UNIVERSITY OF CALGARY. Changes in grizzly bear habitat due to human disturbance in the Rocky Mountain. Foothills of Alberta from 1985 to 2005

Transcription

1 UNIVERSITY OF CALGARY Changes in grizzly bear habitat due to human disturbance in the Rocky Mountain Foothills of Alberta from 1985 to 2005 by Andrea E. Ram A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF GEOGRAPHIC INFORMATION SYSTEMS DEPARTMENT OF GEOGRAPHY CALGARY, ALBERTA FEBRUARY, 2011 Andrea Ram 2011

2 Abstract 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 based on humancaused disturbances in west-central Alberta from 1985 to 2005 using imagery from the Landsat archive. Habitat quality was measured by combining occurrence and risk models to determine indices of attractive-sinks (AS) and safe-harbor (SH) habitats, as well as five habitat-states. Results indicated that the proportion of secondary habitats declined by 17% over the entire study area while primary and secondary sinks increased (104% and 203%, respectively). Mean AS values increased by 61.3% and mean SH values decreased by 3.4% across the monitoring horizon. An analysis of watersheds within the study area was also provided, indicating the most relevant watersheds for protection and mitigation measures within west-central Alberta. The watersheds in most need of mitigation measures were Upper Erith River (UER), McLeod River 4 (MCL4), Pembina River (PEM), Gregg River (GRE), Upper Pembina River (UPE), and Upper McLeod River (UMC). The watersheds in most need of protection measures were Chungo Creek (CHU), Blackstone Creek (BLA), Cardinal River (CAR), and Upper McLeod River (UMC). Understanding how human-caused disturbances have changed the landscape over a wide time span and how this has affected habitat quality will aid in conservation management of grizzly bears. ii

3 Table of Contents Abstract... ii Table of Contents... iii List of Tables... v List of Figures... vi 1.0 INTRODUCTION METHODS Study Area and Data Description Data Sources Disturbance Inventory Disturbance Dating: Change-detection (Figure 2A) Disturbance Inventory Conditioning (Figure 2B) Backdating and Updating of 1998 Reference Land-cover Map Backdating and Updating of 2003 Crown Closure (CC) and Species Composition (SC) Maps Two-Dimensional Habitat Modeling Modeling risk of human-caused grizzly bear mortality (risk model) Modeling the relative probability of adult female grizzly bear occupancy (RSF model) Safe-harbor and attractive-sink indices Five-state Habitats RESULTS AND DISCUSSION Areas that had the greatest- and least-amount of change in habitat quality from 1985 to Attractive-sink (AS) Safe-harbor (SH) Five-state Habitats Temporal Analysis iii

4 3.2.1 Attractive-sink (AS) and Safe-harbor (SH) Indices Five-state Habitats Disturbance Features Watersheds with the greatest conservation concern in Watersheds with the highest proportion of source habitats (and low sink and non-critical habitats) Watersheds with the highest percentage of primary sink (PS) and secondary sink (SS) habitats Watersheds with the least variation between sink (PS and SS) and source habitats (PH and SH) with less than 25% non-critical habitats Watersheds that showed the greatest decline in habitat quality from 1985 to Over-estimated or under-estimated mortality risk and/or RSF models The influence of mortality risk (R) and habitat-occurrence (RSF) models on habitatstates and conditions Conservation Measures CONCLUSION References APPENDIX A: Remote Sensing and Change-detection for Wildlife Habitat Monitoring iv

5 List of Tables Table 1: Remote Sensing Imagery used for disturbance feature detection with time assignments in reference to the methodological framework for generating the disturbance inventory (Linke et al., 2009a). All scenes were pre-processed: orthorectified to reference map (1998), converted to top of atmosphere (TOA) reflectance, normalized to facilitate scene-to-scene comparison, and masked for clouds Table 2: Feature type, disturbance type, overlay order (i.e. order 1 refers to the bottom layer), and land-cover decision rules for disturbance features mapped in the study area. T<D refers to years before the disturbance occurred, T D refers to the year of origin, T D+1 T D+2 refers to years one and two after the disturbance occurred, and T >D+2 refers to years three and beyond Table 3: Best fitting models for crown closure (CC) and species composition (SC) Table 4: RSF model coefficients for adult female GBs in hyperphagia stage; GC refers to the Grandecache GB population unit in the north and composes 16% of the study area; YH refers to the Yellowhead GB population unit in the middle and composes 78% of the total area; CW refers to the Clearwater GB population unit in the south and composes 6% of the study area. All three model coefficients were used to calculate RSF for the entire study area Table 5: Characteristics (mean, standard deviation (SD), and category) of attractive-sink (AS) and safeharbor (SH) indices for 30 watersheds within the study area Table 6: Percent composition of attractive-sink (AS) and safe-harbor (SH) categories including very low (0-20), low (21-40), mid (41-60), high (61-80) and very high (81-100). Very low AS habitat dominated the study area and decreased from 1985 to 2005; while low, mid, high and very high AS habitat increased through time. Very low and very high SH habitat fluctuated from 1985 to 2005 but remained relatively constant; low SH habitat increased slightly; while mid and high AS habitat gradually decreased through time Table 7: Mean and standard deviation summaries of AS and SH habitat across the study area. Average AS values increase by 61.3% across 20 years; the greatest increase occurred between 1995 and Average SH values decreased insignificantly by 3.4%; the greatest decrease occurred between 1990 and Table 8: Percent composition of five habitat-states (non-critical (NC), secondary sink (SS), primary sink (PS), secondary habitat (SH), and primary habitat (PH)) for 30 watersheds within the study area Table 9: Watersheds in 2005 with: (left) greater than 25% primary habitat (PH) and secondary habitat (SH); and (right) greater than 25% primary habitat (PH) and secondary habitat (SH) v

6 List of Figures Figure 1: Study area map depicting town-sites, Jasper National Park (NP) protected area, agriculture, higher elevation areas (>1900 m), and delineated watersheds based on the size of the average female grizzly-bear home range (~700 km2). The area is also subject to oil and gas exploration, forestry, mining, and road development. The map insert on the top right illustrates the study area (24,000-km 2 ) within Alberta, Canada Figure 2: Flowchart of methodology used to develop the disturbance inventory including: A) dating of disturbance objects; and B) disturbance inventory conditioning. ODI refers to the original disturbance inventory (annual disturbances from 1998 to 2005); T<D refers to the time period before the disturbance originated; T>D refers to the time period after the disturbance Figure 3: Index of attractive-sink and safe-harbor habitats for adult female GBs during late hyperphagia. High to very high attractive-sink values represent those habitats where animals are both likely to occur and at high risk of mortality, while high to very high safe-harbor values represent those habitats where animals are likely to occur and are at low risk of mortality Figure 4: Large scale inspection of areas that exhibited the greatest and least amount of change in attractive-sink (AS) habitat from a) 1985 to b) 2005; AS habitat changed from low and very low to high and very high values between the town-sites of Robb, Cadomin and Hinton; AS habitat remained high in the 3 hotspots southeast of Cadomin and along mountainous streams Figure 5: Comparison of proportion of upland tree (p_uptree) and the combined variable ((13.272*p_uptree)-15.4*(p_uptree)). The combined variable was at its highest value where the proportion of upland tree was at its mid-range, and at its lowest where the proportion of upland tree was at its maximum Figure 6: Attractive-sink Index for each watershed within the study area for: a) 1985 and b) Full descriptions of abbreviated watershed names can be found in Table 5. UMC watershed had the highest attractive-sink value in 1985 but remained stable from 1985 to 2005; UER and MCL4 had the highest mean value in EMB, MBR, NOS, and UER showed the greatest increase in AS values from 1985 to Figure 7: Large scale inspection of areas that exhibited the greatest and least amount of change in safe-harbor habitat from a) 1985 to b) 2005; SH habitat changed from high and very high to low and very low values between the town-sites of Robb, Cadomin and Hinton; SH habitat remained high in lower valley mountainous networks at a distance from streams (i.e. >100 m) as well as along a thin strip southeast of Cadomin Figure 8: Safe-harbor Index for each watershed within the study area for: a) 1985 and b) Full descriptions of abbreviated watershed names can be found in Table 5. UMC watershed had the highest SH value in 1985 but remained stable from 1985 to 2005; UMC and BLA had the highest mean value in UER, EMB, WOL, and ERI watersheds showed the greatest decrease in SH values; while UNO and TRO watersheds had the greatest increase in SH values from 1985 to Figure 9: Predicted habitat-states for west-central Alberta based on a two-dimensional classification of habitat occupancy (RSF) and mortality risk (R) estimates vi

7 Figure 10: Large scale inspection of areas that exhibited the greatest and least amount of change in the five habitat states from a) 1985 to b) 2005; habitat-states changed from primary and secondary habitat and non-critical habitat to primary and secondary sink habitat between the town-sites of Robb, Cadomin and Hinton; the strip of area southeast of Cadomin exhibited partially contiguous primary sink habitat (i.e. 3 sink hotspots) adjacent to partially contiguous primary and secondary habitat in both 1985 and 2005 maps Figure 11: Percent composition of five habitat categories across the study area including: (1) noncritical (low RSF); (2) secondary sink (moderate RSF and high risk); (3) primary sink (high RSF and high risk); (4) secondary habitat (low risk and moderate RSF); and (5) primary habitat (low risk and high RSF). The landscape experienced the greatest change between 1995 and 2000 with increases in primary sink and secondary sink habitats, while secondary habitat decreased and primary habitat fluctuated but, cumulatively (over 20 years), remained constant Figure 12: The change in densities (ha/km 2 ) of three different disturbance types from 1985 to Areal features (cut blocks, burns, mines) experienced the greatest increase from 0.17 to 6.25 (ha/km 2 ); followed by linear features (road, pipeline, power line, railway; 2.62 to 5.03 ha/km 2 ), and well sites (from 0.11 to 0.41 ha/km 2 ). The greatest increase in densities for all disturbance types occurred between 1995 and Figure 13: Watersheds with the least variation between the sink habitat categories (i.e. primary sink (PS) and secondary sink (SS)), and the source habitat categories Figure 14: Maps of habitat-states evaluated across watersheds for a) 1985 and b) CHU had the highest proportion of source habitat (primary and secondary habitat), as well as the lowest proportion of sink habitats (primary and secondary sinks) and non-critical habitat combined. UER watershed had the highest proportion of sink habitat, while UPE had the most similar proportions of sink and source habitat. UER also had the greatest decline in amount of source habitats and the greatest increase in amount of sink habitats from 1985 to Figure 15: Watersheds with the greatest incline in sink habitats (primary (PS) and secondary sinks (SS)) associated with the greatest decline in source habitats (primary (PH) and secondary habitat (SH)) from 1985 to Figure 16: Predicted risk of human-cause grizzly-bear mortality in 1985 (A) and 2005 (B) in west-central Alberta, Canada; Predicted relative probability of occurrence (RSF) in 1985 (C) and 2005 (D) for adult female GBs during late hyperphagia (16 August to 15 October) Figure 17: Large scale inspection of an RSF map for 2000 and 2005, indicating higher RSF values along the edge of a cut block (especially newer ones) than in the center of a cut block (especially larger ones) vii

8 1 1.0 INTRODUCTION In the Rocky Mountain ecosystems of North America, industrial resource extraction activities such as road development associated with oil and gas exploration, mining, and forestry, threaten the persistence of grizzly bear (Ursus arctos) populations (Banci et al. 1994; McLellan 1998). In Alberta, grizzly bears (GBs) are currently designated as threatened under Alberta s Wildlife Act with an estimated 691 individuals (including just 359 mature, reproducing females) remaining within the province (ASRD and ACA 2010). For GBs, it is accepted that population growth is primarily dependent on female survival (Boyce et al. 2001; Mattson et al. 1996; Wiegand et al. 1998). In addition, most GB mortalities are human-caused (Benn and Herrero 2002; McLellan et al. 1999) and related to human access features on the landscape (Nielsen et al. 2004a). GB conservation is an important initiative, because the species demonstrates low reproductive rates and densities, requires large tracts of land (ASRD and ACA 2010), and are one of the first species to be lost from an area as a result of land-development activities (Gibeau et al. 2001). Furthermore, GBs are considered a focal species for conservation efforts, since they are an umbrella species and represent an important indicator of ecosystem health (Carroll et al. 2001; Noss et al and 1996). In order to protect remaining GB populations, ecosystem scientists and wildlifeconservation managers require an understanding of how GBs use habitat across space and time, and how habitat use changes spatially and temporally in response to anthropogenic landscape disturbances. As a result, habitat monitoring and landscapechange analysis have become important fields in conservation biology. Ultimately, understanding large-scale patterns and temporal changes to rare threatened or endangered species helps focus conservation needs (Dobson et al. 1996; Mattson and Merrill 2002). Multi-temporal analysis of remotely sensed data has been used to determine the impact of landscape change on wildlife habitat (Berland et al. 2008; Pearson et al. 1999; Reyes

9 2 et al. 2000). Satellite remote sensing is considered a superior technology for monitoring landscape change when compared to traditional field data or aerial-photo interpretation, because it has the ability to cover vast areas across broad time scales in a cost-effective manner (Coppin et al. 2004; McDermid et al. 2005). Remotely sensed data often represents the only way to obtain a multi-temporal data set for some monitoring applications, especially in remote areas which are logistically difficult to access. Change-detection can be accomplished using a wide variety of methods; the selection of which depends on the objective of the analysis and the nature of the data (Coppin et al. 2004). Although change-detection is a common and effective way to assess habitat and monitor habitat change (see Appendix A for a comprehensive review on this subject), there are numerous challenges associated with it. For example, the most common change-detection approach to assess the impact of landscape change on wildlife habitat is post-classification comparison. This technique is attractive because the methods are widely known and the output is highly descriptive and simple to grasp (McDermid et al. 2005). However, the results are often hampered by differential errors in independently classified map products that can propagate through the analysis and seriously distort our measurement of change (Brown et al. 2000, Langford et al. 2006, Shao and Wu 2008, Linke et al. 2009a, Linke et al. 2009b). While careful pre-processing techniques and advanced statistical corrections can be used to reduce these errors, they are very difficult to remove completely from the analysis. In an attempt to improve our handling of multi-temporal image data in operational monitoring applications, Linke et al. (2009a) developed and implemented a semiautomated methodological framework that greatly reduces the problem of propagating errors in change analysis. The strategy involves backdating and updating a reference base map (e.g. land-cover) in the areas of identified change, which are distinguished through bi-temporal change-detection and overlaid systematically onto the base map.

10 3 The methods include accommodations for removing spurious change objects (i.e. slivers) resulting from mis-matches between the boundaries of change features distinguished using change-detection and shared boundaries in the existing reference map. The final result is a multi-temporal series of continuous and/or discrete land-cover maps that establishes the basis for reliable landscape monitoring. Linke et al. (2009a) demonstrated the effectiveness of these methods through an eight-year monitoring application (1998 to 2005) over a large, multi-use study area in west-central Alberta, Canada using Landsat imagery. An effective strategy for use with change-detection involves the Tasseled Cap (TC) transformation of Crist and Cicone (1984). Franklin et al. (2001) applied a simple differencing and thresholding approach called the Enhanced Wetness Difference Index (EWDI) to detect forest change across multi-temporal images in New Brunswick. The approach has become common within the remote-sensing literature, and is considered an effective method for detecting forest changes over vast areas and broad time scales (Franklin et al. 2002; Skakun et al. 2003; Healy et al. 2005; Jin and Sader 2005). Although manually selected thresholds such as those employed in the EWDI approach are subjective and require a significant time commitment and advanced skill, they are still frequently used. Automated methods are limited in availability and have been criticized for their potential to produce inaccurate change results. However, a recent automated object-based method has been developed by Castilla et al. (2009) for image differencing and thresholding called the Land-cover Change Mapper (LCM) tool. This tool overcomes many of the issues associated with manual methods of image differencing in that it is unsupervised, fast, and robust to both mis-registration errors and variations in illumination conditions (Castilla et al. 2009). The LCM was designed to rapidly generate a polygon-vector layer of areas that have undergone significant change in land cover based on two co-registered images of the same scene acquired at different dates (Castilla et al. 2009). Among the outputs from the LCM tool is a difference image where spurious changes due to mis-registration are removed. The difference image can

11 4 then be used in subsequent trials by manually selecting lower and upper thresholds in order to improve the result and eliminate false positives. At a time when large-area and long-term change monitoring is on the rise, novel methods are required to ease the burdens of traditional methods. When used together, the approach presented by Linke et al. (2009a) and the methods described by Castilla et al. (2009) efficiently reduce many of the issues associated with traditional remote-sensing change-detection methods. Numerous studies have modeled GB occurrence across local, regional, and national scales through the integration of landscape-scale environmental variables such as landcover or vegetation type (Apps et al. 2004; Mace et al. 1996; McLellan and Hovey 2001), topography (Apps et al. 2004; Naves et al. 2003; Nielsen et al. 2002), distance-tostreams and forest edge (Nielsen et al. 2002; Theberge 2002), vegetation indices (Apps et al. 2004; Mace et al. 1999), forest structure (e.g. crown closure, leaf area index, age) (Apps et al. 2004; Nielsen 2005), protected areas (Apps et al. 2004), human disturbance (e.g. linear features, cut blocks) (Apps et al. 2004; Nielsen 2005), and human access (Apps et al. 2004). Others have been specifically interested in vegetation disturbances caused by naturally occurring factors such as insect defoliation, forest fires, and flooding, as well as human-caused changes such as resource exploitation and road development (Apps et al. 2004; Coppin et al. 2004; Linke et al. 2005; Nielsen 2005). Optimal GB habitat has generally been considered road-less areas with a mosaic of early succession forests and natural openings in proximity to secure forest stands (Hamer and Herrero 1987). Considering this definition, landscape disturbances play an important role in GB habitat quality. Several studies have explored the influence of disturbed habitats on GB use (McLellan 1998; Banci et al. 1994) and have found that mortality risk increases substantially in disturbed areas due to a higher likelihood of conflict between bears and humans (Schwartz et al. 2003); therefore GBs will tend to avoid certain disturbed areas (Zager et al. 1983). At the same time, depending on landscape and temporal contexts, certain forest disturbances (e.g. cut blocks) in otherwise closed

12 5 forests may be of benefit to bears as they offer specific forage niches during certain seasons (e.g. Nielsen et al. 2004b). Unfortunately, disturbed habitats can become an attractive sink whereby the GB is attracted to the area due to the high forage opportunity and is therefore susceptible to increased mortality risk. This is the premise on which habitat security models have been formulated, which incorporate both selection and risk to explain GB habitat (e.g. Naves et al. 2003, Nielsen et al. 2006). The common model approach simply attempts to correlate animal occurrence with a suite of landscape-scale variables in order to map the one-dimensional function across space (e.g. Mace et al. 1998). In contrast, this multi-dimensional approach incorporates the biological requirements of the species with demographic features, such as occurrence and mortality (e.g. Hall et al. 1997, Naves et al. 2003). In this research, my purpose was to assess the impact of resource development in westcentral Alberta s GB range from 1985 to 2005 (at five-year intervals) with the goal of quantifying the changes in grizzly-bear habitat. The change in landscape conditions as a result of resource development was determined using the methodological framework proposed by Linke et al. (2009a). A two-dimensional habitat model developed by Nielsen (2005) and Nielsen et al. (2004a) was applied to quantify the changes in GB mortality risk and female GB occurrence during the hyperphagia season (16 August to 15 October). Habitat-states and conditions, defined by Nielsen et al. (2006), were derived from their habitat model to measure changes in habitat quality in my study area across the monitoring horizon. Habitat-states were defined as (i) non-critical habitats (low-quality irrespective of risk), (ii) secondary habitats (moderate-quality and secure), (iii) primary habitats (high-quality and secure), (iv) secondary sinks (moderate-quality, but high risk), and (v) primary sinks (high-quality but high risk). Habitat conditions were further defined using safe-harbor (i.e. source-like areas) and attractive-sink (i.e. ecological traps) indices.

13 6 My specific research questions included determining: (1) What areas and watersheds showed the greatest- and least-amount of change in habitat quality from 1985 to 2005? (2) Which five-year time period showed the greatest change in habitat quality across the 20-year monitoring horizon? (3) Which watersheds had the greatest conservation concern as of 2005? In this study, I defined greatest conservation concern as: (i) watersheds that indicated a high proportion of primary and secondary habitat-states (and correspondingly low amounts of sink and non-critical habitats) in 2005; (ii) watersheds that demonstrated a high proportion of primary and secondary sink values in 2005; (iii) watersheds composed of less than 25% non-critical habitat that demonstrated both a high proportion of source habitat as well as a high proportion of sink habitat in 2005; and (iv) watersheds that showed the greatest decline in source habitat and the greatest incline in sink habitats from 1985 to Where Nielsen et al. (2006) estimated habitat-states and conditions for one point in time, this study differs in that it incorporates a temporal dimension spanning across 20 years. In a province that has a high rate of human-caused disturbances, this study can provide valuable insight into the conservation of grizzlybear habitat. The findings can provide a direction for management and contribute to the long-term viability of this umbrella species.

14 7 2.0 METHODS 2.1 Study Area and Data Description The study area for this research was located in west-central Alberta, Canada, along the eastern slope of the Rocky Mountains (Figure 1). The 24,000 km 2 area encompasses a diverse landscape with protected mountains in the southwest within Jasper National Park, and resource-utilized foothills in the east. Elevations range from 772-m in the northeast and 3499-m in the southwest. The foothills are subject to various natural and human disturbances such as forestry, oil and gas development, agriculture, urban development, open-pit coal mining, road construction, forest fires, and insect defoliation. Highway 16 connects Edson and Hinton town-sites in the north part of the study area. Thirty watersheds were delineated across the study area with the approximate size of a female grizzly-bear home range. Based on grizzly bear DNA surveys conducted in 2004, the population of GBs in the area occurs at densities of approximately 4.8 bears/1000-km 2 (Foothills Research Institute Grizzly Bear Project (FRIGBRP) unpublished data 2004).

15 8 Figure 1: Study area map depicting town-sites, Jasper National Park (NP) protected area, agriculture, higher elevation areas (>1900 m), and delineated watersheds based on the size of the average female grizzly-bear home range (~700 km2). The area is also subject to oil and gas exploration, forestry, mining, and road development. The map insert on the top right illustrates the study area (24,000-km 2 ) within Alberta, Canada. The study area is within the Foothills Research Institute Grizzly Bear Research Program (FRIGBRP) region of interest. The FRIGBRP was established in 1999 to provide knowledge and planning tools to land and resource managers to ensure the long-term conservation of GBs in Alberta (FRI 2009). The program takes a large-scale or "landscape level" approach towards GB conservation in which research focuses on habitat and landscape features linked to GB presence, persistence, and health. The results of the current study apply directly to the objectives of FRIGBRP. Existing reference maps that span across the study area, described by McDermid (2005), included a land-cover map (ten classes; 91.8 percent accuracy), and continuous-variable representations of crown closure (90 percent accuracy for two-class configuration) and tree-species composition (90 percent accuracy for two-class configuration). The layers were derived from Landsat imagery, and represented 2003 ground conditions.

16 9 In addition to the 2003 reference maps, a reference disturbance inventory was created in 2009 covering the study area based on a methodological framework described in Linke et al. (2009a). For the purpose of this paper, the inventory will be referred to as ODI (original disturbance inventory). The ODI included all disturbances that occurred on the landscape prior to 1998 (but still discernable in 1998 satellite imagery), and all that occurred annually between 1998 and Disturbance types included cut blocks, well sites, roads, pipelines, power lines, railways, mines, and burns. Annual land-cover maps were also created from the ODI dating from 1998 to For the purposes of this analysis, the ODI was explicitly built-upon in order to extend the time series backwards in five-year intervals to Various layers relevant to the habitat-states modeling were also provided for this analysis (FRI 2009). These included: distance-to-stream, cost surface based on terrainruggedness, adult female grizzly bear range scalar (the probability of female occupancy ranging between 0 and 1), proportion of protected areas (within a 10-km radius), and proportion of agriculture or freehold land (within a 10-km radius). The adult female grizzly bear range scalar is a sub-model developed to reduce overall habitat values in extrapolated areas of the population unit where female bears were predicted to be absent due to displacement from human activities and relatively poor habitat. It accounts for the decreased occupancy of lower-elevation habitat despite greater resource availability. Readers interested in further information about this sub-model should refer to Nielsen et al. (2009). 2.2 Data Sources Data was acquired from various sources to facilitate this study. The study area is covered by Landsat path and row 44/23, and satellite imagery was acquired for 1985, 1990, 1995, and 1998 (Table 1). Satellite imagery for 2000 and 2005 was already acquired through the research by Linke et al. (2009a). The images primarily consisted of Landsat-5 Thematic Mapper (TM) scenes, as well as one Landsat-7 Enhanced Thematic

17 10 Mapper Plus (ETM+) scene for the year Two additional Landsat-5 TM images (path and row 44/22 and 45/23) were acquired to facilitate information-extraction in clouded areas for the beginning of the time series (1985 image, path and row 44/23). All scenes were converted to at-satellite reflectance based on the methods described by Chander and Markham (2003), and relative atmospheric correction was performed as described by Hall et al. (1991) to improve the radiometric consistency between scenes. Each scene was also orthorectified using PCI Orthoengine to match the reference year 1998 and projected to NAD 1983, UTM Zone 11N. The year 1998 was considered the reference year (T 0 ) from which all updating and backdating occurred, while 1985 was labeled T -13 and 2005 was T +7 (Table 1). Table 1: Remote Sensing Imagery used for disturbance feature detection with time assignments in reference to the methodological framework for generating the disturbance inventory (Linke et al., 2009a). All scenes were pre-processed: orthorectified to reference map (1998), converted to top of atmosphere (TOA) reflectance, normalized to facilitate scene-to-scene comparison, and masked for clouds. Landsat Path and Row Image Acquisition Date Sensor Time Assignment (Disturbance Inventory Framework) 44/22 06 June 1985 Landsat-5 TM T /23 16 August 1985 Landsat-5 TM T September 1985 Landsat-5 TM T April 1990 Landsat-5 TM T -8 44/23 24 October 1995 Landsat-5 TM T August 1998 Landsat-5 TM T 0 Landsat Scenes used to generate original disturbance inventory 44/ Landsat-7 ETM+ T Landsat-5 TM T +7 In addition to satellite data, GIS data was acquired to aid in disturbance classification. These included: (1) Alberta (AB) Government 30-m Digital Elevation Model; (2) AB Government road layer; (3) forest fire and cut block database (FRI 2009); (4) Canadian Government watersheds (NRC 2010); and (5) agriculture and settlement masks (FRI 2009). The masks were used to exclude changes occurring within agriculture and settlement zones, which occur along the study area s eastern boundary.

18 Disturbance Inventory The methods used to create temporally and categorically dynamic land-cover map products of the study area followed the disturbance-inventory approach to landscape monitoring described fully in Linke et al. (2009a). The paper represents an exhaustive description of the methods used in this analysis, which are briefly described in this document. The assumption made in order to proceed with the disturbance inventory methodology was that all disturbance objects in the ODI as well as the reference 1998 land-cover map were accurate. Linke et al. (2009a) reported thematic accuracy on change detectability (overall accuracy of 100 percent and a Kappa coefficient of 1.0), disturbance type classification (overall accuracy of 98 percent and a Kappa agreement of 0.97) and land-cover classification (overall accuracy of 80 percent and a Kappa agreement of 0.64). Overall, the accuracy assessment on the ODI produced very good results and it would be expected that this analysis would produce similar accuracy results. Eight types of forest-replacing disturbances were investigated: forest-fire burns, cut blocks, coal and gravel mines, well sites, pipelines, power lines, railway, and mechanized roads. The disturbances were categorized as follows: cut blocks, burns, and mines were considered areal features; well sites were point features; and roads, railways, pipelines, and power lines were linear features. An overview of the methods used to generate the disturbance inventory is outlined in Figure 2. Methods were divided into two parts: (A) dating of the disturbance objects; and (B) conditioning of the disturbance inventory.

19 12 Figure 2: Flowchart of methodology used to develop the disturbance inventory including: A) dating of disturbance objects; and B) disturbance inventory conditioning. ODI refers to the original disturbance inventory (annual disturbances from 1998 to 2005); T<D refers to the time period before the disturbance originated; T>D refers to the time period after the disturbance. Areal Features Disturbance Dating: Change-detection (Figure 2A) In order to detect the appearance of areal features on the landscape between each time-interval in the series, the tasseled-cap wetness layer was derived for use with the Land-cover Change Mapper (LCM) tool (Figure 2, steps 1 and 2a). For a complete description of the LCM methods used to generate the change-detection, interested readers are referred to Castilla et al. (2009). For the purposes of this analysis, the wetness layer was derived for each of the years in the time series: 1985, 1990, 1995, 2000, and The wetness index has shown to effectively detect forest disturbances and has been successfully employed in previous studies (Healy et al. 2005, Jin and Sader 2005). Coefficients from Huang et al. (no date) were used to calculate wetness on 32-bit

20 13 rasters, using top-of-atmosphere (TOA) reflectance values. Resulting wetness values were then re-scaled to 8-bit data for ease of manipulation. The resulting 8-bit rasters of the study area (24,000 km 2 ) were too computationally expensive for the LCM tool to process; therefore, the area for each year was tiled into four parts using PCI Geomatica. LCM was then independently applied: first, to the initial and final images in the series to obtain the cumulative change-detection objects; second, to 12 pairs of temporally consecutive images (e.g and 1990, 1990 and 1995, as well as 1995 and 1998) to obtain bi-temporal change-detection objects. The change of interest involved a decrease of brightness in the final state image. The kernel size used to compute the difference images was 3 x 3 pixels, which proved sufficient due to low mis-registration errors between years. The input variables included the minimum size (MMU) of: (1) change regions (hectares); and (2) remnants within change regions (hectares). Values varied depending on the pair of images. Additional parameters required by the LCM tool included the upper and lower change thresholds. The first time the tool was used on a pair of images, change thresholds were set at automatic. If there were false positives, subsequent trials were conducted by viewing the difference image produced by the tool and selecting thresholds manually until the desired outcome was achieved; values varied depending on the pair of images used. The results were manually inspected for errors of commission or omission. The tiled results of each year were merged together in ArcGIS 9.3. The LCM tool produced smooth polygon edges; however, it was important to match to the boundaries of objects found in the 1998 reference map (T 0 ); therefore, spatial-conversion operations and snap-to-raster functions were performed to create jagged edges that matched the reference objects. The LCM provided results for disturbance mapping over three, five-year intervals, including 1985 to 1990, 1990 to 1995, and 1995 to 1998; however, pre-existing disturbance features (i.e., those occurring prior to 1985 but still visible in the imagery)

21 14 were also required for the analysis. Therefore, the change features existing prior to 1998 from the original disturbance inventory (ODI) were selected; the disturbance features from the new disturbance inventory (NDI; i.e ) were subtracted from the original; and the result was all change features existing prior to 1985 (Figure 2, step 3). Some of the objects that were static (had not changed) from 1998 to 2005 in the ODI, were found to have changed between 1985 and These objects were termed reference (static) objects in Linke et al. (2009a), but were considered dynamic for the purposes of this analysis. Linear and Point Features Although linear and point features were visually detectable from the imagery, they could not be reliably mapped using automated detection methods. The spatial resolution of the Landsat imagery used in this study was too coarse to produce accurate object delineations. Therefore, all linear and point features existing prior to 1998 in the ODI were used as the disturbance objects in the NDI (Figure 2, step 3). Well sites were represented as 3 x 3 pixel squares (90 x 90 m) centered on each well site location; roads, pipelines, power lines, and rail lines were delineated as two-pixel- (60 m) wide polygons. The RGB imagery and wetness indices from 1985, 1990, 1995 and 1998 were manually inspected to establish annual dates-of-occurrence for linear and point objects (Figure 2, step 2b). Objects that appeared in the 1985 imagery were considered pre-1985 features for the purpose of this study. Some objects appeared on the landscape in two different years (e.g. part of a pipeline appeared in 1985, and the other part in 1990); these features were split in two parts and given appropriate dates of origin.

22 15 Gaps in areal features As explained by Linke et al. (2009 a and b), when the ODI was used to update and backdate the reference map, the disturbance features can sometimes display spurious overshoots and undershoots with the boundaries of: (1) coinciding (originating before T 0 ) and adjacent (originating after T 0 ) static features in the reference map; and (2) other disturbance features in the disturbance inventory. These sliver features can lead to significant distortions in change-detection results (Linke et al. 2009b). To overcome the boundary mismatches, a minimum mapping width was selected to specify the threshold beyond which spatial mismatches would define real changes occurring in the time series. Boundaries were trimmed or erased for overshoots and merged or expanded for undershoots. For the purpose of this analysis, all gap features originating prior to 1999 represented all gap features occurring for the 1985 to 1998 time series. As these were dated in the ODI as 1998 pre-existing features, these features needed to be accurately dated according to the NDI (pre-1985 to 1998). Various intersect and boundary proximity tools in ArcGIS were used in order to match each gap feature to the appropriate disturbance object that occurred prior to 1985 or between 1985 and 2005; and therefore apply the correct date (Figure 2, step 2c). Finally, all annual and pre-existing disturbance features (areal, linear, and point) were merged into a disturbance database containing all dynamic features on the landscape from 1985 to 2005 (T -13 to T +7 ; Figure 2, step 4). The database included attributes that specified disturbance type (e.g. cut block), year of origin, and change type. Change type categories included five-year (disturbances detected between five-year intervals from 1985 to 1998), annual (disturbances detected between 1-year intervals from 1998 to 2005 from ODI), or pre-existing (disturbances detected in 1985).

23 Disturbance Inventory Conditioning (Figure 2B) Land-cover labels after disturbance originated (T >D ) In order to adhere to the ODI, disturbance features were assigned land-cover labels for each five-year time period using the same decision rules as reported in Linke et al. (2009a) (Table 2; Figure 2, steps 5a and b). Cutblocks and burn features were assigned to the barren class for the year in which they originated, herbaceous in the two years following the disturbance, and shrub in years three and beyond. Upon visual inspection of the imagery, all 1985 pre-existing disturbance features were still discernable on the landscape in 2005 despite the 20+ year period; therefore the decision rules maintained that disturbance features were not allowed to progress beyond shrub. Roads, railways, well sites, and mine features were labeled as barren for all years, and pipelines and power lines were labeled herbaceous for all years. In reference to Table 2, T <D refers to years before the disturbance occurred, T D refers to the year of origin, T D+1 T D+2 refers to years one and two after the disturbance occurred, and T >D+2 refers to years three and beyond. Since this time series was analyzed in five-year increments, a cut block that occurred in 1985, for example, would be barren in 1985 and shrub in 1990 (five-year time lapse). Table 2: Feature type, disturbance type, overlay order (i.e. order 1 refers to the bottom layer), and land-cover decision rules for disturbance features mapped in the study area. T<D refers to years before the disturbance occurred, T D refers to the year of origin, T D+1 T D+2 refers to years one and two after the disturbance occurred, and T >D+2 refers to years three and beyond. Feature Disturbance Overlay Land-cover (LC) Decision Rules Type Type Order T <D T D T D+1 T D+2 T >D+2 Areal Burn 1 Forest Barren Upland herbs Shrub Areal Cut block 2 Forest Barren Upland herbs Shrub Areal Mine 3 Forest Barren Barren Barren Linear Pipeline 4 No data Upland herbs Upland herbs Upland herbs Linear Power line 5 No data Upland herbs Upland herbs Upland herbs Linear Railway 6 No data Barren Barren Barren Linear Road 7 No data Barren Barren Barren point Well site 8 Context determined Barren Barren Barren

24 17 Land-cover labels prior to the year of origin (T <D ) The land-cover label for the years prior to the disturbance was assigned as forest for all areal disturbance features, under the assumptions that all such disturbances were stand replacing (Figure 2, step 5a). The respective label for linear features was no data to prevent their insertion in the years prior to their occurrence (Figure 2, step 5b). The assumption could not be made that well sites were cut from forests (like the areal features), since they commonly occur on various land-cover types. Therefore, predisturbance land-cover attributes for well sites were assigned based on context within a 60-m buffer (Figure 2, step 5c). Two conditions presented themselves: (1) the well site occurred within an existing reference object (static) or disturbance feature (dynamic); or (2) the well site occurred within two different reference or disturbance objects. Well sites in the first condition were given the land-cover label of the surrounding feature. Well sites in the second condition were split and given two different land-cover labels. Land-cover labels for features originating prior to 1985 (T <1985 ) In order to attach land-cover characteristics to the areal features appearing in the 1985 imagery (i.e., the pre-existing disturbance features), a maximum-likelihood supervised classification was performed using the 1985 Landsat imagery to identify land-cover values (Figure 2, step 6a). The classification involved three classes including: barren, herbaceous, and shrub. The 1985 land-cover classes for the areal disturbance features occurring prior to 1985 (i.e. detected in the 1985 imagery) were added to the disturbance inventory by extracting majority values from the results of the supervised classification. The subsequent succession of each feature followed the decision rules stated in Table 2 for the years 1990 and The original 1998 land-cover values were always adhered to in order to preserve the integrity of the 1998 land-cover reference map.

25 18 Land-cover labels for well sites that occurred prior to 1985 (pre-existing) were barren for the entire time series as they were still discernable on the landscape in T +7 (2005) (Figure 2, step 6b). Linear features attained the same land-cover labels as described in Table 2 for T D (e.g. roads and railways were labeled barren; pipelines and power lines were labeled upland herbs; Figure 2, step 6c). Exceptions to decision rules Some cut block features that were determined to be dynamic from 1985 to 1998 (ODI) were considered static from 1998 to 2005 in the ODI, and required an exception to the decision rules. The 1998 land-cover values were always adhered to in order to preserve the integrity of the 1998 land-cover reference map. Of the total number of cut blocks, 5.8% were determined to be dynamic from pre-1985 to 1995, yet remained static from 1998 to Another exception to the decision rules occurred when 1998 land-cover values of some disturbance objects did not follow the decision rule logic. For example, if a cut block occurred in 1990 and the 1998 land-cover class was wetland herb (from the ODI), the transition could not be from barren in 1990 to shrub in 1995 to wetland herb in 1998 according to the decision rules. Therefore, an exception was made where the land-cover label for 1985 was forest, 1990 was barren, 1995 was wetland herb (rather than shrub), and for 1998 and above, it was also wetland herb (rather than shrub). Land-cover labels for 0.7% of the total cut blocks had to be altered in similar ways and therefore did not represent a large proportion of the areal features. Again, the goal was to adhere to the decision rule as closely as possible, thereby maintaining integrity of the 1998 reference land-cover map.

26 Backdating and Updating of 1998 Reference Land-cover Map Using the methodology described in Linke et al. (2009a), the 1998 reference map was backdated and updated to produce individual land-cover maps for 1985, 1990 and Annual land-cover maps for 1998 to 2005 were already available using the same methods. An assumption was made that the reference map (T 1998 ) was accurate. Creating land-cover maps was accomplished through the systematic overlay of features from the new disturbance inventory (NDI) using the overlay order described in Table 2 above (Figure 2, step 7). Burns were arranged on the bottom layer (order 1); cut blocks were layered on top of burns (order 2); mines could overlap a cut block or burn (order 3); linear features could overlay any areal feature (order 4-7); and well sites were the smallest disturbance type and remained persistent throughout the time period (order 8). First, the 1998 reference map without roads was backdated by removing all burns, cut blocks, mines and well sites that occurred between 1985 and This represented 1985 conditions without disturbance features. Second, all 1985 disturbance features, following the order described above, were overlaid onto this new map to create the new 1985 land-cover map (using Mosaic to New Raster tool in ArcGIS 9.3). These steps were repeated for the 1990 and 1995 land-cover maps. 2.5 Backdating and Updating of 2003 Crown Closure (CC) and Species Composition (SC) Maps In order to backdate the 1998 continuous reference maps (crown closure (CC) and species composition (SC) T 0 ), CC and SC models were developed from spectral variables derived from the 1985 Landsat imagery and 2003 continuous reference maps. The method is comparable to that described in McDermid (2005), but rather than performing a model extension from a source image to an adjacent destination scene

27 20 (i.e. spatial investigation), a model was developed comparing one time period to another (i.e. temporal investigation). There were various steps involved in creating 1985 estimates of CC and SC. First, a 2003 tree mask was developed separating upland tree from all other land-cover types; 600 random points were generated inside the tree mask, and 2003 CC and SC values were extracted. The 2003 maps were used for this exercise, because this was the year in which the baseline crown closure and species composition maps were created (McDermid 2005). Disturbance features, or regenerating forest areas, were not included in the mask because of the variability in rate of succession due to re-planting strategy, scarification, and other silvicultural practices. Second, least-squares regression was performed on both 2003 SC and CC as dependent variables. When the response and predictor variables are both continuous, it is appropriate to use regression analysis. Exploratory data analysis was conducted and various models were attempted to explain the relationship between the dependent variables and the associated independent variables. Independent variables included both topographic (elevation, slope and incidence) and spectral data (1985 TM greenness, wetness and brightness). Coefficients from Huang et al. (no date) were used on TM5 imagery, top-of-atmosphere values, to derive wetness, greenness and brightness indices. Results were verified through F-tests and analyses-of-variance. All statistical analyses were conducted in the software package R version (2010). By utilizing the multivariate regression output, maps of CC and SC were produced using the resulting equations (Table 3). Table 3: Best fitting models for crown closure (CC) and species composition (SC). Response Variable Model Output R 2 Residual Error CC Y = (0.6255*wet) (0.6908*bright) + (0.2846*slp) + (0.6557*inc) on (0.13) (0.08) (0.09) (0.12) (19.11) 559 D.F. SC Y = (0.024*dem) + (1.244*inc) (2.714*green) (1.162*bright) on (0.003) (0.133) (0.174) (0.093) (21.42) 559 D.F.

28 21 Third, 1985 CC and SC estimates were clipped based on all the disturbance features that occurred between 1985 and CC and SC values were used for the areas outside of the disturbance features due to a higher confidence in 2003 values. And finally, 2003 CC and SC were backdated using the clipped 1985 estimates. Backdating methods followed the same principles as described for backdating land-cover maps (Linke et al. 2009a). The areas that exhibited no CC, such as disturbance features, were truncated in ArcGIS so that values were equal to zero; the respective label for species composition was no data. Two assumptions were made in using this method to generate the continuous variable maps: (1) the modeled 2003 crown closure and species composition estimates were reliable and accurate; and (2) crown closure and species composition (outside of the disturbance areas) have not changed significantly between 1985 and Two-Dimensional Habitat Modeling The FRIGBRP developed tools to evaluate the current condition of grizzly-bear habitat in Alberta, and to forecast the effect on habitat quality of proposed developments such as logging, road and pipeline construction, mining, and oil and gas drilling. The GB mortality risk (risk) tool and the resource selection function (RSF) tool, as well as the supporting layers supplied by FRIGBRP were modified and used towards the goals of this analysis Modeling risk of human-caused grizzly bear mortality (risk model) A risk model from Nielsen et al. (2004a), developed just south of the study area, was used to define risk of human-caused mortality for adult GBs as a function of landscape variables. Model coefficients were updated in May 2007 (Nielsen unpublished data 2007). The model was based on multivariate logistic regression analysis of a sample of 297 anthropogenic GB mortalities within the Central Rockies Ecosystem, dating from

29 to Landsat TM imagery from 1995 to 1998 was used to classify the area (Nielsen et al. 2004a). For the purposes of this analysis, mortality risk (R) was calculated for 1985 to 2005 (at each five-year interval) on a pixel-by-pixel basis according to the equation: R = (1.862*TRIcost) + (1.352*dstrm) + (3.573*dfor) (0.064*TRI) + (13.272*p_uptree) [1] *(p_uptree) 2 ) (2.224*protctd6mi) + (3.909*costd_trail*protctd6mi) (3.090*costd_trail*whitez_6mi) where TRI was defined as a terrain-ruggedness index; TRIcost was the cost surface based on a reclassification of TRI into scores from 1 (no cost) to 100 (100 times the cost) and was used as the cost surface in distance-to-roads; dstrm was defined as the negative exponential decay of distance-to-water bodies; dfor was the distance-to-forest edge; p_uptree was the proportion of upland tree within a 17-km radius moving window; protctd6mi was the proportion of protected area (i.e. parks, ecological reserves, and wildland) within a 10-km radius moving window; costd_trail was the cost distance to trail based on TRIcost; and whitez_6mi was the proportion of White Zone (private OR freehold land) within a 10-km radius moving window. Predicted values of R were scaled where the relative risk of mortality ranged from a low of 1 to a high of 10. Some of the layers supplied by FRIGBRP did not need to be re-calculated as they were constant over time or the tool re-calculated them based on layer inputs. These included: TRIcost, dstrm, TRI, protctd6mi, costd_trail, and whitez_6mi. However, all the layers needed to be shifted by various coordinates (less than 1 pixel) north, south, east, or west since they did not line up with each other or to the land-cover products developed for 1985 to Other layers were not static over time and needed to be re-calculated using the 1985 to 2005 time series. These included: p_uptree, landcover (10 classes; used to derive distance to edge variables), trails (used to derive costd_trail), and roads (used to derive TRIcost). Trails were defined as railway, power line, and pipeline features. Land-cover maps and disturbance features for 1985, 1990, 1995,

30 2000, and 2005 were derived by the methods described in this document and Linke et al. (2009a). Trail and road poly-lines were selected out of the disturbance inventory depending on the year of analysis. P_uptree was calculated by using Spatial Analyst Focal Statistics tool based on mean values within a 17 km circle window; processing time was extensive Modeling the relative probability of adult female grizzly bear occupancy (RSF model) A resource selection model (RSF) from Nielsen (2005) was used to define the relative probability of adult female GB occurrence specific to the late hyperphagia stage (16 August to 15 October). Although Nielsen (2005) presents various models that could have been used in this analysis, adult female GBs are the most sensitive sex-age class for fluctuations in population, and the hyperphagia stage represents the most critical GB forage opportunity prior to hibernation (Benn and Herrero 2002); therefore these RSF models were selected for analysis. The RSF model was based on 5172 late hyperphagia radio-telemetry observations collected from 13 adult females between 1999 and 2002; various models were developed specific to the GB population unit. The coefficients were updated in 2009, and separate models were created based on grizzly-bear population units (Nielsen unpublished data 2009). For the purposes of this analysis, the study area spans across three grizzly-bear population units: Grande Cache (GC), Yellowhead (YH), and Clearwater (CW); subsequently, three different models were used to calculate the probability of female GB occurrence over the entire area. The RSF for female GB s in the late hyperphagia phase was calculated for 1985 to 2005 (at each five-year interval) on a pixel-by-pixel basis according to the general equation: RSF = (B 1 X 1 + B 2 X B 18 X 18 ) [2] 23

31 24 where the B s represented coefficients for 18 variables including: seven land-cover categories; three forest-canopy categories; terrain-based soil wetness; five distance-toedge categories; distance-to-stream edge; and female-range scalar. See Table 4 for coefficients for each variable based on grizzly-bear population unit in the late hyperphagia period. Predicted values of RSF were scaled where the probability of occurrence ranged from a low of 1 to a high of 10. All distance input variables had the following general form: d500xxx = 1 (Exp(-1 (distance in meters to edge / 500))) [3] This ensured that the effects of local landscape features eroded sharply beyond a few hundred meters and were irrelevant at large distances. Exponential decays ranged from 1 at the feature to 0 at very large distances. In order to maintain consistency in interpretation of coefficients (i.e., positive coefficients represent further distances and negative coefficients near distances) the exponential decay variable was subtracted from a value of 1 resulting in distance metric that ranged from 0 at the feature to 1 at very large distances.

32 25 Table 4: RSF model coefficients for adult female GBs in hyperphagia stage; GC refers to the Grande-cache GB population unit in the north and composes 16% of the study area; YH refers to the Yellowhead GB population unit in the middle and composes 78% of the total area; CW refers to the Clearwater GB population unit in the south and composes 6% of the study area. All three model coefficients were used to calculate RSF for the entire study area. Variable Model Coefficients based on Grizzly Bear Population Unit GC YH CW Land-cover Upland tree * Wetland tree Regenerating forest Shrub Wetland herb Upland herb Non-vegetated Forest Canopy Crown closure in treed sites Crown closure in regenerating forest Species composition in upland tree sites Terrain-based (DEM) soil wetness Compound topographic index (150 m) Distance to edge (1 negative exp. decay) ** Distance to opening from upland tree Distance to opening from wetland tree Distance to forest edge from upland herb Distance to forest edge from regenerating forest Distance to forest edge from non-vegetated *** Distance to stream (1 negative exp. decay) Distance from stream edge (500 m) Female range Female range scalar * Upland tree used as the reference category in indicator contrasts of land-cover types. ** A 500 m parameter was found to be the most useful scale based on preliminary analyses of 50, 100, 250, 500, and 1000 m. *** Non-vegetated refers to barren, cloud, shadow, snow/ice, water. The layers supplied by FRIGBRP which did not need to be re-calculated included: compound topographic index within a 150-m radius (CTI), distance-factor (500-m exponential) from stream (d500_strm), and female GB range scalar (the probability of female occupancy, ranging between 0 and 1; femalerng). A non-habitat binary mask, derived from a land-cover map, was also applied to the final model including agriculture,

33 26 large bodies of water, snow, shadow, ice, and non-vegetation (i.e. rock) within the alpine sub-region. In addition, all the layers needed to be shifted by various coordinates (less than 1 pixel) north, south, east, or west as they did not line up with each other or to the other land-cover products developed for 1985 to As with the risk model, some of the layers used for the RSF were not static over time and needed to be re-calculated using the 1985-to-2005 time series. These included: crown closure (CC), species composition (SC), land-cover, and regenerating forest (regen). Regen included all areal features from the disturbance inventory that occurred prior to and including the year-of-analysis with a data range of 0 or 1. Land-cover maps and disturbance features for 1985, 1990, 1995, 2000, and 2005 were derived by the methods described in this document and Linke et al. (2009a). CC and SC were also derived from the methods described in this document; however, due to the model variable CC in regenerating forest, further development of CC was required. It was not logical to apply zero CC values to areas that were disturbed by areal features (e.g. cut blocks) as far back as 1960; therefore regeneration rate or growth in these areas needed to be calculated. The following formula was used: CC = ( * [age]) ( * [age] 2 ) [4] where CC refers to crown closure values in regenerating disturbance areas and age refers to the estimated age of the cut block. This formula was derived by John Boulanger using a weighted-least-squares regression analysis of crown-closure values for 35,000 cut blocks harvested between 1955 and The regression weighted the contribution of each measurement by the inverse of its variance, applying less weight to the imprecise measurements compared to the more precise measurements. Crownclosure values for cut blocks were calculated using Spatial Analyst Zonal Statistics with pixel values derived from the 2003 crown-closure layer. The accuracy of crown-closure values in regenerating areas was dependent on the accuracy of the estimated ages of the cut blocks.

34 27 In order to date areal features pre-existing to 1985, digitized polygons with the year of origin attributes representing cut blocks, mines and burns were selected from ancillary GIS sources that matched the areal disturbances existing prior to The boundaries of these objects were matched to the 1998 reference map by using overlay procedures and snap-to-raster functions. Linear features and well sites were not considered regenerating-forest areas, and therefore CC values remained at zero in these areas Safe-harbor and attractive-sink indices In order to measure the change in habitat quality, the mortality risk (R) and adult female GB habitat occupancy (RSF) maps were further used to define safe-harbor and attractive-sink indices (Nielsen et al. 2006). Attractive-sink (AS) and safe-harbor (SH) indices were defined as: AS = RSF x R [5] and SH = RSF x (11 R), [6] where RSF was an index of habitat occupancy for adult females from Eq. (3) scaled from 1 to 10, and R was an index of human-caused mortality risk for adult female GB from Eq. (2) scaled from 1 to 10. High AS values were considered areas where bears were both most likely to occur and most likely to experience human-caused mortality (e.g. low survival). High SH values were considered areas where bears were most likely to occur and experience low risk of human-caused mortality. AS and SH continuous indices were categorized into five levels in order to facilitate interpretation: very low (1-20), low (21-40), mid (41-60), high (61-80) and very high (81-100). Percent-composition of each category and mean values were calculated across the entire study area. In addition, mean AS and SH values based on 30 watersheds for 1985 to 2005 (at 5-year intervals) were derived from the maps to target the watersheds that are most in-need of protection or mitigation. The watershed polygons approximated the average size of a

35 28 female grizzly-bear home range (approximately 700-km 2 ) and were an appropriate medium-scale analysis extent for small disturbances, such as well sites or cut blocks. Major watersheds (acquired from NRC 2010) were subdivided generally along heightsof-land and occasionally along watercourses by the FRIGBRP Five-state Habitats In addition to safe-harbor and attractive-sink indices, a five-state habitat model was used to measure the changes in habitat quality as described in Nielsen et al. (2006). The five states were defined as: (1) non-critical (rare female GB occupancy where RSF<5, regardless of R); (2) secondary sink (high risk and moderate habitat occupancy where 5 RSF 7 values and R 6); (3) primary sink (high risk and high habitat occupancy where RSF>7 and R 6); (4) secondary habitat (low risk and moderate habitat occupancy where 5 RSF 7 and R<6); and (5) primary habitat (low risk and high habitat occupancy where RSF>7 and R<6). Whereas the safe-harbor and attractive-sink conditions represented continuous indices ranging from 0 to 100, the five-state habitats provided discrete categories for targeted management objectives. The five-state habitats allowed for a deeper understanding of GB habitat, representing primary habitat and primary sink values (which highly correspond with high safe-harbor and high attractive-sink pixels, respectively), on a single map. The five-state habitats were derived over the entire study area for 1985 to 2005 (at 5-year intervals). The proportion of each habitat-state was also summarized across watersheds over the 20-year time series.

36 RESULTS AND DISCUSSION 3.1 Areas that had the greatest- and least-amount of change in habitat quality from 1985 to 2005 Habitat conditions (attractive-sink and safe-harbor indices) and habitat-states (noncritical, secondary sink, primary sink, secondary habitat, and primary habitat) were used as surrogates for habitat quality. This component of the analysis was completed based on two spatial scales for attractive-sink and safe-harbor habitat: (1) the entire study area; and (2) 30 watersheds within the study area which were delineated based on the average size of a female GB home range. The evaluation of the five-state habitat model was solely based on the study area. In this paper, for the purposes of all subsequent tables and figures, 1985 refers to features that occurred prior to 1985; 1990 refers to features that occurred between 1985 and 1990; 1995 refers to features that occurred between 1990 and 1995; 2000 refers to features that occurred between 1995 and 2000; and 2005 refers to features that occurred between 2000 and Attractive-sink (AS) Study-area Scale Figure 3 shows the Indices of attractive-sink and safe-harbor habitats for adult female GBs during late hyperphagia across the entire study area.

37 30

38 31 Figure 3: Index of attractive-sink and safe-harbor habitats for adult female GBs during late hyperphagia. High to very high attractive-sink values represent those habitats where animals are both likely to occur and at high risk of mortality, while high to very high safe-harbor values represent those habitats where animals are likely to occur and are at low risk of mortality. Areas that exhibited the greatest change from low to high and very high attractive-sink values through time (1985 to 2005) occurred primarily in the foothills between the town-sites of Hinton, Robb, and Cadomin, as well as along the north and northwest

39 32 edges of the study area (Figure 4). This change can be attributed to forest edges associated with forestry activities (e.g. cut blocks) and some unimproved and gravel roads associated with forestry and oil and gas activities. Well site development also contributed to the increase in attractive-sink locations, but to a lesser degree. Figure 4: Large scale inspection of areas that exhibited the greatest and least amount of change in attractive-sink (AS) habitat from a) 1985 to b) 2005; AS habitat changed from low and very low to high and very high values between the town-sites of Robb, Cadomin and Hinton; AS habitat remained high in the 3 hotspots southeast of Cadomin and along mountainous streams. Areas that exhibited perennially high and very high attractive-sink values from 1985 to 2005 occurred primarily near Cadomin and southeast of Cadomin along the eastern slopes of the Rocky Mountains, as well as disturbance areas within the foothills, and some upper foothill and mountainous river valleys (Figure 4). High attractive-sink values in these areas can be explained by: (1) a high probability of female GB occurrence (femalerng) resulting in high RSF values; (2) pre-existing forestry and oil and gas activity

40 33 resulting in high risk and high RSF; (3) low- to mid-proportion of upland forest covering the landscape within 15 km (p_uptree) resulting in high risk values; and (4) proximity-tostreams (dstrm) resulting in high RSF and high risk (see below). The femalerng was used to reduce overall habitat values in extrapolated areas of the population unit where female bears were predicted to be absent due to displacement from human activities and relatively poor habitat. It was based on natural subregion and accounted for the decreased occupancy of lower-elevation habitat despite greater resource availability. As a result, highest femalerng values occurred along the Rocky Mountains and just east of the mountains, with declining values to the northeast. Female grizzly-bear range only represented the current knowledge of female bear range or occupancy based on GB DNA sampling. If this research was conducted in other parts of Alberta, further investigation of the extent of female bear range in the east would be necessary (i.e. east and southeast of Edson). Referring to Eq. [1] above and Figure 5 below, the combined variable of p_uptree was used to identify areas of higher risk ((13.272*p_uptree) (15.400*(p_uptree) 2 ). This combined variable was at its highest value where the proportion of upland tree was at its mid-range, and at its lowest where the proportion of upland tree was at its maximum. Therefore, the area along the eastern slopes of the Rocky Mountains resulted in high attractive-sink or high risk values. The area within JNP would also be considered high risk according to this variable; however, in this case the variable that represented the proportion of protected area (protctd6mi) counter-acted the effects of the combined p_uptree variable. The coefficient for distance-to-stream (dstrm with a negative exponential decay) is positive for the risk model, indicating that bears closer to streams are at higher risk of mortality. A negative exponential decay is used on the resulting distance grid, to ensure the greatest influence of roads or trails is near the feature and not some large distance away. Values ranged from 1 next to the feature, and fall to almost 0 some large

41 34 Figure 5: Comparison of proportion of upland tree (p_uptree) and the combined variable ((13.272*p_uptree)- 15.4*(p_uptree)). The combined variable was at its highest value where the proportion of upland tree was at its mid-range, and at its lowest where the proportion of upland tree was at its maximum. distance away. In contrast, the coefficient for distance from stream edge (1 negative exp. decay) is negative for the RSF model, indicating higher quality habitat at streams. Therefore, the closer a bear is to a stream, the higher-quality habitat, and the higher the risk for mortality. Watershed Scale The mean, standard deviation (SD) and category of the attractive-sink and safe-harbor indices for 1985 and 2005 were summarized for 30 watersheds and are provided in Table 5.

42 35 Table 5: Characteristics (mean, standard deviation (SD), and category) of attractive-sink (AS) and safe-harbor (SH) indices for 30 watersheds within the study area. Attractive-sink (AS) Index Safe-harbor (SH) Index Watershed Abbreviation MEAN SD category MEAN SD Category MEAN SD Category MEAN SD Category Blackstone Creek BLA very low very low mid mid Cardinal River CAR low low mid mid Chungo Creek CHU very low very low mid mid Dismal Creek DIS very low low low low Edson River EDS very low low low low Elk River ELK very low low low low Embarras River EMB very low low low low Erith River ERI very low low very low very low Gregg River GRE very low low mid mid Lower Brazeau River LBR very low very low very low very low Lower Nordegg River LNO very low very low very low very low Maligne River MAL very low very low mid mid Middle Brazeau River MBR very low low low mid McLeod River 2 MCL very low very low very low very low McLeod River 3 MCL very low very low very low very low McLeod River 4 MCL low low mid mid Middle Pembina River MPE very low very low very low very low Nosehill Creek NOS very low low low low Obed Creek OBE very low low low low Pembina River PEM very low low mid low Shunda Creek SHU very low very low mid mid Southesk River SOU very low very low low low Sundance Creek SUN very low low low low Trout Creek TRO very low very low low low Upper Erith River UER very low low low low Upper McLeod River UMC low low mid mid Upper Nordegg River UNO very low very low low low Upper Pembina River UPE very low low mid mid Upper Rocky River URO very low very low mid mid Wolf Creek WOL very low low very low very low

43 36 Average AS values for the watersheds ranged from low to very low overall ratings from 1985 to In 1985, Upper McLeod River (UMC), McLeod River 4 (MCL4), Gregg River (GRE), and Cardinal River (CAR) were among the highest average AS locations, with UMC representing the highest value (27.2) (Figure 6). Trout Creek (TRO) watershed had the lowest overall AS value (5.4). In 2005, Upper Erith (UER), MCL4, GRE, as well as Upper Pembina River (UPE) and Pembina River (PEM) were among the highest average AS locations, with UER and MCL4 rating the highest (39.6 and 36.3, respectively). Upper Rocky River (URO) and Maligne River (MAL) demonstrated the lowest average AS values (8.0 and 8.4 respectively). Figure 6: Attractive-sink Index for each watershed within the study area for: a) 1985 and b) Full descriptions of abbreviated watershed names can be found in Table 5. UMC watershed had the highest attractive-sink value in 1985 but remained stable from 1985 to 2005; UER and MCL4 had the highest mean value in EMB, MBR, NOS, and UER showed the greatest increase in AS values from 1985 to The watersheds that experienced the greatest increase in mean AS values from 1985 to 2005 were Embarras River (EMB), Middle Brazeau River (MBR), Nosehill Creek (NOS),

44 37 UER, and Elk River (ELK) (166%, 155%, 148%, 145%, 144%, respectively) (Table 5, Figure 6). Not only did UER exhibit the greatest average AS values in 2005, this watershed also experienced one of the greatest increases in AS values from 1985 to Other watersheds that more than doubled in average AS values from 1985 to 2005 included WOL, LNO, LBR, UNO, UPE, and TRO. Although UMC had the highest AS value in 1985 (27.2), this watershed remained relatively stable across the 20-year time span, increasing by 5%. Five watersheds actually decreased in mean AS values across time including CAR, Southesk River (SOU), Blackstone Creek (BLA), URO, and MAL; however, these watersheds changed insignificantly (i.e. <1%) Safe-harbor (SH) Study-area Scale Perennial low values of safe-harbor habitat occurred primarily within the non-vegetated high alpine (JNP), large water-bodies, and the area southeast of Edson along the eastern edge of the study area (Figure 3 above). These areas represented low-quality habitat and/or high mortality risk for GBs. For example, the area surrounding Edson had low values of safe-harbor habitat because the area represented high proportion of white zone (agriculture and freehold land) which translated to high risk values. The white zone is an administrative region of Alberta where the dominant land use is agriculture, ranching, and human settlement. The significance of the white zone for GBs is their attraction to predation opportunities (livestock), which, combined with high human density in the white zone and a conflict with human economic interests, results in very high mortality rates. The area between Edson and Robb (along eastern edge of study area) exhibited low female GB range (femalerng) based on DNA sampling and therefore represented low probability of female GB occurrence and low safe-harbor habitat. Nonhabitat water-bodies and non-vegetated high alpine resulted in low safe-harbor habitat due to the non-habitat mask applied to the RSF model.

45 38 Areas that exhibited perennially high and very high safe-harbor values from 1985 to 2005 occurred primarily immediately south of Cadomin and southeast of Cadomin along a thin strip approximately 10-km east of the Rocky Mountains. In addition, lower valley networks within Jasper National Park (JNP) at a distance from streams (i.e. >100 m) exhibited perennial high and very high SH habitat (Figure 7). High safe-harbor values in these areas can be explained by: (1) a high probability of female GB occurrence (femalerng) within JNP and southeast of Cadomin; (2) a lack of disturbances (i.e. roads, trails); and (3) open meadows (i.e. upland herb and shrub land-cover). In addition, JNP is a protected area and therefore the mortality risk was low within lower valley networks, indicating the importance of the park for GBs. Figure 7: Large scale inspection of areas that exhibited the greatest and least amount of change in safe-harbor habitat from a) 1985 to b) 2005; SH habitat changed from high and very high to low and very low values between the town-sites of Robb, Cadomin and Hinton; SH habitat remained high in lower valley mountainous networks at a distance from streams (i.e. >100 m) as well as along a thin strip southeast of Cadomin. Areas that exhibited the greatest change from high to low and very low SH values through time (1985 to 2005) occurred primarily in the foothills between the towns of

46 39 Hinton, Robb, and Cadomin (Figure 7), as well as along the northwest edge of the study area (Figure 3 above). This can be attributed mostly to cut block and road development associated with forestry in an already fragmented landscape. Although these anthropogenic factors led to an increase in the probability of grizzly bear occurrence (RSF) in this area, the risk of mortality dramatically increased due to a lower proportion of upland tree overall as well as dense road and trail development. Where indices of attractive-sink provide a mechanism for identifying areas in most need of management attention to minimize the likelihood of contact between humans and bears, safe-harbor sites identify habitats in most need of continual protection or inclusion in a system of reserves. The index of safe-harbor habitats within the study area identified high-quality and secure GB habitat. Watershed Scale The mean, standard deviation (SD) and category of the safe-harbor index for 1985 and 2005 were summarized for 30 watersheds and are provided in Table 5 above. Average SH values for the watersheds exhibited very low, low and mid overall ratings from 1985 to In 1985, UMC, CAR, and BLA were among the highest average SH locations, with UMC representing the highest value (51.9). McLeod River 2 (MCL2) watershed had the lowest overall SH value (11.4) (Figure 8). In 2005, UMC, CAR, Chungo Creek (CHU), and BLA were among the highest average SH locations, with UMC and BLA rating the highest (51.3 and 49.0, respectively). Wolf Creek (WOL) and MCL2 watersheds demonstrated the lowest average SH values (12.6 and 10.4, respectively).

47 40 Figure 8: Safe-harbor Index for each watershed within the study area for: a) 1985 and b) Full descriptions of abbreviated watershed names can be found in Table 5. UMC watershed had the highest SH value in 1985 but remained stable from 1985 to 2005; UMC and BLA had the highest mean value in UER, EMB, WOL, and ERI watersheds showed the greatest decrease in SH values; while UNO and TRO watersheds had the greatest increase in SH values from 1985 to The watersheds that experienced the greatest decrease in mean SH values from 1985 to 2005 were UER, EMB, WOL, and ERI (25.4%, 19.6%, 19.2% and 18.4%, respectively) (Figure 8, Table 5). This was primarily the result of a decline in the proportion of upland tree caused by a dramatic increase in human-caused disturbances (e.g. roads and cut blocks). UER experienced many changes across the 20-year span it experienced the greatest decline in safe-harbor habitat and one of the greatest inclines in attractive-sink habitat, as well as the highest attractive-sink values in 2005 when compared across 30 watersheds. Although UMC had the highest SH value in 1985 (51.9), (as well as the highest AS value in 1985 (27.2)), SH values within this watershed remained relatively stable across the 20-year time span, decreasing by 1%.

48 41 A considerable number of watersheds actually increased in mean safe-harbor values across time including Upper Nordegg River (UNO), TRO, CAR, MBR, CHU, SOU, BLA, MAL, and Shunda Creek (SHU). UNO and TRO, located in the southeast corner of the study area, had the greatest increase in SH values (15.4% and 14.7%, respectively). This can be directly attributed to an increase in forest edges caused by forestry activity. The development of cut blocks dramatically increased the probability of grizzly bear occurrence (RSF) and only increased mortality risk to a slight degree (due to a high proportion of surrounding upland tree cover in the area). Road development increased RSF and risk dramatically. Therefore, the development of cut blocks (and increased edge habitat) in UNO and TRO, in a relatively undisturbed area overall, subsequently increased SH values. Areas where roads appeared did not increase SH values due to the strong risk associated with them Five-state Habitats Figure 9 shows the predicted habitat-states for the study area (for each five-year interval) based on a two-dimensional classification of habitat occupancy (RSF) and mortality risk (R) estimates. The area that most notably experienced the least amount of change (in all habitat categories) occurred within the mountainous region and extended out to approximately 30 km northeast of the Rockies (Figure 10). In the area immediately northeast of the Rockies, dense- and partially contiguous primary and secondary sink habitat was observed adjacent to dense- and partially contiguous primary and secondary habitat. This suggests there was a continuous supply of highquality GB resources with a combination of secure and insecure habitat. There were also consistent primary and secondary sink values in the northeast section of the study area suggesting that the area provided high-quality resources but that GBs also suffered a high risk of mortality (due to proximity to the white zone) (Figure 9).

49 Figure 9: Predicted habitat-states for west-central Alberta based on a two-dimensional classification of habitat occupancy (RSF) and mortality risk (R) estimates. 42

50 43 Figure 10: Large scale inspection of areas that exhibited the greatest and least amount of change in the five habitat states from a) 1985 to b) 2005; habitat-states changed from primary and secondary habitat and noncritical habitat to primary and secondary sink habitat between the town-sites of Robb, Cadomin and Hinton; the strip of area southeast of Cadomin exhibited partially contiguous primary sink habitat (i.e. 3 sink hotspots) adjacent to partially contiguous primary and secondary habitat in both 1985 and 2005 maps. Areas which represent primary sink habitat that are adjacent to primary habitat require special conservation attention. Management could consider limiting access to humans in primary habitat and primary sink locations or making the attractive habitat undesirable to GBs, thereby reducing the chance of GB mortalities. For example, the three primary sink or attractive-sink hotspots along the eastern slopes of the Rockies are interspersed with primary and secondary habitat. By removing risk to bears in this area (i.e. limiting human access), a contiguous primary source habitat for GBs would be established, thereby considerably reducing bear-human conflict in the area. Risk in this area was associated with proximity to streams and a mid-proportion of upland tree.

51 44 The greatest amount of change occurred in the foothills between the towns of Robb, Hinton, and Cadomin (Figure 10), as well as within the northwest section of the study area (Figure 9). These areas experienced a change from primary and secondary habitat values to a high proportion of primary and secondary sink values. This change can be attributed to road access for forestry and oil and gas objectives as well as an increase in forest edges caused by forestry activities. By utilizing the AS and SH indices, as well as the five-state habitat model, managers can more efficiently acquire an inventory of grizzly-bear habitats, identify areas in need of protection (i.e. primary and secondary habitats), and determine areas in need of management action (e.g. limiting access to humans and/or transforming attractive habitat for bears in high risk zones). 3.2 Temporal Analysis The temporal analysis involved determining which years showed the greatest change in habitat quality across the study area. For the two indices examined (safe-harbor and attractive-sink), this component of the analysis was evaluated based on change in percent composition for five separate categories (i.e. very low, low, mid, high, and very high) and change in mean values. For the habitat-states, only the change in percentcomposition for the five categories was evaluated. The temporal analysis was also conducted for disturbance features Attractive-sink (AS) and Safe-harbor (SH) Indices The percent-composition of attractive-sink (AS) and safe-harbor (SH) categories is provided in Table 6. The index of AS habitats identified high-quality yet insecure grizzlybear habitat. The majority of the total area was dominated by very low attractive-sink values for all years (83% for 1985, 81% for 1990, 78% for 1995, 71% for 2000, and 66% for 2005) with decreasing amounts of low, mid, high, and very high categories (Table 6,

52 45 Figure 3 above). The combined high and very high AS categories totaled 1.9% of the study area for 1985, 2.0% for 1990, 2.5% for 1995, 3.9% for 2000, and 4.9% for Although high and very high AS habitat represented a low proportion of the total area, this type of habitat increased by 158% over 20 years (from 1.9 to 4.9%), indicating an overall increase of both high mortality risk and high grizzly-bear occurrence. The greatest increase in high and very high categories occurred between 1995 and 2000 (56.0%), followed by 2000 and 2005 (25.6%). The combined low and very low AS categories steadily declined (a total change of 8.5%), with the greatest decline occurring between 1995 and 2000 (4%). Table 6: Percent composition of attractive-sink (AS) and safe-harbor (SH) categories including very low (0-20), low (21-40), mid (41-60), high (61-80) and very high (81-100). Very low AS habitat dominated the study area and decreased from 1985 to 2005; while low, mid, high and very high AS habitat increased through time. Very low and very high SH habitat fluctuated from 1985 to 2005 but remained relatively constant; low SH habitat increased slightly; while mid and high AS habitat gradually decreased through time. Year Percent Composition (%) of two Habitat Indices across the Study Area Attractive-sink (AS) Categories Safe-harbor (SH) Categories Very Low Mid High Very Very Low Mid High Very low High Low High The majority of the total area was dominated by very low safe-harbor values for all years and was less variable across time than the attractive-sink values (45.8% for 1985, 45.6% for 1990, 45.7% for 1995, 45.7% for 2000, and 46.0% for 2005). There were decreasing amounts of low, mid, high, and very high categories (Table 6, Figure 3 above). The combined high and very high SH categories didn t change substantially over the years totaling 14.3% of the study area for 1985, 14.2% for 1990, 13.6% for 1995, 13.3% for 2000, and 13.6% for 2005; the greatest decline occurred between 1990 and High and very high SH habitats declined by 4.9% from 1985 to 2005 within the study area and composed the smallest proportion of the landscape. SH habitat did not change

53 46 substantially through time because the increase in anthropogenic disturbances from 1985 to 2005 both increased RSF and risk values to varying levels depending on the location within the study area. For example, the southeast corner of the study area was relatively less affected by disturbances (i.e. had a higher proportion of upland tree in 2005 and hence less risk); therefore, the development of cut blocks had the affect of improving GB habitat (increase in RSF) while introducing only a small level of risk. As a result, SH habitat was high and actually increased. In contrast, an area with dense disturbances such as between the town-sites of Cadomin, Robb and Hinton, an increase in cut blocks and roads was associated with very high risk (and high RSF); therefore, SH habitat declined. Subsequently, over the entire study area, disturbance features in some areas increased safe-harbor habitat, effectively counter-acting the loss in SH habitat from land-cover changes (e.g. loss of proportion of upland tree) in other, more affected areas. Mean AS and SH values were also calculated across the study area (Table 7). From 1985 to 2005, average AS values increased by 61.3%, with the greatest increase occurring between 1995 and 2000 (21.9%). Average SH values decreased slightly from 1985 to 2005 (3.4%) across the study area (Table 7). The greatest decrease occurred between 1990 and 1995 (1.7%), followed by 1995 and 2000 (1.3%). In summary, based on the change in the percent composition of the five categories as well as the mean values, the greatest change occurred between 1990 and 1995 for the SH index, and between 1995 and 2000 for the AS index. Table 7: Mean and standard deviation summaries of AS and SH habitat across the study area. Average AS values increase by 61.3% across 20 years; the greatest increase occurred between 1995 and Average SH values decreased insignificantly by 3.4%; the greatest decrease occurred between 1990 and Attractive-sink (AS) Safe-harbor (SH) Year Mean SD Mean SD

54 Five-state Habitats Non-critical habitat dominated the landscape within the study area for all years and decreased through time (62% for 1985, 61% for 1990, 60% for 1995, 56% for 2000 and 54% for 2005; Figures 9 and 11). Following non-critical habitat, there were decreasing amounts of secondary habitat, primary habitat, primary sink and secondary sink. Primary sink values corresponded to high attractive-sink values and primary habitat corresponded to high safe-harbor values. Figure 11: Percent composition of five habitat categories across the study area including: (1) non-critical (low RSF); (2) secondary sink (moderate RSF and high risk); (3) primary sink (high RSF and high risk); (4) secondary habitat (low risk and moderate RSF); and (5) primary habitat (low risk and high RSF). The landscape experienced the greatest change between 1995 and 2000 with increases in primary sink and secondary sink habitats, while secondary habitat decreased and primary habitat fluctuated but, cumulatively (over 20 years), remained constant. Primary habitat appeared to fluctuate insignificantly in proportion from 1985 to 2005, where 12.0% characterized the landscape in 1985 and 12.0% in 2005; the greatest decline occurred between 1990 and 1995, and the greatest increase occurred between 2000 and Conversely, secondary habitat decreased steadily from 17.4% in 1985 to 14.4% in 2005 (17% decline); the greatest decline occurred between 1995 and 2000 (8.4%). The landscape changed most significantly for secondary sink habitat which was 3.3% in 1985 and 10.0% in 2005 (increase of 203%) with the greatest change occurring between 1995 and 2000 (54%). Primary sink habitat also increased substantially from

55 48 5.0% in 1985 and 10.1% in 2005 (increase of 104%) with the greatest increase occurring between 1995 and 2000 (38.0%). In summary, the greatest change for secondary habitat and sink habitat occurred between 1995 and 2000 while primary habitat experienced no cumulative change. It is important to note that non-critical bear habitat (where occurrence is rare), can be enhanced by certain human-caused disturbances so that bears will be attracted to the area. This occurs because edge habitat can increase the probability of occurrence in an otherwise closed forest. However, as observed in this study, the decrease in non-critical habitat from 1985 to 2005 (14%) was associated with an increase in both primary (104%) and secondary sinks (203%) and a decrease in secondary habitat (17%). The mortality risk associated with the land-cover change was greater than the associated benefit to bears. As more non-critical habitat was converted to sink habitat than source habitat through various human-induced disturbances, these results show more of a net loss in habitat Disturbance Features In an attempt to correlate the temporal changes in grizzly-bear habitat with changes in disturbance features, the greatest increase in disturbance development was assessed. Various human-caused disturbances were measured for range and variance spatially and temporally in order to generate the NDI including railways, pipelines, power lines, roads, well sites, cut blocks, mines and burns. Figure 12 shows the changes in the densities of the different disturbance types from 1985 to Linear feature density (including roads, pipelines, power lines, and railways) increased by 92% (from 2.62 to 5.04 ha/km 2 ) from 1985 to Areal feature density (including cut blocks, mines and burns) increased by 3600% (from 0.17 to 6.25 ha/km 2 ) from 1985 to Well site density increased by 270% (from 0.11 to 0.41 ha/km 2 ) from 1985 to Over the span of 20 years, the total density of all disturbances increased by 300% from 2.9 to 11.7 ha/km 2. The greatest increase in

56 49 densities for all disturbance types occurred between 1995 and 2000, which coincides with the greatest increase in sink habitats (i.e. primary and secondary sink and attractive-sink) as well as the greatest decrease in secondary habitat. Primary habitat did not change cumulatively while safe-harbor habitat did not change substantially (Table 6 and Figure 11, sections and 3.2.2). Figure 12: The change in densities (ha/km 2 ) of three different disturbance types from 1985 to Areal features (cut blocks, burns, mines) experienced the greatest increase from 0.17 to 6.25 (ha/km 2 ); followed by linear features (road, pipeline, power line, railway; 2.62 to 5.03 ha/km 2 ), and well sites (from 0.11 to 0.41 ha/km 2 ). The greatest increase in densities for all disturbance types occurred between 1995 and Watersheds with the greatest conservation concern in 2005 Determining which watersheds had the greatest conservation concern as of 2005 was evaluated based on the five-state habitat models, and was separated into four specific analyses.

57 Watersheds with the highest proportion of source habitats (and low sink and non-critical habitats) Watersheds exhibited varying proportions of habitat-states in 2005 (Table 8). Table 8: Percent composition of five habitat-states (non-critical (NC), secondary sink (SS), primary sink (PS), secondary habitat (SH), and primary habitat (PH)) for 30 watersheds within the study area. Percent Composition of Five Habitat-states Watershed Abbreviation NC SS PS SH PH NC SS PS SH PH Blackstone Creek BLA Cardinal River CAR Chungo Creek CHU Dismal Creek DIS Edson River EDS Elk River ELK Embarras River EMB Erith River ERI Gregg River GRE Lower Brazeau River LBR Lower Nordegg River LNO Maligne River MAL Middle Brazeau River MBR McLeod River 2 MCL McLeod River 3 MCL McLeod River 4 MCL Middle Pembina River MPE Nosehill Creek NOS Obed Creek OBE Pembina River PEM Shunda Creek SHU Southesk River SOU Sundance Creek SUN Trout Creek TRO Upper Erith River UER Upper McLeod River UMC Upper Nordegg River UNO Upper Pembina River UPE Upper Rocky River URO Wolf Creek WOL LNO, LBR, WOL, MCL2, MCL3, ERI, and MPE watersheds all had high amounts of noncritical habitat (i.e. >75%) where GB occurrence was suggested to be rare. The highest proportion of primary habitats in 2005 occurred in MAL (36.0%), followed by UMC (34.8%), and URO (32.6%), suggesting that these watersheds offered secure habitat and had a high probability of GB occurrence (URO and MAL are located in JNP). The highest

58 51 proportion of primary habitat combined with secondary habitat occurred in CHU (57.8%), followed by BLA (55.7%), CAR (54.9%), UMC (52.7%), and MAL (49.5%) (Table 9). CHU, BLA, CAR and UMC watersheds are located adjacent to each other northeast of the Rocky Mountains. MAL watershed is located within JNP and therefore it is not surprising to observe high-quality habitat. CHU watershed had the highest proportion of primary and secondary habitats combined (57.8%) and the lowest proportion of primary sinks, secondary sinks and non-critical habitat combined (42%), suggesting that CHU was both the most secure and most attractive watershed for bears. This watershed was most secure due to a lack of disturbances and a high proportion of upland tree. Table 9: Watersheds in 2005 with: (left) greater than 25% primary habitat (PH) and secondary habitat (SH); and (right) greater than 25% primary habitat (PH) and secondary habitat (SH). Watersheds with >25% PH and SH Watersheds with >25% PS and SS Watershed Proportion Watershed Proportion CHU 57.8 UER 61.4 BLA 55.7 MCL CAR 54.9 GRE 43.8 UMC 52.7 PEM 42.8 MAL 49.5 UPE 40.4 SHU 47.0 DIS 36.1 URO 41.9 EMB 34.6 MBR 41.2 ELK 29.2 GRE 40.3 SUN 29.0 UPE 38.9 UMC 28.3 SOU 38.2 EDS 27.7 UNO 37.9 MCL NOS 31.1 PEM 30.9 TRO 29.6 OBE 25.1

59 Watersheds with the highest percentage of primary sink (PS) and secondary sink (SS) habitats The highest proportion of primary sink habitats in 2005 occurred in MCL4 (35%), followed by UPE (28.2%), GRE (28.2%) and UER (27.9%), suggesting that these watersheds were non-secure in nature and had a high probability of GB occurrence. The highest proportion of primary sinks combined with secondary sinks occurred in UER (61.4), MCL4 (50.9%), followed by GRE (43.8%), PEM (42.8%), and UPE (40.4%) (Table 9), suggesting that these watersheds were non-secure in nature and had a moderate-tohigh probability of GB occurrence. These watersheds are located adjacent to each other and centered around the town-site of Robb, Alberta. They are also immediately north of the watersheds found to exhibit the highest proportion of source habitat (section 3.3.1) Watersheds with the least variation between sink (PS and SS) and source habitats (PH and SH) with less than 25% non-critical habitats For watersheds composed of less than 25% non-critical habitat, UPE, GRE, PEM, MCL4 and UMC exhibited the most similar proportions of source habitats (i.e. primary and secondary habitats) and sink habitats (i.e. primary and secondary sinks) (Figure 13).

60 53 Figure 13: Watersheds with the least variation between the sink habitat categories (i.e. primary sink (PS) and secondary sink (SS)), and the source habitat categories Watersheds that showed the greatest decline in habitat quality from 1985 to 2005 It is also valuable to investigate the trend in habitat-states across time, isolating watersheds that may have the most immediate conservation concern. The proportions of habitat-states varied among watersheds in 1985 (Table 8). Many watersheds exhibited high proportions of non-critical habitats (i.e. >75%) in 1985 including ELK, UNO, Lower Nordegg River (LNO), Lower Brazeau River (LBR), WOL, MCL2, MCL3, EMB, ERI, TRO and Middle Pembina River (MPE); this suggests that these watersheds had low habitat quality (i.e. where GB occurrence was suggested to be rare) (Figure 14). The highest proportion of primary habitats in 1985 occurred in MAL (36.0%), followed by UMC (35.1%) and URO (32.4%). The highest proportion of primary sink habitats in 1985 occurred in UMC (24.4%), followed by CAR (20.0%) and BLA (15.9%), suggesting that the habitat was non-secure in nature.

61 Figure 14: Maps of habitat-states evaluated across watersheds for a) 1985 and b) CHU had the highest proportion of source habitat (primary and secondary habitat), as well as the lowest proportion of sink habitats (primary and secondary sinks) and non-critical habitat combined. UER watershed had the highest proportion of sink habitat, while UPE had the most similar proportions of sink and source habitat. UER also had the greatest decline in amount of source habitats and the greatest increase in amount of sink habitats from 1985 to

62 55 The watershed that showed the greatest decline in habitat quality from 1985 to 2005 was UER (24.5% decline in primary and secondary habitats, accompanied by a 47.0% increase in secondary and primary sink habitat), followed by MCL4, PEM, and UPE (Figure 15). Specifically, these watersheds had the greatest decline in amount of source habitats and the greatest increase in amount of sink habitats. Figure 15: Watersheds with the greatest incline in sink habitats (primary (PS) and secondary sinks (SS)) associated with the greatest decline in source habitats (primary (PH) and secondary habitat (SH)) from 1985 to Over-estimated or under-estimated mortality risk and/or RSF models There were a number of factors that could have contributed to either over- or underestimating the number of disturbance features in the NDI, thereby influencing the twodimensional habitat models and the habitat quality analysis. These included a lack of certain disturbance types in the analysis, errors of omission in the ODI and the reference land-cover map, and methodological problems associated with land-cover labeling. These factors are addressed in the following paragraphs.

63 56 Seismic lines have an impact on the landscape but mapping this disturbance was not within the scope of this analysis. Including this disturbance type in the analysis may have changed the results in terms of the impact to grizzly-bear habitat. The accuracy of the new disturbance inventory, and in turn, the measure of habitatstates and conditions (i.e. habitat quality), was dependent on the accuracy of the original reference maps and disturbance inventory (i.e. ODI). Refer to Linke et al. (2009a) for thematic accuracy results on the change detectability, disturbance classification type (e.g. burn) and land-cover classification (i.e. land-cover labels assigned to the disturbance features) on the original 1998 to 2005 disturbance database that was used for this analysis. The change detectability for the ODI was assessed at 100 percent overall accuracy and a Kappa coefficient of 1.0 based on 178 random samples distributed between change and no change features with 10% allowable error (Linke et al. 2009a). The disturbance classification type was assessed at an overall accuracy of 98 percent and a Kappa agreement of 0.97 based on 256 random samples distributed proportionally between the disturbance types with 20% allowable error. The Land-cover classification was assessed at an overall accuracy of 80 percent and a Kappa agreement of 0.64 based on 256 random samples with size-proportional representation. Overall, the accuracy assessment on the ODI produced very good results and it would be expected that the NDI would retain similar quality accuracy results. For this analysis, areal change features between pre-1985 and 1998 were identified by using the wetness layer with the Land-cover Change Mapper (LCM) tool. In contrast, Linke et al. (2009a) identified the change features (from pre-1998 to 2005) using the enhanced wetness difference index (EWDI) method developed by Franklin et al. (2001) and conducted object oriented analysis. In general, the LCM tool performed very well. After visually inspecting the results for the 12 independent bi-temporal changedetections ranging from 1985 to 1998, the LCM tool needed to be re-run on an average

64 57 of three times based on the difference image and the manually selected thresholds. The bi-temporal analysis identified 100% of the changes that the cumulative analysis detected (complete change between 1985 and 1998), as well as additional changes which were missed by the cumulative analysis. This is to be expected considering the shorter temporal resolution. The LCM tool detected 100% of the areal changes identified in the ODI as pre-existing 1998 features reported in Linke et al. (2009a). Errors of omission in the ODI, however, contributed to slightly under-estimated cut block features in this analysis. As mentioned previously, only the changes detected that were present in the ODI or in the reference 1998 land-cover map (defined as reference or static objects) were used in order to maintain consistency across the entire time series. For example, if the LCM detection identified cut blocks that occurred between 1985 and 1998 (and were still discernable in the 1998 wetness imagery), but did not exist in the ODI and were labeled as upland tree in the 1998 land-cover reference map, these features were removed from the NDI. Therefore, the cut block disturbances were slightly under-estimated (errors of omission); however, these features did not make up a significant proportion of the total cut blocks in the NDI. Based on visual inspection of the ODI, most of the omitted cut blocks appeared to have originated in the detection of pre-existing 1998 features (all those appearing prior to 1998). The cut block features existing prior to 1998 in the ODI were detected by converting the 2003 reference map to a polygon layer and extracting those polygons that overlapped with pre-existing GIS cut block features (the GIS layer developed by FRI 2009). Another potential source of omission errors in this analysis involved linear and point features. Since this analysis only included pre-existing linear and point features that were detected in the ODI and the ODI was assumed to be correct, there may have been some features that occurred prior to 1985 but were overgrown by 1998 (e.g. pipelines) or missed in the original analysis. Roads, power lines, railways and well sites would most likely still be on the landscape; pipelines could have overgrown but these would

65 58 represent a very small percentage of the landscape prior to Furthermore, based on visual inspection of the 1985 imagery and pre-existing 1999 linear and point features, there did not appear to be any features omitted from the ODI (either in error or overgrown). It was not within the scope of this research to quantify possible omitted linear or point features in the ODI; however, these could have been under-estimated thereby under-estimating risk to GBs. The last factor that could have introduced errors in the new disturbance inventory (and had a significant bearing on the effects of RSF values) included inaccurate land-cover labeling in the backdating and updating process. The RSF model included specific landcover variables and distance to forest edge from specific land-cover types. Each landcover type had varying degrees of strength in the RSF model (e.g. for the main Yellowhead region, the coefficient for shrub was and for upland herb). While an increase in one land-cover type would result in lower RSF values, another would result in higher RSF values. Therefore, care must be taken in applying decision rules and exceptions to decision rules while backdating and updating the land-cover maps, specifically when labeling shrub and upland herb. Beyond the scope of this research, but perhaps a more accurate method to establish succession rates of disturbance features would be to model the Normalized Difference Vegetation Index (NDVI) in disturbed areas. New definitions of land-cover categories would be developed that would most likely be more accurate than simple decision rules. Furthermore, improved labeling techniques would allow for a more accurate understanding of longterm impacts of resource development and forest succession on grizzly-bear habitat needs.

66 The influence of mortality risk (R) and habitat-occurrence (RSF) models on habitat-states and conditions Using Eq. s [1] and [2], human-caused mortality risk (R) and female GB habitatoccurrence (RSF) during late hyperphagia (16 August to 15 October) were calculated for the given study area from 1985 to 2005 at five-year intervals (Figure 16). This twodimensional habitat model provided the foundation for the derivations of the five habitat-states and habitat conditions and therefore warranted a brief summary. The mid-section of the RSF map (Figure 16: C, D), where most of the harvesting occurred and where there was a clear increase in RSF values from 1985 to 2005, has perennially high RSF values, suggesting there were continuous food resources from 1985 to This area contained better grizzly-bear habitat than the eastern margins or the alpine areas in the southwest. It is also evident that risk of grizzly-bear mortality was higher throughout the mid-to-north section of the study area in the foothills (Figure 16: A, B) where there was an increase in disturbances from 1985 to As expected, the risk and RSF values in the mountainous area within JNP (southwest of study area) remained relatively constant; risk and RSF values were high along mountainous river valleys.

67 60 Figure 16: Predicted risk of human-cause grizzly-bear mortality in 1985 (A) and 2005 (B) in west-central Alberta, Canada; Predicted relative probability of occurrence (RSF) in 1985 (C) and 2005 (D) for adult female GBs during late hyperphagia (16 August to 15 October). Both RSF and risk values increased from 1985 to 2005; mean RSF values for the study area were 4.1 (1985), 4.1 (1990), 4.2 (1995), 4.5 (2000) and 4.6 (2005) with a possible range of 1 to 10; mean risk values had more variability and were 2.6 (1985), 2.7 (1990), 3.1 (1995), 3.6 (2000), and 3.9 (2005) with a possible range of 1 to 10. Larger-scale inspection of the RSF map shows that areas near the edge of areal features (e.g. cut blocks), especially newer ones, indicate higher RSF values than the interior of

68 61 openings, especially larger ones (Figure 17). Typically, creating new openings in mature forest increases edge habitat and encourages growth of understory forage, therefore creating better habitat for GBs (higher RSF values). As regenerating cut blocks age, crown closure values increase, thereby altering RSF, sink and source habitat without imposing any additional disturbances in the area. However, this generalization does not account for different silviculture practices, site preparation methods, or pesticide use. Research is needed to account for these different methods and possibly improve the RSF model (and account for varying crown closure succession rates). For example, Nielsen et al. (2004b) found that irregular-shaped cut blocks were more attractive to bears; in addition, silvicultural methods may influence food resource availability (Nielsen et al. 2004c). Larger-scale inspection of the risk map shows that certain sections of road were associated with higher risk if they traversed higher-quality habitat, such as riparian areas, open meadows or cut blocks. Figure 17: Large scale inspection of an RSF map for 2000 and 2005, indicating higher RSF values along the edge of a cut block (especially newer ones) than in the center of a cut block (especially larger ones). In summary, an increase in RSF values represents an increase in habitat resources which means a greater likelihood of a more healthy grizzly bear population. Mean RSF values for the study area, which included three different GB population units, increased by 12% from 1985 to 2005, indicating a benefit to the local GB populations. Similarly, a decrease in risk values represents an improvement in habitat security that will reduce mortalities and aid in grizzly bear recovery strategies. The combination of a mean

69 62 increase in RSF and a mean decrease in risk values would enhance habitat quality. However, mean risk values for the study area increased by 50% from 1985 to 2005, representing a net loss of high-quality habitat. 3.6 Conservation Measures Two types of grizzly bear conservation measures can be applied to west-central Alberta watersheds based on the findings of this analysis: protection and mitigation. First, protection approaches (such as eliminating further industrial development, creating reserves, and limiting human access) can be used in watersheds that showed high proportions of primary and secondary habitat (with low proportions of sink and noncritical habitats). The watersheds in most need of protection measures were Chungo Creek (CHU), Blackstone Creek (BLA), Cardinal River (CAR) and Upper McLeod River (UMC). Maligne River (MAL) was also found to exhibit these qualities but it is located in Jasper National Park and therefore already has protected status. Source habitat in these watersheds was attributed to a lack of disturbances, open meadows, and high femalerng. Second, mitigation approaches (such as limiting human access and industrial development or removing bear attractants) can be used in watersheds that: (1) exhibited a high proportion of source and sink habitat (with less than 25% non-critical habitat) in 2005; (2) showed a combination of the greatest decline in source habitat and the greatest incline in sink habitat from 1985 to 2005; or (3) had a high proportion of sink habitat in The watersheds in most need of mitigation measures based on one or more of these factors were Upper Erith River (UER), McLeod River 4 (MCL4), Pembina River (PEM), Gregg River (GRE), Upper Pembina River (UPE), and Upper McLeod River (UMC). PEM, UER, UPE and MCL4 were not only among the watersheds with the greatest proportion of sink habitat in 2005, but they also exhibited the greatest decline in habitat quality from 1985 to 2005 (increase in sink and decrease in source habitat).

70 63 Furthermore, with the exception of UER, they also had a substantial proportion of source habitat in Sink habitat in these watersheds was mostly associated with a high density of forestry activities, road development and well site construction. UMC was an interesting watershed in that it qualified for both protection and mitigation measures; it ranked in the top 4 watersheds in terms of proportion of primary and secondary habitat (53%) and it exhibited 28% sink habitat. Although there was twice as much source habitat as compared to sink habitat, more than a quarter of the watershed represented a high risk to grizzly bears; therefore, both mitigation and protection measures would be effective in this watershed. The town-site of Cadomin and a rail line is located in the north section of UMC, and therefore limiting human access is not feasible. However, reducing the risk of bear-human conflict by eliminating bear attractants, reducing vehicle and train speeds, and promoting bear education in the towns-site would be effective. The mortality risk associated with the southern section of the watershed was associated with proximity to streams and a gravel (unimproved) road built prior to Since this area is relatively undisturbed, limiting human access in this area would be feasible and effective. Those watersheds that were composed of high proportions of non-critical habitat do not necessarily require mitigation measures. However, if development activities are placed in non-critical habitat areas of dense, closed tree cover where open spaces are limited, there will likely be a net gain in habitat for grizzly bears. In other words, non-critical habitat can be converted to primary habitat through properly placed development activities, providing road access is restricted after the development work is completed. Furthermore, openings such as regenerating cut blocks can be seeded with bear foods such as buffaloberry (Sherperdia canadensis) and sweet-vetch (Hedysarum alpinum) to encourage bear use in secure areas (i.e. no road access). In addition, land managers should consider harvest techniques to increase edge habitat such as a typical 2-pass versus a single-pass harvest clear-cut system.

71 64 Grizzly bear mortality in areas of sink habitats is a function of several landscape layers, but one of the primary factors is distance-to-open, motorized roads (Graham et al. 2010, Roever et al. 2008); therefore it is useful to mitigate the effects of roads. Over 90% of known human-caused grizzly bear mortalities in Alberta have occurred within 500 m of a road (Roever et al. 2008). Graham et al. (2010) found that, in the fall season, sub-adult females were within 200 m of roads more frequently than expected (and more than adult males), indicating that females had a greater chance of encountering humans. In the spring season, females with cubs were also within 200 m of roads more frequently than expected. Potential mitigation measures for roads include, but are not limited to: (1) reducing density of roads in GB habitats; (2) reducing human presence on these roads (e.g. installing locked gates or barriers, or removing access structures especially during fall and spring); (3) creating or leaving dense tree buffers along roads that traverse open habitat; (4) completing work and decommissioning roads during the seasons of lower occupancy; and (5) reducing grizzly bear food near roads by reducing the width of roadside ditches, eliminating the planting of clover and reevaluating road placement (e.g. avoid roads near water) in areas with high grizzly bear density. Considering that the risk of grizzly bear mortalities is high in disturbed areas, and that grizzly bears seem to prefer areas that have been disturbed in certain foraging seasons and land-cover types, controlling human use in high-quality habitats would be a logical step towards the conservation of grizzly bears. Limiting human access to high-quality grizzly bear habitat has been useful in a variety of national parks. For example, Banff National Park closes areas to humans during late hyperhagia season (coinciding with the ripening of Shepherdia canadensis) that are valuable to grizzly bear sows and cubs. This effectively reduces the risk of human-bear encounters during sensitive foraging periods. However, limiting human access as a conservation measure can be challenging due to the attitudes of people - the challenge for land managers is juggling multiple interests over the same landscape. In Alberta, policies to protect grizzlies have been controversial due to the variety of stakeholders interested in using land that grizzly bears and other

72 65 species need to survive (e.g. recreationalists, hunters, developers, etc). Interdisciplinary problem solving (IPS) has been used as a way to establish common ground between stakeholders rather than focusing on the differences (Rutherford et al. 2009). IPS workshops involve guiding stakeholders through the problems of grizzly bear conservation and the sociopolitical and ecological context for those problems. It also requires participants to analyze their own beliefs and see other perspectives. IPS has been successful in grizzly bear conservation in Wyoming, Australia (Clark et al. 2002) and Banff National Park, Alberta (Rutherford et al. 2009). Gathering community support and involvement can make a contribution to grizzly bear conservation in west-central Alberta. Mitigation measures may also involve removing grizzly bear attractants from an area. For example, if a watershed contains a high proportion of sink and source habitat, and limiting human access is not feasible (e.g. near a town-site), removing grizzly bear food resources along trails and open areas may reduce the potential for human-bear encounters. For example, Banff National Park actively removes buffalo berries (Shepherdia canadensis) along high human-use hiking trails.

73 CONCLUSION Combining models of grizzly bear risk and the probability of grizzly bear occurrence (RSF) into attractive-sink and safe-harbor indices as well as habitat-states differs from the traditional habitat quality models. Models that attempt to define habitat quality by determining grizzly bear occurrence without considering the associated risk, greatly misrepresent grizzly bear habitat. The objectives of this research were to determine: (1) what areas and watersheds showed the greatest and least amount of change in habitat quality from 1985 to 2005; (2) which years showed the greatest change in habitat quality across the 20-year time span; and (3) which watersheds had the greatest conservation concern as of Habitat-states and conditions were used as surrogates for habitat quality. The greatest change in habitat quality from 1985 to 2005 occurred between the townsites of Robb, Cadomin and Hinton, as well as around the north and northwest edges of the study area. The areas incurred a change from low-to-high attractive-sink values, and a change from source habitat (primary and secondary habitat) to sink habitat (primary and secondary sinks). These changes were primarily attributed to increased road access from forestry and oil and gas activities as well as an increase in forest edges caused by forestry activities in an already fragmented landscape. Areas of the least amount of change were variable across the indices and states. Three clusters of perennial high AS values were located immediately northeast of the Rockies; streams also exhibited continuously high AS values from 1985 to High risk in these areas was associated with proximity to streams and mid-proportion of upland forest cover while high RSF in this area was attributed to a high probability of female grizzly bear occurrence (i.e. femalerng). Consistently low values of SH habitat occurred in the non-vegetated high alpine, water-bodies, and southeast of Edson along the eastern edge of study area; this was attributed to low RSF (due to non-habitat mask) and proximity to white zone. Consistently high SH values occurred along a thin strip

74 67 southeast of Cadomin and lower valley networks (at a distance of at least 100 m from streams) within Jasper National Park (JNP). These results were attributed to high femalerng, a lack of disturbances, open meadows (i.e. herb and shrub), and proximity to protected area. The analysis of the five habitat-states showed that the least amount of change occurred in the mountainous region within JNP, extended out to 30-km northeast of the Rocky Mountains. This area represented high proportions of partially contiguous source and sink habitat. The north section of the study area exhibited continuously high sink habitat due to proximity to agricultural areas. Grizzly bear habitat incurred the greatest change between the years of 1995 and Attractive-sink habitat, as well as primary and secondary sink habitat increased the most while secondary habitat decreased the most during this time period. Not surprisingly, the landscape also endured the greatest increase in human-caused disturbances during this same time period (i.e. the cumulative development of well sites, areal features and linear features increased by 57.7% from 1995 to 2000). In order to segment the landscape into more appropriate management zones relevant to grizzly bears, conservation strategies were focused on watersheds for this analysis. The watersheds in most need of mitigation measures were Upper Erith River (UER), McLeod River 4 (MCL4), Pembina River (PEM), Gregg River (GRE), Upper Pembina River (UPE), and Upper McLeod River (UMC). These watersheds exhibited either: (1) a high proportion of sink habitat in 2005; (2) a high proportion of source and sink habitat (with mess than 25% non-critical habitat) in 2005; or (3) showed a combination of the greatest decline in source habitat and the greatest increase in sink habitat from 1985 to Mitigation measures include limiting human access and/or removing grizzly bear attractants in order to reduce human-bear encounters and decrease grizzly bear mortality. The watersheds in most need of protection measures were Chungo Creek (CHU), Blackstone Creek (BLA), Cardinal River (CAR) and Upper McLeod River (UMC). These

75 68 watersheds exhibited high source habitat with a combination of low sink habitat and low non-critical habitat. This was attributed to a lack of disturbances, high femalerng, and open meadows. CHU represented both the most secure and most attractive watershed for bears due to the reasons stated above and it was within a high proportion of upland tree cover. These watersheds are located adjacent to one another; therefore by providing protection, GBs could benefit from a contiguous area of high-quality habitat. Protection measures may include limiting human access as much as possible and potentially creating reserves to maintain the existing high-quality habitat. This could foster the wary behavior in grizzly bears that most managers consider desirable, given that habituated bears have a significantly elevated mortality risk. Landscape patterns should be considered when managing watersheds to avoid isolation of sites within a matrix of risky habitat. As west-central Alberta is an important resource for grizzly bears as well as a hotspot for further industrial development, it would be useful for land managers to continue this time series, expanding on the dataset. Further monitoring would provide insight into the long-term effects of human-caused disturbances (i.e. forestry) on grizzly bear habitat. However, when using this methodology for further analysis, the temporal extent must be carefully considered. As the base map is updated and backdated further through time, reference objects inevitably change, and it is possible that as the temporal extent of the monitoring horizon expands, the accuracy of the final map products will decline. Since the reference year was 2003 for the initial study (Linke et al. 2009a), reference features are still recent for this map series and therefore this database could be used for future analysis of west-central Alberta. As this study focused on the late hyperphagia season (16 August to 15 October), it would be useful to assess habitat quality change in other seasons, such as the hypophagia (i.e. 1 May to 15 June) or early hyperphagia (i.e. 16 June to 15 August) to account for variations in habitat use through time.

76 69 This study allowed for an understanding of the trend in grizzly bear habitat from 1985 to 2005 over 20 years. At the rate of sink habitat growth demonstrated in this study, it would be useful to assess the likelihood of grizzly bear persistence using population viability assessments (PVAs). In traditional demographic-based PVAs, habitat was proven as the single most influential parameter for long-term population viability (Boyce et al. 1994). The backdated and updated maps developed from the disturbance inventory framework extending from 1985 to 1998 at five-year intervals and annually from 1998 to 2005 represent a very powerful research tool in terms of grizzly bear conservation. The landscape within the study area experienced increasing disturbances over the 20-year span, including well sites, linear features and cut blocks; furthermore, disturbances were not new to the area in The grizzly bear population density within this study area was estimated to be 4.8 GB/1000-km 2 in 2005 (FRIGBRP unpublished data 2004). Further research is needed to apply the same methodology in an area of higher bear density such as immediately north of the study area in the Grande Cache grizzly bear population unit where there were an estimated 18.1 bears/1000 km 2 in 2008 (FRIGBRP unpublished data 2004). It is possible that the density is higher in the Grande Cache area due to a later surge of human-caused development or perhaps less of a disturbance footprint as compared to the area investigated for this analysis.

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84 77 APPENDIX A: Remote Sensing and Change-detection for Wildlife Habitat Monitoring Many natural and man-made features on the earth s landscape are constantly changing over time, which has a tremendous effect on the physical and human processes occurring around the world. For example, Foody (2001) demonstrated that, at a regional scale, land-cover change was strongly correlated with rainfall variability. O Neal et al. (2010) found a correlation between the reduction in forest land-cover and an increase in summer temperatures. Land-cover change has been attributed to enhanced atmospheric CO 2 that contributes to global warming and which may in turn result in further land-cover changes (McAlpine et al. 2009). Therefore, timely and accurate change-detection of the Earth s surface features provides improved understanding of human-social interactions to sustainably manage and use resources more effectively. Furthermore, large-area processes require an accurate monitoring of land- surface change over at least a few decades. The study of change-detection covers a wide variety of monitoring applications from land-use and land-cover change to glacier mass changes (Lu et al. 2004). Satellite remote sensing has long been considered a cost efficient and feasible method for detecting and monitoring landscape change over vast areas; as such, there are countless techniques to utilize it. This paper is divided into three main sections as follows: (1) the definition of change-detection using remote sensing including its purpose, applications, and the types of satellite sensors used; (2) the common methods and steps used to detect change; and (3) the issues surrounding change-detection when used to monitor habitat. 1.0 Change-detection Change-detection has been defined as the sensing of environmental changes that uses two or more scenes covering the same geographic area acquired over a period of time (NRCAN 2005). The objective of change-detection is to measure changes by comparing spatial representation of two points in time, while controlling all variability caused by differences that are not of interest (Green et al. 1994). Changes that are not of interest

85 78 can be caused by variables such as different sensor types, atmospheric conditions, illumination, viewing angle, and soil moisture; techniques to handle such variables are discussed in section 2.2. Certain criteria should be used to measure change-detection including: area change and change rates, spatial distribution of change types, change trajectories of land-cover types, and accuracy assessments of results (Lu et al. 2004). Remote sensing is considered an essential technology for monitoring change because traditional field data or aerial photo interpretation can be cost prohibitive, as well as limited to a local extent and are not easily accessible to regional or global extents (Coppin et al. 2004, McDermid et al. 2005). Remote sensing has the ability to cover vast areas across broad time scales in a cost-effective manner. Furthermore, remotely sensed data can provide a comprehensive record of land-cover change that may be the only way to obtain a multi-temporal data set for some monitoring applications, especially in remote areas which are logistically difficult to access. Change-detection based on remote sensing imagery uses consistent and repeatable procedures and accesses the non-visible regions of the electromagnetic spectrum (Coppin et al. 2004). The study of change-detection covers a wide variety of monitoring applications such as land-use and land-cover change, deforestation and forest regeneration, forest mortality and defoliation, wetland change, urban change, agricultural development, forest and vegetation land management, forest fire, ice forecasting, and glacier mass changes (Lu et al. 2004). For example, Muchoney and Haack (1994) compared four methods of change-detection to monitor hardwood forest defoliation caused by gypsy moths. Read and Lam (2002) used spatial statistics and selected landscape indices to characterize land cover and detect land-cover changes for the tropics. Lu et al. (2004) provided a full listing of literature related to various applications of change-detection. Many satellite-based remote sensing platforms provide data at a spatial and temporal resolution that are suitable for detecting and monitoring landscape change (Table 1). See section for a full description of spatial and temporal resolution considerations

86 79 when approaching change-detection. Currently available satellite based imagery can be divided into three categories based on its relationship between scale and spatial resolution. Low spatial resolution imagery is most suitable for studying phenomena that vary over hundreds or thousands of meters (i.e. small scale); examples of sensors include NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer), EOS (Earth Observing System) MODIS (Moderate Resolution Imaging Spectrometer), and SPOT (Satellite Pour l Observation de la Terre) VEGETATION. Medium spatial resolution imagery is most suitable for studying phenomena that vary over tens or hundreds of meters (i.e. medium scale); examples of sensors include Landsat (Multispectral Sensory [MSS], Thematic Mapper [TM] and Enhanced Thematic Mapper [ETM+]), SPOT, IRS (Indian Remote Sensing satellite). High spatial resolution imagery is most suitable for studying phenomena that vary over centimeters to meters (i.e. large scale); examples of sensors include IKONOS and QUICKBIRD-2 (Franklin and Wulder 2002). Table 1. Selected remote sensing systems (and major characteristics) used to detect and monitor landscape change (Wang et al. 2009, NASA 2009(a,b), DG 2009, SIC 2009, USGS 2009, Jensen 2005). Sensor AVHRR/3 MODIS SPOT 4 HRVIR SPOT 5 HRG Terra (ASTER) Landsat TM Landsat ETM+ Landsat MSS IRS-P6 IKONOS QUICKBIRD Spatial Resolution (m) * Low (1100) Low (250, 500, 1000) Medium (20) Low (10 MS); Medium (20 SWIR) 15 Medium (30) Medium (30) Medium (79) Medium (23.5) High (4) High (2.44) Swath Width (km) x * MS = multispectral; SWIR = shortwave infrared. Spectral Resolution (nm) Temporal Coverage 1981-present 1999-present 1998-present 2002-present 1999-present 1982-present 1999-present present 1999-present 2001-present Temporal Resolution (days/visit) 14.5 visits/day to 3 1 to <3 1-5 The temporal resolution (i.e. orbital revisit periods) varies between sensors as well as shown in Table 1. Typically, low-spatial-resolution sensors have frequent revisit periods

87 80 while medium-spatial-resolution sensors have less-frequent revisit periods. The swath width determines image size (i.e. spatial extent) and therefore has significance on the analysis. If the study area spans across multiple paths of a low swath sensor system such as IRS, the analyst is required to fuse together multiple images; this potentially results in various complications such as temporal and spatial inconsistencies as discussed in sections 2 and 3. The type of remote sensor selected ultimately depends on the objectives and requirements of a specific project and the data available in the study area. For example, the spatial resolution of Landsat-5 TM sensor (30 x 30 m) allows for land cover changedetection consistent with the scale of land management (Jensen 2005). For long-term change-detection applications, data from different sensors may be used because singlesensor data may not cover the entire time series. For example, MSS data are available after 1972, TM data after 1982, and ETM+ data after Change-detection Methodology 2.1. Common Methods Depending on the research purpose, selection of an appropriate change-detection method has considerable significance in producing a high-quality change-detection product. For example, some techniques such as image differencing can only provide change and non-change information, while other techniques such as post-classification comparison can provide a complete matrix of from-to change information. Several change-detection techniques are often used to implement change-detection, whose results are then compared to identify the best product through visual assessment or a quantitative accuracy assessment.

88 81 Common change-detection techniques are grouped into seven categories: (1) algebra; (2) transformation; (3) classification; (4) advanced models; (5) Geographical Information System (GIS) approaches; (6) visual analysis; and (7) other approaches. The scope of this paper does not warrant an exhaustive description of the various methods associated with each category that are used to perform change-detection between images. Lu et al. (2004), Jensen (2005), and Gong and Xu (2003) provide a detailed summary of the various techniques. The most widely used methods are described below and these include: image differencing, change vector analysis (CVA), principal components analysis (PCA), post-classification comparison, and linear spectral mixture analysis (LSMA) (Lu et al. 2004) Image Differencing Image-differencing is an algebra-based process and involves subtracting the radiometrically and geometrically corrected imagery of one date from that of another (Jensen 2005, Lu et al. 2004). The subtraction involves positive and negative values in areas of radiance change and zero values in areas of no change; results are stored in a new change image. Threshold boundaries between change pixels and no-change pixels, which are found empirically, are selected in a change-image histogram. The advantages of this method are that it is one of the simplest methods commonly used, implementation is not difficult, and the interpretation of results is very straightforward (Lu et al. 2004). However, results depend on selecting thresholds and therefore the amount of change selected is subjective and must be based on prior knowledge of the study area. Furthermore, image-differencing simply identifies areas that may have changed and cannot provide a detailed change matrix; it is valuable when used in combination with other techniques (Jensen 2005). Examples of applications include forest defoliation (Muchoney and Haack 1994), land-cover change (Sohl 1999), and irrigated crops monitoring (Manavalan et al. 1995). Numerous studies have shown that visible red band image differencing is most effective in detecting change in semi-arid

89 82 and arid environments (Lu et al. 2004); though the choice of band depends on the type of environment. A special case of image-differencing involves subtracting transformed variables rather than subtracting raw image bands as described above. For example, the Tasselled Cap Transformation (TC) is often used for detecting change by subtracting the same component (e.g. wetness) from two separate dates. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) are other transformations used in image-differencing change-detection techniques Spectral Change Vector Analysis (CVA) Change vector analysis (CVA) is also an algebra-based process and is a theoretical extension of image differencing. This method generates two outputs: (1) the spectral change vector describes the direction and size of change from date 1 to date 2 (Chen et al. 2003); and (2) the total amount of change per pixel is calculated by determining the Euclidean distance between end points through n-dimensional change space (Michalek et al. 1993). The decision that a change has occurred is made if the threshold is exceeded. The threshold may be selected by examining consistently unchanged areas such as deep water areas (if possible), and recording their scaled magnitudes from the spectral change vector file. In contrast to image differencing, CVA can detect all changes greater than the identified thresholds and can provide detailed from-to change information. In addition, this method processes any number of spectral bands; however, a disadvantage is that identifying land-cover change trajectories is difficult and requires expertise. Examples of applications include disaster assessment (Schoppmann and Tyler 1996) and conifer forest change (Cohen and Fiorella 1998) Principal Components Analysis (PCA) Principal components analysis (PCA) is a transformation-based process and involves two techniques: (1) combining two or more dates of images into a single file, performing

90 83 PCA, and analyzing the minor component images for change; or (2) performing PCA on the multi-temporal imagery separately, then subtracting date two PC image from the corresponding PC image of date one. The methods assume that multi-temporal data are highly correlated and change information can be highlighted in the new components (Lu et al. 2004). This method reduces data redundancy between bands, and emphasizes variable information in the derived components. It is less complex than the other methods in the transformation category such as Gramm-Schmidt (GS) and Chi-square. A disadvantage is that the results are dependent on the analyst s skill in identifying which component best represents change and selecting appropriate thresholds; therefore, it is a subjective process. Furthermore, results are often difficult to interpret and label, the methods do not produce a detailed matrix of change class information, and PCA is scene dependent. Accurate atmospheric calibration for each date of image is required to produce accurate change-detection and avoid spurious change results, (atmospheric correction is described in section ). Examples of applications include land-cover change (Kwarteng and Chavez 1998), tropical forest conversion (Jha and Unni 1994), and forest mortality (Collins and Woodcock 1996) Post-classification comparison (PCC) Post-classification comparison (PCC) is a classification-based process and involves separately classifying multi-temporal images into thematic maps, then implementing pixel-by-pixel comparison of the classified images for change. It works best in a high resolution environment (i.e. when the pixel is smaller than the object of interest) where there are limited problems with mixed pixels. PCC minimizes the impacts of atmospheric, sensor and environmental differences between multi-temporal images, and provides a complete matrix of from-to change information. However, it is time consuming and requires expertise for classifying the

91 84 images. The final accuracy depends on the quality of the classification (and training sample data) for each date; selecting high quality and numerous training sample sets is often difficult and not feasible. As a result, other techniques such as image transformations and vegetation indices are used in conjunction to improve supervised or unsupervised classifications. Examples of applications include land-use and landcover change (Foody 2001) and urban expansion (Ward et al. 2000). In order to perform a classification-based change-detection, an appropriate image classification logic is applied (i.e. supervised, unsupervised, or hybrid) where statistical decision rules are used to separate pixels into categories. Supervised classification is the process of using samples of known identity to classify pixels of unknown identity. This method requires prior knowledge of the area, classes of interest for the application, and the general spectra, temporal and spatial characteristics of those classes, as well as representative ground data (Jensen 2005). Common supervised algorithms include parallelepiped, minimum distance to means, and maximum likelihood. Unsupervised classification is the process of automatically segmenting an image into spectral classes based on natural groupings found in the data; this method does not require prior knowledge of the study area, is more automated, and is based entirely on the spectral characteristics of the image (Jensen 2005). Unsupervised classification sometimes requires further processing such as aggregating classes. Numerous studies use unsupervised classification to gain a better understanding of the imagery, and follow up with a supervised classification to more effectively derive classes of interest Linear spectral mixture analysis (LSMA) Linear spectral mixture analysis (LSMA) is an advanced model-based process and involves comparing mixed spectral signatures with pure reference spectra to determine the mixture of pure end-members that produce the observed reflectance value. The actual proportion or abundance of the pure end-member class within the pixel is reported rather than simply reporting the dominant constituent (Jensen 2005). End-

92 85 members are selected from training areas on the image, or from field spectra of materials occurring in the study area, or from a relevant spectral library. Changes are detected by comparing the before and after fraction images of each end-member. The quantitative changes can be measured by classifying images based on the end-member fractions. The assumption with this method is that the spectral variation observed in an image is caused by mixtures of a limited number of surface materials. This method produces stable, accurate and repeatable results. It is especially useful in a L- resolution environment (i.e. when the objects of interest are smaller than the pixel) where mixed pixels are common. A disadvantage is that is it considered an advanced method and therefore requires expertise, especially in selecting appropriate endmembers. There are also problems associated with representing pure end-members across an image; Roberts et al. (1998) used multiple end-member spectra mixture models where the number of pure end-members and types of end-members varied across the image. In addition, there may also be issues with non-linearity (i.e. other sources of radiance may be reflected in addition to the mixture of end-members). Examples of applications include land-cover change (Roberts et al. 1998) and vegetation change patterns (Rogan et al. 2002) Change-Detection: General Steps Defining the Problem (Figure 1, step 1) There are general steps to perform change analysis using remote sensing as depicted in Figure 1 (Jensen 2005). The first step is defining the problem (step 1). This includes specifying the: (1) geographic region of interest (ROI)); (2) time period (i.e. multiple-date images, daily, seasonal, yearly); (3) land-cover classes of interest; (4) hard and/or fuzzy change-detection logic; and (5) pixel or object-oriented change-detection technique.

93 86 Figure 1. Flowchart depicting the steps involved in performing change-detection on multi-temporal images (Jensen 2005). Step 4 includes acronyms as follows: TC refers to Tasselled Cap Transformation, PCA refers Principal Components Analysis, PCC refers to post-classification comparison, LSMA refers to Linear Spectral Mixture Analysis. When selecting the ROI, it s important that each multi-date image used in the analysis is covered by the ROI and that the images are of high quality (e.g. low cloud cover). Some situations will require mosaicking two different images together in the event one image in the time series is of poor quality; however, this may reduce accuracy of the final change-detection. It is also important to select the correct time period depending on what phenomenon is being analyzed (Jensen 2005, Coppin et al. 2004). For example, seasonal vegetation phenology research would require a shorter temporal frequency than five-year time intervals as it is influenced by seasonal variations in weather. Selecting the appropriate land-cover classes for the research is also valuable in order to meet the research objective and to allow for comparison between studies (Jensen 2005). There are standardized land-cover and land-use classification systems for change-detection such as the American Planning Association Land-based Classification Standard (LBCS) and the U.S. Geological Survey Land Use/Land Cover Classification System for Use with Remote Sensor Data (Jensen 2005).