpath functions within ArcInfo (Figure 24). The least-cost path approach recognises that movement is constrained by landscape elements such as terrain, slope, roads, streams and other anthropogenic activities. As such, the analysis is designed to approximate the actual distance covered by female grizzly bears as it moves from one patch to the next.

Figure 24. Edge Creation using G016 1999 Kernel Homerange.

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12.3.3 Graph Calculations and Comparisons Upon graph completion for each female’s kernel home range, statistical and spatial indices are further calculated based on the previously determined least-cost path distance between connected habitat patches. Simple connectivity indices such as gamma (γ) and alpha (α) are demonstrated in Figure 25. As dispersal distance decreases for G016’s graph, habitat connectivity also decreases. The appearance of smaller subgraphs below 10 kilometers indicates a breakdown of connectedness and an increase in fragmentation within G016’s kernel home range.

3 km5 km10 km γ = 0.9986

α = 0.9888 γ = 0.8986 α = 0.8444

γ = 0.5667 α = 0.3333

Figure 25. Dispersal Distance for G016 1999 Kernel Homerange. Further connectivity analysis includes the calculation of the dispersal probability matrix (P), dispersal flux (f), and average-weighted dispersal flux (W). Additionally, these measures are also used to determine the minimum spanning tree for each landscape graph. Edge removal and node removal graph operations will explore the sensitivity of the landscape to habitat removal and changes to dispersal distances. These operations will further demonstrate the importance of a specific habitat patch to the overall area-weighted dispersal flux (F) and traversability (T). These algorithms are currently being examined and modified to properly model connectivity associated with grizzly bear populations Final validation involves verifying the accuracy of the graph theoretic model output. Locations demonstrating high levels of connectivity and potential corridor areas will be validated using 2001 GPS telemetry data. The benefit of this project is within its ability to utilize existing data and provide new methodologies and tools to allow for improved long-term habitat assessment and monitoring. Results to date demonstrate the effectiveness of the model to quantify connectivity, it is expected that further progress will provide information regarding

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movement patterns and address landscape thresholds within the FMF grizzly bear research program area. Project deliverables include a M.Sc. thesis, joint publications on the research with FMF project partners, and a transferable methodology that can be integrated into the long-term monitoring plan of the FMF grizzly bear research program. 12.4 Research Progress

Research Details and Status Project Task Details Status Thesis Proposal Thesis proposal completion and defense

Entitled: Graph Theoretic Methods for Examining Landscape Connectivity and Spatial Movement Patterns: Application to the FMF Grizzly bear research program

Completed

Field Research Field work Field research assistant recording vegetation and forest plots necessary

for the classification and validation of the new IDT map product, summer 2001

Completed

Data collection Proposed effort to backtrack grizzly bear movement paths in field for further model validation, attempted summer 2001, not completed

Proposed

Spatial Analysis and Model Development Graph Code Stage 1: testing and finalisation of methodology for LCP determination Completed Cost surface development

Creation of three primary cost surfaces (linkage zone model, subjective, and resource selection function) for basis least-cost path modeling

In Progress

Cost surface validation

Validation of cost surfaces with 2001 female grizzly bear GPS data using least-cost path analysis to model movement patterns

Pending

Node creation Selection of habitat patches to create centroids (nodes) for graph theory model

Completed

Edge creation Edge creation using graph code and least-cost path procedure for graph output, distances and connections between habitat patches

Completed

Preliminary graph results

Simple measure of connectivity and exploration of dispersal distances related to individual bears and kernel home ranges

Completed

Graph code Stage 2: testing and finalization of graph metric calculations In Progress Advanced connectivity calculations

Detailed connectivity analysis using dispersal probability matrix (P), dispersal flux (f), and average-weighted dispersal flux (W), overall area-weighted dispersal flux (F) and traversability (T)

Pending

Final model validation

Validation of final graph results to be validated with 2001 female grizzly bear GPS data

Pending

Corridor and threshold identification

Identification of corridors important for movement of female bears as well as identifying landscape thresholds resulting from habitat fragmentation and decreased levels of connectivity

Pending

Documentation and Defense M.Sc. thesis Documentation of analysis and results In Progress M.Sc. defense Thesis completion, final revisions and defense preparations Pending Publications and Presentations Publications Schwab, B.L., Woudsma, C.G., Stenhouse, G.B., and S.E. Nielson, (****),

Connections That Matter: A Graph Theoretic Analysis of Grizzly Bear Movement in the Yellowhead Ecosystem, Alberta, Canada, in preparation for Ursus.

In Progress

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Research Details and Status Project Task Details Status Publications and Presentations Presentations Graph Theoretic Methods and GIS: Modeling Landscape Connectivity

and Spatial Movement Patterns of Grizzly Bears in the Alberta Yellowhead Ecosystem, Canada, Schwab, B.L. and C.G. Woudsma, to be presented at the Western Division of the Canadian Association of Geographers, Simon Fraser University, BC, March 14/16, 2002.

In Progress

Exploring the Application of Graph Theoretic Approaches to Understanding Grizzly Bear Habitat Interactions, Schwab, B.L. and C.G. Woudsma, to be presented at the 17th Annual Symposium International Association for Landscape Ecology, United States Regional Association, Lincoln, Nebraska, April 23/27, 2002.

In Progress

Connections That Matter: A Graph Theoretic Analysis of Grizzly Bear Movement in the Yellowhead Ecosystem, Alberta, Canada, Schwab, B.L., Woudsma, C.G., Stenhouse, G.B. and S.E. Nielson, to be presented at the 14th International Conference on Bear Research and Management, Steinkjer, Norway, July 28/August 3, 2002.

In Progress

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13. GRIZZLY BEAR FOOTHILLS HABITAT FRAGMENTATION BY SEISMIC CUTLINES MAPPED FROM INDIAN REMOTE SENSING (IRS) IMAGERY

Julia Linke (Masters of Science Project Progress Report, University of Calgary) 13.1 Introduction Besides providing habitat to the Grizzly Bear, the Alberta Foothills region contains considerable human activities such as tourism, mining, seismic oil and gas exploration, and forest harvesting. The purpose of this Masters of Science project is to assess the effects of seismic lines on the landscape structure of grizzly bear habitat. Secondly, the relationship between landscape structure and grizzly bear landscape use is to be investigated. None of these topics have been studied previously. Seismic lines have been introduced to the foothills for over half a century and the Government of Alberta had maintained an inventory about the spatial distribution of most cutlines established in the past and present. However, since vegetation also regenerates within seismic lines and at rates dependent on the substrate and the surrounding plant species, seismic lines do not remain an indefinite feature of the landscape. Mapping seismic cutlines therefore becomes a function of newly developed, existing and disappearing cutlines. Any study involving this dynamic landscape feature requires its accurate depiction at the specific time of interest.

Satellite imagery, such as Landsat Thematic Mapper (TM), which was already used for the grizzly bear habitat mapping, offers the opportunity to capture a large area at the same time (e.g. Lillesand and Kiefer 1994). However the resolution of Landsat TM imagery is 30 m, which is too coarse to detect conventional seismic cutlines, which have been regulated to have a maximum width of 6 m in 1995 (Alberta Sustainable Resource Development 1998). Five meter resolution panchromatic Indian Remote Sensing (IRS) satellite imagery however presented the opportunity to map these narrow features, which was to be tested as a reliable and repeatable method for updating seismic cutlines as part of this thesis project.

In 1998, Alberta Environment had started an initiative, called ACCESS 2000, using IRS imagery to update all ACCESS features, including seismic features. In contrast to this Masters project, where satellite imagery forms the sole basis for detecting seismic lines, the Provincial Government used an additional data set: a 1:20,000 mapping program performed between the 1980 and 1994 using aerial photographs (Doug Knight, pers. comm. 2001). Part of the Masters project is to compare the two mapping products.

Before the link between grizzly bear landscape use and seismic lines can be investigated, their effects have to be quantified. It was hypothesized that seismic lines fragment and dissect the grizzly bear habitat, which can be detected by means of landscape metrics, a set of indices to measure landscape structure (i.e. habitat mean

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patch size, habitat patch number, edge density, etc.) (see McGarigal and Marks 1994 for a large set of available landscape metrics). This progress report will include details on the first two parts of the Masters project, i.e. mapping of seismic cutlines with IRS imagery, and effects of cutlines on landscape metrics. The third and last part of the project, the actual link of cutlines to grizzly bear landscape use, will be completed in the year 2002 and subsequently reported on. 13.2 Methods

13.2.1 Mapping and Validating Seismic Cutlines Five different image tiles from the year 1998, where image tile one displays a strong atmospheric segregation into a clear and in a hazy area, were 'pieced together' to form an image mosaic covering the 3200 km2 (about 40 km in width and 80 km in length) large FMF grizzly bear research program area (Figure 26). Exact image date information was unavailable for two image tiles. Using PCI Works 7.0, the remote sensing processing software, an edge detection filter was applied to the panchromatic IRS image, previous to digitizing all straight, explorative seismic cutlines. To assess the accuracy of the IRS digitized seismic map in comparison to the governmental ACCESS seismic map, a field sampling program was carried out in June and July, 2001 inside two sampling areas: a) test area, and b) whole foothills (Figure 26). The test area consisted of one image tile only and therefore image quality influences due to differing image data could be ignored using that specific spatial unit for analysis. Eight five km segments of seismic lines, stratified by the two dominant seismic cutline directions, NE and SE, and by the two atmospheric conditions, clear and hazy, were selected as sampling lines inside the test area. One of these segments had to be divided into two smaller components due to unsuitable field conditions (Figure 26). Along the sampling lines, the presence and absence of intersecting seismic cutlines were recorded. The intersection point was recorded with a hand-held Global Positioning System (GPS). The total of 40 km of sampling lines constituted about 5% of IRS mapped seismic lines inside the test area. The same procedure was applied along sampling lines in the larger FMF grizzly bear research program area, which had a sampling intensity of 5 km (2.5 km NE and 2.5 km SE) per image tile zone (Figure 26). This sampling intensity was designed to satisfy field time constraints while covering all strata combinations. The effort in the foothills area was 30 km, yielding a total effort of all sampling lines combined of 70 km. In order to investigate the characteristics of mapped seismic lines, a systematic sampling regime along all of the sampling lines was designed. Every 300 m along the sampling lines (Figure 27a), a 5 x 5 m ground vegetation plot was taken in the centre of

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the seismic line (see Figure 27 for details). A forest plot was also taken with a prism sweep (see Figure 27 for details) 30 m perpendicularly to the seismic line, which assessed contrast features of the matrix stand. Seismic cutline features, such as width, and terrain, here slope and aspect, were assessed at the centre of the ground vegetation plot (Figure 27b and c).

Figure 26. Image tile stratification of FMF grizzly bear research program area and sampling lines used for field verification.

The objective of the statistical analysis of the 300 m field plots was to assess and to explain differences between IRS mapped field points (hereafter referred to as "handtrace points") and those undetected by the IRS mapping technique (hereafter referred to as "handtrace gap points"). At this stage, only the intensively sampled test area field points (total n = 110) were used in this assessment. The field plot data was analysed using a generalized linear model (logit binomial link) with the handtrace and handtrace gap points as response variable. This variable is explained by three different sets of predictors, explaining topics as a) structural characteristics of line (width and terrain), b) contrast variables (matrix change in crown closure, mean matrix crown closure, matrix change in conifer, mean matrix amount of conifer, matrix change in height, mean matrix height), and c) seismic line vegetation features (seismic % tree cover, seismic % shrub cover, seismic % non-woody vegetation, seismic % rock, seismic % soil, seismic % dead matter, seismic % open water, and seismic ATV usage), The statistical software package S-Plus 2000 (MathSoft, 1999; Venables and Ripley 1994) was used for all analysis.

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Correlations among model coefficients were accepted until a maximum of 50%. For a first selection of predictors Chi-square p-values were used, in addition to Cp (AIC) values (Burnham and Anderson 1998, Venables and Ripley 1994), and an understanding of the physical effects of correlated variables in respect to their visual discrimination. Based on the first selection process, the final model selection was achieved with the help of the automated step Akaike Information Criterion (stepAIC) (Akaike 1973, Venables and Ripley 1994). The signs and the relative magnitude of the GLM coefficients indicated the relationships between predictors and the response variable (positive coefficient indicate relationship with handtrace layer and a negative coefficient indicates relationship with handtracegap layer). In this progress report for brevity reasons, only a summary and the interpretation of this analysis are presented but details in form of regression coefficients can be obtained upon request.

Figure 27. Sampling details of the systematic 300 m field plot data collection.

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13.2.2 Landscape Metric Assessment The IRS map layer of straight explorative seismic lines was created as a 5 m resolution grid data format, with the seismic features being 2 pixels, or 10 m wide. Seismic exploitative lines, which are curvy features used for accessing oil and gas well sites, were extracted from the governmental ACCESS seismic map and also used for fragmentation analyses. Length of seismic features was determined by dividing its area by the width. Since it is landscape structure, which is the main focus of this project, only structurally significant cutline segments were incorporated. Cutline segments dissecting habitat patches of low vertical structure, such as open wetlands, meadows, shrubs, and young cuts, were considered structurally insignificant cutline segments and were removed from both explorative and exploitative seismic layers. The 1999 integrated decision tree grizzly habitat map (IDT) was resampled to 5 m resolution, therefore allowing the incorporation the detail of both explorative and exploitative seismic lines. All landscape metric calculations were performed using ESRI's Arcview 3.2 Geographic Information System Software with the Patch Analyst Grid 2.2 extension (Elkie et al. 1999). 13.3 RESULTS 13.3.1 Seismic Maps Inside the intensively sampled test area, the IRS mapped and the ACCESS seismic layers both had a very low error of omission (5%) (Table 13), translating into a map accuracy of 95%. However, at the foothills scale, where variations in image date and atmospheric conditions exist, seismic feature recognition appears to be reduced slightly when using IRS imagery only as the basis for mapping. This was demonstrated by the higher error of omission of 12%, reducing the accuracy of the IRS-mapped seismic layer down to 88%, while the ACCESS seismic layer remains its low error of omission of 4% (Table 13). Table 13. Accuracy Assessment of IRS-mapped and of ACCESS program Seismic

layers.

Test Area Foothills (incl. test area) Error Type IRS- mapped Seismic Layer

ACCESS Seismic Layer

IRS- mapped Seismic Layer

ACCESS Seismic Layer

Error of Omission (number of mapped lines divided by number of present lines subtracted from 1)

1-(18/19) =

5%

1-(18/19) =

5%

1-(22/25) =

12%

1-(24/25) =

4%

Error of Commission (Number of mis-mapped lines divided by total number of mapped lines)

1/19 =

5%

7/25 =

28%

1/23=

4%

9/33= 27%

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At both spatial scales, the test area and the foothills area, the ACCESS seismic layer performs poorer (with errors of omission of 28% and of 27% respectively) than the IRS-mapped seismic layer (with 5% and 4% errors of omission) (Table 13). In summary, the ACCESS seismic layer seriously overestimates the presence of seismic lines. This is likely due to the old secondary information used in their methodology (aerial photographs from the 80s to early 90s). The IRS-mapped seismic layer, however, hardly overestimates seismic lines while tending to underestimate them slightly. Results from the 300 m test area field plot analysis reveal additional information on the seismic characteristics from these underestimated seismic lines by the IRS-mapping technique. Model a) on structural characteristics presented line width as the main predictor of handtrace layer points (coefficient :0.272, st. error = 0.10), meaning that the IRS-mapped seismic layer tends to underestimate cutlines that are narrow. Mean width ('actual line width', see Figure 27c) of underestimated seismic lines was 5.3 m (st. error = 0.5, n = 24) compared to a mean width of 7.2 m (st.error = 0.3, n = 86 ) of IRS-mapped seismic lines. Model b) on contrast features selected mean crown closure between both matrix stands ('matrix mean crown closure') as the strongest predictor for handtrace points (coefficient : 0.017, st. error =0.008 ). This likely indicates that the IRS-mapped seismic layer generally underestimates seismic lines occurring in a matrix with more open crown closure (mean crown closure = 46%, st.error = 6%, n = 24 ) compared to IRS-mapped lines (mean crown closure = 63%, st.error = 3% n = 86). Model c) on seismic characteristics showed that the amount of open water was the strongest predictor of handtrace points (coefficient: 0.126, st. error = 0.156), suggesting that seismic lines were more frequently detected from the IRS image when they had some amount of open water in contrast to underestimated seismic lines, which generally had no amounts of open water. In summary, mapping seismic lines from IRS images solely proves to be a successful method, which hardly overestimates but slightly underestimates cutlines. Based on the test area field plots only, cutlines that are missed or underestimated tend to be narrow and of lower horizontally structural significance, since they occur more frequently in open habitats (as indicated by mean crown closure). 13.3.2 Seismic Cutlines as Fragmentation Features In 1999, the study area of the FMF grizzly bear research program (as defined by the area of nine Foothills Bear Management Units (BMU)) contained 1365 km of explorative (density: 0.45 km/km2) and 2054 km of exploitative (density: 0.67 km/km2) seismic lines, yielding a total of 3419 km of seismic lines (density: 1.12k km/km2) (Figure 28). Explorative seismic densities in the nine BMU’s ranged from 0.11 km/km2 (Whitehorse BMU, Figure 28, Table 14) to 1.30 km/km2 (Lovett BMU, Figure 28, Table 14). Adding exploitative seismic lines to the explorative lines displayed a range of seismic densities between 0.47 km/km2 (Cardinal BMU, Figure 28, Table 14) and 1.97 km/km2 (McLeod BMU, Figure 28, Table 14).

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Landscape metric changes on the foothills scale included 9.9% and 10.8% respective increases in number of habitat patches and edge density when adding explorative seismic lines.

Figure 28. Seismic Lines in the FMF grizzly bear research program Area stratified into nine BMUs.

Table 14. Densities of Explorative Cutlines and combined Explorative and

Exploitative Cutlines, stratified by BMU.

BMU Explorative Seismic Density

(km/km2)

Explorative and Exploitative Seismic

Density (km/km2) Whitehorse 0.11 0.55 Gregg 0.13 0.94 Cardinal 0.17 0.47 Brazeau 0.37 0.87 Maskuta 0.45 1.20 Beaverdam 0.54 1.15 McLeod 0.63 1.97 Pembina 0.71 1.21 Lovett 1.30 1.92

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The fragmentation of habitat, as indicated by percent changes in landscape metrics, was also detectable at the scale of the BMUs. Having a low explorative seismic density, the habitat structure of Whitehorse BMU was the least fragmented, as indicated by only very small increases in number of patches and in edge density and by the small decrease in mean patch size (Table 14, Figure 29a). This BMU stands in contrast to the Lovett BMU, which was fragmented considerately by explorative cutlines, as shown by its increases in number of patches by 30%, and in edge density by 24%, and also by its decrease in mean patch size by about 23%(Figure 29a).

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Figure 29. Landscape Metric Changes in Nine Foothills BMUs in 1999 by a) explorative seismic lines and b) by explorative and exploitative seismic lines combined.

Assessing the effects of explorative and exploitative cutlines combined, shows the same trends, which are simply more pronounced. The Lovett and McLeod BMUs display around 53% increases in number of patches, 36% increases in edge density, and 35% reductions in mean patch size, responding to cutline densities of 1.92 and 1.97 km/km² respectively (Figure 29b,Table 14). This initial assessment of landscape metrics leads to conclude that seismic cutlines of both types, explorative and exploitative, are a major fragmentation causes for the grizzly habitat in the year 1999, when investigating from a strictly landscape structural perspective. It is to be the objective of the year 2002, to explore further the meaning of these fragmentation levels, in relation to grizzly bear landscape use.

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14. MAPPING AND QUANTIFICATION OF CHANGE IN LANDSCAPE STRUCTURE IN GRIZZLY BEAR HABITAT

P. Kirk Montgomery, (Master of Science Project Report, University of Calgary) 14.1 Introduction This report documents the research that I plan to accomplish within the FMF grizzly bear research program for the years 2002 - 2003. This research is also a critical component towards the completion of the requirements of the degree of Master of Science in Geography awarded by the University of Calgary. The goal of this research is to provide the FMF grizzly bear research program valuable insight into landscape change within the grizzly bear research program area and to provide the broader scientific community with new methods for the creation of multi-temporal mapping products derived from multiple data sources. What follows is a short discussion of the purpose and background to this research, a description of the data and methodologies to be used, as well as the anticipated results and deliverables. 14.2 Purpose and Background The FMF grizzly bear research program studies grizzly habitat and can be subdivided into five areas in terms of the quantification of landscape change. These are:

• Mapping grizzly bear habitat.

• Quantifying habitat and landscape structure.

• Quantifying change in landscape structure.

• Determining grizzly bear habitat suitability and potential.

• Validating and verifying results. The research outlined here fits into this framework under the ‘quantifying change in landscape structure’ portion. Quantifying change in a landscape is critical in understanding the past and predicting future in terms of landscape structure and the FMF goal of quantifying grizzly bear habitat. Specifically, the objectives of this research are to:

i. Develop a method to reconstruct landscape structure in the past century.

ii. Determine the rate of change for the landscape.

iii. Create map products that represent a time series of landscape change in the grizzly bear research program area, for example 1930, 1950, 1970, … today.

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iv. Explore linkages between alternative landscapes, landscape structure, and management needs.

There are two problems of interest in this research; firstly, we know the study area has changed, but we don’t know how much, at what rate, and what causes. Determining the rate and cause of change will provide grizzly bear managers with more information in terms of trends of use in the study area. It will allow them to predict with more accuracy future scenarios of development. Secondly, we will be in a position to roughly estimate landscape use by bears at different times in history. Estimates of bear landscape use have been successful with current-day landscape structure (Popplewell 2001); one method of estimating grizzly bear landscape use in the past is through an investigation of hunting records in order to obtain bear presence records. This type of information will provide a valuable additional source of data that will allow resource managers to have a better understanding of how changes in habitat effect bear distribution and populations. The third reason to undertake this research is to solve the methodological challenge so that this methodology could be applied and generalised. The simulation of historical landscapes based on air photos, remotely sensed imagery, and GIS data layers will be difficult and will add new information to the field as to how this should be accomplished. In effect, a methodology will be provided as to which techniques are valid for the simulation of historical landscapes. The next section of this report describes the sources and types of data required to carry out this research as well as a generalized methodology that will be used to create the final deliverables. 14.3 Data and Methods There are several different types of data required for this research concerning historical landscapes. This project is ambitious because it tries to replicate the kinds of data available today, but as if they were acquired within the last 50-70 years. Data not acquired specifically for use in this research will be supplied from one of these sources:

• Foothills Model Forest.

• Other participants in the research project.

• The University of Calgary Library.

• Resource extraction industries in the grizzly bear research program area. Generally, there are two classes of data that will be used in order to accurately model historical landscape structure; spatial data and ground data. Spatial data refers to data such as aerial photographs, remotely sensed imagery, and GIS data layers. Each is described in more detail below.

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14.3.1 Spatial Data • Airborne Data

Airborne data will be required for the years before remotely sensed imagery was available (before 1972). Aerial photographic missions were flown many times over portions of the study area over the last 50 years. However, complete coverage of the entire study area is only available for a period spanning 1948 through 1952 and in the early 1970’s.

Aerial photographs will be used to make a mosaic of my research area. Each photograph will be orthorectified, geometrically corrected, and then mosaiced to form one continuous image. Once this has been completed, the image will be interpreted and used in conjunction with a GIS to produce thematic coverages that represent the features of interest found on the airphoto mosaic.

Once this is complete, a relationship will be established between elements of the airphoto and field data. This is explained further under the Ground Data section below.

• Spaceborne Data

Remotely sensed data offer many advantages over conventional data sources. It allows us the ability to image large geographic areas in their entirety and evaluate landscape patterns and the aerial extent of resources in a temporally and spatially comprehensive way (Kepner et al. 2000). It also affords us the ability to obtain measurements in areas or at times that are not practical.

Remotely sensed imagery make an ideal data source because the FMF grizzly bear research program study site is large. However, remotely sensed imagery is not generally available before 1972 when the first Landsat satellite was launched and became operational. Prior to the Landsat series of satellites, the United States government operated the top-secret surveillance satellite ‘Corona,’ from 1960 through 1972 (Ruffner 2001). With about one-meter panchromatic resolution, Corona data would be a fantastic source of information.

The methodology required in order to process spaceborne data is similar to airphoto data – orthorectification, geocorrection, and mosaicing (in some cases). Once this has been completed, variables such as greenness (an indicator of plant biomass or the amount of green vegetation present at a location) can be easily produced. Greenness maps have been required by the FMF grizzly bear research program because greenness has been demonstrated as being a good predictor of bear habitat use (Mace et al. 1999; Boulanger 2001).

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Additionally, habitat classification maps will be created for historical landscapes that resemble the modern day results of the classification methodology developed for the creation of the integrated decision tree (IDT) classification maps.

• Geographic Information Systems

The forestry industry uses geographic information systems (GIS) data layers to plan and account for current and future resource extraction activities. The data provided by them regarding their future cut plans will be needed to simulate future landscapes. These GIS coverages include roads and trails, fire history, and cut history as examples.

14.3.2 Ground Data Ground data refers to information collected ‘in the field.’ Two previous field seasons worth of data have been completed and will be a valuable resource in evaluating landscape change over time. This data set includes field plot data from field sampling sites used in the creation of the IDT map products. Field data recorded include a general description of the site, the dominant form and type of vegetation, and tree characteristics such as diameter at breast height (dbh). These data as well as data that are to be collected summer 2002 will be used to evaluate current and past landscape features. A summer field campaign will be required to collect needed forest parameters. These parameters include leaf area index (LAI) using three different methodologies, crown closure, tree height, stand age, and dbh. Once ground data has been collected, a prediction will be made based on collected ground data and forest parameters from the spatial data. For example, the prediction of greenness (an indicator of plant biomass or the amount of green vegetation present at a location) will be made based on relationships between the forest structure parameters (LAI, dbh, etc.) and aerial photo tone, texture, and ground cover. 14.4 Summary The research to be completed and described here will aid resource managers in their ability to understand landscape changes in the FMF grizzly bear research program area over the past 50 years. Deliverables include (1) a methodology for determining past landscapes based on historical aerial photos and which then resemble satellite image products of more recent vintage, (2) a series of map products documenting previous landscape and showing landscape change up to the present including greenness maps and classification maps, and (3) an estimation of landscape use for alternative landscapes by grizzly bears. The data compiled will also allow for the modeling of future scenarios of landscape change in addition to being inputs in RSF modeling activities. The projected completion date for this research is February 2003.

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15. HABITAT STRUCTURE AND FRAGMENTATION OF GRIZZLY BEAR MANAGEMENT UNITS AND HOME RANGES IN THE ALBERTA YELLOWHEAD ECOSYSTEM

Charlene Popplewell M.Sc. Thesis, University Of Calgary, September 2001 15.1 Introduction Grizzly-containing ecosystems, such as the Alberta Yellowhead Ecosystem of West-Central Alberta, Canada, may be assessed by the presence, abundance, and health of grizzly bears to provide insight on the ecological integrity of the environmental conditions and processes that generate and maintain biodiversity and allow natural evolutionary change of the ecosystems. Ecosystem fragmentation occurs when human activities or natural processes divide the landscape. Therefore, having baseline habitat structure data for the landscape-level fragmentation effects of the human activities on the presence of grizzly bears will aid both private and public sectors in planning resource activities in the Alberta Yellowhead Ecosystem. A greater understanding of the relationship between grizzly bear population characteristics, such as bear density, and habitat fragmentation, quantified by landscape metrics, provides an effective management tool useful for land managers to balance the desire for critical grizzly bear habitat with sustainable resource extraction and associated infrastructure. 15.2 Research Objectives and Methods By integrating remote sensing, GIS, and landscape metrics, this study provides an important component to effective management that will enable land managers to monitor and maintain critical grizzly bear habitat. The thesis research looked at the degree of grizzly bear habitat fragmentation present in 1999 in the Foothills Model Forest (FMF) Grizzly bear research program (GBRP) study area in the Alberta Yellowhead Ecosystem. A baseline of landscape structure was established that will be useful for evaluating change and making future land management decisions. Structural differences among Bear Management Units (BMUs) and Minimum Convex Polygon (MCP) home ranges were assessed. Using the Patch Analyst extension to ESRI’s ArcView 3.2 GIS software, landscape metrics were calculated on a remote sensing classification to quantify the composition, structure, and configuration of the IDTA habitat map (Figure 30). The composition and geographical distribution of the habitat class patches were quantified using various landscape structure metrics (Table 15), which were then assessed for determining landscape-habitat relationships. In addition to providing baseline fragmentation information, a goal of this thesis was to determine the relationship of landscape

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structure metrics with grizzly bear habitat fragmentation and population density, in order to provide a quantitative relationship that can facilitate wildlife and land management planning. The level of habitat class aggregation (i.e. attribute scaling) affects fragmentation information within BMUs as measured by the landscape metric approach, and was also assessed. Table 15. Selected landscape metrics that were focused on in the research. Abbreviation Name (Units) Description Interpretation

(Landscape/Class) TLA Total Landscape

Area (ha) Combined area of all patch types within the area of analysis

Measures the overall area; useful when comparing different areas of analysis

CA Class Area (ha) Area, or proportion, of the patch type in the landscape

Measures the amount of each patch type; indicates abundance of patch types relative to one another

NUMP Number of Patches

Number of separate patches in the landscape

Measures patch abundance to determine if patches are more or less numerous; indicative of spatial heterogeneity; indicates landscape fragmentation with higher values

MPS Mean Patch Size (ha)

Average size of patches (area)

Quantifies landscape composition; indicates landscape fragmentation smaller values

PSCOV Patch Size Coefficient of Variance

Relative variation; patch size standard deviation as a percentage of mean patch size

Conveys direct comparison of variability information between landscapes; interpretation may require NUMP or MPS when PSCOV between landscapes has same value

ED Edge Density (m/ha)

Standardised length of all edges per unit area (perimeter/area)

Relative measure of all patch edges; enables direct comparison between landscapes; indicates fragmentation with increasing value

AWMSI Area Weighted Mean Shape Index

Average perimeter-to-area ratio with individual patch area weighting applied to each patch (sum of patch edges/square root of patch areas,

Measures shape complexity adjusted for square standard, as weighted by patch size; indicates square shape when value is one and more complex shape with increasing value; larger patches are weighted

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Abbreviation Name (Units) Description Interpretation (Landscape/Class)

adjusted by constant multiplied by patch area/total area)

more heavily than smaller patches, which may be more useful in landscapes where larger patches are more important to a particular landscape function

MNN Mean Nearest Neighbor (m)

The greater the measure, the more isolated the patch is from similar types; greater overall measure indicates highly fragmented landscape

IJI Interspersion-Juxtaposition Index

Adjacency measure between patches

Ranges from 0 (uneven adjacencies) to 100 (equal adjacencies)

Average, shortest distance between patch edges of same type (class level) or the average of class distances (landscape level)

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7, 8

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istu

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ss ID

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lass

ID1,

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ed D

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L ST

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Figure 30. Methods flow chart.

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The two main hypotheses tested in pursuit of this research were:

• Hypothesis 1: Grizzly bear density class estimates within a BMU are predictable by landscape structure metrics. Grizzly Bear Density = f(Landscape Structure Metrics)

• Hypothesis 2: BMUs having lower fragmentation (resulting from human disturbance) will be similar in landscape structure to actual grizzly bear home ranges. Low-Fragmented BMUs (Structure) ≈ Bear Home Ranges (Structure)

To test these hypotheses, the following analysis steps (outlined in Figure 30) were conducted:

1. Using the IDTA satellite remote sensing classification map, representative BMUs were selected and quantified using landscape structure metrics.

2. The differences between BMUs were statistically compared and ecologically interpreted to predict bear density using discriminant function analysis.

3. A comparison between BMUs and grizzly bear home range structural measures were interpreted to illuminate the effects of fragmentation.

15.3 Discussion and Conclusions The thesis itself more completely introduces the problem and provides background information, including a review of the scientific literature on what grizzly bear habitat is, in what types of ecosystems it can be found, and how it can be mapped and quantified. Similar wildlife habitat studies that have been done, the study area, and data used for this thesis are detailed. The sensitivity of landscape metrics to the level of attribute scaling used to describe the habitat-related land cover classes are explored and applies spatial metrics to management units and ecological areas of analyses. Methods for quantifying landscape structure and understanding its relationships with grizzly bear characteristics are discussed. The thesis also considers the use of habitat structure in predicting bear density by applying classification tools and examining which land management units are comparable in habitat structure to actual bear home ranges. Major findings are summarized, implications for management are addressed, and issues for future research are outlined. Note: the following involves abbreviations for patch classification schemes as detailed

in Figure 30; i.e. CT = 14 Cover Type, LD = 6 Life form/Disturbance classes, and VN = Vegetated and Non-vegetated classes.

Analyzing the sensitivity of landscape metrics to the level of patch aggregation on which they are implemented helped determine which landscape metrics and which patch classification scheme was most ecologically significant in understanding the landscape-level effects of human activity upon grizzly bear habitat. Mean patch size (MPS), patch size coefficient of variance (PSCOV), and mean nearest neighbor (MNN) have been shown not to be sensitive to differences in attribute and responded as

98

expected between low and high fragmentation landscapes with a clear trend as the attributes decreased. Number of patches (NUMP) and edge density (ED) displayed moderate sensitivity in the expected response to fragmentation due to attribute scaling effects. Area weighted mean shape index (AWMSI) and interspersion-juxtaposition index (IJI) exhibited the highest sensitivity to attribute scaling, and also displayed differences in the way they responded to fragmentation. The metrics examined in this study respond to differences in level of fragmentation. Five of the metrics indicated sensitivity to attribute scaling and should be applied with caution; the other three show "predictable" trends and may be applied with more confidence in how they respond to fragmentation. In some cases, the metrics show constant response to fragmentation effects with no regard to attribute scaling; in other cases, the metrics are more sensitive to attribute scaling than to differences in the human impact on landscapes. Generally, fragmentation (as indicated by dispersal of isolated and small-sized patch types throughout the landscape) is seen as similar patterns in all attribute-scaling methods. The Brazeau has less fragmentation than the Gregg River, and the more aggregated and general the classification, the more visible the differences are between the two BMUs. As in all data representations, interpretation must be made with caution since how the data are classified will affect how they get interpreted. The LD patch classification scheme is a good example since the originally dominant cover types will impart heavier weight to the more general habitat classes. Aggregation of the classes may result in more structurally different patch types that are more ecologically meaningful, but too much generalization may dilute the effects of important patch types and have a profound impact on landscape metric response. Discriminant function analysis has been shown to predict grizzly bear density classes from landscape structure metrics in the 1999 study area containing sixteen Bear Management Units. The main purpose of discriminant function analysis is to determine the dimensions along which groups differ and to predict group membership through classification. The first discriminant function enables separation of High bear density groups from Low and Medium groups; the second function separates Low from medium bear densities. This research has also found the most optimal landscape metrics for use in grizzly bear habitat analysis for the FMF GBRP study area. The high percent of cases grouped by bear density that were correctly classified using all sixteen BMUs, testing random six from ten, and testing random four from 12 show the predictability of Low, Medium, and High bear density classes from the following set of landscape metrics: total landscape area (TLA), number of patches (NUMP), mean patch size (MPS), patch size coefficient of variance (PSCOV), edge density (ED), area weighted mean shape index (AWMSI), mean nearest neighbor (MNN), and interspersion-juxtaposition index (IJI). Provided the density classes are appropriate, the results show a range of original grouped cases correctly classified, with an overall accuracy of 93.8%. The discriminatory power of the functions applied to this data set resulted from systematic testing of sets of independent variables. Three sets of landscape metrics that

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were selected from the standard output of Arc View’s Patch Analyst were examined. Better discrimination, i.e. accuracy higher than the lowest random test result of 89.6%, may be attained through the incorporation of alternative metric variables not tested here. A larger population of cases may improve upon the robustness of this methodology. How to predict the bear density group that an unknown BMU probably belongs to can be seen in interpreting Figure 31; the unknown BMU would be categorized as whichever cluster or group centroid the unknown’s functions are closest to. Future research could also build on the predictive ability of the discriminant functions in a temporal analysis of grizzly bear densities, through “what if” scenarios that alter the habitat structure based on resource extraction agendas. For example, change in the landscape structure of a BMU could be modeled and a new bear density class predicted from discriminant function analysis. The discriminant functions support the use of the Main 8 metrics in further analyses of landscape structure. The four metrics having the highest absolute correlations with each function were assessed among BMUs and home ranges to find similarities and determine which BMUs were most like actual grizzly bear home ranges (Figure 31). The proportional differences among number of patches (NUMP), mean patch size (MPS), edge density (ED), and mean nearest neighbor (MNN) for the areas of analysis showed that female home ranges are comparable to BMUs in the Mountains, and male home ranges are most similar to Foothill BMUs. Compositionally, the closed conifer (ClCon), mixed (Mix), open conifer (OpCon), alpine/subalpine (AlpSub), shrub (Shrub), wetland/riparian (WetRip), and no food/cover (NFC) classes dominate the study area. OpCon, AlpSub, and Shrub are the habitat-related classes found most often within female home ranges. WetRip and Shrub patches are more prevalent in male home ranges. The BMUs containing proportionately more forest, AlpSub, WetRip and fewer disturbances correspond most closely to the habitat structure of the individual grizzly bear home ranges under investigation. Based on these results, the most highly fragmented BMUs (i.e. the least like actual grizzly bear home range structure) are the McLeod (MC), Gregg (GR), Pembina (PE), Lovett (LO), Maskuta (MA), and Beaverdam (BE). The other FH BMUs appear to be less fragmented and would be structurally appropriate for inclusion within male home ranges. The Mountain BMUs of Fiddle (FI) and Lower Rocky (LR) are structurally similar to the Foothill BMUs and show signs of being fragmented. Table 16 summarizes the major conclusions of this research. Table 16. Major conclusions of this research in summary. Concept Result Important landscape metrics Class Area, Number of Patches, Mean Patch

Size, Edge Density, Mean Nearest Neighbor Fragmented Foothill BMUs McLeod, Pembina, Lovett, Maskuta, Beaverdam Fragmented Mountain BMUs Fiddle, Lower Rocky

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b) MPS

55.67

59.93

60.46

63.22

63.39

65.64

74.63

75.59

76.67

80.11

80.84

86.72

90.92

93.40

98.36

99.21

107.44

109.69

112.19

113.28

113.79

117.71

123.96

127.91

128.13

131.67

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G010

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9060.00

9273.89

10750.00

10986.00

11709.00

14354.00

15136.00

34635.00

38807.00

42979.00

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Figure 31. Landscape-level metrics for the CT classification: (a) Number of Patches,

(b) Mean Patch Size in hectares, (c) Edge Density in meters/hectare, (d) Mean Nearest Neighbor in meters.

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15.4 Implications for Management The scaling into fewer and broader classes can dilute the information relevant to grizzly bear habitat; for example, differences between the crown closure of forest types and level of regeneration of cut-blocks are lost in aggregation. Although discriminant analysis shows a strong relationship between density and all attribute scaling schemes, caution should be applied since fragmentation information is different for some metrics from one attribute-scaling scheme to another. Even with the optimal fourteen-class classification, there are implications for management: not all types of disturbance are mapped, micro scale habitats of riparian corridors and avalanche chutes are not included, and levels of human activity are not accounted for. The coarseness of the density data means that the discriminant functions are more useful for land and wildlife management applications, and not directly for wildlife population assessment and monitoring since there is no actual population count associated with the predictive ability. More robust DNA analysis results could provide more confident density estimates to be used in the discriminant function methodology built in the present study. The conclusions allow confidence that certain kinds of fragmentation are unfavorable to bears, and show definitively that this fragmentation can be measured by landscape metrics. Therefore the discriminant functions developed here are usable in planning how changes to the landscape will affect bear habitat structure and density. However, the presently sparse bear data means that only very broad classes of bear density can be predicted. This is not sufficiently precise to determine wildlife management objectives such as hunting quotas, culling, or introduction of specific numbers of bears in specific places. Land managers should be able to utilize appropriate spatial metrics when planning for such habitat structure altering human activities as forest harvest and road building. Patch class area (%LAND) is an obvious choice because it shows the percentage of each Cover Type within a grizzly bear home range. %LAND is a good indicator of the habitat-related vegetation cover a bear uses and the amount of disturbance patches it may be tolerating. Mean patch size (MPS), edge density (ED), and mean nearest neighbor (MNN) can provide clues on how much fragmentation the habitat may be able to sustain and remain viable. Shorter MNN distances are desirable for the habitat-related Cover Types. For example, both male and female home ranges have low MNN for the OpCon, AlpSub, Shrub, and WetRip classes. By ensuring that the inter-patch distance remains within the range of MNN values found in actual home ranges, structural changes to these patch types within BMUs that would be harmful to grizzly bear habitat needs can be avoided. Greater MNN distances are appropriate for the disturbance-related Cover Types. The road and cut classes have larger MNN values within home ranges as compared to BMUs; the longer inter-patch distances probably mean that bears are avoiding these undesirable patch types.

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It must be noted, however, that presence of a particular patch type may not actually be meaningful given the nature of the Minimum Convex Polygon method of delineating home ranges. Since the outermost point locations from telemetry data are joined, patch classes that are not even used by bears become part of the MCP because these patch types are surrounded by actual habitat. Also, this is one snapshot in time for grizzly bear habitat structure in the FMF GBRP study area. It must be noted that there is no proof that the bears tracked in 1999 are entirely representative of the population. The baseline data gathered and analysed here should be compared with that gathered in future years in a continual monitoring program to determine the average habitat structure within the grizzly bear home ranges before any “hard numbers” can be determined for landscape threshold targets. 15.5 Recommendations for Future Research Recommendations for addressing present and future research and planning in the FMF grizzly bear research program area for the continual monitoring of grizzly bears using landscape structure measures are offered as follows:

• To assess changes over time, annually or over some other time step that is more ecologically meaningful to the study of grizzly bears, digital imagery and classification methods comparable to the present study must be used. Simple image algebra, such as the subtraction of a future classification from the 1999 baseline, could easily be implemented to detect and quantify changes in the landscape due to human activities. For example, the effects of forest harvesting and road building can be accurately measured through the application of landscape metrics.

• In a similar vein, “what-if” scenarios could be applied to the baseline classification. Such scenarios would alter the habitat structure according to proposed resource extraction and development plans, and effects on the landscape structure could then be quantified and assessed. The a priori results would enable mitigation or prevention of potentially fragmenting activities on grizzly bear habitat.

• The FMF GBRP is species driven; that is, the size and distribution of grizzly bear home ranges under investigation defines the scale of the study. Therefore, the areal extent of the IDTA map limits the bear data that can be incorporated into the landscape structure analysis. Another example of the modifiable areal unit problem, the method of delineating home ranges affects the structure information contained in these areas of analysis. For example, Kernel home ranges (White and Garrott, 1990) are an alternative method that depicts concentrations of telemetry locations, thereby revealing the higher use habitat. Also, experimenting with the minimum mapping unit may provide further ecological insight into the habitat structure.

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• In the year 2000, the study area expanded to encompass areas farther east in the foothills; therefore, the number of BMUs increased from sixteen to twenty nine. When a habitat classification within the extended boundary becomes available for the 1999 baseline year, the methodology from this research should be applied to the new BMUs to facilitate direct comparisons between years for the entire area. With the addition of more BMUs, the population available for discriminant function analysis is increased. This should enable a more robust testing of the ability of landscape metrics to predict bear density within a BMU. Also, predictions could be made by applying the discriminant functions to BMUs, which have as yet unknown bear density. Conducting future DNA studies, thus eliminating the potential for any bias in selecting training and testing areas, would then test the correctness of the predictions.

• The coarseness of bear density groups limits their utility to management; actual population counts are more useful, especially if they provide interval rather than ordinal data. Alternative multivariate statistical techniques, such as multiple regression, could be applied to grizzly bear population counts, when and if this data becomes available, to predict landscape structural change on actual numbers of bears, to aid in hunting quotas, and other land activities.

• This study has focused on the large-scale human disturbance classes of forestry and transportation. Other human activities, such as certain forms of mining and oil and gas development, have small “footprints” in at least one dimension, making them difficult to detect reliably using the present image classification methods. In order to address the fragmenting effects of these activities, alternative classification methodologies would need to be implemented using finer resolution and incorporating more detailed classes.

This research has quantified and assessed the baseline habitat structure for the FMF grizzly bear research program that was implemented in 1999. Theoretical issues relating to interpreting landscape metrics have been addressed and have shown that the best results are obtained when analyzing moderately detailed habitat classes. Methodology has also been developed that would allow resource managers to easily monitor habitat structure over time and predict the effects on grizzly bear population densities. Continual monitoring is the key to understanding grizzly bear habitat structure so that land managers may be able to implement the optimal conservation practices and maintain a viable Canadian grizzly bear population.

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APPENDIX I

Publication/Technical Paper List

1999 Dugas, J. and G.B. Stenhouse. 1999. Grizzly Bear Management: Validating Existing

Cumulative Effects Models. Thirteenth annual conference on geographic information systems. Vancouver, B.C. 1999.

GeoAnalytic Inc. 1999. 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.

Lee, J.L. and G.B. Stenhouse. 1999. Comparison of Grizzly Bear telemetry location data

with a grizzly bear habitat model. Foothills Model Forest Report. 29pp. 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.

Stenhouse, G.B. and R. Munro. 1999. Foothills Model Forest Grizzly Bear Research

Program 2000 Annual Workplan (year 2). 2000 Dechka, J., S. Franklin, D. Peddle, and G. Stenhouse. 2000. Land cover mapping and

landscape fragmentation analysis in support of grizzly bear habitat management. 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.

Franklin, S.E., D.R. Peddle, J.A. Dechka, and G.B. Stenhouse. 2000. 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.

121

Franklin, S.E., G.B. Stenhouse, M.J. Hansen, C.C. Popplewell, J.A. Dechka, and D.R. Peddle. 2000. 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. Canadian Journal of Remote Sensing. (submitted).

GeoAnalytic Inc. 2000. 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.

Skrenek, J., D. Hodgins and G.B. Stenhouse. 2000. 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.

Stenhouse, G.B. and G. Mowat. 2000. 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.

Stenhouse, G.B. and R. Munro. 2000. Foothills Model Forest Grizzly Bear Research

Program 1999 Annual Report. 110 pp. Wasser, S. and G.B. Stenhouse. 2000. 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.

2001 Boulanger, J., G. Stenhouse and R. Munro. 2001. Causes of heterogeneity bias when

DNA mark-recapture sampling methods are applied to grizzly bear populations. Journal of Applies Ecology. In press.

Cattet, M.R.L., N.A. Caulkett, and G.B. Stenhouse. 2001. The comparative effects of

chemical immobilizing drug and method of capture on the health of free-ranging grizzly 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.

122

Cattet, M.R.L., N.A. Caulkett, 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.

Cattet, R.L., K. Christison, N. Caulkett and G.B. Stenhouse. 2001. Effects of method of

capture on chemical immobilization features and physiological values in grizzly bears. Submitted paper, Journal of Wildlife Diseases, December 2001.

Frair, J., E. Merrill, M. Boyce, S. Lele, G. Stenhouse, R. Munro and S. Nielsen. 2001.

Incorporating habitat-biase3d locational error into habitat use models. Paper presented at the 8th Annual Conference of the Wildlife Society. Reno, Nevada, September 2001.

Franklin, S.E., D.R. Peddle, J.A. Dechka, and G.B. Stenhouse. 2001. 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).

Logan, R.J., G.B. Stenhouse, and R.F. Ferster. 2001. Cheviot Mine: A catalyst for space

age research towards regional conservation of the grizzly bear. Paper presented at the 2001 Annual conference of the Canadian Institute of Mining, Metallurgy and Petroleum. Quebec City, Quebec, April 2001.

Mucha, D.M., G.B. Stenhouse and J. Dugas. 2001. 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.

Munro, R.H.M., S.E. Nielsen, G.B. Stenhouse, 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.

Nielsen, S., M. Boyce, and G.B. Stenhouse. 2001. Can you have too much data? The

Problem of spatial autocorrelation in habitat selection studies. Paper presented at the 8th Annual Wildlife Society Conference, Reno Nevada, September 24-30th, 2001.

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Nielsen, S.E., M.S. Boyce, G.B. Stenhouse and R. Munro. 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.

Nielsen, S.E., M. Boyce, G.B. Stenhouse, 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.

Nielsen, S.E., M. Boyce, G. Stenhouse and R. Munro. 2001. Habitat selection by

Yellowhead grizzly bears. Paper presented at the 8th Annual Conference of the Wildlife Society. Reno, Nevada, September 2001.

Nielsen, S., M. Boyce, G.B. Stenhouse and R. Munro. 2001. Incorporating food

phenology models for grizzly bear predictions: can we improve upon static habitat maps? Submitted paper, December 2001.

Popplewell, Charlene. 2001. Habitat structure and fragmentation of Grizzly Bear

management units and home ranges in the Alberta Yellowhead ecosystem. University of Calgary, MSc Thesis.

Popplewell, C. G.B. Stenhouse, M. Hall-Beyer 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.

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.

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.

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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.

Stenhouse, G.B. and R. Munro. 2001. Grizzly Bear Mortality in the Yellowhead

Ecosystem – Anomaly or Trend? Paper presented at the 2001 Annual Conference of the Northwest Section of the Wildlife Society. Banff, Alberta, March 2-4, 2001.

Last modified December 17, 2001.

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APPENDIX II

Foothills Model Forest Grizzly Bear Research Partners

(1999- 2002)

Ainsworth Lumber Alberta Conservation Association Alberta Energy Company Alberta Sustainable Resource Development/Alberta Environment Alberta Newsprint Anderson Resources Ltd AVID Canada BC Oil and Gas Commission Environmental Fund Blue Ridge Lumber (1981 Ltd) BP Canada Energy Company Burlington Resources Canada Centre for Remote Sensing Canadian Resources Ltd Canadian Forest Products Canadian Hunter Canadian Wildlife Service Cardinal River Coals Ltd. Foothills Model Forest GeoAnalytic Ltd. Gregg River Resources Inland Cement Luscar Sterco (1977) Ltd Millar Western Pulp Ltd Mountain Equipment Coop National Science and Engineering Research Council (NSERC) Northrock Resources Parks Canada (Jasper National Park) Petro-Canada Precision Drilling Ltd. PTAC (Petroleum Technology Alliance of Canada) Rocky Mountain Elk Foundation Suncor Sundance Forest Industries Sunpine Forest Products Ltd Telemetry Solutions

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The Centre for Wildlife Conservation (USA) Trans Canada Pipelines University of Alberta University of Calgary University of Washington Veritas Ltd. Weldwood of Canada Ltd Western College of Veterinary Medicine Weyerhaeuser Canada Ltd World Wildlife Fund