gbp 2010 04 annrpt 2009

FOOTHILLS RESEARCH INSTITUTE GRIZZLY BEAR PROGRAM 2009 ANNUAL REPORT

Prepared and edited by Gordon B. Stenhouse and Karen Graham April 2010

(Formerly the Foothills Model Forest)

Disclaimer This report presents preliminary findings from the 2009 research program within the Foothills Research Institute (FRI) Grizzly Bear Program. It must be stressed that these data are preliminary in nature and all findings must be interpreted with caution. Opinions presented are those of the authors and collaborating scientists and are subject to revision based on the ongoing findings over the course of these studies. Suggested citation for information within this report: Karine Pigeon.. 2010. Denning Behaviour, Thermoregulation, And Environmental Variables. In: G. Stenhouse and K. Graham (eds). Foothills Research Institute Grizzly Bear Program 2009 Annual Report. Hinton, Alberta. This is an interim report not to be cited without the express written consent of the senior author.

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ACKNOWLEDGEMENTS A program of this scope and magnitude would not be possible without the dedication, hard work and support of a large number of people. We would like to thank the capture crew members: Bernie Goski, Dave Hobson, Terry Larsen, Bogdan Cristescu and Joe Northrup. Thanks also to the local Fish and Wildlife officers and biologists for their help during the capture season. Exemplary flying (and field!) skills were provided by John Saunders and Steve Wotton of Peregrine Helicopters of Hinton and fixed wing pilot Sean Biesbroek of Wildlife Observation Air Services. Thanks also to the veterinarians who assisted with the captures: Marc Cattet and Bryan McBeth. Appreciation is also extended to the vegetation plot crewmembers Terry Larsen, Karine Pigeon, Gina Sage, Robbie Bonin, Kirsten Hayward, and Robin Strong whose hard work and enthusiasm ensured a successful field season. Special thanks to Tracy McKay for all her great work on the various field programs she was involved with. Also thanks to the volunteers, Kim Forster, Heidi Fengler, Joan Simonton, and Kirsten Hayward for their help with the Whitebark Pine project this year. Alberta Sustainable Resource Development provided logistical support and camp accommodations. The Grizzly Bear Research Partner Group and the Board of Directors of the Foothills Research Institute provided valuable support and assistance that allowed the research to proceed in order to address management needs. Thanks to Julie Duval, Katie Yalte and Debbie Mucha for their help in all areas relating to GIS and data management. A word of praise goes out to Sean Kinney, Joan Simonton and Fran Hanington for their efforts with media needs and the special communication requirements associated with our program. Thank you to Judy Astalos, and Denise Lebel for an excellent job in managing the administrative details of this program and keeping all the crews in line. Definitely not an easy task! 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. Thank you to Jerome Cranston for his ongoing GIS support (and much more!) and to John Boulanger who provided superb statistical advice throughout the year. Dr. David Paetkau at Wildlife Genetics International completed the 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 1).

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ..............................................................................i TABLE OF CONTENTS ................................................................................. ii LIST OF TABLES......................................................................................... vi LIST OF FIGURES ...................................................................................... vii INTRODUCTION.........................................................................................1 CHAPTER 1: SUMMARY OF 2009 CAPTURE PROGRAM..............................3

Introduction ............................................................................................................ 3 Study Areas .................................................................................................................... 3 Grizzly Bear Captures..................................................................................................... 4 GPS Radio‐Telemetry Data ............................................................................................ 6 Grizzly Bear Health Evaluation....................................................................................... 7 Sex and Age Characteristics........................................................................................... 7 Capture Type ................................................................................................................. 8 Bear Captures – Cheviot Mine Area .............................................................................. 8

CHAPTER 2: DENNING BEHAVIOUR, THERMOREGULATION, AND ENVIRONMENTAL VARIABLES ...................................................................9

Introduction ............................................................................................................ 9 Hibernation.................................................................................................................... 9 Thermoregulation.......................................................................................................... 9

Methods................................................................................................................ 10 Study Area ................................................................................................................... 10 Meteorological Variables............................................................................................. 10 Forage Availability ....................................................................................................... 10 Denning:....................................................................................................................... 11

Pre‐ And Post‐Denning Behaviour.......................................................................... 11 Timing Of Den Entry And Den Emergence ............................................................. 11

Thermoregulation:....................................................................................................... 12 Activity, Standard Operative And Operative Temperatures .................................. 12

Habitat Selection In The Context Of Thermoregulation.............................................. 12 Results and Discussion .......................................................................................... 13

Pre‐ And Post‐Denning Behaviour ............................................................................... 13 Den‐environ pre‐denning: ...................................................................................... 13 Den‐environ post‐denning: .................................................................................... 14 Forage availability:.................................................................................................. 14

Timing Of Den Entry And Den Emergence................................................................... 14 Future Analyses..................................................................................................... 15

Pre‐ And Post‐Denning Behaviour ............................................................................... 15 Timing Of Den Entry And Den Emergence................................................................... 15

Climate trends data investigations:........................................................................ 15 Thermoregulation:....................................................................................................... 15

Activity, Standard Operative And Operative Temperatures And Habitat Selection ................................................................................................................. 15

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CHAPTER 3: ENERGETICS FIELD WORK SUMMARY 2009..........................20 Introduction .......................................................................................................... 20 Study area and Methods....................................................................................... 21

Circle plots: .................................................................................................................. 23 Belt transect: ............................................................................................................... 23 Full 30x30m plot survey:.............................................................................................. 23 Additional measurements: .......................................................................................... 24

Shrub Line Intercept ............................................................................................... 24 30 minute berry picking sessions: .......................................................................... 24 Plant Collections: .................................................................................................... 24

Results and Discussion .......................................................................................... 24 CHAPTER 4: WHITEBARK PINE SEEDS AS A FOOD SOURCE FOR GRIZZLY BEARS IN WEST CENTRAL ALBERTA 2008/2009 PILOT STUDY.....30

Introduction .......................................................................................................... 30 Study region .......................................................................................................... 31 Methods................................................................................................................ 32

Transect surveys: ......................................................................................................... 32 Midden surveys: .......................................................................................................... 33 2009 methods:............................................................................................................. 34 Data analysis: ............................................................................................................... 34

Results................................................................................................................... 38 Discussion.............................................................................................................. 42 Conclusions and Future Directions ....................................................................... 45

CHAPTER 5: GRIZZLY BEAR HABITAT ENHANCEMENT TRIAL PROGRESS REPORT..................................................................................48

TAY RIVER ENVIRONMENTAL ENHANCEMENT FUND......................................................... 48 Introduction .......................................................................................................... 48 Reconnaisance and Planting ................................................................................. 48

Burnt Timber 15 (11‐26‐31‐9‐W5) UTM Z11N, 626140E, 5727766N ......................... 49 Burnt Timber 13 (5‐28‐30‐10‐W5) (UTM Z11N 613930E, 5717500N)........................ 52 Limestone East (1‐6‐33‐9‐W5) (UTM Z11N 620428E, 5739687N).............................. 52

Conclusion............................................................................................................. 52 MOOSE MOUNTAIN ENVIRONMENTAL ENHANCEMENT FUND II ....................................... 53

Introduction .......................................................................................................... 53 Reconnaissance and Planting................................................................................ 53

Site 1: (NW‐12‐23‐6‐W5) (UTM Z11N 660110E, 5646152N) ...................................... 55 Site 2: Interconnect pipeline (SW‐3‐23‐6‐W5) (UTM Z11N 657585E, 5643833N) ..... 55

Conclusion............................................................................................................. 57 CHAPTER 6: GRIZZLY BEAR HABITAT PRODUCTIVITY MODELS FOR THE YELLOWHEAD, SWAN HILLS, GRANDE CACHE AND CHINCHAGA POPULATION UNITS OF ALBERTA ............................................................58

Summary ............................................................................................................... 58 Introduction .......................................................................................................... 58 Methods................................................................................................................ 60

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Study area. ................................................................................................................... 60 Field methods. ............................................................................................................. 60 Species (food item) models ......................................................................................... 60

Plant (herbivory, root digging, and frugivory) food models................................... 60 Animal protein (carnivorous) food models ............................................................ 61 Model building ....................................................................................................... 61

Species (food item) maps ............................................................................................ 61 Bi‐weekly estimates of food item importance weights............................................... 61 Estimating potential habitat productivity (PHP).......................................................... 62 Estimating realized habitat productivity (RHP) ........................................................... 63 Estimating habitat deficits (HD)................................................................................... 64

Results................................................................................................................... 64 Species (food item) models ......................................................................................... 64 Potential habitat productivity (PHP)............................................................................ 65 Realized habitat productivity (RHP)............................................................................. 65 Habitat deficits (HD) .................................................................................................... 65

Discussion.............................................................................................................. 65 CHAPTER 7: GRIZZLY BEAR HEALTH‐ENVIRONMENT RELATIONSHIPS......82

Introduction .......................................................................................................... 82 Objective 1 ............................................................................................................ 82 Objective 2 ............................................................................................................ 82

Study Areas and Capture Procedures.......................................................................... 83 Grizzly Bear Health Evaluation..................................................................................... 83

Objective 3 ............................................................................................................ 83 Objectives 4 and 5................................................................................................. 84

CHAPTER 8 REMOTE SENSING UPDATE – UNIVERSITY OF CALGARY & UNIVERSITY OF SASKATCHEWAN ............................................................87

INTRODUCTION AND OVERVIEW ...................................................................................... 87 TOPIC 1: POSSIBLE PROBLEMS IN REMOTE SENSING OF LARGE‐AREA HABITAT MAPPING.......................................................................................................................... 87 TOPIC 2: THE APPLICABILITY OF SMALL‐SATELLITE CONSTELLATION IN CLASSIFICATION FOR LARGE‐AREA HABITAT MAPPING: A CASE STUDY OF DMC MULTISPECTRAL IMAGERY IN WEST‐CENTRAL ALBERTA .............................................................................. 88 TOPIC 3: THE COMPARISON OF LANDSAT MULTISPECTRAL AND IRS PANCHROMATIC IMAGERY ON LANDSCAPE PATTERN ANALYSIS OF GRIZZLY BEAR HABITAT IN THE AGRICULTURAL AREA OF WESTERN ALBERTA.................................................................... 89 TOPIC 4: THE APPLICABILITY OF RADARSAT‐2 IMAGERY IN LARGE‐AREA HABITAT MAPPING.......................................................................................................................... 90 TOPIC 5: GRIZZLY BEAR OCCURRENCE AND CHANGE ......................................................... 91

Methods................................................................................................................ 92 Resulting Explanatory Datasets: ........................................................................... 93

TOPIC 6: CHANGES IN LANDSCAPE DISTURBANCE OF GRIZZLY BEAR HABITAT IN THE ROCKY MOUNTAIN FOOTHILLS OF ALBERTA FROM 1985 TO 2005..................................... 96

Methods................................................................................................................ 97 Conclusions ........................................................................................................... 97

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TOPIC 7: LINKING GRIZZLY BEAR HEALTH WITH REMOTELY‐SENSED VEGETATION PHENOLOGY ..................................................................................................................... 99

Methods................................................................................................................ 99 Results and Discussion ........................................................................................ 100 Summary ............................................................................................................. 101

CHAPTER 9: REMOTE SENSING REPORT ON MOUNTAIN PINE BEETLE / GRIZZLY BEAR PROJECT 2009/2010 ....................................................... 102

EXECUTIVE SUMMARY.................................................................................................... 102 TOPIC 1: IMPROVING THE TEMPORAL RESOLUTION OF BASE MAP TEMPLATES............... 103

Classification of synthetic‐Landsat imagery ....................................................... 103 Implementation of STAARCH over MPB / grizzly bear study area and Implementation of the STARFM component (phenology) over MPB/ grizzly bear study area. .................................................................................................. 103 Attribution of change – Demonstration of methods.......................................... 104 Develop and validate over the full Grizzly Bear study area................................ 106 Role of understory as a driver of conifer canopy spectral reflectance, influence on capture of phenology `Phenological Cameras’.............................. 109 Edges and transitions: Development of landscape edge features from continuous variables ........................................................................................... 114

TOPIC 2. MONITORING MOUNTAIN PINE BEETLE ATTACK AND MITIGATION ACTIVITIES. ..................................................................................................................... 116

Infestation Maps Over MPB Area: Assessment Of Impacts Of MPB And GB Home Ranges In The Alberta Foothills................................................................ 116

Helicopter global positioning system survey data..................................................... 120 TOPIC 3: ONGOING MONITORING OF MPB INFESTATION AND COLLECTION OF GROUND VALIDATION DATA........................................................................................... 126 TOPIC 4: PROJECTING LANDSCAPE RECOVERY AND CHANGE........................................... 129

Scenario‐based modeling approach which combines information on land cover change with forest growth and forest recovery growth rates. ............... 129 Review of options for combining remotely sensed data and spatial data layers to project landscape recovery and change (2010/11) ............................. 129

Develop methods over prototype study area, base level simulations...................... 130 Long Time Series Change Over the Kakwa Greater Ecosystem, Landsat Analysis.................................................................................................. 133

APPENDIX 1: LIST OF PROGRAM PARTNERS (1999 – Present) ................ 138 APPENDIX 2: LIST OF PROGRAM PARTNERS (2009)................................ 139 APPENDIX 3: LIST OF PUBLISHED PAPERS .............................................. 140

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LIST OF TABLES CHAPTER 1: SUMMARY OF 2009 CAPTURE PROGRAM Table 1. Capture summary for 2009 field season. ..........................................................................5 Table 2. Grizzly bear capture types.................................................................................................8 CHAPTER 3: ENERGETICS FIELD WORK SUMMARY 2009 Table 1. Priority grizzly bear foods................................................................................................22 Table 2. Classes for percent cover values .....................................................................................23 Table 3. Sweet vetch (Hedysarum alpinum) densities (plants/m2) determined within

different sampling areas. A dash indicates that a density was not collected .........................25 Table 4. Sweet vetch (Hedysarum alpinum) densities by plot type..............................................25 Table 5. Average buffaloberry (Sheperdia canadensis) densities by plot type.............................27 Table 6. Berry densities within belt transect ................................................................................28 CHAPTER 4: WHITEBARK PINE SEEDS AS A FOOD SOURCE FOR GRIZZLY BEARS IN WEST CENTRAL ALBERTA 2008/2009 PILOT STUDY Table 1. Basal areas of tree species observed (m2/ha), averaged by study area; areas

ordered north to south. ..........................................................................................................38 Table 2. Average whitebark pine cones per tree by area in 2009. ...............................................41 CHAPTER 6: GRIZZLY BEAR HABITAT PRODUCTIVITY MODELS FOR THE YELLOWHEAD, SWAN HILLS, GRANDE CACHE AND CHINCHAGA POPULATION UNITS OF ALBERTA Table 1. Grizzly bear food items considered in the development of habitat productivity

models. ....................................................................................................................................68 Table 2. Predictor variables used to model species occurrence for 20 grizzly bear food items

in western Alberta. ..................................................................................................................69 Table 3. Weights (% digestible matter) used to scale seasonal importance of food items

where present. ........................................................................................................................70 CHAPTER 8 REMOTE SENSING UPDATE – UNIVERSITY OF CALGARY & UNIVERSITY OF SASKATCHEWAN Table 1. Landsat scenes acquired and processed for quantifying human disturbance, land

cover and grizzly bear mortality risk/habitat selection from 1985‐2005................................97 Table 2. GEE model results for Average NDVI vs. BCI .................................................................100 Table 3. GEE model results for Maximum NDVI vs. BCI ..............................................................101 CHAPTER 9: REMOTE SENSING REPORT ON MOUNTAIN PINE BEETLE / GRIZZLY BEAR PROJECT 2009/2010 Table 1. Summary of the camera sites, including forest cover type and coordinates................109 Table 2. Landsat‐5 TM tasseled cap transformation coefficients. ..............................................123 Table 3. Confusion matrix summarizing the accuracy assessment for path 46, row 21. ...........125 Table 4. A range of input climate data has been assembled and provided to FMF

throughout 2009. ..................................................................................................................129 Table 5. Imagery used in the Kawka historical study..................................................................133

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LIST OF FIGURES CHAPTER 1: SUMMARY OF 2009 CAPTURE PROGRAM Figure 1. Kakwa (north) and Cheviot (south) study areas with 2009 capture site locations..........4 Figure 2. Extent of spatial coverage of GPS location data from bears monitored in 2009. ...........6 Figure 3. Extent of sampling area for GPS locations for the period 2005‐2009. ............................7 CHAPTER 2: DENNING BEHAVIOUR, THERMOREGULATION, AND ENVIRONMENTAL VARIABLES Figure 1. Observed movement rate (m/hr) per day prior to den entry for grizzly bear males

(n = 5, t = 5.33, intercept = 3.94) in the WGP and Hinton‐Cadomin study areas (2005‐2008). Movement rates were log‐transformed (ln) to respect the assumptions of normality of residuals and the homogeneity of variance. ..................................................16

Figure 2. Observed mean movement rates (m/hr) per day prior to den entry for grizzly bear females (n = 11, t = 7.0, intercept = 3.68) in the WGP and Hinton‐Cadomin study area (2005‐2008). Movement rates were log‐transformed (ln) to respect the assumptions of normality of residuals and the homogeneity of variance. ......................................................16

Figure 3. Observed mean movement rate (m/hr) per day post den emergence (n = 13, t = 12.4, intercept = 3.65) in the WGP and Hinton‐Cadomin study areas (2006‐2009). Movement rates were log‐transformed (ln) to respect the assumptions of normality of residuals and the homogeneity of variance. ...........................................................................17

Figure 4. Mean biomass of ripe berries per quadrat in 2008 and 2009 (n = 281), biomass estimates calculated by year using the equation: biomass/quadrat = mean weight of individual berry species*(sum of berries / quadrat). Numbers above columns indicate sample size. .............................................................................................................................17

Figure 5. Number of days spent in hibernation for male (n = 10) and female (n = 32) grizzly bears in the WGP and Hinton‐Cadomin study areas (2005‐2008). .........................................18

CHAPTER 3: ENERGETICS FIELD WORK SUMMARY 2009 Figure1. Map showing the multiyear annual home ranges of G003 (1999, 2001‐2003) and

G012 (1999‐2003) from GPS collar data..................................................................................21 Figure 2. Comparison of percent cover estimates and density estimates of sweet vetch

within the circle areas. ............................................................................................................26 Figure 3. Comparison of line intercept data and density estimates of buffaloberry within

the circle areas. .......................................................................................................................27 Figure 4. Buffaloberry fruit counts for 4 plots for G012 across three 10 minute picking

sessions. The solid lines represent the west side counts for each plot, and the corresponding coloured dashed lines indicate the east side counts. .....................................29

CHAPTER 4: WHITEBARK PINE SEEDS AS A FOOD SOURCE FOR GRIZZLY BEARS IN WEST CENTRAL ALBERTA 2008/2009 PILOT STUDY Figure 1. Grizzly bear range and whitebark pine distribution in Alberta......................................30 Figure 2. Map of 2008/2009 study area. ......................................................................................32 Figure 3. Uncorrected versus corrected area for calculating midden densities per

study area. ...............................................................................................................................36 Figure 4. Midden densities by area, areas ordered north to south..............................................39 Figure 5. Map of study areas where bear excavations/scat were observed, within

whitebark pine distribution....................................................................................................40

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Figure 6. Proportion of whitebark pine versus total midden density, by study area. ..................41 CHAPTER 5: ‐GRIZZLY BEAR HABITAT ENHANCEMENT TRIAL PROGRESS REPORT Figure 1. Treated sites in the Tay River area.................................................................................49 Figure 2. Reclamation of BT‐15.....................................................................................................50 Figure 3. Planted Shepherdia ........................................................................................................50 Figure 4. Planted Hedysarum........................................................................................................51 Figure 5. Revegetation of BT‐15....................................................................................................51 Figure 6. Treated sites in the Moose Mountain area...................................................................54 Figure 7. Site 1, in 2006 before green‐up .....................................................................................55 Figure 8. Site 2 in September. 2006..............................................................................................56 Figure 9. Buffaloberry seedlings ...................................................................................................56 CHAPTER 6: GRIZZLY BEAR HABITAT PRODUCTIVITY MODELS FOR THE YELLOWHEAD, SWAN HILLS, GRANDE CACHE AND CHINCHAGA POPULATION UNITS OF ALBERTA Figure 1. Study area map illustrating elevation patterns over the 171,771‐km2 region of

western Alberta and population unit boundaries (dark grey lines). .......................................71 Figure 2. Location of protected areas (yellow overlay thus green in the Rocky Mountains,

orange in the Foothills, light green in the Boreal and light brown in the Parkland) within the 171,771‐km2 study area. .......................................................................................72

Figure 3. Location of 2,388 vegetation plots used for modeling the occurrence of 20 grizzly bear food items. ......................................................................................................................73

Figure 4. Predicted regional occupancy (Ψ) of adult female grizzly bears (see Nielsen et al. 2009 for details). .....................................................................................................................74

Figure 5. Predicted probability of occurrence of blueberry (Vaccinium myrtilloides) and huckleberry (V. membranaceum) in western Alberta.............................................................75

Figure 6. Predicted relative probability of occurrence of moose (Alces alces) in western Alberta. ....................................................................................................................................76

Figure 7. Predicted potential habitat productivity by bi‐weekly period for the month of August......................................................................................................................................77

Figure 8. Predicted potential habitat productivity (sum of seasonal models). ............................78 Figure 9. Predicted realized habitat productivity (RHP) by bi‐weekly period for the month

of August .................................................................................................................................79 Figure 10. Predicted multi‐seasonal realized habitat productivity (RHP).....................................80 Figure 11. Predicted multi‐seasonal habitat deficit based on the difference of realized and

potential habitat......................................................................................................................81 CHAPTER 7: GRIZZLY BEAR HEALTH‐ENVIRONMENT RELATIONSHIPS Figure 1. Conceptual ‘driver‐stressor’ diagram depicting the key factors affecting grizzly

bear health throughout their distributional range in Alberta. (Immun. = immunity, Long. = longevity, Reprod. = reproduction).............................................................................85

Figure 2. Schematic representation of analytical approaches used to relate landscape condition and landscape change (independent factors) to grizzly bear health (dependent factor) based on frequency of measurements for individual bears captured once (static) to many times (dynamic). ...................................................................................86

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CHAPTER 8 REMOTE SENSING UPDATE – UNIVERSITY OF CALGARY & UNIVERSITY OF SASKATCHEWAN Figure 1. Relative abundance of grizzly bears in BMA 3 as derived from early summer 2004

DNA hair sampling summarized at the 7x7 km landscape cell scale.......................................91 Figure 2. Exemplification of the multi‐temporal disturbance and landscape content and

multi‐scale context (using two window sizes) for sampled landscape cells. ..........................92 Figure 3. Distribution and total area of all cumulative disturbances existing in 1998 and in

2004, and total area of all annual new disturbances appearing in each time interval between 1998 and 2004.......................................................................................................... 93

Figure 4. Mean distance from to any nearest cumulative and annual disturbance feature from within each landscape cell, between 1998 and 2004.....................................................93

Figure 5. Amount of specific disturbances cumulatively existing in 1998 and 2004, including cutblocks, surface mines, wellsites, linear access features, and burns...................94

Figure 6. Largest contiguous patch inside each landscape cell, and accessible from within each landscape cell, as well as accessible from within 3.5 and 7 km distances surrounding each landscape cell in 2004. The contiguous patches are defined once by undisturbed forested cover type, and once by undisturbed vegetated cover type (including forest). ....................................................................................................................95

Figure 7. Location of the study area in west‐central Alberta within bear management area (BMA) 3, east of Hinton town‐site...........................................................................................96

Figure 8. Study area and mosaic of kernel‐density grizzly home ranges......................................99 Figure 9. MODIS derived NDVI phenology metrics (from top left): NDVI amplitude, Max

NDVI, Average NDVI, Max Green‐Up, Time Max Green‐Up NDVI, Time Max NDVI, Integrated NDVI, Length of growing season. Not shown: Start of Season, End of Season. ..............................................................................................................................100

CHAPTER 9: REMOTE SENSING REPORT ON MOUNTAIN PINE BEETLE / GRIZZLY BEAR PROJECT 2009/2010 Figure 1. Flowchart of the developed method to predict and label change ..............................105 Figure 2. Location of the Grizzly Bear population units and core and secondary habitat

areas within western Alberta, Canada ..................................................................................106 Figure 3. (a) An example of yearly classified STAARCH disturbances within validation

area A. (b) Validation data set for the same area, derived by producing change masks from intermediate Landsat scenes. Yearly periods run from summer to summer, with exact dates depending on the date of the respective Landsat scenes. ................................107

Figure 4. Total disturbed area within the grizzly bear study area by date. Dates are given as yyyy/mm/dd......................................................................................................................108

Figure 5. Example images from the Cadomin conifer site (see Table 1 for site description). ....111 Figure 6. Example images from the Cadomin mixed site (see Table 1 for site description).......112 Figure 7. Changes through time in the 2G‐RBi and the blue‐green ratio for the Cadomin

mixed and conifer sites .........................................................................................................112 Figure 8. Examples of the 2G‐RBi trajectories for all sites during spring green‐up. ...................113 Figure 9. Histograms of edge contrast for two land cover transitions: Coniferous to

Broadleaf, and Exposed Land to Herbs. ................................................................................114 Figure 10. Comparison of LPI edge density and wombling values indicating the local

nature of wombling BEs. .......................................................................................................115

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Figure 11. Map of the western Alberta grizzly bear management area and population units. Waterton and Alberta North were not included in the analysis. ................................117

Figure 12. Summary and total areas of the circa year 2000 EOSD land cover classes for the Livingstone, Grande Cache, Clearwater, Yellowhead, and Swan Hills grizzly bear population units. ...................................................................................................................118

Figure 13. Heli‐GPS survey data mapping the distribution of mountain pine beetle infestation for the 2005‐2008 beetle years. .........................................................................118

Figure 14. Summary of the total number of hectares exhibiting one or more crowns infested by mountain pine beetle by EOSD vegetated land cover class for the Livingstone, Grande Cache, Clearwater, Yellowhead, and Swan Hills grizzly bear population units. Infestation is represented using the 1 ha tessellated red attack surface. ..................................................................................................................................119

Figure 15. Summary of the total areas of landscape disturbance by vegetated land cover class for the Livingstone, Grande Cache, Clearwater, Yellowhead, and Swan Hills grizzly bear population units. Disturbances were delineated and dated using the Landsat‐ and MODIS‐derived STAARCH product. Note that the data extends only to the summer of 2008. ............................................................................................................. 120

Figure 16. Number of red attack crowns found at locations mapped during the 2008 Heli‐GPS survey for Landsat‐5 TM path 46, row 21. .............................................................121

Figure 17. Examples of Infestation intensities typical of the study area in 2008. Image data is 0.20 m true colour digital aerial imagery collected in 2008. .....................................121

Figure 18. Time 1 and Time 2 Landsat data used to create the mountain pine beetle red attack map for 2008. Time 1 data were acquired on 6 August 2008; Time 2 data were acquired on 24 July 2009....................................................................................................... 122

Figure 19. Example of the forest/non‐forest mask employed during the analysis. Non‐ forest areas are shaded black in the right hand panel..........................................................123

Figure 20. Box and whisker plots displaying mean, standard error of the mean, and ±two standard deviations of the enhanced wetness difference index for selected healthy and attacked sites..................................................................................................................124

Figure 21. Comparison of the 2008 Heli‐GPS survey data (right) and the final red attack classification (left) for a portion of path 46, row 21. ............................................................125

Figure 22. Examples of the 0.20 m spatial resolution airborne images collected along two transects. Panels a) and b) are examples of healthy forests, while infestation intensities shown in panels c) and d) are typical of the area of interest. .............................127

Figure 23. Red attack mapping with green attack back‐casting for G:R estimation...................128 Figure 24. Decision tree developed to predict presence and absence of lodgepole pine,

based on the maximum effect of the four seasonal climate modifiers. ...............................130 Figure 25. Prediction of lodgepole pine distribution under current climate and the three

future 30‐year periods: 2020, 2050 and 2080. ..................................................................... 131 Figure 26. Image stack of the images used in the Kawka study. These images will assist in

showing the location, regionalization, and date of changes occurring within this greater Kakwa ecosystem. ....................................................................................................134

Figure 27. Change by time period from the Landsat time series analysis ..................................135 Figure 28. Decadal summaries of the Landsat time series change.............................................136 Figure 29. Summary of area changed over time (findings on rates and proportions also

available) ...............................................................................................................................136 Figure 30. Summary of change events and change mean change event size.............................137

Introduction

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INTRODUCTION Over the past eleven years the Foothills Research Institute’s (FRI) Grizzly Bear Program (GBP) (FRIGBP) has made significant advances in improving our understanding of how grizzly bears use forested landscapes within their range in Alberta. Our research program continues with the development of new tools and models to assist in sustainable forest and land management practices and decisions concerning the long‐term conservation of grizzly bears. Our health initiative successfully completed its 4th year of a 5 year project. This work involves a multi‐disciplinary team of researchers from the University of Saskatchewan, University of Waterloo, University of Alberta, University of Calgary, the Foothills Research Institute, and eight industry partners (3 forestry, 5 oil and gas) extracting resources across an area of approximately 228,000 km2 in the western part of Alberta coincident with the distributional range of grizzly bears for the province. The broad objective of this research is to investigate relationships between landscape structure, human‐caused landscape change, grizzly bear health and population performance through combined use of remote sensing technology, Global Positioning System (GPS) radio‐telemetry, wildlife health and stress evaluation, and molecular techniques in biomarker development. We have also made significant progress in our ongoing work on documenting and defining grizzly bear health conditions along the eastern slopes. These health condition data have been improved by the inclusion of new laboratory results from the pioneering work we have undertaken on the measurement of chronic stress in grizzly bears. Interest in this work has been received from researchers in other areas of North America as this research can be used on other wildlife species and in other areas. Scandinavian grizzly bear researchers interested in our health work prompted a new collaboration between the FRI GB Program and a long running brown bear (grizzly bear) program based in Sweden and Norway. Under the newly formed Circumboreal Forest Initiative within FRI, the FRI GB Program began sharing data and methodologies with the Scandinavian Brown Bear Project. The objective of this collaboration is to focus on sustainability of brown bear populations in boreal forest landscapes. The Scandinavian brown bear study has been in existence since 1984 and has one of the longest term and unique data sets on brown bears in the world. Brown bears in Scandinavia went from about 130 animals in the 1930s to over 3,300 animals today and illustrate the potential brown bear populations have in recovery and recolonization. In Alberta, wildlife managers are just embarking on recovery efforts for this species and undoubtedly we will learn many things from our Scandinavian counterparts. This is a unique opportunity for groups of internationally recognized scientists, from a wide range of disciplines, to work together to share knowledge, expertise, and experience conserving and managing brown (grizzly) bears in boreal forest and mountain ecosystems. This initiative began late in 2009. Preliminary results will be forthcoming in 2010. In 2008, we embarked on the development of new food based RSF models for the Chinchaga/Clear Hills area. In 2009, we expanded this work to cover an area from the Brazeau River in west‐central Alberta to Grande Prairie and east to Swan Hills. A first draft of this new product is provided in this report. Preliminary work to determine the use of whitebark pine by grizzly bears was also initiated in 2008 and continued in 2009. Other projects continuing from

Introduction

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2008 include the relationship between weather on grizzly bear’s denning activities and habitat use and grizzly bear food energetics. Our mountain pine beetle project to examine mountain pine beetle forestry activities and its impacts on grizzly bears completed its 3rd year of a 5 year program. This work has involved building upon data sets gathered from 2005‐2009 in the Kakwa area and will utilize new approaches to monitor and measure MPB/landscape change, and grizzly bear movements. The remote sensing team from University of British Columbia and University of Victoria has developed new data sets that now allow landscape condition maps to be produced at 16 day intervals. In addition this team has new mapping products that provide information of MPB presence and spread and the distribution of predicted future forests. These products will be combined with hourly GPS locations from collared bears to allow an analysis of detailed movements and response to known MPB activities. The remote sensing work has also developed new approaches and data sets to allow the preparation of plant phenology map layers. It is envisioned that these products will be related back to NDVI layers to show the seasonal phenology of grizzly bear foods across the study area which will be an important component in understanding grizzly bear habitat selection in relation to MPB. This plant phenology remote sensing work is also linked with our ongoing grizzly bear food energetics work. This MPB work was undertaken at the request of SRD and is linked to the FRI Mountain Pine Beetle Ecology Program through funding support and the sharing of remote sensing products for use in both projects. In addition the landscape condition map layers that we are creating have value for other FRI programs (and partners) who need to understand and monitor changing landscape conditions over time. The results of our research efforts will enable all our program partners to make well informed and timely land use and planning decisions to maintain ecosystem health and to support sustainable development in provincial grizzly bear habitat. Although this project focuses on Alberta grizzly bear populations, the concepts, techniques and relationships uncovered can be applied to a variety of species at risk in Alberta and Canada. These leading edge innovative products and techniques will make Alberta a recognized world leader in ecosystem management and monitoring. This annual program report for the Foothills Research Institute’s Grizzly Bear Program (formerly known as the Foothills Model Forest Grizzly Bear Research Program) is divided into separate sections which provide detail on the various program elements within the research effort. These sections have been prepared by the principal investigators of these elements who have or will be publishing most results in scientific peer reviewed journals. A listing of research publications is presented in Appendix 3. Our research team had a great year in 2009, writing a large number of peer reviewed publications along with many that have been submitted. On behalf of our research team we want to extend our gratitude to every program partner for your ongoing support and encouragement of our work. Gordon Stenhouse Karen Graham

Chapter 1 Capture Summary

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CHAPTER 1: SUMMARY OF 2009 CAPTURE PROGRAM

Gordon Stenhouse, FRIGBP Leader Foothills Research institute

Introduction The 2009 grizzly bear capture session was the 11th conducted by the Foothills Research Institute (FRI) Grizzly Bear Program previously known as the Foothills Model Forest Grizzly Bear Research Project. In 1999, our 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. In 2003 the study area 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. In 2006, the study area expanded northwards to include the Chinchaga River, The Hotchkiss River and the Meikle River area. We also included an area south of the Wapiti River and returned to the Swan Hills. In 2007 our capture and collaring efforts were focused in two areas. We returned to the Kakwa and Nose Hill Tower areas of the Weyerhaeuser FMA and the Clear Hills area north of Worsley which included the southern part of the Chinchaga area. In 2008 our capture and collaring efforts was focused in the Kakwa River, Nose Hill Tower and Two Lakes areas which is an area that represents current MPB outbreak and is part of the Weyerhaeuser FMA. In 2009 we again focused our capture and collaring efforts in the Kakwa River and Two lakes area which are within the Grande Cache Bear Management Unit. The focus of this capture effort was to collect grizzly bear movement data that will relate to ongoing mountain pine beetle activities and ongoing forest management efforts in this area. Our research team has recently completed an RSF map product for the Kakwa area with grizzly bear location data obtained prior to mountain pine beetle activity in this area. In addition data was needed from collared bears on habitat use and denning selection. We continue to collect important information on health parameters of all grizzly bear handled during our operations. In 2009 we also provided capture and handling assistance to a study underway by Bogdan Cristescu (University of Alberta) in the Cheviot mine area south of Hinton. This capture/handling work is reported on within this document. Study Areas We captured and sampled grizzly bears in two distinct study areas in 2009 (Figure 1). Eleven bears were captured in a northern area between Grande Prairie and Grande Cache, known as the Kakwa study area and representing our primary research area for 2009. Eight bears were also captured at a more southern location, south of Hinton and east of Jasper National Park,


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known as the Cheviot study area. Bears were captured in this latter area to assist another research project conducted by investigators at the University of Alberta.

Figure 1. Kakwa (north) and Cheviot (south) study areas with 2009 capture site locations. Grizzly Bear Captures Field capture efforts began in May 2009 with field crews working out of camps at Two Lakes and the Kakwa Tower in the Kakwa study area. The crews consisted of biologists with experience in grizzly bear capture. The capture team primarily used ground‐based trapping along existing forest access roads with culvert traps fitted with satellite trap alarm systems. Early in the capture season we did utilize some leg‐hold snares for the capture of three bears. Trap alarms were used on all snares and traps to minimize the amount of time a bear was held after capture. A limited amount of helicopter darting was employed which targeted specific bears for recapture and collar replacement. Primary capture efforts were concluded at the end of June. Aerial capture work occurred primarily in October. An additional eight grizzly bears were also captured, collared, and sampled in the Cheviot study area in conjunction with another research effort underway. Lastly, samples were also obtained from Alberta Sustainable Resource Development staff who captured and handled seven grizzly bears for management purposes. We anaesthetized grizzly bears using a combination of xylazine and Telazol administered by remote drug delivery, e.g., dart rifle. Once immobilized, grizzly bears were weighed, and


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measured (chest girth, zoological length, and straight‐line length). Samples were collected (blood, hair, skin biopsy, and tooth). Radio‐collar and ear tag transmitters were attached. A transponder (microchip) was also inserted beneath the skin for future identification purposes. Vital functions and blood‐oxygen levels were monitored throughout the handling period. Following handling, we administered atipamezole to reverse the effects of anaesthesia and monitored the grizzly bears until they showed imminent signs of recovery. We re‐checked all bears again within 24 hours of capture to ensure they had recovered fully from immobilization. All details of capture operations conformed to national standards on the capture and handling of ursids as well as provincial standards. In total, we captured 19 grizzly bears in our 2009 field season (Table 1). Eleven bears were caught in the Kakwa area, and 8 bears were captured as part of a University of Alberta study by our research team in the Cheviot mine area south of Hinton, Alberta. No black bears were handled this field season and no other non‐target species were captured. No capture related mortalities occurred during the 2009 field season. Table 1. Capture summary for 2009 field season.

Bear ID Capture Date Recapture Area Sex Age Class Cub Status

G023 12‐May‐09 yes Cheviot Female adult 2 Yearlings G053 9‐Oct‐09 yes Cheviot Male adult G112 28‐Apr‐09 no Cheviot Male adult G113 2‐May‐09 no Cheviot Female adult None G114 3‐May‐09 no Cheviot Male adult G115 11‐May‐09 no Cheviot Male adult G116 3‐Oct‐09 no Cheviot Male yearling G117 20‐Oct‐09 no Cheviot Female adult None G238 17‐Oct‐09 yes Kakwa Female adult None G251 17‐Oct‐09 yes Kakwa Female subadult None G254 13‐Jun‐09 yes Kakwa Female adult 2 COY G257 29‐May‐09 no Kakwa Male adult G258 16‐Jun‐09 no Kakwa Female adult None G259 24‐Jun‐09 yes Kakwa Female subadult None G260 16‐Oct‐09 yes Kakwa Female adult 2 COY G262 24‐May‐09 yes Kakwa Male adult G266 24‐Jun‐09 yes Kakwa Male subadult G268 27‐Jun‐09 no Kakwa Male adult G269 29‐Jun‐09 no Kakwa Female adult 2 Yearlings


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GPS Radio‐Telemetry Data We deployed a Global Positioning System (GPS) radio‐collar and (VHF) ear‐tag transmitter on all captured bears. As of mid‐October 2009, all collars were still functioning, and no collars had been pulled off. All radio‐collars have an integrated remote release mechanism in addition to a rot‐off system as a backup in case of electronic failure. Nineteen radio‐collars were deployed. Radio‐collars deployed consisted of 3 types, a single Telemetry Solutions GPS collar, Follow‐it (Tellus II) and a single Follow‐it Satellite GPS collar deployed on a large adult male. Follow‐it collars collect locations on the following schedule: • 1 April to 31 November ‐ 1 location/hour. • 1 December to 31 March ‐ 2 locations/hour. We did monthly data upload flights from all collared bears in the Kakwa study area and, as of 15 October we have recorded over 100,000 GPS location points (Figure 2).

Figure 2. Extent of spatial coverage of GPS location data from bears monitored in 2009. Overall we have collected health and GPS data from collared grizzly bears in the Kakwa study area for the period of 2005‐2009 as part of this project (Figure 3).


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Figure 3. Extent of sampling area for GPS locations for the period 2005‐2009. Additional GPS data collection took place in January 2010 once denning had occurred. As in previous years we used the individual GPS locations to determine locations of all den sites in order to investigate denning behaviour. We installed weather monitoring stations at a number of grizzly bear dens in early January to collect microsite weather data to relate to temperature data being collected from collars in dens. Grizzly Bear Health Evaluation We gathered complete health information from 19 grizzly bears captured in Alberta in 2009 as part of our research activities and partial health information from another seven grizzly bears captured by Alberta Sustainable Resource Development staff. The data from these 26 bears include data on physical and physiological measurements recorded at capture as well as results from subsequent laboratory analyses of blood serum, skin, and hair. All health data for 2009 are now entered into our project health database bringing the total number of health records for the project to 334 cases representing 206 unique animals of which 71 have multiple (2‐8) records over intervals ranging from 1 month to 8 years. Sex and Age Characteristics Of the 19 grizzly bears captured 15 (79%) were adults, 4 (21%) were sub‐adults, 9 (47%) were males, 10 (53%) were females (Table 1). Adult females made up the largest component of captured grizzly bears (42%) while adult males, sub‐adult males, and sub‐adult females comprised 37%, 5%, and 11% of captured grizzly bears respectively. One yearling (G116) was captured in a culvert trap and released with an ear tag transmitter but no GPS radio collar. No cubs of the year were caught.


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Capture Type Capture types were categorized as ground capture with snare, ground capture with culvert trap or helidarted. Most ground capture sites used snares in 3 different formats, pail sets, cubby sets and/or trail sets. Of the 19 capture events (Table 2), helicopter captures accounted for 21% (4) of capture events and ground captures accounted for 79% (15) of capture events. Five ground‐capture events involved snares and 10 involved a culvert trap. Table 2. Grizzly bear capture types.

Cheviot Mine Area Kakwa Grizzly Bear IDs Capture Types Grizzly Bear IDs Capture Types G023 Culvert G238 Culvert G053 Culvert G251 Heli G112 Snare G254 Heli G113 Snare G257 Snare G114 Snare G258 Snare G115 Culvert G259 Culvert G116 Culvert G260 Heli G117 Heli G266 Culvert G262 Culvert G268 Culvert G269 Culvert Bear Captures – Cheviot Mine Area Our research team captured and collared eight grizzly bears in the Cheviot mine area for Bogdan Cristescu. Two of these bears (G23 and G53) had been captured earlier in our research project. These bears were collared to provide additional data on grizzly bear movements and habitat use in the vicinity of the Cheviot mine site.

Chapter 2 Environmental Variables

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CHAPTER 2: DENNING BEHAVIOUR, THERMOREGULATION, AND

ENVIRONMENTAL VARIABLES

Karine Pigeon, PhD Candidate Laval University, Quebec City

Introduction Hibernation Atmospheric concentrations of greenhouse gases have increased to levels generating global climate change. Long‐term changes including widespread shifts in temperature, precipitation, wind patterns, and extreme weather events have been widely recorded (International Panel on Climate Change, 2007; Allan and Soden, 2008). Accordingly, a number of recent studies have documented and modeled the impact of climate change on natural systems (e.g. Parmesan and Yohe, 2003; Rosenzweig et al., 2008). A problematic and potentially detrimental aspect of climate change and its impact on natural systems is that climate change may act as an additional strain on already fragile populations affected by other factors (e.g. habitat loss and over‐exploitation). In this effect, there may be a strong link between the ability for a species to survive and its ability to shift or expand its range into new, more suitable areas (Lister and Stuart, 2008). The effects of climate change on fragile populations will likely be more pronounced during energetically demanding periods such as cub rearing and winters. Understanding species‐specific vulnerability and resilience can therefore be crucial to the successful conservation of fragile populations in the context of climate change. Recent evidence from population census efforts suggests that the grizzly bear population in Alberta is smaller than previously expected (Alberta Sustainable Resource Development and Alberta Conservation Association, 2010). For bears, the denning period is widely viewed as a sensitive period during which disturbances can have significant fitness costs (Swenson et al., 1997; Linnell et al., 2000). Even though bears hibernate in order to reduce their energy expenditure, Farley and Robbins (1995) observed that during hibernation, lactating grizzly bear females lost nearly twice as much weight as non‐lactating females. These large energy costs imply severe consequences from potential winter disturbances and den abandonment. In the context of climate change, the potential for winter disturbances and den abandonment may be heightened by unseasonably warm weather events such as premature spring conditions and/or den flooding occurrences. Thermoregulation There are numerous studies highlighting the importance of environmental conditions on the thermoregulation and behavioural responses of mammals (e.g. Done‐Currie and Wodzicka‐Tomaszewska, 1984; Dussault et al., 2004). Individuals thermoregulate primarily by adopting different behaviours in response to variations in their environment. In the context of climate change, behavioural responses and thermally‐driven habitat selection will undoubtedly have implications for the long‐term conservation of fragile populations (Parmesan and Yohe, 2003; Kearny et al., 2009; Dubois et al., 2009). For grizzly bears, investigating year‐round behaviour and habitat use in relation to meteorological variables and weather patterns may prove to be a key step in understanding the impacts of climate change on population requirements.


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Methods Study Area For the denning portion of this study, we are using all relevant grizzly bear GPS‐collar data gathered by the FRIGBP since 1999 and are currently focusing on collecting new data within the two current study areas. These areas include the Weyerhaeuser Grande Prairie (WGP) Forest Management area (13,031km2) directly south of Grande Prairie, Alberta, and the Hinton‐Cadomin area (18,907km2) directly south of Hinton, Alberta. For the thermoregulation portion of the study we are focusing data collection within the WGP study area only. Meteorological Variables To investigate potential relationships between grizzly bear behaviour, habitat selection, and weather conditions, we are using archived and current weather data from nearby mines (Gregg River Mine, Cardinal River Coal, and Coal Valley Mine in the Hinton‐Cadomin area), and four provincial weather stations located within both study areas. In addition to the mine and provincial weather data, we are also using Onset Micro Station Data Loggers® (hereafter referred to as Micro Stations) installed in five habitat types (pine, black spruce, broadleaf, 0‐10‐yrs old regenerating stand, and 11‐20‐yrs old regenerating stand) of the WGP study area. Micro Stations were installed in 2007 and will be running for one additional year, until spring 2011. These habitat types were chosen based on their abundance and known utilization within the WGP study area. Weather variables recorded by the data loggers include temperature, solar radiation, barometric pressure, relative humidity, and wind. In addition, precipitation data is available from the mine and provincial weather datasets. Yearly average snowpack depth will be inferred from available precipitation data (1999‐2011). The frequency of Chinook conditions (warm wind, warm temperatures, intense solar radiation, and/or rain events) will be determined from the weather station data. Daylight hours will be determined from sunset/sunrise tables associated with GPS‐collar location data. Forage Availability Because habitat selection and behaviour are undoubtedly linked to the availability and abundance of food resources, and because berries are the primary food source for grizzly bears in central Alberta during late hyperphagia (16 Aug – den entry; Larsen and Pigeon, 2006), 575 permanent berry plots (0.5m2) were established in the WGP area in an effort to record fluctuations in berry availability and abundance. Within these 575 permanent berry plots, we monitored replicates of six key berry‐producing shrub species (Vaccinium. membranaceum [100 plots], V. myrtilloides [100 plots], V. caespitosum [100 plots], V. vitis‐idea [200 plots], Empetrum nigrum [30 plots], and Shepherdia canadensis [45 plots]) recognized as important grizzly bear foods (Larsen and Pigeon, 2006). Plots were installed during the summer of 2008, stratified within three classes of shrub percent cover (low: 1‐25%, moderate: 26‐50%, high: > 50%) and positioned on slopes of less than 10º. An equal number of stratified plots (low, moderate, and high % cover of shrub) were evenly distributed within the three main berry‐producing habitat types of the study area (70‐100% pine, black spruce (< 30% pine), and 10‐25 year old regenerating stands). Habitat type, percent canopy cover, and soil texture of individual plots were assessed upon installation and percent cover of shrub, vegetative and reproductive phenology, plant vigour, and the number of individual berries present were recorded periodically (every 3‐weeks) from July to September. Once most berries were ripe within a plot, all berries were picked and weighed for biomass estimates. The same protocol will be repeated for one additional year (2010). In addition to permanent plots, the distribution and occurrence of berry‐producing shrub species was assessed by visiting 5‐hectare quadrats randomly positioned within 70‐100% pine,


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black spruce (< 30% pine), and 10‐25 year old pine stands of the WGP area (2008: black spruce stands = 3 quadrats, regenerating stands = 3 quadrats; 2009: black spruce stands = 9 quadrats, pine stands = 9 quadrats, regenerating stands = 9 quadrats). Within these 5‐hectare quadrats, 50 random plots (0.5m2) were visited in order to record presence / absence and percent cover of the 6 shrub species. From these data we will derive an estimate of yearly berry biomass availability within individual’s home ranges based on the proportion of each habitat present in the home range and investigate the links amongst habitat selection, movement rates, activity patterns, food availability and weather variables. Denning: Pre‐ And Post‐Denning Behaviour To assess the potential links amongst weather variables, food availability, and pre‐ and post‐denning behavioural changes, we will use GPS‐collar data to infer movement rates, activity patterns (diurnal vs. nocturnal), and habitat selection for bears in both study areas. Movement rates of individuals (distance travelled in meters per hour) will be derived from continuous GPS‐collar locations data. Only sets of subsequent locations equal to or less than 2‐hr apart will be used for analyses. Activity patterns (timing and fluctuations of activity peaks) will be determined from the movement rate data. Timing Of Den Entry And Den Emergence We determined den entry dates (n=60) and den emergence dates (n=44) at a day‐level accuracy using hourly (May‐November) and bi‐hourly (December‐April) GPS‐collar data. Sample size varies between den entry and den emergence dates because collars fitted on individuals captured prior to the start of this research were often programmed to release during the denning period in order to facilitate collar and data retrieval. Using ArcGIS 9.3, we mapped all GPS‐collar locations with acceptable degrees of precision, DOP < 7 (Lewis et al., 2007) occurring within the denning period. To accurately delineate individual den clusters, we used the spatial statistic hot spot analysis Getis‐Ord Gi* tool to obtain z‐scores and p‐values representing the statistical significance of the den clustering while taking the spatial relationship and scale of individual GPS‐collar locations into account. To determine the actual den site location, we used all GPS‐collar locations representing the den cluster (z‐scores > 2.58 and p < 0.001) and used the Mean Centre tool to define den site location. The mean error rate between known (field visits) den locations and den site locations estimated with this method was 15.2m ± 2.7 (n =16). Field visits will be conducted for each den site in order to validate each den site location. Using the known or estimated den location, we generated buffers representing the actual den site (buffer = 1sd of all den GPS‐collar locations), the den area (buffer = 3sd of all den GPS‐collar locations), and the den environ (buffer = 1km distance from the centre of den). The timing of den entry and the arrival at the den area and den environ was then determined using the date of the first GPS‐collar location included within the respective buffers. The reverse of this method was used to determine den emergence dates and departures from den area and den environ. We used the standard deviation of individual den site GPS‐collar locations to create den‐site (1sd) and den‐area (3sd) buffers rather than a fixed distance in order to account for variations in GPS fix rate, accuracy, and precision resulting from the different collar types used throughout the study period, as well as to account for differences in GPS fix rates associated with variations in habitat types (Frair et al., 2004; Hebblewhite et al., 2007).


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Thermoregulation: Activity, Standard Operative And Operative Temperatures To investigate whether movement rates and activity patterns are influenced by thermoregulation needs, we will use summer (June, July, and August) movement rates (m/hr), and activity patterns (patterns of activity peaks) obtained from GPS‐collars of individuals captured in the WGP area since 2008. Movement rates will be compared to average daily temperatures and maximum daily temperatures obtained from four permanent Micro Stations installed in pine, black spruce, 0‐10‐yrs old regenerating, and 11‐20‐yrs old regenerating stands approximately in the centre of the WGP study area since 2008. In addition to the permanent weather stations, another four Micro Stations were installed in pine, black spruce, broadleaf, and 0‐10‐yrs old regenerating stands in the western (2008) and northern (2009) portions of the WGP area. In 2010, these weather stations will be re‐installed in the same habitat types of the southern portion of the study area. Weather stations are temporarily installed in different sections of the WGP study area in an effort to obtain adequate spatial coverage and potential variations related to the spatial variability of weather data. In addition to the Micro Stations, we have also installed twenty‐seven operative temperature (Te) data‐loggers within all the main habitat types (3 replicates per habitat type) of the WGP study area. Habitats with operative temperature data‐loggers are pine, black spruce, treed wetland, broadleaf, shrub, and regenerating stands (0‐10 year old, 10‐25 year old, 25‐35 year old, and 35‐45 year old). Operative temperature sensors give an accurate measure of the “perceived temperature”, taking the effects of solar radiation, canopy cover, and wind into account for each temperature record logged within the habitat types mentioned above. Operative temperature sensors were installed in the summer of 2008 and are scheduled to acquire data until spring 2011. At the moment, undergraduate students at Université Laval are working on building prototypes of portable standard operative temperature (Tes) sensors which are scheduled to be deployed in the WGP study area in the summer of 2010. Standard operative temperature data‐loggers differ from operative temperature data‐loggers as the former acts as a warm body (the environment within which the data‐logger is placed is heated to mimic the body temperature of an endotherm). Because the within‐habitat temperature perceived by a mammal is influenced by its potential heat gain and/or heat loss to the environment, and because the heat transfer between a mammal and its environment differs from the heat transfer of an unheated object, standard operative temperature data‐loggers give a more accurate depiction of how a mammal perceives its thermal environment (see Bakken, 1992 and Dzialowski, 2005 for detailed descriptions of Te and Tes models and calculations). Both operative (2008‐2010) and standard operative (2010) temperature data‐loggers will be utilized in an effort to understand the influence of thermoregulation on an individual’s behavioural decisions. Again, movement rates and activity patterns obtained from GPS‐collars will be compared to average and maximum operative, and standard operative temperatures recorded within the various habitat types. Data‐loggers are visited periodically to insure proper equipment performance. Habitat Selection In The Context Of Thermoregulation To investigate whether habitat selection is influenced by thermoregulation needs, we will also compare summer (June, July, and August) GPS‐collar locations of individuals captured in the WGP area since 2008, to average daily temperatures and maximum daily temperatures obtained from weather stations, operative temperature, and standard operative temperature data‐loggers deployed in the study area.


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Based on previous food consumption analyses conducted in the WGP study area (Larsen and Pigeon, 2006), grizzly bears should select key forage resources according to plant phenology, energetic content, and abundance and availability of the different species. Moreover, individuals should also select specific habitat types according to habitat‐based differences in plant phenology, abundance, and availability. Using GPS‐collar location data, we will therefore investigate the potential effects of thermoregulation on the selection of habitats by comparing expected food‐based habitat selection to observed actual habitat selection. If thermoregulation influences habitat selection during the summer, then expected habitat selection should differ the most from observed habitat selection in extreme (e.g. hot days) weather conditions. In order to determine the food‐based expected habitat selection of individuals in the WGP area, I will use a habitat‐based food model (S. Nielsen, unpublished data), and a habitat‐based food biomass model (T. Larsen, unpublished data) currently being developed. Results and Discussion Pre‐ And Post‐Denning Behaviour In order to investigate potential variations in movement rates prior to den entry, we performed general linear mixed models (GLMMs; Proc MIXED, SAS 9.1) of daily mean movement rates during a six‐week period preceding den entry for eleven females and five males using data from both study areas. We accounted for repeated observations of individuals over the years by nesting individual within den‐years and accounted for the variability in location numbers per day per individual by using the Julian day as a random variable. As was observed by Friebe et al. (2001), our preliminary results show a decrease in movement rates for a six‐week period prior to den entry for males (F = 28.4, df = 4, P = 0.006; Figure 1) and females (F = 49, df = 10, P < 0.0001; Figure 2), respectively. The same observation (i.e. an increase in movement rates with an increasing number of days) can be observed for the six‐week period following den emergence, for males and females combined (n = 13; F = 155, df = 9, P < 0.0001; too few individuals to analyze by sex, Figure 3). Unlike our preliminary results, however, Manchi and Swenson (2005) found virtually no reduction in male movement rates until the last two weeks prior to denning during which movement rates dropped abruptly. Both Friebe et al. (2001) and Manchi and Swenson (2005) hypothesized that observed reductions in movement rates prior to denning were associated with the high availability of berries in Sweden during that time of the year. If food is highly available in one area, there may not be any need for the bears to keep moving. In our study areas, however, most berry species generally ripen in mid‐ to late August, well before den entry. In this case, the observed reductions in movement rates are likely a result of some other factors such as lower ambient temperatures, reduced daylight hours, or perhaps the result of a physiological process. Moreover, because our preliminary results show a similar trend in movement rates after den emergence, the high food availability hypothesis proposed for Sweden does not seem appropriate here. In our study areas, bears generally emerge from dens in mid to late April while there are very little food resources available. Den‐environ pre‐denning: To further investigate denning behaviour, we conducted GLMs (Proc GLM, SAS 9.1) of the time spent at the den environ for male and female from 1999 to 2008. Considering the Hinton‐Cadomin and the WGP study areas independently, there were significant annual (F = 7.78, df = 6, P = 0.004) and individual (F = 3.07, df = 27, P=0.04) variations in the time spent within the den environ for the Hinton‐Cadomin area but no variations within the WGP area (year: F = 0.93, df = 3, P = 0.5; individual: F=0.75, df = 11, P=0.7). There were no differences in the time spent within the den environ between sexes for the Hinton‐Cadomin area (F = 0.56, df = 1, P=0.5), for the WGP area, sample sizes are too small to investigate potential variations caused by sex. Still, these preliminary results suggest that differences observed could be due to environmental


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factors such as variations in food availability within the different home ranges or to variations in weather conditions. Den‐environ post‐denning: In the Hinton‐Cadomin area, there were no variations in the time spent within the den environ post‐denning when considering years (F = 1.30, df = 3, P = 0.4), individuals (F = 1.93, df = 21, P = 0.2), or sex (F = 1.61, df = 1, P = 0.2). There were, however, yearly variations in the WGP area (F = 9.51, df = 4, P = 0.03), but no differences amongst individuals (F = 0.91, df = 8, P = 0.6). All bears except two in the WGP area were females; these results thus suggest that yearly variations have an influence on the time spent near the den after emergence. The fact that no yearly variation was observed in the Hinton‐Cadomin area might be a reflection of behavioural differences attributable to mountain vs. foothills bears (Ciarniello et al., 2005). Low food availability near mountain den sites may force bears to rapidly migrate into lower areas to feed while foothills bears may have the option to remain near their dens. Because samples sizes are currently too small to test for the effects of reproductive status, time spent near the den after emergence may also be related to reproductive status or weather conditions. Forage availability: In the WGP, the mean total biomass of ripe berries per quadrat was 29% higher in 2008 than in 2009 (paired t‐test; t = 3.62, df = 158, P=0.0004; Figure 4). When considering species individually, quadrats of Empetrum nigrum (t = 2.96, df = 20, P = 0.008) and Vaccinium caespitosum (t = 2.86, df = 40, P=0.007) were both significantly more productive in 2008 than in 2009. There were however no marked differences for Sheperdia canadensis (t = 0.98, df = 26, P=0.3), V. membranaceum (t = 0.97, df = 63, P = 0.3), and V. myrtilloides (t = 1.58, df = 37, P = 0.1). It is also worth noting that even though quadrats were visited during similar periods between years, there were no ripe V. vitis‐idea berries in 2009 (Figure 4). Once we obtain denning behaviour data for the 2009‐2010 and 2010‐2011 denning periods, we will investigate whether differences in food availability within respective home ranges explain some of the yearly variations observed in pre‐denning behaviour. Our preliminary results suggest, however, that there is a potential for yearly variations amounting to a 30% difference in available berry biomass. Timing Of Den Entry And Den Emergence As previously observed in most other grizzly bear populations (e.g. Haroldson et al., 2002; Manchi and Swenson, 2005), females in both the Hinton‐Cadomin and WGP areas den significantly longer than males (n =42; t = 3.63, df = 40, P =0.0008, Figure 5). In both study areas, there were no differences in the length of the hibernation period amongst years or individuals (Hinton‐Cadomin year: F = 0.72, df = 3, P = 0.6; individuals: F = 1.27, df = 18, P = 0.4; WGP year: F = 0.31, df = 3, P = 0.8; individuals: F = 0.49, df = 6, P = 0.8, the same individuals could be followed for more than one denning period). Because there was only one male within the WGP study area, we could not investigate differences in the length of the hibernation period per sex between the two study areas. Still, our preliminary results fit well with most other studies. Combining both study areas, females entered dens earlier (F=5.68, df = 1, P=0.02) and emerged later than males (F=6.45, df = 1, P=0.02). Our preliminary results support the hypotheses that weather variables and/or differences in food availability affect pre‐ and post‐denning behaviour. Moreover, the observation that the hibernation length, den entry, and den emergence dates do not seem to be affected by year or individuals supports the hypothesis that sex and reproductive status may best determine the timing of den entry and den emergence . These findings also suggest that pre‐and post‐denning


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behaviour, timing of den entry and den emergence, and the hibernation length may be driven by different factors. Future Analyses Pre‐ And Post‐Denning Behaviour In order to assess whether food availability and/or weather variables are linked to the observed decline in movement rates prior to, and post denning, we will perform GLMMs of mean movement rates, timing of activity peaks (activity pattern), weather variables, and mean berry availability per month within individual’s home ranges. Principal component analyses (PCAs) will be performed on weather variables to reduce the multidimensionality of the data and identify combinations of variables which best explain large amount of variations. Mean berry availability per home range and associated yearly fluctuations will be determined for the 2008, 2009, and 2010 summer and fall seasons using berry phenology and biomass data obtained from permanent and random plots. In addition to movement rates and activity patterns, we will further investigate the propensity of individuals to visit future den sites during the summer, stay, or roam in and out of the den environ and den area during the summer and fall prior to, and the spring following the denning period. Timing Of Den Entry And Den Emergence In order to determine the relationships amongst the timing of den entry and den emergence, the duration of the denning period, body condition, sex, age, reproductive status, forage availability and yearly variations in weather conditions, we will build GLMMs of arrival at den environ, den area, den site, and denning length vs. sex, age, and reproductive classes as well as body condition, forage availability, yearly mean monthly temperatures (September‐December) and other meteorological variables (PCAs, September‐December). The same variables will be investigated for the length of the hibernation period, den emergence, and spring departure from den site, den area, and den environ. Climate trends data investigations: Monthly temperature averages (mean, minimum, and maximum) will be derived from archived weather data obtained through the mine and provincial weather stations for the period overlapping available denning behaviour data (1999‐present). Using GLMMs, we will then investigate potential associations amongst the timing and the length of the hibernation period, and ambient temperature changes. Other available meteorological variables such as solar radiation and precipitation data will also be investigated using GLMMs and PCAs. Potential effects of age, sex, and reproductive status will be included in the models as covariates. We will also investigate the potential link between denning behaviour and photoperiod by plotting den entry and emergence dates over average daylight hours/day for the months of September, October, November, and December (1999‐present). Thermoregulation: Activity, Standard Operative And Operative Temperatures And Habitat Selection Accounting for food availability, and again using GLMMs and PCAs, we will determine the relationships amongst meteorological variables, movement rates, activity patterns, and habitat selection from available GPS‐collar data.


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Figure 1. Observed movement rate (m/hr) per day prior to den entry for grizzly bear males (n = 5, t = 5.33, intercept = 3.94) in the WGP and Hinton‐Cadomin study areas (2005‐2008). Movement rates were log‐transformed (ln) to respect the assumptions of normality of residuals and the homogeneity of variance.

3.2

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Figure 2. Observed mean movement rates (m/hr) per day prior to den entry for grizzly bear females (n = 11, t = 7.0, intercept = 3.68) in the WGP and Hinton‐Cadomin study area (2005‐2008). Movement rates were log‐transformed (ln) to respect the assumptions of normality of residuals and the homogeneity of variance.


17

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Figure 3. Observed mean movement rate (m/hr) per day post den emergence (n = 13, t = 12.4, intercept = 3.65) in the WGP and Hinton‐Cadomin study areas (2006‐2009). Movement rates were log‐transformed (ln) to respect the assumptions of normality of residuals and the homogeneity of variance.

Figure 4. Mean biomass of ripe berries per quadrat in 2008 and 2009 (n = 281), biomass estimates calculated by year using the equation: biomass/quadrat = mean weight of individual berry species*(sum of berries / quadrat). Numbers above columns indicate sample size.


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Figure 5. Number of days spent in hibernation for male (n = 10) and female (n = 32) grizzly bears in the WGP and Hinton‐Cadomin study areas (2005‐2008). Literature Cited Alberta Sustainable Resource Development and Alberta Conservation Association. 2010. Status

of the Grizzly Bear (Ursus arctos) in Alberta: Update 2010. Alberta Sustainable Resource Development. Wildlife Status Report No. 37 (update 2010). Edmonton, AB. 44pp.

Allan, R.P. and B.J. Soden. 2008. Atmospheric warming and the amplification of precipitation extremes. Science, 321:1481‐1484.

Bakken, G.S. 1992. Measurement and application of operative and standard operative temperatures in ecology. American Zoologist, 32:194‐216.

Ciarniello, L.M., M.S. Boyce, D.C. Heard, and D.R. Seip. 2005. Denning behavior and den site selection of grizzly bears along the Parsnip River, British Columbia, Canada. Ursus, 16:47‐58.

Done‐Currie, J.R. and M. Wodzicka‐Tomaszewska. 1984. The effects of thermoregulatory behaviour on the heat loss from shorn sheep as measured by a model ewe for micro‐climate integration. Applied Animal Behaviour Science, 13:59‐70.

Dubois, Y., G. Blouin‐Demers, B. Shipley, and D. Thomas. 2009. Thermoregulation and habitat selection in wood turtles Glyptemys insculpta: chasing the sun slowly. Journal of Animal Ecology, 78:1023‐1032.

Dussault, C., J.P. Ouellet, R. Courtois, J. Huot, L. Breton, and J. Larochelle. 2004. Behavioural responses of moose to thermal conditions in the boreal forest. Ecoscience, 11:321‐328.

Dzialowski, E.M. 2005. Use of operative temperature and standard operative temperature models in thermal biology. Journal of Thermal Biology, 30:317‐334.

Farley, S.D. and C.T. Robbins. 1995. Lactation, hibernation, and mass dynamics of American black bears and grizzly bears. Canadian Journal of Zoology, 73:2216‐2222.

Frair, J.L., S.E. Nielsen, E.H. Merrill, S.R. 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.

Friebe, A., J.E. Swenson, and F. Sandegren. 2001. Denning chronology of female brown bears in central Sweden. Ursus, 12:37‐46.

Haroldson, M.A., M.A. Ternent, K.A. Gunther, and C.C. Schwartz. 2002. Grizzly bear denning chronology and movements in the Greater Yellowstone Ecosystem. Ursus, 13:29‐37.


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Hebblewhite, M., M. Percy, and E.H. Merrill. 2007. Are all Global Positioning System collars created equal? Correcting habitat‐induced bias using three brands in the central Canadian Rockies. Journal of Wildlife Management, 71:2026‐2033.

International Panel on Climate Change. 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Eds Solomon, S.D. et al. Cambridge University Press. Cambridge. UK.

Kearny, M., R. Shine, and W.P. Porter. 2009. The potential for behavioral thermoregulation to buffer «cold‐blooded» animals against climate warming. Proceedings of the National Academy of Sciences, 10:3835‐3840.

Larsen, T. and K. Pigeon. 2006. The diet of grizzly bears (Ursus arctos) in North‐Central Alberta, Canada, part of a new food‐based model approach to mapping grizzly bear habitat. In Stenhouse, G. and K. Graham (eds). Foothills Model Forest Grizzly Bear Research Program 2006 Annual Report, Hinton, Alberta, 87 pp.

Lewis, J.S., J.L. Rachlow, E.O. Garton, and L.A. Vierling. 2007. Effects of habitat on GPS collar performance: using data screening to reduce location error. Journal of Applied Ecology, 44:663‐671.

Linnell, J.D.C., B. Barnes, J.E. Swenson, and R. Andersen. 2000. How vulnerable are denning bears to disturbance? Wildlife Society Bulletin, 28:400‐413.

Lister, A.M. and A.J. Stuart. 2008. The impact of climate change on large mammal distribution and extinction: Evidence from the last glacial/interglacial transition. Geoscience, 230:615‐620.

Manchi, S. and J.E. Swenson. 2005. Denning behavior of Scandinavian brown bears Ursus arctos. Wildlife Biology, 11:123‐132.

Parmesan, C. and G. Yohe. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421:37‐42.

Rosenzweig, C., D. Karoly, M. Vicarelli, P. Neofotis, Q. Wu, G. Casassa, A. Menzel, T.L. Root, N. Estrella, B. Seguin, P. Tryjanowski, C. Liu, S. Rawlins, and A. Imeson. 2008. Attributing physical and biological impacts to anthropogenic climate change. Nature, 453:353‐357.

Swenson, J.E., F. Sandegren, S. Brunberg, and P. Wabakken. 1997. Winter den sites abandonment by brown bears Ursus arctos: causes and consequences. Wildlife Biology, 3:35‐38.

Chapter 3 Energetics

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CHAPTER 3: ENERGETICS FIELD WORK SUMMARY 2009

Karen Graham and Tracy McKay

Foothills Research Institute Introduction The theoretical number of grizzly bears that an area could support (i.e. carrying capacity) would be a powerful piece of information for grizzly bear conservation and recovery efforts. Carrying capacity estimates could help land managers prioritize areas for recovery. Feasibility of access management in areas with few grizzly bears relative to the carrying capacity but with suitable connectivity could be assessed. Carrying capacity estimates could be used in determining population targets as outlined in the Alberta Grizzly Bear Recovery Plan 2008‐2013 (2008) as a necessary piece of information for grizzly bear recovery in the province.

To determine the carrying capacity for grizzly bears in an area, the energy used by a bear in its daily activities must be estimated, as well as the potential food energy available on the landscape. Previous research with captive bears have provided estimates of energy required for walking, running and resting, and is valuable information for calculating the energy requirements of grizzly bears (Best 1982, Best et al. 1981, Watt et al. 1991). To determine the energy available to grizzly bears on the landscape, information on the diet of grizzly bears is required. Since 2001, the FRI Grizzly Bear Program has gathered information on the diet of collared grizzly bears throughout north‐central Alberta. We have visited grizzly bear GPS collar locations to quantify the habitat and determine bear activities at those locations. Often, it was determined whether the bear was digging for sweet vetch (Hedysarum alpinus) roots or ants, feeding on berries, grazing on forbs, or feeding on a kill. Any scat found at a location was collected and later visually inspected to determine the items eaten (Munro et al. 2006, Larsen and Pigeon 2006, Larsen and Pigeon 2007 unpublished data). These site visits and scat analyses provided information on the food items ingested, the date the food was eaten, and the relative importance of the food items in the diet (Munro et al. 2006). From this information, spatial food models based on presence‐absence have been developed for over 20 bear foods over a large portion of grizzly bear range in Alberta (See Chapter 6). These food models predict where on the landscape bear foods will occur and is an important step toward determining the food energy available to grizzly bears on the landscape. Data on food densities and energy content will allow these food models to predict the spatial carrying capacity of the landscape.

In 2009, we revisited GPS collar locations from 2001‐2003, that had bear activity and habitat data previously collected. We used this data to maximize the probability of visiting sites with relatively high densities of important bear foods. Our goal was to better quantify bear food densities, and to collect specific bear foods for energy and nutritional analysis. We plan to integrate this food energy data into new spatially explicit food models (See Chapter 6). Results from this research would link to the population status, landscape conditions and food supply and would be critical information needed for understanding health and environment relationships, setting population targets, and making appropriate management decisions for


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species recovery. We also wanted to compare data obtained using different collection methods to determine the most efficient methodology to quantify plant densities for following years and to determine whether data from our 2001‐2003 collection methods could provide density estimates if strong relationships exited between the data collected from different protocols.

Study Area and Methods We utilized capture, habitat and activity information collected from 1999‐2003 for two female grizzly bears (G003 and G012), both born in 1994. In 2003, G003 was 175 cm from nose to tail, and G012 was 188cm. During 1999‐2003, G003 lived in the mountains within Jasper National Park and the Cardinal Divide area, while G012 lived southeast of Hinton in the lower foothills (Figure 1).

Figure 1. Map showing the multiyear annual home ranges of G003 (1999, 2001‐2003) and G012 (1999‐2003) from GPS collar data. We reviewed activity data previously collected for the two bears, and selected foot accessible sites where the bears had fed on sweet vetch (Hedysarum alpine), cow parsnip (Heracleum lanatum), and/or buffaloberry (Sheperdia canadensis), in order to provide density estimates of these three food items, as well as for other priority bear foods as listed in Table 1. We know from scat analyses that sweet vetch is an important spring and late fall food, cow parsnip is an important summer food, and buffaloberry is an important late summer and early fall food. We attempted to revisit sites at approximately the same time of year as the previous bear use date. However, we discovered that sweet vetch plants were not completely above ground in early


22

June. Sweet vetch is often dug up by bears in the spring before the plant has come out of the ground. Without plant emergence, sweet vetch was difficult to detect and quantify; therefore, we delayed visiting some Hedysarum use plots until mid June. Table 1. Priority grizzly bear foods.

Amelanchier spp. (saskatoon) shrub Aralia nudicaulis (sarsaparilla) Herbaceous‐fruit producer Arctostaphylos spp. (bearberry) dwarf shrub‐fruit producer Hedysarum alpinum (sweet vetch). herbaceous Heracleum lanatum (cow parsnip) herbaceous Empetrum nigrum (crowberry) dwarf shrub‐fruit producer Equisetum spp. (horsetail) herbaceous Fragaria spp. (strawberry) herbaceous‐fruit producer Lathyrus ochroleucus (peavine) herbaceous Lonicera spp. (honeysuckle) Shrub‐fruit producer Medicago spp. (alfalfa) herbaceous Prunus spp. (chokecherry) Shrub‐fruit producer Ribes spp. (currents) Shrub‐fruit producer Ribes spp. (gooseberry) Shrub‐fruit producer Rosa (rose) shrub‐fruit producer Rubus spp. (raspberry) Herbaceous‐fruit producer Rubus idaeaus (raspberry) Shrub‐fruit producer Rubus parviflorus (thimble berry) Shrub‐fruit producer Salix sp.(willow) shrub Sambucus racemosa (elderberry) Shrub‐fruit producer Sheperdia canadensis (buffaloberry) Shrub‐fruit producer Taraxacum officinale (dandelion) herbaceous Trifolium spp. (clover) herbaceous Vaccinium caespitosum (dwarf blueberry) dwarf shrub‐fruit producer Vacinnium membranaceum (huckleberry) dwarf shrub‐fruit producer Vaccinium myrtilloides (velvet‐leaved blueberry)

dwarf shrub‐fruit producer

Vaccinium scoparium (grouseberry) dwarf shrub‐fruit producer Vaccinium vitis‐idaea (lingon berry) dwarf shrub‐fruit producer Viburnum spp. (cranberry) Shrub‐fruit producer At each selected bear location, we set up a 30x30m plot. A 30m measuring tape was laid out in a north to south orientation, and 15 m on either side of the tape was paced out and flagged. Location information (UTM, date/time, and plot #) was recorded. Landscape attributes recorded included slope, aspect and elevation. Pictures were taken from the plot centre facing in each of the 4 cardinal directions. Habitat class was recorded based on our landcover habitat maps (Franklin et al. 2001), tree canopy cover across the entire plot was estimated (in classes, 1=<5%; 2=5‐25%; 3=26‐50%; 4=51‐75%; 5=76‐100%), and a prism sweep was conducted to quantify tree density by species.


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To quantify bear food densities we completed 3 different types of assessments: Circle plots: 1m2 circle plots were used at 5m intervals to determine densities and percent cover for herbaceous or dwarf woody bear foods. A 1 inch PVC tube was formed into a circle to encompass a 1m2 area. This tube was laid on the ground at 0, 5, 10, 15, 20, 25, and 30m along the plot centre line. Within the enclosed 1 m2 area, all herbaceous or dwarf shrubs listed as priority bear foods (Table 1) were recorded, including percent cover, plant count (density), fruit count (when applicable), and fruit status (unripe, ripe, rotten). Grasses, sedges and some dwarf shrub species (e.g. Arctostaphlos) growing in dense mats were too difficult to count as individual plants, therefore only percent cover was estimated for those species. Herbaceous species that were not specifically classified as priority bear foods, but were likely grazed by bears, were grouped together as “other forbs”, and percent cover was estimated. Belt transect: A 2m wide belt transect along the plot centre line (1m on each side) was used to quantify the density of bear food shrubs. If the distribution of sweet vetch or cow parsnip was scattered or scarce (and therefore did not occur in the circle areas), these two species were also quantified in the belt transect. Dwarf shrub species were not quantified in the belt transect. Shrubs/plants were counted within a specified area along the belt transect. The area for the density count was chosen based on the distribution (density and patchiness) of the shrubs along the belt transect. For example, if there were numerous shrubs of a species, and the density was consistent along the entire 30m line, only the first 15m along the west side (a total area of 15m2) was counted. If the density was patchy or sparse, we counted the full 30 m along the west side (a total area of 30m2), and if the density was extremely scarce or patchy, we counted the full 30m on both sides of the centre line (a total area of 60m2). If present, berry counts were also completed within the specified area of the belt; berries were not picked during these counts. Full 30x30m plot survey: A full 30x30m plot survey was used to detect and estimate percent cover or plant counts for all bear foods, including those species that were very low in abundance and therefore may not have been detected within the belt or circle areas. The overall percent cover of the priority bear foods across the entire 30x30m plot was estimated based on cover classes (Table 2) Often it was easier to estimate one quadrat (quarter of the plot) at a time, and average the values to determine the overall percent cover of each bear food. Table 2. Classes for percent cover values.

+ < 1% or ~1 to 5 individuals1 < 5% or ~6‐50 individuals 2 5‐25% 3 26‐50% 4 51‐75% 5 76‐100%

If buffaloberry, cow parsnip or sweet vetch were detected during the full plot survey, but had not been quantified in the circle or belt areas, a count of the number of plants (density) and fruit (if present) was recorded within the 30x30m plot.


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While surveying the plot, the total number of sweet vetch diggings (old or recent), anthills or ant logs present (dug up or unused), bee or wasp nests present (dug up or unused), and any other bear sign or wildlife sign was counted and recorded. The phenology stages for both vegetative and reproductive growth were recorded for all bear foods detected. Additional measurements: Shrub Line Intercept Shrub intercept quantification was used by our program in 2001‐2003, but density estimates were not recorded. In 2009, we conducted a shrub intercept assessment as well as a density estimate to investigate whether intercept data correlated with density data, and whether these intercept data could be extrapolated into plant density estimates. The distance measurements along the 30m measuring tape were recorded for any bear food shrub that intercepted the tape (e.g. Shepherdia, 18.1m to 18.3m). 30 minute berry picking sessions: Berry picking sessions were used to investigate and calibrate potential foraging rates, with the possible application of using foraging rates as rapid assessments/indices of fruit in subsequent years. If ripe berries were present, three 10 minute berry picking sessions were completed. One person worked in each half of the plot, picking as many berries of one species as possible in 10 minutes, within the boundaries of the 30x30m plot, and using only one hand. After each 10 minute session, the number, weight and volume of berries was recorded. The 10 minute session was repeated twice more for a total of 30 minutes. A separate 30 minute session was completed for each berry species within the plot. Berries were stored in ziplock bags and frozen. At the end of the field season, frozen samples were sent to a lab at the University of Alberta for nutrient and caloric analysis. Plant Collections: We collected samples of all the herbaceous non‐fruit producing species listed as priority bear foods (sweet vetch, cow parsnip, dandelion, sweet clover, horsetails, and/or pea vine). Only the above ground portion of the plant was collected, with the exception of sweet vetch, for which both the root and the green plant were collected. To reduce bias in selecting plant samples, the plants closest to the plot centre in each of the four quadrats were collected, and the distance from the centre was recorded. If no plant was present in a quadrat, this was noted. Plant samples were put in a freezer at the end of each fieldwork day. At the end of the field season, frozen samples were sent to a lab at the University of Alberta for nutrient and caloric analysis. Results and Discussion We visited 26 GPS collar locations from 2001‐2003 from bears G003 and G012, including twelve sites for G003 and 14 sites for G012. Two sites were visited twice, to assess sweet vetch plant density during the first visit and berry abundance during the second visit. Every site had sweet vetch, cow parsnip and/or buffaloberry present. Of the 24 sites visited, 17 (71%) showed evidence that a bear had fed there the spring of 2009 or in a recent year (old digs). Three sites with diggings recorded in 2001‐2003 turned out to be for ants rather than for sweet vetch. Sweet vetch was present at twelve sites for G003 and 9 sites for G012. The sampling protocol allowed for density estimate comparisons among the three types of assessments (Table 3). The circle plot consistently gave the highest density estimate, the full plot area provided the lowest, and the belt transect was intermediate. Assuming that the full plot density was closest to the “true” density for the 30x30m area, with more samples, a correction factor could possibly be


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determined and applied to the circle and belt densities, in order to produce a more accurate density estimate. This relationship is important, because the habitat data collected in 2001‐2003 included density estimates of sweet vetch within several small (0.5 m2) quadrats. If a correction factor could be determined to improve or standardize these density estimates, the 2001‐2003 data could be corrected and included in the estimation of grizzly bear food energy available on the landscape. Table 3: Sweet vetch (Hedysarum alpinum) densities (plants/m2) determined within different sampling areas. A dash indicates that a density was not collected.

Circle Plot Density*

Belt Plot Density

Full Plot Density

‐ 0.07 0.02 0.14 ‐ 0.08 1.71 0.87 ‐ 0.71 ‐ 0.04 *averaged across 7 circle plots

Sweet vetch densities ranged from 0.02‐13.14 plants/m2 for G003, and 0.02‐6.14 plants/m2 for G012 (Table 4). Densities in the low end of the ranges were obtained using the 30x30m plot surveys, while the high densities were obtained using circle plots. Table 4: Sweet vetch (Hedysarum alpinum) densities by plot type.

Bear plotID_numeric Density Plot type

G003 1017 2.14 circle plots



G003 1118 13.14 circle plots G003 1119 0.02 30x30m plot G003 1121 0.56 30x30m plot G003 1185 2.14 circle plots G003 1187 3.71 circle plots G003 1193 2.43 circle plots G003 1195 1.29 circle plots G003 1202 0.03 30x30m plot G003 1978 0.20 30x30m plot AVERAGE 2.76 G012 1011 1.57 circle plots G012 1049 0.08 30x30m plot G012 1102 0.02 30x30m plot G012 1157 0.63 belt transect G012 1161 0.04 30x30m plot G012 1224 2.29 circle plots G012 1232 0.86 circle plots G012 1236 0.29 circle plots G012 1747 6.14 circle plots AVERAGE 1.32 *Circle plot density is the average across 7 circle areas within the 30x30m plot


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We compared sweet vetch cover estimates with densities within the circle plots, and found a weak relationship (R2 = 0.53). Our 2001‐2003 data contain percent cover estimates for sweet vetch and many other bear foods within similar quadrats, therefore, if a relationship was found, we could use our percent cover data as a possible source of density estimates. Further data from circle plots with both density and percent cover estimates are needed to determine if a strong enough relationship exists.

Percent Cover vs Density of Sweet Vetch in Circle plots

R2 = 0.5276

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Figure 2. Comparison of percent cover estimates and density estimates of sweet vetch within the circle areas. Only two sites visited (both for G003) had cow parsnip present. Density ranged from 0.002 ‐ 2.71 plants/m2. As before, the lowest estimate was from the full plot survey, and the high estimate was from the circle plots. More sites with cow parsnip need to be quantified to better estimate the abundance of this species. Buffaloberry shrubs were present at 7 and 13 locations for G003 and G012, respectively (Table 5). Typically, only the belt transect had a density estimate for buffaloberry, therefore a comparison of shrub densities using the different assessment types was not possible. One exception occurred, where a full plot survey and belt transect from the same plot resulted in the same buffaloberry density value. Two buffaloberry sites were visited twice (plots 1154 and 1157), and the density estimates obtained from the belt transect for the two visits were similar (0.33 vs 0.53 plants/m2 and 0.6 vs 0.4 plants/m2), which suggests that the belt sampling protocol produces reliable density estimates for buffaloberry. Buffaloberry shrub densities ranged from 0.02 to 0.63 plants/m2 for G003 and 0.02 to 1.30 plants/m2 for G012 (Table 5). There was a very weak relationship (r2 = 0.27) between the shrub intercept data and plant density (Figure 3). Again, our 2001‐2003 data contain intercept data for buffaloberry shrubs, therefore, if a relationship was found, we could use our shrub intercept data as a possible source of density estimates. Further data from density and shrub intercept estimates are needed to determine if a strong enough relationship exists.


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Table 5. Average buffaloberry (Sheperdia canadensis) densities by plot type.

Bear plotID_numeric density Plot Type G003 1019 0.02 Belt G003 1185 0.40 Belt G003 1187 0.23 Belt G003 1193 0.63 Belt G003 1195 0.32 Belt G003 1202 0.27 Belt G003 1978 0.60 Belt AVERAGE 0.35 G012 1011 0.07 Belt G012 1102 1.07 Belt G012 1154 0.33 Belt G012 1154 0.53 Belt G012 1157 0.60 Belt G012 1157 0.40 Belt G012 1161 0.03 Belt G012 1163 0.50 Belt G012 1224 0.02 Belt G012 1229 1.30 Belt G012 1232 0.12 Belt G012 1236 0.27 Belt

G012 1747 0.02 Belt and 30x30m area gave same density

AVERAGE 0.40

Buffaloberry Density vs Line Intercept

R2 = 0.2721R2 = 0.2721

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Only 3 sites (2 for G003 and 1 for g012) had berries within the circle plots. G012’s plot had 5 dwarf blueberries and 1 strawberry. G003 had one plot with 2 alpine bearberries and one site with 4 strawberries. Six plots had berries present within the belt transect. Species and densities are given in Table 6. Table 6. Berry densities within belt transect.

Bear_ID PlotSite Date Shrub species Berries/m2 G012 1102 28‐Jul‐09 Bracted honeysuckle 0.07 G012 1161 11‐Aug‐09 Bracted honeysuckle 0.03 G003 1187 18‐Aug‐09 Buffaloberry 0.03 G012 1102 28‐Jul‐09 Buffaloberry 3.50 G012 1154 29‐Jul‐09 Buffaloberry 24.80 G012 1157 29‐Jul‐09 Buffaloberry 29.80 G003 had 6 sites with buffaloberry fruit present; however, there were less than 12 berries in total within the 30x30m plots. This was not enough for the 30 minute picking sessions. G012 had berries present in 4 plots; all of which had enough berries to complete the berry picking sessions. Of these four sites, one site included berry counts with corresponding weights and volumes; the remaining sites had only weights and volumes measured. However, we found a good relationship between berry weight and berry number (r2=0.97), therefore a berry count could be estimated for the 3 plots lacking berry counts. Berry counts across the three 10 minute sessions were plotted. We assumed that if the number of berries picked decreased by the third session, then a large portion of the berries in the plot were picked. Two of the four plots showed a decline in the number of berries picked over time. However, the other 2 plots showed some variation in the number of berries picked over time. It is possible that the person picking the berries improved their efficiency in searching and/or picking the berries, or perhaps a shrub was found after the first session with many berries, and less time was spent searching. We noted that the distribution of buffaloberry fruit could be quite scattered within a plot, with one shrub loaded with berries while many other shrubs in the area had few to no berries. We could not distinguish male and female plants at that time of year; therefore, we could not determine whether the patchiness was a result of male plants dominating a site, or whether some female plants within the plot had berries while other female plants did not. Further berry picking data will be needed to calibrate potential foraging rates.


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Buffaloberry fruit counts over time

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Figure 4. Buffaloberry fruit counts for 4 plots for G012 across three 10 minute picking sessions. The solid lines represent the west side counts for each plot, and the corresponding coloured dashed lines indicate the east side counts. Plants and fruits collected were sent to the lab at the University of Alberta for nutrient and caloric analysis and are currently being analyzed. Literature Cited Alberta Grizzly Bear Recovery Plan 2008‐2013. 2008. Alberta Sustainable Resource

Development, Fish and Wildlife Division, Alberta Species at Risk Recovery Plan No. 15. Edmonton, AB. 68 pp.

Best, R.C. 1982. Thermoregulation in resting and active polar bears. Journal of Comparitive Physiology, 146: 63‐73.

Best, R.C., Ronald, K. and Oritsland, N.A. 1981. Physiological indices of activity and metabolism in the polar bear. Comparative Biochemistry and Physiology, 69(A): 177‐185.

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.

Larsen T. and Karine Pigeon 2006, The diet of grizzly bears (Ursus arctos) in North‐Central Alberta, Canada, part of a new food‐based model approach to mapping grizzly bear habitat In: Stenhouse, G. and K. Graham (eds). Foothills Model Forest Grizzly Bear Research Program 2006 Annual Report. Hinton, Alberta. 87 pp

Munro, R.H.M, S.E. Nielsen, M.H. Price, G.B. Stenhouse, M.S. Boyce. 2006. Seasonal and diel patterns of grizzly bear diet and activity in west‐central Alberta. Journal of Mammology 87:1112‐1121.

Watts, P.D., Ferguson, K.L. and Draper, B.A. 1991. Energetic output of subadult polar bears (Ursus maritimus): resting, disturbance and locomotion. Comparative Biochemistry and Physiology, 98(A): 191‐193.

Chapter 4 White‐bark pine

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CHAPTER 4: WHITEBARK PINE SEEDS AS A FOOD SOURCE FOR GRIZZLY BEARS IN WEST CENTRAL ALBERTA 2008/2009 PILOT STUDY

Tracy McKay and Karen Graham

Foothills Research Institute

Introduction Whitebark pine (WBP) trees are found at high elevations, from treeline down into the subalpine forest. In Alberta, WBP are distributed from the Kakwa Wildland Park in the north along the continental divide through Jasper and Banff National Parks, to the southern edge of Waterton Lakes National Park at the U.S. border. The entire range of whitebark pine distribution in Alberta falls within grizzly bear range (Figure 1). In 2009, whitebark pine trees were classified as endangered by Alberta's Endangered Species Conservation Committee (ESCC), under threat by an introduced blister rust and mountain pine beetle. To facilitate recovery planning, there is a need for a better understanding of the ecological significance of whitebark pine and its importance for wildlife, including grizzly bears.

Figure 1. Grizzly bear range and whitebark pine distribution in Alberta. In the United States, whitebark pine seeds have been documented as a major source of energy for bears in the Greater Yellowstone Ecosystem (GYE) and along the east slopes of the Montana Rocky Mountains (Mattson et al., 2001). WBP seeds are much larger than other conifer seeds,


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and have a high fat content. Mattson et al. (1991) reported that when whitebark pine seeds were available and abundant, grizzly bears in the GYE ate almost nothing else; however, the use of whitebark seeds was subject to large‐scale variation between years. Whitebark pine cone crops are highly variable; “mast” (peak) cone crops occur every three to five years, with very low or no seed production in between (McCaughey & Tomback, 2001). In the GYE, the level of whitebark pine consumption by bears has been directly related to cone crop size, with highest use in mast years (Kendall, 1983; Mattson and Reinhart, 1997). The research in Yellowstone indicated that almost all WBP seeds eaten by bears were obtained by excavating red squirrel middens, where WBP cones are cached by squirrels after the cones have matured (Mattson & Reinhart, 1997). In Canada, throughout many years of grizzly bear research, WBP seeds have not been reported as a significant food source for bears. A food and habitat study in Banff National Park did not detect WBP seeds as a food item (Hamer & Herrero, 1987). Similarly, WBP was not reported as a food source in a study completed in Jasper National Park (Russell et al., 1979), or in an analysis of scat collected on the east slopes of West Central Alberta (Munro et al., 2006). However, none of the previous bear food research in Alberta specifically considered whitebark pine stands, subalpine study areas in some cases did not include whitebark pine forest types (e.g. Hamer & Herrero, 1987), and study years may not have included mast years for whitebark pine cone production.1 Although grizzly bear range and general whitebark pine distribution overlap, there is a lack of comprehensive mapping of whitebark pine at the forest stand level. Therefore, at this time, it is difficult to interpret bear use of WBP habitat using GPS collar data. Grizzly bears are known to consume a wide variety of foods, and foods consumed vary by ecological region. Therefore, although not previously reported, it was conceivable that a relationship could exist between WBP and grizzly bears in Alberta, similar to that observed in Yellowstone. As a high quality (high calorie) food source, whitebark pine seeds may have importance to the energetic balance of grizzly bears, even with a relatively low distribution of whitebark pine seeds in comparison to other bear foods across the province. In the spring of 2008, Alberta Tourism, Parks and Recreation (AB TPR) and the Foothills Research Institute Grizzly Bear Program (FRIGBP) initiated a study in the Willmore Wilderness Park to investigate grizzly bear use of whitebark pine seeds as a food source. Based on preliminary results obtained in 2008 (FRI, 2009), this study was continued in 2009, and the study area was expanded to include additional areas of whitebark pine distribution in west central Alberta. Data from both years were combined for analysis following completion of the 2009 field season. Study region The 2008/2009 study region included whitebark pine stands in west central Alberta, with study sites in the Willmore Wilderness Park, Jasper National Park (JNP), Banff National Park (BNP), and the Siffleur Wilderness Area (Figure 2). Point data of known WBP locations provided by JNP, AB TPR, and Alberta Sustainable Resource Development (ASRD) were used to delineate different study areas. Study areas were selected and prioritized based on the size of the WBP stands (area >2.0 hectares), size of trees within WBP stands (where DBH data were available, areas with average DBH >10cm for WBP were selected), potential overlap with grizzly bear collar

1 2007 was a known mast year for whitebark pine; data regarding mast years previous to 2007 were unavailable for the study area.


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points (where GPS data were available), and accessibility for field crews (in 2009, foot accessible sites were given priority).

Figure 2. Map of 2008/2009 study area. Methods Fieldwork was completed during June to September, 2008 and June to August, 2009. Over the two field seasons, study areas included walk‐in sites, fly‐in camps (multi‐day sampling), and fly‐in sites (day access) based near or within known whitebark pine stands. Within the stands sampled, aspect was highly variable, average slope angle was 23 degrees, and the elevation ranged from 1600m to 2300m. The elevation of sites was not specifically selected, but was somewhat dependent on accessibility (sampling at helicopter accessed sites started at the upper range of whitebark pine stands, while at foot‐accessed sites we started sampling in the lower range). As a result, sites were located anywhere from the lower edge of the alpine zone down into the lower subalpine, and canopy cover along transects ranged from 1 to 90%. All WBP stands were located in mixed subalpine forests with various proportions of Engelmann spruce, Engelmann/white spruce hybrids, subalpine fir, Lodgepole pine, black spruce, and white spruce. Transect surveys: Some initial basic research questions were developed in 2008, including: 1) Are WBP seeds available as a food source for bears in the Willmore?

What is the density of WBP trees? What is the density of squirrel middens in WBP stands?

Transect surveys were completed to estimate WBP and red squirrel midden densities. For the development of our sampling protocols, we initially planned to use randomly generated points in WBP stands as the starting points for survey transects; however, this was not possible, due to


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the incomplete mapping of whitebark pine in Alberta.2 In order to complete the maximum number of transects within WBP stands, we located WBP stands by observations on the ground, then traveled to the upper or lower elevation limits of the observed WBP stands, and started midden survey transects at a horizontal break (e.g. a streambed or gully) or at the beginning of a stand. However, researchers were attempting to maximize the probability of detecting bear activity in whitebark pine stands, and sometimes started or ended transects at middens that were found during travel through a stand. This non‐random selection of start and end points for transect surveys resulted in some known bias in results, including potential overestimation of midden densities. This bias was addressed when determining areas for calculating midden densities; see data analysis. Data collected at the transect start included slope, aspect, elevation, percent tree canopy cover (estimated), bear foods (plants) present, a prism sweep3 of all tree species (2x or 4x prism), and a general description of habitat. From the transect start, based on a sightability distance of approximately 5m along the ground in each direction, researchers spaced themselves apart at a distance of approximately 10m (e.g. one researcher was located at the transect start point, and additional researchers placed themselves 10m upslope and/or 10m downslope). Research groups ranged from 2 to 4 people, therefore, in each case a 20m, 30m or 40m wide area was searched for middens. Researchers followed the contour of the slope, moving at a speed that maximized detection of middens within that range, until an obstacle or significant change in the landscape occurred, such as a gully, rockslide, avalanche slope, creek, or cliff. Transect lengths ranged from 75 to 570m. A prism sweep of all tree species present was completed every 50m along each transect. A full midden survey was completed for all middens observed along each transect (see below). At the end of each transect, researchers moved up or downhill within the WBP stand and started a new transect back in the opposite direction from the first. This pattern was repeated back and forth across the slope until essentially the entire WBP stand on that slope had been sampled, or as time permitted. At some helicopter‐accessed day sites, only two transects were completed at each site due to budget/time/weather restrictions. Midden surveys: Preliminary results at the beginning of the 2008 field season indicated that WBP seeds would likely be available for bears; therefore, the second basic research question addressed in 2008 included: 2) Are bears using WBP seeds as a food source in the Willmore?

Is there evidence of bear activity at squirrel middens? Is there direct evidence of bear consumption of WBP seeds?

2 Areas (polygon spatial data) of WBP stands were not comprehensively mapped as of 2008; only point data of known whitebark stands were available. We used GIS processing to select areas within the elevation range of WBP in areas where known WBP stands occurred, and random study sites were generated within these areas. In 2008, once in the field, it was obvious that this method was not effective, as the random points were not necessarily within actual WBP stands on the ground. This suggests that current WBP mapping was inadequate to determine the spatial extent of the entire stand where WBP was found. 3 A prism sweep is a forestry technique used to estimate basal area (cut stem area at ~1.4m) of tree species present, and is an indicator of tree density or productivity. The number of trees >/=1.4m tall are counted in the prism sweep, then multiplied by the prism factor to estimate the basal area of trees present in m2/hectare (Luckai, 1997).


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Use of WBP seeds was investigated by completing midden surveys at any middens found during the transect surveys. General data collected included slope, aspect, elevation, percent tree canopy cover (estimated), and bear foods (plants) present. A prism sweep was completed at the midden centre as well as 25 to 50m away from the centre in all four cardinal directions4. The midden was measured (length, width), midden tree species (central tree) was recorded, any cached cones visible at the midden were speciated and counted, and cone scale samples (approximate volume of 125mL) were counted (or collected for later counting) from each of the four cardinal directions from the tree centre (cone scale counts are relevant for squirrel predation studies, collaborative work with Dr. Vern Peters). The midden was defined as active or inactive, based on the presence/absence of observed squirrel signs such as squirrel presence, squirrel heard rattling, fresh cone piles, tree clippings, or piles of fresh cone scales. The midden and surrounding area were investigated for signs of bear activity, such as diggings (midden excavations) or bear scat. Any scat observed was collected and/or recorded, scat contents were estimated, and approximate age of bear activity (recent or from previous years) was recorded. 2009 methods: The above methods for transect and midden surveys were continued throughout the 2009 field season, as the study area was expanded from the Willmore to address the preliminary research questions for additional areas of west Central Alberta (Jasper National Park, Banff National Park, and the Siffleur Wilderness Area). 2009 data analysis was also directed towards investigating potential relationships between forest stand characteristics, squirrel midden densities and different levels of bear use of WBP. Information regarding these relationships could assist in describing forest stands that may support different levels of bear use of whitebark pine. An additional focus for 2009 included applying this research to ongoing FRIGBP energetics work. To estimate availability and potential energy provided by whitebark pine, 2009 methods included surveying the number of whitebark pine cones produced per tree, preliminary energetic testing of whitebark pine seeds, and analysis of existing 2008/2009 data to investigate how availability affects the level of bear use. At each study area, ten mature (potentially cone‐bearing) trees were chosen at random, and all visible cones were counted, using binoculars. A sample of whitebark pine seeds collected from one study area (Astoria River, Jasper National Park) was sent to the University of Alberta for calorimetric (gross energy) analysis; analysis was completed by Sean Coogan in the laboratory of Dr. Scott Neilsen. At the U of A, the seeds were dried at 60 degrees Celcius for 48 hours, then ground up. Approximately 1 gram of ground whitebark pine seed was formed into a pellet and burned in a calorimetry analyzer (adiabatic bomb calorimeter); two sample runs were completed (Sean Coogan, personal communication, 22 December 2009). Rough estimates of the potential energy provided by whitebark pine seeds were calculated from the number of cones counted per tree, cones produced by basal area (from the literature), seeds per cone (literature), and approximate calories per seed. Data analysis: Tree densities (basal areas in m2/hectare) were calculated by multiplying the prism sweep count by the prism factor (2x or 4x) for each tree species. Spruce species (black, white, and Engelmann) were grouped together for the analysis, due to common hybridization between Engelmann and white spruce, and potential errors in distinguishing spruce species during prism

4 Based on an estimated home range of 100m by 100m for a red squirrel in spruce forest, as indicated in the literature: densities of 1 to 2 squirrels per hectare (Wheatley et al., 2002) correspond with approximate home ranges of 5000 to 10000m2 (100m x 100m).


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sweeps. Basal areas along transects were averaged by study area, i.e. all basal area measurements taken along all transects within each study area were averaged together to estimate the average density of each tree species across each study area. To calculate percentage composition of tree species by study area, basal areas for each species were summed and divided by the total basal area (all species) for the area. Individual basal area values recorded at the midden centre and in the four directions 50m from the midden were converted to averages across each midden for use in regression analysis as described below. Midden sizes were calculated as areas (length x width, in square metres). Cone counts were averaged per study area when available. When estimating wildlife densities, a commonly used transect survey method includes randomly placed transects through a study area, and densities are calculated using estimated detection functions to account for detectability (e.g. Mattson & Reinhart, 1997). The initial focus of this study was to document whether or not bears use whitebark pine seeds; therefore, the preliminary aim was to locate as many squirrel middens as possible, rather than to simply determine the midden density. For this reason, and because randomly generated transect start points were not possible (due to limited whitebark mapping, see Midden transect surveys), we chose to cover as much area as possible within each study area, rather than place random transects within each stand. 100% detectability of middens was not possible, however, detectability was likely very high due to the grid search pattern, the close spacing of researchers walking the transects, and the large size and visibility of middens. Based on these sampling methods, densities were calculated by study area, rather than estimated from transect counts and detection functions; although some error may have been introduced by detectability and other bias (as described below), this error is likely comparable or less than the inherent error in estimating probability detection functions. As previously described, the tendency to start and end transect surveys at middens may have created bias in the data in the form of potential overestimation of midden densities. In contrast, in cases where successive transects were not directly adjacent (i.e. within sighting distance) to each other, some area may not have been completely surveyed in between transects, and midden densities could be underestimated. In the interest of being conservative in estimating the availability and significance of whitebark pine seeds for bears, researchers determined that underestimation was preferable to overestimation; in this way, any biological significance suggested by this study will be valid and defensible. Therefore, a correction was applied to account for the potential positive bias created by the sampling techniques. In cases where middens were located at or near the start, end, or edge of transects, the effective area surveyed was increased. Middens were buffered using a 50 metre radius; this distance was selected for three reasons: 1) this area was effectively surveyed when researchers completed prism sweeps 25‐50m away in the four cardinal directions from the midden centre, 2) this distance encompassed the approximate maximum distance observed between middens (based on the average distance between middens, as calculated in ArcView5), and 3) this distance corresponds with approximate home range sizes/midden densities reported in the literature (Wheatley et al., 2002). By applying this estimated maximum distance between

5 The distance between middens was calculated in ArcView using the nearest neighbor function. Average distance between middens varied from 60m to 99m between study areas; half this distance translates to an average midden radius of 30m to 49m, this buffer radius around each midden falls within the 50m value applied in the analysis.


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middens, the “empty” area surrounding each midden may be more accurately estimated (this area is unlikely to contain any middens). Study area polygons were manually drawn in ArcView, using GPS tracks of transects and the buffered middens and midden densities were calculated for each study area based on the number of middens observed within each study area polygon. See Figure 3, below, for an example of the uncorrected versus corrected areas used for calculating midden densities.

Figure 3. Uncorrected versus corrected area for calculating midden densities per study area. The total (uncorrected) area covered during transect surveys varied widely between study areas, due to accessibility and time constraints, and this difference in area surveyed corresponded to a high level of variance between corrected areas. Researchers could be more likely to find middens with more hectares surveyed. Therefore, particularly in study areas where a very small area was surveyed, this variance had potential to bias midden density results. A correlation analysis of total midden density versus (corrected) area surveyed showed a small positive correlation (r = 0.42), indicating that measured midden density values may tend to increase with area surveyed. However, when included in the regression analysis (see below), (corrected) area surveyed was not a significant predictor of midden density (p = 0.29). Knowledge regarding relationships among forest stand composition, squirrel midden densities, and bear use of whitebark pine in the study area is very preliminary, therefore, an exploratory approach to analysis was appropriate, and a model selection process was not applied for data analysis. Multiple regression analyses were utilized to investigate the relationships between forest stand composition (tree densities and proportional composition) and squirrel midden densities. The dependent variable analyzed was total midden density (middens per hectare) by study area. Total midden density was chosen rather than active midden density (used by Mattson & Reinhart, 1997), as difficulties were encountered in confirming whether some middens were currently active or inactive, and researchers were unable to determine if a midden was active or inactive before it was excavated by a bear (all excavations were from previous years). Independent variables included basal areas for whitebark pine, spruce,


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subalpine fir, and lodgepole pine, in m2 per hectare. Total basal area could not be included in the multiple regression analysis, as it is collinear with basal areas of the individual tree species; a separate regression was completed with total basal area as the single independent variable. Elevation was also excluded as an independent variable in the multiple regression, as it is correlated with basal area (r = ‐0.85; tree density decreases with increasing elevation); elevation was also analyzed in a separate simple linear regression. A regression analysis was also completed using proportions of tree species (percent of total basal area) for whitebark pine, spruce species, subalpine fir, and lodgepole pine as the independent variables. Percentage of whitebark pine had to be excluded from the multiple linear regression, as this parameter caused errors in the analysis due to a nonlinear (polynomial) relationship with the dependent variable; a separate polynomial regression was completed in Stata 10.1™. Total midden density results were checked for normality in Stata 10.1™ using the Shapiro‐Wilk (SW) test, review of histograms, and the skewness and kurtosis test. The SW test produces the W statistic; if the W statistic is close to 1, distribution is close to normal; an alternate interpretation is that the null hypothesis for the SW test is a normal distribution; a significant test statistic (p ≤ 0.05) results in rejection of Ho and therefore indicates a non‐normal distribution. The SW test of midden density results produced a W statistic of 0.90, and a probability (PW) of 0.289, indicating that the distribution of active midden densities was close to that of a normal distribution. Similarly, in interpreting the skewness and kurtosis test for normality, a significant result (p≤ 0.05) results in rejection of Ho and indicates a non‐normal distribution. The skewness and kurtosis test produced a result of p=0.564, indicating that neither the skewness nor kurtosis were significant. Based on the above results, the distribution of the dependent variable was close to a normal distribution, and an ordinary least squares (OLS) multiple linear regression function was used for analysis, with α set at 0.05. Residuals were checked for normality and homogeneity of variance (assumptions that must be met for linear regression). Residuals were within range for homogeneity (a plot of residuals versus predicted values showed a mostly random distribution of points around zero), and close to normally distributed (a probability plot of residuals versus estimated values displayed a nearly linear curve). In previous research, excavated midden density was used to describe the level of bear use of whitebark pine (Mattson & Reinhart, 1997). To investigate how forest stand composition characteristics may predict what level of bear use of whitebark pine may take place within a local area, excavated midden densities by study area were analyzed as the dependent variable in a multiple regression analysis, with basal areas of whitebark pine, spruce, subalpine fir, and lodgepole pine, in m2 per hectare as the independent variables. Excavated midden density results were checked for normality (in Stata 10.1™) using the Shapiro‐Wilk (SW) test, review of histograms, and the skewness/kurtosis test. Although the excavated midden density dataset included a number of zero values, the data fell within tolerance limits for normal distribution, as indicated by the SW test results (W = 0.89, PW = 0.232) and skewness/kurtosis test (p=0.259). Therefore, ordinary least squares (OLS) multiple linear regression function was used for analysis, with α set at 0.05. As described above, total basal area is collinear with basal areas of individual tree species; therefore, a separate simple linear regression analysis was completed with excavated midden density as the dependent variable and total basal area as the independent variable.


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To investigate how local availability of whitebark pine seeds might influence the probability that a bear would excavate an individual midden, a logistic regression analysis was applied. Midden data from all study areas were pooled. The dependent variable was midden excavation; all middens were given a value of 1 (excavated) or 0 (not excavated), and factors affecting availability were applied as the independent variables: 1) midden size (area in square metres), 2) whitebark pine basal area at each midden (m2/hectare, averaged from measurements at midden centre and four directions from centre), and 3) midden density surrounding each midden (total middens per hectare, averaged by study area). It is likely that the activity status of a midden (active/inactive) and the whitebark cone crop from the year of excavation would also affect availability. However, due to the fact that all excavations were from the previous year or older, it was uncertain if the middens were active or inactive before excavations took place, and cone crop data were not available from the year the excavations took place; therefore, these factors could not be included in the analysis. Results Preliminary results from 2008 indicated that bears were eating whitebark pine seeds in the Willmore Wilderness area, and in 2009 the study area was expanded to determine if bears are eating pine seeds in other areas of WBP distribution in Alberta. Data from the 2008 and 2009 field seasons were combined for the final analysis. During June to September, 2008, sixteen transects were completed in four different areas of the Willmore, and from June to August 2009, 28 transects were completed in six different areas within the Willmore, JNP, and the Siffleur Wilderness. Midden surveys (but not transects) were also completed along the Sunset Lookout Trail in Banff National Park. Over the two years, transect surveys covered a total area of approximately 67 hectares; the area covered per study area varied from 1.89 to 22.2ha. Basal area for whitebark pine ranged from 0.23 to 7.33m2/ha in the areas studied. Basal areas for other tree species (Subalpine fir [Fa], Lodgepole pine [Pl], and Engelmann, white and black spruce) also ranged widely between study areas (Table 1). Table 1. Basal areas of tree species observed (m2/ha), averaged by study area; areas ordered north to south.

WBP Fa Pl Spruce Study area m2/ha % m2/ha % m2/ha % m2/ha % Total Featherstonhaugh, Willmore 7.33 37.9 10.22 52.9 0.00 0.0 1.78 9.20 19.3 DeVeber North, Willmore 3.16 19.7 11.02 68.5 0.00 0.0 1.91 11.85 16.1 Hardscrabble Creek, Willmore 3.26 50.0 1.47 22.6 0.11 1.6 1.68 25.81 6.5 Smoky/Jackpine Confluence, Willmore 0.23 2.5 4.92 53.8 1.77 19.3 2.23 24.37 9.2 Palisades Lookout, JNP 0.70 4.5 3.40 21.7 1.40 8.9 10.20 64.97 15.7 Astoria River Trail, JNP 4.12 25.5 5.49 33.9 1.84 11.4 4.73 29.21 16.2 Geraldine Lookout Trail, JNP 3.43 12.0 14.19 49.8 2.29 8.0 8.57 30.10 28.5 Siffleur/Mt. Loudon, Siffleur 0.63 9.4 3.37 50.0 0.21 3.1 2.53 37.50 6.7

Forty‐six red squirrel middens were observed during the two field seasons. Midden density by study area ranged from 0 to 0.88 middens per hectare (mean = 0.46). Figure 4 illustrates total midden densities and excavated midden densities for each study area.


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Figure 4. Midden densities by area, areas ordered north to south. Intact whitebark pine cones were observed at only two of the 46 middens, although old whitebark pine scales were observed at the vast majority (89%) of middens across all study areas. Bear use of whitebark pine seeds was observed in six out of the eleven areas visited (Figure 5), as indicated by the presence of bear scat containing whitebark pine seed casings6, and/or excavations at middens. The excavated midden density ranged from 0 to 0.71 middens/ha (Figure 4, above). All diggings and whitebark pine scat appeared to be old (estimated 2 years or more); no new (2008 or 2009) bear activity was observed at middens, although recent bear sign (e.g. berry scats, hedysarum digs) was observed along several transects.

6 In 2008, lab analysis of scat samples confirmed field identification of whitebark pine seed casings.


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Figure 5. Map of study areas where bear excavations/scat were observed, within whitebark pine distribution. Out of the eleven areas visited, eight study areas had complete basal area (tree density) data and were included in the analysis of the influence of stand composition on midden density. The multiple linear regression analysis did not indicate significant effects of basal area for any of the individual tree species (whitebark pine, subalpine fir, lodgepole pine, and spruce species), but total basal area was a significant predictor of total midden density (p = 0.041) in a separate simple linear regression. Significant effects were not observed in the separate simple linear regression analyses of total midden density versus elevation (p = 0.375), nor were significant effects observed in the multiple linear regression of proportion (percentage of total basal area) of spruce, subalpine fir, and lodgepole pine against total midden density. A non‐linear relationship was observed between percentage of whitebark pine and total midden density; midden density increased with increasing percent WBP to a maximum, then decreased with a continuing increase in proportion of WBP (Figure 6). This pattern suggested a polynomial relationship between the proportion of whitebark pine present and total midden density. The polynomial regression analysis indicated a statistically significant relationship between percentage of WBP and midden density (p = 0.002); graphing the relationship as a polynomial equation (Figure 6) produces an R2 value of 0.816, and the equation for the best‐fit line (Microsoft Excel) was y = ‐0.0013x2 + 0.0651x ‐ 0.0015.


41

0.000.100.200.300.400.500.600.700.800.901.00

0 10 20 30 40 50 60

Percent WBP

Tota

l mid

den

dens

ity (m

2/ha

)

Figure 6. Proportion of whitebark pine versus total midden density, by study area. The regression analysis of forest stand composition (basal areas) as predictors of excavated midden density did not produce any statistically significant results. The logistic regression of excavated versus unexcavated middens (N = 36) indicated that midden size significantly affected the probability that an individual midden was excavated (p = 0.015), but basal area of whitebark pine surrounding the midden (p = 0.326) and midden density in the surrounding study area (p = 0.295) did not have a significant effect. Cone counts were obtained for 5 study areas in 2009; counts ranged from zero to 45 cones per tree. Averages by study area are presented below (Table 2). Table 2. Average whitebark pine cones per tree by area in 2009.

Study area Average cones per tree

Hardscrabble Creek, Willmore 3.57 Palisades Lookout, JNP 6.50 Astoria River Trail, JNP 4.21 Geraldine Lookout Trail, JNP 6.50 Sunset Lookout Trail, BNP 18.4 Preliminary calorimetric analysis of whitebark pine seeds collected from one cone in Jasper National Park (Astoria River Trail) produced results of approximately 5.49kcal/g (Sean Coogan, personal communication, 7 December 2009). Female whitebark pine cones have been reported to produce 75 seeds per cone on average,7 with a mean weight of about 300mg per seed (ASRD & ACA, 2007). As a rough estimate, this corresponds to approximately 124 kcal per cone (75 seeds per cone x 0.3g/seed x 5.49 kcal/g). Fieldwork measurements in 2008 and 2009 did not involve fixed area plots with counts of mature WBP trees, therefore calories available by study

7 Researchers counted 52 seeds in one cone collected in 2009; based on a sample size of only 1, the reported average value from the literature was considered more accurate.


42

area cannot be estimated based on calories per tree x number of trees in each area. However, prism sweeps were completed and basal areas were calculated for each study area; therefore, estimates can be made based on basal areas (tree densities). McKinney et al. (2009) estimated that approximately 1000 cones/ha can be produced in forests with live whitebark pine basal area ≥5.0 m2/ha; this would correspond with approximately 200 cones per m2 (basal area) of whitebark pine. Based on these values, very approximate calculations suggest that thousands of potential kilocalories from whitebark pine seeds could be available in each study area. Future (anticipated) nutritional analysis of whitebark pine seeds may include percent fibre (digestibility) and improved accuracy of calorimetry measurements (repeat analysis). Discussion Basal areas of WBP varied (from 0.23 up to 7.33m2/ha) between study areas, indicating different levels of availability in different areas of whitebark pine distribution. Basal areas in this study were in the lower range of those observed in the GYE in areas of documented bear WBP use. Mattson and Reinhart (1997) reported WBP basal areas ranging from 2.2 to 23.4m2/ha in the Yellowstone region. The lower basal areas in our study indicate a lower availability of whitebark pine seeds in Alberta as compared to the GYE, with possible implications for the relative importance of WBP seeds in the overall diet of bears in Alberta’s whitebark pine zone. Potential methods for estimating the relative importance of whitebark pine seeds in grizzly bear diet are currently being investigated (see below). Midden densities calculated for this report (0.0 – 0.88, mean = 0.46middens/ha) are lower than those previously reported in 2008, due to the correction applied in 2009 for calculations of study areas. These densities may be underestimated, as previously described, and as such may be considered as minimum counts. However, midden densities were similar to those previously reported in other studies of WBP stands (0.23 to 1.09 per hectare in the GYE, Mattson & Reinhart, 1996). Between study areas, midden density varied considerably; indicating that availability of whitebark pine seeds for bears varies between study areas, possibly as a result of differences in stand composition or other landscape features. Basal areas of each tree species, total basal area, and elevation were considered as possible predictors of squirrel midden density. Although potential relationships were suggested by early results (2008), no significant relationships were detected between midden density and individual tree densities or elevation in the analysis of 2008/2009 data. This may be due to the small sample size available for analysis (N=8), or due to variation of basal areas within study areas. Analysis by transect (instead of by study area) would increase sample size and reflect tree density values at a smaller scale. Another possible explanation for the lack of significant relationships in the analysis is simply that midden densities are not significantly affected by basal areas of different tree species. Total basal area was a significant predictor of midden density, suggesting that for red squirrel populations in our study area, the overall density of conifer species may be more important than the densities of individual species. Other authors have reported that the density of red squirrels does not necessarily reflect differences in forest type (i.e. spruce dominated, mixed conifer, or lodgepole pine dominated); rather that it follows cone availability (Wheatley et al., 2002). A relationship was observed between the proportion of whitebark pine and midden density; a graph of this relationship suggests that as the percentage of whitebark pine in the forest stand increases, midden densities increase to a maximum, then decrease as the proportion of whitebark continues to increase above about 20%. Other authors report that squirrels usually inhabit stands with multiple conifer species, as opposed to pure whitebark pine stands (Lorenz


43

et al., 2008), due to the large inter‐annual variations in availability of whitebark pine. Mattson and Reinhart (1997) reported the highest midden densities in forest types dominated by lodgepole pine, and lowest midden densities in forests dominated by a mix of whitebark and lodgepole pine. These results suggest that there may be an optimal proportion of whitebark pine within a forest stand that support a maximum number of squirrel middens and availability of whitebark pine cones for bears. Since bears get whitebark pine seeds almost exclusively from squirrel middens, increasing proportions of whitebark pine within a forest stand may not necessarily increase availability for bears. At the present time, whitebark pine stands in Alberta are not extensively mapped; however, work is currently underway to apply remote sensing techniques for whitebark pine mapping. Future data describing the proportion of whitebark pine within forest stands will assist in modeling WBP seed availability for bears. The vast majority of middens included at least some WBP cone scales, confirming that squirrels were collecting and using WBP cones in the areas studied, making WBP seeds available for bears. However, very few intact WBP cones were cached at the middens observed in 2008 and 2009. Researchers did not visit middens after August in 2009, and it is possible that the peak of cone caching was missed. Only surface cones were counted in this study; middens were not disturbed to investigate buried cones. Therefore, any WBP cones cached under the midden surface were not detected by our methods, and WBP cone caching by squirrels was likely somewhat underestimated. It is difficult to determine true WBP seed availability without digging up middens, which are crucial to winter survival for squirrels. Although the number of cones cached in middens would provide a more direct estimate of WBP seed availability for bears, cone counts may be an appropriate surrogate measure of availability without disturbing middens. Similarly, Mattson (1994, as cited in Mattson et al., 2001) measured cones per WBP tree in relation to bear use of seeds. In 2009, cone counts were completed at a number of study areas, and cone count data were available from one study area for 2008. Annual cone production for whitebark pine can vary from zero up to around 300 cones per tree (Haroldson, 2000). Mast years occur every three to five years, with little to no cone production during intervening years. Cone counts in this study were highly variable from tree to tree (from 0 to 45), but an approximate value for each study area gives an indication of whether there was a crop failure (close to zero cones present), a moderate cone crop (a few cones per tree) or a mast year (hundreds of cones per tree). Results from 2008 suggest a cone crop failure in the Willmore, while cone counts from 2009 indicate a moderate crop in the areas studied. Cone counts during a mast year would provide an estimate of potential maximum availability of whitebark pine seeds. The most recent mast year was 2007, and based on cone development in 2009 (cones that will mature in 2010), it is predicted that 2010 will not be a mast year for WBP within the study area. Collaborative researchers monitoring annual WBP cone development will know one year in advance of a peak cone production year, allowing time for planning and financing to collect important data during a mast year. In spite of the cones present in 2009, there was no bear use of whitebark pine seeds observed during 2009; all cases of documented bear use of WBP seeds observed during this study were from previous years (<2008). Researchers did not visit middens after August in 2009, and it is possible that cone caching and bear excavations could have taken place at middens in September or October. Mattson et al. (2001) report a relationship between grizzly bear consumption of WBP seeds and the size of the cone crop, suggesting that there may be a minimum threshold of availability of WBP seeds for bear use to occur. Other authors have also noted inter‐annual variation in WBP use, related to the inter‐annual variation in cone production. In a ten‐year study of food habits of Yellowstone grizzly bears, use of WBP seeds


44

varied substantially between years, including years with no consumption, as well as years when WBP seeds made up most of the food consumed in summer and fall (Mattson et al., 1990). Kendall (1983) reported annual variation in bear use of pine nuts, depending on availability (timing of cone maturity and squirrel caching) and abundance (peak cone crop years). In order to more accurately record bear use of whitebark pine seeds in Alberta, it would be useful to repeat surveys of known squirrel middens during a mast year. Based on documented bear activity (excavations and scat) at squirrel middens, and direct evidence of WBP seed consumption by bears (presence of WBP seed casings in scat), results indicate that bears are using WBP seeds as a food source in a number of areas in west‐Central Alberta (see Figure 5). However, the importance of WBP to grizzly bears in these areas is unknown relative to other foods utilized. Excavated midden densities may be used to estimate and compare levels of bear use of WBP seeds (Mattson & Reinhart 1997). Excavated midden densities varied among the study areas in this study. Of the ten study areas for which midden densities were calculated, five had no excavated middens, while other areas had up to 0.71 excavated middens/ha. These results indicate that bear use of WBP differs between the areas. The range of 0 to 0.291 excavated middens/ha reported in the GYE (Mattson & Reinhart, 1997) is considerably lower than the values obtained in our study, although total midden densities were similar between the two studies. The higher rate of excavations in some areas in this study may indicate areas repeatedly visited by bears. Lower availability of whitebark pine on a regional scale could result in more intense use of certain whitebark pine stands. The presence of multiple scat piles observed near middens also suggests that a bear may return to the same midden and/or remain at middens for several feeding sessions, or that multiple bears visit the same midden. Other research has reported repeat excavations of squirrel middens over the course of months (Kendall, 1983). The regression analysis of forest stand characteristics versus excavated midden densities in our study did not produce statistically significant results; therefore, results from this study do not allow prediction of forest stands where more bear use of whitebark pine may take place. Mattson and Reinhart (1997) reported that the highest probability of bear excavations of middens occurred in stands with WBP basal areas in the range of 7.1 – 23.4m2/ha. It was expected that regional midden densities and/or whitebark pine basal area surrounding each midden would affect the probability of bear excavation of middens in this study, since these parameters affect availability of whitebark pine seeds for bears. However, neither basal areas of whitebark pine around middens nor regional midden densities were significant predictors of the probability of a midden being excavated. Midden size was a significant predictor of midden excavation. Mattson and Reinhart (1997) also reported that larger middens were excavated more often than smaller middens. These results suggest that bears may concentrate their efforts on larger middens, in order to maximize the energy obtained. At this time, midden size is the only local availability parameter that may be applied to predict probability of bear use of whitebark pine; future investigation could include analysis of what stand composition factors may influence midden size. WBP seed casings were observed in numerous bear scat samples near excavated middens, but it is difficult to determine the relative role of WBP seeds in the overall diet of grizzly bears in west central Alberta. It was not possible for scat sample collection to be temporally and spatially random or equally distributed across the landscape (scat samples were recorded and collected


45

only along transects). Currently, an alternative to scat analysis is being investigated for estimating the relative importance of whitebark pine seeds, see Conclusions and Future Directions. In calorimetry analysis, whitebark pine seeds collected in this study (2009) had the highest gross calorie content (5.49kcal/g) of any plants tested. This value is preliminary, as it is based on only one cone collection, but it is comparable to that reported in other studies. Mealey (1980) reported 3.9 kcal/g for the whole (undigested) seed, while Lanner & Gilbert (1994) reported 27kJ/g, or approximately 6.45kcal/g (as cited in Felicetti et al., 2003). When taking digestibility into account, whitebark pine seeds provide less digestible energy than meat, but at least twice that of most common plant foods (Mealey, 1980). Rough approximations of potential availability of calories from whitebark pine seeds in different study areas suggest that WBP seeds have the potential to provide thousands of calories per hectare. Previous research in Alberta has not reported WBP seeds as a food source for grizzly bears, however, as previously discussed, research did not focus on areas within WBP distribution. Based on a relatively lower density of whitebark pine in west central Alberta as compared to the Yellowstone area, WBP seeds probably do not hold the same significance for bears as in the GYE, but results from this study confirm that they are being used as a food source. Bears are known to be generalists, consuming a diverse range of foods, depending on seasonal, annual, and regional availability (Munro et al., 2006). Although WBP seed availability and use by bears may be localized, and may change significantly from year to year, WBP seeds could still serve as an important energy source for grizzly bears in areas of WBP distribution. The high fat content of WBP seeds (30‐50%) makes them a high quality food item, both due to the calories they contain and the efficiency with which they can be converted to body fat (Mattson et al., 2001). In the GYE, female bears have been reported to eat twice as many pine seeds as males. Consumption of WBP seeds may facilitate the accumulation of body fat (Mattson et al., 2001), and hence play an important role in reproductive performance and cub survival. Hair isotope analysis methods currently being considered (see below) could demonstrate the relative importance of whitebark pine, and could indicate if females are using whitebark pine more than males. Conclusions and Future Directions Fieldwork conducted in 2008/2009 was intended as exploratory research, as the relationships among whitebark pine, red squirrels and grizzly bears had not previously been documented in Alberta. Data confirmed that bears use squirrel‐cached WBP seeds as a food source in a number of areas within whitebark pine distribution in west central Alberta. Current threats to whitebark pine in Alberta include white pine blister rust, mountain pine beetle, climate change, and prolonged fire suppression (ASRD & ACA, 2007). The forest changes resulting from mountain pine beetle infestation are being considered in current Grizzly Bear Program research (see FRI, 2007); establishing relationships between bears and whitebark pine may provide more information on the potential impacts of mountain pine beetle on grizzly bear populations. Results from this study contribute to a better understanding of the ecological significance of whitebark pine. If whitebark pine seeds are a significant grizzly bear food, the potential loss of WBP trees through blister rust or pine beetle could affect the reproduction and survival of grizzly bears in areas of WBP distribution. WBP distribution includes a considerable area of grizzly bear range in Alberta, from the Kakwa Wildland Park south along the continental divide to the United States. As another species at risk, the connection of this endangered tree


46

species to grizzly bears could make WBP conservation efforts8 even more of a priority in Alberta, and in certain areas, the coordinated management of the two species could be useful. Different areas within WBP distribution may support different levels of bear use of pine seeds. Information regarding areas of high availability and levels of grizzly bear WBP use could assist in prioritization of WBP conservation efforts and/or influence bear conservation strategies in areas within WBP distribution. Total basal area and proportion of whitebark pine were significant predictors of midden density. These parameters may be used to model whitebark pine availability and use, with applications for food models and energetics working in areas where grizzly bear range overlaps whitebark pine distribution. Isotope analysis of bear hair has been used in the Yellowstone region to estimate the dietary contributions of whitebark pine seeds. The sulfur isotope signature of whitebark pine is very distinct from that of other foods (Felicetti et al., 2003). In 2008, the GBP collected hair samples in the Willmore/Grand Cache region for DNA analysis; these samples were collected in an area of whitebark pine distribution in the spring following a mast year for whitebark pine, and extra hair is available. The GBP is currently evaluating the feasibility of sending a pilot sample of bear hair for sulfur isotope analysis. This project will depend upon availability of outside funding in 2010. Acknowledgements This research was supported by the Parks Division of Alberta Tourism, Parks and Recreation. Thanks to Joyce Gould, Vernon Peters, Matthew Wheatley and Brooks Horne for providing background information, logistical support and/or advice in choosing study sites and developing field methods. Field assistance from Heidi Schindler, Lynae Vandervalk, Kim Forster, Heidi Fengler, Joan Simonton, and Kirsten Hayward was greatly appreciated. Literature cited Alberta Sustainable Resource Development (ASRD) and Alberta Conservation Association (ACA).

2007. Status of the Whitebark Pine (Pinus albicaulis) in Alberta. Alberta Sustainable Resource Development, Wildlife Status Report No. 63, Edmonton, AB. 22pp.

Hamer, D. and Herrero, S. 1987. Grizzly bear food and habitat in the front ranges of Banff National Park. International Conference of Bear Research and Management, 7: 199‐213.

Felicetti, L.A., Schwartz, C.C., Rye, R.O., Haroldson, M.A., Gunther, K.A., Phillips, D.L. and Robbins, C.T. 2003. Use of sulfur and nitrogen stable isotopes to determine the importance of whitebark pine nuts to Yellowstone grizzly bears. Canadian Journal of Zoology, 81: 763–770.

Foothills Research Institute (FRI). 2007. Foothills Research Institute Grizzly Bear Program 2007 Annual Report.

Foothills Research Institute (FRI). 2009. Foothills Research Institute Grizzly Bear Program 2008 Annual Report.

Haroldson, M.A. 2000. Whitebark pine cone production. pp. 44–47 In C.C. Schwartz and M.A. Haroldson (Eds) Yellowstone grizzly bear investigations: annual report of the Interagency Grizzly Bear Study Team. U.S. Geological Survey, Bozeman, Montana.

8 A Whitebark and Limber Pine Recovery Team has been formally assembled, with a tentative timeline of March 2010 for the recovery plan.


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Kendall, K.C. 1983. Use of pine nuts by grizzly and black bears in the Yellowstone area. International Conference of Bear Research and Management, 5: 166‐173.

Lanner, R.M., and Gilbert, B.K. 1994. Nutritive value of whitebark pine seeds, and the questions of their variable dormancy. U.S. For. Serv. Gen. Tech. Rep. INT‐GTR‐309. pp. 206–211.

Luckai, F. 1997. Forest measurements field manual. Lakehead University Faculty ofForestry, Thunderbay, ON.

Lorenz, T.J., Aubry, C. and Shoal, R. 2008. A Review of the literature on seed fate in whitebark pine and the life history traits of Clark’s nutcracker and pine squirrels. General Technical Report PNW‐GTR‐742. United States Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, Oregon. 62pp.

Mattson, D.J., Blanchard, B.M. and Knight, R.R. 1991. Food habits of Yellowstone grizzly bears, 1977‐1987. Canadian Journal of Zoology, 69: 1619‐1629.

Mattson, D.J., Blanchard, B.M. and Knight, R.R. 1992. Yellowstone grizzly bear mortality, human habituation, and whitebark pine seed crops. Journal of Wildlife Management, 56(3): 432‐442.

Mattson, D.J., Kendall, K.C., and Reinhart, D.P. 2001. Whitebark pine, grizzly bears, and red squirrels. Chapter 7 in Tomback, D.F., Arno, S.F. and R.E. Keane, eds. Whitebark Pine Communities, Ecology and Restoration. Island Press, Washington, 440 pp.

Mattson, D.J. and Reid, M.M. 1991. Conservation of the Yellowstone grizzly bear. Conservation Biology, 5(3): 364‐372.

Mattson, D.J. and Reinhart, D.P. 1994. Bear use of whitebark pine seeds in North America. Pages 212‐220 in W. C. Schmidt and F. K. Holtmeier, compilers. Proceedings – International workshop on subalpine stone pines and their environment: The status of our knowledge. USDA Forest Service, Intermountain Research Station, General Technical Report INT‐GTR‐309. Ogden, Utah.

Mattson, D.J. and Reinhart, D.P. 1996. Indicators of red squirrel (Tamiasciurus hudsonicus) abundance in the whitebark pine zone. Great Basin Naturalist, 56(3): 272‐275.

Mattson, D.J. and Reinhart, D.P. 1997. Excavation of red squirrel middens by grizzly bears in the whitebark pine zone. Journal of Applied Ecology, 34: 926‐940.

McCaughey, W.W.; Tomback, D.F. 2001. The natural regeneration process. Chapter 6 in Tomback, D.F., Arno, S.F. and R.E. Keane, eds. Whitebark Pine Communities, Ecology and Restoration. Island Press, Washington, 440 pp.

Mealey, S.P. 1980. The natural food habits of grizzly bears in Yellowstone National Park, 1973‐1974. Bears, their biology and management; International Conference on Bear Research and Management, 4: 281‐290.

Morgan, P. and Bunting, S.C. 1992. Using cone scars to estimate past cone crops of whitebark pine. Western Journal of Applied Forestry, 7: 71‐73.

Munro, R. H., Nielsen, S.E., Price, M.H., Stenhouse, G.B., and Boyce, M.S. 2006. Seasonal and diel patterns of grizzly bear diet and activity in west‐central Alberta. Journal of Mammalogy, 87(6):1112–1121.

Russell, R.H., Nolan, J.W., Woody, N.G. and Anderson, G.H. 1979. A study of the grizzly bear in Jasper National Park, 1975 to 1978. Final Report, prepared for Parks Canada by Canadian Wildlife Service, Edmonton.

Struebel, D. 1989. Small Mammals of the Yellowstone Ecosystem. Roberts Rineharts, Inc., Publishers, Boulder, Colorado.

Wheatley, M., Larsen, K.W., Boutin, S. 2002. Does density reflect habitat quality for North American red squirrels during a spruce‐cone failure? Journal of Mammalogy, 83(3):716–72.

Chapter 5 Enhancement Trials

48

CHAPTER 5: GRIZZLY BEAR HABITAT ENHANCEMENT TRIAL PROGRESS REPORT

Jerome Cranston

Arctos Ecological Services, Hinton, [email protected]

TAY RIVER ENVIRONMENTAL ENHANCEMENT FUND Introduction This interim report describes the surveys and treatments applied by the Grizzly Bear Habitat Enhancement Trial, conducted by the Foothills Research Institute Grizzly Bear Program (formerly the Foothills Model Forest Grizzly Bear Research Program) in the Tay River area in Clearwater, Alberta, in 2008 and 2009. The objective of the Grizzly Bear Habitat Enhancement Trial is to determine:

i) whether grizzly bear foods such as buffaloberry (Shepherdia canadensis) and alpine sweet‐vetch (Hedysarum alpinum) can be established on reclaimed oil and gas sites; and ii) once established, whether grizzlies will be attracted to these sites.

Funding for this project was provided by Shell Canada and Mancal Energy through the Tay River Environmental Enhancement (TREE) fund. Similar treatments were carried out for the trial in 2007, 2008, and 2009 in Kananaskis, with funding provided by Shell Canada and Husky Energy under the Moose Mountain Environmental Enhancement Fund (MMEE I and MMEE II). Reconnaissance and Planting Reconnaissance of potential treatment sites was conducted on 14 July 2007. Three reclaimed well sites were ultimately selected for treatment (Figure 1): Burnt Timber 15 at SE‐3‐23‐W5, Burnt Timber 13 at NW‐12‐23‐W5, and Limestone East at 1‐06‐33‐09‐W5.


49

Figure 1. Treated sites in the Tay River area. Burnt Timber 15 (11‐26‐31‐9‐W5) UTM Z11N, 626140E, 5727766N This site was reclaimed in 2007 and extensive micrositing and slash dispersal was employed (Figure 2).


50

Figure 2. Reclamation of BT‐15. This site was planted with 300 Buffaloberry seedlings on 7 July 2008 (Figure . 3); these showed good survival the following spring. The site was also planted on 9 and 10 June 2009 with 4620 Buffaloberries, (including 1740 that came from 2007 seed and had overwintered in the nursery). A third planting treatment was carried out on 18 August 2009, with 490 sweet‐vetch (Figure 4) and 190 buffaloberry seedlings from Alberta Research Council. The site was very heavily vegetated at that time, and wild horses obviously frequented the area. (Figure 5). A total of 5600 seedlings were planted over 1.5 ha on BT‐15.

Figure 3. Planted Shepherdia.


51

Figure 4. Planted Hedysarum.

Figure 5. Revegetation of BT‐15.


52

Burnt Timber 13 (5‐28‐30‐10‐W5) (UTM Z11N 613930E, 5717500N) This well site was reclaimed in 2008 and is flat, low‐elevation, furrowed and had considerable slash. Soil was soft and silty, and there was minimal competing vegetation at the time of planting. 2160 buffaloberries were planted on this 0.5 ha site on 12 June 2009. Limestone East (1‐6‐33‐9‐W5) (UTM Z11N 620428E, 5739687N) This well site had been planted about ten years previously with Lodgepole pine which will offer sheltered microsites for the Buffaloberry seedlings. 1080 buffaloberries were planted on this 1.0 ha site on 13 June 2009. Conclusion In terms of the project objectives,


the success of the planting effort can not be determined until establishment and growth have been monitored for several years. The second objective is predicated upon the success of the first, therefore it cannot yet be determined whether grizzlies will be attracted to these sites. It is recommended that funds remaining in the TREE account be allocated to manual removal of competing vegetation in the summer of 2010. These three sites (BT‐15, BT‐13, and Limestone East) should continue to be monitored annually for seedling establishment and vigour, human traffic, and evidence of grizzly bear use.


53

MOOSE MOUNTAIN ENVIRONMENTAL ENHANCEMENT FUND II

Introduction This interim report describes the surveys and treatments applied by the Grizzly Bear Habitat Enhancement Trial, conducted by the Foothills Research Institute Grizzly Bear Program (formerly the Foothills Model Forest Grizzly Bear Research Program) in the Moose Mountain area in Kananaskis, Alberta, in 2008 and 2009. The objective of the Grizzly Bear Habitat Enhancement Trial is to determine:


Funding for this project was provided by Shell Canada and Husky Energy through the Moose Mountain Environmental Enhancement (MMEE 2) fund. Reccnnaissance and Planting Reconnaissance of potential treatment sites was conducted on 28 July 2006, and 7 September 2006. Two sites were ultimately selected for treatment (Figure 6): a portion of an access road leading to a well site reclaimed by Petro Canada at NW‐12‐23‐W5, and a section of the Interconnect pipeline right‐of‐way. (Note that the well site at NW‐12‐23‐W5 was planted with buffaloberry and sweet‐vetch in 2007 under the initial Moose Mountain Environmental Enhancement (MMEE 1) fund).


54

Figure 6. Treated sites in the Moose Mountain area.


55

Site 1: (NW‐12‐23‐6‐W5) (UTM Z11N 660110E, 5646152N) This site is a small (0.12 ha) tilled section at the front of a 250m access road from the West Bragg Creek Road to a well site drilled by Petro‐Canada in 2005 (Figure 7). Lodgepole pine seedlings had been planted in spring 2006 on the well site and access road but did not survive past 2007. The well site and access road had been planted with 1080 buffaloberry and 580 Hedysarum on 9th and 10th July 2007 under the initial Moose Mountain Environmental Enhancement fund (MMEE 1). Mortality over the winter of 2007/2008 was very high due to extreme heat and drought conditions following planting. The tilled section at the front of the access road was planted again on 3rd and 4th July 2008, with 495 Buffaloberry and 400 sweet‐vetch seedlings. Mortality on these plants was also high due to compaction by cattle, also competition from grass, clover, and dandelion, as indicated by inspection on 12 June 2009. This section was planted with 230 buffaloberry on 4 July 2009.

Figure 7. Site 1, in 2006 before green‐up. Site 2: Interconnect pipeline (SW‐3‐23‐6‐W5) (UTM Z11N 657585E, 5643833N) This portion of the Interconnect (1.3 ha) (Figure 8) was planted on 3 and 4 July 2009 with 4625 buffaloberry seedlings. The seedlings had been growing in containers since February 2009 and were in excellent condition (Figure 9). The soil was dry at the time of planting, but extensive thunderstorms on the evening of 4 July made it very likely that the plants were well‐watered after planting.


56

Figure 8. Site 2 in September. 2006.

Figure 9. Buffaloberry seedlings.


57

Conclusion In terms of the project objectives,


the success of the planting effort can not be determined until establishment and growth have been monitored for several years. The second objective is predicated upon the success of the first, therefore it cannot yet be determined whether grizzlies will be attracted to these sites. Both treated sites were considerably less compacted than the two sites planted in 2007 under the MMEE 1 fund. Site 1 had been mechanically tilled and Site 2 on the Interconnect pipeline had been tilled and strewn with coarse woody debris. The primary growth‐limiting factor on both sites is competition from herbaceous vegetation such as clover and dandelion, but compaction due to cattle apparently led to considerable mortality on the initial treatment of Site 1 in 2008. It is recommended that the funds remaining in the MMEE II fund be allocated to manual removal of competing vegetation around planted seedlings in the summer of 2010, and that the performance of planted seedlings be monitored annually until the seedlings are able to outgrow competing vegetation.

Chapter 6 Food Model

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CHAPTER 6: GRIZZLY BEAR HABITAT PRODUCTIVITY MODELS FOR THE YELLOWHEAD, SWAN HILLS, GRANDE CACHE AND CHINCHAGA

POPULATION UNITS OF ALBERTA

Scott Nielsen

Department of Renewable Resources, University of Alberta Edmonton, Alberta

With field and laboratory assistance from:

Karen Graham, Terrance Larsen, Tracy McKay and Robin Munro Foothills Research Institute, Hinton, Alberta

Summary This report summarizes the methodology and outputs of a diet‐based grizzly bear habitat model that predicts habitat productivity for a 171,771‐km2 landscape that encompasses the Yellowhead, Grande Cache, Swan Hills, and Chinchaga (far north) grizzly bear population units of Alberta. The model of habitat productivity is based on spatial predictions of occurrence for 20 critically important grizzly bear food items and the seasonal (10 bi‐weekly periods from May through September) weighting of those food items based on scat analyses performed in the region over the past decade. These spatial‐temporal models of habitat productivity illustrate the dynamic nature of grizzly bear habitat and measure habitat quality more directly by quantifying bottom‐up limitations in food resources that ultimately influence wildlife health and potential population density (carrying capacity). As well as providing seasonal estimates of habitat productivity, multi‐seasonal definitions of potential habitat (sum of seasonal models), realized habitat (sum of seasonal models after considering loss of habitat due to range contraction) and deficit habitat (difference between potential and realized habitat) are summarized. Such metrics of habitat productivity provide a framework for population management and land use planning, including the prioritization of sites for habitat restoration (access management, road decommissioning, etc.) and approaches for quantifying population recovery targets. Future research is focused on converting seasonal importance weights to units of available (digestible) energy and addressing inter‐annual variation in certain food resources. Introduction Grizzly bear populations are regulated not only through top‐down processes influencing survival (particularly human‐caused mortality), but also by bottom‐up processes that influence spatial and temporal patterns of resource (food) abundance. These bottom‐up processes influence wildlife health (e.g., body mass, body condition, etc.), reproductive events, fecundity and carrying capacity (potential population density). To date, most habitat models for grizzly bears in Alberta have relied upon telemetry‐based approaches to define habitat (e.g., Nielsen 2005; Nielsen et al. 2002, 2006, 2009). Although telemetry‐based approaches predict locations of bears and their habitats well, they are more limited in their ability to relate habitat to population processes (e.g., growth/health of animals, fecundity and population density).


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Moreover, telemetry‐based approaches are unlikely to measure habitat productivity at local (patch‐level) scales, since re‐locations of bears represent not only foraging bouts, but also a number of other behaviours, such as bedding, movements between food patches or to and from bedding sites (Munro et al. 2006), and mating or other social interactions that don’t directly relate to use of food resource patches (Stenhouse et al. 2005). Assuming that the most critical limiting factors affecting bear populations are human‐caused mortality (survival) and habitat productivity (reproduction and carrying capacity), telemetry‐based models attenuate or misjudge both of these limiting factors, especially when in the presence of maladaptive habitat selection where bears use human‐dominated habitats increasing their mortality risk (Nielsen et al. 2006). More direct or mechanistic measures of grizzly bear habitat are therefore needed that consider patterns (spatial and temporal) in habitat productivity, such as that produced by Mattson et al. (2004) for the Yellowstone Ecosystem. This bottom‐up approach focuses on understanding the patterns of food resource abundance and its relationship to wildlife health and population dynamics. Such an emphasis would presumably allow for direct manipulation (enhancement) of reproductive events, fecundity, carrying capacity and ultimately rates of population recovery for declined populations. Given that Alberta’s grizzly bear population for the majority of its range on Provincial lands is most likely below carrying capacity, such information would be critical to setting population targets and accelerating population recovery in association with management actions that limit human access to increase survival. In this report, I define grizzly bear habitat productivity for a 171,771‐km2 landscape of western Alberta (Yellowhead, Grande Cache, Swan Hills, and Chinchaga populations) using a bottom‐up approach first described for the Yellowhead population by Nielsen (2010), but more generally following the concepts of Mattson et al. (2004) for the Yellowstone Ecosystem. My approach is based around spatial predictions of known grizzly bear food items (for example, see Nielsen et al. 2003) and subsequently the temporal weighting study pixels (30‐m raster) for each food item predicted using bi‐weekly estimates of percent digestible matter (0 to 100%) from scat analyses (Nielsen et al. 2010). Bi‐weekly models of potential habitat productivity are produced, as well as a multi‐seasonal habitat productivity model based on the sum of bi‐weekly models. Because potential habitat does not consider loss of grizzly bear range, I estimate realized habitat based on the product of potential habitat and regional patterns of occupancy (probability of occurrence) influenced by broad‐scale patterns of human land use conditions (Nielsen et al. 2009). I assume therefore that factors influencing occupancy at regional scales (range of the species in Alberta) are consistent with those factors affecting local survival and thus limiting the true potential of the habitat to sustain a population of grizzly bears at carrying capacity. Even at regional scales, the same concept applies. Grizzly bear foods may be found in abundance outside the current range of the species and in those situations the critical limiting factor is not food resource abundance, but survival. Consideration of both resource abundance and survival (including regional patterns in occupancy) is therefore necessary to properly predict current habitat conditions (i.e., realized habitat quality). The objectives of this report are to describe the methodology used to estimate habitat productivity in both potential and realized conditions, as well as illustrate where habitat deficit is greatest.


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Methods Study area This study takes place over a 171,771‐km2 of western Alberta representing the northern‐most distribution of grizzly bears in Alberta and 71.5% of the area defined as grizzly bear range in Alberta (Figure 1). Elevation ranges from 3,666 metres along the border with British Columbia in Jasper National Park to 259 metres in the north along the Peace River. Given the broad gradient in elevation, the area encompasses a diverse array of ecosystems from alpine meadows in the mountains, to boreal forests in the north, as well as agricultural white‐zone (private lands) around Grande Prairie. Large protected areas occur in the mountains (Jasper National Park, Willmore Wilderness, Kakwa Wildlands) and to a lesser extent the foothills and boreal forests (Figure 2). Field methods Vegetation characteristics (crown closure, tree basal area, shrub cover, and ground‐layer cover) were measured at 2,388 field plots between the years 2001 and 2008 as part of the Foothills Research Institute Grizzly Bear Program (Figure 3). This included only those plots that did not exhibit disturbance (clear‐cuts or fire) since first being established to minimize possible sources of bias with remote sensing‐derived variables (landcover and canopy). For each location, the presence‐absence (detected‐undetected) of 20 selected grizzly bear food items were classified and imported into a GIS. More details on field plot methodology can be found in Nielsen et al. (2004a) and the Foothills Research Institute Grizzly Bear Program annual reports. Species (food item) models Plant (herbivory, root digging, and frugivory) food models A total of 20 food items were considered locally important for grizzly bears (Table 1). For each plant species, excluding a general forbs‐grasses‐sedges group (see details below), species occurrence models using logistic regression were estimated for the study area using the presence‐absence of species at field plots and the environmental predictors of climate, geology (geological formation or surficial material), terrain, land cover type and stand canopy conditions (see Table 2 for a full list of predictor variables). In some cases, more than one species from the same Genera were combined into a single species complex or group. One group in particular where this occurred was the huckleberry (Vaccinium membranaceum)‐velvet leaf blueberry (V. myrtilloides) complex. Although relatively easy to identify differences between the species in the field, it appeared that some field staff did not differentiate the two species correctly, often referring to all large Vaccinium’s as huckleberry. Just as problematic was the fact that scat analyses also combined the two species into a single group. Therefore, I have analyzed the huckleberry‐velvet leaf blueberry complex as a single group. Another complex where species were combined into a single food item category was the unidentified forbs, grass and sedge group. Since this represented a rather ubiquitous group of species with little knowledge of specific species that are used or preferred by grizzly bears (often just referred to in scat analyses as unidentified forb or grass), I qualitatively ranked the relative abundance of forbs‐grasses‐sedges in each study area pixel on a scale of 0 to 1 for each land cover type. More specifically, herbaceous and shrub habitats were given an abundance value of 1; barren cover types outside of the alpine natural sub‐region (most of these are clear‐cuts), mixed forests, broadleaf forests, and open wetlands an abundance value of 0.75; open conifer and treed wetland an abundance value of 0.5; moderate conifer an abundance value of 0.25; and finally dense conifer, agriculture (assumed to be non‐range for grizzly bears), water, snow/ice, cloud, shadow, and alpine barren areas an abundance value of 0.


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Animal protein (carnivorous) food models For moose (Alces alces), rodents (numerous species), ants (Formicidae) and wasps/bees (Hymenoptera), field visited grizzly bear locations where the use of each food item was recorded was used to model relative probability of occurrence using a resource selection function (RSF; Manly et al. 2002). Specifically, those sites where moose carcasses, rodent digs, ant digging or destruction of coarse woody debris (myrmecophagy) and the digging for ground nesting wasps and bees were recorded and compared to pseudo‐absent locations randomly drawn from the study area. The same environmental predictors used for predicting occurrence of plant food were used for animal foods, although for the moose model, moving window averages were used to summarize conditions at two additional scales (51.5 ha and 51.6‐km2) relating to local patch and moose home range conditions. To ensure that model predictions were scaled similarly to other models (i.e., 0 to 1), random pseudo‐absence locations were assigned sample importance weights that equalled (sum of weights) the total sample size of kill sites for each animal species. Model building I followed a Hosmer & Lemeshow (2000) model building approach where univariate models (and quadratic models where appropriate) were first assessed for each variable and ranked according to their importance (in this case according to percent deviance explained). A multivariate model was then selected for each food item by adding the most important uncorrelated (Pearson correlations < |0.7|) univariate (or quadratic) factors. Where appropriate, interaction terms were considered. Model significance, fit and predictive accuracy were assessed using a model likelihood χ2 statistic, a Hosmer & Lemeshow goodness‐of‐fit statistic Ĉ (Hosmer & Lemeshow 2000), and area under the curve (AUC) Receiver operating characteristic (ROC) respectively. AUC values above 0.9 were considered to have high model accuracy, 0.7‐0.9 good model accuracy and < 0.7 low model accuracy (Swets 1988; Fielding & Bell 1997). Specificity and sensitivity values were also estimated, as well as the predicted probability of occurrence, for each observation in order to identify the optimal cut‐off probability for classifying species occurrence in each study area pixel. The cut‐off probability for classifying species presence was selected as the probability where the absolute value of the difference between sensitivity and specificity curves was minimized (Liu et al. 2005), thus balancing type I and II error rates. Species (food item) maps Model coefficients were used to predict probability of occurrence in each study area pixel for each species using a GIS (raster calculator function). A non‐habitat mask was also used to ‘zero out’ species occurrence for non‐habitat sites based on reclassified landcover (remote sensing classification) productions from McDermid et al. (2009). This included the landcover types: water; snow/ice; cloud; shadow (mostly occurring in steep areas of rock); barren for only the alpine natural sub‐region where it represents the high‐elevation mountain rocks (lower elevation barren areas are often classified as recent clear‐cuts); and finally areas of agriculture (based on grizzly bear landcover classification). Presence‐absence of each species in each study area pixel was generated by reclassifying species probability maps into a 0 (absent) or 1 (present) grid using the optimal cut‐off probability estimated from sensitivity and specificity values. Bi‐weekly estimates of food item importance weights For each of the 10 bi‐weekly periods from 1 May to 30 September and for four different regions of the study area (Yellowhead & Grande Cache mountain natural region, Yellowhead Foothills natural region, Grande Cache Foothills natural region, Swan Hills unit, and Chinchaga area),


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importance weights were estimated from local diet studies of grizzly bears based on scat analyses. More specifically, percent digestible matter values from Munro et al. (2005) were used as seasonal importance weights in the Foothills and Mountain natural regions of the Yellowhead population unit, as well as the mountains of the Grande Cache population unit where no diet study has been completed (Table 3). Where diet items showed high inter‐annual variation in importance, such as in fruit of buffaloberry and huckleberry, the maximum observed value among years was used, which results in index values exceeding 100 for some seasons where all possible food items were present in a pixel. Given the importance of fruit in regulating populations, this approach emphasizes habitats that may in certain years be critically important and thus the management of sites that provide a basis for these ‘bonanza’ years. Caution should therefore be given when interpreting the index as being consistent among years or being able to predict animal locations equally‐well among years. Importance weights (percent digestible matter) for the Swan Hills and Grande Cache Foothills were based on values reported in Larsen & Pigeon (2006). However, since some bi‐weekly periods from Larsen & Pigeon (2006) did not contain estimates of percent digestible matter or were based on small sample sizes, modifications were made to smooth seasonal values (where sample size was low) or extrapolate missing values from the more well‐studied Yellowhead population (Munro et al. 2005). In addition, one major food item, beaver, was well‐represented in the summer diets (especially late July through August) of Swan Hill grizzly bears (Larsen & Pigeon 2006), but not observed elsewhere. Since our field plots do not measure the presence of beaver or their sign, this food item was ignored (future work on beaver habitat in the Swan Hills should be considered). Finally, importance values for the Chinchaga region were based on extrapolation from the Yellowhead Foothills with modifications made given general knowledge of black and grizzly bear diets in the region from McKay & Graham (2009) and recent evidence that the vast majority of scats opportunistically collected and used for analysis of diets in the Chinchaga were from black bears. Estimating potential habitat productivity (PHP) I estimated potential habitat productivity for any study area habitat patch (30‐m pixel) in each of 10 bi‐weekly periods as the sum of resource‐weighted items in a habitat patch, or more formally,

)(1

m

n

mmijk wRPHP ×=∑

=

where potential habitat productivity for pixel i of ecosystem j during season k (i.e., PHPijk) was estimated as the sum of the product between resource abundance R for the m‐th resource item and a seasonal importance weight w for the same resource item (Nielsen et al. 2010). Resources represented food items and where measured as the abundance within a study pixel, while importance weights represent the seasonal significance of the food item by ecosystem. In the simplest situation involving a single ecosystem (a region over which a population has similar diets), potential habitat productivity for any particular site would be estimated from a single list of food items, their predicted distribution (resource patches) and a matrix of seasonal importance weights by resource item. Here I measured species abundance as simply the predicted presence‐absence of species and use bi‐weekly estimates of percent digestible matter for importance weights. Due to variation in diets across the study area, five ecosystems were


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considered: (1) the mountain natural region of the Yellowhead and Grande Cache population units; (2) the Foothills natural region of the Yellowhead population unit; (3) the Foothills of the Grande Cache population unit; (4) the Swan Hills population unit; and (5) Chinchaga region of the far north population unit. An annual (multi‐seasonal) index of potential habitat productivity for any study area habitat patch was based on the sum of seasonal potential habitat productivity values, or more formally,

)(11

km

n

mkm

n

kij wRPHP ×= ∑∑

==

where potential habitat productivity for pixel i of ecosystem j (PHPij) was estimated as the sum of the product between resource abundance R in pixel i of ecosystem j for the m‐th resource item during the k‐th season and an importance weight w assigned to pixel i of ecosystem j for the same m‐th resource item and k‐th season (Nielsen et al. 2010). Potential habitat productivity does not consider historic displacement (extirpation) of grizzly bears and thus predicts habitat conditions outside the current range of the species. Although this may initially seem to be problematic for management purposes, potential habitat productivity does provide an estimate of habitat conditions in the absence of human‐altered populations and thus offers information for actions relating to population recovery, the current management phase for grizzly bears in Alberta. Estimating realized habitat productivity (RHP) To adjust habitat conditions for currently occupied habitat, realized habitat productivity was estimated for each study area pixel. Realized habitat productivity for any particular season was measured as the product of that season’s potential habitat productivity and the predicted regional occupancy for grizzly bears for any study area pixel (Ψi) from Nielsen et al. (2009; Figure 4) or,

iijkijk PHPRHP ψ×=

Annual (multi‐seasonal) realized habitat productivity was simply the sum of seasonal realized habitat productivity, or more formally,

( )∑=

×=n

kikij PHPRHP

1

ψ

Realized habitat productivity was assumed to relate to regional patterns of animal density and population size, since it reduces habitat conditions in areas where survival is low and in areas where populations have been extirpated. Here, local factors of mortality risk, such as that used by Nielsen et al. (2004b, 2006, 2008), were not considered, but could be subtracted from regional RHP’s to reflect patch‐level population sinks associated with human access features.


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Estimating habitat deficits (HD) Habitat deficits (seasonal or annual) measure the loss of habitat potential due to historic extirpation of populations or local reductions in population density due to excessive rates of human‐caused mortality (Nielsen et al. 2010). Assuming a relationship between observed density and realized habitat productivity, this measure would measure the increase in population density in the absence of human‐caused mortality and range loss. Habitat deficits (HD) were measured as simply the difference between potential and realized habitat productivity. For seasonal (bi‐weekly) periods, this would be estimated for any study area pixel as,

ijkijkijk RHPPHPHD −=

where HDijk represents the estimated habitat deficit for pixel i of ecosystem j in season k and PHPijk and RHPijk the potential habitat productivity and realized habitat productivity respectively for that same pixel, ecosystem and season. Total habitat deficit is measured as the sum of multi‐seasonal deficits, or more formally,

( )∑=

−=n

kkkij RHPPHPHD

1

Where habitat deficit (HDij) for any study area pixel i in ecosystem j was estimated as the sum of seasonal (k) differences between potential habitat productivity PHPk and realized habitat productivity RHPk. Results Species (food item) models I do not describe here each of the 20 food items, but instead I illustrate two examples: (1) the Huckleberry (Vaccinium membranaceum) and velvet‐leaved blueberry (V. myrtilloides) group for an example of a frugivory food item; and (2) moose (Alces alces) for an example of a carnivorous food item. Below each is described. Huckleberry/velvet‐leaved blueberry was predicted by the climate variables of mean annual precipitation (quadratic hump‐shaped response) and mean minimum January temperature (negative response), the geological bedrock formations of Dunvagan (negative response), Lower Palaeozoic (negative response), Scollard (positive response) and Upper Palaeozoic (negative response), the surficial geology of colluvial (positive response), till blanket (positive response) and till veneer (positive response) materials, the terrain variables of topographic position (quadratic hump‐shaped response), terrain wetness (quadratic hump‐shaped response) and June to August solar radiation (negative response), and finally the forest stand variable of canopy (positive response). Predicted probability of occurrence for huckleberry/blueberry suggested widespread occurrence throughout the foothills illustrating its importance in the diet of foothills bears. It was predicted to be absent, however, in the far north Chinchaga region and most mountain valleys (Figure 5). Moose (Alces alces) occurrence (relative probability of being at a site consistent with a moose kill) was predicted by the terrain variable of terrain wetness (positive response), the land cover categories of herbaceous (negative response) and shrub (quadratic hump‐shaped response), and


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finally the forest stand variable of canopy cover (quadratic hump‐shaped response). Predicted probability of relative occurrence for moose suggested it was most common to boreal forests of the Chinchaga region and the foothills between the Kakwa and Grande Prairie area, but also scattered throughout the foothills where terrain soil conditions were moist and shrub cover (this land cover type in the foothills generally reflects recent clear‐cut forests) moderate (Figure 6). Potential habitat productivity (PHP) Potential habitat productivity (PHP) was predicted from the sum of weighted food models for each season (Figures 7). Although 10 bi‐weekly periods were developed, I illustrate here an example the PHP for the month of August. During August, PHP was highest in the lower to moderate elevations of the foothills and lower elevations in the mountains where fruit, which were the most important food item during that time, were readily available. Multi‐seasonal PHP was again highest in the valley bottoms, but also in some high alpine meadows of the Rocky Mountains and semi‐disturbed sites throughout the Swan Hills (Figure 8). Realized habitat productivity (RHP) Realized habitat productivity (RHP) reflected the down‐weighting of habitat due to the influence of human activity on mortality risk of grizzly bears. As an example of seasonal estimates of RHP, I illustrate RHP for the month of August. During August RHP was highest in the mountain valleys where both the security and food resource abundance (mostly fruiting species) during that month was highest (Figure 9). Multi‐annual RHP was again highest in the mountain valleys due to influences of reduced habitat potential caused by human activity displacing bears from productive habitats in the lower Foothills (Figure 10). Habitat deficits (HD) A map of habitat deficits illustrates the areas where food resource abundance is greatest, but the displacement of animals from those habitats is greatest. In particular, the lower foothills surrounding the Swan Hills, the lower foothills of the Grande Cache and Yellowhead units, and much of the Parkland in the Grande Prairie region showed the greatest current habitat deficit (Figure 11). Where connectivity occurs, locations of restoration projects that would enhance recovery of grizzly bear populations should be considered. Discussion In this report I a describe new approach for measuring and mapping habitat quality that focuses on tradeoffs in food resource abundance and survival (Nielsen et al. 2010). The assumption with this approach is that both bottom‐up (food resource abundance) and top‐down (human‐caused mortality) factors regulate or limit grizzly bear populations by influencing individual health (e.g., body mass), habitat‐based carrying capacity, fecundity and survival. My approach to defining bottom‐up resources rests upon the knowledge of seasonal diets of grizzly bears that are already well‐described across the study area (Munro et al. 2006; Larsen & Pigeon 2006). Diet studies alone, however, cannot describe spatial patterns of habitat quality at the scales which land management and conservation planning occur. Knowledge of food resource distribution, abundance and nutritional quality at these same scales is therefore also needed. I used species distribution models based on information from vegetation field plots to estimate the location of important food patches (Nielsen et al. 2003) and weighted those patches based on percent digestible dry matter from diet studies (Munro et al. 2006; Larsen & Pigeon 2006). Although I used resource patches defined by the presence‐absence of food items to measure resource availability, actual abundance (density or grams of resource), or better yet resource nutrition


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(e.g., starch, protein, and carbohydrate) and energetics (e.g., kilocalories or potential net digestible energy) should be considered to facilitate accurate comparisons among seasonal habitat conditions and overall multi‐seasonal estimates of habitat quality. Laboratory and field research on energetics is currently underway which will improve models by scaling maps into units of potential energy (available kcal or potential digestible energy). This will facilitate analysis of spatial patterns in health, population density and land use planning. References

Anonymous. 2003. Alberta Climate Prediction Model (ACPM) to provide climate predictions for any location in Alberta from its geographic coordinates. Unpublished report to Alberta Environment/Sustainable Resource Development, Edmonton, Alberta ‐ pp 41.

Anonymous. 2010. Alberta Bedrock Geology. Alberta Geological Survey. Available at: http://www.ags.gov.ab.ca/GIS/download_gis.htm

Barry, S.C., Welsh, A.H., 2002. Generalized additive modelling and zero inflated count data. Ecological Modelling, 157, 179–188.

Fielding, A.H. & Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38–49.

Hosmer, D.W. & Lemeshow, S., 2000. Applied logistic regression. John Wiley & Sons, Inc., New York.

Larsen, T. & Pigeon, K., 2006. The diet of grizzly bears (Ursus arctos) in north‐central Alberta, Canada, part of a new food‐based model approach to mapping grizzly bear habitat. In: Stenhouse, G. & K. Graham (eds). Pg. 43‐54. Foothills Model Forest Grizzly Bear Research Program 2006 Annual Report. Hinton, Alberta. 87 pp.

Liu, C., Berry, P.M., Dawson, T.P. & Pearson, R.G., 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28, 385–393.

Long, J.S. & Freese, J., 2003. Regression models for categorical dependent variables using stata. Revised ed. Stata Press, College Station, Texas.

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.

Mattson, D.J., Barber, K., Maw, R., & Renkin, R., 2004. Coefficients of productivity for Yellowstone’s grizzly bear habitat. U.S. Geological Survey, Biological Science Report USGS/BRD/BSR‐2002‐0007. 76pp.

McDermid, G., Collingwood, A., Cranston, J., He, Y., Hird, J., Franklin, S., Guo, X., Laskin, D., Linke, J., McLane, A., Pape, A. & Smith, I., 2009. Remote sensing mapping research update (UofC, UofS). In: Stenhouse, G. & Graham, K. (eds). Pp. 20‐38. Foothills Research Institute Grizzly Bear Program 2008 Annual Report. Hinton, Alberta. 204 pp.

McKay, T. & Graham, K., 2009. Availability of bear foods in the Chinchaga region: Field data collection for grizzly bear food‐habitat models. In: Stenhouse, G. & K. Graham (eds). Pg. 113‐121. Foothills Research Institute Grizzly Bear Program 2008 Annual Report. Hinton, Alberta. 204 pp.

Munro, R.H.M., Nielsen, S.E., Price, M.H., Stenhouse, G.B., & Boyce, M.S., 2006. Seasonal and diel patterns of grizzly bear diet and activity in west‐central Alberta. Journal of Mammalogy, 87, 1112‐1121.

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.


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Nielsen, S.E., 2009. A resource‐based habitat model for grizzly bears (Ursus arctos) in west‐central Alberta, Canada. In: Stenhouse, G. & Graham, K. Pg. 122‐155. Foothills Research Institute Grizzly Bear Program 2008 Annual Report. Hinton, Alberta. 204 pp.

Nielsen, S.E., Boyce, M.S., Stenhouse, G.B., & 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., & Munro, R.H.M., 2003. Development and testing of phenologically driven grizzly bear habitat models. Ecoscience, 10, 1‐10.

Nielsen, S.E., Munro, R.H.M., Bainbridge, E., Boyce, M.S., & Stenhouse, G.B., 2004a. Grizzly bears and forestry II: distribution of grizzly bear foods in clearcuts of west‐central Alberta, Canada. Forest Ecology and Management, 199, 67‐82.

Nielsen, S.E., Herrero, S., Boyce, M.S., Benn, B., Mace, R.D., Gibeau, M.L., & Jevons, S., 2004b. Modelling the spatial distribution of human‐caused grizzly bear mortalities in the Central Rockies Ecosystem of Canada. Biological Conservation, 120, 101‐113.

Nielsen, S.E., Boyce, M.S., & Stenhouse, G.B., 2006. A habitat‐based framework for grizzly bear conservation in Alberta. Biological Conservation, 130, 217‐229.

Nielsen, S.E., Cranston, J., & Stenhouse, G.B., 2009. Identification of priority areas for grizzly bear conservation and recovery in Alberta, Canada. Journal of Conservation Planning, 5, 38‐60.

Nielsen, S.E., McDermid, G., Stenhouse, G.B., & Boyce, M.S., 2010. Dynamic wildlife habitat models: seasonal foods and mortality risk predict occupancy‐abundance and habitat selection in grizzly bears. Biological Conservation, In press.

Stenhouse, G., Boulanger, J., Lee, J., Graham, K., Duval, J., Cranston, J., 2005. Grizzly bear associations along the Eastern Slopes of Alberta. Ursus, 31–40.

Swets, J.A., 1988. Measuring the accuracy of diagnostic systems. Science, 240, 1285–1293. Vuong, Q., 1989. Likelihood ratio tests for model selection and non‐nested hypotheses.

Econometrica, 57, 307–334.


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Table 1. Grizzly bear food items considered in the development of habitat productivity models.

Foraging category Item (species) Peak season of use Grazing/herbivory Cow‐parsnip (Heracleum lanatum) mid‐summer Clover (Trifolium spp.) spring‐fall Dandelion (Taraxacum officinale) spring‐summer Horsetails (Equisetum arvense) spring General forbs, grasses & sedges spring‐fall Root digging Sweet vetch (Hedysarum alpinum) spring & fall Frugivory Buffaloberry (Shepherdia canadensis) mid‐late summer

Blueberry/Huckleberry (Vaccinium membranaceum & V. myrtilloides) late‐summer

Lingonberry (Vaccinium vitis‐idaea) fall & spring Bearberry (Arctostaphylos uva‐ursi) Raspberry (Rubus idaeus) late summer Currants (Ribes spp.) late summer Saskatoon (Amelanchier alnifolia) late summer Strawberry (Fragaria virginiana) High‐bush cranberry (Viburnum trilobum) late summer Wild sarsaparilla (Aralia nudicaulis) late summer Carnivory Moose (Alces alces) early summer Rodents (numerous species) spring‐fall Ants (Formicidae) mid‐summer Wasp (Hymenoptera) summer


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Table 2. Predictor variables used to model species occurrence for 20 grizzly bear food items in western Alberta.

Units Scale(s) Source Units Scale(s) Source

Mean annual precipitation cm 500‐m 1 Elevation m 30‐m 6Growing season precipitation cm 500‐m 1 Topographic position unitless 30‐m 7May‐Sept precipitation cm 500‐m 1 CTI (terrain wetness) unitless 30, 90, 150m, 51.5 ha 7Summer moisture index unitless 500‐m 1 shortwave solar radiation (d. 172) w/m2/day 30‐m 7March precipitation cm 500‐m 1 diffuse solar radiation (d. 172) w/m2/day 30‐m 7June precipitation cm 500‐m 1 gobal solar radiation (d. 172) w/m2/day 30‐m 7December precipitation cm 500‐m 1 June‐August solar radiation w/m2/day 100‐m 2Mean min. temp. deg. C 500‐m 1 June‐September solar radiation w/m2/day 100‐m 2Mean max. temp. deg. C 500‐m 1Mean min. January temp. deg. C 500‐m 1Mean min. July temp. deg. C 500‐m 1 Barren 0 or 1 30‐m 8Mean max. July temp. deg. C 500‐m 2 Herbaceous 0 or 1 30‐m, 51.6‐km2 8Mean July temp. deg. C 500‐m 2 Upland herbaceous 0 or 1 30‐m 8First day of frost Julian day 500‐m 1 Agriculture 0 ‐ 1 51.6‐km2 8Frost free period days 500‐m 1 Shrub 0 or 1 30‐m, 51.6‐km2 8Growing degree days‐ base 0 deg. days 500‐m 1 Shrub in regenerating forest 0 or 1 30‐m 8

Open wetland 0 or 1 30‐m 8Treed wetland 0 or 1 30‐m 8

Alberta group 0 or 1 300‐m 3 Upland treed 0 or 1 30‐m 8Brazeau 0 or 1 300‐m 3 Broadleaf forest 0 or 1 30‐m 8Coalspur 0 or 1 300‐m 3 Mixed forest 0 or 1 30‐m 8Dunvegan 0 or 1 300‐m 3 Open conifer forest 0 or 1 30‐m 8Lower Mesozoic‐L. Cretaceous 0 or 1 300‐m 3 Moderate conifer forest 0 or 1 30‐m 8Lower Paleozoic 0 or 1 300‐m 3 Dense conifer forest 0 or 1 30‐m 8Paskapoo 0 or 1 300‐m 3Scollard 0 or 1 300‐m 3Upper Paleozoic 0 or 1 300‐m 3 Canopy percent 30‐m, 3x3 8Wapiti group 0 or 1 300‐m 3 Canopy variability percent 3x3 scale (StDev) 8

Species composition percent 30‐m 8Deciduous canopy percent 30‐m 8

Alpine complexes 0 or 1 300‐m 4 Canopy in clear‐cut percent 30‐m 8Colluvial 0 or 1 300‐m 4Glaciolacustrine 0 or 1 300‐m 4Glaciofluvial 0 or 1 300‐m 4 CTI (150 m) x canopy unitless 30‐m 7Till blanket 0 or 1 300‐m 4Till veneer 0 or 1 300‐m 4

Canopy

Interactions

VariableVariableClimate

Geological formation

Surficial geology

Terrain

Landcover

References (sources): 1‐ Anonymous (2003); 2‐ Wulder & Coops (UBC) 2009 deliverables of the FRI Grizzly Bear Project; 3‐ Anonymous (2010); 4‐ map 1880A, Natural Resources Canada; 6‐ 30 m DEM from FRI Grizzly Bear Project; 7‐ derived from the author (UA); 8‐ McDermid and others (UC) 2009 deliverables of the FRI Grizzly Bear Project.


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Table 3. Weights (% digestible matter) used to scale seasonal importance of food items where present.

HeLa Trif EqAr TaOfForbs/Grass HeAl ShCa VaMe VaVi ArUv RuId Ribes AmAl FrVi ViEd ArNu Moose Rodent Ants Wasp

1 7‐May YH & GC Mtns 0 0 0 0 7 90 0 0 2 1 0 0 0 0 0 0 0 0 0 0 100

2 21‐May YH & GC Mtns 0 0 0 1 15 55 0 0 1 1 0 0 0 0 0 0 25 0 1 0 993 7‐Jun YH & GC Mtns 0 0 3 2 6 53 0 0 0 1 0 0 0 0 0 0 33 1 0 0 994 21‐Jun YH & GC Mtns 0 0 1 1 51 34 0 0 0 0 0 0 0 0 0 0 8 2 0 0 97

5 7‐Jul YH & GC Mtns 11 6 10 1 54 10 0 0 0 0 0 0 0 0 0 0 5 1 0 1 996 21‐Jul YH & GC Mtns 23 0 6 0 39 19 0 0 0 0 0 0 1 0 0 0 0 1 5 1 957 7‐Aug YH & GC Mtns 25 0 2 0 31 21 11 0 0 0 0 0 1 0 0 0 3 1 3 1 99

8 21‐Aug YH & GC Mtns 0 0 2 0 13 27 45 0 0 0 0 0 1 1 0 0 5 1 2 2 999 7‐Sep YH & GC Mtns 0 0 1 0 6 3 79 0 0 0 0 0 0 1 0 0 2 0 1 1 94

10 21‐Sep YH & GC Mtns 0 0 0 0 0 3 94 0 0 0 0 0 0 0 0 0 0 0 0 0 971 7‐May YH Fthls 0 0 7 0 2 75 0 0 5 1 0 0 0 0 0 0 5 0 0 0 952 21‐May YH Fthls 0 0 7 1 15 53 0 0 3 1 0 0 0 0 0 0 15 1 0 0 96

3 7‐Jun YH Fthls 0 0 6 2 33 10 0 0 1 1 0 0 0 0 0 0 37 2 0 1 934 21‐Jun YH Fthls 3 3 3 1 52 0 1 0 1 1 0 0 0 0 0 0 23 1 1 1 915 7‐Jul YH Fthls 3 5 3 1 65 1 1 0 0 0 0 1 0 0 0 0 14 1 2 1 98

6 21‐Jul YH Fthls 16 2 0 0 47 0 14 1 0 0 1 2 1 0 0 0 4 1 3 2 947 7‐Aug YH Fthls 8 1 1 0 36 8 25 1 0 0 1 1 2 1 1 2 5 1 1 2 97

8 21‐Aug YH Fthls 6 1 0 0 19 22 28 3 0 0 1 1 1 1 1 2 3 1 3 5 989 7‐Sep YH Fthls 3 0 0 0 24 12 12 34 0 0 2 0 0 1 1 1 2 0 0 1 9310 21‐Sep YH Fthls 0 0 0 0 24 47 17 0 0 0 0 0 0 0 1 0 4 0 2 0 95

1 7‐May GC Fthls 0 0 5 0 2 0 0 0 1 1 0 0 0 0 0 0 5 0 0 0 142 21‐May GC Fthls 0 0 10 1 15 0 0 0 0 1 0 0 0 0 0 0 15 0 1 0 433 7‐Jun GC Fthls 0 0 10 2 33 0 0 0 0 1 0 0 0 0 0 0 37 2 1 1 87

4 21‐Jun GC Fthls 0 0 11 1 10 0 0 3 0 1 0 0 0 0 0 0 31 6 3 2 685 7‐Jul GC Fthls 39 0 33 1 17 0 0 0 0 0 0 1 0 0 0 0 5 0 4 0 101

6 21‐Jul GC Fthls 36 0 29 0 3 0 20 3 0 0 1 2 1 0 0 0 0 0 1 0 967 7‐Aug GC Fthls 11 7 6 0 6 0 47 8 2 0 1 1 2 1 1 2 9 0 0 2 1068 21‐Aug GC Fthls 35 0 2 0 9 0 33 3 0 0 1 1 1 1 1 2 0 0 8 12 109

9 7‐Sep GC Fthls 22 0 5 0 7 47 4 0 1 0 2 0 0 1 1 1 0 0 0 17 10810 21‐Sep GC Fthls 0 0 1 0 3 48 3 0 0 0 0 0 0 0 1 0 20 0 3 22 1011 7‐May SH Fthls 0 25 36 0 39 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 102

2 21‐May SH Fthls 0 10 10 1 85 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1073 7‐Jun SH Fthls 0 70 21 2 8 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 104

4 21‐Jun SH Fthls 26 17 15 1 24 0 0 0 1 1 0 0 0 0 0 0 2 6 8 1 1025 7‐Jul SH Fthls 74 3 5 1 17 0 2 1 1 0 0 1 0 0 0 0 0 0 1 1 1076 21‐Jul SH Fthls 20 13 5 0 27 0 12 17 0 0 1 2 1 0 0 0 0 0 1 2 101

7 7‐Aug SH Fthls 9 13 0 0 13 0 9 56 1 0 1 1 2 1 1 2 5 0 1 2 1178 21‐Aug SH Fthls 10 17 1 0 6 0 1 51 1 0 1 1 1 1 1 2 5 0 1 5 1059 7‐Sep SH Fthls 2 32 0 0 4 27 0 67 1 0 2 0 0 1 1 1 14 0 0 1 153

10 21‐Sep SH Fthls 0 32 0 0 4 27 0 30 2 0 0 0 0 0 1 0 14 0 0 0 1101 7‐May CHIN Boreal 0 5 5 0 2 0 0 0 15 1 0 0 0 0 0 0 5 0 0 0 33

2 21‐May CHIN Boreal 0 10 10 1 15 0 0 0 10 1 0 0 0 0 0 0 15 0 1 0 633 7‐Jun CHIN Boreal 0 10 10 2 33 0 0 0 5 1 0 0 0 0 0 0 37 2 1 1 1024 21‐Jun CHIN Boreal 0 10 11 1 10 0 0 0 0 1 0 0 0 0 0 0 31 2 3 2 71

5 7‐Jul CHIN Boreal 39 5 33 1 17 0 0 0 0 0 0 1 0 0 0 0 5 1 4 0 1066 21‐Jul CHIN Boreal 36 2 29 0 3 0 1 0 0 0 1 2 0 0 0 0 0 1 1 0 767 7‐Aug CHIN Boreal 11 1 6 0 6 0 5 0 0 0 1 1 0 1 1 2 9 1 0 2 47

8 21‐Aug CHIN Boreal 35 1 2 0 9 0 3 15 0 0 1 1 0 1 2 2 0 0 8 12 929 7‐Sep CHIN Boreal 22 0 5 0 7 0 0 25 0 0 2 0 0 1 2 1 0 0 0 17 82

10 21‐Sep CHIN Boreal 0 0 1 0 3 0 0 15 5 0 0 0 0 0 2 0 20 0 3 22 71

SumHerbivory/Root digging Frugivory CarnivorySeason

#mid‐ date

Study area

Natural region


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Figure 1. Study area map illustrating elevation patterns over the 171,771‐km2 region of western Alberta and population unit boundaries (dark grey lines).


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Figure 2. Location of protected areas (yellow overlay thus green in the Rocky Mountains, orange in the Foothills, light green in the Boreal and light brown in the Parkland) within the 171,771‐km2 study area.


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Figure 3. Location of 2,388 vegetation plots used for modeling the occurrence of 20 grizzly bear food items.


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Figure 4. Predicted regional occupancy (Ψ) of adult female grizzly bears (see Nielsen et al. 2009 for details).


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Figure 5. Predicted probability of occurrence of blueberry (Vaccinium myrtilloides) and huckleberry (V. membranaceum) in western Alberta.


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Figure 6. Predicted relative probability of occurrence of moose (Alces alces) in western Alberta.


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Figure 7. Predicted potential habitat productivity by bi‐weekly period for the month of August.


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Figure 8. Predicted potential habitat productivity (sum of seasonal models).


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Figure 9. Predicted realized habitat productivity (RHP) by bi‐weekly period for the month of August.


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Figure 10. Predicted multi‐seasonal realized habitat productivity (RHP).


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Figure 11. Predicted multi‐seasonal habitat deficit based on the difference of realized and potential habitat.

Chapter 7 Health and Environment

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CHAPTER 7: GRIZZLY BEAR HEALTH‐ENVIRONMENT RELATIONSHIPS

Marc Cattet1, David Janz2, Matt Vijayan3, Scott Nielsen4, John Boulanger5, Jerome Cranston6, and Gordon Stenhouse7

1 Canadian Cooperative Wildlife Health Centre, 2 University of Saskatchewan, 3 University of Waterloo, 4 University of Alberta, 5 Integrated Ecological Research, 6 Arctos Ecological Services , 7 Foothills Research

Institute Grizzly Bear Program

Introduction This research is co‐sponsored by the University of Saskatchewan, the Foothills Research Institute, and eight industry partners (3 forestry, 5 oil and gas) extracting resources across an area of approximately 228,000 km2 in the western part of Alberta coincident with the distributional range of grizzly bears for the province. The broad objective of this research is to investigate relationships between landscape structure, human‐caused landscape change, grizzly bear health and population performance through combined use of remote sensing technology, Global Positioning System (GPS) radio‐telemetry, wildlife health and stress evaluation, and molecular techniques in biomarker development. Specific objectives of the project are to: 1) Enhance use of geospatial tools for monitoring landscape structure over large geographic

areas, with particular emphasis on detecting changes likely to negatively affect the health of resident grizzly bears,

2) Determine home ranges and evaluate health and stress status of free‐ranging grizzly bears captured throughout their distributional range in Alberta,

3) Develop and validate sensitive techniques, with emphasis on proteomics, for detecting long‐term physiological stress in grizzly bears based on analysis of stress‐activated substances found in blood serum, skin, and hair of grizzly bears,

4) Determine relationships between long‐term physiological stress and other measures of health (longevity, growth, reproduction, immunity, and activity) in grizzly bears, and

5) Establish linkages between stress levels and health profiles of individual grizzly bears, performance of grizzly bear populations, and landscape structure and change within home ranges and population ranges along a gradient of human‐caused alteration.

We have made significant progress on all objectives in 2009 and continue to meet the milestones outlined in the activity schedule of our original proposal to the Natural Sciences and Engineering Research Council (NSERC) Canada. Specific details are: Objective 1 – See Chapters 8 & 9 Objective 2 The following summarizes activities completed related to the collection of grizzly bear home range, movement, and health data in 2009:


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Study Areas and Capture Procedures The study areas, as well as capture and handling procedures related to deployment of GPS radio‐collars, were described in Chapter 1. Grizzly Bear Health Evaluation We gathered complete health information from 19 grizzly bears captured in Alberta in 2009 as part of our research activities and partial health information from another seven grizzly bears captured by Alberta Sustainable Resource Development staff. The data from these 26 bears include physical and physiological measurements recorded at capture as well as results from subsequent laboratory analyses of blood serum, skin, and hair. All health data for 2009 are now entered into our project health database bringing the total number of health records for the project to 334 cases representing 206 unique animals of which 71 have multiple (2‐8) records over intervals ranging from 1 month to 8 years. Objective 3 Development and validation of long‐term stress biomarkers is following two complementary paths. One path, led by Matt Vijayan (University of Waterloo) is focused toward blood serum‐based indicators of long‐term stress. The other path, led by David Janz (University of Saskatchewan), is focused toward development of a sensitive protein array and hair cortisol extraction procedure for detecting long‐term physiological stress in skin and hair samples collected from grizzly bears. At the University of Waterloo, we are developing new tools to detect long‐term stress/health status of wildlife. Specifically we are developing novel serum proteins as markers of stress/health status of grizzly bears. To this end, we have cloned and sequenced for the first time grizzly bear corticosteroid‐binding globulin (CBG) and from the deduced amino acid sequence, generated a peptide sequence for antibody production. This affinity purified anti‐bear CBG cross reacts very well with CBG in grizzly bear and polar bear serums. Using this antibody we have characterized CBG expression in serum of grizzly bears subjected to different capture stress and also in animals from different populations. We are in the process of developing an enzyme‐linked immunosorbent assay (ELISA) for quantifying serum CBG concentrations in bear serum. To this end we have purified recombinant grizzly bear CBG for use as standards for the ELISA. The serum CBG concentration from all the samples will be measured in the next six months. In addition, we are also identifying differentially secreted proteins and/or peptide fragments in bear serum using proteomics. Using differential ingel electrophoresis (DiGE) we have identified several proteins that are differentially expressed in the serum of “stressed” bears. The majority of these proteins belong to the acute phase proteins, a family of proteins that are part of the innate immune response process, suggesting that they may be indicative of the health status of the animal. We have used heterologous antibodies (generated from human or mouse protein), that are commercially available, for detecting the expression of these proteins in bear serum. The protein expression using the antibody corroborates the expression pattern seen with the DiGE method. We have identified transferrin as a key acute phase protein that is differentially expressed in serum of stressed bears. We are in the process of characterizing the utility of these acute phase proteins as markers of long‐term stress/health status of grizzly bears. The work mentioned above is part of the PhD project of Mr. Brian Chow that involves identification and development of serum proteins as biomarkers of long‐term stress in grizzly bears. We have completed measuring total cortisol, hsp70 and hsp60 levels in serum samples of grizzly bears for all the captured bears so far.


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At the University of Saskatchewan, we have conducted extensive analyses of the protein array data in 2009. We performed correlations among expression of stress proteins within individual bears, and found many of the 31 proteins to be positively (directly) correlated in skin, with no negative (inverse) correlations, indicating the biological relevance of this technique. We further validated the array in several respects, observing: (1) low intra‐ and inter‐assay variation, indicating the reproducibility and reliability of the technique; (2) no difference between protein expression in skin and muscle tissues, indicating the applicability of the technique in different biopsy tissues; (3) significant interactions in protein expression among body regions, capture method, and sex, indicating that samples should be collected from a similar body region (e.g., thigh), and that method of capture and animal gender may influence results; (4) performing the array using different amounts of protein indicated that 80 µg was optimal; (5) optimal antibody dilution occurred at a 1:1 dilution of antibody: print buffer; (6) no protein degradation effect on protein expression when holding samples at room temperature for up to 48 hours; and (7) no effect of preserving samples in RNase inhibitors commonly used in DNA microarray techniques. Overall, these extensive laboratory validation steps indicated the reliability, sensitivity and robustness of the protein array under conditions that were reflective of issues related to sample collection in the field. The work described above is being incorporated into a manuscript (2nd draft currently being edited by Janz) to be submitted to the Canadian Journal of Zoology in March 2010. Ongoing analyses are currently being conducted investigating the relationships between protein array data and other measures of long‐term stress (i.e., serum and hair markers), animal health, habitat quality and environmental (landscape) change. We have also successfully developed and validated a reliable and sensitive technique to measure cortisol, the primary stress hormone associated with the hypothalamic‐pituitary‐adrenocortical axis, in hair collected from grizzly bears. We have determined cortisol in n=151 bears, including 18 animals captured on multiple occasions within and between years. The major observations from this work included: (1) a significant difference in hair cortisol between hair types (guard hair > undercoat hair); (2) a significant difference among body regions (neck hair > shoulder, rump and abdomen; (3) no difference among hair colour ranging from light tan to black; (4) no difference in hair cortisol among different locations along the hair shaft; (5) hair cortisol was measurable in hair samples as small as 5 mg (i.e., about 5 guard hairs); (6) no effect of weathering in the field, where hair samples were exposed to the elements for up to 18 days; (7) no effect of storing intact hair for up to 18 months on cortisol concentration; (8) a significant increase in hair cortisol in bears captured in culvert traps compared to leg snares and helipcopter darting approaches; (9) a significant positive effect of animal age; and (10) no effect of sex on hair cortisol. Overall, we believe this to be the most comprehensive investigation of hair cortisol ever conducted in vertebrate animals, including human, and a manuscript based on this work was submitted to the Canadian Journal of Zoology in January 2010. Similar to the protein array data, ongoing analyses are currently being conducted investigating the relationships between hair cortisol concentration and other measures of long‐term stress (i.e., serum and skin markers), animal health, habitat quality and environmental (landscape) change. Objectives 4 and 5 Over the first 3 years of this project, we developed extensive datasets to describe the spatial configuration of landscape structure and the health of grizzly bears across the distributional range of the species in Alberta. We also quantified the spatial configuration of annual landscape change (emphasizing anthropogenic‐caused change) for two areas, the Yellowhead and Grande


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Cache population units, inhabited by different grizzly bear populations within the province. In late 2007, we began merging datasets, calculating variable values over different temporal and spatial scales, and exploring linkages between landscape, stress, and health as identified in our working hypothesis, and in accordance with the conceptual model shown in Figure 1. We are assuming the primary driver in this process is landscape structure and change brought about mostly by a range of human developments, i.e., resource extraction, agriculture, recreation, and urbanisation. Landscape stressors associated with these developments result in the modification of habitat and more generally increased human access/activity. Where human development is most extensive and recent increases in human activity most pronounced, we have hypothesized a biological effect to manifest itself in the health of grizzly bears. Specifically, we have hypothesized long‐term stress leads to increased activity (movement rates) and reduced immunity, growth, reproduction, and longevity.

Figure 1. Conceptual ‘driver‐stressor’ diagram depicting the key factors affecting grizzly bear health throughout their distributional range in Alberta. (Immun. = immunity, Long. = longevity, Reprod. = reproduction) To evaluate this driver‐stressor model and more specifically individual linkages among landscape‐health components, we are using three analytical approaches (Figure 2). These approaches include a static measurement (point‐in‐time) of landscape and health (I; see Figure 2), a dynamic measurement of landscape (rate of change) and a static measurement of health (II), and finally a dynamic measurement of landscape and health (III). Dynamic analyses represent repeated measures over time (longitudinal data), although they do not necessarily exclude static measures, since overall landscape conditions are also important to consider when examining rates of change. As grizzly bears were not chosen based on level of human disturbance prior to the study, the repeated measures of individuals in the dynamic analyses (design II and III) represent what best would be referred to as a retrospective cohort study. Each


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analytical approach has advantages and disadvantages, but together provides in our opinion the most comprehensive strategy to establishing linkages between landscape stressors and biological effects (stress and health) in grizzly bears.

Figure 2. Schematic representation of analytical approaches used to relate landscape condition and landscape change (independent factors) to grizzly bear health (dependent factor) based on frequency of measurements for individual bears captured once (static) to many times (dynamic). Our first complete analysis of landscape and health datasets occurred March‐June 2008 and provided preliminary support for linkages between landscape structure and change, long‐term stress, and grizzly bear health. However, the relationships among variables were often more complex than predicted due to inter‐annual variations in bears’ resources and sex‐age segregation across habitats. Further, because human activity was directly correlated with habitat quality and elevation, we were challenged to separate the differential effects of these confounding factors. Since September 2008, we have modified datasets to allow more flexible merging of landscape and health data such that spatial and temporal scales can be adjusted in accordance with biological hypotheses. In December 2009, we initiated a second round of statistical analyses that is scheduled to be completed in April 2010. In addition to making improvements in our analytical procedures over the past year, we are using substantially larger datasets than were available during the first round. The results of this analysis will provide the foundation for what we anticipate to be several seminal publications establishing conclusive evidence of relationships between landscape structure and change, long‐term stress, and grizzly bear health.

Chapter 8 U of C / U of S ‐ Remote Sensing Update

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CHAPTER 8 REMOTE SENSING UPDATE – UNIVERSITY OF CALGARY &

UNIVERSITY OF SASKATCHEWAN

Greg McDermid1, Kai Wang2 Julia Linke1, Andrea Ram1, David Laskin1, Xulin Guo2, Steven Franklin2,

1Foothills Facility for Remote Sensing and GIScience Department of Geography, University of Calgary 2500 University Dr. NW Calgary, AB T2N 1N4

2Environmental Remote Sensing Laboratory

Department of Geography and Planning, University of Saskatchewan 9 Campus Drive Saskatoon, SK S7N 5A5

INTRODUCTION AND OVERVIEW 2009/10 marks the tenth year in which researchers and technicians from the University of Calgary and the University of Saskatchewan have contributed towards remote sensing research and mapping initiatives in the Foothills Research Institute Grizzly Bear Program (FRIGBP). In this report, we summarize the activities taking place within the Foothills Facility for Remote Sensing and GIScience at the University of Calgary and the Environmental Remote Sensing Laboratory at the University of Saskatchewan. TOPIC 1: POSSIBLE PROBLEMS IN REMOTE SENSING OF LARGE‐AREA HABITAT MAPPING Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species‐at‐risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large‐area habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on the relevant scientific research. Pioneering work has achieved promising results in addressing these issues, but a tremendous amount of research yet remains, especially regarding the limitations and uncertainties of Landsat‐series data, clouds and cloud shadows in optical imagery, and landscape analysis and remote sensing. Advanced optical and radar sensors, e.g. DMC and Radarsat‐2, may provide answers to the above questions. Future research should explore the applicability of these sensors in large‐area wildlife habitat mapping, and develop reliable and efficient methods for supporting diverse environmental, ecological and resource management applications. Key areas to address are: fusing the complementary optical and radar images to improve classification accuracy; diminishing the difference of classified maps from diverse sensors on landscape pattern analysis via adjusting object‐oriented classification parameters; developing a cloud classification scheme according to their impacts on ground objects; and applying radar to remove cloud contamination in optical imagery.

• Wang, K., S. E. Franklin, X. Guo, Y. He, and G. J. McDermid, 2009: Problems in remote sensing of landscapes and habitats. Progress in Physical Geography, 33(6): 747‐768.


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TOPIC 2: THE APPLICABILITY OF SMALL‐SATELLITE CONSTELLATION IN CLASSIFICATION FOR LARGE‐AREA HABITAT MAPPING: A CASE STUDY OF DMC MULTISPECTRAL IMAGERY IN WEST‐CENTRAL ALBERTA Small‐satellite constellation has become the cutting edge of development in remote sensing for recent years, with the advantage of efficient operation and cost, global surveying, and increased revisit frequency. Disaster Monitoring Constellation (DMC), the first successful practice of the concept of the earth‐observation constellation, is considered to be an alternative satellite addressing the issue of large‐area habitat mapping and provides a new opportunity for extending the applicability of the satellite imagery. The objective of this research was to exploit the applicability of small‐satellite constellation in large‐area habitat mapping, through exemplifying DMC multispectral imagery in classification of grizzly bear habitat in west‐central Alberta. A hierarchical land cover classification scheme composed of three levels of detail was adopted to assess the DMC classification. The overall accuracies were 95.6% in the coarsest Level I, and 82.4% in the finest Level III classifications. It was proven that the DMC multispectral imagery had acceptable capability of describing the physiognomy of earth’s surface with more than 80% accuracy, especially for large area. Moreover, Landsat‐based classification, the most popular satellite product used, was chosen to compare the classification results to demonstrate the applicability of DMC imagery.

• Wang, K., S.E. Franklin, and X. Guo, 2010: The applicability of small‐satellite constellation in classification for large‐area habitat mapping: a case study of DMC multispectral imagery in west‐central Alberta. Canadian Journal of Remote Sensing, in review.


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TOPIC 3: THE COMPARISON OF LANDSAT MULTISPECTRAL AND IRS PANCHROMATIC IMAGERY ON LANDSCAPE PATTERN ANALYSIS OF GRIZZLY BEAR HABITAT IN THE AGRICULTURAL AREA OF WESTERN ALBERTA The grizzly bear (Ursus arctos L.) is a species that is widely recognized as an indicator of ecosystem health in west‐central Alberta. Agricultural activities, oil and gas exploration and extraction, forestry, and recreation can all contribute to grizzly bear habitat fragmentation and loss. The purpose of this research was to compare two models of grizzly bear activity in agricultural areas of western Alberta, Canada, developed from landscape pattern metrics derived from Landsat‐ and IRS (Indian Remote Sensing)‐based land cover classifications and assess if these models statistically converged on the same landscape metrics. Results were further explained by considering the influence of spatial, spectral and thematic resolution, along with previous knowledge on grizzly bear habitat preference. The Landsat‐ and IRS‐based analyses were compared using relationships between landscape metrics and both grizzly bear presence/absence data and frequency of use data. Results indicated that landscape spatial structure had at least some role in determining whether or not grizzly bears would use an area in an agricultural landscape. It was concluded that the thematic resolution represented the greatest impact on compositional metrics for both the grizzly bear presence/absence and frequency of use analyses; i.e., the Landsat‐based product was more suited to revealing the function of the compositional metrics than IRS‐based product. Configurational metrics, however, were more sensitive to the higher spatial resolution map derived from the IRS data. Landscape management recommendations are suggested in the context of these geospatial results.

• Wang, K., S. E. Franklin, X. Guo, A. Collingwood, G. B. Stenhouse, and S. Lowe, 2009: The comparison of Landsat‐multispectral and IRS‐panchromatic imagery on landscape pattern analysis of grizzly bear habitat in the agricultural area of western Alberta. Canadian Journal of Remote Sensing, conditionally accepted.


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TOPIC 4: THE APPLICABILITY OF RADARSAT‐2 IMAGERY IN LARGE‐AREA HABITAT MAPPING The Radarsat‐2 (R‐2) imagery has been intended to be applied in improving classification accuracy and removing clouds and cloud shadows (CCS) of DMC multispectral imagery through the approach of data fusion. Seven R‐2 images have been acquired including three Standard Dual‐Pol and four Fine Quad‐Pol products in the format of SGX (Path Image Plus). Preprocessing has been implemented to correct geometric errors and suppress speckle effect, i.e. orthorectification and speckle filter. The next step is to classify the R‐2 images under different spatial resolution, polarization and classification algorithms. It is assumed to underlie the subsequent data fusion and CCS removal.


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TOPIC 5: GRIZZLY BEAR OCCURRENCE AND CHANGE (Work in progress by Julia Linke – please do not reproduce figures or tables) The goal of this study is to understand the multi‐scale links between landscape pattern and multi‐temporal pattern change to temporally static DNA census‐based grizzly bear occurrence. The primary objectives of this project are to quantify metrics for the existing (temporally coinciding) and former (previous years) land cover and human disturbance patterns based on the annual land cover map series and disturbance inventory dataset (Linke et al. 2009) within and surrounding bear occurrence sampling units in BMA 3 and to document how these metrics explain the relative abundance of bears within the extent of the occupancy‐sampling grid (Figure 1 and Figure 2). Specific questions will be directed at the relative importance of current and former conditions of landcover and disturbance patterns and the temporal dynamics of disturbance patterns, and how these links differ across different scales of the landscape context (Figure 3). Since this work is in progress, this report will be limited to the demonstration of the explanatory datasets that were derived as the basis for this investigation.

Figure 1. Relative abundance of grizzly bears in BMA 3 as derived from early summer 2004 DNA hair sampling summarized at the 7x7 km landscape cell scale.


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Methods Disturbance change will be related against grizzly bear abundance at the same stratification as the DNA census was performed; the 7 x7 km cell scale. Each 7x7 km landscape cell will be described in terms of its disturbance content at the time of the census (2004), at the time of the beginning of our existing time series (1998), and at equal intervals in‐between. To characterize the disturbance present in the surrounding neighourhood, the cell context for each landscape cell will also be measured at two different window extents: a) 14 x14 km, and b) 21x21km (Figure 2).

Figure 2. Exemplification of the multi‐temporal disturbance and landscape content and multi‐scale context (using two window sizes) for sampled landscape cells.


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Resulting Explanatory Datasets:

Figure 3. Distribution and total area of all cumulative disturbances existing in 1998 and in 2004, and total area of all annual new disturbances appearing in each time interval between 1998 and 2004.

Figure 4. Mean distance from to any nearest cumulative and annual disturbance feature from within each landscape cell, between 1998 and 2004.


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Figure 5. Amount of specific disturbances cumulatively existing in 1998 and 2004, including cutblocks, surface mines, wellsites, linear access features, and burns.


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Figure 6. Largest contiguous patch inside each landscape cell, and accessible from within each landscape cell, as well as accessible from within 3.5 and 7 km distances surrounding each landscape cell in 2004. The contiguous patches are defined once by undisturbed forested cover type, and once by undisturbed vegetated cover type (including forest).


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TOPIC 6: CHANGES IN LANDSCAPE DISTURBANCE OF GRIZZLY BEAR HABITAT IN THE ROCKY MOUNTAIN FOOTHILLS OF ALBERTA FROM 1985 TO 2005. Landscape change studies are often necessary to effectively assess wildlife habitat and monitor changes through time. In the Rocky Mountain foothills of Alberta, Canada, human disturbances such as mining, oil and gas extraction, forestry and road development have increased dramatically over the past 30 years, and exert a profound effect on present‐day landscape structure. This region also provides valuable habitat to grizzly bears (Ursus arctos) whose population is in decline, and may be adversely affected by the rate and pattern of landscape change. The goal of this research was to explore the spatial‐temporal changes in habitat quality of grizzly bears in the Alberta foothills from 1985 to 2005 using imagery from the Landsat archive. We selected four five‐year time intervals ranging from 1985 to 2005 to monitor changes in human‐induced disturbances and their impact on grizzly bear mortality and habitat selection. We employed satellite imagery and change‐detection procedures to generate spatially‐explicit layers of cut‐blocks and roads across an area east of Hinton, Alberta (Figure 7) within bear management area (BMA) 3, then applied these disturbances to generate land‐cover maps across the time series. The next steps involve deriving GIS layers and applying a Mortality Risk / Habitat Selection model to the data.

Figure 7. Location of the study area in west‐central Alberta within bear management area (BMA) 3, east of Hinton town‐site.


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Methods The methods used to create the 5‐year map products of the study area followed the disturbance inventory approach to landscape monitoring described fully in Linke et al. (2009a). Landsat Thematic Mapper imagery was acquired and pre‐processed to track change patterns across the study area from 1985 to 2005 (Table 1). Pre‐processing involved geometric and radiometric correction. Determining change patterns involved semi‐automated change detection for each bi‐temporal series (e.g. 1985‐1990, etc) and for the entire time series (i.e. 1985‐1998) using the Landcover Map tool (LCM) (Castilla et al. 2009). Change features were visually inspected and manually corrected where necessary. New disturbances were then applied to the existing disturbance inventory created by Linke et al. (2009a) which covered the years 2000 and 2005. Disturbance features were subjected to boundary conditioning routines designed to reduce the occurrence of slivers (Linke et al. 2009b). Disturbance objects were transformed to land cover classes using decision rules (e.g. road features = barren), and spatially mosaicked to create backdate layers. Finally, the annual backdate layers were overlaid on the co‐registered 1998 base land cover map to backdate land cover products for 1995, 1990 and 1985. With this backdated temporal analysis derived from Landsat imagery, I will then determine the change in primary grizzly bear habitat for each time interval using Scott Nielsen’s Occurrence‐Mortality Risk model (Nielsen et al. 2006). The model will illustrate areas of primary habitat (low mortality risk coupled with attractive habitat), and primary sink (attractive habitat and high mortality risk). Table 1. Landsat scenes acquired and processed for quantifying human disturbance, land cover and grizzly bear mortality risk/habitat selection from 1985‐2005.

Landsat Path/Row Image Acquisition Date Sensor 45/23 16 August 1985 Landsat 5 TM

10 September 1985 Landsat 5 TM 17 April 1990 Landsat 5 TM

24 October 1995 Landsat 5 TM

44/23

29 August 1998 Landsat 5 TM Landsat Scenes from existing inventory

27 September 2000 Landsat 7 ETM+ 44/23 13 September 2005 Landsat 5 TM

Conclusions Bear management is currently a highly controversial issue in Alberta involving various stakeholders (e.g. hunters, recreationalists, government, and biologists). The results of this research can be applied immediately in management plans to address conservation concerns. Understanding how human‐caused disturbances have changed the landscape over a wide time span and how this has affected habitat quality is an important step in protecting primary habitat areas from further disturbances and identifying those areas in greatest need of management attention.


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References Castilla, G., Guthrie, R. and G.J. Hay. 2009. The Landcover Change Mapper (LCM) and its

application to timber harvest monitoring in Western Canada. Photogrammetric Engineering and Remote Sensing, 75(8): 941‐950.

Linke, J., G.J. McDermid, A.D. Pape, A.J. McLane, D.N. Laskin, M. Hall‐Beyer, and S.E. Franklin, 2009a: The influence of patch delineation mismatches on multitemporal landscape pattern analysis. Landscape Ecology, 24(2): 157‐170.

Linke, J., McDermid, G.J., Laskin, D., McLane, A., Pape, A., Cranston, J., Hall ‐Beyer, M., and S.E. Franklin. 2009b. A Disturbance‐Inventory Framework for Flexible and Reliable Landscape Monitoring. Photogrammetric Engineering & Remote Sensing, 75(8): 981‐995.

Nielsen, S., Stenhouse, G., M. Boyce. 2006. A Habitat‐Based Framework for Grizzly Bear Conservation in Alberta. Biological Conservation, 30: 217‐229.


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TOPIC 7: LINKING GRIZZLY BEAR HEALTH WITH REMOTELY‐SENSED VEGETATION PHENOLOGY The grizzly bear (Ursus arctos L.) is an umbrella species whose populations in Alberta are threatened by habitat loss and high rates of human‐caused mortality (Nielsen et al., 2006). Understanding the impact of landscape disturbance on grizzly habitat is a serious management concern, especially considering the temporal brevity and spatial limitations of optimal forage sites (Berland et al., 2008). Current grizzly bear habitat‐use models in Alberta rely almost entirely on habitat‐quality indices such as landscape pattern and forest structure, and do little to incorporate mechanistic variables such as vegetation distribution and abundance. Furthermore, the potential of recently available satellite imagery to map the quality and spatio‐temporal distribution of herbaceous food resources remains largely untapped (Nielsen et al., 2003). The objective of this study is to identify a link between grizzly bear health and vegetation phenology derived from the orbiting Moderate Resolution Imaging Spectroradiometer’s (MODIS) Normalized Difference Vegetation Index (NDVI). Specifically, does the natural variability of remotely‐sensed vegetation abundance within a grizzly bear’s home range relate to overall animal body condition? Such a relationship promises to provide key insights into the ecology of grizzly bears, with direct applications to their management and conservation. Methods The analysis will be conducted on home ranges of individual grizzly bears in the Foothills Research Institute’s Grizzly Bear Program core population, east of Jasper, Alberta. This region covers a topographical gradient that is perceptible with MODIS NDVI data, and encompasses a variety of ecologically‐productive subregions (Figure 8). This area offers the longest history of GPS telemetry (bear location) data, and the greatest knowledge regarding animal foods and diet. The population in this core area comprises 44 collared grizzly bears, with location, growth, health, and reproductive data available between 1999 and 2007.

Figure 8. Study area and mosaic of kernel‐density grizzly home ranges. A series of NDVI metrics were derived from MODIS imagery obtained from 2000 to 2007. Each metric was assessed for quality and noise‐filtered using methods developed by Hird and McDermid (2009), representing the average combined phenology values per growing season. A total of ten metrics were used including measures such as the length of growing season, time of maximum vegetation green‐up, and maximum NDVI. The metrics were clipped using grizzly


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bear home ranges from the corresponding year of occupation. Home ranges were characterized by kernel density polygons derived from annual telemetry locations. The pixel values within each home range were averaged ‐ elevation and home range area were also considered in the models.

Figure 9. MODIS derived NDVI phenology metrics (from top left): NDVI amplitude, Max NDVI, Average NDVI, Max Green‐Up, Time Max Green‐Up NDVI, Time Max NDVI, Integrated NDVI, Length of growing season. Not shown: Start of Season, End of Season. These phenology data were regressed against individual grizzly health variables. Body Condition Index (BCI) is a measure of total body mass against straight‐line body length and is a means of standardizing overall physical condition so that different individuals can be compared (Cattet et al., 2002). A cross‐sectional time‐series analysis was used to account for the repeated occurrence of some bears throughout the study period. Recurring individuals were aggregated, and a generalized estimating equation (GEE) was used to predict BCI values from early season captures the following spring. Three separate models were developed to explain BCI using various metric groupings: 1) NDVI metrics (average, maximum, amplitude…), 2) Temporal metrics (season start date, length of season…), and 3) Spatial metrics (elevation, home range area). Results and Discussion Maximum NDVI and Average NDVI were found to be significant predictors of BCI while controlling for NDVI Amplitude and the subject ID effect (repeated occurrences of individual bears in the data set). This signifies a positive relationship where BCI increases as a result of high maximum and average NDVI values from the previous season (Figures 8 & 9). GEE models use a relative measure of goodness of fit; quasi likelihood under independence model criterion (QIC), where a smaller value indicates better fit. Therefore average NDVI (QIC = 43.4) is a better predictor of BCI than maximum NDVI (QIC = 90.9). Both model results are logical considering that maximum and average NDVI are the two metrics that most directly indicate vegetation health and abundance on the landscape. Table 2. GEE model results for Average NDVI vs. BCI.

Parameter β Std. Error Wald Sig. > 4* Sig. p<0.05* Intercept ‐ 4.65 2.63 3.12 0.07 Average NDVI 0.00045 .00021 4.37* 0.03* NDVI Amplitude 0.00061 .00045 1.88 0.17


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Table 3. GEE model results for Maximum NDVI vs. BCI.

Parameter β Std. Error Wald Sig. > 4* Sig. p<0.05* Intercept ‐19.34 8.90 4.72 0.03 Maximum NDVI 0.0019 0.00086 5.10* 0.02* NDVI Amplitude 0.0011 0.00060 3.09 0.08

The beta coefficients of the explanatory variables are low, suggesting a subtle influence upon BCI. A likely reason contributing to this is the rarity of early season bear captures which limited the degrees of freedom to approximately fifty observations. This limited number of observations is borderline, yet statistically acceptable given the number of explanatory variables used. Physiological factors influencing these models include hibernation occurring between the end of the foraging season, and animal capture the following spring. However, it can be assumed that a bear with a high BCI in the autumn will have a relatively high BCI in the spring. Finally, if more observations were available, a control for gender should be employed to account for behavioural differences in foraging habits and home range sizes. This model result is encouraging, and can only improve as more telemetry data is acquired over future seasons. Summary This was an exploratory investigation used to establish the straightforward hypothesis that remotely‐sensed NDVI values indicating abundant vegetation within a grizzly home range will indicate higher BCI values for the bear occupying that home range. It was determined that average and maximum NDVI values within a home range explain increased BCI values for that bear the following season. This study acts as a first step in using the remote sensing of vegetation phenology in wildlife habitat quality assessment and management. Future efforts will act to (i) develop a method for linking ground‐based phenophase measurements to satellite derived phenological data; (ii) derive spatially explicit herbaceous food models representing intra‐ and inter‐annual distribution of grizzly bear foods, including variations in quality and quantity; and (iii) contribute to a better understanding of the role and influence of dynamic patterns and variability of food resources on grizzly bear health and habitat carrying capacity in Alberta. References Berland, A., Nelson, T., Stenhouse, G.S., Graham, K., and Cranston, J. (2008) The impact of

landscape disturbance on grizzly bear habitat use in the Foothills Model Forest, Alberta, Canada. Forest Ecology and Management. 256(11): 1875‐1883.

Cattet, M.R.L., Caulkett, N.A., Obbard, M.E., and Stenhouse, G.B. (2002) A body condition index for ursids. Canadian Journal of Zoology. (80): 1156‐1161.

Hird, J. and G.J. McDermid, 2009: Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113(1): 248‐258.

Nielsen, S.E., Stenhouse, G.B., and Boyce, M.S. (2006) A habitat‐based framework for grizzly bear conservation in Alberta. Biological Conservation. 130: 217‐229.

Nielsen, S.E., Boyce, M.S., Stenhouse, G.S., and Munro, H.M. (2003) Development and testing of phenologically driven grizzly bear habitat models. Ecoscience. 10(1): 1‐10.

Chapter 9 UBC/CFS/UVic Remote Sensing Update

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CHAPTER 9: REMOTE SENSING REPORT ON MOUNTAIN PINE BEETLE /

GRIZZLY BEAR PROJECT 2009/2010

PIs: Mike Wulder (CFS), Nicholas Coops (UBC) Team: Thomas Hilker (UBC), Chris Bater (UBC), Rachel Gaulton (UBC), Trisalyn Nelson (UVic), Ben Stewart (UVic), and Joanne White (CFS).

University of Victoria, Canadian Forest Service, University of British Columbia

EXECUTIVE SUMMARY This document reports upon the progress made in 2009/10 fiscal year by the groups located at the University of British Columbia, the Canadian Forest Service, and the University of Victoria as part of the Grizzly Bear Program which was initiated by the Foothills Research Institute in Hinton, Alberta, Canada. In 2009/10, our research has focussed on four main areas, the improvement of temporal resolution of base map templates, the monitoring of Mountain Pine Beetle attack and mitigation activities, the collection of ground validation data in particular automated phenological data , and the projection of landscape recovery and change. We have made significant progress in a number of areas. Most critically we have completed our analysis of disturbance using the Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) algorithm which allows for the generation of high‐spatial (30m) and ‐temporal (weekly or bi‐weekly) disturbance sequences using fusion of Landsat TM or ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. A disturbance sequence representing stand‐replacing events for the period 2001 to 2008, over almost 6 million ha of grizzly bear habitat along the eastern slopes of the Rocky Mountains in Alberta is complete, 1 year ahead of schedule. This is an important dataset and one that can form the basis of a number of ongoing studies, most critically, linking disturbance type to bear movement and health. Methodological advances have also been made in: 1) classifying edges and bringing these into resource selection functions; 2) algorithm developed for automating the attribution of disturbance; and 3) routine production of mountain pine beetle damage maps which are still significantly impacting the pine land base. Last but not least, the first year of imagery from the understory phenology camera network has been collected resulting in over 6,700 images acquired in 8 months across seven sites throughout the growing season, providing a rich database of phenological activity of both the under‐ and overstory forest components. Collaborations within the team, and with other project members has been highly successful and rewarding and a total six new papers have been submitted to refereed journals in 2009/2010, two have been published and another four are in preparation, which were either fully or partly funded by this research, indicative of a highly successful year.


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TOPIC 1: IMPROVING THE TEMPORAL RESOLUTION OF BASE MAP TEMPLATES Classification of synthetic‐Landsat imagery Measurement of forest biophysical properties has been a long standing goal in remote sensing applications, and shows a diverse array of approaches and results. The relationship between biophysical properties, such as leaf area index (LAI), and remotely sensed imagery has been established, but its operational application in predicting across space and time has been less successful. Estimates across time are of particular importance in boreal forests, as they undergo dramatic change throughout the growing season. While the measurement of this change is desirable from a forest monitoring standpoint, in situ data collection is prohibitively expensive, and calculation based on remotely sensed imagery may be obscured due to persistent cloud cover. The solution to this problem arises from newly developed data fusion techniques (STARFM and STAARCH) that combine the high spatial and spectral resolution of Landsat imagery, with the high temporal resolution of MODIS data. The output synthetic Landsat data has an 8‐day temporal resolution, which will allow the continuous monitoring of biophysical variables throughout the growing season. This study will analyze the change in LAI as an example of the viability of measuring biophysical variables with STAARCH outputs. The calculation of LAI has resulted in a series of images for a growing season measuring LAI. A comparison between different land covers shows that the value of LAI changes during the year. In the figure below, we see the comparison of a conifer forest, a broadleaf forest and an area of harvest (conifer becomes bare ground). Currently we are developing a series of metrics (mean, coefficient of variation, area under the curve) that will summarize the change in LAI during the growing season on a pixel by pixel basis, showing that the change in LAI can be influential in analyzing forest dynamics. Outcome: Stewart, B.P., T. Nelson, M.A. Wulder. et al. (2010). Spatial and temporal patterns to LAI over a montane forest environment. (journal to be advised). Implementation of STAARCH over MPB / grizzly bear study area and Implementation of the STARFM component (phenology) over MPB/ grizzly bear study area. Habitat structure and the distribution of wildlife species, such as grizzly bears, can be strongly influenced by the spatial and temporal distribution of vegetation structure and disturbance events and by vegetation phenology. Landsat imagery, with a spatial resolution of 30m, has been shown to allow the monitoring of land cover and ecosystem disturbance over large areas, but the minimum 16‐day revisit cycle of the platform limits the ability to accurately determine dates of disturbance events, especially in regions where cloud contamination is a frequent occurrence. Fusion of high spatial resolution Landsat imagery with high temporal resolution imagery such as Moderate Resolution Imaging Spectroradiometer (MODIS) data can allow the generation of synthetic, Landsat‐like, observations with high spatial (30m) and temporal (potentially daily) resolution. Synthetic data generated using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) (Gao et al. 2006) has been shown to allow monitoring of seasonal patterns of vegetation change and large scale changes in land use.


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The Spatial Temporal Adaptive Algortihm for mapping Reflectance Change (STAARCH) (Hilker et al. 2009) was developed as an extended version of STARFM to allow the detection of disturbance events at spatial scales smaller than that of a MODIS pixel, through the generation of a spatial change mask derived from Landsat and an image sequence recording the temporal evolution of disturbance events (based on MODIS). STAARCH has been applied and verified to produce dates of disturbance and synthetic imagery (at a 8‐day interval) for a single Landsat scene in west‐central Alberta (Hilker et al. 2009), but has not previously been applied over a large study area covered by multiple scenes. By implementing the STAARCH algorithm on imagery covering the core grizzly bear study area, large scale spatial and temporal patterns in disturbance regimes and changes in vegetation structure and phenology can be examined, with implications for grizzly bear habitat modeling and management. Outcome: Full details on the background to these methods can be found in previous years reports. Papers covering the two major components of this work were both published this year: Hilker, T., Wulder, M.A., Coops, N.C., Seitz, N., White, J.C., Gao, F., Masek, J., & Stenhouse, G.B. (2009). Generation of dense time series synthetic Landsat data through data blending with MODIS using the spatial and temporal adaptive reflectance fusion model (STARFM). Remote Sensing of Environment 113: 1988‐1999 Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J., Gao, F. and White, J.C., 2009. A new data fusion model for high spatial‐ and temporal‐resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113, 1613‐1627.

Attribution of change – Demonstration of methods As anthropogenic landscape disturbances continue to increase in frequency and severity, there is a need for information to be more readily available for the forest manager. Remotely sensed imagery allows for disturbances to be detected at high spatial and temporal frequencies; however, the ability to label these disturbances is not as easy, often requiring manual interpretation and field visits. For many species, detecting the type of disturbance is equally important to management decisions, as detecting the disturbance itself. Grizzly bears, for example, have been known to utilize the edges of clearcuts for foraging, but have shown markedly different reactions to numerous other disturbances, including roads, exploratory well‐sites and pipelines; all of which are common in Western Alberta. This project developed a framework for automatically detecting disturbances, through a combination of high and medium resolution imagery, and labelling disturbances based on a combination of disturbance shape and reflectance attributes. In this study, we developed a framework (Figure 1) to automatically detect and label disturbances derived from remotely sensed images. Disturbances were detected through traditional image differencing of medium‐resolution imagery (Landsat‐7 Enhanced Thematic Mapper Plus [ETM+], resampled to 30 m), but were refined and augmented through comparison with edge features extracted from high‐spatial‐resolution satellite imagery (Indian Remote Sensing [IRS] satellite 1C panchromatic imagery, resampled to 5 m). By incorporating spectral information, derived composite band values (Tasselled Cap Transformations), spatial and


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contextual information, and secondary datasets, we were able to capture and label disturbance features with a high level of overall agreement (91%). Areal features, such as harvest areas, are captured and labelled more reliably than linear features such as roads, with 92% and 72% agreement when compared to control data, respectively. By incorporating rule‐based disturbance attribution with remote sensing change detection, we envision the update of land cover databases with reduced human intervention, aiding more rapid data integration and opportunities for timely managerial responses.

Figure 1. Flowchart of the developed method to predict and label change.

The research provided insights into the automated labeling of Landsat images using spatial feature rules. Limits remain due to Landsat detectablity limitations. Detection limits are compensated for by a dense time series and large area coverage. Outcome: Stewart, B.P., M.A. Wulder, G.J. McDermid, T. Nelson. (2010). Disturbance capture and attribution through the integration of Landsat and IRS‐1C imagery. Canadian Journal of Remote Sensing. (accepted 26 December 2009).


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Develop and validate over the full Grizzly Bear study area The distribution and habitat use of wildlife species are known to be influenced by anthropogenic and natural disturbances. As a result, timely and accurate mapping of disturbance patterns can be used to better understand the nature of wildlife habitats, distributions, and movements. One common approach to map forest disturbance is by using medium spatial resolution satellite imagery, such as Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper (ETM+) imagery acquired at a 30 m spatial resolution. However, the low revisit times of these medium spatial resolution satellites acts to limit the capability to accurately determine actual disturbance dates for a sequence of disturbance events, especially in regions where cloud contamination is a frequent occurrence. In addition, the widespread use of new technologies (GPS or Satellite radio collars) used to monitor wildlife habitat use and movement, provides data at more refined time scales. As wildlife habitat use can vary significantly seasonally, annual patterns of disturbance are often insufficient in assessing relationships between disturbance and, for example, in the case of grizzly bears (Ursus arctos L.) foraging behaviour or movement patterns which may be recorded on an up to hourly basis. The Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) allows the generation of high‐spatial (30m) and ‐temporal (weekly or bi‐weekly) disturbance sequences using fusion of Landsat TM or ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. In this research, the STAARCH algorithm is applied to generate a disturbance sequence representing stand‐replacing events for the period 2001 to 2008, over almost 6 million ha of grizzly bear habitat along the eastern slopes of the Rocky Mountains in Alberta (Figure 2).

Figure 2. Location of the Grizzly Bear population units and core and secondary habitat areas within western Alberta, Canada.


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The STAARCH algorithm incorporates pairs of Landsat images to detect the spatial extent of disturbances; information from the bi‐weekly MODIS composites is used in this study to assign a date of disturbance (DoD) to each detected disturbed area over 1 ha in size. Dates of estimated disturbances are validated by comparison with a yearly Landsat‐based change sequence with produces accuracies ranging between 15 – 85% (average overall accuracy 62%) (Figure 3) depending on the size of the disturbance event. Once validated, the spatial and temporal patterns of disturbances within the region and in smaller subsets, representative of the size of a grizzly bear annual home range, are then explored.

Figure 3. (a) An example of yearly classified STAARCH disturbances within validation area A. (b) Validation data set for the same area, derived by producing change masks from intermediate Landsat scenes. Yearly periods run from summer to summer, with exact dates depending on the date of the respective Landsat scenes.


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Figure 4 shows the temporal distribution of disturbance, as predicted by STAARCH, for the entire study area. Disturbances are most frequently predicted as occurring in the first few dates of the growing season (representing both disturbances during those periods and those occurring outside of the sequence, such as winter harvesting and snow‐related crown damage) as well as late in the season, during late August and September. Large amounts of disturbance (on average, 23 677 ha year‐1 or 0.4% of the total area) occurred in all years, but 2003 had noticeably higher predicted disturbance levels than other years. Disturbance levels are shown to increase later in the growing season, with most disturbances occurring in late August and September. Individual events are generally small in area (less than 5 ha) except in the case of wildfires, with, on average, 0.4% of the total area disturbed each year. With further work to assign the nature and cause of individual disturbance events the disturbance sequence has significant potential for improving understanding of grizzly bear habitat use with implications for the long term conservation of this species.

Figure 4. Total disturbed area within the grizzly bear study area by date. Dates are given as yyyy/mm/dd. The majority of individual disturbance events were small in terms of area (mean patch size of 3.84 ha, standard deviation of 7.2 ha) with the most covering between 1 and 5 ha (Figure 6). A smaller number of larger disturbance events also occurred, with the largest covering an area of 1028 ha. Outcome: Gaulton, R., Hilker, T.H., Wulder, M.A., Coops, N.C., Stenhouse, G (2010). Characterising Stand Replacing Disturbance In Western Alberta Grizzly Bear Habitat, Using A Satellite‐Derived High Temporal And Spatial Resolution Change Sequence. Remote Sensing of Environment (in review)


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Role of understory as a driver of conifer canopy spectral reflectance, influence on capture of phenology `Phenological Cameras’ Developments in distributed sensing, web camera image databases, and automated data visualisation and analysis, among other emerging opportunities, have resulted in a suite of new techniques for monitoring habitat at many different scales. Data from these networks can provide important information on the timing of plant phenology with implications to habitat status and condition. Further, of more broad global relevance, information on phenology is also useful to better understand the relationship between canopy structure and seasonal dynamics of CO2 uptake by forests, especially under changing climatic conditions. In this paper we describe the design and deployment of a small network of cameras (Table 1) established along an elevation gradient in western Alberta, Canada, with the dual purpose of: (a) developing a more comprehensive understanding of seasonal phenophases and the reproductive timing (budding, fruit production) of understory forest vegetation, and (b) to support modelling efforts to validate phenological properties derived from satellite‐based remote sensing systems, principally Landsat and MODIS. Table 1: Summary of the camera sites, including forest cover type and coordinates.

Site name Easting (m)1

Northing (m)1

Forest type

Overstory species mix Elevation above

ellipsoid (m)2

Fickle Lake 518537 5916058 Coniferous Mature White Spruce 951

Fickle Lake 519136 5916668 Mixed White Spruce, Trembling

Aspen 970

Bryan spur 501950 5898930 Coniferous black spruce, lodgepole pine 1,092

Bryan spur 502319 5899684 Mixed Trembling aspen, lodgepole

pine, white spruce 1,093

Cadomin 480660 5879755 Coniferous Black spruce, Lodgepole

pine 1,458

Cadomin 478427 5877276 Mixed White spruce, Balsam

Poplar, willow 1,484

Prospect Creek

478036 5868840 Coniferous Lodgepole pine 1,714 1 Horizontal Datum: World Geodetic System 1984. Projection: Universal Transverse Mercator. Zone: 11 north During an 8 month period in 2009, over 6,700 images were acquired across seven sites throughout the growing season, providing a rich database of phenological activity of both the under‐ and overstory forest components. Strong elevation and climate responses were observed. Example images from a pair of the mid‐elevation sites are shown in Figures 5 and 6. These images demonstrate the variability of the scenes across the elevation gradient with respect to the overstory and understory composition, as well as snow cover at the commencement and end of the growing season.


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14th April

7th August

4th May

22nd September

9th June

6th October

17th July 27th October


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Figure 5. Example images from the Cadomin conifer site (see Table 1 for site description).

12th April 11th August

7th May 10th September

26th June 1st October

21st July 22nd October


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Figure 6. Example images from the Cadomin mixed site (see Table 1 for site description). We then extracted the temporal pattern from the images (Figure 7) based on the sigmoid function which fitted the start of green up closely, with r2 values ranging from 0.74 – 0.95. The ratio of the c/d parameters provides an indication of the half maximum green‐up time, and demonstrates that, as expected, the lower elevation sites had earlier green‐up dates than the higher elevation sites. Similarly, coniferous sites across the transect were more similar than the mixed sites, which contained a significant proportion of deciduous vegetation. The fit of the sigmoid models to the normalized individual brightness channels (blue, green and red) was poor, with noise and background effects clearly impacting the extraction of the 4 sigmoid parameters. In those cases with evident noise and / or background effects, the coefficient of variation (r2) values ranged from 0.0 – 0.7, with a wide variety of fitting accuracies. A comparison of the fitted sigmoid functions over the 7 sites, using the index with the best fit to the function (2G‐RBi) is shown in Figure 8.

Figure 7. Changes through time in the 2G‐RBi and the blue‐green ratio for the Cadomin mixed and conifer sites.


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Figure 8. Examples of the 2G‐RBi trajectories for all sites during spring green‐up. Outcome: Bater, C.W., Coops, N.C., Wulder, M.A., Nielsen, S., McDermid, G, Stenhouse, G. (2010). Design And Installation Of A Phenological Camera Network Across An Elevation Gradient For Habitat Assessment. Computers and Electronics in Agriculture (in review). Bater, C.W., Coops, N.C., Wulder, M.A., Nielsen, S.E., Proulx, R., and Stenhouse, G. (2010). Monitoring plant phenology using indices derived from a network of ground‐based digital cameras. Ecological Indicators (in internal review).


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Edges and transitions: Development of landscape edge features from continuous variables Edges between different land cover types or functional vegetation classes represent important transition zones for abiotic and biotic processes. However, boundary detection methods often identify edges solely in areas of high contrast, such as transitions between forested and non‐forested areas, and are also insensitive to the relative contrast and orientation of different transitions. Edge contrast and orientation can determine the magnitude and even the occurrence of ecological edge effects, and should be measured to provide information on landscape condition and habitat potential. All of these variables are standard outputs of wombling, a relatively underused automated edge detection algorithm. We applied wombling to the wetness component of a tasselled cap transformation (TCT) of a Landsat scene acquired over the eastern slopes of the Rocky Mountains in Alberta, Canada. By incorporating wombled edge contrast and orientation, and edge class transition type obtained from a land cover dataset, the nature of all transitions between land cover classes (Figure 9) within the image were characterized and quantified. We also assessed the consistency between edges identified by wombling and other common methods of edge delineation (such as spatial clustering) and methods of edge quantification (such as landscape pattern indices [LPI]).

Figure 9. Histograms of edge contrast for two land cover transitions: Coniferous to Broadleaf, and Exposed Land to Herbs. Land cover transitions showed a broad range of edge contrast. Comparisons of edge contrast and the LPI of density generally showed some correlation (r2=0.33), however, the contrast of this relationship varied with the dominant land cover (e.g., r2=0.016 for broadleaf open forest to r2=0.48 for dense coniferous forest). When edge contrast values were stratified to values > 1


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standard deviation and compared to the LPI of density, agreement between the data types increased which indicates that the LPI is preferentially relating with high contrast edges (Figure 10).

Figure 10. Comparison of LPI edge density and wombling values indicating the local nature of wombling BEs. Through this research we demonstrate the unique edge information that may be generated from a remotely sensed continuous variable (TCT wetness) and show the complementarity of these values to the more typically generated LPI of density. Knowledge of the location and magnitude of contrast at the edges will enable stratification and selection of particular land cover transitions, allow insights upon the nature of edge effects and enable insights on the driving physical and biological processes and species distributions. Outcome: Wulder, M.A., B.P. Stewart, M.E. Andrew, M. Smulders, T. Nelson, N.C. Coops, and G.B. Stenhouse. (2010). Remote sensing derived edge location, magnitude, and class transitions for ecological studies. Canadian Journal of Remote Sensing. (accepted 17th December, 2009).


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TOPIC 2. MONITORING MOUNTAIN PINE BEETLE ATTACK AND MITIGATION ACTIVITIES. Infestation Maps Over MPB Area: Assessment Of Impacts Of MPB And GB Home Ranges In The Alberta Foothills. Numerous studies have shown that moderate spatial resolution remote sensing imagery can be useful for change detection and characterizing forest disturbance. Recently, Landsat‐5 Thematic Mapper (TM) and Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) imagery have been proven effective in mapping the mountain pine beetle infestation in British Columbia, and also demonstrating an applicability elsewhere in western North America. Typically based on indices derived from Tasseled Cap transformations (TCT), these studies have been successful in producing both discrete (categorical) and probabilistic infestation maps. To establish the context within which mountain pine beetle mapping activities are occurring, we produced a report which begins with a description of the nature of mountain pine beetle infestation and landscape disturbance in portions of the grizzly bear management area. The report then describes the results of the 2008 red attack mapping exercise. To examine infestation and disturbance in the grizzly bear management area, land cover data representing circa year 2000 conditions were employed to stratify the landscape, and were then compared to helicopter global position system (Heli‐GPS) data mapping mountain pine beetle red attack locations, and Landsat‐ and MODIS‐derived data capturing landscape disturbance. The land cover data used for stratification were developed by the Earth Observation for Sustainable Development of Forests (EOSD) project, and consist of a Landsat‐derived, 23 class land cover map of the forested ecozones of Canada representing circa year 2000 conditions (Wulder et al. 2003; Wulder et al. 2009). To determine the distribution of red attack, Heli‐GPS data is collected by experienced surveyors who record the coordinates and the number of crowns exhibiting infestation at a given location. As previous experience has shown that Heli‐GPS points are often poorly registered to the actual location of red attack crowns, the discrete point data were tessellated using a 1 ha grid. Each binary grid cell then records the presence or absence of mountain pine beetle infestation. Heli‐GPS data were available for the 2005‐2008 beetle years. Landscape disturbance was captured using the Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH), which is a change detection algorithm designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM+ and MODIS reflectance data. The two main outputs of STAARCH are 1) a spatial change mask of forest disturbances and 2) an image sequence which records the temporal evolution of these disturbance events at an 8 day time interval and spatial resolution of 30 m. As the STAARCH disturbance product does not include the Waterton or Alberta North grizzly bear population units (Figure 1), these areas were excluded from the analysis. STAARCH data were available from 2005 through to the summer of 2008. In its entirety, the grizzly bear management area is approximately 24,000,000 ha size (Figure 11). The region analysed includes the Livingstone, Grande Cache, Clearwater, Yellowhead, and Swan Hills grizzly bear population units, which include approximately 12,800,000 ha of the


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management area. Together, the majority of these population units consist of dense coniferous forest (42% of the area) (Figure 12). Areas classified as dense broadleaf forest (13%), various types of shrubland (13%), herbaceous (11%), and rock/rubble (8%) make up much of the remainder. According to the Heli‐GPS survey data, mountain pine beetle infestation was limited to the western portion of Grande Cache in 2005, but expanded north, south and east in following years, reaching its height in 2007 before diminishing in 2008 (Figure 13). At the peak of the infestation in the 2007 beetle year, approximately 47,000 ha contained one or more trees exhibiting red attack; this number fell to 17,000 ha in 2008. Although widespread in 2006‐2008, infestation intensities were light, with the majority of red attack locations having fewer than ten crowns exhibiting red attack. For all years, the vast majority of infestation was located in areas classified as dense coniferous forest (Figure 14). Areas classified as dense broadleaf forest, which is defined by the EOSD as consisting of 75% or more broadleaf species by basal area, were impacted to a lesser degree, as were mixedwood and wetland areas.

Figure 11. Map of the western Alberta grizzly bear management area and population units. Waterton and Alberta North were not included in the analysis.


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Figure 12. Summary and total areas of the circa year 2000 EOSD land cover classes for the Livingstone, Grande Cache, Clearwater, Yellowhead, and Swan Hills grizzly bear population units.

Figure 13. Heli‐GPS survey data mapping the distribution of mountain pine beetle infestation for the 2005‐2008 beetle years.


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Figure 14. Summary of the total number of hectares exhibiting one or more crowns infested by mountain pine beetle by EOSD vegetated land cover class for the Livingstone, Grande Cache, Clearwater, Yellowhead, and Swan Hills grizzly bear population units. Infestation is represented using the 1 ha tessellated red attack surface. Amounts of landscape disturbance as captured by the STAARCH product were greatest in 2005 and 2007, totalling approximately 53,000‐54,000 ha for both years, while the total area disturbed in 2006 was approximately 47,000 ha (Figure 15). As STAARCH extends only to the summer of 2008, totals for that year cannot be compared, although relative amounts of disturbance by land cover class are similar to other years. For all years, disturbance is concentrated in open and dense coniferous forest, both in terms of total area, and the proportion of area disturbed relative to class size. Dense broadleaf forest, wetland and shrubland exhibit less disturbance as compared to the conifer classes, but are similar to each other in proportion relative to their class sizes.


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Figure 15. Summary of the total areas of landscape disturbance by vegetated land cover class for the Livingstone, Grande Cache, Clearwater, Yellowhead, and Swan Hills grizzly bear population units. Disturbances were delineated and dated using the Landsat‐ and MODIS‐derived STAARCH product. Note that the data extends only to the summer of 2008. Helicopter global positioning system survey data Tactical infestation surveys are undertaken by experienced surveyors from helicopters whereby the location (using global position systems, GPS) and the number of trees at a given site are recorded. Heli‐GPS surveys provided infestation data depicting mountain pine beetle red attack across the area of interest. The survey data, collected in 2008, consists of discrete points with the number of red crowns at a given location stored as an attribute, and were used to identify infested areas. Figure 16 shows the distribution of the number of red attack crowns found at the survey points. While infestation was widespread across the area of interest, intensity at any given location was relatively light, with most locations having fewer than 10 crowns exhibiting signs of red attack (Figure 16, Figure 17).


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Figure 16. Number of red attack crowns found at locations mapped during the 2008 Heli‐GPS survey for Landsat‐5 TM path 46, row 21.

Figure 17. Examples of Infestation intensities typical of the study area in 2008. Image data is 0.20 m true colour digital aerial imagery collected in 2008.


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Under optimal conditions for the production of an EWDI product, Landsat images should be acquired the year prior to, and the year following, the “beetle attack year” to be mapped (Wulder et al. 2006b). In other words, to map beetle infestation for 2008, Landsat data from 2007 and 2009 should be employed. Because of persistent cloud cover over the area of interest throughout 2007, however, Landsat scenes from 2008 and 2009 were obtained. Time 1 (T1) data consisted of a scene acquired on 6 August 2008, while Time 2 (T2) data consisted of a scene collected on 24 July 2009. Both scenes were acquired from path 46, row 21 (Figure 18).

Figure 18. Time 1 and Time 2 Landsat data used to create the mountain pine beetle red attack map for 2008. Time 1 data were acquired on 6 August 2008; Time 2 data were acquired on 24 July 2009. The Landsat‐5 TM scenes were processed following the methods outlined in (Wulder et al. 2006). Shadow, snow and other non‐forest areas were masked out using band thresholding, while cloud and haze were manually delineated (Figure 19). The images were converted to top of atmosphere (TOA) reflectance values using published post‐launch gains and offsets. The images were of sufficiently high spatial accuracy that no georeferencing or registration were required. The T2 image was normalized to T1 using mean TOA reflectance values collected from bright and dark pseudo‐invariant targets. Wetness indices were calculated using a TCT; the coefficients applied to the individual bands are shown in Table 2. An enhanced wetness difference index (EWDI) was then calculated by subtracting T2 wetness from T1 wetness.


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Figure 19. Example of the forest/non‐forest mask employed during the analysis. Non‐forest areas are shaded black in the right hand panel.

Table 2. Landsat‐5 TM tasseled cap transformation coefficients.

Feature Band 1 Band 2 Band 3 Band 4 Band 5 Band 7

Greenness ‐0.285 0.244 0.544 0.724 0.084 ‐0.180

Wetness 0.151 0.197 0.328 0.341 ‐0.711 ‐0.457

To characterize healthy forest, forested sites with high TCT greenness (Table 2) values were randomly selected from each scene. Locations exhibiting red attack were manually identified using the Heli‐GPS survey data as a guide, which indicated where infestation was relatively severe. Calibration and validation data each consisted of 102 points. Using a Python script automating procedures described in (Wulder et al. 2006b), initial EWDI values were established using the calibration data, and then iteratively adjusted to determine threshold values for areas likely experiencing red attack. Validation data were then used for the final accuracy assessment. Final steps in the development of the red attack product included the application of a 3x3 median filter to reduce random noise, and the incorporation of the Heli‐GPS locations into the final surface.


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Figure 20 displays the EWDI distributions of healthy and infested stands. The resulting classification has an overall accuracy of 82% (Table 3). The producer’s accuracy for red attack is 78%, indicating that over half the reference red attack locations were correctly classified. The user’s accuracy for the attack class indicates that 85% of attacked locations were correctly mapped. Figure 11 provides a visual comparison between the Heli‐GPS survey data and the final red attack classification for a portion of the study area. Although classification accuracies are generally high given the non‐optimal dates of the Landsat imagery and the widespread but relatively light intensities of infestation, results of the validation should be interpreted with caution. As it is impossible to verify the accuracy of the red attack product without additional ground truth data, users are encouraged to interpret the results with caution. While the capacity to map mountain pine beetle infestation using moderate resolution remote sensing data is well‐established, success depends on the timing and severity of attack. For instance, Skakun et al. 2003 found significant accuracy improvements for sites containing 30‐50 read attack trees versus those containing 10‐29. While widespread, the low severity of the infestation is a confounding factor. Nonetheless, the low cost and large area coverage of Landsat data, and the proven capability of Landsat‐derived products, makes it a critical tool for forest health monitoring. Further, the utility of these maps may be increased when combined with survey information from other sources, and forest inventory based information on susceptibility to mountain pine beetle infestation.

Figure 20. Box and whisker plots displaying mean, standard error of the mean, and ±two standard deviations of the enhanced wetness difference index for selected healthy and attacked sites.


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Table 3. Confusion matrix summarizing the accuracy assessment for path 46, row 21.

Model

Not red Red Total Producer's accuracy Omission

Not red 44 7 51 86.3% 13.7%

Reference Red 11 40 51 78.4% 21.6%

Total 55 47 82.35%

User's accuracy 80.0% 85.1%

Commission 20.0% 14.9%

Overall Accuracy 82.4% True positive rate 78.4% False positive rate 13.7% True negative rate 86.3% False negative rate 21.6% Precision 85.1% n 102

Figure 21. Comparison of the 2008 Heli‐GPS survey data (right) and the final red attack classification (left) for a portion of path 46, row 21.


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TOPIC 3: ONGOING MONITORING OF MPB INFESTATION AND COLLECTION OF GROUND VALIDATION DATA A ground‐based digital camera network was installed across an elevation transect to monitor plant phenology during the 2009 growing season. Seven cameras each recorded five images daily. Teams from UC, UA, FRI and UBC undertook a field program from April to October 2009. The field program included: Site selection and installation of seven cameras in early April. Bi‐weekly site visits to collect under‐and overstory phenophase data. Bi‐monthly visits to download camera data. Tall tree survey to collect data on overstory species, height, diameter, and basal area. Mapped understory species within camera fields of view at each site. Removed cameras at end of October. Species‐specific data is extracted from regions of interest within images, from which true‐colour indices of greenness and structural complexity are then derived. Linkages between digital indices and field‐based phenophase observations are in development to track the timing of phenological events across the elevation transect. In addition to the field data we have the capacity to utilise high spatial resolution airborne imagery (Figure 22). This imagery provides crown scale imagery for phenological camera placement as well as detailed, plot level information particularly on crown condition. No imagery was acquired in 2009 however supplemental activity emerged with a report provided to the Government of Alberta on utility of airborne data.


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Figure 22. Examples of the 0.20 m spatial resolution airborne images collected along two transects. Panels a) and b) are examples of healthy forests, while infestation intensities shown in panels c) and d) are typical of the area of interest. In addition as mountain pine beetle population trends are often expressed through the green attack to red attack ratio (G:R), which compare the number of current successfully attacked trees (G) to trees attacked successfully the previous year (R). This ratio is often used in an operational context to estimate the rate of change in beetle populations. A ratio that is less than one (i.e., 0.5:1) indicates that a beetle population is declining, as there are fewer successful attacks in the current year than the previous year. A ratio that is greater than one (i.e., 2.75:1) indicates that the beetle population is increasing. Wulder et al. (2008b) used annual high spatial resolution satellite imagery to successfully calculate retrospective G:R ratios. To obtain the ratio for the first year of imagery, a count of the number of red attack trees in the imagery for that year will provide the red attack count for the ratio. Since the new red attack in the imagery of


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the second year will likely have been green attack in the previous year, this will provide the value for the green attack component of the ratio for the first year (Wulder et al., 2008b). Collection and interpretation of multiple years of imagery will allow the estimation of annual G:R ratios, thereby providing information about trends in the population rate of change. This logic may also be applied to high spatial resolution aerial imagery (Figure 23).

Figure 23. Red attack mapping with green attack back‐casting for G:R estimation. Outcome: Wulder, M.A., Ortlepp, S., White, J.C., Coops, N.C (2009) Opportunities for the use of low‐cost digital very high spatial resolution aerial imagery to support mountain pine beetle monitoring. Mountain Pine Beetle Program Working Paper. Pacific Forestry Centre, Canadian Forest Service.

Chapter 9 Health & Environment

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TOPIC 4: PROJECTING LANDSCAPE RECOVERY AND CHANGE. Scenario‐based modeling approach which combines information on land cover change with forest growth and forest recovery growth rates. (2011/12 – overall) and Review of options for combining remotely sensed data and spatial data layers to project landscape recovery and change (2010/11) Once land cover change has occurred due to harvesting, development, or beetle activity, it is critical to understand how the landscape will recover through time, and the effect this recovery will have on future grizzly bear habitats and populations. To examine these issues, the final set of objectives (2.6) works towards designing and implementing a scenario‐based modeling approach (known as “Future of Alberta’s forests for Grizzly Bears“) which combines information on land cover change with forest growth and forest recovery growth rates. The first stage is the development of the relevant environmental layers required for predicting stand growth into the future under current and future climate (2.6.1) with a discussion document and data prepared and provided by mid 2009. Table 4. A range of input climate data has been assembled and provided to FMF throughout 2009.

Climate Layer Scenario Spatial Resolution

Extent

Average Monthly Minimum temperature Current Climate 100m Griz Bear Study Area Average Monthly Maximum temperature Current Climate 100m Griz Bear Study Area Average Monthly Frost Days Current Climate 100m Griz Bear Study Area Average Monthly Solar Radiation Current Climate 100m Griz Bear Study Area Average Monthly Precipitation Current Climate 100m Griz Bear Study Area Average Monthly Minimum temperature 2020, 2050, 2080. A2

Scenario 100m Griz Bear Study Area

Average Monthly Maximum temperature 2020, 2050, 2080. A2 Scenario

100m Griz Bear Study Area

Average Monthly Frost Days 2020, 2050, 2080. A2 Scenario


Average Monthly Precipitation 2020, 2050, 2080. A2 Scenario



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Develop methods over prototype study area, base level simulations In 2009/2010 predictions on the distribution of lodgepole pine, the major species in the region was assessed under current and future climate. Lodgepole pine (Pinus contorta Dougl.) is a widely distributed species in the Pacific Northwest of North America. The extent to which the current distribution of lodgepole pine may be altered under a changing climate is an important question for managers of wood supply, wildlife and for the conservation of subalpine ecosystems. In this first scenario component of this theme, we addressed the question, how much might the current range of the species shift under a changing climate? We first assessed the extent that suboptimal temperature, frost, drought, and humidity deficits affect photosynthesis and growth of the species across the Pacific Northwest with a process‐based model (3‐PG). We then entered the same set of climatic variables into a decision‐tree model, which creates a suite of rules that differentially rank the variables, to provide a basis for predicting presence or absence of the species under current climatic conditions.

Figure 24. Decision tree developed to predict presence and absence of lodgepole pine, based on the maximum effect of the four seasonal climate modifiers. The derived decision‐tree model successfully predicted weighted presence and absence recorded on 12,660 field survey plots with an accuracy of ~70%. The analysis indicated that sites with significant spring frost, summer temperatures averaging <15oC and soils that fully recharged from snowmelt were most likely to support lodgepole pine. Based on these criteria, we projected climatic conditions through the 21st century as they might develop without additional efforts to reduce carbon emissions using the Canadian Climate Centre model (CGCM2). In the 30‐year period centred around 2020, the area suitable for lodgepole pine in the Pacific Northwest was projected to be reduced only slightly (8%), (Figure 25). Thereafter, however, the projected climatic conditions appear to


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progressively favor other species, so that by the last 30 years of 21st century, lodgepole pine could be nearly absent from much of its current range.

Current 2020

2050 2080

Figure 25. Prediction of lodgepole pine distribution under current climate and the three future 30‐year periods: 2020, 2050 and 2080.


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We conclude that process‐based models, because they are highly sensitive to seasonal variation in solar radiation, are well adapted to identify the importance of different climatic variables on photosynthesis and growth. These same variables, once identified, and run through a decision‐tree model, provide a reasonable approach to predict current and future patterns in a species’ distribution. Outcome: Coops, N.C., Waring, R.H (2010) Estimating Lodgepole Pine (Pinus contorta Dougl.) Distribution in the Pacific Northwest under Climatic Change. (in press).


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Long Time Series Change Over the Kakwa Greater Ecosystem, Landsat Analysis The primary objective of this work is to document and quantify the rate of landscape change within an area of grizzly habitat over an extended period of time (1973–2008, Table 5). This is due to long‐term landscape change having an impact on grizzly habitat. In addition to the amount of area that has changed, the rate at which the change has occurred and the variability in that rate of change—over time—is of interest. A sub‐area of Landsat Path 46 / Row 22. A time series of Landsat MSS, TM, and ETM+ images (1973–2008, Figure 26) with the requisite geometric and radiometric correction and normalization applied: Table 5: Imagery used in the Kawka historical study.

The approach uses the angle of Tasseled cap greenness to brightness components as the spectral feature for capturing changes in land cover [Angle = Arctan (G/B)]. We are then summarizing change events to a 250m analysis unit for reporting; focus on stand‐replacing disturbance.

Sensor Path/Row Date Sensor Path/Row Date

MSS 50/22 1973‐09‐16 TM5 46/22 1997‐09‐25 MSS 50/22 1976‐09‐27 ETM 46/22 2000‐09‐25 MSS 50/22 1978‐07‐25 ETM 46/22 2001‐09‐28 MSS 50/22 1981‐08‐14 ETM 46/22 2002‐09‐15 TM5 46/22 1990‐09‐06 TM5 46/22 2004‐08‐11 TM5 46/22 1991‐07‐23 TM5 46/22 2006‐06‐30 TM5 46/22 1995‐09‐04 TM5 46/22 2008‐08‐06


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Figure 26. Image stack of the images used in the Kawka study. These images will assist in showing the location, regionalization, and date of changes occurring within this greater Kakwa ecosystem.


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Figure 27. Change by time period from the Landsat time series analysis.


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Figure 28. Decadal summaries of the Landsat time series change.

Figure 29. Summary of area changed over time (findings on rates and proportions also available).

1970s15%

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1990s27%

2000s46%

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16765

5381

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5411

8979

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7343

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5317

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36129

15075

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Figure 30. Summary of change events and change mean change event size. Outcome: Analysis complete and manuscript in preparation for Remote Sensing of Environment. A summary report will also be provided for year end.

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APPENDIX 1: LIST OF PROGRAM PARTNERS (1999 – Present)

Ainsworth Lumber Co. Ltd. Alberta Advanced Education and Technology (formerly Innovation and Science) Alberta Conservation Association

Alberta Environment Alberta Fish & Game Association

AB Innovates (ARC) Alberta Newsprint Company Alberta Sustainable Resource Development (formerly Natural Resources Service) Alberta Tourism, Parks and Recreation Anadarko Canada Corporation Anderson Exploration Ltd. AVID Canada B P Canada Energy Company BC Oil and Gas Commission Buchanan Lumber – Tolko OSB Canada Centre for Remote Sensing Canadian Association of Petroleum Producers (CAPP) Petroleum Technology Alliance Canada

(PTAC) Environmental Research Advisory Council (ERAC) Fund Canadian Natural Resources Ltd. Canfor Corporation Center for Wildlife Conservation ConocoPhillips Canada (formerly Burlington Resources Canada Ltd.) (formerly Canadian Hunter Exploration Ltd.) Conservation Biology Institute Daishowa Marubeni International Ltd. Devon Canada Corp Enbridge Inc. Encana Corporation

ENFORM

Foothills Research Institute (formerly Foothills Model Forest)

Forest Resources Improvement Association of Alberta (FRIAA)

G&A Petroleum Services GeoAnalytic Inc. Government of Canada Canadian Forest Service, Natural Resources Canada Canadian Wildlife Service Environment Canada – HSP Human Resources and Skills Development Canada Natural Sciences and Engineering Research Council of Canada (NSERC) Parks Canada Banff National Park Jasper National Park Grande Cache Coal Corporation Hinton Fish and Game Association Hinton Training Centre

Husky Energy Inc. Komex International Ltd. Lehigh Inland Cement Limited Luscar Ltd. Coal Valley Resources Inc Gregg River Resources Ltd. Manning Diversified Forest Products Ltd. Manning Forestry Research Fund Millar Western Forest Products Ltd. Mountain Equipment Co‐op

Nature Conservancy NatureServe Canada Nexen Inc. Northrock Resources Ltd.

Peregrine Helicopters Persta Petro Canada Ltd. Peyto Energy Trust Precision Drilling Corporation Rocky Mountain Elk Foundation ‐ Canada Shell Canada Limited

Sherritt – Coal Valley Slave Lake Division – Alberta Plywood Spray Lake Sawmills Ltd. Suncor Energy Inc. Sundance Forest Industries Ltd. Talisman Energy Inc. TECH – Cardinal River Operations (formerly Elk Valley Coal) Telemetry Solutions TransCanada Pipelines Ltd. University of Alberta University of Calgary University of Lethbridge University of Saskatchewan Western College of Veterinary Medicine University of Washington University of Waterloo Veritas DGC Inc. West Fraser Mills Ltd. Alberta Plywood Blue Ridge Lumber Inc. Hinton Wood Products Slave Lake Pulp Sundre Forest Products Weyerhaeuser Company Limited Wilfred Laurier University

World Wildlife Fund Canada

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APPENDIX 2: LIST OF PROGRAM PARTNERS (2009) AB Innovates (ARC) AB Newsprint Company Alberta Tourism, Parks and Recreation Alberta Sustainable Resource Development B P Canada Energy Company Canfor Corporation ConocoPhillips Canada Daishowa Marubeni International Ltd. Devon Canada Corp ENFORM Foothills Research Institute (Core) Mountain Pine Beetle Program Forest Resources Improvement Association of Alberta (FRIAA)

Grande Cache Coal Corporation Government of Alberta

Alberta Tourism, Parks and Recreation Alberta Sustainable Resource Development Alberta Employment and Immigration (STEP)

Government of Canada Canadian Forest Service, Natural Resources Canada

Human Resources and Skills Development Canada (CSJ) Parks Canada

Natural Sciences and Engineering Research Council of Canada (NSERC) Husky Energy Inc. Nature Conservancy Nexon Petro Canada Ltd. (now part of Shell Canada Limited) Shell Canada Limited Sherritt – Coal Valley Suncor Energy Inc. Sundance Forest Industries Ltd. Talisman Energy Inc. TransCanada Pipelines Ltd. Weyerhaeuser Company Ltd

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APPENDIX 3: LIST OF PUBLISHED PAPERS

Berland, A., T.Nelson, G. Stenhouse, K.Graham, J.Cranston. 2008. The impact of landscape disturbance on grizzly bear habitat use in the Foothills Model Forest, Alberta Canada. Forest Ecology and Management 256:1875‐1883.

Boulanger, J., G.C. White, M. Proctor, G. Stenhouse, G. Machutchon, S. Himmer. 2008. Use of occupancy models to estimate the influence of previous live captures on DNA‐based detection probabilities on grizzly bears. Journal of Wildlife Management 72:589‐595.

Boulanger, J., M. Proctor, S. Himmer, G. Stenhouse, D. Paetkau, J. Cranston. 2006. An empirical test of DNA mark‐recapture sampling strategies for grizzly bears. Ursus 17:149‐158.

Boulanger, J., G. Stenhouse, R. Munro. 2004. Sources of heterogeneity bias when DNA mark‐recapture sampling methods are applied to grizzly bear (Ursus arctos) populations. Journal of Mammalogy 85:618‐624.

Boyce, M.S., S.E. Nielsen, G.B. Stenhouse. 2009. Grizzly Bears Benefit from Forestry ‐ Except for the Roads. BC Forest Professional. January‐February:19.

Cattet, M., G. Stenhouse, and T. Bollinger. 2008. Exertional myopathy in a grizzly bear (Ursus arctos) captured by leg‐hold snare. Journal of Wildlife Diseases 44:973‐978.

Cattet, M., J. Boulanger, G. Stenhouse, R.A. Powell, and M.J. Reynolds‐Hogland. 2008. An evaluation of long‐term capture effects in Ursids: Implications for wildlife welfare and research. Journal of Mammology 89:973‐990.

Cattet, M.R., A. Bourque, B.T. Elkin, K.D. Powley, D.B. Dahlstrom, N.A. Caulkett. 2006. Evaluation of the potential for injury with remote drug‐delivery systems. Wildlife Society Bulletin 34:741‐749.

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.

Chow, B.A., J.W. Hamilton, D. Alsop, M. Cattet, G. Stenhouse, and M.M. Vijayan. 2010. Grizzly bear corticosteroid binding globulin: cloning and serum protein expression. General and Comparative Endocrinology (in press)

Collingwood, A., S.E. Franklin, X. Guo, and G. Stenhouse. 2009. A medium‐resolution remote sensing classification of agricultural areas in Alberta grizzly bear habitat. Canadian Journal of Remote Sensing 35:23‐26.

Collingwood, A. 2008. Satellite image classification and spatial analysis of agricultural areas for land cover mapping of grizzly bear habitat. MSc. Thesis. Department of Geography, University of Saskatchewan, Saskatoon, Saskatchewan.

Curry, B.L., 2008. A comparison of relative and absolute change detection for measuring forest disturbance. MGIS Thesis, Department of Geography, University of Calgary.

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., Y.He, A.D. Pape, and G. J. McDermid. 2010. Landsat‐comparable land cover maps using the ASTER and SPOT images; a case study for large‐are mapping programs. International Journal of Remote Sensing (in press).

Franklin, S. E. 2009. Remote Sensing for Biodiversity and Wildlife Management: Synthesis and Applications. McGraw‐Hill Professional, New York. 346p. (http://www.mhprofessional.com/product.php?isbn=0071622470)

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.

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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.

Graham, K.G., J. Boulanger, J. Duval, G. Stenhouse.. Spatial and temporal use of roads by grizzly bears in west‐central Alberta. Ursus (in press)

Hamilton, J.W. 2007. Evaluation of indicators of stress in populations of polar bears (Ursus maritimus) and grizzly bears (Ursus arctos). M.Sc. Thesis. Department of Biology, University of Waterloo, Waterloo, Ontario.

He, Y., S. E. Franklin, X. Guo, and G. B. Stenhouse. 2009. Narrow‐Linear and small‐area forest disturbance detection and mapping from high spatial resolution imagery. Journal of Applied Remote Sensing 3: 033570

Hilker, T., Wulder, M.A., Coops, N.C., Linke, J., McDermid, G., Masek, J., Gao, F., & White, J.C. 2009. A new data fusion model for high spatial‐ and temporal‐ resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment 113:1613–1627

Hilker, T., Wulder, M.A., Coops, N.C., Seitz, N., White, J.C., Gao, F., Masek, J., & Stenhouse, G.B. 2009. Generation of dense time series synthetic Landsat data through data blending with MODIS using the spatial and temporal adaptive reflectance fusion model (STARFM). Remote Sensing of Environment. 113:1988‐1999.

Hird, J. and G.J. McDermid, 2009: Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113(1): 248‐258

Hird, J.N. 2008. Noise reduction for time series of the Normalized Difference Vegetation Index (NDVI): An empirical comparison of selected techniques. MSc Thesis, Department of Geography, University of Calgary

Huettmann, F., S.E. Franklin, G.B. Stenhouse. 2005. Predictive spatial modelling of landscape change in the Foothills Model Forest. Forestry Chronicle 81:525‐537.

Hunter, A. 2007. Sensory Based Animal Tracking. PhD Thesis. Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada.

Lindsjo, H.J.A. 2009. Development and application of a health function score system for grizzly bears (Ursus arctos) in western Alberta. MSc. Thesis. Department of Veterinary Pathology, University of Saskatchewan, Saskatoon, Saskatchewan.

Linke, J. and G.J. McDermid. A conceptual model for multi‐temporal landscape monitoring in an object‐based environment. Journal of Selected Topics in Earth Observation and Remote Sensing (in press).

Linke, J., G.J. McDermid, D.N. Laskin, A.J. McLane, A.D. Pape, J. Cranston, M. Hall‐Beyer, and S.E. Franklin. 2009. A disturbance‐inventory framework for flexible and reliable landscape monitoring. Photogrammetric Engineering and Remote Sensing 75 (8): 981‐9969.

9 Third‐place recipient of the 2010 ERDAS Award for Best Scientific Paper in Remote Sensing

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Linke, J., G.J. McDermid, A.D. Pape, A.J. McLane, D.N. Laskin, M. Hall‐Beyer, and S.E. Franklin. 2009. The influence of patch delineation mismatches on multitemporal landscape pattern analysis. Landscape Ecology 24(2): 157‐170.

Linke, J., M. Betts, M.B. Lavigne, and S.E. Franklin. 2007. Landcape structure, function and change. Chapter 1 in Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches. CRC Press (Taylor and Francis), Boca Raton, 1‐30.

Linke, J., and S.E. Franklin. 2006. Interpretation of landscape structure gradients based on satellite image classification of land cover. Canadian Journal of Remote Sensing 32:367‐379.

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.

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.

McDermid, G.J., N.C. Coops, M.A. Wulder, S.E. Franklin, and N. Seitz, 2009: Critical Remote Sensing Data Contributions to Spatial Wildlife Ecological Knowledge and Management, invited chapter in F. Huettmann and S. Cushman (Eds.), Spatial Complexity, Informatics, and Wildlife Conservation, pp 193‐221, Springer, Tokyo ISBN: 978‐4‐431‐87770‐7.

McDermid, G.J., J. Linke, A.D. Pape, D.N. Laskin, A.J. McLane, and S.E. Franklin. 2008. Object‐based approaches to change analysis and thematic map update: challenges and limitations. Canadian Journal of Remote Sensing 34(5): 462‐466.

McDermid, G.J., R.J. Hall, G.A. Sanchez‐Azofeifa, S.E. Franklin, G.B. Stenhouse, T. Kobliuk, E.F. LeDrew. 2009. Remote sensing and forest inventory for wildlife habitat assessment. Forest Ecology and Management 257:2262‐2269.

McDermid, G. J., S. E. Franklin, and E. F. LeDrew (in press). Radiometric normalization and continuous‐variable model extension for operational mapping of large areas with Landsat imagery. International Journal of Remote Sensing 00:000‐000.

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.

McLane, A.J., G.J. McDermid, and M.A. Wulder, 2009: Processing discrete‐return profiling LiDAR data to estimate canopy closure for large‐area forest mapping and management. Canadian Journal of Remote Sensing, 35(3): 217‐229.

McLane, A.J. 2007. Processing discrete‐return profiling LiDAR data to estimate canopy closure for the purpose of grizzly bear management in northern Alberta. MGIS Thesis, Department of Geography, University of Calgary.

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.

Munro, R.H.M, S.E. Nielsen, M.H. Price, G.B. Stenhouse, M.S. Boyce. 2006. Seasonal and diel patterns of grizzly bear diet and activity in west‐central Alberta. Journal of Mammology 87:1112‐1121.

Nielsen, S.E., G.B. Stenhouse, J. Cranston. 2009. Identification of priority areas for grizzly bear conservation and recovery in Alberta, Canada. Journal of Conservation Planning: 38‐60.

Nielsen, S.E., Stenhouse, G.B., Beyer, H.L., Huettmann, F., Boyce, M.S., 2008. Can natural disturbance‐based forestry rescue a declining population of grizzly bears? Biological Conservation 141:2193‐2207.

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 199:51–65.

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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 199:67–82.

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.

Pape, A. 2006. Multiple Spatial Resolution Image Change Detection for Environmental Management Applications. MSc. Thesis. Department of Geography, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

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.

Pape, A. D. and S. E. Franklin. MODIS‐based change detection for grizzly bear habitat mapping in Alberta. Photogrammetric Engineering and Remote Sensing 74:973‐985

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.

Roever, C.L. 2006. Grizzly Bear (Ursus arctos L.) Selection of Roaded Habitats in a Multi‐use Landscape. M.Sc. Thesis, University of Alberta, Edmonton, Alberta, Canada.

Roever, C.L., M.S. Boyce, G.B. Stenhouse. 2008. Grizzly bears and forestry I: Road vegetation and placement as an attractant to grizzly bears. Forest Ecology and Management 256:1253‐1261.

Roever, C.L., M.S. Boyce, G.B. Stenhouse. 2008. Grizzly bears and forestry II: Grizzly bear habitat selection and conflicts with road placement. Forest Ecology and Management 256:1253‐1261.

Sanii, S. 2008. Assessing the effect of point density and terrain complexity on the quality of LiDAR‐derived DEMs in multipe resolutions. MGIS Thesis, Department of Geography, University of Calgary.

Schwab, C., B. Cristescu, M.S. Boyce, G.B. Stenhouse, M.Ganzle. 2009. Bacterial populations and metabolites in the feces of free roaming and captive grizzly bears. Canadian Journal of Microbiology 55:1335‐1346.

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.

Smulders, M.C.A. 2006. Spatial‐Temporal Analysis of Grizzly Bear Habitat Use. MSc. Thesis. Department of Geography, University of Victoria, Victoria, British Columbia

Sobol, A. 2009: The impact of sampling intensity and failed GPS acquisition on animal home range estimation. MSc. Thesis, Departement of Geography, University of Calgary, Alberta

Stenhouse, G.B., J. Boulanger, M. Cattet, St.Franklin, D.Janz, G.McDermid, S.Nielsen, M.Vijayan. 2008. New Tools to Map, Understand, and Track Landscape Change and Animal Health for Effective Management and Conservation of Species at Risk. Final Report for Alberta Advanced Education and Technology and Alberta Sustainable Resource Development. 391 pp.

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.

Stewart, B.P., M.A. Wulder, G.J. McDermid, and T. Nelson. Distrubance capture and attribution through the integration of Landsat and IRS‐1C imagery. Canadian Journal of Remote Sensing (in press)

Wang, K., S.E. Franklin, X. Guo, Y. He, and G.J. McDermid. 2009.Problems in remote sensing of landscapes and habitats. Progress in Physical Geography 33:747‐768.

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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.

McDermid, G.J., N.C. Coops, M.A. Wulder, S.E. Franklin, and N. Seitz, 2009: Critical Remote Sensing Data Contributions to Spatial Wildlife Ecological Knowledge and Management, invited chapter in F. Huettmann and S. Cushman (Eds.), Spatial Complexity, Informatics, and Wildlife Conservation, pp 193‐221, Springer, Tokyo ISBN: 978‐4‐431‐87770‐7.

Wulder, M.A., S.E. Franklin. 2007. Understanding forest disturbance, information needs, and new approaches. Chapter 9 in Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches. CRC Press (Taylor and Francis), Boca Raton, 233‐243.

Wulder, M. A., and S. E. Franklin, eds., 2003, Remote Sensing of Forest Environments: Concepts and Case Studies, Kluwer Academic Publishers, Boston, MA, 519p.

Wunderle, A.L. 2006. Sensitivity of multi‐resolution satellite sensor imagery to regenerating forest age and site preparation for wildlife habitat analysis. M.Sc. thesis, Department of Geography, University of Saskatchewan, 89p.

Yalte, K. 2007. Relative change detection: an overlooked advantage. MGIS Thesis, Department of Geography, University of Calgary.

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