Grizzly Bear Habitat Suitability Modeling in the Central Purcell Mountains, British Columbia

Transcription

1 Grizzly Bear Habitat Suitability Modeling in the Central Purcell Mountains, British Columbia Clayton D. Apps, RPBio 1 Prepared for Ministry of Water, Land and Air Protection Victoria, British Columbia July, Aspen Wildlife Research 2708 Cochrane Road NW Calgary, Alberta T2M 4H aspen@cadvision.com

2 Acknowledgements This project was completed under contract to the British Columbia Ministry of Water, Land and Air Protection (WLAP). Direction and contract supervision were provided by Matt Austin of WLAP. Data acquisition was assisted by Tim Brierley of the Ministry of Sustainable Resource Management (SRM), and by Michael McLorg of ENKON Environmental Ltd. Tony Hamilton of WLAP contributed in the development of modeling parameters. Recommended Citation Apps, C. D Grizzly bear habitat suitability modeling in the central Purcell Mountains, British Columbia. Aspen Wildlife Research, Calgary, AB. Prepared for Ministry of Water, Land and Air Protection, Victoria, British Columbia. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

3 Table of Contents Acknowledgements...1 Table of Contents...2 List of Tables...3 List of Figures...3 List of Acronyms Used...4 List of Acronyms Used...4 Introduction...5 Methods...7 Data Sources and Variables...7 Model Development...9 Suitability Model: Biophysical-based...9 Green Vegetation Index-based Suitability Model...12 Model Evaluation...13 Results...13 Model Performance Against TEM Habitat Ratings...13 Model Performance Against Sampled Grizzly Bear Occurrence...13 Discussion and Recommendations...15 Digital Products...15 Literature Cited...18 Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

4 List of Tables Table 1. Variables and associated classes derived for modeling grizzly bear habitat suitability in the central Purcell Mountains, British Columbia...8 Table 2. Grizzly bear habitat suitability class rating system, and assumed translation to population density, independent of historic mortality (A. Hamilton unpublished report) Table 3. Parameters for modeling grizzly bear habitat suitability from existing biophysical inventories in the central Purcell Mountains, British Columbia Table 4. Index values and associated classes reflecting grizzly bear habitat suitability in the central Purcell Mountains, British Columbia List of Figures Figure 1. Central Purcell Mountains regional overview study area (red) in southeastern British Columbia....6 Figure 2. Hierarchical structure for knowledge-based modeling of grizzly bear habitat suitability, using biophysical inventories, in the central Purcell Mountains, British Columbia Figure 3. Linear regression analysis results in evaluating the correspondence of grizzly bear habitat ratings derived from TEM mapping with (A) biophysical- and (B) green vegetation index - based habitat suitability models Figure 4. Grizzly bear habitat suitability class in the central Purcell Mountains, British Columbia. Suitability here reflects current landscape potential to support grizzly bears given existing vegetative conditions, and does not account for human influence Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

5 List of Acronyms Used BEC BEI BEU BTM DEM FIP GIS GVI RMZ SRM TEM TRIM WLAP Biogeoclimatic Ecosystem Classification Broad Ecosystem Inventory Broad Ecosystem Unit Baseline Thematic Mapping Digital Elevation Model Forest Inventory Planning Geographic Information System Green Vegetation Index Resource Management Zone Ministry of Sustainable Resource Management Terrestrial Ecosystem Mapping Terrain Resource Information Management Ministry of Water, Land and Air Protection Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

6 Introduction Landscape quality for grizzly bears (Ursus arctos) can be characterized in terms of habitat capability, suitability and effectiveness. Habitat capability refers to a landscape s inherent potential to support a species under ideal conditions of vegetation composition and structure, while habitat suitability is the landscape s current capacity given existing vegetative conditions. Habitat effectiveness equates to the realized ability of a species to inhabit and persist within a landscape after human influence factors are accounted for. I was contracted by the British Columbia Ministry of Water, Land and Air Protection to derive a knowledge-based grizzly bear habitat suitability model for the Central Purcell Mountains regional overview study area (Figure 1). For this, I relied on existing and digitally available biophysical and satellite imagery data, and I employed methods recently developed for grizzly bear habitat modeling based on expert-assessment (Apps and Hamilton 2002). I evaluated model output against actual grizzly bear occurrence as indicated by an existing DNA hair-snag database (Strom et al. 1999). I also compared model output to grizzly bear habitat ratings applied to Terrestrial Ecosystem Mapping (TEM) that have been completed for a portion of the study area. In this report, I describe model development and evaluation, and I discuss results as they pertain to the predictive confidence of habitat model output, and subsequent application in cumulative effects assessment. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

7 Golden Invermere Kimberley Nelson Cranbrook Figure 1. Central Purcell Mountains regional overview study area (red) in southeastern British Columbia. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

8 Methods Data Sources and Variables I obtained geographic data from several sources. Planimetric data of hydrography and point and linear human features were derived from 1:20,000 Terrain Resource Information Management (TRIM) files (Surveys and Resource Mapping Branch 1992). Terrain coverages were derived from a 1:20,000 digital elevation model (DEM; Geographic Data BC 1996). A 1:20,000 coverage of forest inventory planning (FIP) data depicting forest overstory conditions were acquired for forest districts within the study area and including the Purcell Wilderness Conservancy (Resources Inventory Branch 1995). Landsat 5 Thematic Mapper multispectral data were acquired for the for the study area from scenes taken during August 1995 and Other habitat data included 1:250,000 biogeoclimatic ecosystem classification (BEC; Meidinger and Pojar 1991), baseline thematic mapping (BTM; Surveys and Resource Mapping Branch 1995), and resource management zones (RMZ; Land Use Coordination Office 1997). All data were rasterized to 50 m for model development and evaluation. Terrestrial Ecosystem Mapping (TEM) that has been completed at 1:50,000 scale for portions of the study area was obtained from ENKON Environmental Ltd. I derived several variables from the above data sources (Table 1). From FIP data, I defined forest cover type and overstory structure, canopy cover and canopy height variables. From the DEM, I derived 5 slope classes, an index of slope position (Pelgerini 1996), and 2 continuous variables depicting east west and north south aspects. Hydrographic features were extracted from TRIM planimetric data. From Landsat data, I derived the green vegetation index of the tasseled cap transformation (GVI; Crist and Cicone 1984), which has shown to correlate strongly with preferred grizzly bear habitat (Mace et al. 1999). From TEM mapping, I themed grizzly bear habitat ratings for use in model evaluation. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

9 Table 1. Variables and associated classes derived for modeling grizzly bear habitat suitability in the central Purcell Mountains, British Columbia. Variable Class Variable Class COVER TYPE Spruce fir ASPECT North South Cedar East West Hemlock Douglas-fir SLOPE Flat <10% Lodgepole pine Gentle 10-30% Larch Moderate 30-50% Whitebark pine Steep 50 80% Ponderosa pine Very steep >80% Deciduous overstory spp. Non-forested vegetated SLOPE Bottom Non-forested unvegetated POSITION Lower Mid OVERSTORY Non-forested barren Upper STRUCTURE Non-forested vegetated Top Non-forested open shrub forested yrs TERRAIN Very low (< 0.2) forested yrs COMPLEXITY Low ( ) forested yrs Moderate ( ) forested yrs High ( ) forested yrs Very high ( 0.8) forested >140 yrs HYDROGRAPHY <50 m of feature CANOPY 0 5% PROXIMITY m of feature COVER 6 30% m of feature 31 50% m of feature 51 70% % AVALANCHE Presence CHUTES Absence FOREST High (site index >20) PRODUCTIVITY Medium (site index 16-20) GREEN Continuous index of primary Low (site index 11-15) VEGETATION vegatation productivity Very low (site index <11) INDEX Non-forest - vegetated Non-forest - unvegetated Non-forest - water Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

10 Model Development A dataset of grizzly bear occurrence derived from DNA hair-snag methods was earlier analyzed to derive a predictive model of bear occurrence and distribution in the central Purcell Mountains (Boulanger and Apps 2002). However, this empirical model was not appropriate for site-specific environmental impact assessment because (1) model output is specific to a broad home range spatial scale more appropriate for regional evaluations, and (2) model predictions are not independent of human influence and associated effects on bear displacement and mortality. Therefore, I developed 2 models of grizzly bear habitat suitability, both of which apply a knowledge-based rather than an empirical approach (sensu Apps and Hamilton 2002). That is, model parameters are based on expert assessment and not data specific to grizzly bear habitat selection within the study area. The 2 models can be considered alternatives, and the decision as to which should be used for management and impact assessment was to be based on testing and evaluation of output using an independent dataset of bear occurrence. Suitability Model: Biophysical-based I developed a biophysical-based grizzly bear habitat suitability model using existing 1:20,000 forest inventory, planimetric and terrain data. I have assumed that grizzly bear habitat suitability in the central Purcell Mountains is largely determined by the abundance and distribution of bear plant foods, and that habitat potential, or capability, is ultimately limited by terrain conditions. Thus, I applied a hierarchical modeling structure that considered habitat factors associated with slope condition, avalanche chutes, riparian habitats, and berry potential (Figure 2). I identified avalanche chutes using the 1:250,000 BTM land cover classification. I used the following 4 variables to define riparian habitat potential: seepage and floodplain potential as defined by slope condition, proximity to hydrographic features, overstory cover type, and forest structure. I also used 4 variables to define potential berry productivity: forest structure, cover type, canopy cover, and aspect. I derived model elements at a single, relatively fine spatial scale using a GIS moving window routine (Bian 1997). Moving window size was defined by a 1.7 km landscape radius, which is the average daily linear movement of female grizzly bears in the central Canadian Rocky Mountains (Gibeau 2000) and likely approximates the scale at which grizzly bears in the central Purcells make daily habitat choices. To derive each model element, I applied coefficients to variable classes and I combined variables using weighting factors. These values were assigned such that model output translated directly to the 6-point class system that has been applied in earlier grizzly bear habitat modeling and population estimation exercises (A. Hamilton unpublished report) (Table 2). The values reflected by each model element ranged from 0 to 1 and were applied as multipliers to step down a maximum rating that is assumed to equate to a density of 100 bears/1000 km 2. Habitat Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

11 suitability was defined as the weighted sum of the bear plant food factors, with a final stepdown to account for inherent capability as determined by slope conditions. Specific model parameters (Table 3) reflect the best available knowledge of grizzly bear ecology and habitat associations. Submodel Factor Variable Capable Habitat Enduring Features Slope Suitable Habitat Avalanche Chutes BTM Avalanche Chutes Slope Bear Plant Foods Riparian Habitats Hydrography Cover Type Overstory Structure Berry Production Overstory Structure Cover Type Canopy Cover Aspect Figure 2. Hierarchical structure for knowledge-based modeling of grizzly bear habitat suitability, using biophysical inventories, in the central Purcell Mountains, British Columbia. Table 2. Grizzly bear habitat suitability class rating system, and assumed translation to population density, independent of historic mortality (A. Hamilton unpublished report). Habitat Class Bears / 1000 km Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

12 Table 3. Parameters for modeling grizzly bear habitat suitability from existing biophysical inventories in the central Purcell Mountains, British Columbia. HABITAT SUBMODEL WeightFACTOR WeightVARIABLE WeightCLASS Coeff. Suitable Habitat Capable Habitat 0.5 Enduring Features 0.5 Slope 1.0 Flat <10% 1.0 Gentle 10-30% 1.0 Moderate 30-50% 1.0 Steep 50-80% 0.5 Very steep >80% 0 Bear Plant Foods 0.5 Avalanche Chutes 0.33 Avalanche Chutes 0.33 BTM Avalanche 1.0 Riparian 0.33 Slope 0.25 <2% % % % % 0.2 >10% 0 Hydrography Proximity 0.25 <50 m of feature m of feature m of feature m of feature 0.5 Cover Type 0.25 Spruce - fir 0.9 Cedar - hemlock 0.8 Douglas-fir 0.2 Lodgepole pine 0.4 Larch 0.2 Whitebark pine 0.1 Ponderosa pine 0.1 Deciduous overstory spp. 1.0 Non-forested vegetated 1.0 Non-forested unvegetated 0.3 Structure 0.25 Non-forested Barren 0.3 Non-forested Vegetated 1.0 forested yrs 0.5 forested yrs 0.5 forested yrs 0.5 forested yrs 0.6 forested yrs 0.8 forested >140 yrs 1.0 Table continues on next page. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

13 Table 3. Continued. HABITAT SUBMODEL WeightFACTOR WeightVARIABLE WeightCLASS Coeff. Berries 0.33 Structure 0.3 Non-forested Barren 0.1 Non-forested Vegetated 1.0 forested yrs 0.9 forested yrs 0.8 forested yrs 0.7 forested yrs 0.6 forested yrs 0.5 forested >140 yrs 0.6 Cover Type 0.2 Spruce - fir 0.8 Cedar - hemlock 0.6 Douglas-fir 0.6 Lodgepole pine 1.0 Larch 0.6 Whitebark pine 0.5 Ponderosa pine 0.3 Deciduous overstory spp. 0.8 Non-forested vegetated 0.9 Non-forested unvegetated 0.3 Canopy Cover % % % % % 0.2 Aspect 0.2 South 0.9 West 0.7 Green Vegetation Index-based Suitability Model Due to limitations of existing data sources in their ability to directly account for grizzly bear plant foods, I also modeled habitat suitability using an alternative approach. This used the Landsat green vegetation index (GVI) as a direct surrogate of habitat suitability based on bear plant foods (Mace et al. 1999, K Ache, USDA Forest Service, personal communication). GVI values were transformed such that all values reflecting unvegetated sites were forced to 0, and the lowest GVI value on vegetated sites was 1. This was then re-scaled to a 0 1 index. I then adjusted this index using a step-down multiplier for slope condition, such that the maximum possible suitability index was limited by terrain conditions and bear movement potential. Specifically, slopes of 50 80% were downgraded by 0.5, and slopes >80% were forced to an index of 0. As described for the biophysical-based model, the GVI-based model was derived at a spatial scale corresponding to the expected average daily foraging radius of a female grizzly bear. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

14 Model Evaluation I tested the output of each alternative model against grizzly bear habitat ratings derived from TEM data that exists for a portion of the study area. I employed a linear regression analysis to evaluate the correspondence between the two ratings systems. I then applied a logistic regression analysis (Hosmer and Lemeshow 1989) to evaluate the ability of each habitat suitability model to predict known grizzly bear occurrence within a sampling grid that was encompassed within the study area. Using the Strom et al. (1999) DNA hair-snag survey database, each detection of a different bear at each site and sampling session combination represented an independent occurrence. This, and non-detections of grizzly bears at respective site/session combinations, represented the binary dependent variable. I acknowledge that bear occurrence data derived from hair-snag methods are not appropriate for fine-scale habitat selection analyses (Apps et al. 2002). Hence, this analysis was conducted at the broader spatial scale that used the 1.7 km landscape radius, assumed to exceed the maximum attraction radius of hair-snag stations (Ibid.). Equating to a landscape area of 900 ha, this scale is also expected to approximate a core habitat area for a female grizzly bear in the central Rocky Mountains (Gibeau 2000). Analysis methods followed Apps et al. (2002). Results Model Performance Against TEM Habitat Ratings Results of regression analyses suggest that each model exhibits a notable correspondence with TEM grizzly bear habitat ratings available for a portion of the study area. The biophysicaland GVI-based models explained 24 and 37% of variation in TEM habitat ratings respectively (Figure 3). Model Performance Against Sampled Grizzly Bear Occurrence Results of the logistic regression analysis of each model s output against sampled grizzly bear occurrence suggested that both models carry significant predictive power (χ 2 > 9.94, 1df, P < 0.002). However, in all possible pairwise comparisons of bear-detections with non-detections, the c statistic (Norusis 1999) indicated that the biophysical- and GVI-based models correctly assigned a higher occurrence probability value to bear detections in 55 and 63% of cases respectively. This is strong evidence to suggest that the GVI-based suitability model performed better than the biophysical-based suitability model. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

15 A. B. Figure 3. Linear regression analysis results in evaluating the correspondence of grizzly bear habitat ratings derived from TEM mapping with (A) biophysical- and (B) green vegetation index- based habitat suitability models. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

16 Discussion and Recommendations Results of the comparison of model output against TEM habitat ratings suggests that the GVI-based model is more consistent with the TEM habitat rating system, which is based on landform/soils/vegetation units mapped at 1:50,000. However, because the TEM grizzly bear ratings are themselves subjective, the actual levels of explained variation (i.e., coefficient of determination) cannot be used to infer model performance. A better indicator of model performance comes from the ability of each model to predict grizzly bear occurrence. Results of this analysis confirm that each model is a useful predictor, but that the GVI-based model performs better. However, it must be acknowledged that actual grizzly bear occurrence will be influenced by human factors that, in conjunction with suitability, will determine habitat effectiveness. Because human disturbance and mortality risk factors may greatly affect grizzly bear persistence and distribution in the central Purcells, it cannot be expected that suitability models alone will explain most of the variation in grizzly bear detection rates. That said, the GVI-based model in particular appeared to perform quite well. Given this result, and the higher correspondence of this model with the TEM habitat ratings, I recommend that the GVI-based model be adopted as the habitat suitability value input to management applications and cumulative effects analyses within the study area. It should be remembered that the habitat suitability model developed here is specific to a relatively fine spatial scale (i.e., expected daily foraging radius of a female grizzly bear). However, grizzly bears respond to some habitat factors and several human factors are broader spatial scales (i.e., home range), and it appears that these broader-scale effects are important in explaining grizzly bear population distribution (Apps et al. 2002). Therefore, application of the suitability model in cumulative effects analyses should consider both a fine scale (i.e., within home range ) and a broad scale (i.e., local population). Digital Products The output of the GVI-based habitat suitability model is a relative index of habitat quality and has not been calibrated to grizzly bear density. Within the study area, I assumed that the highest pixel value generated by the model equates to an optimal composition of bear plant foods in association with appropriate terrain conditions. Using this value, I thus re-scaled the model to a 0 1 index to facilitate management application. I then grouped values into 4 habitat suitability classes (Table 4). Classified model output is illustrated in Figure 4. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

17 Table 4. Index values and associated classes reflecting grizzly bear habitat suitability in the central Purcell Mountains, British Columbia. Index Range Class Suitability Descriptor < None Low Moderate High The following digital products are provided with this report (UTM, Zone 11, NAD83): raster output (ArcInfo binary raster exchange format): griz-suitability_central-purcells_index_scaled - raw (0 1) suitability index values scaled to daily foraging radius griz-suitability_central-purcells_index_non-scaled - raw (0 1) suitability index values at the original pixel level (i.e., no scaling) vector output (Arcview shapefile format) griz-suitability_central-purcells_class_scaled - Vector polygons of reclassified habitat values using the 4-point rating system (see Table 4). Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

18 Figure 4. Grizzly bear habitat suitability class in the central Purcell Mountains, British Columbia. Suitability here reflects current landscape potential to support grizzly bears given existing vegetative conditions, and does not account for human influence. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

19 Literature Cited Apps, C. D., and A. N. Hamilton Grizzly bear habitat effectiveness and connectivity in southwestern British Columbia. Aspen Wildlife Research and Ministry of Water, Land and Air Protection, Victoria, British Columbia. Apps, C. D., B. N. McLellan, J. G. Woods, and M. F. Proctor Estimating grizzly bear distribution and abundance relative to habitat and human influence. Aspen Widlife Research, Calgary, Alberta, and Research Branch, Ministry of Forests, Revelstoke, BC. Bian, L Multiscale nature of spatial data in scaling up environmental models. Pages in D. A. Quattrochi and M. F. Goodchild, editors. Scale in remote sensing and GIS. Lewis Publishers, New York, New York, USA. Boulanger, J., and C. D. Apps Development of quantitative tools to predict and monitor grizzly bear population response to landscape change. Integrated Ecological Research, Nelson, British Columbia, and Aspen Wildlife Research, Calgary, Alberta. Crist, E.P., and R. C. Cicone Application of the tasseled cap concept to simulated thematic mapper data. Photogrammetric Engineering and Remote Sensing 50: Geographic Data BC Gridded DEM specification, release 1.1. Ministry of Environment, Lands and Parks, Victoria, British Columbia, Canada. Gibeau, M. L A conservation biology approach to management of grizzly bears in Banff National Park, Alberta. Ph.D. Dissertation. Resources and the Environment Program, University of Calgary, Calgary, Alberta, Canada. Hosmer, D. W., and S. Lemeshow Applied logistic regression. John Wiley and Sons, New York, New York, USA. Land Use Coordination Office Integrated land use planning for public lands in British Columbia. Government of British Columbia, Victoria, Canada. Mace, R. D., J. S. Waller, T. L. Manley, K. Ake, and W. T. Wittinger Landscape evaluation of grizzly bear habitat in western Montana. Conservation Biology 13: Meidinger, D. V., and J. Pojar Ecosystems of British Columbia. British Columbia Ministry of Forests Special Report Series 4. Norusis, M. J SPSS regression models SPSS Inc., Chicago, Illinois, USA. Pellegrini, G. J Terrain shape classification of Digital Elevation Models using eigenvectors and Fourier transforms. Dissertation. New York State University, New York, New York, USA. Resources Inventory Branch Relational data dictionary (RDD) 2.0. British Columbia Ministry of Forests, Victoria, British Columbia, Canada. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July

20 Strom, K., M. Proctor, and J. Boulanger Grizzly bear population survey in the central Purcell Mountains, British Columbia. Prep. for BC Environmental Assessment Office and Glacier Resorts Ltd. AXYS Environmental Consulting Ltd., Calgary, Alberta. Surveys and Resource Mapping Branch Digital baseline mapping at 1:20,000. British Columbia specifications and guidelines for geomatics, content series volume 3, release 2.0. British Columbia Ministry of Environment, Lands and Parks, Victoria, British Columbia, Canada. Surveys and Resource Mapping Branch Baseline thematic mapping present land use mapping at 1:250,000. British Columbia specifications and guidelines for geomatics, content series volume 6, part 1, release 1.0. British Columbia Ministry of Environment, Lands and Parks, Victoria, British Columbia, Canada. Grizzly Bear Habitat Suitability in the Central Purcell Mountains C.D. Apps July