Nimpkish Black Bear Study: Habitat Analyses

Prepared for: Canadian Forest Products Ltd. Woss, BC

February 2003

This report has not been peer-reviewed and is not suitable for citation or distribution. Please refer to Davis et al. (in submission) for citation or distribution of the information contained in this report.

Prepared by:

Helen Davis, M.Sc., R.P.Bio. Richard D. Weir, M.Sc., R.P.Bio.

Artemis Wildlife Consultants 4515 Hullcar Road Armstrong, BC V0E 1B4 (250) 546-0531

Final Report – Nimpkish Black Bear Habitat Analyses

Executive Summary We examined habitat selectivity by American black bears (Ursus americanus) at 2 spatial scales (within home ranges and patches within stands) in coastal British Columbia, Canada from 1992–1995. We monitored 13 radio-collared males, but could only gather unbiased data for 4. We used information-theoretic inference to assess the effect of 18 habitat and spatial variables in 22 candidate models to explain selection of sites within home ranges. We examined patch scale selection within stands for 4 food and security variables by comparing the sites used by radio-collared males to typical stand conditions. Male black bears exhibited selectivity for a variety of resources that provided food and security at different spatial scales. The probability of use of sites by adult males increased with increasing values of berry-producing and succulent forage plants as well with increased security cover. Juvenile male black bears, however, did not make similar selections and may be relegated to using poor quality habitats with higher mortality risk because of social factors. The male black bears that we studied appeared to make the majority of their site selection decisions at the scale of the home range (i.e., for stands within their home ranges), although some selection was evident at the patch spatial scale. These results increase our understanding of the ecology of black bears and have several implications for the appropriate management of their habitats in coastal British Columbia. Data from 9 female bears were analysed and written as a scientific manuscript submitted for publication.

This report has not been peer-reviewed and is not suitable for citation or distribution. Please refer to Davis et al. (in submission) for citation or distribution of the information contained in this report.

ARTEMIS WILDLIFE CONSULTANTS i Final Report – Nimpkish Black Bear Habitat Analyses

Acknowledgements Funding for the field portion of the study was provided by the Habitat Silviculture Protection Account of the joint Federal and Provincial Forest Resource Development Agreement, the Habitat Conservation Trust Fund, Canadian Forest Products Ltd., and Wildlife Habitat Canada. Assistance during the collection of the field data was provided by A. Friedman, A. Hahn, P. Kaczensky, M. Kellner, C. Mueller, R. Ramcharita, D. Wellwood, and numerous volunteers. Alton Harestad (SFU) also provided crucial support and helpful ideas during the execution of the study. The habitat analyses of the Nimpkish Black Bear Study data would not have been completed without the support of the Forest Investment Account through Canadian Forest Products Ltd.’s Recipient Agreement and the efforts of John Deal. Canadian Forest Products Ltd. supplied all the TEM and road data that made such a complex habitat analysis possible. Tony Hamilton (MWLAP) provided the radiotelemetry and patch-scale habitat data and played an instrumental role in the analyses by answering endless questions. Volker Michelfelder (SFU) entered the vegetation data. People who rounded up obscure information that helped with the analysis included Doug Janz of MWLAP, Karl Wilson of DFO, Bert Svanvik of the Gwanii Hatchery, and Pat Bryant and Bev Webber of Canfor.

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Table of Contents Executive Summary...... i Acknowledgements ...... ii Introduction ...... 1 Data Constraints...... 3 Study Area ...... 3 Methods...... 5 Radiotracking ...... 5 Within home range selection...... 6 Habitat measurements ...... 7 Data Analysis...... 8 Model parameterization...... 9 Model selection and averaging ...... 9 Patch (within-stand) selection...... 10 Error Checking of Data ...... 10 Results...... 12 Radiotelemetry monitoring ...... 12 Selection within home ranges ...... 12 Patch scale selection...... 13 Discussion ...... 14 Management Implications ...... 16 Modelling Non-denning Habitat Suitability...... 17 Female Best Model...... 17 Adult Male Best Model ...... 18 Literature Cited ...... 18 Appendix A: Variable definitions...... 33 Appendix B: Groupings of variants and site series...... 37 Appendix C: Excluded model variables...... 40 Appendix D: Plant rankings for bear foods ...... 41 Appendix E: Plant phenology curves ...... 44 Appendix F: Values of canopy closure, horizontal visibility and food values recorded at random locations within each stand type...... 46

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Introduction From the dry pine forests of Arizona to wet temperate rainforests of western North America, American black bears (Ursus americanus) exploit a wide variety of different habitats for resources needed for survival and reproduction. Wet meadows (California; Kelleyhouse 1980), conifer stands and clearcuts (Washington State; Lindzey and Meslow 1977, Barber 1983), forests that produce hard mast (Powell et al. 1997), and aspen stands (Pelchat and Ruff 1986) are all habitats that black bears utilize successfully throughout their range. Site selection, the process by which bears choose a point in space at which to acquire resources, is affected by many factors, which can be grossly divided in to habitat and non-habitat factors. Habitat factors influence site selection through the distribution and abundance of resources, such as forage, needed by each individual. Non-habitat factors affect the probability of use of sites through the effects of either point or linear features, typically through either attraction or displacement. An example of a non-habitat factor that affects site selection may be heavily travelled roads (Brody and Pelton 1989). The influence of habitat factors on site selection is not limited to the distribution and abundance of resources. The effect of habitat on site selection is hierarchical; that is, bears make simultaneous decisions about habitat resources at several different “selection orders.” Johnson (1980) hypothesised that these selection orders occur at the geographic extent of the species (first order), the selection of a home range (second order), the selection of stands within the home range (third order), and the selection of particular sites for feeding or resting (fourth order). Lofroth (1993) identified an additional selection level between the third and fourth order: selection of patches within larger tracts of habitat that are homogeneous with regard to broad habitat characteristics (i.e., stands). Resources required by a species may be found at any or all of these scales. These habitat selection orders are similar to the spatial scales used to describe forest ecosystem dynamics (Pickett and Thompson 1978). These dynamics can generally be classified from coarse- to fine-grained, into stand, patch, and element scales. Within this scale-based system, selection order is nested and hierarchical: stand-scale habitat selection occurs during the selection of stands within the home range, patch-scale selection occurs for patches within stands, and element-scale selection occurs for elements (e.g., a hollow tree for a winter den) within patches (Weir and Harestad 2003). Patches have often been referred to as microhabitats (e.g., Kolowski and Woolf 2002) or microsites (e.g., Zimmerman and Glanz 2000). Non-habitat factors can also strongly influence the selection of sites by black bears. These factors, while non-habitat in nature, can play a key role in habitat effectiveness (Hood and Parker 2001). For example, human activities can result in displacement of bears in their normal day-to-day activities (McLellan and

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Shackleton 1988). Although much of the selection of habitats by black bears has been linked to food productivity (Powell et al. 1997), other resources are needed by some segments of the population for habitats to be effective. Specifically, females and subordinate individuals are susceptible to attack from other animals, including male bears (Davis and Harestad 1996). To avoid agonistic events with dominant bears, these animals must temper their selection for food resources with requirements for security cover. Indeed, Lindzey and Meslow (1977) showed that female black bears in Washington State used areas with less food productivity because these habitats were more secure. In the coastal forests of British Columbia, forest harvesting has greatly modified the distribution and abundance of food and security resources needed by black bears. Historically, most coastal temperate forests were typically comprised of canopy gaps (Lertzman et al. 1996) that produced small patches of foods in juxtaposition to dense forest structure. The conversion of these forests into relatively large, food-rich openings may have substantial consequences for black bears because these openings, although rich in food resources, may have relatively little security value. The objectives of our study were to identify the habitat and non-habitat factors that affected site selection by black bears at the within-home range and patch spatial scales in coastal British Columbia. Harvesting of late-successional forests for timber has changed the distribution and abundance of resources, such as food and security cover that black bears use. Information on the influence of habitat change on black bears is needed by forest managers to ensure that habitat changes are not detrimental to the health and fitness of black bear populations. The Nimpkish Black Bear Study was conducted in the Nimpkish Valley from 1992 to 1995. The broad objective of the study was to collect ecological information on radio-collared black bears that would help with the prudent management of habitat for black bears in coastal British Columbia. Two publications have been produced from this study, 1 that examined denning (Davis 1996) and 1 that reported on population factors (Davis and Harestad 1996). An analysis of the home ranges and spatial organization of the radio- collared bears was completed in a previous contract in 2002 and prepared as a separate scientific manuscript. The non-denning habitat data from this study had remained un-analyzed until this report. This report is intended as an addendum to the scientific manuscript that was prepared for this contract: Davis, H., R. D. Weir, A. N. Hamilton, and J. A. Deal. in submission. Factors affecting site and habitat selection by female American black bears (Ursus americanus) in coastal British Columbia. Journal of Wildlife Management 0:000-000.

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Data Constraints The data that was presented in Davis et al. (in submission) was limited to female bears that were followed during the study. We did not include data from male bears in the manuscript because radiolocations were likely biased for most of the males that we radio-collared. The large home ranges of the males, which were approximately 10 times the size of female home ranges, made it extremely difficult to locate individuals consistently throughout their home ranges. The lack of ground access in remote portions of the study area resulted in our sampling being dependent upon the location of each bear within their respective home ranges. That is, we were unable to reliably radiolocate bears in remote, generally un-harvested, portions of their home ranges. Indeed, we occasionally were unable to locate radio- collared males for lengthy periods from the ground. Because we were only able to locate males when they were in relatively close proximity to roads, this introduced a bias in our sampling. Aerial telemetry would have allowed us to reduce this bias, however, financial constraints during the study made this option impossible. In addition, large portions of the home ranges of 9 of the 13 males that we radio- collared occurred outside of the area for which terrestrial ecosystem mapping (TEM) had been completed. Our analytical results for these males would have been misleading because of the limited coverage of this important spatial data for assessing factors affecting site selection. For example, many of the radio-collared males moved outside of the mapped area to feed on berries in the summer and some individuals fed on spawning salmon in the Artlish, Tahsish and Kaouk drainages during the fall. Fortunately, some data were useful for preliminary analyses for the males. Four of the 13 males (2 adults, 2 juveniles) that we radio-collared lived almost entirely within the mapped area and we were able to collect relatively unbiased radiolocation data on these individuals. We included these 4 bears in the analyses for this ancillary report, but did not include them in the scientific manuscript because of the relatively small sample size. Additionally, all of the radiolocations at which we conducted patch assessments (i.e., site investigations) were unbiased because the analyses at this scale were conducted in reference to the stand in which the site was located. Thus, analyses of these patch-scale data for males are also included in this report.

Study Area The 540-km2 study area was located at the south end of Nimpkish Lake, approximately 40 km south of Port McNeill on northern Vancouver Island, British Columbia, Canada (Fig. 1). The study area was characteristic of the Northern Island Mountains ecosection of the West Vancouver Island ecoregion

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(Demarchi 1995). Elevations ranged from 10 m to over 1500 m. Winters were cool and wet, as was typical in coastal temperate forests. Annual precipitation at Woss (southeast end of the study area) ranged from 1,600 to 2,610 mm during the study (Ministry of Forests climate data). Seventy-seven percent of the mean annual precipitation fell between October and March with approximately 15% falling as snow during this time (Rochelle 1980). Mean maximum daily temperature for July was 19.8oC and for December was 3.2oC (Ministry of Forests climate data). The low and middle elevations of the study area were within the very dry maritime (CWHxm2), submontane moist maritime (CWHmm1), submontane very wet maritime (CWHvm1), and montane very wet maritime variants (CWHvm2) of the Coastal Western Hemlock biogeoclimatic zone (Green 2000). Upper elevations were in the windward moist maritime and moist maritime parkland variants (MHmm1, MHmmp) of the Mountain Hemlock zone, with the treeless Alpine Tundra zone near mountain tops (Green 2000). In the climax state, vegetation in the 2 forested biogeoclimatic zones was dominated by western hemlock (Tsuga heterophylla), Douglas-fir (Pseudotsuga menziesii), western redcedar (Thuja plicata), mountain hemlock (Tsuga mertensiana), yellow-cedar (Chamaecyparis nootkatensis) and Pacific silver fir (Abies amabilis) trees. Red alder (Alnus rubra), big-leaf maple (Acer macrophyllum), and black cottonwood (Populus trichocarpa) trees occurred as minor deciduous species, none of which produced hard mast crops suitable as food for black bears. Shrubs and herbaceous vegetation that produced food for bears were numerous. Berry-producing shrubs included Vaccinium species, salal (Gaultheria shallon), red elderberry (Sambucus racemosa), Ribes species, and thimbleberry (Rubus parviflorus). Succulent herbaceous vegetation included young growth or flowers of plants such as Equisetum species, sedges (Carex species), skunk cabbage (Lysichiton americanus), clovers (Trifolium species), graminoids and some weeds (wall lettuce, Lactuca muralis; hairy cat’s-ear, Hypochaeris radicata). Some species, such as salmonberry (Rubus spectabilis) produced both succulent vegetation (i.e., shoots) and berries. Prior to extensive timber harvesting, stand-initiating events within forests of the study area were rare or infrequent (British Columbia Ministry of Forests and British Columbia Ministry of Environment, Lands and Parks 1995). Resultantly, the forests were historically dominated by late-successional (i.e., mature and old forest) structural stages, although forest disturbance caused by wind and fire occurred occasionally. Forest harvesting, primarily using clearcut methods, began in the study area in 1923 and resulted in large tracts of even-aged stands with dense canopies surrounded by either remnants of old forest or regenerating clearcuts. Approximately 45% of the forests in the study area were exposed to disturbance (primarily forest harvesting) between 1923 and 1995 (Green 2000), with most forest harvesting having occurred in the valley bottom.

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Harvested areas initially regenerated with productive herbs and berry plants, especially salal and Vaccinium species. Regenerating conifers often form a continuous tree canopy within 50 years of stand initiation that lead to an understory with few plants that provide forage for black bears (Barber 1983). Climax tree species become dominant in the canopy after approximately 80 years, but forests do not reach old-forest conditions until approximately 200 years (British Columbia Ministry of Forests and British Columbia Ministry of Environment, Lands and Parks 1995). Human activity, other than that associated with the forest industry, was limited. The North Island Highway ran through the northeast portion of the study area, which had a small community of approximately 5 people and a gas station along it. A minor railway paralleled the highway, which was used only by the forest company for transporting logs. Five campsites were scattered in the study area. Access to the study area was extensive on active and unused log-hauling roads. The road density was highest in the valley bottom (¯x = 1.91 km/km², CWHxm2 variant), followed by the CWHvm1 (1.39 km/km²), the CWHmm1 (0.85 km/km²), the CWHvm2 (0.81 km/km²), and the MHmm1 (0.25 km/km²). There were no roads in the MHmmp or Alpine Tundra biogeoclimatic subzones. Hunting of bears occurred in the study area during April 1 to June 15 and September to mid-December of each year. Hunters were not allowed to bait bears, but the use of hounds was allowed.

Methods We used Aldrich foot snares and modified culvert traps to capture bears between May 1992 and August 1994. We affixed radiocollars equipped with motion sensors to bears that we captured. Live-trapping and radio-collaring protocols were approved by the Animal Care committee of Simon Fraser University as being in accordance with the principles and guidelines of the Canadian Council on Animal Care. Based on observed breeding behaviour, we considered female bears to be adults at 4 years of age and males to be adults at age 5.

Radiotracking We collected point locations of radio-collared black bears from May 1992 to June 1995. We primarily obtained radiolocations from the ground (94.7% of radiolocations), but occasionally conducted aerial tracking surveys when bears could not be found for extended periods of time. Only those radiolocations for which the observer confidently determined the stand in which the location occurred were used for the analysis of selection within home ranges. We limited our patch-scale analyses to radiolocations for which we definitively identified (either visually or through sign) the exact point that was used by the radio- collared bear. We excluded radiolocations from the analyses that were not

ARTEMIS WILDLIFE CONSULTANTS 5 Final Report – Nimpkish Black Bear Habitat Analyses temporally independent (i.e., separated by <16 hours) or were repeated observations at or near winter dens.

Within home range selection We estimated multi-annual home ranges for male black bears to determine areas that were available to each bear. We used the fixed kernel method with the smoothing parameter generated by least-squares cross validation to estimate the utilization distribution (UD; Worton 1989) for each black bear for the entire period that it was monitored. We did not include radiolocations from winter dens or during the pre-denning loafing period because these data may have overly skewed the UD estimate. We employed a minimum of 30 radiolocations for each estimate (Seaman et al. 1999). For bears with repeated observations at one point (e.g., feeding site, bed site), we initially estimated the smoothing parameter for the fixed kernel for a dataset without the repeated observations. Using the value of the smoothing parameter generated with this technique, we re-ran the fixed kernel on the complete dataset. We approximated the home range boundary from the 95% isopleth of the UD. All home range calculations were completed using the Animal Movement extension to ArcView 3.1 (Hooge and Eichenlaub 1999). For each radiolocation that we obtained, we also generated a paired point that was randomly located within each animal’s home range for comparison purposes. We used several sources of spatial data to determine the values of 18 habitat and non-habitat variables (Appendix A) associated with each radiolocation and random paired point. We used a 1:15,000 ecosystem map of the study area (Green 2000) to identify the stands in which each radiolocation and random paired point occurred. Stands were delineated on the basis of relatively homogenous moisture and nutrient regimes, structural stages, site modifiers, terrain and soil components, and site attributes (British Columbia Ministry of Environment, Lands and Parks and British Columbia Ministry of Forests 1998). Stands ranged in size between 1 and 55 ha. The forests in the study area were assigned 1 of 6 structural stages, ranging from non-vegetated to old forest (Appendix A). Because there were 63 forested site series and 18 non-forested units identified in the study area, we amalgamated these units into 15 site groups on the basis of floristic and structural similarities (Appendix B). We used the site group and structural stage information to identify the stand type (i.e., site group-structural stage combination) for each radiolocation and random paired point. We also determined the proximity to security trees, which we defined as those stands with trees suitable for climbing (i.e., young forest or older structural stages with trees >10m tall), for each radiolocation and random paired point from the ecosystem map. We used Terrain Resources Inventory Management topographical data to determine the proximity to water and proximity to salmon-bearing streams of each radiolocation and random paired point. We also used digital road data maintained by Canadian Forest Products Ltd. to determine proximity to various

ARTEMIS WILDLIFE CONSULTANTS 6 Final Report – Nimpkish Black Bear Habitat Analyses road types for each radiolocation and random paired point. Several model variables included in the initial modelling process were determined to be either inconsequential or we did not have enough accurate data to adequately determine their effect (Appendix C).

Habitat measurements We collected habitat information at 2 types of plots: at radiolocations and random points within the various stand types (i.e., stand description plots). These plots were meant to reflect the values of a patch of 400 m² that occurred at (or around) points used by black bears and representative points within stands. We used these patch-scale data for 2 purposes: to quantify habitat variables in patches used by black bears and to provide estimates of normal (i.e., expected) values of these variables for each stand type. This sampling approach allowed us to assess the habitat factors affecting site selection at both the within-home range and patch spatial scales. The techniques that we used to assess habitat were identical among plots at radiolocations and at stand description points. At each plot, we recorded biogeoclimatic variant and site series, slope, aspect, elevation and the percent cover of vegetation (trees, shrubs, herbs and mosses) in a 400-m2 plot around a central point. We assessed horizontal visibility at these patches by measuring the average distance at which a 1 m tall bear would be obscured by vegetation, debris, or topography in 4 cardinal directions from the plot centre. We identified 85 species of shrubs and herbs that occurred in the study area that could provide forage for black bears (Appendix D). This was derived from previous studies on the feeding ecology of black bears in similar coastal forests (Barber 1983, MacHutchon 1999). We ranked each species of food plant for its peak capability to supply soft mast (e.g., berries, fruit) and succulent vegetation that the bears could consume at some point during the year. Each species of food plant was assigned a ranking of 0.5 (low utility), 0.75 (medium), or 1 (high) for both herbaceous growth and fruit production. Rankings were slightly adjusted to account for differences in fruit volume that were typically produced per unit of percent cover. For example, Vaccinium species are tall and produce more berries/percent cover than low growing species such as salal (Barber 1983), so they received higher ranks for the equivalent amount of cover. Most food plants were classified as either succulent (i.e., young growth, forbs, shoots, flowers, graminoids) or berry-producing. Some plants, such as salal, had rankings in both the herbaceous and fruit categories, because both the flowers and the fruit of this plant were used by bears as food. We also derived phenology curves for each food plant (Appendix E). These curves predicted the date-specific relative abundance of available forage associated with each species of plant. These phenology curves were based on

ARTEMIS WILDLIFE CONSULTANTS 7 Final Report – Nimpkish Black Bear Habitat Analyses field observations of berry and succulent abundance at typical sites throughout the year and published reports (e.g., Barber 1983). Most of the 11 curves were unimodal, bell-shaped curves. For each radiolocation and stand description point, we estimated the phenologically adjusted berry and phenologically adjusted succulent value. This value was derived from the product of the species-specific ranking, date-specific phenology, and percent cover of each species of food plants. We summed the phenologically adjusted berry value of each food plant for each radiolocation and stand description point to derive a total phenologically adjusted berry value. This process was repeated for the phenologically adjusted succulent cover as well. Because the amount of light that reaches berry-producing plants affects the productivity of berries, we also estimated light levels in each stand by calculating the inverse of the tree canopy cover. The typical values for berry and succulent values for each site group are listed in Appendix F.

Data Analysis We used information-theoretic methods to assess the spatial factors that affected the selection of sites within home ranges by male black bears during the non- denning period. We developed a set of a priori candidate models for each period to explain selection based on published literature and suspected ecological relationships. The set of candidate a priori models that we generated was composed of the global model, which included of all the variables of interest, as well as various reduced models that incorporated subsets of the global model variables. We then used an information-theoretic approach (Burnham and Anderson 1998) to determine which model was best supported by the data for each individual bear. Consequently, we ended up with 4 best models. We assessed multicolinearity among the variables in each model with ordinary least squares regression. In cases of high correlation (r² ≥ 0.4), we excluded 1 set of the correlated variables on the basis of a priori understanding. We developed a set of candidate models (Table 1) that represented several different possible combinations of variables that were expected to affect site selection by black bears. These models were based on published results from studies conducted in other areas as well as on hypothesised relationships specific to our study area. Each of these models included combinations of variables that were expected to influence the quality of a site for its food, security, or both. Each candidate model was slightly different in the hypothesised role that the variables played in affecting food or security available at specific sites. We assessed 22 models for fit, given the data. Of these 22 models, 7 included only food variables, 7 were comprised of security variables, and 8 included both food and security variables.

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We included interactions in our models when the effect of one variable on the probability of use was likely affected by the value of another variable. For instance, it is reasonable to assume that the probability of use of a site in close proximity to spawning grounds would be affected by the time of year. That is, a site 20m away from spawning grounds would be more likely to be used in October when spawning fish were present than in April, when they were not. We also used 1 non-spatial variable (hunting season) that was included in several models interacting with non-habitat variables (e.g., hunting season*proximity to roads).

Model parameterization We employed maximum likelihood estimation using 1-1 matched logistic regression methods (Hosmer and Lemeshow 2000:226) to parameterize the candidate models for each bear. Model parameterization involved the 1-1 comparison of a site used by a radio-collared bear to a simultaneously unused site that was randomly located within its home range (i.e., the random paired point). Thus, for each radiolocation, we had a comparison between a site that was selected to one that was not selected at a specific instant in time. We used this conditional 1-1 matched pair approach because it allowed us to incorporate linear spatial variables (e.g., proximity to roads) into our analyses, which are difficult to model using standard resource selection functions (e.g., Manly et al. 1993). Because logistic regression techniques require either binary or continuous data (Hosmer and Lemeshow 2000:57), we created binary design variables for 3 categorical data variables (Appendix A). The reference value for each binary design variable was set to the most frequently observed category for that variable. For instance, we set the reference value for structural stage to the herb- shrub stage, which allowed us to compare the effect of other structural stages compared to the herb-shrub stage on the use of sites by radio-collared bears. For the preliminary analysis of the 9 males whose home ranges lay partially within the mapped area, we only included radiolocations that occurred within the mapped area and limited our selection of random paired points within their home ranges to the mapped area (sensu Mykytka and Pelton 1990).

Model selection and averaging

We calculated the AICc score (Burnham and Anderson 2001) for each model and ranked the relative support for each by comparing the scores among competing models for each bear. For each model in the candidate set, we calculated the log likelihood (log L), number of estimated parameters (K), second-order Akaike information criterion (AICc, Burnham and Anderson 1998), difference between AICc score and the minimum AICc score for the candidate set (∆AICc), and Akaike weight (strength of evidence, wi; Burnham and Anderson 1998). We then identified the best model for each animal from the candidate set by selecting the

ARTEMIS WILDLIFE CONSULTANTS 9 Final Report – Nimpkish Black Bear Habitat Analyses model with the lowest AICc score. We used Akaike weights (wi) to quantify strength of evidence about model-selection uncertainty among the candidate set of models for each bear. We constructed 95% confidence interval sets of the candidate set for each bear based on the Akaike weights. That is, we identified a set of models in which we were 95% confident that the true best model occurred. We used multi-model inference (Burnham and Anderson 1998) to estimate model-averaged parameters and unconditional 95% confidence intervals in the production of a best predictive model for each individual. We also assessed the relative importance of each variable from the weighted value of variables in the candidate set.

Patch (within-stand) selection We completed detailed habitat assessments of patches used by bears at randomly selected radiolocations. At these patches, we documented the activity of the bear (e.g., feeding, bedding, or marking) in addition to the suite of structural variables measured at all other habitat plots. For each plot, we calculated phenologically adjusted berry value interacting with light, phenologically adjust succulent value, horizontal visibility, and canopy closure. We compared the values of these 4 structural variables in patches used by radio- collared bears to typical conditions for the same stand type (i.e., site group and structural stage combination) in which they were located. We conducted this analysis by comparing the difference of each of the 4 structural variables at patches (i.e., in the 400-m2 plot centred on the radiolocation) to the mean value of the respective parameters for that stand type. We calculated the difference between the patch and stand-averaged value for each radiolocation and regressed this difference on the stand-average value. The resultant regression line allowed us to determine if a relationship existed between the patches selected by radio-collared bears and representative patches for each stand type. We considered selection of atypical patches within stands to have occurred when the analysis suggested a non-zero slope of the regression line (i.e., bears used patches with more or less of the habitat feature, relative to stand mean). It also provided us with a predictive equation for identifying the values of each parameter at which patch-scale selection occurred.

Error Checking of Data Initial work with the study data indicated considerable problems with the mapping of radiolocations obtained early in the study. In 1992 and 1993, radiolocations were plotted on GIS maps with minimal spatial data that were produced using NAD27 datum. In 1994 and 1995, we began to use NAD83 orthophotographs for plotting field data. The first step in error checking was to

ARTEMIS WILDLIFE CONSULTANTS 10 Final Report – Nimpkish Black Bear Habitat Analyses convert the 1992 and 1993 radiolocations into NAD83 and then assess the accuracy of the converted radiolocations. We error-checked all of the 1992 and 1993 radiolocations of males and females, as well as many from 1994 and 1995 radiolocations (61%, 1,685 of 2,765). Many radiolocations were replotted on digital orthophotographs (29%, n=810) to ensure accuracy. We assumed that the majority of the 1994 and 1995 radiolocations were plotted accurately. These adjustments often varied only very slightly from the original coordinates, but helped to ensure that the radiolocations were situated in the correct stands. We used the original data sheets to determine the correct location and stand type for each of these radiolocations. We also used the original radiolocation marks on the paper GIS maps and information from the daily telemetry log sheets to ensure that each radiolocation was replotted as accurately as possible. Radiolocations were also assessed for their accuracy. At the time of the radiolocation, the accuracy was assigned a score of 1 (±200m) through 4 (very accurate, ±10 m, usual a visual radiolocation). The accuracy of the original telemetry plus the ability to replot the radiolocation accurately were then used to determine whether the radiolocation was sufficiently accurate to identify the stand in which the bear had been and thus whether it was suitable for stand scale habitat analyses. We made several changes to the variables that we included in the within-home range analysis: • We adjusted structural stages for cutblocks harvested between 1993 and 2000 to their status in the summer of 1993, when the orthophotographs were taken. This involved adjusting approximately 1,400 polygons. • We did not adjust other structural stages (1-7) for elapsed time. We assumed that the structural stage in 1993 was the same as 2000, except for the above exceptions. • We noted many discrepancies between structural stage 0 as identified in the TEM and actual on-the-ground structural stage. This was especially true for road and railway right-of-ways, southeast of Nimpkish Camp, some gravel pits and regenerating urban areas, as well as some wetlands and lake edges (e.g., SW Anutz lake). Because some of these areas were assigned structural stage 0, but were vegetated with grasses and forbs, we amalgamated structural stages 0 and 1 (except for water) for our analyses. • For site series groups/structural stages (i.e., stand types) that were not sampled in the field, we estimated horizontal visibility measures, vegetative cover of berries and forbs, and tree canopy closure. These estimates were based on other structural stages of the same site series groups or other similar habitats. • Stand tending activities such as juvenile spacing, burning and herbicide applications were not included in the model because they were not

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accurately captured within the TEM mapping. This likely strongly affected the fit of several of the food-based models in the candidate set because stands thinned early in development (juvenile spacing) produced substantially more forage in the 40-60 year stand age bracket than those that were not thinned (Table 2). • The ability to document the behaviour of radio-collared bears at each radiolocation was somewhat dependent on the type of habitat that was used by the bear. For example, we collected many radiolocations of bears feeding within clearcuts because bears were relatively easy to find and observe their behaviour when using these habitats. However, when we located bears in heavily forested areas, it was often difficult to visually locate the bear and determine its behaviour. Thus, bear behaviour was not considered in the stand scale analyses because of the bias. Behaviour was used in the patch scale analyses because the data was based on investigated sites at which behaviour was determined.

Results

Radiotelemetry monitoring We captured and radio-collared 27 bears between 15 May 1992 and 11 November 1993. We collected sufficient radiolocations to estimate home ranges of 22 radio- collared bears (13 M, 9F) between May 1992 and June 1995. The home ranges of all of the females and 4 of 13 males lay almost entirely within the area for which we had detailed habitat information. For 9 of the males, only portions of the home ranges lay within the mapped area. We limited the results in the following sections to male radio-collared bears. For results pertaining to female bears, see Davis et al. (in submission).

Selection within home ranges We collected between 23 and 179 radiolocations that were suitable for inclusion in the stand scale analysis for each male bear (x¯ = 92, SD = 43, n = 13). We observed several general trends among the best models that were selected for the 4 male bears for which we had relatively unbiased radiolocation data. Three different models were identified as the best model for these bears and the 95% CI set of best models included between 2 and 8 models (Table 3). The S6 model, which predicted the probability of use in relation to horizontal visibility, presence of escape trees, and proximity to roads, was identified as the best model for bears M11 and M23. The other 2 best models (F18, Global 2) were very similar in that they both incorporated phenologically adjusted berry value interacting with light, phenologically adjusted succulent value, and proximity to fish dependent upon salmon availability. Generally, the strength of evidence (wi) for

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the best model was higher for males (x¯ wi = 0.705, wi range: 0.489 – 0.890, n = 4) than females (x¯ wi = 0.445, wi range: 0.174 - 0.696, n = 9). The parameterization of the variables in the best model varied among bears (Table 4). The probability of use of sites by bear M12 increased with increasing proximity to spawning grounds during spawning, decreasing berry value interacting with light, and increasing succulent value. Bear M14 selected sites with less horizontal visibility (i.e., greater horizontal cover), closer to highways and railroads than main logging roads, farther away from spur roads than main logging roads, increased berry value interacting with light, higher succulent value, closer to forested areas (i.e., escape trees), closer to spawning grounds during spawning, and areas that had been harvested. The probability of use of sites by both M11 and M23 (juvenile males) increased with decreased horizontal visibility (i.e., increased horizontal cover), no escape trees (i.e., structural stage 4 and younger), and with decreased distance from any type of road (i.e., closer to roads). The relative importance of the variables in the process of site selection at the stand spatial scale also varied among bears (Table 4). Horizontal visibility was the variable that most affected site selection for 3 of the 4 males. Other top- ranked variables for males included proximity to fish in relation to salmon abundance, and proximity to various classes of roads.

Patch scale selection We collected patch-scale data on 122 radiolocations of 16 radio-collared male bears between 13 June 1992 and 17 May 1995. Of these 122 radiolocations, 64 were at feeding sites (52%), 49 were at bed sites (40%) and the remaining 9 were at traveling, kill, or mark sites. Between 3 and 17 patch-scale assessments were completed for each radio-collared male (x¯ = 7.6, SD = 5.0, n = 16). We observed less selection at the patch scale for variables related to security when radio-collared males were bedded compared to radio-collared females. The males that we followed bedded in patches within stands that had more canopy closure, but this occurred only in stands with less than 45% canopy closure (r2 = 0.087, MSE= 258, slope = 0.2, intercept = -9.9%, n = 47). We did not detect any substantial patch-scale selection by radio-collared males bears for horizontal visibility while bedded (r2 = 0.017, MSE = 0.0.4, n = 46). Male bears exhibited relatively little patch-scale selection when foraging. We did not detect any substantial selection at the patch scale for berry value interacting with light (r2 = 0.001, MSE = 124, n = 62) or horizontal visibility (r2 = 0.004, MSE = 0.01, n = 60) at foraging sites of males. We did note, however, that males selected patches with higher succulent value than the stand average (r2 = 0.090, MSE = 323, slope = 0.4, intercept = -7.6, n = 62), when foraging in stands with succulent value scores of <18.

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Discussion

The following discussion is based on results obtained from 4 radio-collared male bears. Thus, the conclusions drawn should be considered as very preliminary and may not be representative of male black bears as a whole. The males for which we collected relatively unbiased data had both similarities and differences with females in the factors that affected selection of sites. Like females, males showed most of their selectivity when selecting sites within their home ranges (i.e., stand scale) and relatively little patch-scale selection. Several of the factors that affected selection within the home ranges were similar among sexes and individuals, whereas some strong differences occurred at this scale between juvenile males and other bears. The selection for sites with less horizontal visibility (i.e., those with greater horizontal cover) may have occurred because males, like females, require some component of visual security to effectively use habitat. Despite high food productivity within clearcuts, several researchers have noticed that use of these forest openings by black bears may be restricted due to a lack of protective cover (Lindzey and Meslow 1977, LeCount and Yarchin 1990). Black bears in Washington State used older clearcuts with greater cover and less forage than younger clearcuts with greater forage production but less cover (Lindzey and Meslow 1977). Our results suggest that the reasons for the selection of these sites were similar among both male and female segments of the population. Adult male bears also seemed to make decisions regarding site selection within their home ranges on the basis of food resources. The best model for bears M12 and M14 included phenologically adjusted berry value dependent upon light and phenologically adjusted succulent value, similar to that seen by female bears. The reasons for including these food resources are likely also similar to those for females (see Davis et al. [in submission] for details). One substantial difference between the adult males and females was the influence of spawning fish in their selection of sites. Bear M12 fed on spawning salmon near Woss during the fall and his best model included this factor. The best model for bear M14 also included proximity to fish interacting with salmon abundance. Several of the 9 male bears that were not included in this analysis also made long-range excursions for fish far outside the mapped area. These results are consistent with the observations of Reimchen (1998) who suggested that spawning salmon are an important component of diets of black bears in coastal forests of British Columbia. Based on these 2 adult males, it appears that male bears exhibit similar selection patterns to those seen by female bears, with a stronger emphasis on feeding on salmon in the fall.

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We did, however, observe considerable differences in factors that affected site selection between the adult and juvenile males that we had radio-collared. The selection of sites that we observed by the 2 juvenile male bears may be based more on interactions or avoidance with conspecifics than habitat features. The 2 juvenile males selected sites that were farther away from escape trees and closer to roads than expected, whereas the adult males selected sites primarily on the basis of food. These results suggest that the juvenile males may be relegated to using these less preferred habitats because of their relatively subordinate social stature. The relative concentration of the food resources in food-rich anthropogenic openings may have partially contributed to the pattern that we documented with the 2 juvenile males. Rogers (1987) observed that social organization among black bears was governed by the distribution and abundance of food. He found that bears were dispersed when food was dispersed but that a hierarchy developed among bears when food resources were clumped. Juvenile black bears in south-western Washington State were found farther into clearcuts (and thus, farther from trees) than other age classes (Barber 1983). Our habitat data suggests that food resources for bears were distributed in a clumped manner in the study area. Our documentation of the factors affecting site selection by the 2 juvenile males may therefore have been the results of their relatively low social stature. Indeed, 1 of the juveniles (M11) was shot in a clearcut during the hunting season, perhaps because he was using these less preferred habitats that exposed him to greater mortality risk. Unfortunately, we did not collect sufficient behavioural data on the effect of conspecifics to be able to thoroughly examine this effect. Future research that documents the proximity of other bears may be able to better elucidate the relationship between conspecifics and site selection among juvenile bears. Contrary to female black bears, we observed relatively little selection at the patch scale for either food or security resources by male bears. Males did, however, select for patches that had more canopy cover when bedded and higher succulent value when foraging. Selection for higher succulent values at the patch scale was also demonstrated by female bears and may be due to the need to include non-berry foods in the diet to prevent potential nutritional deficiencies and to reduce energy metabolism (Rode and Robbins 2000). Our results suggest that males make the majority of their decisions regarding site selection at the within-home range spatial scale. This may be because males need large home ranges so that they overlap with females to ensure potential mating opportunities. Large home ranges likely provide a variety of stands with sufficiently dense supplies of resources so that selecting atypical patches is not necessary.

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Management Implications

The following recommendations detail habitat management recommendations pertaining to the effects of habitat modifications on forage supply and mortality risk, which apply to both sexes. Additionally, females have stringent requirements for security cover that need to be incorporated into habitat management prescriptions. The management implications with respect to forest development and habitat management for female black bears are discussed at length in Davis et al. (in submission). Forest managers have several options available to enhance or maintain foraging habitats and minimize mortality risk for black bears. Silvicultural practices that attempt to emulate the natural disturbance regime of coastal temperate rainforest likely provide the best balance between food productivity and security. Small gaps resulting from the extraction of 3-10 trees in an otherwise continuous forest matrix (Lertzman et al. 1996) would better mimic historical natural conditions for black bears than that found under past management practices. This approach would have the net result of enhancing forage potential while maintaining the large trees and dense shrub layers needed for security cover by black bears in coastal British Columbia. Various post-harvesting stand tending techniques can also increase or decrease the utility of habitats to provide forage for bears. Forage production is typically reduced by the growth of newly planted trees, herbicide treatments, and scarification. Conversely, reduced stocking standards, pruning, planting trees in clusters (A. N. Hamilton, unpublished data), prescribed burning (Martin 1983), and seeding with grasses following road deactivation can enhance forage production for bears. We noted that stand tending activities had considerable effect on the cover of berry-producing shrubs within the young forests of our study area (Table 2). Young forests (i.e., structural stage 5, approximately 40-80 years of age) formed dense tree canopies that reduced the amount of sunlight that reached the forest floor. Most shrubs that provide bear forage cannot tolerate these low-light conditions and either die or fail to produce fruit (A. N. Hamilton, Ministry of Water, Land and Air Protection, unpublished data). We noted that stand tending activities that reduced the tree canopy to on average 39%, such as that which occurred with juvenile spacing at about 10-15 years of age, produced young forests that had greater cover of berry shrubs (x¯ = 41%, SD = 26.9, n = 2) than those without these treatments (x¯ = 21%, SD = 13.6, n = 13). These spaced stands may be particularly useful for black bears because they provide abundant forage in areas with substantial security cover in the form of both horizontal cover and escape trees. However, commercial thinning of young forests (i.e., structural stage 5) did not appear to reduce the tree cover sufficiently to elicit an increase in cover in berry-producing shrubs. This lack of effect may be the result of

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commercial thinning occurring primarily in vigorous stands with extremely high tree canopy cover (e.g., >75%) and the reduction in tree cover not being sufficient to provide enough light for berry-producing shrubs to become re-established. Succulent forage plants are important to black bears in coastal environments despite their relative rarity. For example, we observed considerable feeding by bears on hydro-seeded roadsides. This strategy may help fulfill the requirements of bears for succulent vegetation (Rode and Robbins 2000). However, these food sources may also be associated with increased mortality risk because of the increased visibility to hunters and by direct threat of vehicle collisions. Thus, seed mixes used for hydro-seeding treatments should not include species preferred by bears (e.g., clovers) in areas with vehicle access. In other areas with lower mortality risk, such as skid trails in commercially thinned areas, these treatments may have a beneficial effect on bear populations. The effects of maintaining road access into food-rich clearcuts may have negative consequence on populations of black bears in coastal forests. By creating food- rich openings with high accessibility, forest development activities have the potential to increase the vulnerability of bears to hunting mortality. Thus, forest management that eliminates vehicular access following forest harvesting, especially to regenerating stands that have high value for both food and security cover, would clearly be beneficial to populations of black bears. While the importance of food and security requirements were the focus of this analysis, black bears in coastal British Columbia have other requirements that need to be supplied by forested habitats within their home ranges. Specifically, a sufficient supply of winter dens is likely critical for the maintenance of stable bear populations. Black bears in our study area exclusively used structures for denning that were associated with large trees (i.e., large standing trees, logs, root boles, and stumps; Davis 1996). These critical habitat elements develop only in late-successional forests and prudent management of black bear habitat must consider the long-term supply of these elements, along with the supply of foraging and security habitats.

Modelling Non-denning Habitat Suitability One of the greatest utilities of multi-model inference is the application of the best model as a predictive tool. In this case, we can use the averaged best model for each sex to estimate the probability of use for any point in the landscape under varying forest management scenarios.

Female Best Model Probability of use = exp(0.106[SUCC_VALUE] + 0.387[BERRY_VALUE*LIGHT] - 0.032[HORIZ_VIS] – 0.001[PROX_ESC] – 0.008[PROXROAD (if nearest road is a spur road)] + 0.022[PROXROAD (if

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nearest road is a railroad)] + 0.042[PROXROAD (if nearest road is a highway)])/(1 + exp(0.106[SUCC_VALUE] + 0.387[BERRY_VALUE*LIGHT] - 0.032[HORIZ_VIS] – 0.001[PROX_ESC] – 0.008[PROXROAD (if nearest road is a spur road)] + 0.022[PROXROAD (if nearest road is a railroad)] + 0.042[PROXROAD (if nearest road is a highway)]))

Adult Male Best Model (based on 2 adults and likely unreliable): Probability of use = exp(-0.008[PROX_FISH*SALMON] – 0.004[HORIZ_VIS] + 0.064[SUCC_VALUE] + 0.065[BERRY_VALUE*LIGHT] – 0.002[PROX_ESC] + 0.001[PROXROAD (if nearest road is a spur road)] – 0.004[PROXROAD (if nearest road is a railroad)] + 0.725[PROXROAD (if nearest road is a highway)] + 0.847[FOREST_HARV])/(1 + exp(-0.008[PROX_FISH*SALMON] – 0.004[HORIZ_VIS] + 0.064[SUCC_VALUE] + 0.065[BERRY_VALUE*LIGHT] – 0.002[PROX_ESC] + 0.001[PROXROAD (if nearest road is a spur road)] – 0.004[PROXROAD (if nearest road is a railroad)] + 0.725[PROXROAD (if nearest road is a highway)] + 0.847[FOREST_HARV])

Literature Cited

Barber, K. R. 1983. Use of clearcut habitats by black bears in the Pacific northwest. Thesis, Utah State University, Logan, Utah, USA. British Columbia Ministry of Environment, Lands and Parks, and British Columbia Ministry of Forests. 1998. Field manual for describing terrestrial ecosystems. Government of British Columbia. Land management handbook number 25. Victoria, British Columbia, Canada. British Columbia Ministry of Forests, and British Columbia Ministry of Environment, Lands and Parks. 1995. Biodiversity guidebook. Province of British Columbia. Victoria, British Columbia, Canada. Brody, A. J., and M. R. Pelton. 1989. Effects of roads on black bear movements in western North Carolina. Wildlife Society Bulletin 17:5-10. Burnham, K. P., and D. R. Anderson. 1998. Model Selection and Inference: A Practical Information-Theoretic Approach. Springer-Verlag, New York, New York, USA. _____, and _____. 2001. Kullback-Leibler information as a basis for strong inference in ecological studies. Wildlife Research 28:111-119.

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Davis, H. 1996. Characteristics and selection of winter dens by black bears in coastal British Columbia. Thesis, Simon Fraser University, Burnaby, British Columbia, Canada. _____, and A. S. Harestad. 1996. Cannibalism by black bears in the Nimpkish Valley, British Columbia. Northwest Science 70:88-92. _____, R. D. Weir, A. N. Hamilton, and J. A. Deal. in submission. Factors affecting site and habitat selection by female American black bears (Ursus americanus) in coastal British Columbia. Journal of Wildlife Management 0:000-000. Demarchi, D. A. 1995. Ecoregions of British Columbia (fourth edition). 1:2,000,000 map. British Columbia Ministry of Environment, Lands and Parks. Victoria, British Columbia, Canada. Green, R. N. 2000. Terrestrial ecosystem mapping of Canadian Forest Products' Tree Farm License 37. _____, and K. Klinka. 1994. A field guide to site identification and interpretation for the Vancouver Forest Region. B. C. Ministry of Forests. Land management handbook #28. Victoria, British Columbia, Canada. Hood, G. A., and K. L. Parker. 2001. Impact of human activities on grizzly bear habitat in Jasper National Park. Wildlife Society Bulletin 29: 624-638. Hooge, P. N., and B. Eichenlaub. 1999. Animal movement extension to ArcView, version 2.04. Alaska Biological Sciences Center, US Geological Survey, Anchorage, Alaska, USA. Hosmer, D. W., and S. Lemeshow. 2000. Applied Logistic Regression. Second edition. John Wiley & Sons, New York, New York, USA. Johnson, D. H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65-71. Kelleyhouse, D. G. 1980. Habitat utilization by black bears in northern California. International Conference on Bear Research and Management 4:221-227. Kolowski, J. M., and A. Woolf. 2002. Microhabitat use by bobcats in southern Illinois. Journal of Wildlife Management 66:822-832. LeCount, A. L., and J. C. Yarchin. 1990. Black bear habitat use in east-central Arizona. Arizona Game and Fish Department. Technical report #4. Lertzman, K. P., G. D. Sutherland, A. Inselberg, and S. C. Saunders. 1996. Canopy gaps and the landscape mosaic in a coastal temperate rain forest. Ecology 77:1254-1270. Lindzey, F. G., and E. C. Meslow. 1977. Home range and habitat use by black bears in southwestern Washington. Journal of Wildlife Management 41:413-425. Lofroth, E. C. 1993. Scale dependent analyses of habitat selection by marten in the Sub-Boreal Spruce biogeoclimatic zone, British Columbia. Thesis, Simon Fraser University, Burnaby, British Columbia, Canada.

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MacHutchon, A. G. 1999. Black bear inventory, Clayoquot Sound, B.C. - Volume 1: habitat inventory. B. C. Ministry of Environment, Lands and Parks. Nanaimo, British Columbia, Canada. Manly, B. F. J., L. L. McDonald, and D. L. Thomas. 1993. Resource selection by animals: Statistical design and analysis for field studies. Chapman & Hall, London, England. Martin, P. 1983. Factors influencing globe huckleberry fruit production in northwestern Montana. International Conference on Bear Research and Management 5:159-165. McLellan, B. N., and D. M. Shackleton. 1988. Grizzly bears and resource- extraction industries: effects of roads on behaviour, habitat use and demography. Journal of Applied Ecology 25:451-460. Mykytka, J. M., and M. R. Pelton. 1990. Management strategies for Florida black bears based on home range habitat composition. International Conference on Bear Research and Management 8:161-167. Pelchat, B. O., and R. L. Ruff. 1986. Habitat and spatial relationships of black bears in boreal mixedwood forest of Alberta. International Conference on Bear Research and Management 6:81-92. Pickett, S. T. A., and J. N. Thompson. 1978. Patch dynamics and the design of nature reserves. Biological Conservation 13:27-37. Powell, R. A., J. W. Zimmerman, and D. E. Seaman. 1997. Ecology and Behaviour of North American Black Bears: Home Ranges, Habitat and Social Organization. Chapman and Hall, London, United Kingdom. Reimchen, T. E. 1998. Nocturnal foraging behaviour of black bears, Ursus americanus, on Moresby Island, British Columbia. Canadian Field Naturalist 112:446-450. Resources Inventory Committee. 1998. Standards for terrestrial ecosystem mapping. Province of British Columbia. Victoria, British Columbia, Canada. Rochelle, J. A. 1980. Mature forests, litterfall and patterns of forage quality as factors in the nutrition of black-tailed deer on Northern Vancouver Island. Dissertation, University of British Columbia, Vancouver, British Columbia, Canada. Rode, K. D., and C. T. Robbins. 2000. Why bears consume mixed diets during fruit abundance. Canadian Journal of Zoology 78:1640-1645. Rogers, L. L. 1987. Effects of food supply and kinship on social behavior, movements, and population growth of black bears in northeastern Minnesota. Wildlife Monograph No. 97. Seaman, D. E., J. J. Millspaugh, B. J. Kernohan, G. C. Brundige, K. J. Raedeke, and R. A. Gitzen. 1999. Effects of sample size on kernel home range estimates. Journal of Wildlife Management 63:739-747. Weir, R. D., and A. S. Harestad. 2003. Scale-dependent habitat selectivity by fishers in south-central British Columbia. Journal of Wildlife Management

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67: in press. Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70:164-168. Zimmerman, G. S., and W. E. Glanz. 2000. Habitat use by bats in eastern Maine. Journal of Wildlife Management 64:1032-1040.

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Table 1. Candidate set of a priori models used for the information-theoretic examination of factors affecting site selection within home ranges by American black bears during the non-denning period in coastal British Columbia, 1992-1995. The first letter in the model ID refers to the type of variables included in the model: F = food only, S = security/displacement only, C = combination food and security/displacement.

Model ID Variables of interest Model statement K F1 canopy closure CAN_CLOSE 1 F2 site group (relative to FSA site group), S_GROUP_X1_FSA*ST_STAGE_F + 15 food-adjusted structural stage S_GROUP_X2_FSA*ST_STAGE_F + … + S_GROUP_Xn_FSA*ST_STAGE_F F6 berry cover, succulent cover, proximity to BERRY_CVR + SUCC_CVR + PROX_FISH 3 fish F8 berry cover, succulent cover BERRY_CVR + SUCC_CVR 2 F10 structural stage (relative to herb-shrub ST_STAGE0_3 + ST_STAGE4_3 + ST_STAGE5_3 + 5 stage) ST_STAGE6_3 + ST_STAGE7_3 F18 ranked and phenologically adjusted berry BERRY_VALUE* LIGHT + SUCC_VALUE + 3 value dependent upon light, ranked and PROX_FISH*SALMON phenologically adjusted succulent value, proximity to fish dependent upon salmon availability F19 ranked and phenologically adjusted berry BERRY_VALUE* LIGHT + SUCC_VALUE 2 value dependent upon light, ranked and phenologically adjusted succulent value S1 structural stage (relative to herb-shrub ST_STAGE0_3*HUNTING + ST_STAGE4_3*HUNTING + 5 stage) dependent upon hunting season ST_STAGE5_3*HUNTING + ST_STAGE6_3*HUNTING + ST_STAGE7_3*HUNTING S6 horizontal visibility, presence of escape HORIZ_VIS + ESC_TREES + PROXROAD 3 trees, proximity to road S7 presence of escape trees ESC_TREES 1 S8 horizontal visibility, presence of escape HORIZ_VIS + ESC_TREES 2 trees

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Model ID Variables of interest Model statement K S9 structural stage, proximity to road ST_STAGE_F + PROXROAD*ROADCLASS_S_M + 4 dependent upon type of nearest road PROXROAD*ROADCLASS_R_M + PROXROAD*ROADCLASS_H_M S10 horizontal visibility, presence of escape HORIZ_VIS + ESC_TREES + PROXROAD*HUNTING 3 trees, proximity to road dependent upon hunting season S11 distance to escape trees PROX_ESC 1 C3 berry cover, succulent cover, distance to BERRY_CVR + SUCC_CVR + PROX_ESC + HORIZ_VIS + 7 road, type of nearest road, proximity to PROXROAD*ROADCLASS_S_M + escape trees, horizontal visibility PROXROAD*ROADCLASS_R_M + PROXROAD*ROADCLASS_H_M C6 ranked and phenologically adjusted berry BERRY_VALUE* LIGHT + SUCC_VALUE + PROX_ESC + 7 value dependent upon light, ranked and HORIZ_VIS + PROXROAD*ROADCLASS_S_M + phenologically adjusted succulent value, PROXROAD*ROADCLASS_R_M + proximity to escape trees, horizontal PROXROAD*ROADCLASS_H_M visibility, distance to road dependent on type of nearest road C11 berry cover, succulent cover, proximity to BERRY_CVR + SUCC_CVR + PROX_ESC 3 escape trees C12 berry cover, succulent cover, horizontal BERRY_CVR + SUCC_CVR + HORIZ_VIS 3 visibility C15 ranked and phenologically adjusted berry BERRY_VALUE*LIGHT + SUCC_VALUE + PROX_ESC 3 value dependent upon light, ranked and phenologically adjusted succulent value, proximity to escape trees C16 ranked and phenologically adjusted berry BERRY_VALUE* LIGHT + SUCC_VALUE + HORIZ_VIS 3 value dependent upon light, ranked and phenologically adjusted succulent value, horizontal visibility

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Model ID Variables of interest Model statement K Global All variables (except multicollinear BERRY_CVR + SUCC_CVR + PROX_FISH*SALMON + 10 variables), berry and succulent based on % CAN_CLOSE + PROX_ESC + HORIZ_VIS + cover only PROXROAD*ROADCLASS_S_M + PROXROAD*ROADCLASS_R_M + PROXROAD*ROADCLASS_H_M + FOREST_HARV Global2 All variables (except multicollinear BERRY_VALUE* LIGHT + SUCC_VALUE + 9 variables), berry and succulent based on PROX_FISH*SALMON + PROX_ESC + HORIZ_VIS + phenology, ranking, and % cover PROXROAD*ROADCLASS_S_M + PROXROAD*ROADCLASS_R_M + PROXROAD*ROADCLASS_H_M + FOREST_HARV

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Table 2. Effects of juvenile spacing, and commercial thinning on horizontal visibility, food-producing shrubs, and tree cover in a young forest Fir-salal (FSA) unit. Berry-producing shrubs Trees >10 m Horizontal visibility (m) (% cover) (% cover) Type of stand tending Mean SD Mean SD Mean SD n Juvenile spacing 5 2.1 41 26.9 39 17.7 2 Commercial thinning 12 6.8 10 10.1 59 17.6 4 No thinning 10 4.0 21 14.1 47 13.6 13

Table 3. The 95% CI set of models for factors that affected site selection within the home range of each radio-collared male American black bear monitored in coastal British Columbia, Canada, 1992-1995. The 95% CI set represents the set of models in which we are 95% confident that the best model was included. The best model in the candidate set for each male, given the data, is in bold. Model selection for bears listed below the solid line were based on biased data, so the resultant best model is likely inaccurate. Bear Model ID ID Model log(L) K AICc ∆i wi M11 S6 horizontal visibility, presence of escape trees, proximity to road -57.79 3 121.74 0.00 0.85 S10 horizontal visibility, presence of escape trees, proximity to road -59.70 3 125.57 3.83 0.13 dependent upon hunting season M12 F18 ranked and phenologically adjusted berry value dependent upon -36.86 3 80.10 0.00 0.49 light, ranked and phenologically adjusted succulent value, proximity to fish dependent upon salmon availability Global All variables (except multicollinear variables), berry and succulent -28.98 10 81.89 1.79 0.20 based on cover only S7 presence of escape trees -40.90 1 83.87 3.77 0.07 S6 horizontal visibility, presence of escape trees, proximity to road -39.00 3 84.38 4.28 0.06

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Bear Model ID ID Model log(L) K AICc ∆i wi Global2 All variables (except multicollinear variables), berry and succulent -31.79 9 84.74 4.64 0.05 based on phenology, ranking, and cover S8 horizontal visibility, presence of escape trees -40.79 2 85.78 5.68 0.03 S1 structural stage (relative to herb-shrub stage) dependent upon hunting -37.48 5 85.95 5.85 0.03 season S10 horizontal visibility, presence of escape trees, proximity to road -39.87 3 86.12 6.01 0.02 dependent upon hunting season M14 Global2 All variables (except multicollinear variables), berry and -96.59 9 212.25 0.00 0.60 succulent based on phenology, ranking, and cover C6 ranked and phenologically adjusted berry value dependent upon light, -100.06 7 214.78 2.53 0.17 ranked and phenologically adjusted succulent value, proximity to escape trees, horizontal visibility, distance to road dependent on type of nearest road S6 horizontal visibility, presence of escape trees, proximity to road -104.58 3 215.29 3.04 0.13 M23 S6 horizontal visibility, presence of escape trees, proximity to road -31.46 3 69.31 0.00 0.89 S10 horizontal visibility, presence of escape trees, proximity to road -34.20 3 74.77 5.46 0.06 dependent upon hunting season

M03 S6 horizontal visibility, presence of escape trees, proximity to road -63.23 3 132.66 0.00 0.96 M04 S6 horizontal visibility, presence of escape trees, proximity to road -53.09 3 112.38 0.00 0.97 M05 F10 structural stage -25.31 5 62.02 0.00 0.33 F19 ranked and phenologically adjusted berry cover dependent upon light, -29.40 2 63.07 1.05 0.19 ranked and phenologically adjusted succulent cover F18 ranked and phenologically adjusted berry value dependent upon light, -28.68 3 63.89 1.88 0.13 ranked and phenologically adjusted succulent value, proximity to fish dependent upon salmon availability

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Bear Model ID ID Model log(L) K AICc ∆i wi C15 ranked and phenologically adjusted berry value dependent upon light, -29.40 3 65.33 3.31 0.06 ranked and phenologically adjusted succulent value, proximity to escape trees C16 ranked and phenologically adjusted berry value dependent upon light, -29.40 3 65.34 3.32 0.06 ranked and phenologically adjusted succulent value, horizontal cover S9 structural stage, proximity to road dependent upon type of nearest -30.71 2 65.68 3.67 0.05 road F1 canopy closure -32.17 1 66.42 4.40 0.04 S7 presence of escape trees -32.31 1 66.70 4.68 0.03 S8 horizontal visibility, presence of escape trees -31.90 2 68.05 6.03 0.02 S6 horizontal visibility, presence of escape trees, proximity to road -30.86 3 68.25 6.24 0.01 F8 berry cover, succulent cover -32.06 2 68.39 6.37 0.01 S10 horizontal visibility, presence of escape trees, proximity to road -31.06 3 68.66 6.64 0.01 dependent upon hunting season M06 C6 ranked and phenologically adjusted berry value dependent upon -35.20 3 76.72 0.00 0.60 light, ranked and phenologically adjusted succulent value, proximity to escape trees, horizontal visibility, distance to road dependent on type of nearest road C16 ranked and phenologically adjusted berry value dependent upon light, -36.03 3 78.37 1.65 0.26 ranked and phenologically adjusted succulent value, horizontal cover Global2 All variables (except multicollinear variables), berry and succulent -32.37 7 80.32 3.60 0.10 based on phenology, ranking, and cover M08 S6 horizontal visibility, presence of escape trees, proximity to road -66.33 3 138.88 0.00 0.50 S9 structural stage, proximity to road dependent upon type of nearest -66.02 4 140.42 1.54 0.23 road C3 berry cover, succulent cover, distance to road, type of nearest road, -63.18 7 141.43 2.55 0.14 proximity to escape trees, horizontal visibility

ARTEMIS WILDLIFE CONSULTANTS 27 Final Report – Nimpkish Black Bear Habitat Analyses

Bear Model ID ID Model log(L) K AICc ∆i wi C12 berry cover, succulent cover, horizontal visibility -68.62 3 143.46 4.58 0.05 Global All variables (except multicollinear variables), berry and succulent -61.29 10 144.76 5.88 0.03 based on cover only M15 S6 horizontal visibility, presence of escape trees, proximity to road -37.15 3 80.60 0.00 0.98 M16 C16 ranked and phenologically adjusted berry value dependent upon -33.17 3 72.74 0.00 0.82 light, ranked and phenologically adjusted succulent value, horizontal cover F19 ranked and phenologically adjusted berry cover dependent upon light, -36.76 2 77.72 4.97 0.07 ranked and phenologically adjusted succulent cover F18 ranked and phenologically adjusted berry value dependent upon light, -36.26 3 78.92 6.17 0.04 ranked and phenologically adjusted succulent value, proximity to fish dependent upon salmon availability C6 ranked and phenologically adjusted berry value dependent upon light, -31.51 7 79.03 6.28 0.04 ranked and phenologically adjusted succulent value, proximity to escape trees, horizontal visibility, distance to road dependent on type of nearest road M18 S6 horizontal visibility, presence of escape trees, proximity to road -45.13 3 96.56 0.00 0.87 S10 horizontal visibility, presence of escape trees, proximity to road -47.05 3 100.40 3.84 0.13 dependent upon hunting season M20 C15 ranked and phenologically adjusted berry value dependent upon -10.25 3 27.76 0.00 0.21 light, ranked and phenologically adjusted succulent value, proximity to escape trees C3 berry cover, succulent cover, distance to road, type of nearest road, -5.52 6 28.29 0.53 0.16 proximity to escape trees, horizontal visibility S6 horizontal visibility, presence of escape trees, proximity to road -10.57 3 28.40 0.64 0.16

ARTEMIS WILDLIFE CONSULTANTS 28 Final Report – Nimpkish Black Bear Habitat Analyses

Bear Model ID ID Model log(L) K AICc ∆i wi S10 horizontal visibility, presence of escape trees, proximity to road -11.16 3 29.58 1.81 0.09 dependent upon hunting season F8 berry cover, succulent cover -12.64 2 29.88 2.12 0.07 C11 berry cover, succulent cover, proximity to escape trees -11.70 3 30.66 2.90 0.05 F19 ranked and phenologically adjusted berry cover dependent upon light, -13.13 2 30.86 3.09 0.05 ranked and phenologically adjusted succulent cover C12 berry cover, succulent cover, horizontal visibility -11.91 3 31.09 3.32 0.04 C6 ranked and phenologically adjusted berry value dependent upon light, -7.29 6 31.82 4.06 0.03 ranked and phenologically adjusted succulent value, proximity to escape trees, horizontal visibility, distance to road dependent on type of nearest road S8 horizontal visibility, presence of escape trees -13.68 2 31.95 4.19 0.03 C16 ranked and phenologically adjusted berry value dependent upon light, -12.53 3 32.32 4.56 0.02 ranked and phenologically adjusted succulent value, horizontal cover S7 presence of escape trees -15.12 1 32.43 4.67 0.02 F6 berry cover, succulent cover, proximity to fish -12.63 3 32.52 4.75 0.02

ARTEMIS WILDLIFE CONSULTANTS 29 Final Report – Nimpkish Black Bear Habitat Analyses

Table 4. Multi-model inference parameterization and unconditional 95% CI of the odds ratio for the best models selected to describe the factors affecting site selection within home ranges of radio-collared male black bears in coastal British Columbia, Canada, 1992-1995. Relative importance of variables (Σ wi) represented the Akaike-weighted importance of each variable that appeared in the best models for each bear. Model selection for bears listed below the solid line were derived from biased data, so the resultant best models are likely inaccurate.

Model- averaged Odds ratio 95% CI Age parameter Unconditional a Bear ID class Parameter Σ wi estimate SE Odds ratio Lower Upper M12 ad PROX_FISH*SALMON 0.738 -0.014 49.877 0.71 0.00 >1,000 BERRY_VALUE*LIGHT 0.568 -0.007 21.324 0.97 0.00 >1,000 SUCC_VALUE 0.568 0.062 34.392 1.06 0.00 >1,000 M14 ad HORIZ_VIS 0.990 -0.009 0.007 0.96 0.95 0.97 PROX_ESC 0.859 -0.002 0.001 0.95 0.94 0.95 PROXROAD*ROADCLASS_H_M 0.858 -0.009 0.003 0.80 0.68 0.94 PROXROAD*ROADCLASS_R_M 0.858 -0.004 0.003 0.89 0.78 1.03 PROXROAD*ROADCLASS_S_M 0.858 0.001 0.002 1.03 0.94 1.13 BERRY_VALUE*LIGHT 0.773 0.117 0.031 1.79 1.69 1.90 SUCC_VALUE 0.773 0.066 0.024 1.07 1.02 1.12 PROX_FISH*SALMON 0.689 -0.001 0.001 0.98 0.98 0.98 FOREST_HARV 0.682 0.386 0.300 1.47 0.82 2.65 M11 juv HORIZ_VIS 0.977 -0.023 0.011 0.89 0.87 0.91 ESC_TREES 0.977 -2.220 0.464 0.11 0.04 0.27 PROXROAD 0.846 -0.005 0.002 0.89 0.89 0.89 M23 juv HORIZ_VIS 0.960 -0.027 0.012 0.88 0.86 0.90 ESC_TREES 0.950 -0.764 0.500 0.47 0.17 1.24 PROXROAD 0.890 -0.007 0.002 0.84 0.84 0.84

ARTEMIS WILDLIFE CONSULTANTS 30 Final Report – Nimpkish Black Bear Habitat Analyses

Model- averaged Odds ratio 95% CI Age parameter Unconditional a Bear ID class Parameter Σ wi estimate SE Odds ratio Lower Upper M03 ad HORIZ_VIS 0.999 -0.025 0.034 0.88 0.83 0.94 ESC_TREES 0.998 -1.139 0.350 0.32 0.16 0.64 PROXROAD 0.960 -0.003 0.001 0.93 0.93 0.93 M04 ad HORIZ_VIS 0.996 -0.025 0.011 0.88 0.86 0.90 ESC_TREES 0.996 -1.428 0.410 0.24 0.11 0.54 PROXROAD 0.966 -0.003 0.001 0.93 0.93 0.93 M05 ad ST_STAGE4_3 0.327 14.994 0.001 >1,000 >1,000 >1,000 ST_STAGE5_3 0.327 1.087 2.148 2.97 0.04 199.64 ST_STAGE7_3 0.327 15.108 0.002 >1,000 >1,000 >1,000 ST_STAGE6_3 0.327 1.319 1.548 3.74 0.18 77.72 M06 ad HORIZ_VIS 1.000 -0.219 7.437 0.33 0.00 >1,000 BERRY_VALUE*LIGHT 0.924 1.175 1.651 356.23 14.02 >1,000 SUCC_VALUE 0.924 0.012 42.285 1.01 0.00 >1,000 M08 ad HORIZ_VIS 0.724 0.002 0.014 1.01 0.98 1.04 ESC_TREES 0.646 -0.157 0.333 0.85 0.44 1.64 PROXROAD 0.501 -0.002 0.001 0.94 0.94 0.94 M15 ad HORIZ_VIS 0.993 -0.010 0.013 0.95 0.93 0.98 ESC_TREES 0.987 -0.007 0.396 0.99 0.46 2.16 PROXROAD 0.983 -0.004 0.001 0.91 0.90 0.91 M16 ad BERRY_VALUE*LIGHT 0.991 0.241 0.574 3.33 1.08 10.26 SUCC_VALUE 0.991 -0.025 1.104 0.98 0.11 8.49 HORIZ_VIS 0.869 -0.093 0.647 0.63 0.18 2.23 M18 ad HORIZ_VIS 0.998 -0.015 0.007 0.93 0.92 0.94 ESC_TREES 0.998 -0.800 0.362 0.45 0.22 0.91 PROXROAD 0.868 -0.005 0.002 0.88 0.87 0.88

ARTEMIS WILDLIFE CONSULTANTS 31 Final Report – Nimpkish Black Bear Habitat Analyses

Model- averaged Odds ratio 95% CI Age parameter Unconditional a Bear ID class Parameter Σ wi estimate SE Odds ratio Lower Upper M20 ad PROX_ESC 0.471 0.015 106.339 1.45 0.00 >1,000 BERRY_VALUE*LIGHT 0.323 -1.160 1.034 0.00 0.00 0.02 SUCC_VALUE 0.323 0.230 0.785 1.26 0.27 5.87 a Change in likelihood of use with 1-unit increase in value of the parameter, except for BERRY_CVR and CAN_CLOSE (5% increase), HORIZ_VIS (5m increase), and PROXROAD, PROX_ESC, and PROX_FISH (25m increase).

ARTEMIS WILDLIFE CONSULTANTS 32 Final Report – Nimpkish Black Bear Habitat Analyses

Port McNeill #

N Vancouver #

50510Kilometers

Figure 1. Location of study area in the Nimpkish Valley, Vancouver Island, British Columbia, Canada.

ARTEMIS WILDLIFE CONSULTANTS 33 Final Report – Nimpkish Black Bear Habitat Analyses

Appendix A: Variable definitions Spatial and habitat variables used in set of candidate models for the examination of factors affecting site selection within home ranges by radio-collared American black bears in coastal British Columbia, Canada, 1992-1995. Variable codea Definition Data type Effect BERRY_CVR Cover (%) of berry-producing shrubs continuous Food known to be used by bears for fruit SUCC_CVR Cover (%) of succulent plant species continuous Food (herbaceous new growth including graminoids, horsetails, shoots, leaves, flowers, and forbs) known to be used by bears BERRY_VALUE Phenologically adjusted estimate of ranked continuous Food berry value and productivity. Phenological development was specific to the date of each radiolocation. Derived from the product of BERRY_CVR, food value, and date-specific phenological development of each berry-producing species. SUCC_VALUE Phenologically adjusted estimate of ranked continuous Food succulent value and productivity. Phenological development was specific to the date of each radiolocation. Derived from the product of SUCC_CVR, food value, and date-specific phenological development of each succulent-producing species. PROX_FISH Proximity to rivers with spawning continuous Food anadromous salmon (Oncorhynchus spp.) present (DFO and MWLAP data). These include the lower 900m of Tlakwa (Willow) Creek, the entire length of the Nimpkish River, the Woss River between Woss Lake and Nimpkish River, the back- channel on the Nimpkish River immediately southeast of the Nimpkish bridge, the lower Canōn Ck, lower 1.8 km of Kinman creek and the lower 1.5 km of Atluck Creek. SALMON Seasonally adjusted abundance of salmon continuous Food

ARTEMIS WILDLIFE CONSULTANTS 34 Final Report – Nimpkish Black Bear Habitat Analyses

Variable codea Definition Data type Effect ST_STAGE_F Structural stage transformed to continuous continuous Food variable based on logistic transformation of predicted food productivity in each structural stage. Food values were ranked as: Recently disturbed or sparsely vegetated = 0.1 Herb-shrub = 0.9 Pole-sapling = 0.5 Young forest = 0.1 Mature forest = 0.2 Old forest = 0.4 LIGHT Relative measure of light making its way continuous Food to the shrub layer. Derived from inverse of canopy closure (1/[CAN_CLOSE+1]). CAN_CLOSE Canopy closure (%). Derived from continuous Security randomly located plots throughout the study area, augmented by data from TEM plots for stand types where data was lacking. We assigned 0% cover for age classes 0-3. ST_STAGE Structural stage: categorical variable with 6 binary Food + levels (Resources Inventory Committee Security 1998): 0/1, water, railway, road, urban, rural, or sparsely vegetated 2/3, shrub/herb, >20% shrub cover, ≤10% tree cover ≤10m tall 4, pole/sapling, > 10 m tall and ≤40 years (conifer) ≤30 years (deciduous) 5, young forest, >10 m tall, 40-80 years (conifer) or 30-60 years (deciduous) 6, mature forest, >10 m tall and 81-249 years (conifer) or >60 years (deciduous) 7, old forest, >10 m tall and ≥250 years Transformed into 5 binary design variables, with reference to the herb-shrub structural stage (3): ST_STAGE0_3, ST_STAGE4_3, ST_STAGE5_3, ST_STAGE6_3, ST_STAGE7_3

ARTEMIS WILDLIFE CONSULTANTS 35 Final Report – Nimpkish Black Bear Habitat Analyses

Variable codea Definition Data type Effect S_GROUP Site group based on amalgamation of site binary Food + series (Green 1994) with similar floristic Security and structural characteristics. Categorical variable transformed into 14 design variables (S_GROUP_XXX_FSA), with reference value set to the Fir-salal site group. FOREST_HARV Whether stand had been harvested or not binary n/a ESC_TREES Presence or absence of escape trees – binary Security defined as stands with climbable trees (i.e., young forest and older structural stages) PROX_ESC Proximity from site to stands with escape continuous Security trees (i.e., stands in young forest or older structural stage). Values range from 0 (in secure habitat, see next field) to 1,100 m. HORIZ_VIS Average distance at which black bears continuous Security would be hidden by vegetation or topography PROXROAD Proximity to nearest road. Derived from continuous Security Canfor digital road data. Includes all roads that were active and passable (by pick-up truck or all terrain vehicle) in 1993. This variable interacted with ROADCLASS in many models. ROADCLASS Type of road (railway, logging spur, main binary Security haul road, highway). Categorical variable transformed into 3 binary design variables, with main haul road as the reference category (ROADCLASS_S_M, ROADCLASS_R_M, ROADCLASS_H_M) HUNTING Hunting season (opened, closed) binary Security

ARTEMIS WILDLIFE CONSULTANTS 36 Final Report – Nimpkish Black Bear Habitat Analyses

Appendix B: Groupings of variants and site series Site series from Green and Klinka (1994) and TEM map codes from Green (2000). Site BEC group Site group name variant Site series or TEM map code ANTH Anthropogenic CWHvm1 BU – buildings, parking, etc. CWHxm2 BU – buildings, parking, etc. CWHxm2 GP- gravel pit CWHxm2 MS – rubbly mine spoils CWHxm2 RN – railway CWHxm2 RP – road surface CWHxm2 TS – mine tailings AVA Avalanche chute CWHvm1 51 – Sitka alder – salmonberry avalanche track CWHvm2 51 – Sitka alder – salmonberry avalanche track MHmm1 51 – Sitka alder – salmonberry avalanche track BBM Blueberry moss CWHvm1 01 CWHvm1 03 CWHvm1 06 CWHvm2 01 CWHvm2 03 CWHvm2 06 MHmm1 01 MHmm1 27 – late snow-lie “zonal” sites with open canopy DCS Devil's club seepage CWHvm1 04 CWHvm1 05 CWHvm1 07 CWHvm2 04 CWHvm2 05 CWHvm2 07 FSA Fir salal CWHmm1 01 CWHmm1 06 CWHvm1 01s – salal phase CWHvm1 06s – salal phase CWHxm2 01 CWHxm2 03 CWHxm2 06 FSF Fir swordfern CWHmm1 05 FSF Fir swordfern CWHmm1 07

ARTEMIS WILDLIFE CONSULTANTS 37 Final Report – Nimpkish Black Bear Habitat Analyses

Site BEC group Site group name variant Site series or TEM map code CWHxm2 04 CWHxm2 05 CWHxm2 07 FLP Floodplain CWHvm1 09 CWHxm2 08 CWHxm2 09 CWHxm2 26 – regularly flooded lakeshore FRB Forested rock bluff CWHvm1 20 – forested rock bluffs CWHvm2 20 – forested rock bluffs CWHxm2 20 – forested rock bluffs MHmm1 21 - scrubby rock bluffs MHmmp 21 - scrubby rock bluffs MHF Mountain hemlock forest MHmm1 03 MHmm1 05 MHmm1 07 MHP Mountain hemlock MHmmp 22 – “zonal” parkland complex parkland MHmmp 23 – dry rocky parkland complex MHmmp 24 – moist to wet parkland complex OW Open water CWHmm1 LA - lake CWHvm1 LA – lake CWHxm2 LA - lake CWHxm2 PD - pond CWHxm2 RI - river MHmm1 PD - pond PCL Pine cladina CWHvm1 02 CWHvm2 02 CWHxm2 02 MHmm1 02 RO Rock outcrop CWHvm1 RO - rock CWHvm2 RO - rock CWHxm2 RO - rock MHmm1 RO - rock MHmmp RO - rock SCW Skunk cabbage wetland CWHvm1 14 CWHvm1 32– Hardhack – Sweetgale wetland CWHvm2 11 CWHxm2 12 CWHxm2 32 – Hardhack – Sweetgale wetland SCW Skunk cabbage wetland MHmm1 09

ARTEMIS WILDLIFE CONSULTANTS 38 Final Report – Nimpkish Black Bear Habitat Analyses

Site BEC group Site group name variant Site series or TEM map code SPB Sphagnum pine bog CWHvm1 13 CWHvm1 31 – Sphagnum – Carex bog CWHvm2 09 CWHvm2 31 – Sphagnum – Carex bog CWHxm2 11 CWHxm2 25 – poorly drained swamp forest CWHxm2 31 – Sphagnum – Carex bog MHmm1 06 MHmm1 31 – Sphagnum – Carex bog

ARTEMIS WILDLIFE CONSULTANTS 39 Final Report – Nimpkish Black Bear Habitat Analyses

Appendix C: Excluded model variables Several model variables included in the initial modelling process were determined to be either inconsequential or we did not have enough accurate data to adequately determine their effect. Variables that were used and then excluded from the analyses included: Warm/cool aspect site modifiers (SL_ASP_X): categorical data taken from TEM maps and derived from the digital elevation model (DEM). • Gentle slope: gently sloping topography (<35%), no aspect • Cool aspect: cool northerly or easterly aspects (285˚-135˚) with slope 35 - 100% • Very steep cool aspect: very steep slopes (>100%) with cool northerly or easterly aspects (285˚-135˚) • Warm aspect: warm, southerly or westerly aspects (135˚-285˚) with slope 35 -100% • Very steep warm aspect: very steep slopes (>100%) with warm, southerly or westerly aspects (135˚-285˚) Maximum temperature (MAX_TEMP): maximum daily temperature as recorded at the Ministry of Forests (MOF) weather station in Woss. There were a few days with missing readings - these were estimated from the other 3 years worth of data, telemetry data sheets and the MOF Menzies Camp data for those days. Precipitation (PRECIP): total precipitation for the day. Data collected as per maximum temperature (above). We removed the use of stand age (STAND_AGE) as a variable from models because we were unable to determine the age of the forests prior to harvesting in the 1,400 polygons that were harvested between 1993-2000. Proximity to human development (PROX_HUM) was removed from the model because of relatively few developments in the study area. Proximity to water (PROX_H20) was not used as a model variable because water did not appear to be a limiting factor for black bears in the coastal temperate rainforest of B.C. We did not observe any difference in distance to water regardless of temperature (overall x¯ = 166 m; x¯ = 166 when maximum daily temperature < 20 C; x¯ = 163 m when maximum daily temperature ≥ 20 C). However, other studies in more arid areas have found that bears use sites significantly closer to water than available (Arizona; LeCount and Yarchin 1990) and should be considered a variable in models for these types of areas.

ARTEMIS WILDLIFE CONSULTANTS 40 Final Report – Nimpkish Black Bear Habitat Analyses

Appendix D: Plant rankings for bear foods Herbaceous Fruit Fruit Herbaceous phenology food phenology Scientific Name Common Name Code food ranka curve ranka curve Amelanchier alnifolia saskatoon AMELALN 0.75 V Arctostaphylos uva-ursi kinnikinnick ARCTUVA 0.5 G Athyrium filix-femina lady fern ATHYFIL 0.75 SU Bromus vulgaris Columbia brome BROMVUL 0.5 SF Calamagrostis rubescens pinegrass CALARUB 0.75 SF Carex aquatilis water sedge CAREAQU 1 SU Carex atratiformis black sedge CAREATA 1 SU Carex obnupta slough sedge CAREOBN 1 SU Carex spectabilis showy sedge CARESPE 1 SU Carex sp. sedge CAREX 1 SU Cirsium arvense Canada thistle CIRSARV 0.5 SF Cirsium sp. thistle CIRSIUM 0.5 SF Cornus canadensis bunchberry CORNCAN 0.5 V Cornus stolonifera red-osier dogwood CORNSTO 0.75 V Dryopteris expansa spiny wood fern DRYOEXP 0.5 SU Empetrum nigrum crowberry EMPENIG 0.5 V Epilobium angustifolium fireweed EPILANG 0.75 SF Equisetum arvense common horsetail EQUIARV 1 SU Equisetum pratense meadow horsetail EQUIPRA 1 SU Equisetum scirpoides dwarf scouring-rush EQUISCI 1 SU Equisetum sp. horsetail EQUISET 1 SU Equisetum telmateia giant horsetail EQUITEL 1 SU Festuca sp. fescue FESTUCA 1 SU Fragaria sp. strawberry FRAGARI 1 F Fragaria vesca wood strawberry FRAGVES 1 F Fragaria virginiana wild strawberry FRAGVIR 1 F Fritillaria affinis chocolate lily FRITAFF 1 SF Gaultheria shallon salal GAULSHA 0.5 R 0.75 G Heracleum maximum cow-parsnip HERAMAX 0.75 SF Hieracium scouleri var. western hawkweed albertinum HIERSCO2 0.5 SU Hypochaeris radicata hairy cat's-ear HYPORAD 0.75 SU Lactuca muralis wall lettuce LACTMUR 1 SU Lactuca sp. lettuce LACTUCA 1 SU Lonicera involucrata black twinberry LONIINV 0.5 SF 0.75 V Lysichiton americanus skunk cabbage LYSIAME 0.75 SU Maianthemum false Solomon's-seal racemosum MAIARAC 0.5 SF 0.5 VP Maianthemum stellatum star-flowered false Solomon's-seal MAIASTE 0.5 SF Oenanthe sarmentosa Pacific water-parsley OENASAR 0.5 V Oplopanax horridus devil's club OPLOHOR 0.5 SF 0.75 P Oryzopsis asperifolia rough-leaved ricegrass ORYZASP 1 SU Oxycoccus oxycoccos bog cranberry OXYCOXY 0.75 G Petasites frigidus var. palmate coltsfoot palmatus PETAFRI3 0.5 SF

ARTEMIS WILDLIFE CONSULTANTS 41 Final Report – Nimpkish Black Bear Habitat Analyses

Herbaceous Fruit Fruit Herbaceous phenology food phenology Scientific Name Common Name Code food ranka curve ranka curve Petasites sagittatus arrow-leaved coltsfoot PETASAG 0.5 SF Poa sp. bluegrass POA 1 SU Poa secunda Sandberg's bluegrass POA SEC 1 SU Prunus emarginata bitter cherry PRUNEMA 0.75 P Prunus virginiana choke cherry PRUNVIR 0.75 P Pyrus sp. pear PYRUS 1 P Rhamnus purshiana cascara RHAMPUR 0.5 V 0.5 P Ribes bracteosum stink currant RIBEBRA 0.5 P Ribes divaricatum wild black gooseberry RIBEDIV 0.75 P Ribes lacustre black gooseberry RIBELAC 0.75 P Ribes laxiflorum trailing black currant RIBELAX 0.75 P Ribes oxyacanthoides northern gooseberry ssp. irriguum RIBEOXY3 0.75 P Ribes sanguineum red-flowering currant RIBESAN 0.75 SF 0.5 P Rosa sp. rose ROSA 0.75 O Rosa gymnocarpa baldhip rose ROSAGYM 0.75 O Rosa nutkana Nootka rose ROSANUT 0.75 O Rubus laciniatus cutleaf evergreen blackberry RUBULAC 1 V Rubus leucodermis black raspberry RUBULEU 0.75 V Rubus parviflorus thimbleberry RUBUPAR 0.75 SF 0.75 V Rubus pedatus five-leaved bramble RUBUPED 0.5 VP Rubus pubescens dwarf red raspberry RUBUPUB 0.5 V Rubus spectabilis salmonberry RUBUSPE 1 SF 0.75 R Rubus ursinus trailing blackberry RUBUURS 1 RU Salix lucida ssp. Pacific willow lasiandra SALILUC2 0.5 SF Salix scouleriana Scouler's willow SALISCO 0.5 SF Salix sp. willow SALIX 0.5 SF Sambucus racemosa red elderberry SAMBRAC 1 E Sonchus asper prickly sow-thistle SONCASP 0.5 SF Stachys mexicana Mexican hedge-nettle STACMEX 0.5 SF Streptopus amplexifolius clasping twistedstalk STREAMP 0.5 SF 0.5 VP Streptopus lanceolatus rosy twistedstalk var. curvipes STRELAN1 0.5 SF 0.5 VP Streptopus streptopoides small twistedstalk STRESTR 0.5 SF 0.5 VP Taraxacum officinale common dandelion TARAOFF 0.75 SF Trautvetteria false bugbane caroliniensis TRAUCAR 0.5 SF Trifolium sp. clover TRIFOLI 1 SU Trifolium repens white clover TRIFREP 1 SU Vaccinium alaskaense Alaskan blueberry VACCALA 1 V Vaccinium caespitosum dwarf blueberry VACCCAE 1 V Vaccinium sp. blueberry, huckleberry VACCINI 1 V Vaccinium black huckleberry membranaceum VACCMEM 1 V

ARTEMIS WILDLIFE CONSULTANTS 42 Final Report – Nimpkish Black Bear Habitat Analyses

Herbaceous Fruit Fruit Herbaceous phenology food phenology Scientific Name Common Name Code food ranka curve ranka curve Vaccinium ovalifolium oval-leaved blueberry VACCOVL 1 V Vaccinium parvifolium red huckleberry VACCPAR 1 VP Vaccinium uliginosum bog blueberry ssp. pubescens VACCULI1 1 V Valeriana sp. valerian VALERIA 0.5 SF Viburnum edule highbush-cranberry VIBUEDU 0.5 V a ranked as 0.5 (low), 0.75 (medium) or 1.0 (high) for food preference and relative productivity/cover.

ARTEMIS WILDLIFE CONSULTANTS 43 Final Report – Nimpkish Black Bear Habitat Analyses

Appendix E: Plant phenology curves

100% R RU O 75%

50%

Percent of peak phenology 25%

0% 1-Jan 2-Mar 1-May 30-Jun 29-Aug 28-Oct 27-Dec Date

Appendix D-2. Development curves used to estimate phenological abundance for R,

RU, and O plant species.

100%

SU

SF

F

75%

50%

Percent of peak phenology






25%

0%

1-Jan 2-Mar 1-May 30-Jun 29-Aug 28-Oct 27-Dec Date

Appendix D-3. Development curves used to estimate phenological abundance for SU, SF, and F plant species.

ARTEMIS WILDLIFE CONSULTANTS 44

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100%

VP

V

P

75%

50%

Percent of peak phenology 25%

0%


1-Jan 2-Mar 1-May 30-Jun 29-Aug 28-Oct 27-Dec Date

Appendix D-4. Development curves used to estimate phenological abundance for VP, V, and P plant species.

100% E G

75%

50%

Percent of peak phenology 25%

0% 1-Jan 2-Mar 1-May 30-Jun 29-Aug 28-Oct 27-Dec Date

Appendix D-5. Development curves used to estimate phenological abundance for E and G plant species.

ARTEMIS WILDLIFE CONSULTANTS 45 Final Report – Nimpkish Black Bear Habitat Analyses

Appendix F: Values of canopy closure, horizontal visibility and food values recorded at random locations within each stand type. Berry Canopy value Maximum Site Structural closure Horizontal Maximum berry interacting succulent value group stage (%) visibility (m) value (score) with light (score) ANTH 4 80.0 13.3 4.5 0.1 2.5 BBM 3 0.0 7.3 25.3 25.3 5.0 4 33.0 5.9 31.4 0.9 1.5 5 57.2 7.6 32.4 0.6 7.3 6 34.8 7.4 21.0 0.6 4.0 7 28.4 6.4 24.6 0.8 1.1 DCS 3 0.0 5.6 39.9 39.9 23.7 4 22.8 7.8 30.4 1.3 16.1 5 47.5 7.9 22.0 0.5 14.2 6 27.5 6.9 23.5 0.8 15.9 7 36.3 9.6 21.0 0.6 9.8 FLP 3 0.0 4.8 27.4 27.4 57.9 4 57.0 7.3 43.5 0.8 70.8 5 41.0 6.4 62.3 1.5 70.5 6 50.0 15.3 13.0 0.3 6.3 7 29.3 11.8 23.5 0.8 14.1 FSA 3 0.9 8.8 38.0 20.2 26.0 4 20.8 5.8 17.8 0.8 8.6 5 53.1 9.7 19.9 0.4 11.8 6 34.2 10.9 19.0 0.5 9.7 FSF 3 0.0 6.4 26.7 26.7 46.6 4 12.5 3.8 30.3 2.2 29.9 5 60.9 11.5 9.1 0.1 9.2 6 17.5 30.0 8.9 0.5 9.3 7 27.6 12.8 14.5 0.5 10.3 MHF 7 47.8 6.6 10.8 0.2 0.6 PCL 3 0.0 7.5 12.3 12.3 6.3 5 3.0 7.8 10.0 2.5 8.5 6 3.4 10.5 10.7 2.4 4.5 7 11.0 13.0 18.4 1.5 6.4 SCW 3 0.0 19.0 25.3 25.3 65.5 5 9.0 5.3 10.5 1.1 76.3 6 11.2 10.8 23.3 1.9 28.8 SPB 7 39.2 9.2 45.5 1.1 4.6

ARTEMIS WILDLIFE CONSULTANTS 46