GRIZZLY BEAR HABITAT SELECTION:
Along the Parsnip River, British Columbia
Prepared for: British Columbia Ministry of Forests
Dr. Dale R. Seip, Wildlife Ecologist
Prince George Forest Region
1011 - 4th Avenue,
Prince George, BC, V2L 3H9
Report Date: 22 March 2002
Prepared by: Lana M. Ciarniello
Mark S. Boyce
Hawthorne Beyer
Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E9, Canada
Habitat Selection by Mountain and Plateau Grizzly Bears
Ministry of Forests
Abstract
Resource Selection Functions (RSF) were estimated to map the relative probability of occurrence
of habitats for 49 radiocollared grizzly bears along the Parsnip River, British Columbia, Canada
between April 1998 and February 2002. Twenty-two of these bears ranged in the plateau, a
landscape heavily manipulated by humans. In contrast, 27 bears ranged in a relatively pristine
mountainous landscape. Bears were monitored with VHF radiotelemetry twice per week using a
fixed-wing aircraft. Population level RSF models were estimated at the landscape scale for the
mountains and the plateau. In both landscapes greenness was the most significant predictor of
bear occurrence followed by warm, southwest facing aspects (hillshade). Mountain bears
selected for sub-alpine/balsam fir and non- forested mountain (e.g., alpine grasslands or
avalanche chutes) greater than their reference habitat of white spruce. Surprisingly, at the
landscape scale we could not detect significant selection for any of the landcover types by
plateau bears. However, they selected against deciduous mixed forest, balsam fir, pine and nonforested plateau categories. Overall the mountain bears were more affected by covariates than
plateau bears, with the proportion of variance explained in the mountains being greater.
Therefore, the mountain model did a better job of predicting spatial patterns in the probability of
use by grizzly bears than the plateau model.
Resource Selection Ratios were examined for model covariates that could not be GIS referenced
as well as to uncover mechanisms tha t would aid in increasing the predictive capability of the
plateau model. Common to mountain and plateau bears was a significant selection for the
ESSFwk2 biogeoclimatic zone. In the mountains, the AT zone was used in proportion to its
availability. On the plateau, the SBSwk1 zone was highly selected by plateau bears but avoided
by mountain bears. In both landscapes, bears selected areas at a straight-line distance of 5 to 11
km from a road far greater than expected by chance. The Limited Entry Hunt and Problem
Wildlife databases were examined to determine areas of mortality risk for grizzly bears.
Seventy-three percent of grizzly bears were killed within 1 km of a road. In order of importance,
kills occurred most frequently near Highway 97, then secondary logging roads, primary trunk
roads, and decommissioned roads. Bears who resided within the plateau were subjected to
differential mortality risks than from bears residing in the mountains. We suggest that plateau
bear occurrence is a combination of habitat related attributes and avoidance of high-risk areas.
GIS layers that reflect active landscape change are currently being developed. In addition, more
information on plateau bear habitat selection is required to adequately predict occurrence by
grizzly bears in human- manipulated landscapes.
Key words: British Columbia, forestry, grizzly bear, habitat availability, habitat selection,
mountains, plateau, radiotelemetry, Geographic Information Systems, Resource Selection
Function, Ursus arctos.
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Table of Contents
1.0 INTRODUCTION .................................................................................................................... 1
2.0 STUDY AREA ......................................................................................................................... 1
3.0 METHODS ............................................................................................................................... 4
3.1 Capture and radiotelemetry monitoring ................................................................................ 4
3.1.1 Mountains and Plateau: a definition.............................................................................. 4
3.2 Resource Selection Function Models.................................................................................... 4
3.2.1 Mapping Procedures and Covariates ............................................................................. 5
3.2.2 Habitat Covariates.......................................................................................................... 5
3.2.3 Forest Age Classes ......................................................................................................... 6
3.2.4 Hillshade ........................................................................................................................ 6
3.2.5 Greenness....................................................................................................................... 6
3.2.6 Distance to the Nearest Road and Road Type ............................................................... 6
3.2.7 Data Analysis ................................................................................................................. 6
3.2.8 Model Selection............................................................................................................. 7
3.3 Resource Selection Ratios..................................................................................................... 7
3.3.1 Limited Entry Hunt and Problem Wildlife Data............................................................ 8
4.0 RESULTS ................................................................................................................................. 8
4.1 Habitat Model Results........................................................................................................... 9
4.1.1 Model 1. The Mountains ................................................................................................ 9
4.1.2 Model 2. The Plateau................................................................................................... 11
4.1.3 Comparing the Mountain and Plateau Models............................................................. 13
4.2 Resource Selection Ratios................................................................................................... 14
4.2.1 Age Classes.................................................................................................................. 14
4.2.2 Distance to the nearest road ......................................................................................... 15
4.2.3 Road Type.................................................................................................................... 17
4.2.4 Biogeoclimatic Zone .................................................................................................... 18
4.2.3 Limited Entry Hunt and Problem Wildlife Kills.......................................................... 19
5.0 DISCUSSION......................................................................................................................... 21
6.0 ACKNOWLEDGEMENTS.................................................................................................... 22
7.0 LITERATURE CITED........................................................................................................... 23
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List of Figures
Figure 1. Map of the Parsnip Grizzly Bear Project Study Area Boundary. All analysis refers
to the study area ................................................................................................................3
Figure 2. Map of the Relative Probability of Use Across the Mountains. Darker areas represent
an increased probability of use (i.e., higher RSF values) ................................................ 11
Figure 3. Map of the Re lative Probability of Use Across the Plateau. Darker areas represent an
increased probability of use (i.e., higher RSF values) .....................................................13
Figure 4. Ratio of Used Versus Available Landscape Age Classes by Mountain and Plateau
Grizzly Bears, 1998 to 2001 ............................................................................................15
Figure 5. Grizzly Bear Locations Versus Distance to the Nearest Road for both Landscapes,
1998 to 2001.....................................................................................................................15
Figure 6. Grizzly Bear Locations Versus Distance to Nearest Road for the Mountainous
Landscape, 1998 to 2001 .................................................................................................16
Figure 7. Grizzly Bear Locations Versus Distance to Nearest Road for the Plateau Landscape,
1998 to 2001.....................................................................................................................16
Figure 8. Grizzly Bear Locations Versus Type of Road for the Mountainous Landscape, 1998
to 2001..............................................................................................................................17
Figure 9. Grizzly Bear Locations Versus Type of Road for the Plateau Landscape, 1998 to
2001 ................................................................................................................................18
Figure 10. Grizzly Bear Locations of Biogeoclimatic Zones in Mountainous and Plateau
Landscapes, 1998 to 2001................................................................................................ 19
Figure 11. Grizzly Bear Kills in Relationship to Distance to the Closest Road in Mountainous
and Plateau Landscapes, 1998 to 2001 ............................................................................20
Figure 12. Grizzly Bear Kills in Relationship to Distance to the Closest Road in Mountainous
and Plateau Landscapes, 1998 to 2001 ............................................................................20
List of Tables
Table 1. Resource selection function coefficients, standard errors, and P-values based on Wald
statistics for the mountain portion of the Parsnip River study area ....................................10
Table 2. Resource selection function coefficients, standard errors, and P-values based on Wald
statistics for the plateau portion of the Parsnip River study area........................................12
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1.0 INTRODUCTION
The management of grizzly bears (Ursus arctos) and their habitat is a high profile conservation
issue in British Columbia. Because grizzly bears are sensitive to human activity, there is
considerable concern about the rapidly expanding development of forest and mineral resources in
western Canada. Intense public concern regarding B.C.’s grizzly management practices occurs
at the international, national, provincial and local level. In 1991 the Committee on the Status of
Endangered Wildlife in Canada listed the grizzly bear as vulnerable. In B.C., grizzly bears are a
blue-listed species (vulnerable). The B.C. Forest Practices Code requires that the needs of red
and blue listed species be addressed during forest management activities. Forest companies that
have, or are trying to obtain 3rd party environmental certification for their products must
implement acceptable practices to protect threatened and endangered species within their
operating area. Developing an understanding of the nature of habitat loss and fragmentation will
be crucial to the development of sound management practices for grizzly bears.
Habitats for grizzly bears can be modeled statistically using resource selection functions (RSF;
Manly et al. 1993) facilitated by applications of geographical information systems (GIS; Mace et
al. 1996, 2000). RSF offers a statistically rigorous way to quantify bear use of the landscape
through a use versus availability analysis using multiple logistic regression. In this paper we will
examine grizzly bear use of the study area by developing Resource Selection Functions for two
adjacent landscapes, one mountainous and one plateau. Further we characterize habitat use by
calculating resource selection ratios, which are the proportion used o ver the proportion
available. The objectives of this report were to predict areas with higher probabilities of grizzly
bear occurrences as well as to examine whether or not these results were consistent with our
knowledge of grizzly bear use of the study area.
2.0 STUDY AREA
The study area was approximately 17,500-km2 centred along the Parsnip River, British
Columbia, north by northeast of Prince George, and encompassed the Parsnip River and its
tributaries including the Missinka, Hominka, Table, Anzac and Chuchinka river drainages, but
also extended past the Crooked River drainage in the west, and the Wolverine River drainage in
the east (Figure 1). The core study area was within the Arctic watershed. To the south, the
boundary extended past the Salmo n River, which was known to support some small salmon runs,
however, salmon was not known to be a food source and study bears lacked this high-quality,
concentrated, and predictable food supply that is important to bears along Pacific watersheds
(Hilderbrand et al. 1999). The study area depicted in Figure 1 was derived by manually
connecting the outside locations of radiocollared bears gathered during 1998-2001, excluding
outliers in the western portion (i.e, bears who went to Vanderhoof district or Fort St. James).
The rationale for limiting our western boundary was that we did not have a representative sample
of bears using this region. Consequently, this would affect the results of a use versus availability
analysis.
Two distinct topographical areas were represented: the plateau which contains rolling hills and
flat valleys, and the west and east slopes of the Hart Ranges of the Rocky Mountains with steepsided bowls, avalanche chutes and upper elevation valleys. The plateau was within the Nechako
Lowland and the McGregor Plateau Ecosections of the Fraser Basin Ecoregion. The
mountainous habitat was primarily within the Hart Ranges and the Hart Foothills Ecosections of
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the Central Canadian Rocky Mountains Ecoregion. Three biogeoclimatic zones were contained
within the study area. The sub-boreal spruce zone (SBS) is the montane zone and occurred
primarily in the plateau and some lower-elevation areas in the mountains (e.g., along major
rivers). Plateau subzones included moist cool, wet cool, dry warm, and dry cool. Mountainous
subzones included very wet cool and very cool. The Engelmann spruce – subalpine fir zone
(ESSF) occurred above the sub-boreal spruce zone and predominated in the mountainous portion
of the study area. The subzone on the west side of the Rocky Mountains was wet and cool, while
the eastern slopes were moist very cold. The alpine-tundra zone (AT) occurred in high-elevation
habitats beginning at approximately 1,400 m. The AT zone typically consisted of small shrubs
or krummholtz, heath communities, barren rock, or alpine snow and ice. Less than 1 percent of
the study area was rock and ice at elevations over 2,400 m. Elevations ranged from 750 m in the
plateau to 2,500 m in the mountains.
Lower elevation valley bottoms of the Missinka, Hominka, Table, and Anzac Rivers drain from
the mountains into the plateau and were dominated by a mix of white spruce and subalpine fir,
with the proportion of subalpine fir becoming progressively greater with increasing elevation.
High elevation mountain habitats were classified as subalpine parkland, which as elevation
increased become subalpine fir krumholtz tree formations. Subalpine grassland slopes and
avalanche chutes were comprised of glacier lily (Erythronium grandiflorum), Indian helabore
(Veratrum viride), and arrow- leaved groundsel (Senecio triangularis). The highest elevation
alpine areas were alpine tundra communities, barren rock, or ice/snow. Large burns within the
mountains had abundant huckleberries (Vaccinium membranaceum), blueberries (Vaccinium
myrtilloides), and Canadian buffalo-berry (Shepherdia canadensis). The mountainous portion
of the study area occurred in a bottleneck of the Rocky Mountains (Hart Ranges) and may be
important in providing connectivity between the southern and northern Rocky Mountains.
White spruce (Picea glauca) forests existed mainly in the wetter portion of the plateau, while
lodgepole pine (Pinus contorta) occurred primarily in the dryer portions. Most of the plateau
habitats were a mix of white spruce and pine or spruce and subalpine fir (Abies lasiocarpa).
Although rare, small pockets of subalpine fir forests also existed on the plateau. Black spruce
bogs (Picea mariana ) occurred in lower elevation wet areas. Mixed wood forests were lar gely
the result of older cutblocks and consisted of regenerations of balsam poplar (Populus
balsamifera), trembling aspen (Populus tremuloides), and paper birch ( Betula papyrifera).
Interior Douglas fir (Pseudotsuga menziesii spp.), western hemlock (Tsuga heterophylla), and
larch (Larix spp.) also occurred in small portions on the plateau and lower elevation mountain
valley bottoms.
Forestry was the predominant industry in the study area and the plateau contained the majority of
forestry activities. Howe ver, logging activities were expanding up the 4 main river valleys
(Missinka, Hominka, Table and Anzac Rivers) leading into mountainous areas. The majority of
logging on the plateau had taken place within the last 45 years, resulting in a mosaic of forest
habitats in various successional stages. The mountainous area had experienced much less
industrial development, although all major watersheds had logging roads along the valley bottom
and varying proportions of previous harvesting at lower elevations. However, upper elevations
and the back ends of most watersheds were undeveloped wilderness.
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Other potential disturbances to grizzly bears included the town of Bear Lake, Highway 97, a
railway line through the mountains for coal extraction, two sawmills, two logging camps, and
various consumptive and non-consumptive recreational activities, such as hunting, fishing,
snowmobiling and hiking. With the exception of two small provincial parks, there were no
protected areas within the study area.
The location of the Parsnip Grizzly Bear Project provided a number of opportunities to better
understand grizzly bear movements and habitat requirements: 1) the study area ranged from
wilderness mountain habitat to plateau habitat that had extensive road access and for est
harvesting activities. Prior to this study, little was known about grizzly bear habitat use on the
sub-boreal plateau; 2) the majority of the area was in the Arctic watershed so bears did not have
access to spawning salmon; and 3) the area occurred in a bottleneck of the Rocky Mountains
(Hart Ranges) and may be important in providing connectivity between the southern and
northern Rocky Mountains.
Figure 1. Map of the Parsnip Grizzly Bear Project Study Area Boundary. All analysis
refers to the study area.
Study
Area
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3.0 METHODS
3.1 Capture and radiotelemetry monitoring
We captured 49 grizzly bears and fitted them with VHF radiocollars between August 1997 and
October 2001 using a combination of aerial darting, leg snares, and culvert traps. Study bears
were monitored by fixed-wing aircraft twice per week or as often as weather permitted. Once
the bear was located a Universal Transverse Mercator (UTM) coordinates (x, y grid system) was
taken with a hand-held 12 Channel Garmin GPS unit. If a position was taken in
latitude/longitude it was converted to UTM (NAD 83) using the Geographic Calculator (Blue
Marble Geographics). A biologist onboard the aircraft recorded the bear number, collar
frequency, date, time, habitat type and a location confidence code on a telemetry data form.
Confidence codes ranged from 1 (absolute certainty), to 2 (difficult to pinpoint due to terrain or
the bear moving but generally certain), to 3 (heard but unable to pinpoint). Only confidence
codes of 1 and 2 were used in this analysis.
3.1.1 Mountains and Plateau: a definition
The Parsnip River provided a natural divide between the plateau landscape and the mountainous
landscape. We digitized a line down the middle of the River based on the Landsat TM imagery
using Geographic Information System (GIS; Arc View). Bears that had > 50% of their locations
on the east side of the Parsnip River were classified as mountain bears, while bears that resided
primarily on the west side of the River were referred to as plateau bears. Likewise, random
points that fell west of the Parsnip River were identified as ‘mountain’ while those that fell on
the east side of the river were identified as ‘plateau’. Thus, a bear that traveled between the two
landscapes would have some plateau and some mo untain locations.
3.2 Resource Selection Function Models
Resource Selection Functions reflecting the relative probability of use of a habitat type were
estimated using logistic regression. We employed a Design 2 (Manly et al. 1993), third-order
selection (Johnson 1983) study design at the population level because the entire study boundary
extent was likely occupied by grizzly bears. With this design, data from individual animals were
pooled and use and availability was censused for the entire study area. Bear locations gathered
during 1998, 1999, 2000, and 2001 along with over 33,800 randomly generated computer points
were overlaid on map images using a Geographic Information System (GIS; Arc Info). Only
locations for which we were confident of the position of the animal were used in analysis.
Repeat locations, for example multiple den-site locations, were removed from analysis. In
addition, if a family group was collared (e.g., GF35, GM36 and GF37) only the locations of the
mother were included. However, if an offspring split from its mother for a time (e.g., GM36 in
2001) its locations were considered independent until it returned to its mother, when locations
were again removed from analysis.
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3.2.1 Mapping Procedures and Covariates
Digital elevation maps were built from terrain resources inventory map (TRIM; British Columbia
Ministry of Land, Air and Water Protection) images and were used to obtain elevation, slope,
and aspect data for bear use and random locations. Four Landsat 5 TM images were obtained
from Spatial Mapping (on behalf of Canadian Forest Products Ltd.). These four images were all
collected within one month (mid- August to mid-September 1999) and were mosaiced together
to form a continuous image of the study area. Erdas Imagine was used for all image processing.
Forest Cover Maps (FCM) and road networks were obtained from the BC Ministry of Forests,
Canfor East, and Canfor West. These map layers were used to obtain the habitat type, stand age,
distance to roads, and type of road information. Vegetation Resources Inventory data was
obtained from Canfor West and applied to areas where it existed.
3.2.2 Habitat Covariates
The availability of landcover types for each bear was characterized from forest cover maps.
These maps provided the following primary tree species classifications:
AC
AT
BA
BL
DH
DR
E
EP
ES
FD
HW
PA
PL
SB
SW
LA
LT
LW
Cottonwood
Aspen
Subalpine fir
Balsam fir
Douglas hemlock
Red alder
Birch species general
Birch
Engelmann Spruce
Douglas fir
Western Hemlock
White bark pine
Pine
Black spruce
Spruce white
Alpine larch
Tamarack
Western Larch
To minimize the number of landcover classes tree species were combined into 7 primary
landcover categories: ACATEP (cottonwood, aspen, and common paper birch), BBABL
(subalpine and balsam fir), FDHWL (Douglas fir, western hemlock, larch species), SSW (spruce
species and white spruce), SB (black spruce), PL (pine species), NPF (non-forested mountain)
and NFP (non-forested plateau). Non-forested plateau included those habitats of noncommercial value, such as non-commercial brush, non-productive brush, and non-productive
forest. Thus, the majority of non-forested plateau habitats were those related to riparian areas.
Non-forested mountain habitat also included those habitats of non-commercial value. However,
in the mountains these landcover types consisted largely of alpine or meadow habitats as
described by the non-forest or non-productive descriptor columns provided in the database.
Because the landcover types added up to 100% we withheld white spruce as reference habitat
(i.e. coefficients for those variables were not included in the models). We did this because white
spruce occured in abundance in both the mountain and plateau landscapes.
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3.2.3 Forest Age Classes
Forest cover maps provide a projected age for all commercial forest types. From these projected
ages forests were classified into the following categories: young (0 to 45 years and all not
sufficiently restocked areas), mature (46 to 99 years), old (>100 years). However, habitats of
non-commercial value do not contain age information. Therefore, if an age class was not
assigned we went to the Non-Productive Descriptors and Non-Commercial Descriptors to gain
information on these landcove r types. From these, we added the age classes of alpine for all
locations with a descriptor of A (alpine) or AF (alpine forest). Finally, an ‘other’ category was
used to classify the remaining locations without ages. Other included habitats of noncommercial brush, non-productive brush, swamps, and meadows.
Mature forest was used as a reference category to which all other categories were compared
because it commonly occurred in both the mountain and plateau landscapes. Therefore, a
coefficient was not obtained for the mature age class.
3.2.4 Hillshade
Aspect and slope were used to calculate hillshade values. Aspect was set at 225° while degree
slope was set at 45° . Southwest slopes receive high hillshade values while northeast slopes
correspond to low hillshade values. In the transformation, negative coefficients indicate
selection for cooler, northeast aspects, while positive coefficients reflect selection for warmer
southwest aspects.
3.2.5 Greenness
A tasseled-cap transformation of Landsat Thematic Mapper (TM) satellite images using Erdas
Imagine was used to calculate greenness. We used images from mid-August to mid-September
with a 30 x 30 m pixel size. The higher the greenness scores the more green vegetation present.
3.2.6 Distance to the Nearest Road and Road Type
Road network data from FCM, TRIM, Canfor East and Canfor West was amalgamated and used
to query both distance to the nearest road and the type of road. For each bear and random
location the straight- line distance to the nearest road was calculated in GIS.
The type of road was categorized into 4 classes: highway, primary trunk logging roads,
secondary logging road, and deactivated. The highway category referred to Highway 97.
Primary trunk roads included any main stem road used to transport logs to the point of the
Highway. An example of a primary trunk road would be the Anzac 2000 road or the 100 road.
Secondary logging roads stem off of primary trunk roads and were used to access the cutblock.
Decommissioned roads were at one time secondary logging roads that have not had a drainage
ditch or some other type of obstruction (e.g., piling up stumps on road) put in place by the
logging company that made the site less accessible.
3.2.7 Data Analysis
Prior to model construction a correlation matrix was examined for all model variables. Because
all correlations were less than 0.7 we concluded that colinearity was not a serious concern (Sokal
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and Rohlf 2000). The following log-linear equation was assumed to characterize the influence of
covariates on relative use, w(x):
w(x) = exp(β 1x1 + β2x2 + β3x3…βpx p )
(1)
where β i are selection coefficients for each covariate, x i, for i = 1,2,. . .p, estimated using logistic
regression. Bear locations were the used sites, 1, while random sites were assigned 0. Negative
β coefficients imply avoidance, while positive coefficients imply selection. We constructed one
RSF model for the entire 2001 study area. Next, to examine the relative contribution of the
habitats across the study area, we constructed separate RSF models for mountain bears and for
plateau bears. Models were estimated using SATISTICA (StatSoft 1999).
3.2.8 Model Selection
Initially, model selection was based on Akaike Information Criteria (AIC; Burnham and
Anderson 1998). However, to examine whether the large home range size and lower density of
plateau bears was a function of the different landcover types, we wanted the same coefficients in
both the plateau and mountain models. Therefore, to make the models comparable we did not
necessarily choose the individual model with the lowest AIC score but rather the best models in
which each variable occurred within both landscapes. Model estimates for the mountains and the
plateau were interfaced with GIS to create maps of relative probability of grizzly bear use across
the study area.
3.3 Resource Selection Ratios
We also provided Resource Selection Ratios (Manly et al. 1993), which is the ratio of the
proportion used to the proportion available. In this population- level design (i.e., all animals
pooled), use for all animals (wi) is characterized by the following form:
w( x ) = (Ui / U + ) /( Ai / A+ )
(2)
Where (Ui / U + ) refers to the proportion used of bear use locations, while ( Ai / A+ ) refers to the
proportion available for randomly generated locations. If use is proportional to availability (i.e.,
no selection) then the number is approximately 1. If use is greater than availability (i.e., numbers
greater than 1) there is said to be selection for the covariate, whereas use less than availability
(i.e., a number less than 1) implies avoidance.
There were two covariates that we wanted to examine that could not be used in the logistic
regression RSF model because currently they cannot be applied back to the GIS. These were
biogeoclimatic zone, subzone and variant as well as the Limited Entry Hunt data for the study
area.
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3.3.1 Limited Entry Hunt and Problem Wildlife Data
The grizzly bear limited entry hunt and problem wildlife database was obtained through the
Ministry of Water, Land and Air Protection for 1977 to 2001. Locations were plotted in Arc
View and only locations within the study area were considered. Some animals appeared in the
database twice and therefore one location was removed. All locations pertaining to the grizzly
bears killed in association with the landfill closure in Mackenzie were left in the database.
There were 13 grizzly bear kills that occurred in the database that were in a burn off the ‘700
road.’ This was a known guide-outfitter hunting area which was known to contain many ATV
trails. However, because we could not obtain information on the trail network, we excluded
these 13 locations from analysis.
4.0 RESULTS
Of the 49 bears captured, 22 were plateau bears (12 female; 10 male), while 27 were mountain
bears (18 female; 9 male). In 2000 and 2001, we also monitored a plateau female and plateau
male that were captured as part of another study (Peace-Williston Compensation Program).
From 1998 to 2001, a total of 2,307 bear locations met the criteria for analysis on 49 grizzly
bears (58P: 367M in 1998; 133P: 465M in 1999; 238P: 383M in 2000; 358P: 305M in 2001). Of
these 2,307 locations, 1,520 were from the mountains while 787 were from the plateau.
Mountain bears have significantly smaller home ranges than plateau bears (Ciarniello et al.
2001). For all years combined, male grizzly bears that live on the plateau have 5 times the home
range size of males that live in the mountains. Furthermore, the home range size of plateau
males is likely underrepresented because their necks were often larger than their heads allowing
them to slip their radiocollars. In addition, with the exception some subadult males, we were
frequently unable to locate plateau males. Due to intense search efforts for missing bears we
believe they moved extended distances outside the study area boundary. Females that live in the
plateau have 6 times the home range sizes of mountain females (Ciarniello et al. unpublished).
Project capture success was also higher in the mountains than the plateau. For example, despite
significant effort we could not catch any female plateau bears in 1998. A DNA census
conducted in the spring of 2000 yielded a density estimate of 12 bears per 1000 km2 in the
plateau, and 49 bears per 1000 km2 in the mountains (Mowat et al. 2002). Compared to other
DNA-based population estimates in interior British Columbia, grizzly bear density in the
mountains is high (McLellan 1989; Hovey and McLellan 1996), while the density in the plateau
is low (Mowat and Strobeck 2000).
We examined movements of bears to evaluate barriers between the mountains and the plateau.
On the plateau, both male and female bears regularly crossed Highway 97 as well as the Parsnip
River. In addition, 3 male mountain bears moved between the mountains and the plateau.
Furthermore, male and female bears moved between the west and east slopes of the Rocky
Mountains. In November 2000, one female family group moved a straight- line distance of 40.5
km from the plateau to the mountains for denning. These bears moved back to the plateau after
den emergence the next spring. These have been the only bears monitored to spend spring,
summer and fall in the plateau and den in the mountains. Based on the movements of bears there
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does not appear to be significant barriers to movement within the study area and bears are free to
select habitats within the mountains or the plateau (see Fretwell and Lucus 1970).
4.1 Habitat Model Results
A number of differences existed between the mountain and plateau habitats. These differences
occurred in available habitats, length of foraging and denning seasons, as well as consumptive
and non-consumptive human use. In addition, due to the uneven sample sizes (i.e., 1,520
mountain locations versus 787 plateau locations), and diverse bear use selection patterns, we felt
that generating separate models with the same covariates and then comparing the results would
be a better categorization of relative bear use of the landscape than one model for the entire
landscape. Figure 2 provides a map of the relative probability of use across the mountainous
landscape, while Figure 3 provides the relative probability of use across the plateau. Higher
probability of use by grizzly bears is represented by darker areas. The following sections will
examine each of the model coefficients by their respective landscapes.
4.1.1 Model 1. The Mountains
The most significant predictor of mountain bear landscape use was greenness followed by
hillshade (Table 1). Therefore, mountain bears were more likely to be found on warm aspects
that contain a high green herbaceous phytomass. Elevation was found to be negative, simply
meaning that use was higher on the sides of mountains as opposed to the tops or ridges.
Mountain bears exhibited selection for sub-alpine fir and balsam fir (BBABL) as well as nonforested mountain (NF_M) landcover types. These forest t ypes were normally mid-to-high
elevation habitats that consist of krumholtz trees, slide alder, and avalanche chutes. The upper
limit of the sub-alpine parkland was classified as BBABL habitat. These findings are consistent
with radio-telemetry observations of habitat types recorded by the biologist. In addition, visual
rates on telemetry flights for mountain bears were over double those of plateau bears, which
were related to the ease of viewing the ground in open mountain habitats
Mountain bears selected for alpine and young age classes in reference to mature age class. This
was not surprising because mountain bears were often located in alpine landscapes. The
selection for young-age landscapes within the mountains was a reflection of bears moving to
burns that produced an abundance of blueberries. In addition, cutblocks that were difficult to
access by ground occurred in the northeastern portion of the mountains (upper Sukunka River
area). In the spring of 2001, one mountain male bear was often located within these young-age
(i.e., 0-45 years) cutblocks. Mountain bears were seldom located in the low elevation valley
bottoms, which consisted primarily of old age forest stands. In relationship to mature forests,
mountain bears selected against old age forests. However, site visits to old-growth forest habitat
in the mountains revealed well-used travel routes that were largely associated with adult male
locations. Further investigation of old- growth habitat type is required to better interpret this
coefficient. Mountain bears also selected against ‘other’ landcover types. This was not
surprising because in the mountains the ‘other’ habitat category largely refers to rock or talus
terrain.
Mountain bears were located a greater distance to roads than random (Table 1). Because the
coefficient for distance is positive, the greater the distance from roads the more likely there are to
Ciarniello, Boyce & Beyer
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Habitat Selection by Mountain and Plateau Grizzly Bears
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be bears. For mountain bears with roads within or adjacent to their home range a general
avoidance of roads has also been noted on telemetry flights. In addition, all of the cutblocks
used by mountain bears have had to be accessed by helicopter due to decommissioning of the
road. During 2000 and 2001, active logging operations and new cutblocks have been recorded
during aerial telemetry flights within some of the mountain bear home ranges. However, we
have never recorded a mountain bear near active logging activity or on a logging road.
Table 1. Resource selection function coefficients, standard errors, and P-values based on Wald
statistics for the mountain portion of the Parsnip River study area. These estimates for
P-values using the Wald Statistics are conservative given our study design (Boyce et al.
2002).
Variables
GREENNESS
ELEVATION
HILLSHADE
DISTANCE RD
ACATEP
BBABL
PL
NF_M
Categorical Age Classes
AGE
Alpine
AGE
Old
AGE
Other
AGE
Young
AGE
Mature
ß
Standard Error
P-Value
+.036455
-.001322
+.003188
+.000087
-.036463
+.013488
-.026964
+.019632
.001958
.000174
.000586
.000009
.008612
.001730
.006791
.002438
<.00001
<.00001
<.00001
<.00001
.000023
<.00001
.000072
<.00001
+.511529
-.404497
-1.967851
+.084712
--------
.238955
.139493
.299779
.189942
--------
.032299
.003734
<.00001
.655603
--------
Deviation Explained = 22.52%, AIC = 7729.14
Ciarniello, Boyce & Beyer
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Figure 2. Map of the Relative Probability of Use Across the Mountains. Darker areas
represent an increased probability of use (i.e., higher RSF values).
4.1.2 Model 2. The Plateau
Plateau bears also exhibited a selection for higher greenness scores and positive hillshade values.
The selection for lower elevation areas within the plateau was due to a lack of use by bears of the
two small pockets of alpine habitats that existed within the plateau. One of these areas is a small
ridge west of the Williston Reservoir. This ridge was quite different than the alpine habitat
within the mountains but was classified as alpine within the forest cover database. The second
area was in the southeastern portion of the plateau by the divide to Arctic Lake. This area was
similar to lower elevation alpine habitat found within the mountains. The absence of bear use in
this area may be due to the lack of study bears rather than an avoidance of this habitat type.
Remarkably, in the plateau, we were unable to detect significant positive selection for landcover
types (Table 2). Rather, bears significantly avoided the deciduous, pine, and subalpine fir
Ciarniello, Boyce & Beyer
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Habitat Selection by Mountain and Plateau Grizzly Bears
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landcover types. It must be mentioned that this avoidance is relative to the withheld habitat
types of white spruce, black spruce, and Douglas fir. In a previous analysis, selection for spruce
dominated forests was found to be weakly positive (Ciarniello et al. unpublished), while
selection for Douglas fir habitats was positive at the de n-site selection scale. Subalpine firdominated habitats occurred in small pockets on the plateau, whereas they dominate within the
mountains. We have recorded a male denning in higher elevation subalpine fir habitat in the
plateau.
Pine-dominated habitats are typically much dryer than spruce-dominated habitats. Therefore
they lack the abundance of herbaceous vegetation suitable for grazing that our habitat plots have
revealed important for bears (Ciarniello et al. 2001). In 1998 and 1999, avoidance of pinehabitats was recorded on telemetry flights. However, in 2000 an abundant berry crop resulted in
more bears using the dryer pine habitats during berry season. Therefore, use of pine forests was
dependent upon the productivity of the berry crop.
Contrary to primary landcover types, there was selection for all age classes in relation to the
mature forest reference category, however, only the ‘other’ age class was significantly selected.
In the plateau, ‘other’ referred primarily to riparian areas, bogs and fens for which age
information was not available. Selection for wet areas adjacent to rivers and ponds was
consistent with aerial telemetry findings. These riparian areas were also known to contain
important forage for bears during all seasons . We have provided a resource selection ratio for
age classes for both the mountains and plateau under section 4.2.1.
Distance to roads is a positive beta, meaning the further from a road the more likely there are to
be bears, however, this coefficient is not significant. This selection was likely a function of the
density of roads within the plateau – in the plateau it may be difficult to be far from a roaded area
while also meeting nutritional requirements. We were unable to apply type of road to this
analysis because this information is currently being incorporated into the GIS so it may be
referenced, however, this information appears to be important to more adequately assess relative
bear use of the plateau (please refer to Section 4.2.3).
Table 2. Resource selection function coefficients, standard errors, and conservative P-values
based on Wald statistics for the plateau portion of the Parsnip River study area.
Variables
ß
Standard Error
P-Value
GREENNESS
ELEVATION
HILLSHADE
DISTANCE RD
ACATEP
BBABL
PL
NF_P
Categorical Age Classes
AGE
Old
AGE
Other
AGE
Young
AGE
Mature
Ciarniello, Boyce & Beyer
+.01984
-.00128
+.00410
+.00003
-.00351
-.00628
-.01387
-.03952
.003779
.000310
.001963
.000031
.001706
.002070
.001634
.004599
<.00001
.000035
.036699
.265928
.039685
.002423
<.00001
<.00001
+.03650
+3.57140
+.16013
--------
.106596
.454416
.117943
--------
.732075
<.00001
.174569
--------
12
Habitat Selection by Mountain and Plateau Grizzly Bears
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Figure 3. Map of the Relative Probability of Use Across the Plateau. Darker areas
represent an increased probability of use (i.e., higher RSF values).
4.1.3 Comparing the Mountain and Plateau Models
Eleven of the 15 variables measured were significant predictors of mountain bear landscape use
(Table 2), while 8 of variables were significant predictors of plateau-bear use of the landscape
(Table 3). Common to both RSF models was selection for higher greenness scores and
southwest facing aspects. Southwest aspects are snow free earlier, and remain snow free longer,
than cooler aspects, which is an important consideration for food availability in northern
environments. Also common to both models was a selection for lower elevation habitats,
however, for mountain bears this was a reflection of use of mid-elevation habitats, while for
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Habitat Selection by Mountain and Plateau Grizzly Bears
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plateau bears this reflected an avoidance of the small pockets of alpine habitat that occurred in
two locations within the plateau landscape.
Overall, mountain bears were more affected by the covariates in the RSF models than plateau
bears. The mountain model does a better job of predicting spatial patterns in the probability of
use by grizzly bears than the plateau model. Furthermore, the density of bears is higher in the
mountains than the plateau (Mowat et al. 2002). We examined resource selection ratios to
determine the type of information that may be required to improve the plateau model.
4.2 Resource Selection Ratios
Resource selection ratios are similar to Resource Selection Functions (Manly et al. 1993). Their
main advantage is that they allow one to view the selection pattern in a graphical format. In
addition, at this stage of analysis some information has not yet been created as a spatial explicit
GIS layer and therefore cannot be used to create GIS maps. This section provides selection
ratios for some of the model covariates, information not contained in the models, as well as
further information on road types. A ratio of 1 indicates use in proportion to availability (no
selection), whereas greater than 1 indicates selection, and less than 1 indicates avoidance. The
ratio can be thought of as an odds ratio, so the higher the number the more it is selected.
4.2.1 Age Classes
We included a ratio of the forest age class to explain a limitation in the Resource Selection
Function models presented above (Figure 4). In ratio form, the histogram depicts the
information contained within the models. For example, one can clearly see that alpine age
classes were highly selected for by mountain bears, whereas plateau bears selected for ‘other’
and young age classes. There are two things to note on this histogram. The first is that the
selection for young age classes now appears significant. This is because a ratio is a direct
proportion, whereas the RSF models used deviance coding and referenced the age classes to
mature. Therefore, although the beta value was positive, in relation to mature age classes there
was not a significant selection for young habitat, however, in ratio comparison there appears to
be selection for this age class.
Complete avoidance was also a problem for the RSF models and can be seen when examining
the urban category. Random points fell within non-forest descriptors that were labeled urban,
however, we did not have any bear use points in urban. Therefore, we had to eliminate all
random urban points from RSF analysis. In the ratio depicted in Figure 4, these also come out as
0 because there was no bear use. However, complete avoidance of an area is an extremely
important feature of grizzly bear habitat use.
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Habitat Selection by Mountain and Plateau Grizzly Bears
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Figure 4. Ratio of Used Versus Available Landscape Age Classes by Mountain and Plateau
Grizzly Bears, 1998 to 2001.
Ratio of Used Versus AvailableForest Age Classes
3.5
3
Ratio
2.5
2
Mountain
Plateau
1.5
1
0.5
0
Alpine
Mature
Old
Other
Urban
Young
Age Class
4.2.2 Distance to the nearest road
Distance to the nearest road is provided for both landscapes combined (Figure 5), for the
mountains (Figure 6), and for the Plateau (Figure 7). For the study area, there was an overall
avoidance of roads by grizzly bears until approximately 2.5 km. There was neither selection nor
avoidance associated with roads from 2.5 to 4.5 km. Overall, grizzly bear use of the landscape
increased with further distances from roads.
Figure 5. Grizzly Bear Locations Versus Distance to the Nearest Road for both
Landscapes, 1998 to 2001.
11.5
11
10.5
10
9.5
9
8.5
8
7.5
7
6.5
5
4.5
4
3.5
3
2.5
2
1.5
1
12
11
10
9
8
7
6
5
4
3
2
1
0
0.5
Ratio
Bear Locations vs. Distance to Nearest Road
(Study Area)
Distance to Road (KM)
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Habitat Selection by Mountain and Plateau Grizzly Bears
Ministry of Forests
For the mountains the selection ratio provides a depiction of avoidance of areas within 1
kilometer of roads (Figure 6). At 1.5 kilometers use began to increase and was in proportion
with availability at approximately 2.5 km. At 5 kilometers from the road use was greater than
availability and remained so until 11.5 km. This reveals an overall avoidance of road networks
by mountain bears.
Figure 6. Grizzly Bear Locations Versus Distance to Nearest Road for the Mountainous
Landscape, 1998 to 2001.
Grizzly Bear Locations versus Distance to Nearest Road
(Mtns. Only)
8
7
Ratio
6
5
4
3
2
11.5
11
10.5
10
9.5
9
8.5
8
7.5
7
6
6.5
5.5
5
4
4.5
3.5
3
2.5
2
1
1.5
0
0.5
1
Distance to Nearest Road
Figure 7. Grizzly Bear Locations Versus Distance to Nearest Road for the Plateau
Landscape, 1998 to 2001.
Grizzly Bear Locations versus Distance to Closest Road
(Plateau Only)
6
5
Ratio
4
3
2
1
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
6.5
7
7.5
Distance to Road (KM)
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Habitat Selection by Mountain and Plateau Grizzly Bears
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For the plateau use was in proportion to availability within 500 m of the road, which was likely a
function of plateau bears being attracted to the new growth along roadsides during the spring
foraging season. Also, plateau grizzly bears have been recorded to travel along deactivated
logging roads on a number of occasions. From 1 to 5 km from the road selection fluctuated
slightly above and below (avoidance) 1. At 6.5 km use became increasingly greater than
availability. An interesting note is that this is very similar to the pattern observed in the
mountains, where at approximately 5 km use became greater than availability and remained so
up to 11 km from the road. Therefore, in either landscape the further from a road the more likely
there are to be grizzly bears.
4.2.3 Road Type
We also examined the types of roads that were closest to the bear’s locations (Figure 8 and
Figure 9). Mountain bear locations were closer to roads that had been decommissioned while
they most strongly avoided the highway. Possibly the majority of the collared sample did not
actively avoid the highway but simply did not move out of the mountainous habitat. Therefore,
road types other than highway were more applicable for mountain bears that resided in the
interior mountain. As mentioned, all of the cutblocks we visited in the mountains to conduct onsite habitat investigations were classified as decommissioned or largely inaccessible.
Figure 8. Grizzly Bear Locations Versus Type of Road for the Mountainous Landscape,
1998 to 2001.
Mountain Bear Ratio of Nearest Road Type
6
5
Ratio
4
3
2
1
0
Highway
Logging Road
Secondary
Logging
Decommissioned
Type of Roads
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Figure 9. Grizzly Bear Locations Versus Type of Road for the Plateau Landscape, 1998 to
2001.
Plateau Bear Ratio of Nearest Road Type
1.6
1.4
1.2
Ratio
1
0.8
0.6
0.4
0.2
0
Highway
Logging Road
Secondary Logging
Type of Road
Plateau bear locations were closer to primary logging roads than the highway or secondary
logging roads. However, the plateau landscape results for type of road should be viewed with
caution because there were no roads listed as deactivated within the database. However, our onsite knowledge of the land base reveals that there were places on the plateau that had been
deactivated making some inaccessible or ATV-only accessible. For example, on several
occasions we investigated a known area of deactivated road networks by Summit Lake. In the
GIS database these roads were classified either as logging roads or secondary logging roads.
Therefore, for this analysis to accurately reflect the use of areas associated with various types of
road, the databases must be updated and reclassified. Thus, the selection for logging road and
use in proportion to secondary logging road may not be accurate.
4.2.4 Biogeoclimatic Zone
We also examined the biogeoclimatic zones in which the locations occurred (Figure 10). For
mountain and plateau bears there was a selection for the ESSFwk2 biogeoclimatic zone.
Meidinger and Pojar (1991) considered this biogeoclimatic zone to be year round high-quality
grizzly bear habitat. The ESSFwk2 zone occurred in a relatively small proportion on the plateau
but bears were located within this zone greater than its availability. The majority of this use
occurred along the mountain ridge west of McLeod Lake.
The Alpine Tundra (AT) biogeoclimatic zone occurred in very small proportions on the plateau
and there was no use recorded by bears in this zone. However, this may be due to a lack of study
animals in the area where this zone occurs (i.e., by the divide of Arctic Lake). In the mountains
use of the AT zone was in proportion to its availability. Meidinger and Pojar (1991) state that
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Habitat Selection by Mountain and Plateau Grizzly Bears
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the AT alpine zone offers important habitats for grizzly bears, particularly where the soils allow
easy digging.
Figure 10. Grizzly Bear Locations of Biogeoclimatic Zones in Mountainous and Plateau
Landscapes, 1998 to 2001.
Ratio of Used Versus Available Biogeoclimatic Zones
2.5
Ratio
2
1.5
1
Mountain
Plateau
0.5
SBSwk2
SBSwk1
SBSvk
SBSmk2
SBSmk1
SBSmh
SBSdw3
ESSFwk2
ESSFmv3
ESSFmv2
BWBSwk1
BWBSmw1
AT p
0
Biogeoclimatic Zone
4.2.3 Limited Entry Hunt and Problem Wildlife Kills
The plateau model did not perform well in predicting bear use of the landscape (deviation
explained was 3.5%). We believe that this may be because the majority of the covariates in the
model were habitat related, whereas bear use in the plateau may be dictated by risk of mortality
and avoidance of active resource extraction activities rather than driven by habitat related
variables. To increase the predictive capability of the plateau model we are currently developing
a risk layer for bears that will be used to reflect avoidance of areas that based on habitat variables
alone may predict bear use. For example, it is possible that plateau bears were actively avoiding
areas of high mortality risk due to human access. To investigate this hypothesis we are
examining the Limited Entry Hunt (LEH) and Problem Wildlife (PW) database (Figure 11).
Figure 11 depicts the number of grizzly bears killed as a function of the distance to the nearest
road. Seventeen percent (n=54) of the bears killed within the study area were within 0-100 m of
a road, 32% (n=99) within 0-200 m, 39% (n=121) within 0-300m, 43% (n=133) within 0-400 m,
and 58% (180) within 0-500 m of the closest road. 73% (n=227) occurred within 1 km of a road.
Hunters are only required to report their kill site to within the nearest kilometer. Thus, it is likely
that the majority of these kills occurred closer to a road than 1 km. Overall, the majority of
grizzly bears killed within the study area occurred closer to roads than random and the frequency
decreased as the distance to the road increased (Figure 11). The majority of the kills occurring
furthest from roads occurred at a fly in guide outfitting camp within the mountains. A few of
Ciarniello, Boyce & Beyer
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Habitat Selection by Mountain and Plateau Grizzly Bears
Ministry of Forests
the bears in the database had been hit by a train along the railway used to access coal from two
mines within the mountains.
Figure 11. Grizzly Bear Kills in Relationship to Distance to the Closest Road in
Mountainous and Plateau Landscapes, 1998 to 2001.
Grizzly Bears Killed 1977 - 2001 in the PGBP Study Area
250
Number of Bears Killed
200
150
100
50
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Distance to Road (KMs)
We also examined the types of roads closest to the mortality locations (Figure 12). The majority
of grizzly bears killed occurred near the highway, followed by secondary logging roads, primary
trunk roads, and decommissioned logging roads (Figure 12). Deactivating roads may offer an
increased advantage to grizzly bear survival.
Figure 12. Grizzly Bear Kills in Relationship to Distance to the Closest Road in
Mountainous and Plateau Landscapes, 1998 to 2001.
Grizzly Bear Mortality - Relationship to Nearest Road Type
1.2
1
Ratio
0.8
0.6
0.4
0.2
0
Highway
Primary Trunk
Secondary
Logging
Deactivated
Road Type
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Habitat Selection by Mountain and Plateau Grizzly Bears
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5.0 DISCUSSION
The location of the Parsnip Grizzly Bear Study provided a number of special opportunities to
better understand grizzly bear habitat requirements. In particular, the study area ranged from
wilderness mountain habitat to plateau habitat that had extensive road access and forest
harvesting activities. Prior to this study, little was known about habitat use of grizzlies on the
sub-boreal plateau. Similar to results of other studies, we thought that most of the bears would
den in the mountains, move to lower, snow- free habitat in the plateau at den emergence and then
move back to the mountains as foods became available, resulting in a population of migrating
bears (McLellan and Hovey 2001). However, we found that mountain bears tended to live solely
in the mountains and plateau bears on the plateau. During our 4 years of monitoring an average
of 19 grizzly bears per year only 1 large adult male frequently traveled between the mountains
and plateau, although he primarily lived in the mountains. One male dispersed from the
mountains to the plateau and another occasionally traveled between landscapes. However, both
were killed in the limited entry hunt. Only 1 female, accompanied by 2, 3- yr-old offspring,
moved from the plateau to the mountains for denning. This family group returned to the plateau
at den emergence.
The density of bears within the two landscapes also differed markedly (Mowat et al. 2002). In a
previous analysis we examined the link between bear density and landscape structure by
investigating the changes in the RSF models as a result of applying the mean values of the
different habitat types as well as the average density of roads from one landscape to the other
(Ciarniello et al. in review). Although the probability of use by grizzly bears accounted for by
the model is higher in the mountains than the plateau we could explain only 5 to 6 % of the
difference in density between the 2 areas by examining the habitat variables and/or the distance
to roads. Therefore, we were unable to support the hypothesis that 4- fold differences in bear
density between the mountains and the plateau were attributable to differences in the respective
habitats or road density. However, such a conclusion would have a high probability of a Type II
error, i.e., a difference might exist that we are unable to detect for whatever reason. Because
there is a four- fold difference in grizzly bear density as identified by the DNA census, and we
could only account for a 5-6% difference when switching the road and habitat attributes,we
concluded that we did not captured the mechanism behind the difference in population density
between the 2 areas.
We believe that more information on plateau bears is required to sort out habitat selection by
these bears. We state this because we have over twice the number of locations in the mountains
than we have in the plateau, plateau bears were more prone to slipping their collars and therefore
we do not have a full year of data on a number of plateau bears, and the habitat model has
trouble predicting bear use of the plateau landscape. In addition, uncovering the mechanisms of
bear use on the plateau will allow us to hypothesize about future use of the mountain landscape
by bears as it becomes increasingly fragmented. We present a further hypothesis that selection
by plateau bears may be a combination of habitat related attributes and avoidance of high-risk
areas. To adequately predict use of landscapes with a myriad of human activities an
avoidance/risk layer must be developed that can be interfaced with GIS and applied back to the
landscape. We are currently developing a risk layer that will be applied to RSF models and the
GIS database. The analysis presented in this paper reveals that bears were selecting for habitats
further from roads than was available. Therefore, a model based mainly on habitat-related
Ciarniello, Boyce & Beyer
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Habitat Selection by Mountain and Plateau Grizzly Bears
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covariates would predict the animal to be in an area, however, the risk of mortality likely
outweighs the benefits of foraging close to roads. Based on RSFs characterizing female grizzly
bear habitat use in Montana, Mace et al. (1999) recommended management to reduce road
density. Although our results indicate a significant influence of roads, the magnitude of response
is unlikely to account for the observed differences in bear density between the mountains and the
plateau (Ciarniello et al. in review). Clearly the extensive road network on the plateau subjects
bears to higher risk of human- induced mortality than in the mountains by providing access for
human activities. However, adding layers will require more locations to have the statistical
power to accurately predict relative use of the plateau landscape.
Another possible explanation for our results might be that bears have been avoiding areas that
are being harvested for timber. A limitation with the data obtained from forest cover maps is that
the information is not updated yearly. Therefore, map layers do not reflect current manipulation
of the landscape, which may have an influence on the distribution and dens ity of bears. Our next
step will be to update GIS layers with timber harvesting patterns by year to account for present
timber harvesting, and to build new RSF models. Mortality and current land-use data will be
added to the model to explicitly consider risks in areas of concentrated human activity associated
with living in the plateau. Addition of these variables should better predict relative use of the
plateau landscape as well as explaining the 4-fold differences in grizzly bear densities found
between the mountains and the plateau. This information will help land managers in government
and forest industry develop land- use practices that are compatible with the conservation of
grizzly bears and their habitat.
6.0 ACKNOWLEDGEMENTS
The Parsnip Grizzly Bear Project is supported by Forest Renewal British Columbia (FRBC),
Canadian Forest Products Ltd. (Canfor), and the Pas Lumber. Additional support was provided
by: BC Ministry of Environment, Lands and Parks, BC Ministry of Forests, BC Conservation
Foundation, Spruce City Wildlife Association, Natural Science and Engineering Research
Council of Canada scholarship to L. Ciarniello, and the University of Alberta. This project
report was initiated by Dale Seip, BC Ministry of Forests, and supported by the Crown Land Use
Planning Enhancement Fund. We thank our funding agencies.
We thank John Paczkowski for work on the project and for the1998 data. Elena Jones was the
field technician and aided in some of the resource selection ratio analysis. The success of the
Parsnip Project would not have been possible without the assistance of Dale Seip, Doug Heard,
Glen Watts, Carla Wainwright, Ian Ross, Charles Mamo, Doug Wilson, Kerry Deschamps,
Andrew De Vries, and Joe Kavanagh. We also thank Greg Altoft, Pie rre Bock, Ken Knight,
Chris Norman, and Biker Mike for helicopter skills. Fixed-wing airplane pilots Larry Frey and
Eric Stier kept us safe on telemetry flights. We also thank Mari Woods of the McLeod Lake
Landfill Study (Peace-Williston Compensation Program) for providing data on 2 of their study
animals, which have been included in our 2000 and 2001 databases. Last but certainly not least,
we thank all the study bears.
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Habitat Selection by Mountain and Plateau Grizzly Bears
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7.0 LITERATURE CITED
Burnham, K. P., and D. R. Anderson. 1998. Model selection and inference: a practical
information theoretic approach. Springer-Verlag, New York, New York, USA.
Ciarniello, LM, J. Paczkowski, D. Heard, I. Ross and D. Seip. 2001. Parsnip Grizzly Bear
Population and Habitat Project: Progress Report for 2000. Unpub lished report for Canadian
Forest Products Ltd. And BC Ministry of Forests, Prince George, BC, Canada. 55pp.
Ciarniello, L.M., M.S. Boyce, D.R. Seip, D.C. Heard. In review. Grizzly bear habitat selection
in mountain and plateau landscapes along the Parsnip River, BC. Ursus.
Committee on the Status of Endangered Wildlife in Canada (COSEWIC). 1996. Canadian
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