FOOTHILLS MODEL FOREST GRIZZLY BEAR RESEARCH PROGRAM 2005 ANNUAL REPORT
Prepared and edited by Gordon Stenhouse and Karen Graham March 2006
Disclaimer: Opinions presented are those of the authors and collaborating scientists and are subject to revisions based on ongoing findings over the course of this study.
Suggested Citation for information within this report: Nielsen, S.E., R.H.M. Munro, M.S. Boyce. 2006. Modeling Grizzly Bear Activity By Time Of Day And Habitat In West-Central Alberta. In Stenhouse, G. and K.Graham (eds). Foothills Model Forest Grizzly Bear Research Program 2005 Annual Report. Hinton, Alberta.
ACKNOWLEDGEMENTS We would like to thank the capture crew members: Bernie Goski, Charles Mamo, Dave Hobson, Terry Larsen, Rick Booker, and Jay Honeyman. Without the safely executed hard work and perseverance of these individuals we would not have had another successful capture season. Thanks also to Fish and Wildlife officers and biologists for all their help during the capture season: Greg Gilbertson, Mark Heckbert, Dwayne Matier, Shane Ramstead, Dave Robertson, Dave Stepnisky, Lewis Watson, and especially Mike Ewald, and Ken Schmidt. Thanks also to Jim Robertson and Karen Stroebel for the lovely job of bait collection. Exemplary flying (and field!) skills were provided by John Saunders of Peregrine Helicopters of Hinton and fixed wing pilot Mike Dupuis of Wildlife Observation Air Services. Thanks also to the veterinarians that ensured all captures went safely: Marc Cattet, Nigel Caulkett, Erin Geymonat, Tammy Orban, and Ruth Carlson. Appreciation is also extended to the vegetation plot crewmembers Robin Munro and Chelsey Whenham whose hard work and enthusiasm ensured a successful fall field season. A huge thank you to Cliff Henderson for his assistance with aircraft support and Alberta Sustainable Resource Development for their help with logistical support and camp accommodations. Also a big word of thanks to Bernie Morin of Canfor Corporation for assisting with field staff accommodations. The Grizzly Bear Research Program Steering Committee and the Board of Directors of the Foothills Model Forest provided valuable support and assistance that allowed the research to proceed in order to address management needs and we thank Don Podlubny and Jim LeLacheur 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 multidisciplinary program. Special thanks to Julie Duval, Jerome Cranston, and Christian Weik for their expertise in all areas relating to GIS. A word of praise goes out to Lisa Jones and Greg Nelson for keeping up with media needs and the special communication requirements associated with our program. Thank you to Sheri Fraser, Fran Hanington, Denise Lebel, and Angie Larocque for an excellent job in managing the administrative details of this program. The staff at the Hinton Training Centre provided a great deal of assistance in many ways this year including food and lodging for field crews during their short stays. John Boulanger provided superb statistical advice throughout the year. Dr. David Paetkau at Wildlife Genetics International completed lab work on all DNA hair samples and Matson’s Lab conducted our tooth aging. This program would not have been possible without the many program sponsors (See Appendix 2).
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TABLE OF CONTENTS ACKNOWLEDGEMENTS............................................................................................... i TABLE OF CONTENTS .................................................................................................ii LIST OF TABLES........................................................................................................... iv LIST OF FIGURES .......................................................................................................... v LIST OF FIGURES .......................................................................................................... v GENERAL INTRODUCTION ........................................................................................ 1 SUMMARY OF 2005 SPRING CAPTURE PROGRAM ............................................... 3 Introduction............................................................................................................... 3 Methodology............................................................................................................. 3 Results....................................................................................................................... 5 ECOLOGY AND BEHAVIOUR ..................................................................................... 9 Modeling Grizzly Bear Activity By Time Of Day And Habitat In West-Central Alberta ......................................................................................................................... 9 Introduction............................................................................................................... 9 Methods .................................................................................................................... 9 Results..................................................................................................................... 11 Discussion............................................................................................................... 13 A Preliminary Assessment of Grizzly Bear Activity Patterns in the Swan Hills, Alberta ....................................................................................................................... 16 Introduction............................................................................................................. 16 Methods .................................................................................................................. 16 Results..................................................................................................................... 17 Conclusion .............................................................................................................. 19 Understanding Grizzly Bear Associations With Roads in The Foothills Of WestCentral Alberta ......................................................................................................... 21 Introduction............................................................................................................. 21 Methods .................................................................................................................. 22 Expected Results..................................................................................................... 24 Conclusion .............................................................................................................. 25 REMOTE SENSING ...................................................................................................... 28 Map Production Update........................................................................................... 28 Introduction............................................................................................................. 28 Methods .................................................................................................................. 28 Results and Discussion ........................................................................................... 30 Sensitivity Of High-Spatial-Resolution Satellite Imagery To Forest Disturbance, Regenerating Age, And Site Preparation In The Foothills Model Forest ........... 34 Introduction............................................................................................................. 34 Methods .................................................................................................................. 34 Results and Discussion ........................................................................................... 36 Multiple Spatial Resolution Image Change Detection for Environmental Management Applications ....................................................................................... 43 Introduction............................................................................................................. 43 Methods .................................................................................................................. 43 Preliminary Results and Discussion ....................................................................... 46
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Parks Canada Montane Bioregion Landcover Mapping Project ........................ 51 Introduction............................................................................................................. 51 Methods .................................................................................................................. 51 Preliminary Results and Discussion ....................................................................... 55 Literature Cited....................................................................................................... 55 MODELING UPDATE .................................................................................................. 56 Grizzly bear habitat modeling and mapping for the Grande Cache to Waterton region of Alberta’s foothills and mountains........................................................... 56 Introduction............................................................................................................. 56 Methods .................................................................................................................. 56 Results..................................................................................................................... 57 Discussion............................................................................................................... 60 Dynamic grizzly bear habitat maps: Spatial-temporal predictions of food resources for a generalist species ............................................................................ 61 Introduction............................................................................................................. 61 Methods .................................................................................................................. 61 Results..................................................................................................................... 62 Discussion............................................................................................................... 67 Graph theory generated corridors and identified RSF-based patches important for maintaining connectivity (100k results)............................................................ 68 Introduction............................................................................................................. 68 Methods .................................................................................................................. 68 Results..................................................................................................................... 70 Discussion............................................................................................................... 73 GIS APPLICATIONS .................................................................................................... 75 Introduction............................................................................................................. 75 Other outreach tasks included writing an executive summary for the RSF models; serving as the Grizzly Bear Research Program representative on the Natural Disturbance Highway 40 Project Planning committee; and compiling program deliverables, collecting data-sharing agreements, and tracking the distribution of products.TRAINING AND TECHNOLOGY TRANSFER........................................... 76 TRAINING AND TECHNOLOGY TRANSFER.......................................................... 77 HEALTH ........................................................................................................................ 80 Understanding Grizzly Bear Health in the Context of Changing Landscapes ... 80 Introduction............................................................................................................. 80 Methods .................................................................................................................. 81 Anticipated Results................................................................................................. 84 APPENDIX 1. Publication/Technical Paper List ........................................................... 87 APPENDIX 2. Foothills Model Forest Grizzly Bear Research Partners – 2005............ 90 APPENDIX 3. Program Deliverables - January 2006................................................... 91
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LIST OF TABLES Table 1. Number of collars allotted to each capture area. ............................................... 4 Table 2. Grizzly bears captured in each capture area. ..................................................... 5 Table 3. Sex and age of captured grizzly bears. .............................................................. 6 Table 4. Captured female grizzly bears with cubs........................................................... 6 Table 5. Grizzly bear capture types. ................................................................................ 7 Table 6. Grizzly bear fates............................................................................................... 8 Table 7. Seasonal delineation of activities based on diets of grizzly bears in the mountains or foothills of west-central Alberta, Canada (Munro et al., 2006)........ 11 Table 8. Estimated probability of activity (90% confidence intervals-C.I.) by time of day (crepuscular, diurnal, or nocturnal) for field visited female grizzly bear GPS locations.................................................................................................................. 11 Table 9. Probability of bear activity by time of day and habitat. .................................. 12 Table 10. Microsite use table describing late summer activity patterns by percent use based on 57 visited GPS-radiotelemetry locations in the Swan Hills of Alberta. 18 Table 11. Grizzly bear foods sampled in west-central Alberta (adapted from Nielsen et.al, 2004a) ............................................................................................................ 22 Table 12. Frequency of occurrence of grizzly bear foods important to spring diet....... 24 Table 13. Acquisition period of Landsat and MODIS images ....................................... 45 Table 14: Results of Cohen’s Kappa Analysis ............................................................... 47 Table 15. Pixel-based change derived using 2 standard deviations from the mean. ..... 49 Table 16. Polygon-based change derived using pixel based change detection with image interpretation ................................................................................................ 49 Table 17. List of Presentations to Program Partners ..................................................... 78
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LIST OF FIGURES Figure 1. Locations of capture areas................................................................................ 4 Figure 2. Map of west-central Alberta study area showing study boundary, towns, mountains (>1700 m elevation), and 1,032 grizzly bear (by individual) global position system (GPS) radiotelemetry locations visited between 2001 and 2003.. 10 Figure 3. Location of grizzly bear use locations visited in the Swan Hills during the late summer of 2005 (G218 locations (n=4) were 50 km east of Grande Cache and not depicted in this figure). ........................................................................................... 17 Figure 4. Location of study area in Phase 5 expansion. ................................................ 29 Figure 5. Landcover map for the Phase 5 study area..................................................... 31 Figure 6. 16-day NDVI phenology composites for the 2005 growing season, Phase 5 study area. ............................................................................................................... 32 Figure 7. Comparison of SPOT-5 original 10m (a) and pan-sharpened 2.5m (b) imagery; R=SWIR, G=red, B=NIR ........................................................................ 35 Figure 8. Subset of SPOT-5 image showing original image (left) and classified forest disturbance (right)................................................................................................... 37 Figure 9. Results of discriminant analyses for age classification of regenerating boreal forest with 5 year age classes to age 19 and 10 year age classes to age 50............ 38 Figure 10. Extracted Resource Selection Functions (1-7) for female grizzly bear in Fall for all field sampled stands versus age class. ......................................................... 40 Figure 11. 2001 & 2005 Landsat TM & MODIS Imagery showing an area of extensive forestry activity....................................................................................................... 44 Figure 12. Location of Study Area near Hinton, Alberta, Canada ................................. 44 Figure 13. Conceptual framework of the change detection methods used within this study........................................................................................................................ 46 Figure 14. Example of successful MODIS change detection: Polygon.......................... 47 Figure 15. Preliminary Results of Change Detection Tests........................................... 48 Figure 16. Parks Canada Montane Bioregion Landcover Mapping Project study area. 52 Figure 17. Landsat footprint boundaries (left) and completed orthomosaic (right). ..... 52 Figure 18. PCMBLMP sample locations (left) and ground plot layout diagram (right). ................................................................................................................................ 53 Figure 19. Model extension strategy used to apply models from “source” work zones to “destination” work zones in order to produce seamless continuous-variable map products................................................................................................................... 54 Figure 20. Land cover, crown closure, and species composition products for Waterton Lakes National Park................................................................................................ 55 Figure 21. Predicted grizzly bear habitat use for the late hyperphagia season in the Phase 4 region of west-central Alberta................................................................... 58 Figure 22. Predicted grizzly bear habitat use by season for southwestern Alberta (Phase 4 region).................................................................................................................. 59 Figure 23. Predicted distribution of ants (green), a mid-summer food for grizzly bears in the foothills.. ....................................................................................................... 63 Figure 24. Predicted distribution of sweet vetch (Hedysarum spp.), a critical food resource excavated (the root/tubers) by grizzly bears in the spring and autumn.... 64
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Figure 25. Predicted distribution of fruiting species Shepherdia canadensis, Vaccinium membranaceum, and Vaccinium vitis-idaea in various shades of green (light brown green-1 spp. present, medium green- 2 spp. overlap, dark green- 3 spp. overlap).. ................................................................................................................. 65 Figure 26. Distribution of food values (0 to 1,000) from 1 May to 31 September bimonthly periods (sum of 10 maps each ranging from 0% to 100% of potential diet).. ....................................................................................................................... 66 Figure 27. a) Expanded study region depicting mountain (dark grey) and foothill (white) landscapes used in analysis with current imagery extent (DEM), and b) basic graph structure (nodes) with RSF-based cost surface used to generate LCP corridors.... 69 Figure 28. Final GT identified RSF-based habitat patches important to maintaining overall connectivity for a) minimum spanning tree connections and b) areaweighted patch connections.................................................................................... 71 Figure 29. Graphs generated for 100k region showing variation to GT edge connections defined by a) daily movement distance threshold (4942 m) and b) 95th percentile distance threshold (6247 m).................................................................................... 72 Figure 30. Final GT generated corridors important to maintaining overall connectivity (raster format) where dark represents high importance combined with higher path quantities and light represents less importance and lower path quantities available for travel.................................................................................................................. 73 Figure 31. Human activities change the structure of the landscape, and may be perceived as long-term stressors by grizzly bears.. ................................................ 81 Figure 32. An example map of the 2001 FMFGBRP study area that illustrates the probability of occurrence of grizzly bears does not necessarily correlate with their health....................................................................................................................... 85
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GENERAL INTRODUCTION Gordon Stenhouse, Foothills Model Forest, Hinton, Alberta, T7V 1X6. The Foothills Model Forest Grizzly Bear Research Program began in 1998 and has a longterm goal to provide new knowledge and planning tools to assist managers with the conservation of grizzly bears in Alberta. Over the past seven years our program has expanded from our original study area of 10,000 km2, south of Hinton, to an area of approximately 140,000 km2 in 2005. This expansion was undertaken in order to provide our program partners with the same suite of products developed for our original study area. In 2006, and for the next three to five years, our research team will complete the development and delivery of these products for the remainder of the 228,000 km2 of grizzly bear habitat in the province. This plan represents a tremendous amount of work and a huge undertaking in terms of the size of the area to cover, the logistics associated with capturing grizzly bears in a variety of habitats, and maintaining the partnerships and support necessary to complete this work. This would not be possible without the important contributions and diligent work that each of our research team members puts into this program each year. The entire research team also recognizes that the support of our many partners is key to the ongoing achievements and success of our program. The products that our team has developed have been focused on “useable� maps and models that resource and land managers can use to aid in planning decisions in recognized grizzly bear habitat. We now have tested and validated spatially explicit map products (RSF models) that identify the probability of grizzly bear occurrence on the landscape. We use these RSF map products as a surrogate for habitat quality, and new data from DNA inventories has now shown that these maps have predictive value in determining grizzly bear densities. We have also developed map products that show current mortality risk on the landscape and also where safe harbours are for grizzly bears at the present time. New maps of grizzly bear movement corridors (graph theory models), which have been developed and tested within the program, also show where the most important movement corridors are for bears on the landscape. When these products are used together it is now possible to have a clear picture of the habitat requirements, movement routes and mortality risks of grizzly bears along the eastern slopes of Alberta. This represents a significant step for sustainable forest and land management in Alberta. In 2005 our research team continued the ongoing improvement and refinement of products for the areas of the province where we had created these in 2004. These areas now have Version 2 products developed and delivered to all our program partners. For new areas (highway 1- Montana border) we created and tested Version 1 products (maps and models) in 2005 which are now available. Our team continued with capture and collaring efforts in 2005 in order to gather needed grizzly bear habitat use and movement information to allow the creation and testing of new
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maps and models for the areas in Willmore Wilderness Park, Kakwa River and east towards the Swan Hills. Data collected from the 23 bears collared this year will be the first of two years of data needed to allow the full development of these products. Remote sensing based habitat maps have been completed for these new areas and are now available. We have also made the distribution and training necessary to utilize these new products a top priority within our program in 2005. To this end our GIS analyst has worked on the creation of new GIS applications to allow the automated use of the new products in an evaluation context. These GIS applications are being delivered to all program partners when they receive all program deliverables. We continue to make presentations and explain the use and utility of these products to partners and their planning staff. In addition this year we have focused significant efforts on communicating and explaining our research program and the results to date to the general public and stakeholder groups. This report is an overview of the activities and achievements of our research program in 2005.
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SUMMARY OF 2005 SPRING CAPTURE PROGRAM David Hobson, Foothills Model Forest, Hinton, Alberta. T7V 1X6.
Introduction This year’s grizzly bear spring capture session was the seventh conducted by the Foothills Model Forest Grizzly Bear Research Program. The original study area encompassed 10,000 km2 in an area south of Highway 16, between Edson and Jasper in the north and the Brazeau River in the south. Since 2003 the study area has expanded to include all of the grizzly bear range between the Berland River and the Montana border (62% of grizzly bear range in Alberta). In 2005, the study area expanded again to include areas between the Berland and Wapiti Rivers plus the Swan Hills. Spatial data generated by radio-collared grizzly bears captured during this session will be used to verify the accuracy of the RSF maps for this year’s expansion area. Tooth, blood and tissue samples collected from captured grizzly bears will also be used to help understand population dynamics, assist in defining population units and track population health. The goal of this years capture session was to deploy 20-25 GPS radio-collars on grizzly bears between the Berland and Wapiti Rivers and the Swan Hills. This report summarizes the results of the 2005 capture season. Methodology Four individual capture areas were designated within the study area between the Berland and the Wapiti Rivers and the Swan Hills (Figure 1). These capture areas were as follows: 1. 2. 3. 4.
The Willmore Wilderness Park and Grande Cache area. The Kakwa River area. The Simonette River area. The Swan Hills.
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Figure 1. Locations of capture areas. Capture efforts were conducted by 1 helicopter-based crew and 3 ground crews. Each ground crew consisted of biologists with experience in grizzly bear capture. The helicopter crew began capture efforts in capture area 1 on April 18, 2005. Ground crews began in capture areas 1 and 4 on April 25, 2005. Crews moved into new capture areas when the allotted collars were deployed (Table 1) or when it was deemed that enough time had been spent in the area. Capture efforts ended on June 17, 2005. Table 1. Number of collars allotted to each capture area. Capture Area 1 2 3 4
Collars Allotted 5 (FMF) + 5 (West Fraser) 5 5 5
Bears were immobilized using a drug combination of Telazol and xylazine (XZT). The drugs were administered by rifle-fired dart from a helicopter (Helicopter crew) or by rifle/pistol once the bear had been restrained in a snare (ground crews). Atipamazole was used to reverse the xylazine.
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Once immobilized, grizzly bears were weighed, and measured (chest girth, zoological length, and straight-line length). Samples were collected (blood, hair, ear plug tissue and tooth). Radio-collar and ear tag transmitters were attached. Vital functions and bloodoxygen levels were monitored during the processing. A wildlife veterinarian was present at most grizzly bear captures. Black bears and other non-target species were released from the snare after immobilization. Vital conditions were monitored while under anaesthesia but measurements and samples were not collected. After administering the reversal, all bears were monitored until they became mobile. As per the capture permit conditions, all captured grizzly bears were checked within 24 hours of capture to ensure that they had recovered from immobilization. Results Capture Locations In total, 23 grizzly bears and 22 black bears were captured (Table 2). Of the grizzly bear captures, 6 were caught in capture area 1, 6 in capture area 2, 5 in capture area 3, 6 in capture area 4 (Table 2). In some cases (G200 and G202), the captured grizzly bears were too small for radio-collars. These bears were tagged with ear tag transmitters. Table 2. Grizzly bears captured in each capture area. Capture Area
Captured Grizzly Bear IDs
1
G206, G208, G209, G210, G231, G232
2 3 4
G225, G226, G227, G228, G229, G230 G207, G216, G217, G218, G219 G200, G201, G202, G203, G204, G205
Sex and Age Characteristics Of the 23 grizzly bears captured, 12 (53%) were adults (> 5 yrs), 10 (43%) were subadults, and 1 (4%) was a yearling. Eighteen (78%) were male and 5 (22%) were female (Table 3). Adult and subadult males made up the largest component of captured grizzly bears (39% and 35% respectively) while adult females, subadult females and a yearling male comprised 13%, 9% and 4% of captured grizzly bears respectively.
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Table 3. Sex and age of captured grizzly bears. Grizzly Bear IDs G200 G201 G202 G203 G204 G205 G206 G207 G208 G209 G210 G216 G217 G218 G219 G225 G226 G227 G228 G229 G230 G231 G232
Age Subadult Subadult Yearling Adult Subadult Adult Subadult Subadult Subadult Adult Adult Adult Adult Adult Adult Subadult Subadult Adult Adult Subadult Adult Adult Subadult
Sex Male Male Male Female Female Female Male Male Male Male Male Male Male Female Male Female Male Male Male Male Male Male Male
Of the 5 captured females, 2 had cubs (Table 4). Table 4. Captured female grizzly bears with cubs. Grizzly Bear IDs G203 G205
Cubs 3 yearlings 3-two year olds
Capture Type Capture types were categorized as capture from helicopter, ground capture with snare and ground capture with culvert trap. Most ground capture sites used snares in 3 different formats, pail sets, cubby sets and/or trail sets. Culvert traps were used in only a few easily accessible ground sites. Of the 24 capture events (G208 was captured twice)(Table 5), helicopter captures accounted for 8% (2) of captures and ground captures accounted for 92% (22) of captures. All groundcaptured grizzly bears were captured in snares.
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Table 5. Grizzly bear capture types. Grizzly Bear IDs G200 G201 G202 G203 G204 G205 G206 G207 G208 G208 2nd G209 G210 G216 G217 G218 G219 G225 G226 G227 G228 G229 G230 G231 G232
Capture Type Snare Snare Snare Snare Snare Snare Snare Snare Snare Snare Snare Helicopter Snare Snare Snare Snare Snare Snare Snare Helicopter Snare Snare Snare Snare
Telemetry Twenty-one radio-collars were deployed. Two captured grizzly bears were not radiocollared due to their small size. Twenty-three grizzly bears were tagged with an ear-tag transmitter. Radio-collars deployed consisted of 4 types, Televilt, Tellus (new Televit model), ATS and SATLINK. Televilt and ATS radio-collars collect GPS coordinates every 4 hours, which were stored in the collar until uploaded into an airborne receiver (Televilt) or the collar was retrieved (ATS). The SATLINK collar transmitted GPS coordinates to a satellite for relay back to earth. These coordinates were then e-mailed to the Foothills Model Forest. Tellus collars collected locations on the following schedule: April 1 to May 31 - 1 location/hour. June 1 to September 30 - 1 location every 20 minutes. October 1 to November 15 - 1 location/hour. November 16 to March 31 - 1 location/day. All radio-collars were outfitted with a timed release or remote release mechanism with a rotoff as a backup.
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Fates of Captured Grizzly Bears As of November 2005, all bears except for those noted in Table 6 still had functioning collars. Table 6. Grizzly bear fates. Grizzly Bear IDs G200 G201 G202 G203 G204 G205 G206 G207 G208 G209 G210 G216 G217 G218 G219 G225 G226 G227 G228 G229 G230 G231 G232
Fate OK Dead. Killed by another bear. OK Drop-off malfunctioned; collar came off in fall. Collar malfunctioning. OK Pulled collar off. Dead. Illegal kill. Dead. Problem wildlife mortality. Collar failed, dropped and retrieved. OK Dead. Probable illegal kill. Collar retrieved as planned. OK Collar retrieved as planned. OK Collar dropped off successfully (as planned) Dead. Probable illegal kill. OK OK OK Drop-off malfunctioned; collar came off. OK
Capture Related Mortalities A sow and cub caught in snares at a site near Grande Cache were found dead and partially consumed on 16 May 2005. A necropsy was conducted on both bears and a report provided to Enforcement Field Services. There were no mortalities associated with capture myopathy. Black Bears As with the captured grizzly bears, adult males constituted the largest number of 22 captured black bears (59%). Adult females, subadult females and subadult males comprised 18%, 14% and 9% of captured black bears respectively. Male/female percentages of captured black bears were 68/32 and adult/subadult percentages were 77/23. Other Non-Target Species Two moose were captured and released.
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ECOLOGY AND BEHAVIOUR Modeling Grizzly Bear Activity By Time Of Day And Habitat In WestCentral Alberta Scott E. Nielsen, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Robin H.M. Munro, Contract Biologist, Foothills Model Forest, Hinton, AB T7V 1X6. Mark S. Boyce, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Introduction Grizzly bear habitat use has been widely documented within the Central Rockies region of Canada (Hamer and Herrero 1987; Hamer et al. 1991; Gibeau 1998; McLellan and Hovey 2001; Nielsen et al. 2002; 2003; 2004a; Stevens 2002; Theberge 2002; Apps et al. 2004; Mueller et al. 2004). Although providing initial information on habitat use and selection, such studies do not consider the activity of the animal (bedding, root digging, etc.) to be important (see however, Hamer and Herrero 1987). Consequently, interpretations are generalized and our understanding of the important differences among activities lost. As well as considering animal activity, few have also considered time of day, despite probable difference in habitat use. In fact, to our knowledge no one has published activity patterns for grizzly bears relative to both habitat and time of day in the Central Rockies region of Canada. Here we examine 5 common activities (bedding, sweet vetch digging, insect feeding, frugivory, and ungulate kills) through visitation of animal use locations. We predict the probability of activity by time of day (crepuscular, diurnal, and nocturnal) and habitat type and discuss important differences and patterns in animal activity. Methods Field methods During April to October 2001 to 2003, we visited grizzly bear GPS use locations (no more than 1 location/day/animal and generally not older than 2 weeks from the time the bear had been there) from 9 female animals to assess bear activity near Hinton, Alberta (Figure 2). At each use location, site personnel systematically searched a 20-m radius around GPS use waypoints for evidence of bear sign. Five activities were recorded at each site: bedding (depression in vegetation and soil), sweet vetch digging (disturbed soil with vetch present), frugivory (shrub damage, berries missing or on ground, etc.), insect feeding (dug up mounds, or turned over logs and stumps, rocks, etc.), and ungulate kills (presence of bones and hair). Other activities, such as herbaceous feeding and movement, left little if any obvious sign or were too ephemeral in nature to consistently identify. We concentrated instead on the assessment of the 5 primary activities that left the most obvious and therefore, the most easily identifiable sign. Probability of activity by time of day and habitat For each animal location we defined the activity (1 for activity present, 0 if not present) of
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the animal. Some locations contained multiple activities at a site and thus had multiple activity records. As some activities can occur only during a particular time of the year, we defined relevant seasons for each activity based on diet volumes of selected foods (Table 7). Time of day was classified into crepuscular, diurnal, and nocturnal periods, thought to be relevant a priori to grizzly bear activity. Sunrise, sunset, and civil twilight tables (http://www.cmpsolv.com/los/sunset.html) were used to define crepuscular (morning twilight to sunrise and sunset to evening twilight), diurnal (sunrise to sunset), and nocturnal (evening twilight to morning twilight) periods. All tables were based on expected conditions for the centre of the study area (Robb, Alberta; 53째 N and 117째 W) and a Mountain Time zone. For each activity, a random effect (using a random intercept for individual animal) logistic regression model was estimated using the 3 time-of-day categories as predictors of activity. Indicator contrasts were used with the diurnal period chosen as the reference category. For each activity, probabilities were estimated with 90% confidence intervals and significance (p < 0.1) compared among categories. Resulting probabilities reflected the likelihood of an activity by time-of-day relative to all other activities.
Figure 2. Map of west-central Alberta study area showing study boundary, towns, mountains (>1700 m elevation), and 1,032 grizzly bear (by individual) global position system (GPS) radiotelemetry locations visited between 2001 and 2003.
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After determining the significance of time-of-day, we estimated probability of activity relative to each of 10 habitat types. For each activity, we again estimated a random-effect logistic regression model predicting probability of activity, but included habitat as an additional predictor of activity. Indicator contrasts were used with closed forest chosen as the reference category. Time periods were included for only those categories found to differ significantly from one another based on analyses from the previous section. Probability of activity and 90% confidence intervals were estimated. Results In total, 1,032 GPS radiotelemetry locations were visited between 2001 and 2003. Of 1,032 locations visited, bedding was detected at 179 sites (17%). Bears were significantly more likely to bed during the nocturnal period (27.9%), than either the diurnal (13.0%) or crepuscular (8.2%) periods (Table 8). There was no detectable difference in the probability of bedding between diurnal and crepuscular periods. Table 7. Seasonal delineation of activities based on diets of grizzly bears in the mountains or foothills of west-central Alberta, Canada (Munro et al., 2006). Activity type Mountains (â&#x2030;Ľ1700 m) Bedding entire period
Foothills (<1700 m) entire period
Sweet vetch late April through late June; digging late July through late August
late April through early June; early August to early October
Frugivory
early August through late September late May through early June
late July through early October
late July through late August
late June through late August
Ungulate kills Insect feeding
late April through late July
Table 8. Estimated probability of activity (90% confidence intervals-C.I.) by time of day (crepuscular, diurnal, or nocturnal) for field visited female grizzly bear GPS locations. Number Grizzly bear activity:
Of
Crepuscular Prob.
90% C.I.
locations activity lower upper
Prob.
90% C.I.
activity lower upper
Prob.
90% C.I.
activity lower upper 0.279b 0.221 0.346
0.076a 0.033 0.165
0.069a 0.043 0.108
0.059a 0.031 0.112
391
0.236ab 0.151 0.349
0.262a 0.212 0.320
0.157b 0.106 0.229
Insects
529
0.133ab 0.079 0.215
0.217a 0.168 0.276
0.137b 0.090 0.201
Digging
703
0.367a 0.263 0.485
0.349a 0.282 0.423
0.238b 0.174 0.318
1,032
Kill site
620
Frugivory
0.082
0.048 0.136
0.130
a
Nocturnal
0.101 0.165
Bedding
a
Diurnal
Note: unlike superscript letters indicated significant (p<0.1) differences among temporal groups
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Sweet vetch digging was the only activity with an early and late season (Table 7). Based on seasonal delineations, a total of 703 locations were considered potential root digging sites and of these, rooting activity was detected at 253 sites (36%). Root digging was primarily a diurnal and crepuscular activity. Bears were more likely to dig for roots during daylight (diurnal, 34.9% and crepuscular, 36.7%), than night time periods (23.8%; (Table 8). Insect foraging began earlier in the foothills than the mountains, occurring from late June to late August in the foothills compared with late July to late August for the mountains (Table 7). In total, 529 locations were considered potential insect foraging sites and of these, sign was detected at 213 sites (40 %). Insect feeding was most likely during the diurnal time period (21.7%; Table 8). There was no detectable difference in the probability of insect feeding between either crepuscular and diurnal or crepuscular and nocturnal periods. Table 9. Probability of bear activity by time of day and habitat. a) Crepuscular Habitat type alpine/subalpine anthropogenic closed forest herbaceous mixed forest non-vegetated open forest regenerating forest shrub wet forest
Bedding 0.095 0.025 0.213 0 0.153 0 0.192 0.062 0.027 0.212
Digging Frugivory Insects Ungul. kills 0.589 0.119 0.118 0.021 0.071 0.104 0.155 0.015 0.197 0.168 0.077 0.113 0.269 0.353 0 0.04 0.21 0.489 0.288 0.094 0.183 0.168 0 0.151 0.225 0.425 0.243 0.128 0.366 0.275 0.55 0.043 0.727 0.317 0.244 0.074 0.076 0.087 0.218 0.101
b) Diurnal Habitat type alpine/subalpine anthropogenic closed forest herbaceous mixed forest non-vegetated open forest regenerating forest shrub wet forest
Bedding 0.095 0.025 0.213 0 0.153 0 0.192 0.062 0.027 0.212
Digging Frugivory Insects 0.589 0.098 0.145 0.071 0.086 0.189 0.197 0.14 0.096 0.269 0.305 0 0.21 0.435 0.338 0.183 0.14 0 0.225 0.373 0.289 0.366 0.234 0.607 0.727 0.271 0.29 0.076 0.071 0.261
Ungul kills 0.021 0.015 0.113 0.04 0.094 0.151 0.128 0.043 0.074 0.101
c) Nocturnal Habitat type alpine/subalpine anthropogenic closed forest
Bedding 0.228 0.067 0.433
Digging Frugivory Insects 0.491 0.064 0.089 0.049 0.055 0.118 0.142 0.092 0.058
Ungul kills 0.021 0.015 0.113
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c) Nocturnal (conâ&#x20AC;&#x2122;t) Habitat type herbaceous mixed forest non-vegetated open forest regenerating forest shrub wet forest
Bedding Digging Frugivory Insects Ungul kills 0 0.198 0.215 0 0.04 0.336 0.151 0.325 0.228 0.094 0 0.131 0.092 0 0.151 0.401 0.164 0.271 0.19 0.128 0.158 0.28 0.16 0.471 0.043 0.072 0.642 0.189 0.191 0.074 0.431 0.052 0.046 0.169 0.101
Frugivory started earlier and lasted longer in the foothills than the mountains (Table 7). In total, 97 of 391 (25 %) potential locations were known to have been frugivory sites. Temporally, frugivory was highest during diurnal (26.2%) and crepuscular periods (23.6%; Table 8). Only diurnal (26.2 %) and nocturnal (15.7%) periods, however, were significantly different from one another (Table 8). Duration of seasonal ungulate feeding was dependent on region. Ungulate use occurred later and was of shorter duration in the mountains than the foothills (Table 7). Based on seasonal delineations, a total of 620 locations were considered potential kill sites and of these, kills were located at 54 locations (9 %). As we are unsure of whether these sites represented predation by the collared animal (could have been another bear or carnivore), we can only infer that the carcass was visited. No temporal trend in time-of-day was evident for ungulate carcass use (Table 8). The probability of bedding was highest during nocturnal periods in forested habitats, including closed, open, mixed, and wet forests (Table 9). Sweet vetch digging, on the other hand, tended to be most prevalent in open shrub and alpine/sub-alpine habitats, with only marginally less activity during nocturnal times. Frugivory occurred across a variety of habitat types, but was most prevalent within mixed and open forests, and generally during daylight hours. Insect foraging occurred most frequently within regenerated forests, especially during diurnal times (Table 9). Finally, ungulate feeding sites were more likely to be located in closed forests, open forests, wet forests, and non-vegetated areas, regardless of time of day. Discussion To our knowledge, we are the first to quantify grizzly bear activity by time of day and habitat. We found that despite high levels of human activity, especially in the foothills, bears were active most during diurnal and crepuscular periods, with bedding occurring most frequently at night. This supports the generally held belief that bears are primarily diurnal throughout their range in North America (Craighead et al. 1995; Gilbert and Lanner 1995). However, it contradicts studies that have shown bears to be more nocturnal in areas where human activity is high (Gibeau et al. 2002). This suggests that the level of human activity in the foothills and mountains of west-central Alberta are not high enough to disrupt the typical daylight activity pattern of bears.
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Overall, no single habitat characterized all grizzly bear activities but rather bears used different habitats for specific activities. Sweet vetch digging was more likely to occur in herbaceous and alpine habitats, insect feeding was concentrated in regenerating forests, and finally frugivory tended to occur in mixed and open forests. Moreover, bears were more likely to bed in forested communities. These findings reveal the complexity of grizzly bear habitat selection, suggesting that activity and time of day be considered in analyses of habitat use. Examination of habitat selection based on behaviour allowed for mechanistic explanations of grizzly bear habitat selection and assessments of the seasonal importance of different habitats. Managing habitats and foods for grizzly bears is complicated given the diverse nature of food resources (as well as annual and seasonal variations) and habitats used by bears. In one sense, the flexible omnivorous nature of bears allows these animals to adapt to changes in food resources. Despite this flexible nature, some habitats are more productive than others and populations with low reproductive rates (Garshelis et al. 2005) may be sensitive to certain modifications and human activities. We suggest that grizzly bear habitat models consider more directly the spatial and temporal nature of food resources and activity patterns. We further suggest that when possible, food models be developed and combined into composite seasonal habitat maps that score the importance of individual seasonal food items (e.g., Nielsen et al. 2003; 2004b), habitats, and activities. Maps derived from such models are likely to be more predictive and relevant to the needs of bears. Literature Cited Apps, C.D., McLellan, B.N., Woods, J.G., and Proctor, M.F. 2004. Estimating grizzly bear distribution and abundance relative to habitat and human influence. J. Wildl. Manag. 68: 138â&#x20AC;&#x201C;152. Craighead, J.J., Sumner, J.S. and Mitchell, J.A. 1995. The grizzly bears of Yellowstone. Their ecology in the Yellowstone Ecosystem, 1959-1992. Island Press. Washington, D.C. Garshelis, D.L., Gibeau, M.L., Herrero, S. 2005. Grizzly bear demographics in and around Banff National Park and Kananaskis Country, Alberta. J. Wildl. Manag. 69: 277â&#x20AC;&#x201C;297. Gibeau, M.L. 1998. Grizzly bear habitat effectiveness model for Banff, Yoho, and Kootenay National Parks, Canada. Ursus 10: 235â&#x20AC;&#x201C;241. Gibeau, M.L., Clevenger, A.P., Herrero, S., and Wierzchowski, J. 2002. Grizzly bear response to human development and activities in the Bow River Watershed, Alberta, Canada. Biol. Conserv. 103: 227-236. Gilbert, B.K., and Lanner, R.M. 1995. Energy, diet selection and restoration of brown bear populations. In Density-dependent population regulation of black, brown and polar bears. Edited by M. Taylor. Proceeding of the 9th International Conference on Bear Research and Management, Missoula, Mont., 23-28 February 1994. International Association for Bear Research and Management, Washington, D.C. Monogr. Ser. No. 3. pp 231-240. Hamer, D., and Herrero, S. 1987. Grizzly bear food and habitat in the front ranges of Banff National Park, Alberta. In Bears-Their Biology and Management: Proceedings of the 7th International Conference on Bear Research and Management, Williamsburg, Va., U.S.A. and Plityice Lakes, Yugoslavia, February and March 1986. Edited by P. Zager. International Association of Bear Research and Management, Washington, D.C. pp. 199213.
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Hamer, D., Herrero, S., and Brady, K. 1991. Food and habitat used by grizzly bears, Ursus arctos, along the continental divide in Waterton Lakes National Park, Alberta. Can. Field-Nat. 105: 325–329. McLellan, B.N., and Hovey, F.W. 2001. Habitats selected by grizzly bears in a multiple use landscape. J. Wildl. Manag. 65: 92–99. Mueller, C., Herrero, S., and Gibeau, M.L. 2004. Distribution of subadult grizzly bears in relation to human development in the Bow River Watershed, Alberta. Ursus 15: 35–47. Munro, R.H.M., Nielsen, S.E., Price, M.H., Stenhouse, G.B., and Boyce, M.S. 2006. Seasonal and diel dynamics of diet and activity patterns in grizzly bears in Alberta. Journal of Mammalogy: In review. Nielsen, S.E., Boyce, M.S., Stenhouse, G.B., and Munro, R.H.M. 2002. Modeling grizzly bear habitats in the Yellowhead Ecosystem of Alberta: Taking autocorrelation seriously. Ursus 13: 45–56. Nielsen, S.E., Boyce, M.S., Stenhouse, G.B., and Munro, R.H.M. 2003. Development and testing of phonologically driven grizzly bear habitat models. Ecoscience 10: 1–10. Nielsen, S.E., Boyce, M.S., and Stenhouse, G.B. 2004a. Grizzly bears and forestry I: selection of clearcuts by grizzly bears in west-central Alberta, Canada. For. Ecol. Manag. 199: 51–65. Nielsen, S.E., Munro, R.H.M., Bainbridge, E.L., Stenhouse, G.B., Boyce, M.S. 2004b. Grizzly bears and forestry II: distribution of grizzly bear foods in clearcuts of westcentral Alberta, Canada. For. Ecol. Manag. 199: 67–82. Stevens, S. 2002. Landsat TM-based greenness as a surrogate for grizzly bear habitat quality in the Central Rockies Ecosystem. M.Sc. thesis, University of Calgary, Calgary, Alberta, Canada. Theberge, J.C. 2002. Scale-dependent selection of resource characteristics and landscape pattern by female grizzly bears in the eastern slopes of the Canadian Rocky Mountains. Ph.D. dissertation, University of Calgary, Calgary, Alberta, Canada. Acknowledgements We wish to thank J. Bell, M. Cattet, N. Caulkett, M. Dupuis, B. Goski, J. Lee, C. Mamo, G. MacHutchon, J. Saunders, M. Urquhart and a number of Alberta Conservation Officers and Jasper National Park wardens who provided expertise and support during bear capture. We are also grateful to vegetation crew members C. Aries, E. Bainbridge, A. Iglesias, T. Larsen, M. McLellan, E. Moore, M. Piper, and S. Robertson and GIS staff member J. Duval. A special thank you goes to R. Riddell for his assistance with the scat analysis.
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A Preliminary Assessment of Grizzly Bear Activity Patterns in the Swan Hills, Alberta Scott E. Nielsen, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Robin H.M. Munro, Contract Biologist for Foothills Model Forest, Hinton, AB T7V 1X6. Mark S. Boyce, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Introduction Diet and activity of grizzly bears is important for understanding mechanisms of habitat use and animal behaviour, as well as differential reproduction and survival of individuals. Previous assessments of diet and activity have been completed for the front ranges of Albertaâ&#x20AC;&#x2122;s Rocky Mountains (Hamer and Herrero 1987; Hamer et al. 1991; Munro et al. 2006), but little if anything is known about grizzly bears in Alberta outside this region, including the Swan Hills population. The Swan Hills currently represent a unique population of grizzly bears being isolated from the mountains. Given high levels of anthropogenic activity and the isolated nature of the population, long-term persistence is questionable. Continued persistence might be maintained if: (1) high quality and/or abundance of foods, along with a longer growing season, result in better body condition of animals which is translated into higher levels of fecundity (offsetting high levels of mortality); or (2) dispersal from distant mountain populations, such as Willmore Wilderness Park to the west, maintain the existing population in the Swan Hills, and is therefore a population sink. As a precursory examination of this issue, we assessed the diet and activity of 4 GPS-collared grizzly bears from the Swan Hills to make comparisons with previous work (e.g., Munro et al. 2006, see also previous section) from the Foothills near Hinton, Alberta. Our goal was to determine whether diet and activity vary between the Hinton and Swan Hills areas. Methods Field Methods We visited 57 grizzly bear GPS radiotelemetry locations in the Swan Hills region for 4 individual grizzly bears (Figure 3). All locations were visited during August and September 2005, but also represented radiotelemetry locations from the month of July. Each location was randomly sampled from a list of potential use locations, stratified by time of day (diurnal, crepuscular, and nocturnal). No more than one observation per day, per bear was chosen from the list of use locations. Although four animals were followed, only seven observations were recorded for two animals. After navigating to each location using a handheld GPS, activity (bedding, insect use, ungulate use, root digging, and frugivory) of the animal was recorded, along with vegetation and site characteristics. Random locations were paired with each use observation by choosing a random cardinal direction and moving 300 m from the centre point of the GPS use location. Sampling of use and random locations were designed to allow case-control analyses of fine-scale habitat selection relating to individual activities.
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Figure 3. Location of grizzly bear use locations visited in the Swan Hills during the late summer of 2005 (G218 locations (n=4) were 50 km east of Grande Cache and not depicted in this figure). Analytical Methods We summarize the percent of observations within the defined five activity types to generalize patterns of microsite habitat use. Logistic regression was used to assess significance of time of day and habitat in each use activity. When necessary, certain activities were limited to their relevant time period following that of Munro et al. (2006). Although case-control logistic regression was explored, low sample sizes precluded further analysis. Results Bedding We found bears to be bedded at 37% of the sites visited (Table 10). However, there was a strong relationship with time of day, and bedding was more than 13 times more likely during nocturnal periods (LR Ď&#x2021;2 = 16.86, p<0.001), having 74% of all nocturnal observations being bedding locations. Although preliminary and based on low sample sizes, nocturnal bedding appears much more prevalent than that recorded in the core Foothills Model Forest study area near Hinton, Alberta (Munro et al. 2006). We are unsure of causes of disparity between the two sites for the nocturnal period. Diurnal and crepuscular, however, appeared to be within the range of that expected in the Foothills study (~15%). Habitat relationships confirm that bears were bedding most in forests similar to the relationship observed by Munro et al. (2006).
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Table 10. Microsite use table describing late summer activity patterns by percent use based on 57 visited GPS-radiotelemetry locations in the Swan Hills of Alberta. Habitat rank reflects the ranked sum of activities by habitat, not selection.
Habitat
Bedding
Insect Ungulate Root use use digging Frugivory
% Activity
36.8
41.9
12.3
0
10.5
Activity by habitat:* decid/mixed forest conifer forest shrub fire & clearcut anthropogenic herbaceous
(57) 42.9 60 50 30.0 15.4 0
(43) 69.2 37.5 50 12.5 44.4 0
(57) 21.4 26.7 0 0 0 0
(41) 0 0 0 0 0 0
(57) 0 6.7 0 40.0 7.7 0
Habitat rank of activity
1 2 3 4 5 6
*low sample sizes required that like habitats be combined (e.g., decid/mixed forest & fire/clearcut); no use observations were recorded in wet forest communities; sample size varies by activity based on predefined seasons from Munro et al. (2006)
Insect use (ants and wasps) Insect use occurred at 42% of the sites visited during the period considered relevant for insect foraging (all sites through late August) (Table 10). We found that anting was more likely to occur during daylight hours than the nighttime period, consistent with Hinton study (Munro et al. 2006). Only nocturnal and diurnal periods were significantly (p <0.1) different from one another. Use of wasps relative to time of day was much less clear, likely reflecting low power. Unlike the foothills study, which suggested substantial use of ants in clearcuts (Munro et al. 2006), little insect foraging (ants and wasps) was witnessed in clearcuts. The majority of clearcuts in the Swan Hills, however, were represented by salvage logging operations in post-burn sites, which may contribute to the differences observed. Most insect foraging in the Swan Hills appeared to occur in deciduous and mixed forests (Table 10). Compared with the Hinton area, the use of wasps by bears was much more common. Ungulate use (kill sites) Overall 12% of locations visited were considered to be kill sites, but it was unknown whether it was predatory in nature or scavenged (Table 10). Time of day was not significant, similar to what was found by Munro et al. (2006) in the Hinton area. Habitats associated with kill sites included deciduous/mixed forest and conifer forests. No kill sites were found in the remaining, more open habitats. All kill sites appeared to be adult moose (Alces alces). Neonate moose were determined to be important in the Hinton area (Munro et al. 2006), but due to the late nature (July-September) of the field visits in the Swan Hills, the most vulnerable period for neonates was missed. Additional late spring and early summer visits for the Swan Hills would be necessary to establish any relationship between neonate and adult moose use/vulnerability.
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Root digging We did not find root digging at any grizzly bear use location visited. This may reflect the season sampled (late summer/early fall) or the unimportance of roots in the diets of grizzly bears in the Swan Hills. Further assessments of diet, especially in the spring, are necessary before definitive conclusions can be made regarding root digging in the Swan Hills. Frugivory Only 11% of use locations were determined to be frugivory activities (Table 10). No obvious time of day patterns were evident, although diurnal and crepuscular periods were found to be more selective in the Hinton area (Munro et al. 2006). Habitats used tended to be fire/clearcut sites, followed by conifer forest and anthropogenic sites. Hillside blueberry (Vaccinium myrtilloides), and to a lesser extent Canada buffaloberry (Shepherdia canadensis), were species most sought out by those bear locations visited. Use of hillside blueberry was much greater than that observed in the Hinton study, where big huckleberry (Vacccinium membranaceum) and Canada buffaloberry were more dominant (Munro et al. 2006). Overall habitat rank Assuming each activity to be equally important to grizzly bears, we summed percent activities for each habitat to determine the ranked importance of each habitat. We found deciduous and mixed forests to be the most used habitat, followed by conifer forest, shrub, fire/clearcut, anthropogenic, and herbaceous (Table 10). As availability of these habitats vary substantially, actual selection of habitats may differ from ranked use. Conclusion Grizzly bear activity in the Swan Hills of Alberta appeared to be relatively consistent with the Hinton study reported by Munro et al. (2006). Principal differences included much higher bedding probability during nocturnal periods, no root digging (at least within the late season digging normally observed in the mountains and upper foothills near Hinton), less buffaloberry use than expected, and an increase in insect use, especially that of wasps. However, markedly different habitat conditions were present in the Swan Hills region having a substantial presence of post-fire forest stands, many of which have been recently salvage logged. Differences in availability of habitats and thus associated foods likely contributed to variations observed in microsite use (activity) patterns. Sample sizes were too low to provide conclusive relationships. As such, continued fieldwork in the Swan Hills is necessary to draw firm conclusions about the activity patterns and diets of grizzly bears in the Swan Hills. Presently, we are unable to conclude whether food resources in the Swan Hills are more abundant or diverse than that of the Foothills. Processing of field-collected scats should provide preliminary assessments of diet composition and a more complete picture of late season food and habitat resources. Animal health, genetic relatedness, human-caused mortality, graph theory connectivity, and diet relationships will all be necessary elements in determining the reasons for the current persistence of grizzly bears in the Swan Hills. Management activities that maintain long-term connectivity among the Rocky Mountain populations and Swan Hills will likely be necessary for continual persistence of the Swan Hills population.
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Literature Cited Hamer, D., and Herrero, S. 1987. Grizzly bear food and habitat in the front ranges of Banff National Park, Alberta. In Bears-Their Biology and Management: Proceedings of the 7th International Conference on Bear Research and Management, Williamsburg, Va., U.S.A. and Plityice Lakes, Yugoslavia, February and March 1986. Edited by P. Zager. International Association of Bear Research and Management, Washington, D.C. pp. 199213. Hamer, D., Herrero, S., and Brady, K. 1991. Food and habitat used by grizzly bears, Ursus arctos, along the continental divide in Waterton Lakes National Park, Alberta. Can. Field-Nat. 105: 325â&#x20AC;&#x201C;329. Munro, R.H.M., Nielsen, S.E., Price, M.H., Stenhouse, G.B., and Boyce, M.S. 2006. Seasonal and diel dynamics of diet and activity patterns in grizzly bears in Alberta. Journal of Mammalogy: In review.
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Understanding Grizzly Bear Associations With Roads in The Foothills Of West-Central Alberta Carrie L. Roever, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Mark S. Boyce, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Gordon Stenhouse, Foothills Model Forest, Hinton, AB T7V 1X6. Editorâ&#x20AC;&#x2122;s Note: This research is being conducted as partial fulfillment toward a Masterâ&#x20AC;&#x2122;s of Science Degree. It must be stressed that these data are preliminary in nature and all findings must be interpreted with caution and are subject to revision based on the ongoing findings over the course of this study.
Introduction Throughout North America, a growing human population and a high standard of living increases the pressure to expand into undeveloped land for resources, settlement, and economic growth. As a result, the consequence of land development on wildlife has become a topic of concern. Historically, areas with high human presence have experienced substantial declines in grizzly bear (Ursus arctos) populations due to both human caused mortality and habitat loss (Brown, 1985; Mattson, 2002). The forests of Alberta, Canada only recently have been opened up to industrial development, yet the resulting landscape change has been considerable (Schneider, 2002). In rugged, uninhabitable areas, grizzly bear populations continue to be secure (Mattson, 2002), but in the foothills where pressure from development is highest, the future persistence of grizzly bear populations is less certain. Not all development practices are detrimental to grizzly bears. Some have been shown to be beneficial, for example, logging creates early successional stage forests that open canopy cover and allow for the growth of bear foods (Bratkovich, 1986; Hillis, 1986; Nielsen et al., 2004a). In west-central Alberta, bears within managed forests have shown lower levels of cortisol and higher levels of progestin than neighboring bears in National Parks, suggesting that bears within managed forests experience less stress and have higher levels of reproductive activity (Wasser et al., 2004). While timber harvesting has its benefits, industrial development creates a permanent network of roads on the landscape. Increasing human access has proven to be detrimental to bear survival by increasing vehicular collisions, hunter access, and illegal poaching (McLellan and Shackleton, 1988; Mace et al., 1996; Johnson et al. 2004; Nielsen et al., 2004b). Benn and Herrero (2002) found that 90% of grizzly bears die within 500 m of a road. The apparent response of grizzly bears to roads has been variable. Weilgus and Venier (2003) found that grizzly bears avoid roads; however, the extent of avoidance seems to be contingent on the traffic volume (Mattson et al., 1987; Archibald et al., 1987). Others have found a neutral or positive selection (Mace et al., 1996) particularly when roads are near an important food source (Chruszcz et al., 2003) or when alternative foods are less abundant (Mattson et al., 1992). There is also variation in the response between the sexes. Some studies observed females using habitats near roadways more readily than males (Mattson et
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al., 1987; Mattson et al., 1992) particularly when they had cubs (McLellan et al., 1988). Conversely, Gibeau (2002) found that females remained further from roads, but were more tolerant of human settlement. In the foothills of Alberta, Nielsen et al. (2004b) has shown that grizzly bears suffer higher mortality when in close proximity to roads, but unfortunately, bears continue to use these high-risk areas (unpublished data). One hypothesis is that roads often are built in highquality bear habitats, thus bears are not attracted to the road, but instead were already using the area prior to road development. An alternative hypothesis is that roads introduce an additional food source into the grizzly bearsâ&#x20AC;&#x2122; generalist diet. Roads often are planted with clover and contain early-successional and nutrient-rich plants such as dandelions (T. officinale) and alfalfa (Trifolium spp.). The main objective of this study is to distinguish between these two hypotheses with a two-fold approach. We will first examine the prevalence of grizzly bear foods around roads, which might act as an attractant. We will then study grizzly bear movements before and after road development to determine if bears are using these habitats prior to road development or are later attracted to the area because of some feature associated with the road. Methods Grizzly bear foods around roads and streams Fieldwork was conducted in the summer of 2005 to assess differences in bear food abundance between roads and roads associated with riparian habitats. We used a nested design to reduce noise created by environmental heterogeneity. Sites were selected randomly using a compound topographic index (CTI) provided by Nielsen et al. (2004c) and human development layers provided by the Foothills Model Forest. Within each site, three plots were identified: 1) a road greater than 200 m from a riparian area; 2) a roads less than 150 m from a riparian area; and 3) a riparian area greater than 200 m from human disturbance features. We used a minimum distance rule of 500 m between plots to maintain independence. Within each plot, subplots were identified and placed in 1) the ditch, 2) inside the forest edge and greater than 20 m from the ditch subplot, and 3) at least 200 m away from any human disturbance feature. Table 11. Grizzly bear foods sampled in west-central Alberta (adapted from Nielsen et.al, 2004a) Food Ants A. uva-ursi (bear berry) Equisetum spp. (horse tail) Hedysarum spp. H. lanatum (cow parsnip) S. canadensis (buffalo berry) T. officinale (dandelion) Trifolium spp. (clover) Ungulate pellets V. caespitosum V. membranaceum V. myrtilloides V. vitis-idaea
Food Type or Feeding Activity Myrmecophagy Fruits Herbaceous Roots/ tuber digging Herbaceous Fruits Herbaceous Herbaceous Carnivorous Fruits Fruits Fruits Fruits
Season of Use Summer Spring and late summer Spring and summer Spring and fall Summer Late summer and fall Spring and summer Spring and summer Spring and early summer Late summer and fall Late summer and fall Late summer and fall Late summer and fall
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Subplots consisted of one 20 m long belt transect laid parallel to the road or stream. Quadrats (0.25m2) were established every 5 m along the belt transect, following the methods of Nielsen et al. (2004a). The presence of all bear foods (Table 11) were assessed in each quadrat and ripe berries were counted and weighed. A 10 m meander search for ants and ungulate pellets was conducted on either side of the belt transect. In addition, stand composition and terrain variables were assessed. Canopy cover was measured using a spherical densiometer in the four cardinal directions at the 0, 10, and 20 m mark. Understory density also was measured using a Robel pole (Robel, 1970) because vertical hiding cover may be an important factor in bear presence around roads. Finally, road surface and/or stream type was noted. For future analysis, we will use a mixed effect logistic regression model. All vegetation measurements will be averaged over the subplot scale, and random intercepts will be placed on the site and plot level to account for the hierarchical structure of the data. All continuous variables will be assessed for collinearity using Pearsonâ&#x20AC;&#x2122;s correlations (r). Finally, we will use K-fold cross validation (K=3) to validate all models. Grizzly bear movements near roads We will examine the movements of 18 radiocollared grizzly bears that have home ranges in areas of road development. Bears were captured and fitted with GPS radiocollars between 1999 and 2004 (detailed description of capture procedure in Nielsen et al., 2004c). The majority of collars acquired locations every four hours; however, four of the collars were programmed to obtain locations at two-hour intervals. Comparison resource selection functions (Lele et al., in development) and step selection functions (Fortin, 2005) will be used to analyze the movements of individuals before and after road development. We will examine how grizzly bear behaviour changes with new road development, taking into account factors such as time of day, traffic volume on the nearest road, and the sex/age class of the individual. Since the inception of the Foothills Model Forest Grizzly Bear Research Program in 1999, many new roads have been built in the 10,000 km2 study area south of Hinton. New road development was identified using Landsat TM satellite images taken between August and September of every year. Landsat images also were used to classify landcover and identify habitat change on a yearly basis (Franklin et al., 2001). Traffic volume also may play an important role in grizzly bear proximity to roads. We plan to create traffic indices based on least-cost paths of industrial traffic and diffusion approximations from population centers for residential traffic. For industrial traffic, we will obtain the dates of timber harvest and locations of active wellsites and create least cost paths between those locations and the processing center. Residential traffic will be estimated following the protocol of Apps (2004) whereby a diffusion index is created based on the unwillingness to travel as travel time increases. Average travel velocity per road and distance from a population center will be used to calculate the time required to access any point within the road network. These two variables will then be combined to classify roads into low, medium, and high volume.
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Expected Results Based on preliminary analysis, we suspect that grizzly bear foods that are used in the spring will be more prevalent near roads than in the forest interior. Dandelions and clover occurred more often in the ditch and edge than in the forest (Table 12). Horsetails (Equisetum spp.) were common around roads associated with riparian (72.4%), and they were also common in ditches not associated with streams (51.7% of plots). This is potentially due to the water settling in the ditch even around upland roads. Hedysarum spp. has been shown to be an important part of grizzly bearâ&#x20AC;&#x2122;s early-spring diet (Munroe, in review), however it was not very prevalent in our study and was present at only 5% of our total plots. Ungulates were used by grizzly bears during spring, but results suggest that they usually do not occur near roads. This may be misleading, however, because Collins and Urness (1979) have shown that a bias exists in ungulate detectability between forested and open areas. Tentative observations suggest that bears are using roaded areas when other food sources are less abundant, but analysis still needs to be done to determine if these results are significant. Table 12. Frequency of occurrence of grizzly bear foods important to spring diet
A. uva-ursi Equisetum spp. Hedysarum spp T. officinale Trifolium spp. Ungulate pellets Total # of plots
Road (n=87) Ditch Edge 10.3 17.2 51.7 24.1 0 3.4 72.4 10.3 69.0 3.4 17.2 41.4 29 29
Forest 10.3 17.2 3.4 3.4 0 44.8 29
Road and Riparian (n=86) Ditch Edge Forest 13.8 31 17.9 72.4 48.3 21.4 3.4 10.3 7.1 48.3 13.8 0 69 10.3 0 10.3 24.1 32.1 29 29 28
Previous work has suggested that grizzly bears in the foothills of west-central Alberta select roadside habitats (Nielsen, unpublished data), but when other factors, such as time of day, traffic volume, and new road development are taken into account, we might find that bears are in fact avoiding areas where mortality risk is high. Preliminary analysis suggests that bears are displaced after a new road is built. Bears also move further away from preexisting roads, possibly due to increased traffic. When new roads are built, not only does activity increase while building the road, but also traffic flow throughout the area increases as new cutblocks, wellsites, and coal and gravel mines are established. We also have evidence that grizzly bear use of roads increases at night, suggesting that bears may mediate the mortality risk associated with road use by selecting the lowest risk times of day to visit. More work still needs to be done to test whether these results are significant. We must also investigate differences in selection between sex/age groups to determine if subordinate individuals and females with cubs are utilizing roadsides to avoid dominant males. Care must be taken when stating that bears are attracted to roads because when other factors are considered, this may not be the case. If we do discover that bears are attracted to roads because of the abundance of food resources, we may be creating an attractive sink due to the high mortality risk associated with human access features.
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Conclusion With increasing habitat fragmentation due to development and industry, grizzly bears are being forced into closer proximity to humans in order to gain access to resources. The effect of roads on grizzly bear mortality has become a notable problem, and with the slow reproductive cycle of bears, it is important that we understand causes of mortality and the means to avoid it in the future. Grizzly bears in the foothills of west-central Alberta may not exhibit the same avoidance of roadways that has been seen in other areas throughout their range, but no work has been done to examine this association more closely. By gaining a greater understanding of this relationship, it will allow us to make better management decisions for the future persistence of grizzly bears in the foothills landscape. References Cited Apps, C.D., B.N. McLellan, J.G. Woods, and M.F. Proctor. 2004. Estimating grizzly bear distribution and abundance relative to habitat and human influence. Journal of Wildlife Management 68(1): 138-152. Archibald, W.R., R. Ellis, and A.N. Hamilton. 1987. Responses of grizzly bears to logging truck traffic in the Kimsquite River Valley, British Columbia. Ursus 7: 251-257. Bratkovich, A.A. 1986. Grizzly bear habitat component associated with past logging practices on the Libby Ranger District, Kootenai National Forest. ProceedingsGrizzly Bear Habitat Symposium: 180-184. Benn, B., and S. Herrero. 2002. Grizzly bear mortality and human access in Banff and Yoho National Parks, 1971-1989. Ursus 13: 213-221. Brown, D.E. 1985. The grizzly of the Southwest: documentary of an extinction. University of Oklahoma Press, Norman. Chruszcz, B., A.P. Clevenger, K.E. Gunson, and M.L. Gibeau. 2003. Relationship among grizzly bears, highways, and habitat in the Banff-Bow Valley, Alberta, Canada. Canadian Journal of Zoology 81: 1378-1391. Collins, W.B., and P.J. Urness. 1979. Elk pellet group distribution and rates of deposition in aspen and lodgepole pine habitats. In: Boyce, M.S., and L.D. Hayden-Wing (Eds), North American Elk: Ecology, Behavior, and Management. The University of Wyoming, Laramie, Wyoming, pp. 140-144. Franklin, S.E., G.B. Stenhouse, M.J. Hansen, C.C. Popplewell, J.A. Dechka, and D.R. Peddle. 2001. 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 27: 549-592. Fortin, D., H.L. Beyer, M.S. Boyce, D.W. Smith, T. Duchesne, and J.S. Mao. 2005. Wolves influence elk movements: behavior shapes a trophic cascade in Yellowstone National Park. Ecology 86: 1320-1330. Gibeau, M.L., A.P. Clevenger, S. Herrero, and J. Wierschowski. 2002. Grizzly bear response to human development and activities in the Bow River Watershed, Alberta, Canada. Biological Conservation 103: 227-236.
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Hillis, M. 1986. Enhancing grizzly bear habitat through timber harvesting. In: Contreras, G.P. and K.E. Evans (Eds). Proceedings- Grizzly Bear Habitat Symposium, Missoula, Montana. USDA Forest Service, Intermountain Research Station, Ogden, Utah, pp. 176-179. Johnson, C.J., M.S. Boyce, C.C. Schwartz, and M.A. Haroldson. 2004. Modeling Survival: Application of the Andersen-Gill model to Yellowstone grizzly bears. Journal of Wildlife Management 68: 966-978. Mace, R.D., J.S. Waller, T.L. Manley, L.J. Lyon, and H. Zuuring. 1996. Relationships among grizzly bears, roads, and habitat in the Swan Mountains, Montana. Journal of Applied Ecology 33: 1395-1404. Mattson, D. J., and T. Merrill. 2002. Extirpations of grizzly bears in the contiguous United States, 1850-2000. Conservation Biology 16: 1123-1136. Mattson, D.J., R.R. Knight, and B.M. Blanchard. 1987. The effects of development and primary roads on grizzly bear habitat use in Yellowstone National Park, Wyoming. Ursus 7: 259-273. Mattson, D.J., B.M. Blanchard, and R.R. Knight. 1992. Yellowstone grizzly bear mortality, human habituation, and whitebark pine seed crops. Journal of Wildlife Management 56: 432-442. McLellan, B.N., and D.M. Shackleton. 1988. Grizzly bears and resource-extraction industries: Effects of roads on behavior, habitat use, and demography. Journal of Applied Ecology 25: 451-460. McLellan, B.N., F.W. Hovey, R.D. Mace, J.G. Woods, D.W. Carney, M.L. Gibeau, W.L.Wakkinen, and W.F. Kasworm. 1999. Rates and causes of grizzly bear mortality in the interior mountains of British Columbia, Alberta, Montana, Washington, and Idaho. Journal of Wildlife Management 63(3): 911-920. Munro, R.H.M., S.E. Nielsen, M.H. Price, G.B. Stenhouse, M.S. Boyce. Seasonal and diel dynamics of diet and activity patterns in grizzly bears in Alberta. Journal of Mammology. in review. Nielsen, S.E., R.H.M. Munro, E.L. Bainbridge, G.B. Stenhouse, and M.S. Boyce. 2004a. Grizzly bears and forestry II. Distribution of grizzly bear foods in clear-cuts of westcentral Alberta, Canada. Forest Ecology and Management 199: 67-82. Nielsen, S.E., S. Herrero, M. S. Boyce, R.D. Mace, B. Benn, M.L. Gibeau, and S. Jevons. 2004b. Modeling the spatial distribution of human-caused grizzly bear mortalities in the Central Rockies ecosystem of Canada. Biological Conservation 120: 101-113. Nielsen, S.E., M.S. Boyce, and G.B. Stenhouse. 2004c. Grizzly bears and forestry I. Selection of clearcuts by grizzly bears in west-central Alberta, Canada. Forest Ecology and Management 120: 101-113.
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Munro, R.H.M., S.E. Nielsen, M.H. Price, G.B. Stenhouse, and M.S. Boyce. 2006. Seasonal and diel dynamics of diet and activity patterns of grizzly bears in Alberta. Journal of Mammalogy; in review. Robel, R.J., J.N. Briggs, A.D. Dayton, L.C. Hulbert. 1970. Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management 86: 1320-1330. Schneider, R.R. 2002. Alternative Futures: Albertaâ&#x20AC;&#x2122;s Boreal Forest at the Crossroads. The Federation of Alberta Naturalists and the Alberta Centre for Boreal Research, Edmonton, Alberta. Wasser, S.K., B. Davenport, E.R. Ramage, K.E. Hunt, M. Parker, C. Clarke, and G.B. Stenhouse. 2004. Scat detection dogs in wildlife research and management: Application of grizzly and black bears in the Yellowhead Ecosystem, Alberta, Canada. Canadian Journal of Zoology 82: 475-492. Wielgus, R.B., and P.R. Vernier. 2003. Grizzly bear selection of managed and unmanaged forests in the Selkirk Mountains. Canadian Journal of Forest Resources 33: 822-829.
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REMOTE SENSING Map Production Update Greg McDermid, Foothills Facility for Remote Sensing and GIScience, Department of Geography, University of Calgary, 2500 University Dr. NW Calgary, AB T2N 1N4. Alysha Pape, Environmental Remote Sensing Laboratory, Department of Geography, University of Saskatchewan, 9 Campus Drive Saskatoon, SK S7N 5A5. David Laskin, Foothills Facility for Remote Sensing and GIScience, Department of Geography, University of Calgary, 2500 University Dr. NW Calgary, AB T2N 1N4.
Introduction 2005 marked the sixth year in which researchers and technicians from the University of Calgary and University of Saskatchewan have contributed remote sensing map products in support of grizzly bear ecology and conservation efforts within the Foothills Model Forest Grizzly Bear Research Program. McDermid (2005) and McDermid et al. (2006a) described a methodological framework for creating landcover and vegetation information over large areas from remote sensing and digital elevation models (DEMs). The approach generates an information base consisting of four attributes: land cover, crown closure, species composition, and normalized difference vegetation index (NDVI) phenology. The products have been demonstrated to provide both high-quality representation of land and vegetation conditions over large, multi-jurisdictional landscapes as well as an effective foundation for explaining observed patterns of grizzly bear occurrence (McDermid et al., 2006b). The McDermid (2005) framework has been adopted as the standard for on-going mapping work. This report documents mapping activities in the Phase 5 expansion on the northern portion of the Project’s study area. Methods The Phase 5 expansion covers approximately 15 million hectares split across the two “shoulder” regions of the Phase 5 study area, and includes portions of the Willmore Wilderness region to the west and Swan Hills region to the east (Figure 4).
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Figure 4. Location of study area in Phase 5 expansion. Field Sampling Field work in support of the Phase 5 mapping expansion was carried out in the summer of 2005 by personnel from the University of Saskatchewanâ&#x20AC;&#x2122;s Environmental Remote Sensing Laboratory. Field crews relied heavily on helicopter support due to the remote and inaccessible nature of the terrain, combining traditional ground surveys with ocular estimates acquired from hovering aircraft. Ground surveys consisted of observations, measurements, and hemispherical photography designed to characterize land cover, crown closure, and tree species composition (proportion coniferous) across a sample plot roughly analogous to a 30 x 30 metre Landsat pixel. Air calls consisted of straight ocular estimation while hovering over a sample point. The total field data set consisted of 613 points, composed of 31 ground points and 582 air calls. An additional 47 ground points collected by University of Calgary field crews operating in the northern portion of Jasper National Park were also integrated into the data set. Image Acquisition and Pre Processing Remote sensing imagery from two different satellite sensor systems were acquired in support of this research: Landsat Thematic Mapper (TM) and the Moderate Resolution Imaging Spectrometer (MODIS). Multispectral TM imagery from Landsat 5 were used to map land cover, crown closure, and species composition, while MODIS data were used to track NDVI.
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The study area was composed of three separate Landsat scenes: 44/22, 46/22, and 46/23. Each image was orthorectified, transformed to top-of-atmosphere radiance, and radiometrically normalized to a pre-existing master image (45/22 from the Phase 3 study area) using the linear transformation procedure described in McDermid (2006). The tasseled cap transformation of Crist and Ciccone (1984) was used to generate the standard orthogonal components brightness, greenness, and wetness. MODIS data from MOD13Q1 (Huete et al., 1999) were used to monitor NDVI across the 2005 growing season. A series of 16-day NDVI composites from the Terra satellite were acquired across MODIS tiles h10v03 and h11v03 from April 7 to October 31, 2005. The MODIS Reprojection Tool was used to mosaic the required scenes and reproject to UTM zone 11 (NAD 83). A DEM from DMTI Spatial was acquired for this study through an academic agreement with the University of Calgary library. Wall-to-wall DEM coverage of the study area was achieved by mosaicking 1:250,000 map sheets in ArcMap. Further morphometric processing was used to derive topographic variables of slope and angle of incidence for use in subsequent mapping activities. Land Cover Classification Land cover classification was performed using object-oriented image processing techniques implemented within the software package eCognition. The procedure involved performing a multi-resolution segmentation of the study area to identify a nested hierarchy of image object primitives: homogeneous groups of pixels that formed the basis of all subsequent processing. Classification was performed using fuzzy rule-based and nearest neighbour analysis. Crown Closure and Species Composition Crown closure and species composition were modeled as continuous variables using regression techniques in the software package SPLUS. Models of species composition were generated with binomial-family generalized linear models with a logit link (Crawley 2002). Crown closure was analyzed using conventional regression models following arcsine transformation. In both cases, a stepwise procedure based on Akaikeâ&#x20AC;&#x2122;s Information Criterion (AIC) was used to select the best-fitting model with the fewest number of predictor variables, following the principle of parsimony, and verified the results through F-tests and analyses of variance. Results and Discussion Land Cover The composite land cover map over the Phase 5 study area is shown in Figure 5. The overall accuracy of the Phase 5 extension was calculated at 81%.
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Figure 5. Landcover map for the Phase 5 study area. Crown Closure and Species Composition Statistical models of crown closure and species composition based on response variables quantified with ocular estimates from low-hovering helicopters produced poor results. For example, the crown closure model from the Foothills region of 44/22 had a coefficient of variation of 0.14. The species composition model had residual/null deviance of 691/947. Consequently, the map production phase was not pursued. Additional ground points acquired in the forthcoming field season will be used to complete this phase of mapping in the fall of 2006. NDVI Phenology Figure 6 shows the 13 NDVI composites tracking vegetation phenology across the 2005 growing season. NDVI is different from the other maps because it is an index - the normalized difference vegetation index - rather than a physical ground attribute. The index values (-1 to +1) are directly related to vegetation amount (-1=low; +1=high). NDVI is
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perhaps the best-known and most widely-used remote sensing index and is linearly related with leaf area index (leaf surface per unit ground area, McDermid 2005).
Figure 6. 16-day NDVI phenology composites for the 2005 growing season, Phase 5 study area.
Literature Cited Crawley, M. J., 2002: Statistical Computing, an Introduction to Data Analysis Using S-Plus. Wiley, West Sussex, England. Crist, E. P. and R. C. Cicone, 1984: A physically based transformation of Thematic Mapper data - The TM Tasseled Cap. IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-22, pp. 256 - 263. McDermid, G. J., 2005: Remote Sensing for Large-Area, Multi-Jurisdictional Habitat Mapping. Unpublished Ph.D. Thesis, Department of Geography, University of Waterloo, 258 p.
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McDermid, G. J., 2006: Radiometric normalization of Landsat imagery for the operational mapping of vegetation and land cover over large areas. International Journal of Remote Sensing, in review. McDermid, G. J., S. E. Franklin, and E. F. LeDrew, 2006a: A flexible, attribute-based framework for mapping land cover and vegetation over large areas. Canadian Journal of Remote Sensing, in review. McDermid, G. J., R. J. Hall, S. E. Franklin, A. Sanchez-Azofeifa, S. Nielsen, G. B. Stenhouse, and E. F. LeDrew, 2006b: Remote sensing and forest inventory for grizzly bear habitat mapping and resource selection analysis. Forestry Chronicle, in review.
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Sensitivity Of High-Spatial-Resolution Satellite Imagery To Forest Disturbance, Regenerating Age, And Site Preparation In The Foothills Model Forest Ame Wunderle, Environmental Remote Sensing Laboratory, Department of Geography, University of Saskatchewan, 9 Campus Drive Saskatoon, SK S7N 5A5. Steven Franklin, Environmental Remote Sensing Laboratory, Department of Geography, University of Saskatchewan, 9 Campus Drive Saskatoon, SK S7N 5A5. Introduction Resource extraction activities in previously remote areas potentially increase landscape fragmentation (Mattson et al., 1996). Although fragmentation is not always considered negative in terms of wildlife habitat (Nielsen et al., 2005), it increases human activity within remote areas (Mattson et al., 1996; Craighead et al., 1995), thus endangering vulnerable wildlife species to their largest predator (Craighead et al., 1995). This is currently a problem in the Central Rockies Ecosystem in west-central Alberta as resource management practices expand further into grizzly bear (Ursus arctos L.) homeland ranges (Nielsen et al., 2005; Wielgus, and Vernier, 2003; Gibeau et al., 2002; Mattson et al., 1996). Not only is it difficult for these bears to find areas where they can be secure (Nielsen et al., 2005; Mattson et al., 1996; Craighead et al., 1995), but they must also rediscover old habitats within the newly fragmented area (Nielsen et al., 2005). Recently, grizzly bears in the Foothills Model Forest near Hinton, Alberta, were found to select clear cut areas of different age ranges as habitat. They also selected or avoided certain cut areas depending on the site preparation process employed, leading Nielsen et al., (2005) to conclude that availability of attractive food sources in a cut block varied with both age and site preparation. The data used in these studies were obtained using a comprehensive Geographic Information System (GIS) of forestry inventory parameters--obtained from the Foothills Model Forest--over an area of approximately 10,000 square kilometres. A GIS can be a useful tool, when regularly updated, however, such substantial area coverage is rarely the case, due to cost and time constraints of ground level field data collection. Additionally, large areas do not necessarily coincide with forest management units of large forest products companies, resulting in remote areas without available information. The grizzly bear range and the area of interest in population viability analysis can be greater than 10,000 square kilometres (McDermid, 2005; Gibeau et al., 2002, Nielsen et al., 2003) rendering any effort to improve a GIS using typical field methods unfeasible. Satellite remote sensing, however, is an alternative for classifying large areas at multiple spatial and temporal resolutions (McDermid, 2005). Methods Imagery Acquisition and Pre-processing SPOT-5 10 m multispectral and 2.5 m panchromatic imagery were acquired on July 21, 2005 over the core study area and were ortho-rectified within 0.49 pixel root mean square
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error (RMSE). To obtain a higher spatial resolution dataset, SPOT-5 panchromatic band was fused with the multispectral channels. This was completed using the Advanced Pansharpening approach developed by Zhang (1999, 2002). This method is derived from a least-squares method to determine the best-fit between the multispectral, panchromatic and the fused images with the dual purpose of incorporating the higher resolution spatial detail and preserving the spectral information. The latter is necessary for classification of biophysical parameters. Figure 7 illustrates the comparison of the multispectral near infrared band 3 and its pan-sharpened counterpart.
a.
b.
Figure 7. Comparison of SPOT-5 original 10m (a) and pan-sharpened 2.5m (b) imagery; R=SWIR, G=red, B=NIR. Texture Calculation The texture analyses were based upon the pan-sharpened near infrared channel following Gong et al. (2003), as it showed the greatest degree of spectral variation for vegetation. First-order measures were used to evaluate the standard deviation of spectral reflectance under each moving window. Second-order grey level co-occurrence matrix (GLCM) textural analyses which measures the pair-wise occurrence of grey values within the moving window (Haralick et al., 1973) were examined. For this study, the statistical measures of homogeneity, entropy, correlation, and contrast were employed based on their sensitivity to forest structural parameters (Franklin, 2001a,b,c; Coburn and Roberts, 2004). Moving window sizes of 5x5, 11x11, and 25x25 were tested with both first- and second-order texture measures. These moving window sizes were chosen based on research with conifer species (Franklin et al., 2001b; Coburn and Roberts, 2004). Classification Techniques and Product Validation eCognition version 4.0 was used for object-oriented analysis and classification of forest disturbance over the SPOT-5 pansharpened image. To create image object primitives, a scale parameter of 50 was visually chosen as a result of the 2.5m pansharpened spatial resolution of the SPOT-5 dataset. The spectral versus shape parameter was set to 0.2:0.8 and smoothness versus compactness was set to 1:9 to emphasize the compact nature and unnatural shape of cut blocks as per Flanders et al. (2003). For a more detailed discussion of 35
the software, the reader is referred to Flanders et al. (2003), and McDermid (2005). Three spectral bands were used for the classification based on the results of the discriminant analyses, the normalized difference moisture index (NDMI), green, and short wave infrared index (SWIR) bands. This method has the advantage over the more popular pixel-based maximum likelihood classifier as it classifies polygons as a whole, and uses several features (such as mean, distance to neighbour, etc.) to create the most accurate â&#x20AC;&#x153;pictureâ&#x20AC;? of the class before classification begins. A standard nearest neighbour feature optimization was run to include the indices that would help separate three classes of stands: disturbed (aged 0-20), older disturbed (aged 21-50), and undisturbed (aged 50+). The resulting features were the minimum distance between each neighbour based on all three spectral values, the mean of all three spectral values, and the maximum difference between them per class. Once completed, a Kappa statistics test (Jensen, 2005) was completed over the entire area based first on initial samples, and secondly on a set of 50 random points throughout the image not used in the analysis. Statistical Analysis Age classes were determined prior to the statistical analysis based on the stated needs of the end-users. Nielsen (2005) had stated that a maximum ten year range per age class would be needed for the data to be applicable to the resource function modeling for grizzly bear habitat analysis. However, five year age ranges to age 20 and ten year from 21-50 were chosen in order to determine how sensitive the classification analysis could be to smaller differences. To calculate the Structural Complexity Index (SCI) to be used for the statistical classification, the first loading of a principal components analysis using the correlation matrix of field level inventory variables per stand was measured (Hansen et al. 2001; Cohen and Spies, 1992). The predictive model (a function of the green reflectance, NDMI, standard deviation 5x5, and correlation texture measure 5x5) for SCI was used and applied to the image. Mean SCI values were calculated over each sampled cutblock and included in the statistical analysis. All age and site preparation classifications were obtained using stepwise linear discriminant analysis similar to the procedures used by Kimes et al. (1996, 1999). This analysis technique is based on canonical correlation analysis and plots each sample point as a function of two parameters highly correlated to the input parameters. All results were cross-validated and both classification accuracies are noted. Results and Discussion Classification of two levels of forest disturbances (recent: 0-20 years; older: 21-50 years) versus old growth (50+ years) was 94.8% using the 352 sampled stands from the GIS database (Figure 8). Post-classification analysis using 50 different random samples from a GIS database of all three classes reached 89.8%. The overall accuracy of the eCognition based map is similar to results presented by Flanders et al. (2003) however, the results of this study classify forest disturbance past 20 years, unlike the previous study which focused on blocks as recent or old-growth. In addition, three spectral bands were used for this classification, the SWIR, green, and the NDMI which further support previous research by Wulder et al. (2004), Franklin et al. (2000), and Wilson and Sader (2002) indicating that the removal of canopy moisture in these areas is the most distinguishable characteristic identifiable using remotely sensed imagery. These results are of importance to the analysis of grizzly bear habitat in the Foothills Model Forest of Alberta as it validates our use of
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pansharpened imagery for forest biophysical classification. In addition, it has resulted in the creation of a consistent forest disturbance layer for areas of high grizzly occurrence. Although water was not used in the classification, this layer will be of great use in yearly change detection models, a very important aspect of the Foothills Model Forest Grizzly Bear Research Program. Results of the linear discriminant analysis over the 44 field sampled stands in the study area represent the separability of five year age classes up to age 20, and ten year age classes from 21 to 50 using the most robust and accurate model. This model was created using the SWIR and green bands, and the correlation texture measure under the 25x25 window with an accuracy of 76.7%. The spectral and textural parameters were chosen using the stepwise
Figure 8. Subset of SPOT-5 image showing original image (left) and classified forest disturbance (right). method not only to ensure high classification accuracy but also to decrease collinearity. When adding the predicted SCI to the model classification accuracy jumps to 82.5%. Pearsonâ&#x20AC;&#x2122;s correlation between the SCI and age class in this study was 0.92 (p=0.01, 2-tailed) showing that the structure and age are inextricably linked. Figure 9 shows the the most accurate model with age class. There was a clear visual trajectory of clusters with age following a horseshoe shape which helped show the clear separation of these classes This result was stronger in the boreal forest than in more complex forested systems examined by Hansen et al. (2001) and Cohen and Spies (1992), which was surprising as the boreal forest in this study area is very simple. This result is partially attributable to the 44 regenerating stands, and the differing structure that they portrayed. These stands were planted in a similar fashion, with similar species (pine or spruce), and followed accordingly similar structural trajectories. Thus it could be expected that similarly aged stands would display a similar 37
structural complexity captured by the SCI. This relationship helps to explain the increase in accuracy once the SCI is added. The results support work completed by Wulder et al. (2004) in that the green and SWIR bands are important to age classification of forest. The only textural measure of importance to age classification was second-order correlation texture under the 25x25 window size. This measure is a complex measure of spatial smoothness, a reflection of local homogeneity versus global variance, i.e. a measure of linear dependency similar to spatial auto-correlation (van der Sandman and Hoekmann, 2005). Conceptually, the correlation texture measure will increase in value with a decrease in surface roughness or variance in local reflectance. In forested stands, this value would be high for debris covered cuts, increase with regeneration until the values are similar to the homogeneous recent cut once again. Thus to differentiate between the old growth stands and the recent cuts, the spectral reflectance of SWIR of the polygon is the determinant parameter. Franklin et al. (2001b) had previously determined homogeneity to be of strongest import to age class determination in the North West Territories, also a measure of spatial variance or smoothness within the window. However, they did not test the spatial autocorrelation or the second-order correlation texture measure which may have improved their model performance.
Figure 9. Results of discriminant analyses for age classification of regenerating boreal forest with 5 year age classes to age 19 and 10 year age classes to age 50. Site preparation class was separated into two classes, mounding versus furrow in 25% of the sampled stands in the field (aged 0-5). Fully excavated sites were unavailable for testing in the study area. Results from the stepwise linear discriminant analysis showed that classification accuracy was 90.9% using the NDMI and homogeneity texture measure under the 5x5 window size. Both of these parameters were highly correlated to one single 38
canonical discriminant function, hence the lack of a corresponding graph depicting the classes for two functions. The NDMI in this model represents the level of debris and litter over the cut block. Cut blocks that have been subjected to a furrow style site preparation will have rows of debris interjected with rows of brown or black dirt, perhaps with small seedlings interspersed. Those subjected to mounding will have large piles of debris either throughout the block or along the sides. Either way, these different features will change the value of the NDMI. As discussed earlier, homogeneity measures â&#x20AC;&#x153;smoothnessâ&#x20AC;? over the set window size. Although the results from this analysis are quite promising, classification was completed over a small sample, i.e. 11 stands (33 plots). This suggests that more research should be undertaken in this area for confirmation of these results. Application to Grizzly Bear Habitat Modeling This research provides valuable habitat information content of high spatial resolution imagery, and develops an appropriate model to determine both age class and the structural complexity of a stand. Although this research was undertaken as a result of grizzly bear habitat requirements outlined by Nielsen (2005), it is clear that the products are applicable to other studies involving wildlife habitat. By providing the products and methods created here to all wildlife studies, it is possible that they may be able to make a clearer link with habitat, its change, and thus strongly affect wildlife conservation policies. To determine the utility of the three map products produced, the age class and SCI layers were compared to the Resource Selection Functions (RSF) created by Nielsen (2005) for the same area. These functions display a low value (on a scale of 1 to 7) where grizzly bear occurrence is unexpected and high where habitat is highly suitable. The RSF values were averaged under each polygon within the 44 field sampled stands. The shape-based segmentation had resulted in several polygons per stand, culminating in 372 sampled polygons overall. Pearsonâ&#x20AC;&#x2122;s correlation (two-tailed) coefficients were calculated to evaluate the relationship between these stands, RSF, and age class. Results between age class and RSF (r = 0.60, p<0.01) were stronger than between the predicted SCI and RSF (r = 0.50, p<.01). This situation indicates that SCI would not be an appropriate surrogate for age as previously hypothesized. Figure 10 illustrates the changing relationship between RSF and age class. Although the graph shows RSF increasing with age, there are some small variations based on food quality, quantity, and access along this trend. For a more detailed analysis of the results and indications of RSF modeling and its link with forest structure, the reader is referred to Nielsen (2005). As shown in Figure 10, the map layers resulting from this thesis are valuable, enforcing the idea that consistent remote detection of regenerating forest age of cut areas is critical in grizzly bear habitat analysis and other wildlife studies. In addition, this thesis offers a prospect for future classification of site preparation activities within areas where GIS is unavailable. The product of this research thus provides a practical alternative to expensive field work in the areas of forest inventory and wildlife habitat analysis.
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Mean RSF value for female grizzly fall
7.00000000
44 stands; 372 sampled polygons
6.00000000
5.00000000
4.00000000
3.00000000
2.00000000
1.00000000
0.00000000 0-5
6-10
11-15
16-20
21-30
31-40
41-50
50+
Age Class
Figure 10. Extracted Resource Selection Functions (1-7) for female grizzly bear in Fall for all field sampled stands versus age class. Sampled polygons are segments within each stand; Standard error of the mean was 0.08. Literature Cited Coburn, C.A. and Roberts, A.C.B. 2004. A multiscale texture analysis procedure for improved forest stand classification. International Journal of Remote Sensing, 25 (20), 4287-4308. Cohen, W. B. and Spies, T. A., 1992. Estimating structural attributes of Douglas-Fir / Western Hemlock forest stands from Landsat and SPOT Imagery. Remote Sensing of Environment, 41, 1-17. Craighead, J.J., Sumner, J.S., and Mitchell, J.A. 1995. The Grizzly Bears of Yellowstone: Their ecology in the yellowstone ecosystem, 1959-1992. Island Press: Washington, D.C. Flanders, D., Hall-Beyer, M., and Pereverzoff, J., 2003. Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Technical note in: Canadian Journal of Remote Sensing, 29 (4), 441-452. Franklin, S. E., Lavigne, M. B., Moskal, L. M., Wulder, M. A., and McCaffrey, T. M., 2001a. Interpretation of forest harvest conditions in New Brunswick using Landsat TM enhanced wetness difference imagery (EWDI). Canadian Journal of Remote Sensing, 27, 118-128.
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Franklin, S. E., Wulder, M. A., and Gerylo, G. R., 2001b. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. International Journal of Remote Sensing, 22 (13), 2627-2632. Franklin, S.E. 2001c. Remote sensing for sustainable forest management. New York: Lewis Publishers. Franklin, S. E., Moskal, L. M., Lavigne, M., and Pugh, K. 2000. Interpretation and classification of partially harvested forest stands in the Fundy Model Forest using multitemporal Landsat TM digital data.. Canadian Journal of Remote Sensing, 26, 318333. Gibeau, M. L., Clevenger, A. P., Herroro, S., and Wierzchowski, J., 2002, Grizzly bear response to human development and activities in the Bow River Watershed, Alberta, Canada. Biological Conservation, 103, 227-236 Gong, P., Mahler, S.A., Biging, G.S., and Newburn, D.A. 2003. Vineyard identification in an oak woodland landscape with airborne digital camera imagery. International Journal of Remote Sensing, 24 (6), 1303-1315. Hansen, M. J., Franklin, S. E., Woudsma, C., and Peterson, M., 2001. Forest structure classification in the North Columbia Mountains using the Landsat TM Tasseled Cap Wetness Component. Canadian Journal of Remote Sensing, 27 (1), 20-32. Haralick, R.M., Shanmugan, K., and Dinstein, I., 1973, Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-3, pp. 610-621. Kimes, D. S., Holben, B. N., Nickeson, J. E., and McKee, W. A., 1996, Extracting forest age in a Pacific Northwest Forest from Thematic Mapper and topographic data. Remote Sensing of Environment, 56, 133-140. Kimes, D.S., Nelson, R.F., Salas, W.A., Skole, D.L. 1999. Mapping secondary tropical forest and forest age from SPOT HRV data. International Journal of Remote Sensing, 20 (18), 3625-3640. Mattson, D.J., Herrero, S., Wright, R.G., and Pease, C.M. 1996. Science and management of Rocky Mountain Grizzly Bears. Conservation Biology, 10 (4), 1013-1025. McDermid, G. 2005. PhD thesis Remote estimation of land cover and LAI over large areas for wildlife habitat applications, Faculty of Environmental Studies, University of Waterloo. Nielsen, S. E. 2005. Habitat ecology, conservation, and projected population viability of grizzly bears (Ursus arctos L.) in west-central Alberta, Canada. PhD Dissertation, University of Alberta, Edmonton, Alberta, Canada. Nielsen, S. E., Boye, M. S., Stenhouse, G. B., and Munro, R. H. M., 2003. Development and testing of phonologically driven grizzly bear habitat models. Ecoscience, 10 (1), 1-10. van der Sandman, J. J. and Hoekman, D. H., 2005. Review of relationships between greytone co-occurrence, semivariance, and autocorrelation based image texture analysis approaches. Canadian Journal of Remote Sensing, 31 (3), 207-213. Wielgus, R. B. and Vernier, P. R., 2003. Grizzly bear selection of managed and unmanaged forests in the Selkirk Mountains. Canadian Journal of Forest Research, 33 (5), 822-829. Wilson, E. H., and Sader, S., 2002, Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80, 385-396.
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Wulder, M.A., Skakun, R. S., Kurz, W. A., and White, J. C., 2004. Estimating time since forest harvest using segmented Landsat ETM+ imagery. Remote Sensing of Environment, 93 179-187 Zhang, Y. 1999. A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20, 2003-2014. Zhang, Y. 2002. A new automatic approach for effectively fusing Landsat 7 as well as IKONOS images. IEEE/IGARSS Conference, Toronto, Canada, June 24-28.
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Multiple Spatial Resolution Image Change Detection for Environmental Management Applications Alysha Pape, Environmental Remote Sensing Laboratory, Department of Geography, University of Saskatchewan, 9 Campus Drive Saskatoon, SK S7N 5A5. Steven Franklin, Environmental Remote Sensing Laboratory, Department of Geography, University of Saskatchewan, 9 Campus Drive Saskatoon, SK S7N 5A5. Editorâ&#x20AC;&#x2122;s Note: This research is being conducted as partial fulfillment toward a Masterâ&#x20AC;&#x2122;s of Science Degree. It must be stressed that these data are preliminary in nature and all findings must be interpreted with caution and are subject to revision based on the ongoing findings over the course of this study.
Introduction Many resource-rich forests across Canada have been subject to change via anthropogenic activities such as mining, forestry, recreation, and oil and gas exploration. The impacts of these activities often cover large areas and may have a negative influence on the natural processes of ecosystems. This in turn, affects the habitat of many different species including the grizzly bear (Ursos arctos L.) that attempt to coexist in these areas. In an attempt to ensure forests are managed sustainably, environmental managers are constantly seeking innovative tools. One example is Satellite Remote Sensing (RS), which has been developed to detect and identify various types of change at a variety of scales across large areas. For this research, a relatively coarse spatial resolution (250m) sensor, Moderate Resolution Imaging Spectroradiometer (MODIS) is considered. This study develops the best method of change detection and change labeling for this sensor and compares the results to those resulting from Landsat TM data (30m spatial resolution). This study will enable the comparison of sensor sensitivity to change and determine the optimal scale at which different types of changes are identified. The methods will then be applied to a larger area encompassing the entire study area of the Foothills Model Forest Grizzly Bear Research Program (FMFGBRP) encompassing more than 100,000 km2. The results of this change detection will then be used to update the multi-scene landcover mapping initiative for the FMFGBRP. This research will enable environmental managers to study the relationships between anthropogenic change and grizzly bear biophysical response using large area maps, further allowing them to make appropriate decisions regarding land use. Methods Study Area and Data Set The resource-rich eastern slopes of the Rocky Mountains have fostered extensive industry activities over the past century, for example, mining, forestry, oil and gas development, camping and commercial outfitting (Stenhouse & Munro, 2003). Landscape change from lush forests to bare soil, roads, well sites and other urban features has occurred as the rate of these activities grow (Figure 11). The alterations occurring in this area provide practical examples of land cover changes that are likely to be visible across other, similar forest types as well. The study location (Figure 12) also includes areas that have not changed during the 5-year time period. Various remotely sensed scenes were collected in order to complete this change detection research outlined in Table 13. 43
Figure 11. 2001 & 2005 Landsat TM & MODIS Imagery showing an area of extensive forestry activity. The cutblocks/non vegetated areas are visible in pink for Landsat TM and black for the MODIS NDVI dataset. 2005 Landsat 5 TM
Figure 12. Location of Study Area near Hinton, Alberta, Canada.
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Table 13. Acquisition period of Landsat and MODIS images Data MODIS
Acquisition Date 2001, 2002, 2003, 2004, 2005
LS Path-Row/ MODIS Graticule 10v03; 10v04; 11v03
Landsat 7 ETM+ Landsat 5 TM
2001 2005
44/23 44/23
Classification Techniques and Product Validation The two change detection techniques – Normalized Difference Vegetation Index (NDVI) (Jensen, 1996) and Enhanced Vegetation Index (EVI) – were used for the MODIS image data sets and the Enhanced Wetness Difference Index (EWDI) (Franklin et al., 2000) was used for the Landsat change detection. This comparison study of methods and scale would determine the most efficient method for detecting change and provide information on those methods best suited for the detection of a certain type of change. There is little research on this topic at present. The techniques are exemplified in Figure 13 and the analysis was executed as follows: Part I 1) Image differencing comparisons of the NDVI and EVI MODIS products were conducted to establish the most useful vegetation index for change detection. 2) Annual change (2000-2001, 2001-2002, 2002-2003, 2003-2004, and 2004-2004) was detected with the MODIS NDVI dataset. Each resultant change layer was compiled into a cumulative change detection layer. 3) Change over a five-year time period (2000-2005) was detected, again with MODIS data using NDVI (∆M5NDVI), and with Landsat TM using the EWDI (∆TMBest) 4) The results from the two methods used for the MODIS change detections were statistically compared. A proportional, stratified random sample of 200 points was collected from the hand-digitized change layer and an error matrix was created to derive the best change detection technique (∆MBest) for use in Part II. Additional statistical tests are currently being performed. Part II 5) Change was labeled for ∆MBest and ∆TMBest using a decision tree method and two classes: 1) cutblock and, 2) well site. 6) An accuracy assessment is currently being performed on each of the products however, preliminary results suggest that it is not possible to accurately classify smaller features like well sites.
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Vectorized polygon layer
Landsat TM orthomosaic
=
Segmentation
Polygon-Based Change
Apply Segments to Pixel-Based Change
Image 1
-
Image 2
=
Pixel-Based Change
Figure 13. Conceptual framework of the change detection methods used within this study. Preliminary Results and Discussion Results prove that it is possible to detect change using MODIS data (Figure 14), the challenge now is to quantify the accuracy or map agreement to ground truth data; a process which is currently underway. Preliminary results are promising and are outlined below. A manual change detection was performed by hand digitizing all changes visible in the 2005 Landsat TM dataset and merging it with a cutblock GIS layer from the Foothills Model Forest database. This reference layer is compiled of 2003 polygons representing change that were included in an error matrix (Lillesand & Kiefer, 2003) and Kappa test (Congalton & Mead, 1983). The error matrix is commonly used in assessing the accuracy of maps and several statistical values can be derived from it. First, the overall accuracy of the classification is calculated by dividing the total number of correctly classified reference points with the total number of reference points. The most successful method was with the Single Image MODIS NDVI change detection, giving a 56% overall accuracy. Secondly, within the error matrix the error of omission (producerâ&#x20AC;&#x2122;s accuracy) and error of commission (userâ&#x20AC;&#x2122;s accuracy) is derived by dividing the total number of correct pixels in a category with its column and row totals respectively. The producer accuracy calculated was 0.99 and 0.46 for no change and change respectively (Table 14). These are significant statistics because it enables the producer to quantify the ability to reproduce the same results. For example, there is a 99% chance that the no change class will be reproduced in future attempts however, only a 46% chance for the change class. The error of commission is typically an indication of map reliability and therefore, a value the user is most interested in. The calculated userâ&#x20AC;&#x2122;s accuracies were 0.3 for no change and 0.995 for change. This indicates that 99% of the time, the polygons included in the change class correctly represents change on the ground. The Kappa analysis is one other useful statistical test of map agreement between reference and classified datasets in categorical mapping (Congalton, 1991). The Khat of the MODIS NDVI change detection is 0.76 showing that there is a 76% map agreement between the two datasets. The final map agreement is still being assessed however there are several issues discovered in this research project that require further attention. 46
Table 14: Results of Cohenâ&#x20AC;&#x2122;s Kappa Analysis ERROR MATRIX NO NO CHANGE SUM
CHANGE 382 3 385
872 746 1618
SUM 1254 749 2003
ACCURACY REPORT PRODUCER USER NO 382/385=0.99 382/1254=0.3 CHANGE 746/1618=0.46 746/749=0.995 (1128 / 2003) = 0.563155267 Overall Accuracy KHAT 0.244
2001 TM
MODIS Change
2005 TM Figure 14. Example of successful MODIS change detection: Polygon. Determining the threshold of change/no change is one of the largest challenges within this study. A threshold is set to identify pixels that have changed, for example by digital number or reflectance value. For this study, the threshold was selected based on two standard deviations from the mean of the differenced images however, this did not prove to be the most effective method. Within the 2001 single-image (Figure 15b) and cumulative (Figure 15e) MODIS change detections, problems associated with threshold values are evident. Tables 15 and 16 and Figure 15 illustrate differences in change detection results between Landsat and MODIS sensors for both pixel-based and polygon-based methods. In addition they compare the cumulative and single image changes that could be perceived by the imagery. Using the reference layer (Figure 15a), it is clear that MODIS detected similar area 47
of change within the study area for the pixel-based single image method (from 2002 to 2005, Figure 15f). The poor results from the single image (2001-2005, Figure 15e) detection may be the result of changes that MODIS made to its sensor and datasets that year. The cumulative methods for both years (i.e. 2001 or 2002 to 2005, Figures 15a, 15b) over predict the changes. This is simply the result of change that occurs throughout the period in question slowly transforming back to the original state, which at the end point is not within the threshold of change. Results were similar with the polygon-based changes, although most values showed a considerable increase in area of predicted change. As can be seen in Figure 15, change that does not completely cover the polygon will still affect its classification as change. For that reason, area will be over estimated more so with this method than with the pixel-based method. However, there is less of difference between the cumulative and single image area of change detected for the 2002-2005 image indicating that this method removes the uncertainty caused by â&#x20AC;&#x153;changingâ&#x20AC;? areas that caused over prediction in Table 15. It is important to also note that the accuracy of changes detected is highly dependent on threshold selection. Further research is required in this area.
a
b
c
d
e
f
Figure 15. Preliminary Results of Change Detection Tests. a. 2001-2005 Landsat Manual Image Interpretation, 31850 Ha of Change; b.2001-2005 Cumulative MODIS NDVI, 63964 Ha of Change; c. 2002-2005 Cumulative MODIS NDVI, 38868 Ha of Change; d. 2001-2005 Single-Image Landsat TM EWDI, 33135 Ha of Change; e. 20012005 Single-Image MODIS NDVI, 21780 Ha of Change; f. 2002-2005 Single-Image MODIS NDVI, 38725 Ha of Change.
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Table 15. Pixel-based change derived using 2 standard deviations from the mean. Sensor
Year
Change Detection Type
Hectares (m)
Difference
Modis 250m
2001-2005
Cumulative
47582
152%
Single Image
28151
90%
Cumulative
34776
111%
Single Image
30569
98% 58%
2002-2005
LANDSAT TM - 30m
2001-2005
SINGLE-IMAGE
18207
LANDSAT TM & GIS
2001-2005
MANUAL IMAGE INTERPRETATION
31324
Table 16. Polygon-based change derived using pixel based change detection with image interpretation Sensor
Year
Change Detection Type
Hectares (m)
Difference
Modis 250m
2001-2005
Cumulative
63964
204%
Single Image
21780
70%
Cumulative
38868
124%
Single Image
38725
124% 106%
2002-2005
LANDSAT TM - 30m
2001-2005
SINGLE-IMAGE
33135
LANDSAT TM & GIS
2001-2005
MANUAL IMAGE INTERPRETATION
31324
These results are, however, preliminary. Full results from these analyses and those relating to the labeling of the changes that were detected will be forthcoming.
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Literature Cited Congalton, R.G., and R.A Mead. 1983, A quantitative Method to test for Consistency and Correctness in Photointerpreation, Photogrammetric Engineering & Remote Sensing, 49(1):69-74 Congalton, R.G.,1991, A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37:35-46. Coppin, P.R., and M.E. Bauer, 1996, Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery. Remote Sensing Reviews, Vol. 13:207-234. Franklin, S. E., E. E. Dickson, D.M. Farr, M. J. Hansen, and L. M. Moskal, 2000, Quantification of landscape change from satellite remote sensing. Forestry Chronicle. 76(6):877-886. Jensen, J.R. 1996, Introductory to Digital Image Processing. A Remote Sensing Perspective. Second Edition. Prentice Hall. New Jersey. Lunetta, R.S., and C.D. Elvidge, 1998, Remote sensing change detection; environmental monitoring methods and applications. Ann Arbor Press. Chelsea, MI, United States. Smith, G. M. and R.M. Fuller, 2001, An integrated approach to land cover classification: an example in the Island of Jersey. International Journal of Remote Sensing, 22(16):31233142. Stenhouse, G. and R. Munro, 2003, Foothills Model Forest Grizzly Bear Research Program 2003 Annual Workplan (Year 4). Unpublished
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Parks Canada Montane Bioregion Landcover Mapping Project Greg McDermid, Foothills Facility for Remote Sensing and GIScience, Department of Geography, University of Calgary, 2500 University Dr. NW Calgary, AB T2N 1N4. David Laskin, Foothills Facility for Remote Sensing and GIScience, Department of Geography, University of Calgary, 2500 University Dr. NW Calgary, AB T2N 1N4. Adam McLane, Foothills Facility for Remote Sensing and GIScience, Department of Geography, University of Calgary, 2500 University Dr. NW Calgary, AB T2N 1N4.
Introduction In 2005, the Foothills Model Forest Grizzly Bear Research Program (FMFGBRP) partnered with Parks Canada to produce remote sensing map products within the five Rocky Mountain national parks: Banff, Jasper, Kootenay, Yoho, and Waterton Lakes. Funding for this arrangement was handled through a research agreement administered by the Foothills Model Forest, and managed operationally as the Parks Canada Montane Bioregions Landcover Mapping Project (PCMBLMP). The purpose of this initiative is to generate a series of vegetation and landcover maps that are (i) consistent to those available on neighbouing public lands and (ii) capable of supporting a variety of resource management objectives. The remote sensing strategy described by McDermid et al. (2006) and adopted by the FMFGBRP was selected for this project. This paper describes activities from the first of this planned two-year initiative. Methods Study Area In keeping with a strategy that would see this initiative merge cleanly with existing FMFGBRP activities, a single contiguous study area encompassing the three primary Rocky Mountain natural subregions – montane, alpine, and subalpine – was established along the crest of the continental divide, terminating in the west along the Rocky Mountain trench. The study area contains the five national parks of interest (Figure 16). Image Acquisition and Mosaic Development A mosaic of the study area was constructed using data from 11 Landsat Thematic Mapper (TM) images (Figure 17). Each image was orthorectified using a DEM from DMTI Spatial, transformed to top-of-atmosphere radiance, and radiometrically normalized using the linear transformation procedure described in McDermid (2006). Orthorectification and mosaicking was completed in PCI Orthoengine. Stratification and Field Sampling The study area was stratified into bioregions based on the natural subregions from Alberta (Achuff, 1992) and biogeoclimatic data from British Columbia. These bioregions were further split into 38 workzones designed to divide the Landsat data into ecologically similar zones with consistent radiometric scales. Workzones falling within national parks were designated as “source” workzones; those falling outside of parks boundaries were designated “destination” zones. A preliminary land cover classification was completed within the
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source workzones and used to anchor a multi-stage sampling strategy stratified by workzone, land cover, and tasseled cap greenness.
Figure 16. Parks Canada Montane Bioregion Landcover Mapping Project study area.
Figure 17. Landsat footprint boundaries (left) and completed orthomosaic (right). 52
Field work was conducted during the summer and early fall of 2005, during which time field crews consisting of personnel from the Foothills Facility for Remote Sensing and GIScience (F^3GISci) and backcountry wardens from Parks Canada visited 972 ground plots (Figure 18, left). At each location, field crews conducted surveys and measurements designed to characterize land cover, crown closure, and tree species composition across an area roughly equivalent to a 30-metre Landsat pixel (Figure 18, right). Hemispherical photography was used to capture permanent records of tree canopy architecture, and processed for measures of gap fraction (crown closure equivalent) with WinsCANOPY software. Species composition proportions were derived from stem counts sampled from BAF-2 prisms. Field calls regarding moisture regime and land cover were made on the basis of pre-established decision rules.
Figure 18. PCMBLMP sample locations (left) and ground plot layout diagram (right). Image Processing and Map Product Development Image processing and map product development procedures following the methods established by McDermid (2005) and McDermid et al. (2006) are currently on-going, moving systematically across work zones from south to north. Land cover classification is being performed using object-oriented image processing techniques implemented within the software package eCognition. Classification is performed using fuzzy decision rules and nearest neighbour analysis of spectral and topographic variables.
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Crown closure and species composition attributes are being modeled as continuous variables using regression techniques within the software package SPLUS. Models of species composition are being generated using binomial-family generalized linear models with a logit link (Crawley, 2002). Crown closure is being analyzed using conventional regression models following arcsine transformation. In both cases, a stepwise procedure based on Akaike’s Information Criterion (AIC) is being employed to select the best-fitting model with the fewest number of predictor variables, following the principle of parsimony, and verifying the results through F-tests and analyses of variance. Statistical models of crown closure and species composition are being constructed for each “source” work zone in the study area, and applied spatially using the raster calculator in ArcMap. Previous experience (McDermid, 2005; McDermid, 2006) has shown that attempts to model continuous variables across Landsat scene boundaries produce undesirable seam lines in the completed map product. To avoid these spatial inconsistencies, the model extension procedure described by McDermid (2006) is being employed. The procedure involves four steps designed to derive new model coefficients for “destination” scenes based on explanatory variables modeled response derived from the overlap portion of adjacent Landsat images (Figure 19).
Figure 19. Model extension strategy used to apply models from “source” work zones to “destination” work zones in order to produce seamless continuous-variable map products. 54
Preliminary Results and Discussion Mapping and analysis for the PCMBLMP is still underway; proceeding in an orderly fashion from south to north. Land cover, crown closure, and species composition products from the work zone covering Waterton Lakes National Park are shown in Figure 20 to illustrate the results emerging from this effort. The accuracy of the land cover map shown is 89%; the coefficient of variation for the crown closure model is 0.72; the residual/null deviance value for the species composition model is 129/938.
Figure 20. Land cover, crown closure, and species composition products for Waterton Lakes National Park. Literature Cited Crawley, M. J., 2002: Statistical Computing, an Introduction to Data Analysis Using S-Plus. Wiley, West Sussex, England. McDermid, G. J., 2005: Remote Sensing for Large-Area, Multi-Jurisdictional Habitat Mapping. Unpublished Ph.D. Thesis, Department of Geography, University of Waterloo, 258 p. McDermid, G. J., 2006: Radiometric normalization of Landsat imagery for the operational mapping of vegetation and land cover over large areas. International Journal of Remote Sensing, in review. McDermid, G. J., S. E. Franklin, and E. F. LeDrew, 2006a: A flexible, attribute-based framework for mapping land cover and vegetation over large areas. Canadian Journal of Remote Sensing, in review. McDermid, G. J., R. J. Hall, S. E. Franklin, A. Sanchez-Azofeifa, S. Nielsen, G. B. Stenhouse, and E. F. LeDrew, 2006b: Remote sensing and forest inventory for grizzly bear habitat mapping and resource selection analysis. Forestry Chronicle, in review.
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MODELING UPDATE Grizzly bear habitat modeling and mapping for the Grande Cache to Waterton region of Albertaâ&#x20AC;&#x2122;s foothills and mountains Scott E. Nielsen, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9 Mark S. Boyce, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9 Introduction Grizzly bear habitat modeling efforts through the Foothills Model Forest previously concentrated on a core 10,000-km2 region centered on Robb, Alberta (e.g., Nielsen 2005; Nielsen et al. 2002; 2003; 2004; 2006). Products (maps) describing habitat use from these efforts have shown to be predictive (Nielsen 2005) and useful for numerous applications including examinations of connectivity through graph theory, assessments of future habitat conditions, and predictions of animal density and carrying capacity. Since the development of the habitat mapping products for the core area, there has been demand for production of similar products to other regions of Alberta, particularly to the south. During 2004 and 2005, efforts have been underway to collar animals from Grande Cache, Alberta to the Montana border in Waterton National Park. Here we report on our preliminary mapping products produced for these foothills and mountainous regions of Alberta. Further modifications to these products should be expected. Methods We developed habitat models describing relative use of grizzly bear habitats in two stages for two regions of Alberta. For our first stage of analysis in early 2005, we modeled habitat use for a region referred to as Phase 3, encompassing a 101,237-km2 region from approximately the Trans Canada Highway in Banff National Park north to Grande Cache and northwest to the Swan Hills. For our second stage of modeling, we assessed habitats for a 20,575-km2 region of southwest Alberta (also referred to as Phase 4) from the Montana border north to the Trans Canada Highway. Resource selection functions (Manly et al. 2002) were used to estimate habitat use and generally follow that found in Nielsen (2005). During these expanded analyses, however, RSF models were produced individually for each landcover category rather than lumped into a single model. Model constants were therefore retained to scale habitat differences. To produce a single habitat map, predictions for each habitat were merged into a single final product. This modification was necessary to account for the broader scale (extent) of the analysis. Interaction terms between landcover type and each linear covariate in the model could have been used instead, but we felt it was easier to understand and follow individual landcover types. Map predictions were binned into five habitat selection categories: (1) strongly avoid to absent, w*(x) <0.1; (2) avoid, w*(x) = 0.1 to 0.75; (3) use = available, w*(x) = 0.75 to 1.25; (4) select, w*(x) = 1.25 to 2.5; and (5) strongly select, w*(x) >2.5, based on assessments of use and availability for model building GPS locations and independent datasets. A further difference between previous models and the expanded regional models was the process for defining and sampling available
56
resources. Given the larger extents of the analysis, which includes areas where bear occurrence was unlikely, multiple scales of habitat selection (Thomas & Taylor 1990) were operating. We chose to sample available resources randomly across the entire extent to incorporate 1st (range distribution), 2nd (home range selection), and 3rd (patch selection) orders of selection (Johnson 1980). This follows a design II protocol whereby resource use is collected for each animal, but availability is measured at the level of the population (Thomas & Taylor 1990, Manly et al. 2002). Finally, we used a random effect on individual animal to account for differences in sample sizes among animals and to estimate a population level response (Gilles et al. 2006). Previous modeling methods described for the area have either ignored differences in sample sizes or used sample weighting. Random effect models preclude the use of sample weighting and therefore GPS collar bias (e.g., Frair et al. 2004) was ignored. Recent technological advances in GPS collars, as well as more regular acquisition of locations, appears to be reducing fix bias and is therefore less of a concern. For the northern analysis, a total of 5,763 GPS radiotelemetry locations were used from 24 female bears to model late hyperphagia habitats for female bears. In the southern region, 8,493 locations from 17 animals were used to estimate seasonal habitat models for both sexes. We report the late hyperphagia model for females in the north (Phase 3) and the three seasonal models for both sexes in the south (Phase 4). Results Phase 3-Northern region habitat map Maps describing habitat use for Phase 3 suggested that the majority of the region (44%) contained poor habitat (strongly avoided to absent habitats), with similar amounts (18%) of avoided and use = availability categories (Figure 21). Selected habitats totaled approximately 11% of the landscape, while strongly selected habitats occupied only about 9% of the total region. Model fit and validation pointed to very low use of the strongly avoided regions and nearly exponentially increasing use of subsequently categorized habitats. Notable problems were encountered when estimating habitat use for herbaceous areas, as the Landsat classification provided was based on land cover, not land use, and therefore did not distinguish between agricultural areas and alpine meadows. We used an elevation interaction in the habitat model in an attempt to distinguish between the two different habitats. In some agricultural areas outside of our assessment zone (validation polygon, Figure 21), it was apparent that extrapolations were poor, likely due to the relatively high elevations of the â&#x20AC;&#x2DC;herbaceousâ&#x20AC;&#x2122; land cover category. This was particularly evident in the Sundre and Cochrane areas east of Banff National Park. Phase 4-Southern region habitat map For Phase 4, Albertaâ&#x20AC;&#x2122;s natural sub-region classification was used to constrain the analysis to areas outside of the mixed grassland, which contained no observable grizzly bear locations. The western boundary of the habitat map (Figure 22) therefore reflects the western boundary of the mixed grassland natural sub-region. Strongly avoided and absent habitats were again common to the region, but most prevalent in the northeast and southeast corresponding to areas of human settlement. In the central portion of the study area, the porcupine hills were predicted to contain high quality (selected) habitats, despite having no observed telemetry locations. Seasonal variation in habitat use was most prevalent during late hyperphagia when bears occurred at lower elevations and appeared to be more diffuse overall. This
57
change in habitat use was likely due to frugivory activities. Hypophagia and early hyperphagia appeared to be rather consistent in nature.
Figure 21. Predicted grizzly bear habitat use for the late hyperphagia season in the Phase 4 region of west-central Alberta. Validation polygon (MCP) represents the area of model development and testing (validation). Areas outside of this region represent extrapolations. Use of this product in these areas should therefore be cautioned.
58
a. hypophagia
b. early hyperphagia
c. late hyperphagia
Figure 22. Predicted grizzly bear habitat use by season for southwestern Alberta (Phase 4 region).
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Discussion Initial predictions of habitat use beyond the original core study area near Hinton show promise. However, it is evident that at these larger scales that the broader distribution of bears in Alberta needs consideration. Some extrapolated predictions of habitat appear to be over-estimated and outside the current range of observed grizzly bears. Existing range maps and white zones, although crude could be used as a first stage analysis to restrict or adjust habitat scores for these areas. An alternative and preferred approach would be the combination of the DNA mark-recapture site abundance with radiotelemetry-based assessments of habitat use to estimate animal density or some index of habitat quality (including accurately mapping areas without bears). We are currently working to update, combine, and validate Phase 3 and Phase 4 regions to further provide an integrated model/map that considers more directly human settlement, range of grizzly bears, scale of selection, and major differences in environmental conditions, such as natural sub-regions and the broad north-south gradient that now exists. Further integration of the DNA work with habitat modeling/mapping is also being explored. We expect this work to be completed in 2006. With these refined products on the near horizon, we suggest that users consider updating their definitions of grizzly bear habitat as more refined products are produced. Literature Cited Gillies, C.S., Hebblewhite, M., Nielsen, S.E., Krawchuk, M.A., Aldridge, C.L., Frair, J.L., Saher, D.J., Stevens, C.E., Jerde, C.L., 2005. Application of random effects to the study of resource selection by animals. Journal of Animal Ecology, In press. Johnson, D.H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65â&#x20AC;&#x201C;71. Manly, B.F.J., McDonald, L.L., Thomas, D.L., McDonald, T.L., Erickson, W.P. 2002. Resource Selection by Animals: Statistical Design and Analysis for Field Studies, 2nd ed. Kluwer Academic Publishers, Dordrecht, the Netherlands. Nielsen, S.E. 2005. Habitat ecology, conservation, and projected population viability of grizzly bears (Ursus arctos L.) in west-central Alberta, Canada. PhD Dissertation, University of Alberta, Edmonton, Alberta, Canada. Nielsen, S.E., Boyce, M.S., and Stenhouse, G.B. 2004. Grizzly bears and forestry I: selection of clearcuts by grizzly bears in west-central Alberta, Canada. Forest Ecology and Management 199, 51â&#x20AC;&#x201C;65. Nielsen, S.E., Boyce, M.S., Stenhouse, G.B., and Munro, R.H.M. 2003. Development and testing of phenologically driven grizzly bear habitat models. Ecoscience 10, 1-10. Nielsen, S.E., Boyce, M.S., Stenhouse, G.B., and Munro, R.H.M. 2002. Modeling grizzly bear habitats in the Yellowhead Ecosystem of Alberta: Taking autocorrelation seriously. Ursus 13, 45-56. Thomas, D.L., Taylor, E.J. 1990. Study designs and tests for comparing resource use and availability. Journal of Wildlife Management 46, 629â&#x20AC;&#x201C;635.
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Dynamic grizzly bear habitat maps: Spatial-temporal predictions of food resources for a generalist species Scott E. Nielsen, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Mark S. Boyce1, Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9. Editorâ&#x20AC;&#x2122;s Note: This research is work in progress. It must be stressed that these data are preliminary in nature and all findings must be interpreted with caution and are subject to revision based on the ongoing findings over the course of this study.
Introduction Grizzly bears have largely been thought of as habitat generalists, being omnivorous and wide ranging in distribution throughout the northern hemisphere. Although being extraordinarily adaptable in numerous environments from the Arctic to the Gobi desert of China, their low levels of fecundity, late maturity, and low density make them sensitive to population decline. Currently, the Alberta population appears to be threatened by high mortality, making habitat mapping a conservation and management goal. Habitat maps to date have generally been radiotelemetry-based assessments of habitat use. Although such data is useful for informing habitat models, they have not fully considered the importance of activity (bedding versus foraging) or fine scale temporal â&#x20AC;&#x2DC;switchesâ&#x20AC;&#x2122; in resources. As an alternative to radiotelemetry modeling of grizzly bear habitat, we explore here bimonthly food-based grizzly bear habitat and resource maps for the west-central Alberta population. Methods During the summers of 2001 and 2002, we visited 642 stratified random habitat locations (296 in un-harvested forests, 247 in clearcuts of various ages, and 99 open sites) within the core study area near Hinton, Alberta in order to evaluate the distribution of grizzly bear food resources. A full description of field methods can be found in Nielsen et al. (2004). Using random locations, we modeled the probability of occurrence in each major habitat for each of 11 principal grizzly bear food items identified in a diet of female grizzly bears by Munro et al. (2006). Independent environmental variables used to predict occurrence of food items included sub-habitat type (e.g., for forests: deciduous forest, mixed forest, closed conifer forest, etc.), climate (growing degree days, frost free period, mean annual temperature, mean annual precipitation, etc.), terrain (compound topographic index, topographic position, and solar radiation), and forest stand characteristics (age, canopy, leaf area index, and distance to edge). Univariate analyses and user-defined forward model building of non-correlated factors. For each final food model, sensitivity and specificity values were predicted and used to determine the optimal cutoff (where the two curves intersected) for predicting presence of food items. The result was 11 binary maps (1-present, 0-absent). We weighted each binary map for each of 10 bi-monthly periods starting on 1 May and ending on 31 September based on reported diet volumes in Munro et al. (2006). Weights reflected diet volumes for the specified period within either the mountain or foothills environment. 61
Natural region boundaries were used to specify whether the study pixel was in a mountainous or foothill environment. We summed individual food-habitat â&#x20AC;&#x2DC;scoresâ&#x20AC;&#x2122; for each bi-monthly period to estimate a food-based habitat value. As well as having seasonal maps, a final multi-seasonal food-habitat map was estimated as the sum of all bi-monthly maps. As all random and use diet locations were sampled within about 100 km of Robb, Alberta, areas outside of the core study area represent extrapolations. Although predictions are made outside of the original region, interpretation of results in these areas should be cautioned. Results Climate, terrain, habitat, and stand characteristics were all important predictors of food items. Receiver operating characteristic (ROC) area under curve estimates and Hosmer and Lemeshow goodness of fit statistics (Ä&#x2C6;) suggested that food models fit the data well and had high predictive accuracy (for further information on food models contact S. Nielsen). Spatial distribution of food resources varied substantially between the mountains and foothills, suggesting flexibility in resource and ultimately, habitat use depending on availability of foods. For instance, ant resources were most prevalent in the foothills (Figure 23), while sweet vetch (Hedysarum spp.) was most common in the mountains (Figure 24). Even within a particular bi-monthly period, spatial disparities in food resource distribution was evident. During late August and early September, fruit from both Shepherdia canadensis and Vaccinium spp. were used (Munro et al. 2006), but largely missing from higher elevation sites within Jasper National Park (Figure 25). Animals residing in high elevation sites instead appeared to rely on autumn digging of sweet vetch (Figure 24). Examination of the multi-seasonal food-habitat map suggested that the richest sources of food were found mostly within mountain valleys (Figure 26). Some of these areas, including the Athabasca Valley in Jasper National Park, lack sightings or the frequent use by radiocollared animals. This suggests that grizzly bears are either making trade-offs between food resources and security (human disturbance) or historic displacement of animals has left a ghost-of-history-past signal.
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Figure 23. Predicted distribution of ants (green), a mid-summer food for grizzly bears in the foothills. Area of enlargement represents the BrulĂŠ Lake and Gregg River areas where sites with residual coarse woody debris, low canopy, and dry southwest slopes or valley winds provides excellent ant habitat.
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Figure 24. Predicted distribution of sweet vetch (Hedysarum spp.), a critical food resource excavated (the root/tubers) by grizzly bears in the spring and autumn. Sweet vetch distribution is especially prevalent in alpine areas of the mountains, but also in river valleys and small-disturbed hydric sites in the foothills.
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Figure 25. Predicted distribution of fruiting species Shepherdia canadensis, Vaccinium membranaceum, and Vaccinium vitis-idaea in various shades of green (light brown green-1 spp. present, medium green- 2 spp. overlap, dark green- 3 spp. overlap). The former two species are especially sought by grizzly bears from about 15 July through September, while the latter tends to be foraged late in the fall or even on over wintered berries the following spring. Although distribution of species with potential fruit is rather widespread, large masts of fruit are most typical of open forest stands (<50% canopy) and forest edges (within ~15m of a forest edge).
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Figure 26. Distribution of food values (0 to 1,000) from 1 May to 31 September bimonthly periods (sum of 10 maps each ranging from 0% to 100% of potential diet). Enlarged area centered on Maligne Lake in Jasper National Park shown.
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Discussion Spatial predictions of critical grizzly bear foods and subsequent food-based habitat maps show promise as an alternative to radiotelemetry-based habitat mapping. Some advantages of food-based models are: (1) maps are more likely to directly link to resources that influence reproduction and animal density; (2) animal activities, such as bedding and movement, are not emphasized as critical habitat like they are in RSF designs; (3) seasons are defined a priori based on diet; (4) temporal detail is retained and depicted at bi-monthly periods, which are likely to be necessary for showing phenological changes within an area and between the mountains and foothills; (5) cross-sectional time series analyses can be used on temporally varying habitat conditions resulting in a single model with more power (versus multiple seasonal models each with a set of radiotelemetry data and predictor variables); and (6) the ability to identify potential (i.e., assuming no human disturbance) versus realized habitat conditions. For the last point, trade-offs between human disturbance and food resources can be examined and used to infer current or future cumulative human impacts on grizzly bear distribution and abundance. For instance, it was evident that the Athabasca Valley in eastern Jasper National Park contained very high food-habitat conditions (Figure 26). Distributions of grizzly bears, however, were much restricted to the area suggesting displacement of historic habitat, consistent with records and observations. Identifying thresholds where animals less likely to access high-quality habitats due to human disturbance or where survival is reduced would be greatly beneficial for grizzly bear management. Currently, we are validating models, assessing trade-off situations, and using for predictions of home range size, animal density, health, and population demography. Literature Cited Munro, R.H.M., Nielsen, S.E., Price, M.H., Stenhouse, G.B., and Boyce, M.S. 2006. Seasonal and diel dynamics of diet and activity patterns in grizzly bears in Alberta. Journal of Mammalogy, In review. Nielsen, S.E., Munro, R.H.M., Bainbridge, E.L., Stenhouse, G.B., Boyce, M.S. 2004b. Grizzly bears and forestry II: distribution of grizzly bear foods in clearcuts of westcentral Alberta, Canada. Forest Ecology and Management, 199:67â&#x20AC;&#x201C;82.
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Graph theory generated corridors and identified RSF-based patches important for maintaining connectivity (100k results) Barbara L. Schwab, Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, Ontario, N2L 3C5, Canada, email: blschwab@shaw.ca Editorâ&#x20AC;&#x2122;s Note: This research is being conducted as partial fulfillment toward a PhD. It must be stressed that these data are preliminary in nature and all findings must be interpreted with caution and are subject to revision based on the ongoing findings over the course of this study.
Introduction Maintaining connections for movement across fragmented landscapes is important for the long-term conservation of grizzly bear populations. As such, it is important to address movement within and across habitats as grizzly bears utilize the entire landscape and respond to gradients of habitat quality. Here, the surrounding spatial environment will facilitate or impede movement between resource patches and therefore is considered a vital modeling component. The graph-theoretic model described here identifies resource selection function (RSF) preferred movement corridors between habitat patches across multiple spatial scales (home range to landscape levels) (Schwab 2003). This approach further provides a platform for measuring and comparing connectivity indices across heterogeneous landscapes characterized by varying levels of human development. Methods Study Area and Graph Creation The study area encompassed approximately 62,000 km2 of mountainous and foothills habitats situated along the eastern slopes of the Rocky Mountains (Figure 27a). To examine potential differences between regions, graph theory analyses were completed for 20 watershed units: 8 mountain (approximately 23,000 km2) and 12 foothills (approximately 39,000 km2). Mountain landscapes were selected to represent environments with limited human disturbance while foothill landscapes represent environments modified by various forms of human activity. Major data inputs included a RSF grizzly bear habitat model defining patches (basis for nodes), GIS grid-based cost surfaces (basis for least-cost path corridor creation), and GPS data for habitat model development and validation. See previous reports for detailed descriptions on graph theory (GT) model development and implementation (e.g. Schwab 2004), while additional information regarding RSF-based habitat models can be found in Nielsen (2002). Patches were chosen based on pixels where the relative probability of adult female grizzly bear occurrence exceeded a threshold of +1.5 standard deviations. The centroids of each patch were used for node creation with specific patch attributes recorded for later use in graph analyses. Patches smaller than 5.0 ha were not selected as nodes but were maintained in graph analyses as suitable low-cost habitat within the cost surface and implicitly included in edge creation as stepping-stones (Bunn et al. 2000).
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We used a habitat-based cost surface to define â&#x20AC;&#x2DC;edgesâ&#x20AC;&#x2122; or connections between patches which formed the underlying landscape graph structure (Schwab 2003). The original RSF habitat model, which was scaled between 1 and 10 to represent low to high ranking in relative occurrence, was inversed to represent resistance to movement across the landscape. More specifically, low resistance (low movement cost) patches represented attractive bear habitats, while high resistance (high movement cost) patches represented low ranked or nonattractive habitats.
Figure 27. a) Expanded study region depicting mountain (dark grey) and foothill (white) landscapes used in analysis with current imagery extent (DEM), and b) basic graph structure (nodes) with RSF-based cost surface used to generate LCP corridors. We further re-sampled the cost surface to 500 m and least-cost path (LCP) connections or edges were generated to represent all possible connections between identified nodes or patches. Each LCP edge approximated the actual landscape distance traversed by a grizzly bear as it moved from one patch to the next in a heterogeneous landscape (Boone and Hunter 1996; Bunn et al. 2000). Figure 27b demonstrates the resulting patch (node) layer and RSF-based cost surface which provided the basis for LCP generation. Edges within the graph structure were further modified by a functional distance D. We explored the effect of distance on maintaining connections between patches or nodes. High distance thresholds allowed for all patches across the landscape to be connected. Conversely, low distance thresholds limited the number of connections existing between identified patches. Resulting graph structures were analyzed to quantify the subsequent change to connectivity resulting from changes in potential travel distance (see Figures 28a and 2b for examples). 69
Detailed Graph Analyses Graph habitat and edge analyses were completed using FORTRAN modules independent of GIS to evaluate the importance of individual elements (edges and nodes) for the entire graph structure (Bunn et al. 2000; Urban and Keitt 2001). Edge importance was evaluated using a technique termed edge thresholding (Bunn et al. 2000). Edge thresholding is the iterative removal of LCP edges based on movement or potential travel distance. After each iteration, the resulting graph structures were reevaluated to assess responding levels of connectivity and identify where graphs begin to disconnect or fragment into subgraphs. Habitat importance was identified through node removal where the importance of each habitat patch was assessed to the overall graph structure (Bunn et al. 2000). As nodes were removed, all edges incident to each node were also removed (Bunn et al. 2000). The overall area-weighted dispersal flux (F) calculated the relative contribution of individual nodes to total landscape connectivity where si is the size (total area) of node i and pij the dispersal probability for nodes i and j. To determine F for each habitat patch, we calculated F for the entire landscape before and after node removal. As such, F indicates the strength or contribution of the removed habitat patch to graph or landscape structure. Results Analysis was completed for 20 watershed units (8 mountain and 12 foothill) to assess connectivity. The mountain GT structures had a total patch area (high quality RSF habitat) of 420,180 ha with a mean between patch distance of 42.37 km. The foothill GT structures had a total patch area of 351,227 ha with a mean between patch distance of 31.73 km. Overall, foothill environments had less available habitat (smaller patches) with smaller movement distances between available patches.
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Figure 28. Final GT identified RSF-based habitat patches important to maintaining overall connectivity for a) minimum spanning tree connections and b) area-weighted patch connections. Node removals resulted in different spatial patterns across mountain and foothill landscapes for minimum spanning tree (MST) patch importance and area-weighted patch dispersal flux (F). Results for MST node removals indicate smaller, high-quality habitat patches evenly distributed throughout both landscapes to be important for maintaining the â&#x20AC;&#x2DC;backboneâ&#x20AC;&#x2122; of landscape connectivity. The removal of these patches will affect connectivity by increasing the presence of subgraphs. Currently, these patches are important stepping stones for movement within and across the landscape (Figure 28a). Iterative node removal for areaweighted patch dispersal highlighted a linear portion of high-quality habitat patches integral to maintaining overall connectivity in foothill environments (Figure 28b). These patches are larger, contiguous habitat patches located along the transition zone between foothill and mountain environments and represent habitats needed to maintain populations outside national parks.
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Figure 29. Graphs generated for 100k region showing variation to GT edge connections defined by a) daily movement distance threshold (4942 m) and b) 95th percentile distance threshold (6247 m). Mean graph sinuosity or topological complexity for the mountain and foothill GT landscapes were 1.32 and 1.25, respectively. Results for mountain GT structures (1.36 e.g.) indicate higher topological complexity or sinuosity associated with movement corridors which traverse higher elevation landscapes. A value of 1.0 would indicate a straight-line connection. Straighter-line movement paths are more likely to be found in foothill landscapes where bears are using human development features to travel (e.g. utility corridors, seismic lines and road) and are not limited by topography. Changes in functional edge distance resulted in large changes to landscape structures. Graph edges were defined by daily movement rate (Figure 29a) and 95th percentile distance (Figure 29b) thresholds. As distance thresholds decreased, the graph structure appeared less connected with an appearance of sub graphs occurring in the northeast. It is apparent from visual interpretation of the resulting graph structures above that fragmentation occurred first in the foothills. This coincides with increased human disturbance features such as road structures and decreased numbers of large, contiguous habitat patches. Edge and node removals resulted in connected graphs occurring for 5 out of 8 (63 %) mountain watershed units and for 3 out of 12 (25 %) foothill watershed units. Foothill landscapes demonstrated the highest levels of fragmentation or decreased connectivity with 11 and 14 graph components (number of graph break-downs or subgraphs) occurring across both the daily and 95th percentile distance thresholds. Foothill landscapes also were found to have lower corresponding total graph diameters. Edge corridor results were further assessed 72
to understand the relative spatial configuration of movement patterns (edges) ranked by importance and overall level of connectivity. We created a grid-based version of GT corridor importance for maintaining landscape connectivity using the kernel density estimation (KDE) function in ArcGIS (Figure 30). All resulting high probability corridors were weighted by probability for movement as well as total number of paths.
Figure 30. Final GT generated corridors important to maintaining overall connectivity (raster format) where dark represents high importance combined with higher path quantities and light represents less importance and lower path quantities available for travel. Discussion Model developments for wildlife studies are increasingly combining the functional capabilities of remote sensing, GIS and GPS (Anderson and Danielson 1997; Roberts et al. 2000). The graph-theoretic approach proposed here takes advantage of the power of combining spatial analysis abilities of GIS and GPS grizzly bear movement data allowing for the identification of spatial movement patterns in relation to habitat RSF models. Assessing grizzly bear movement and habitat use through GIS-based methods has the potential to assist resource managers with land-use decisions related to the conservation of grizzly bears (Dugas and Stenhouse 1999). Iterative removal of nodes demonstrated the affect of losing habitat patches to both spatial dispersal patterns and resulting connectivity rates. As habitat patches were removed from the landscape a bearâ&#x20AC;&#x2122;s ability to traverse the landscape was shown to decrease. For grizzly bears, such node removal techniques allow land use managers to envision the quantity of habitat
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loss acceptable to grizzly bear movements based on graph structure. Furthermore, the use of empirical habitat-based nodes representing high-quality habitat patches or food resources specific to grizzly bears (Nielsen et al. 2002) strengthens the utility of our modeling results. We recommend that high-quality habitat patches be maintained to supply both resources to bears and safe movement, while medium and low-quality classed patches are reserved to provide stepping-stones (Figures29a and 29b). We demonstrated here that graph-theory provides a useful tool for examining habitat fragmentation and connectivity, a critical wildlife management and landscape ecology topic. Literature Cited Anderson, G. S., and B. J. Danielson. 1997. The effects of landscape composition and physiognomy on metapopulation size: the role of corridors. Landscape Ecology 12:261-271. Boone, R. B., and M. L. Hunter. 1996. Using diffusion models to simulate the effects of land use on grizzly bear dispersal in the Rocky Mountains. Landscape Ecology 11:51-64. Bunn, A. G., D. L. Urban, and T. H. Keitt. 2000. Landscape connectivity: A conservation application of graph theory. Journal of Environmental Management 59:265-278. Dugas, J., and G. B. Stenhouse. 1999. Grizzly bear management: validating existing cumulative effects models. Pages 157-160 in Proceedings of Thirteenth Annual Conferences on Geographic Information Systems: 157-160. Roberts, S. A., G. B. Hall, and P. H. Calamai. 2000. Analysing forest fragmentation using spatial autocorrelation, graphs and GIS. International Journal of Geographical Information Science 14:185-204. Schwab, B.L. 2004. Graph Theoretic Methods for Examining Landscape Connectivity and Spatial Movement Patterns: An Update, in Stenhouse, G.B., Munro R.M, and K. Graham (eds). Foothills Model Forest Grizzly Bear Research Program 2003 Annual Report. Hinton, Alberta, 87 pp. Schwab, B. L. 2003. Graph theoretic methods for examining landscape connectivity and spatial movements patterns: application to the FMF Grizzly Bear Research Project. Masters of Science Thesis, University of Calgary, Calgary. Nielsen, S. E., M. S. Boyce, G. B. Stenhouse, and R. H. M. Munro. 2002. Modeling grizzly bear habitats in the Yellowhead Ecosystem of Alberta: taking autocorrelation seriously. Ursus 13:45-56. Urban, D., and T. Keitt. 2001. Landscape connectivity: a graph-theoretic perspective. Ecology 82:1205-1218.
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GIS APPLICATIONS Jerome Cranston, Foothills Model Forest, Hinton Alberta T7V 1X6 Introduction The GIS component of the GBRP can be divided into 5 broad categories: Mapping Maps are the primary means of communicating spatial information. In 2005 maps were created for a variety of purposes: • Notifying affected parties of the location and timing of baited DNA snare sites; • Planning maps for capture/collaring program; • Maps for DNA census project planning, for field crews, and for reports; • Maps for presentations Spatial Data Management • Collar data processing (remote uploads and direct downloads); importing new GPS data into existing GIS datasets. • Organizing and updating base GIS datasets, including the migration of existing datasets into geodatabase format; • Metadata documentation; • Ordering new datasets as study areas expanded north of Highway 16; • Extracting subdatasets for program collaborators; • The creation of new datasets for special purposes, including: A dated road layer for the core study area, created by manual classification using imagery of various dates, and dated wellsites and cutblock vector layers, used for road crossing analyses; Subdivision of the Phase 3 and Phase 4 areas (140,000 sq km in total) into 200 watershed basins, chosen to correspond to the average size of an adult female grizzly bear home range (~700 sq km). These will become the standard unit of landscape-level GB habitat planning and analysis. A raster-based forest age layer for Phase 3 and Phase 4, compiled from provincial and industry AVI, fire boundaries, and remote sensing layers; used as an input to the RSF models. Analysis Various analyses were performed for Luscar, Rangeland Consulting, Elk Valley Coal (Cheviot), ASRD (R11 and E8 Forest Management Units), and various FMA-holders, focused on summarizing habitat quality for specified areas. GIS was a core component of a road crossing analysis presented at the International Bear Association Conference in Riva del Garda, Italy in September 2005. Final results from this analysis will be published in a scientific journal in 2006.
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The GIS analyst also carried out investigations into such questions as: Is grizzly bear health related to position of home ranges? Do grizzly bears use regenerating forests during second-pass harvesting the way mature forests are used during first-pass harvesting? Can habitat use be distinguished from travel between habitats based on GPS location data? Tool and Model Development Python scripts were written to regenerate the RSF and mortality risk models for a userspecified area, with the option of including proposed developments such as roads and cutblocks. This will allow resource planners to predict the effect of industrial development on grizzly bear habitat quality and explore options for reducing impact. This new tool was first demonstrated at the ASRD (Fish and Wildlife) workshop in October. Communications and Extension The GIS analyst has taken on the role of communicating research results to both program partners and the general public. In 2005 the GIS analyst and Program Leader made 19 presentations to program partners throughout Alberta. In addition, meetings were held with an ASRD committee struck in 2004 with the purpose of piloting the use of new models and tools in 3 forest districts; Hinton, R11 (Rocky Mountain House), and C5 (Cochrane).
Other outreach tasks included writing an executive summary for the RSF models; serving as the Grizzly Bear Research Program representative on the Natural Disturbance Highway 40 Project Planning committee; and compiling program deliverables, collecting data-sharing agreements, and tracking the distribution of products.
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TRAINING AND TECHNOLOGY TRANSFER Gordon Stenhouse, Foothills Model Forest, Hinton, Alberta. T7V 1X6 Jerome Cranston, Foothills Model Forest, Hinton Alberta T7V 1X6 In 2005 we have put significant effort into two areas related to the actual use and implementation of our research products (maps and models). These efforts include: 1. Development of GIS applications to enable the evaluation of different land use planning scenarios as these relate to change in RSF values, mortality risk and safe harbour values. These GIS applications are designed to run on ArcGIS 9. 2. Working with program partners to increase their understanding of our research findings and explain how to utilize the products we have delivered to them. In addition to these presentations and training sessions we have also been working with SRD Forest Planning Division on a pilot program developing criteria and approaches to assist department staff in both understanding and utilizing the research products. We are now inviting comment and feedback from our program partners on these proposed approaches to using these products. In 2006 we will develop a formal 2-day training course that will give end users of the models a better understanding of the key elements that contribute to their creation, and to then utilize our GIS applications to incorporate these models into land use planning activities. Through these ongoing efforts the research team is very pleased that our program partners are now attempting to use the research products in their planning efforts throughout grizzly bear range in Alberta. Over the course of the past year we have made a total of 22 presentations and training sessions for various program partners (Table 17)
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Table 17. List of Presentations to Program Partners #
Date
Location1
Presenters
Audience
1
13 Dec. 2004
ASRD, Calgary
Gordon Stenhouse, Jerome Cranston
11 (SRD Staff)
2
14 Dec. 2004
Gordon Stenhouse, Jerome Cranston
20 (SRD Staff)
3
15 Dec. 2004
ASRD, Rocky Mountain House ASRD, Edmonton
Gordon Stenhouse, Jerome Cranston
11 (SRD staff)
4
17 Jan. 2005
CAPP, Calgary
~40 (Oil and Gas Industry reps)
5
19 Jan. 2005
AFPA, Edmonton
6
17 Mar. 2005
ASRD Edmonton
Gordon Stenhouse (program overview) Greg McDermid (Remote-sensing products) Scott Nielsen (RSF and risk models) Jerome Cranston (GIS applications) Gordon Stenhouse (program overview) Greg McDermid (Remote sensing products) Scott Nielsen (RSF and risk models) Jerome Cranston (GIS applications) Gordon Stenhouse, Jerome Cranston
7
23 Mar. 2005
Jasper National Park
15 (JNP staff)
8
8 Apr 2005
ASRD, Hinton
Gordon Stenhouse (program overview) Greg McDermid (Remote sensing products) Scott Nielsen (RSF and risk models) Jerome Cranston (GIS applications) Jerome Cranston
9
22 April 2005
Jerome Cranston
12 (FMA GIS people)
10 27 April 2005
GIS User Group, BRL Whitecourt ASRD, Whitecourt
Jerome Cranston
12 (SRD staff)
11 19-20 Oct. 2005
ASRD: Fish & Wildlife, Edmonton
Gordon Stenhouse (program overview) Greg McDermid (Remote sensing products) Scott Nielsen (RSF and risk models)
20 (Fish and Wildlife staff)
~40 (forestry industry reps)
pilot team meeting
4 (SRD staff)
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Location1
Presenters
Audience
12 26 Oct. 2005
ASRD, Edmonton
Jerome Cranston (GIS applications) Marc Cattet (capture and handling) John Boulanger (DNA census) Barb Schwab (Graph Theory) Dave Hobson (relocated bears) Gordon Stenhouse, Jerome Cranston
5 (SRD staff)
13 27 Oct. 2005
Jasper National Park
Gordon Stenhouse, Jerome Cranston
9 (JNP staff)
14 2 Nov. 2005
West Fraser, Hinton
Jerome Cranston
8 (WF staff)
15 16 Nov. 2005
Canfor, Grande Prairie
Gordon Stenhouse, Jerome Cranston
4 (Canfor staff)
16 28 Nov. 2005
ACA, Rocky Mountain House
Gordon Stenhouse, Jerome Cranston
1
17 29 Nov. 2005
Encana, Calgary
Gordon Stenhouse, Jerome Cranston
2
18 30 Nov. 2005
EUB, Calgary
Gordon Stenhouse, Jerome Cranston
8
19 30 Nov. 2005
Shell/Husky, Calgary
Gordon Stenhouse, Jerome Cranston
11
20 1 Dec. 2005
PetroCanada, Calgary
Gordon Stenhouse, Jerome Cranston
17
21 1 Dec. 2005
ASRD, Calgary
Gordon Stenhouse, Jerome Cranston
7
22 6 Dec. 2005
ANC, Whitecourt
Jerome Cranston
1
#
Date
1
ACA=Alberta Conservation Association, AFPA=Alberta Forest Products Association, ANC=Alberta Newsprint Company, ASRD=Alberta Sustainable Resource Development, BRL=Blue Ridge Lumber, CAPP=Canadian Association of Petroleum Producers, EUB=Energy and Utilities Board
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HEALTH Understanding Grizzly Bear Health in the Context of Changing Landscapes Marc Cattet, Canadian Cooperative Wildlife Health Centre, University of Saskatchewan, Saskatoon, SK. Ruth Carlson, Toxicology Centre, University of Saskatchewan, Saskatoon, SK. Jason Hamilton, Department of Biology, University of Waterloo, Waterloo, ON. David Janz, Department of Veterinary Biomedical Sciences and the Toxicology Centre, University of Saskatchewan, Saskatoon, SK. Matt Vijayan, Department of Biology, University of Waterloo, Waterloo, ON. John Boulanger, Integrated Ecological Research, Nelson, BC. Editor’s Note: This research will begin the spring of 2006 and is being pursued as part of an Alberta Innovation and Science Project grant. This work will build upon previous research conducted by the Foothills Model Forest Grizzly Bear Research Program. For more information on prior work see Stenhouse, G.B. and K.Graham. (Eds.). 2005. Foothills Model Forest Grizzly Bear Research Program 1999-2003 Final Report. 289 pp.,
Introduction In general, current understanding of how landscape change affects wildlife populations is poor; much of the existing research is in large part correlative and speculative. The detrimental effects of human activities on grizzly bears are well recognized and, in general, the species is believed to show a lack of resilience to anthropogenic disturbance (Weaver et al. 1996). Recent evidence suggests such adverse effects are already occurring in areas of the Canadian Rocky Mountains (Benn and Herrero 1992, McLellan et al. 1999, Nielsen et al. 2004b). Ongoing and increasing human activities in this region raise serious questions about the long-term conservation of grizzly bears. Determination of the persistence of grizzly bears is impeded by our general lack of understanding of relationships between grizzly bear health and landscape structure and change. The status of grizzly bear populations in Alberta has been assessed largely by estimates of abundance and determination of rates of survival, mortality, and reproduction (Wielgus and Bunnell 1994, Garshelis et al. 2005). This wildlife management approach lacks precision and provides limited insight into the health of the bear population. Moreover, determination of classical population-level parameters in bears (abundance, mortality and reproductive rates) requires long-term data collection, an approach that is too slow, costly, and insensitive to provide early warning of the potential impact of changing landscapes. Although some forms of landscape change have been shown to be favorable to grizzly bears (Nielsen et al. 2004a, 2004c), our working hypothesis is that landscape change can negatively affect grizzly bear populations, largely as a consequence of long-term physiological stress in individual bears. In other words, long-term physiological stress is the underlying mechanism by which landscape change adversely affects bear health (Fig. 31). The physiological stress response is an evolutionarily conserved process in vertebrate animals. The short-term stress response is a beneficial response to immediate stressors such as predators (the classic “fight or flight” response). However, the physiological stress response to long-term stressors can result in
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negative effects on health, including decreased immune function, reproduction, and growth (Kültz 2005). Our research is based on the premise that, if the health of grizzly bears is affected adversely by human activity and landscape change, it will manifest first as long-term physiological stress in individual animals before effects (e.g., impaired reproduction, diminished growth, reduced survival) occur at the population level. Thus, sensitive and reliable measures of stress and health in individual bears are urgently needed as part of a working model to forecast the potential effects of landscape change (Clark et al. 2001). Consequences exist for both bears and humans when interactions occur as a result of stress.
Figure 31. Human activities change the structure of the landscape, and may be perceived as long-term stressors by grizzly bears. A prolonged physiological stress response can adversely affect measures of wildlife health. The research objectives of the wildlife health unit of the Foothills Model Forest Grizzly Bear Research Program (FMFGBRP) are as follows: • To develop and validate long-term stress biomarkers in grizzly bears. • To determine relationships between long-term physiological stress and other measures of health (longevity, growth, reproduction, immunity, and activity) in grizzly bears. • To establish linkages between the health profiles of individual grizzly bears and the landscape structure and change within their home ranges along a gradient of human use. Methods The development of long-term stress biomarkers is occurring along two complementary paths. One path is the development of blood serum-based indicators of long-term stress. For this, we are developing and validating an assay for cortisol binding globulin (CBG), a protein that binds and transports the stress hormone cortisol in the blood. To purify CBG, we first ran grizzly bear sera through a Sepharose-cortisol affinity column to remove the bulk of the serum proteins by preferentially binding CBG and other proteins that bind cortisol, and then ran the sera through 81
fast performance liquid chromatography, which left CBG as the sole protein in the purified homogenate. We verified the identity of the protein through a sodium dodecyl sulfatepolyacrylamide gel (SDS-PAGE) and Western Blotting using a commercially available mammalian CBG antibody. We injected the purified CBG into two rabbits with three booster injections given at three-week intervals, in order to allow the rabbits to raise an antibody against bear CBG. We collected blood samples from the rabbits before each inoculation and tested the samples for antibody titres. By using SDS-PAGE and Western Blotting of purified bear CBG, we confirmed that the generated antibody was indeed specific to bear CBG. We are now using this antibody to develop an enzyme-linked immunosorbent assay (ELISA) for quantifying CBG levels in grizzly bear serum. In addition, we are using commercial kits for the detection of heat shock proteins (hsps) to measure levels of hsps 60 and 70 in grizzly bear sera collected for the FMFGBRP since 1999. Although CBG and hsps hold promise as reliable biomarkers of longterm stress, their application is primarily limited to live-captured bears. The other path is the development of a sensitive protein array for detecting long-term physiological stress. The protein array offers several important advantages over serum-based indicators. First, because the array will yield expression profiles for multiple stress-activated proteins, it will provide insight into the nature of the long-term stressors (e.g., contaminants? reduced food availability) and their likely health effects (e.g., reduced immunity? stunted growth?), information that cannot be gleaned from any single measure of stress. Second, because the array will include evolutionarily conserved proteins, its application has potential for other species, including those at risk such as woodland caribou and the wolverine. Third, and of particular importance from an animal welfare perspective, the array will yield expression profiles for stress-activated proteins found in many body tissues. Therefore, sampling should not require the capture and handling of large numbers of animals to collect sera. Instead, remote biopsy techniques can be used to quickly sample small portions of tissue (e.g., skin, muscle) from freeranging animals; viable samples also may be opportunistically collected from recently deceased animals (e.g., hunter-killed, road killed). We are using three different approaches to develop and validate a custom protein array for grizzly bears. First, we are using Western Blotting to identify and evaluate commercially available antibodies for their ability to cross-react with grizzly bear proteins. Second, we are using commercial protein arrays developed for the evaluation of human health and disease as a screening tool to identify stress-activated proteins occurring in bears. Third, beginning in April 2006, we will use two-dimensional electrophoresis and mass spectrometry to determine proteins that are differentially expressed in bears affected by long-term stress. We anticipate the custom protein array for grizzly bears will be composed of around 40 antibodies, each specific to a different stress-activated protein. To use the protein array, we will homogenize and label grizzly bear skin and muscle samples with biotin. The sample homogenate and an internal standard homogenate labeled with dinitrophenyl will then be mixed and incubated on the surface of the protein array, and conditions will be further optimized for antibody-protein binding. We will quantify levels of stress-activated proteins on individual arrays by using a laser array reader to compare fluorescence between the individual and internal standard samples. Once the protein array is fully operational for quantifying the initial panel of 40 stress-activated proteins, we will analyze archived bear samples that have been collected from a range of study sites since 2004, as
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well as samples that will be collected routinely from study animals over the duration of the project. Twenty-five to forty grizzly bears will be captured annually from 2006 to 2009 across all grizzly bear range in Alberta (~228,000 km2). At present, detailed health information is available for approximately 120 grizzly bears, some of which have been captured and sampled multiple times over the past 7 years. Although similar information will continue to be collected over the next 4 years, the sampling procedures are being augmented to improve health assessments for individual grizzly bears. Bears will be captured either by remote drug delivery from helicopter, leg-hold snare, or culvert trap depending on the openness of the terrain and road access. The physical attributes and health of captured bears will be assessed as follows: • • • • • • •
Stress profile – by serum-based indicators and by skin/muscle biopsy and protein array as described above. Sex – by examination of external genitalia. Age – by extraction of a premolar tooth and counting of cementum annuli. Body condition – by determination of body mass and calculation of the body condition index (Cattet et al. 2002). Body size – by measurement of straight-line body length (an index of skeletal growth). Reproduction – by blood collection and laboratory evaluation of reproductive hormone levels, by examination of external genitalia and mammary development, by vaginal cytology, and by observation of accompanying bears, e.g., family groups, breeding pairs. Immunity – by blood collection and laboratory evaluation of innate and acquired immunity.
We will deploy Global Positioning System (GPS) collars on all captured bears, programmed to record locations every hour from April to October, and every 4 hours in November and March, for 2 years. We will upload the GPS data monthly from fixed wing aircraft. We will use sequential locations to estimate movement rates (e.g., m/hr) as an index of activity for individual bears. We will also use the GPS locations to determine home ranges for bears. The collars will be programmed to release and fall off after 2 years of wear. We will construct mathematical models to integrate the long-term stress data with the other health data. The data will be analyzed using mixed model repeated measures analysis of covariance models (Milliken and Johnson 2002) because of their robustness to missing values and because they allow flexible and parsimonious modeling of covariance matrices (Littell et al. 1996). The most parsimonious structure (i.e., the fewest variables to explain the greatest amount of variation) will be selected using Akaike’s Information Criterion. Statistical analyses will determine if expression profiles for stress-activated proteins in individual bears can be used to accurately predict their health status. To link the health and landscape data, we will first connect the health scores of individual bears to the landscape and human use data in both a spatial and temporal context. We will establish the spatial connection by determining the 95% Kernel home range for each bear by GPS radiotelemetry. The temporal connection will be the year of capture. So, for example, if road density is the landscape variable of interest for a bear captured in 2006, it would be expressed as 83
km of road/km2 home range/yr. Similarly, if vehicular traffic is the human use variable of interest, it would be expressed as the average number of vehicles/km2 home range/yr. To determine how landscape change affects grizzly bear health, we will combine geospatial and health data and GPS location information to generate explicit, predictive, health-based resource selection models (Manly et al. 2002). We will use scaled values from the models to estimate and project spatially a relative probability of landscape use for healthy and diseased (stressed) bears across grizzly bear habitat in Alberta. These final products, the models and maps, will not only predict where grizzly bears are likely to occur on the landscape, but will also provide insight into where bears are healthy or where bears are not. This additional â&#x20AC;&#x153;health layerâ&#x20AC;? will provide resource managers with much greater capacity to make informed decisions regarding human use of the landscape than basing decisions on resource selection functions alone. Anticipated Results Partners of the FMFGBRP will receive detailed maps linking landscape structure with grizzly bear occurrence and health across Alberta (Fig. 32). When combined with predictive models of the effects of landscape change, these maps will provide a solid decision-making framework for resource managers. For example, the models could be used to predict the threshold at which landscape change (or elements thereof) negatively affects grizzly bear populations. Improved understanding of the relationships between landscape structure and change, and grizzly bear health will allow resource managers to plan landscape activities with minimal disruption to grizzly bears and their ecosystem.
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Figure 32. An example map of the 2001 FMFGBRP study area that illustrates the probability of occurrence of grizzly bears does not necessarily correlate with their health. The dark brown water sheds in the RSF surface (right) are areas where the probability of occurrence is high. However, the health overlay (left) indicates that the health status of individual grizzly bears living in these areas range from poor (red) to good (blue). Literature Cited Benn, B. and S. Herrero. 2002. Grizzly bear mortality and human access in Banff and Yoho National Parks, 1971-98. Ursus 13:213-221. Cattet, M.R.L., Caulkett, N.A., Obbard, M.E., and G.B. Stenhouse. 2002. A body condition index for ursids. Canadian Journal of Zoology 80:1156-1161. Clark, J.S., Carpenter, S.R., Barber, M. et al. 2001. Ecological forecasts: an emerging imperative. Science 293:657-660. Garshelis, D.L., Gibeau, M.L., and S. Herrero. 2005. Grizzly bear demographics in and around Banff National Park and Kananaskis Country, Alberta. Journal of Wildlife Management 69:277-297. K端ltz, D. 2005. Molecular and evolutionary basis of the cellular stress response. Annual Review of Physiology 67:225-57. Littell, R.C., Milliken, G.A., Stroup, W.W., and R.D. Wolfinger. 1996. SAS system for mixed models. SAS Publishing, Cary, NC. 656 pp.
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Manly, B.F.J., McDonald, L.L., Thomas, D.L., et al. 2002. Resource selection by animals â&#x20AC;&#x201C; statistical design and analysis for field studies (second edition). Kluwer, Dordrecht, Netherlands. 240 pp. McLellan, B.N., Hovey, F.W., Mace, R.D. et al. 1999. Rates and causes of grizzly bear mortality in the interior mountains of British Columbia, Alberta, Montana, Washington, and Idaho. Journal of Wildlife Management 63:911-920. Milliken, G.A. and D.E. Johnson. 2002. Analysis of messy data, volume III: analysis of covariance. Chapman and Hall, NY. 624 pp. Nielsen, S.E., Boyce, M.S., and G.B. Stenhouse. 2004a. Grizzly bears and forestry I: selection of clear-cuts by grizzly bears in west-central Alberta, Canada. Forest Ecology and Management 199:51-65. Nielsen, S.E., Herrero, S., Boyce, M.S. et al. 2004b. Modeling the spatial distribution of human-caused grizzly bear mortalities in the Central Rockies ecosystem of Canada. Biological Conservation 120:101-113. Nielsen, S.E., Munro, R.H.M., Bainbridge, E.L. et al. 2004c. Grizzly bears and forestry II: distribution of grizzly bear foods in clear-cuts of west-central Alberta, Canada. Forest Ecology and Management 199:67-82. Weaver, J.L., Paquet, P.C., and L.F. Ruggiero. 1996. Resilience and conservation of large carnivores in the Rocky Mountains. Conservation Biology 10:964-976. Wielgus, R.B. and F.L. Bunnell. 1994. Dynamics of a small, hunted brown bear Ursus arctos population in southwestern Alberta, Canada. Biological Conservation 67:161-166.
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APPENDIX 1. Publication/Technical Paper List Boulanger, J., G. Stenhouse, R. Munro. 2004. Sources of heterogeneity bias when DNA markrecapture sampling methods are applied to grizzly bear (Ursus arctos) populations. Journal of Mammalogy 85:618-624. Cattet, M.R.L., K. Christison, N.A. Caulkett and G.B. Stenhouse. 2003. Physiologic responses of grizzly bears to different methods of capture. Journal of Wildlife Diseases 39(3):649-654. Cattet, M.R.L., N.A. Caulkett, and G.B. Stenhouse. 2003. Anesthesia of grizzly bears using xylazine-zolazepam-tiletamine or zolazepam-tiletamine. Ursus 14(1):88-93. Cattet, M.R.L., N.A. Caulkett, M.E. Obbard and G.B. Stenhouse. 2002. A body-condition index for ursids. Canadian Journal of Zoology 80:1156-1161. Frair, J.L., S.E. Nielsen, E.H. Merrill, S. Lele, M.S. Boyce, R.H.M. Munro, G.B. Stenhouse, and H.L Beyer. 2004. Removing GPS-collar bias in habitat-selection studies. Journal of Applied Ecology 41, 201-212. Franklin, S. E., P. K. Montgomery, and G. B. Stenhouse. 2005. Interpretation of land cover using aerial photography and satellite imagery in the Foothills Model Forest of Alberta. Canadian Journal of Remote Sensing 31:304-313. Franklin, S.E., D.R. Peddle, J.A. Dechka, G.B. Stenhouse. 2002. Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping. International Journal of Remote Sensing 23(21):4633-4652. Franklin, S.E., M.B. Lavigne, M.A. Wulder, and G.B. Stenhouse. 2002. Change detection and landscape structure mapping using remote sensing. The Forestry Chronicle 78(5):618-625. Franklin, S.E., M.J. Hansen, G.B. Stenhouse. 2002. Quantifying landscape structure with vegetation inventory maps and remote sensing. The Forestry Chronicle 78(6):866-875. Franklin, S.E., G.B. Stenhouse, M.J. Hansen, C.C. Popplewell, J.A. Dechka, D.R. Peddle. 2001. An integrated decision tree approach (IDTA) to mapping land cover using satellite remote sensing in support of grizzly bear habitat analysis in the Alberta Yellowhead ecosystem. Canadian Journal of Remote Sensing 27(6):579-592. Gau, R.J., R. Mulders, L.M. Ciarniello, D.C. Heard, C.B. Chetkiewicz, M. Boyce, R. Munro, G. Stenhouse, B. Chruszcz, M.L. Gibeau, B. Milakovic, K. Parker. 2004. Uncontrolled field performance of Televilt GPS-Simplex collars on grizzly bears in western and northern Canada. Wildlife Society Bulletin 32:693-701. Huettmann, F., S.E. Franklin, G.B. Stenhouse. 2005. Predictive spatial Modeling of landscape change in the Foothills Model Forest. Forestry Chronicle 81:525-537. Hunter, A., N. El-Sheimy, G. Stenhouse. 2005. GPS/Camera Collar Captures Bear Doings. http://www.gpsworld.com/gpsworld/article/articleDetail.jsp?id=146689 (this will only be here until March 2006 or so). Linke, J. 2003. Using Landsat TM and IRS imagery to Detect Seismic Cutlines: Assessing their Effects on Landscape Structure and on Grizzly Bear (Ursus arctos) Landscape Use in Alberta. MSc. Thesis. Department of Geography, University of Calgary, Calgary, Alberta, Canada.
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Linke, J., S.E. Franklin, F. Huettmann and G.B. Stenhouse. 2005. Seismic cutlines, changing landscape metrics and grizzly bear landscape use in Alberta. Landscape Ecology 20:811-826. McDermid, G. J., S.E. Franklin and E.F. LeDrew. 2005. Remote sensing for large-area habitat mapping. Progress in Physical Geography 29:449-474. McDermid, G.J. 2005. Remote Sensing for Large-Area, Mult-Jurisdictional Habitat Mapping. PhD. Thesis. Department of Geography, University of Waterloo, Waterloo, Ontario, Canada. Mowat, G., D.C. Heard, D.R. Seip, K.G. Poole, G.Stenhouse, D.W. Paetkau. 2005. Grizzly Ursus arctos and black bear U. americanus densities in the interior mountains of North America. Wildlife Biology 11: 31-48. Nielsen, S.E., G.B. Stenhouse, M.S. Boyce. 2006. A habitat-based framework for grizzly bear conservation in Alberta. Biological Conservation 130:217-229. Nielsen, S.E. 2004. Habitat ecology, Conservation, and Projected Population Viability of Grizzly Bears (Ursus arctos L.) in West-Central Alberta, Canada. PhD Thesis. Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada. Nielsen, S.E., M.S. Boyce, and G.B. Stenhouse. 2004. Grizzly bears and forestry I: selection of clearcuts by grizzly bears in west-central Alberta. Canada.Forest Ecology and Management 199:51â&#x20AC;&#x201C;65. Nielsen, S.E., R.H.M. Munro, E. Bainbridge, M.S. Boyce, and G.B. Stenhouse. 2004. Grizzly bears and forestry II: distribution of grizzly bear foods in clearcuts of west-central Alberta, Canada. Forest Ecology and Management 199:67â&#x20AC;&#x201C;82. Nielson, S.E., M.S. Boyce, G.B. Stenhouse, R.H.M. Munro. 2003. Development and testing of phenologically driven grizzly bear habitat models. Ecoscience 10(1):1-10. Nielson, S.E., M.S. Boyce, G.B. Stenhouse, and R.H.M. Munro. 2002. Modeling grizzly bear habitats in the Yellowhead ecosystem of Alberta: taking autocorrelation seriously. Ursus 13:45-56. Pereverzoff, J.L. 2003. Development of a Rapid Assessment Technique to Identify Human Disturbance Features in a Forested Landscape. MSc. Thesis, Department of Geography, University of Calgary, Calgary, Alberta, Canada. Popplewell, C., S.E. Franklin, G.B. Stenhouse, and M. Hall-Beyer. 2003. Using landscape structure to classify grizzly bear density in Alberta Yellowhead Ecosystem bear management units. Ursus 14: 27-34. Popplewell, C. 2001. Habitat Structure and Fragmentation of Grizzly Bear Management Units and Home Ranges in the Alberta Yellowhead Ecosystem. MSc. Thesis. Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada. Ritson-Bennett, R.A. 2003. Assessing the Effects of a Heli-portable 3D Seismic Survey on Grizzly Bear (Ursus arctos horribilis) Distribution. MSc. Thesis, Department of Geography, University of Calgary, Calgary, Alberta, Canada. Schwab, B.L. 2003. Graph Theoretic Methods for Examining Landscape Connectivity and Spatial Movement Patterns: Application to the FMF Grizzly Bear Research. MSc Thesis. Department of Geography, University of Calgary, Calgary AB.
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Stenhouse, G.B., J. Boulanger, J. Lee, K. Graham, J. Duval, J. Cranston. 2004. Grizzly bear associations along the eastern slopes of Alberta. Ursus 16:31-40. Stenhouse, G.B. and K.Graham. (Eds.). 2005. Foothills Model Forest Grizzly Bear Research Program 1999-2003 Final Report. 289 pp. Stenhouse, G.B., R. Munro and K.Graham. (Eds.). 2003. Foothills Model Forest Grizzly Bear Research Program 2002 Annual Report. 162 pp. Stenhouse, G.B., R. Munro. (Eds.). 2002. Foothills Model Forest Grizzly Bear Research Program 2001 Annual Report. 126 pp. Stenhouse, G. and R. Munro. (Eds.). 2001. Foothills Model Forest Grizzly Bear Research Program 2000 Annual Report. 87 pp. Stenhouse, G. and R. Munro. (Eds.). 2000. Foothills Model Forest Grizzly Bear Research Program 1999 Annual Report. 98 pp. Wasser, S.K., B. Davenport, E.R. Ramage, K.E. Hunt, M. Parker, C. Clarke, and G.B. Stenhouse. 2004. Scat detection dogs in wildlife research and management: Application to grizzly and black bears in the Yellowhead Ecosystem, Alberta, Canada. Canadian Journal of Zoology 82:475-492. Wulder, M. A., and S. E. Franklin, eds., 2003, Remote Sensing of Forest Environments: Concepts and Case Studies, Kluwer Academic Publishers, Boston, MA, 519p. Wulder, M.A., S.E. Franklin, J.C. White, J.Linke and S. Magnussen. 2005. An accuracy assessment framework for large-are land cover classification products derived from mediumresolution satellite data. International Journal of Remote Sensing: in press.
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APPENDIX 2. Foothills Model Forest Grizzly Bear Research Partners – 2005 Ainsworth Engineered Canada Alberta Conservation Association Alberta Environment Alberta Newsprint Company Alberta Sustainable Resource Development Anadarko Canada Corporation Banff National Park Buchanan Lumber – Tolko OSB Burlington Resources Canada Ltd. Canadian Association of Petroleum Producers Canfor Corporation Canadian Forest Service ConocoPhillips Canada Resources Ltd. Devon Canada Corporation Elk Valley Coal – Cardinal River Operations EnCana Corporation Environment Canada (HSP) Forest Resources Improvement Association of Alberta Human Resources and Skills Development Canada (SCP) Husky Energy Inc. Jasper National Park
Manning Diversified Forest Products Ltd. Millar Western Forest Products Ltd. Peregrine Helicopters Petro Canada Ltd. Peyto Exploration Petroleum Technology Alliance Canada ERAC Fund Shell Canada Limited Spray Lake Sawmills Ltd. Suncor Energy Inc. Sundance Forest Industries Ltd. Talisman Energy Inc. TransCanada Pipelines Ltd. University of Alberta University of Calgary University of Saskatchewan University of Waterloo Western College of Veterinary Medicine West Fraser Mills Ltd. Hinton Wood Products Blue Ridge Lumber Inc. Slave Lake Division – Alberta Plywood Sundre Forest Products Weyerhaeuser Company Limited
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APPENDIX 3. Program Deliverables - January 2006 1) For the Phase 3 study area (right): Grizzly bear Resource Selection Function (RSF) map (version 2.0), showing the probability of grizzly bear occurrence; validated and tested with 6 years of GPS data. Grizzly bear mortality risk map, showing the probability of human-caused grizzly bear mortality. Graph Theory-based movement corridor map (version 1.0), showing the primary travel routes used by grizzly bears.
2) For the Phase 4 study area (Highway 1 south to Montana border): Grizzly bear Resource Selection Function (RSF) map (version 1.0), validated and tested with 2 years of GPS data.
3) For the combined Phase 3/Phase 4 study area: Remote-sensing based Landcover map and associated vegetation maps (crown closure, percent conifer). Map of watershed units summarized by mean RSF score.
4) GIS Applications: Python geoprocessing scripts, and associated GIS input layers, will allow the user to predict changes to grizzly bear habitat caused by industrial development. Planned development features (roads, cutblocks, wellsites, pipelines) are incorporated into the landscape variables,
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and the RSF and mortality risk models are then regenerated for Phase 3 study area. These scripts require ESRI ArcGIS 9x with Spatial Analyst extension. GIS layers include: terrain grids: o DEM o Compound Topographic Index o Terrain Ruggedness Index o Topographic class o solar radiation o distance to water features vegetation grids: o forest age o greenness o Leaf Area Index o Crown Closure Anticipated Deliverables for January 2007: For the Phase 5 Study area (right): Remote-sensing based Landcover map and associated vegetation maps (crown closure, leaf area index, percent conifer). Grizzly bear Resource Selection Function (RSF) map (version 1.0), validated and tested with 2 years of GPS data. Grizzly bear mortality risk map. Graph Theory-based movement corridor map. GIS applications, as above, that allow long-term landscape modeling of grizzly bear habitat.
Long-term habitat mapping plan: By 2008, we plan to have completed habitat mapping for the entire range of grizzly bears in Alberta, an area of over 350,000 sq. km.
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