Grizzly Bear Habitat Selection and
Mortality Coefficients of Southern Alberta:
Estimates for the Southern Alberta Regional
Strategy (SARS)-ALCES Project
April 2003
Prepared by:
Scott E. Nielsen1 and Mark S. Boyce1
In collaboration with the East Slopes Grizzly Bear Project
(S. Herrero, M. Gibeau, and B. Benn), Brad Stelfox, and Jim Schieck
1
University of Alberta, Department of Biological Sciences, Edmonton, Alberta, T6G 2E9
Contact: [email protected]
This report may be cited as:
Nielsen, S.E., and M.S. Boyce. 2003. Grizzly bear habitat selection and mortality coefficients of
southern Alberta: Estimates for the Southern Alberta Regional Strategy (SARS)-ALCES
project. A report for Forem Technologies. 21 pp.
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Table of Contents
Title Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Table of contents, list of figures and list of tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4. Methods
4.1 ALCES geographic information system (GIS) variables and data . . . . . . . . . . 5
4.2 Grizzly bear habitat selection modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4.3 Grizzly bear mortality modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
5. Results
5.1 Grizzly bear habitat selection modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
5.2 Grizzly bear mortality modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
6. Suggestions, limitations and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
List of Tables
Table 1. ALCES landcover categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Table 2. VHF radiotelemetry data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Table 3. Sampling points and landcover categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Table 4. Estimated coefficients for habitat selection model . . . . . . . . . . . . . . . . . . . . . . . . 16
Table 5. Estimated coefficients for mortality/random model . . . . . . . . . . . . . . . . . . . . . . . 17
Table 6. Estimated coefficients for mortality/radiotelemetry model . . . . . . . . . . . . . . . . . 18
List of Figures
Figure 1. Study area map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Figure 2. Exposure index variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Figure 3. Location of MCP study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
SARS–ACLES Report
2
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
1. Summary
Southern Alberta has witnessed substantial recent growth in local human population
concurrent with an increasing demand on natural resources. This growth is expected to continue
for the foreseeable future. A Southern Alberta Region Strategy (SARS) was formed to address
potential economic and ecological benefits and/or impacts of projected regional change. To
examine these relationships in a quantitative and structured manner, SARS settled on the use of
A Landscape Cumulative Effects Simulator (ALCES). One resource sector outlined in SARS
and modeled in ALCES is wildlife, with grizzly bears (Ursus arctos L.) chosen as one focal
conservation species for the process. Grizzly bears are a species of special concern in Alberta,
currently considered ‘may be at risk’. For the ALCES modeling process, information on habitat
relationships or habitat suitability indices (HSI) are required. In this report we describe the
results of empirical modeling exercises undertaken to provide coefficients of habitat selection
and mortality. We further provide suggestions for incorporating the two indices into a single
synthetic index we refer to as exposure.
For derivation of habitat selection coefficients, we used 2,764 VHF radiotelemetry
locations retrieved from 45 male and female grizzly bears in the Central Rockies Ecosystem
(CRE) of southwest Alberta from 1994 to 2001 (East Slope Grizzly Bear Project; ESGBP).
Using these data, we developed general population-level habitat selection models using standard
resource selection function (RSF) methods. Data were partitioned into training (80%) and
testing (20%) datasets used for model building and validation respectively. RSFs were
developed by contrasting model training radiotelemetry locations with random available samples
using logistic regression and the ALCES covariates of landcover and human linear feature
density. We found that bears selected landcover classes differentially and without much respect
to human access features. The lack of a negative response for access suggests that under current
levels of access, the pooling of seasonal data, and the scale of analysis for linear features (1-km2
moving windows), that bears were often within regions of human impact. This is likely an
artifact of scale and landscape configuration (valleys and mountains) where both bears and
humans are competing for similar resource areas (e.g., high quality habitats are overlapping with
humans). Smaller scale responses (i.e., direct avoidance of nearby roads) were likely, but not
examined here. By assessing the predictive accuracy of the model using model testing
(validation) data, we found the model to be generally predictive, but lacking both substantial
explanation and strong validation suggesting that ALCES GIS variables were either poor
predictors of grizzly bear habitat or that the pooling of data caused responses that were too
general to be of importance.
For derivation of mortality coefficients, we used 235 spatially registered human-caused
grizzly bear mortalities from the CRE. These data, compiled by the ESGBP (B. Benn),
represented recorded mortalities and management relocations (considered equivalent to a
mortality) within the region from 1971 to 2002. We contrasted these data using the same
methods described for habitat selection under two sampling protocols: (1) a comparison with
random available locations; and (2) a comparison VHF radiotelemetry data. Both provide an
index of risk, although the second approach (mortality versus radiotelemetry) provides a more
direct index of risk given that animals can only be killed if they are present. We found that
human-caused mortality was largely related to the density of linear human-use features, with the
relationship to roads stronger than the relationship with other linear features (seismic lines,
transmission lines, pipelines, railroads, and trails). Regardless of the effects of linear features,
we found that crop/agricultural/rural, forest shrub, grassland, and lentic/lotic ALCES classes had
SARS–ACLES Report
3
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
the highest risk of mortality, although a few of these categories were inherently coupled with
access suggesting possible confounding effects. Assessments of model predictive capacity,
however, revealed that models were predictive when compared with out-of-sample model testing
(validation) data, especially for the mortality and radiotelemetry comparison.
We provide tables of model coefficients for the ALCES modeling process and validation
metrics. We caution, however, that these coefficients have limitations; most notably that of data
pooling for the habitat selection model leading to potentially poor model performance and the
loss of seasonally important relationships. Furthermore, differences in individual selection
behaviors (e.g., habituated versus wary bears) are important and not considered here. Overall,
grizzly bear habitat selection is noisy and variable (generalist species), unlike that of humancaused mortality processes that are highly predictable and consistent. Measurement error
associated with GIS covariates, VHF radiotelemetry locations, and mortality sites also needs
additional exploration into implications of spatial uncertainty and model influences. Moreover,
there is a question as to whether the current ALCES classes are applicable for measurements of
grizzly bear habitat selection. Some grizzly bear studies have show that spatial variables (e.g.,
patchiness, distance to edge, etc.) can be good predictors of bear occurrence, but many of these
are not possible for an a-spatial approach like ALCES. Lastly, we advocate that coefficients
presented herein be viewed in an adaptive management context, where adjustments are made as
additional information (data) and models become available across the range of habitats
considered in SARS. For instance, this report notably lacks information in the Crowsnest and
Waterton areas of SARS, an important region of grizzly bear habitat and conservation concern.
Qualitative assessments of habitat use and mortality from this area can be used to adjust
coefficients required for ALCES. We suggest that an exposure index be used within the ALCES
modeling framework as it incorporates both habitat selection and mortality risk. We caution the
interpretation of actual values of this index as the scaling of use and mortality models are not
validated. However, the ranking of potential future scenarios are considered robust and of
greatest use. Without incorporating mortality risk, a biased assessment of the impact of
landscape change on populations of grizzly bears would likely result.
2. Introduction
Southern Alberta has witnessed substantial recent growth in local human population
concurrent with an increasing demand on natural resources. This growth is expected to continue
for the foreseeable future. Recently, a Southern Alberta Region Strategy (SARS) was formed to
address potential economic and ecological benefits and/or impacts of projected regional change.
To examine these relationships in a quantitative and structured approach, SARS settled on the
use of A Landscape Cumulative Effects Simulator (ALCES). ALCES is a resource
management-modeling tool useful for tracking resource changes given future scenario
relationships. One resource sector outlined in SARS and modeled within ALCES is wildlife. A
small number of indicator and/or focal conservation species were chosen following consultation
of wildlife personnel and experts. This included grizzly bears (Ursus arctos L.), a species of
special concern in Alberta currently considered ‘may be at risk’ (Kansas 2002).
Grizzly bears are a classic ‘umbrella’ species as the large landscape requirements
necessary to sustain viable populations of bears are likely to further maintain numerous other
species and natural processes. Grizzly bears are also largely considered to be keystone species
through their actions as ecosystem engineers (Tardiff & Stanford 1998), transport of nutrients
(Tardiff and Stanford 1998; Hilderbrand et al. 1999), movement and germinations of seeds
SARS–ACLES Report
4
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
(Krefting & Roe 1949; Applegate et al. 1979; Welch et al. 1997), and the excavators of roots,
tubers and small mammals enhancing local plant diversity (Tardiff & Stanford 1998). The
primary threat to the long-term persistence of healthy grizzly bear populations is widely
acknowledged to be the expansion and development of resources extraction activities and rural
populations leading to increased human access and fragmentation of grizzly bear habitat (Banci
et al. 1994; McLellan 1998). Examinations of historic extirpations provide convincing evidence
of correlations among human expansion and the local disappearance of grizzly bears (Woodroffe
2000; Matson & Merrill 2002).
Although numerous habitat studies for grizzly bears in the overall region have been
completed (Mace et al. 1996, 1999; McLellan and Hovey 2002; Nielsen et al. 2002a, 2003a),
there is a general lack of readily transferable information for the ALCES modeling process. No
landcover classifications from these studies match the classes that ALCES tracks. The grizzly
bear working group also felt that expert opinion-driven coefficients would be unsuitable and
tenuous extrapolations of relationships from elsewhere or local imperfect knowledge. Moreover,
there is very little if any information and methods available for incorporating human-caused
mortality (see however, Nielsen et al. 2003b), a critical component required to fully understand
the impact of landscape change. Instead, the working group suggested that an empirical,
objective approach be undertaken to derive relationships specific for ALCES from the wealth of
data already available to the area from the East Slopes Grizzly Bear Project (ESGBP). In this
report, we describe the results of modeling exercises undertaken to support these needs.
3. Objectives
The objectives of this report were to develop coefficients of habitat selection and
mortality for grizzly bears in the Central Rockies Ecosystem of Alberta in support of the SARS
ALCES modeling process. The approaches and methods follow closely to that of the Northeast
Slope (NES) ALCES process for grizzly bears (Nielsen et al. 2002b). Readers are referred to
this document if they are interested in initial analyses and comparisons of the predictive
performance of ALCES landcover classifications with other GIS and remote sensing
information. In this report, we do not compare mapping products, but simply describe habitat
selection and mortality coefficients for variables tracked in ALCES. Approaches used in this
report, however, differ from Nielsen et al. (2002b) in that we also focused on the incorporation
of mortality sites in assessments of habitat quality. Although habitats may initially appear to be
relatively high in habitat quality based on habitat selection coefficients, some sites may be risky
habitats. From a population perspective, the use of these risky areas can largely be considered to
be attractive sinks (Delibes et al. 2001). Tracking and mitigating risky high quality habitat is
likely the primary management goal of resource personnel and scenario modeling used in
ALCES. We suggest an approach for combining the habitat selection and mortality processes
into a synthetic index representative of the overall goals of tracking habitat quality with changing
land-use. This index, which we call exposure, is considered to be the most appropriate
generalized representation of the processes influencing grizzly bear population viability. Tracing
this index along with general habitat quality (RSF) and mortality risk is an appropriate approach
for comparing future scenarios derived from ALCES.
4. Methods
4.1 ALCES geographic information system (GIS) variables and data.—We were provided
GIS data for 2 primary variable groups necessary for ALCES modeling efforts. These data
SARS–ACLES Report
5
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
corresponded to a 27,900-km2 region of the northwest SARS study area where modeling efforts
for grizzly bears in this report took place (Figure 1). This does not include the far southern area
of current grizzly bear distribution in the province and hence the SARS study, south of
Kananaskis Country to the U.S.A. border. Our first variable of interest for describing habitat
selection and mortality of grizzly bears was current landcover, categorized into 27 separate
classes from numerous sources, but most notably that of Alberta Vegetation Inventory (AVI)
(Table 1). However, given the rarity and or ecological similarity of many of these classes, we
were forced to reduce these to 14 more general classes (Table 1). Our second variable of interest
and the one that will undoubtedly change the most in future decades is human linear access
features. Access features included pipelines, transmission lines, seismic lines, railways, trails,
and roads. We categorized these into two groups: 1) roads, and 2) other human use access
features. All features were transformed into density estimates within 1-km2 moving windows
and reported on a km/km2 basis. Responses for these features in this report therefore represent
landscape scale influences more than local patch-level processes. Although you would expect
road and other human use features to be highly correlated and therefore problematic from a
model co-linearity standpoint, we found the two variables to only be marginally correlated (r =
0.278) and therefore included both variables within our models.
4.2 Grizzly bear habitat selection modeling.—We used 2,764 VHF radiotelemetry
locations retrieved from 45 male and female grizzly bears (Ursus arctos L.) in the Central
Rockies Ecosystem (CRE) of southwest Alberta (Table 2). These data were collected by the East
Slopes Grizzly Bear Project (ESGBP, M. Gibeau and S. Herrero) from 1994 and 2001 and only
represent aerial locations for bears with at least 10 total observations. Using these data, we
developed general population-level habitat selection models using standard resource selection
function (RSF) methods on use-availability data (Manly et al., 1993, 2002). The relative
probability of occurrence for a RSF is determined from the following relationship described in
Manly et al. (1993, 2002):
w(x) = exp(β1 x1 + β2 x2 + . . . + βk xk)
(eqn 1)
where, w(x) is the resource selection function, βi the coefficient of habitat selection for variable xi
based on logistic regression estimates. We followed a patch-level habitat selection scale and a
design III approach, where individuals are identified and censused throughout the modeling
process (use and available samples). Given this design, we were able to use robust estimates of
variance identifying the individual bear as the unit of replication (Otis and White 1999; Nielsen
et al. 2002). Data were partitioned into training (80%) and testing (20%) datasets used for model
building and validation respectively. RSFs were developed by contrasting model training
radiotelemetry locations with random available samples generated within individual grizzly bear
MCP (minimum convex polygon) home ranges (1 point/km2) and the ALCES covariates of
landcover and human linear access density (km/km2). Following model development, we used
testing data to validate the predictive performance of the model based on the methods outlined in
Boyce et al. (2002). Briefly, this method compares the frequency of withheld radiotelemetry
points with ranked habitat quality bins (10 bins from low to high in this case). Bins are simply
classified ordinal regions of the RSF model predictions. A significant predictive model would be
one that has an increasing frequency of bear locations for each subsequent habitat bin. We
assessed this assumed relationship using a Somer’s D statistic, which is interpreted in a similar
SARS–ACLES Report
6
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
manner to that of a Spearman rank correlation ranging from –1 to 1, but allowed jackknifed
estimates of standard errors. A Somer’s D with a 1.0 would represent perfect performance
between model prediction and observed validation data, while 0 would represent random noise.
We used a quantile classification (reclassification) of the raw RSF prediction to derive 10
categories (bins) using the spatial analyst extension of ArcMap 8.2 (ESRI). Bin value was then
correlated against adjusted frequency of occurrence for validation locations.
4.3 Grizzly bear mortality modeling.—We used 235 spatially registered human-caused
grizzly bear mortalities from the CRE to model ALCES factors (landcover and human linear
feature density) affecting mortality. Mortality data were compiled by the ESGBP (B. Benn and
S. Herrero) and represented recorded mortalities and management relocations (considered
equivalent to a mortality) within the CRE from 1971 to 2002. We contrasted these data using
similar methods described in the above section on habitat selection, but using two separate
sampling designs: (1) a comparison of mortality points with random available locations; and (2)
a comparison of mortality points with radiotelemetry data. RSF methods differed for mortality
analyses compared with habitat selection analyses as we followed a design II approach for
sampling of use and available resources (Manly et al. 1993, 2002). Here, individual animals
were identified, but available sampling is measured at the population level (either random or
radiotelemetry points). Again, we use the model testing (validation) data to describe the
predictive capacity of the two approaches, although here we binned (quantile classification) the
final model predictions into 5 categories (bins) corresponding to the qualitative ranks of very
low, low, intermediate, high, and very high risk of mortality.
Differences between sampling design 1 and 2 were that results from the comparison with
the random available samples (#1) were interpreted as the likelihood (relative) of a mortality
occurring within an area with respect to the entire extent and composition of habitats. This may,
for example, show that animals are unlikely to be killed in rock and ice habitat. However, bears
are not likely to be present in these areas in the first place, so we need to consider some form of
conditional probability to derive an index meaningful for the process of mortality risk. That is to
say, risk of mortality for a bear should be conditional on the animals being present in the first
place. Conditional properties for two dependent processes would look like,
P(A∩B) = P(B|A) × P(A)
(eqn 2)
where we let P(A) be the relative probability of occurrence, P(B) the relative probability of
mortality, and P(B|A) is the relative probability of mortality given that the bear used (relative
probability of occurrence) that habitat. If we assume that P(B|A) is the product that compares
mortality locations with radiotelemetry locations (use), we can use the multiplicative relationship
between P(B|A) and P(A) to derive P(A∩B). We call the variable P(A∩B) exposure. Exposure
is maximized where both P(A) and P(B|A) are maximized (Figure 2). More specifically, this
index represents risky high quality habitats where bears are not only likely to be spending large
amounts of time, but also within areas that are the most dangerous to them. We feel that this is
an important variable to track in ALCES, together with habitat quality, as it is an effective means
of describing the impacts of future landscape changes for grizzly bears. Those scenarios that
reduce exposure, while maintaining habitat quality (relative probability of occurrence) would be
considered the most beneficial for conservation of grizzly bear populations. It is important to
note that these are relative probabilities and therefore only an index. We do not have access to
SARS–ACLES Report
7
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
true probabilities given that we do not have presence-absence data, but instead use-availability
data. We recommend therefore, caution in interpreting actual values. Instead, emphasis should
be given on the ranking difference between future scenarios. Ideally in the future and with the
right data, survival models could be used to derive true probabilities of mortality, while there are
some possible approaches for deriving a RSPF (resource selection probability function).
5. Results
5.1 Grizzly bear habitat selection modeling.—Based on the 2,764 radiotelemetry use
locations and the sample of random available locations (27,022) within MCP home range
polygons, grizzly bears selected forb, forest shrub, and major road, while avoiding
cropland/agriculture/rural, rock/ice, and recreation/anthropogenic using simple RSF ratios (Table
3). RSF models using these same classes and human linear density variables describe these
selection ratios as coefficients (Table 4). Overall, the habitat selection model was significant (χ2
= 195.2, d.f. = 15, p <0.001), however, only the coefficients for forb (+), forest shrub (+), and
rock/ice (-) were significant at the p = 0.05-level when compared with the pine (indicator)
reference category. It appeared that samples of grizzly bear locations occurring from 1994 to
2001 and for all seasons were too general to describe in detail the dynamics of selection for the
variables examined here. Considering all seasons and data, it appears that we can only say with
confidence that there was consistent selection for forb and forest shrub areas and avoidance of
rock/ice habitats. All other factors, including human linear access feature densities within a 1km2 window, were not showing consistent patterns of selection.
The predictive capacity of the model using withheld model-testing data (n = 565) proved
to be significant overall (D = 0.711 ±0.145 S.E., p <0.001). However, these data were tested
from the existing distribution of grizzly bear samples (e.g., within MCP home ranges of bears
used in the model) and not from other areas of the study region (Figure 3). Extrapolations
outside of this area may not be representative of the model. This maybe particularly evident
given that access density and some habitats are quite different and hence the predictions are
made outside of the distribution of the data. Also, the generalist nature of the selection process
for this species and the pooling of important seasonal habitat selection processes suggest
marginally important relationships were uncovered. Excluding the high elevation areas of rock,
snow, and ice, grizzly bears use most, if not all of the remaining habitats during some season or
behavioral activity (foraging, bedding, and movement). It is the displacement of animals,
particularly through human-caused mortality, that can largely be considered the conservation
concern. Taken together, we caution the user on relying too heavily on habitat selection
coefficients derived from this product and instead focus the reader to results of the mortality
analysis.
5.2 Grizzly bear mortality modeling.—Based on the 235 grizzly bear mortalities, overall
significance of models comparing radiotelemetry (χ2 = 222.7, d.f. = 13, p <0.001) and random
(χ2 = 294.8, d.f. = 13, p <0.001) locations were both good suggesting strong explanation of the
data and mortality patterns. For both modelling methods, we found consistent positive
significant relationships between human linear access feature density and mortality (Table 5, 6).
Interestingly, the random versus mortality model showed a weaker, albeit still strongly
significant, relationship with roads when compared with the other linear features variable.
Comparing landcover categories with the reference category pine, the random versus mortality
model indicated that mortalities were significantly related to forest shrub (+) and lentic/lotic (+)
SARS–ACLES Report
8
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
categories. Non-significant, but directional patterns in mortality were seen in alpine (-),
hardwood (-), major road (+), and rock/ice (-) categories (Table 5). The model describing grizzly
bear mortality from comparisons with radiotelemetry locations indicated that mortalities were
significantly related to forest shrub (+), grassland (+) and lentic/lotic (+) landcover categories
when compared to pine stands (Table 6). In summary, models for mortality largely agreed with
one another and were strongly influenced by human linear feature densities and to some degree
the landcover category present.
Assessments of model predictive capacity, however, revealed that the performance of the
two mortality RSF models were variable. Using out-of-sample withheld mortality testing
locations (n = 50) we found that the mortality versus random model had less predictive support
(D = 0.600 ±0.267, p = 0.024) than the mortality versus radiotelemetry model (D = 1.0, p
<0.001). This suggests that it is important to consider a comparison that is relevant to where
animals are likely to be in the first place (conditional probability). Given the greater predictive
capacity of the mortality and radiotelemetry RSF model, we generated an index of exposure from
the habitat selection model above and the mortality/radiotelemetry model. This model appeared
to reduce the regions of over-prediction for those habitats that bears infrequent and would
therefore be at less risk than an area used frequently (all other factors held constant, e.g., road
density).
6. Suggestions, limitations and conclusions
We suggest that coefficients herein may be useful for regional land-use planning and
cumulative effects assessments. We caution users, however, to realize that these estimated
coefficients are quite generalized to the actual habitat selection processes occurring. We have
previously found for nearby populations that selection varies substantially between individuals
(Nielsen et al. 2002a) and among seasons at scales as small as one month (Nielsen et al. 2003a).
Selection behaviors are likely to further vary among years (Schooley 1994), as resources used by
grizzly bears are temporally dynamic at annual periods (e.g., berry productivity). Moreover, we
have found evidence that current remote-sensing and forest GIS-based vegetation maps (such as
those used in this report) may not reflect the habitats that grizzly bears perceive when selecting
resources (Nielsen et al. 2003a). Remote sensing and forest GIS layers relate more to the
perception and scale of resources that we find important for management and land-use planning,
not necessarily that of grizzly bears. Furthermore, current GIS data contain unknown
measurement and classification error. Despite possible limitations in GIS data and scale, we
found that models in this report were significantly predictive using out-of-sample (not out-oftime or out-of-space, however) testing data suggesting some useful correlation between GIS
variables used and either the occurrence or mortality of grizzly bears.
We recommend that coefficients from this report be considered under an adaptive
management context, where adjustments can be made in cumulative effects assessment modeling
using ALCES as more information becomes available. Beyond updating coefficients, assessing
differences between habituated and non-habituated animals, sexes, seasons, and years might be
further explored. Secondly, we recommend that SARS consider additional habitat related
surrogates, such as habitat diversity within 2.25 ha windows (Nielsen et al. 2002b). Secondly,
we must point out that coefficients reflect the 27,900-km2 area sampled within the SARS region
(see Figure 1). We are currently largely unaware of the limitations and possibilities of
extrapolating these models in space and time. We suspect, however, that such models would be
robust across similar habitats within the area, but probably not for the entire SARS grizzly bear
SARS–ACLES Report
9
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
extent. The inclusion of information gleaned from the Pincher Creek, Blairmore, and Crowsnest
Pass region should be considered and integrated within the SARS process. Finally, we suggest
that the exposure index derived in this report be the primary index focused on for the ALCES
model process, with the habitat (RSF) and mortality risk models focused as secondary indicators.
We must strongly warn, however, that the exposure index presented herein is not scaled so as to
track actual changes on the landscape, but rather as the ranked differences between possible
future scenarios. Exposure derived from basic scaled (0-1) risk and habitat selection models may
result in strongly different values if scales are modified. If exposure is excluded, we feel that the
risk model would be the most appropriate parameter to compare against scenarios, as it is the
change in landscape risk that will likely be the ultimate factor determining grizzly bear
persistence. Much of the grizzly bear range in SARS is within controlled land-use and despite
potential succession of early seral stage habitats (highest quality areas) with fire suppression, the
immediate risk is more in controlling mortality factors or areas where co-existence of humans
and grizzly bears are unlikely than in attempting to increase habitat quality for a generalist
species.
7. Acknowledgements
This work would not have been possible without the data from the East Slopes Grizzly Bear
Project (ESGBP). Steve Herrero, Mike Gibeau, and Bryon Benn invested a substantial amount
of time and energy securing these data and we are thankful for their help and generosity. Tom
Churchill and Lana Robinson provided GIS support for ALCES land-use and land-cover data,
while Charlene Popplewell provided invaluable support to the authors in cleaning and querying
data, mapping, and numerous other GIS related issues. We thank the efforts of Jim Schieck in
organizing local grizzly bear experts in the fall of 2003 and allowing alternative empirical
approaches for calibrating ALCES. Of course we are indebted to Brad Stelfox for his continual
insight and willingness to adjust ALCES based on our specific and unique suggestions. We only
hope that these methods, although not perfect, provide the best possible information and
approaches to understanding grizzly bear populations and habitats in southern Alberta.
8. References
Applegate, R.D., L.L. Rogers, D.A. Casteel, and J.M. Novak. 1979. Germination of cow parsnip
seeds from grizzly bear feces. Journal of Mammology 60, 655.
Banci, V., D.A. Demarchi, W.R. Archibald. 1994. Evaluation of the population status of grizzly
bears in Canada. International Conference on Bear Research and Management 9, 129142.
Boyce, M.S., P.R. Vernier, S.E. Nielsen, and F.K.A. Schmiegelow. 2002. Evaluating resource
selection functions. Ecological Modeling 157, 281-300.
Delibes, M., P. Gaona, and P. Ferreras. 2001. Effects of an attractive sink leading to maladaptive
habitat selection. American Naturalist 158, 277-285.
Hilderbrand, G.V., T.A. Hanley, C.T. Robbins, and C.C. Schwartz. 1999. Role of brown bears
(Ursus arctos) in the flow of marine nitrogen into a terrestrial ecosystem. Oecologia 121,
546-550.
SARS–ACLES Report
10
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Kansas, J.L. 2002. Status of the grizzly bear (Ursus arctos) in Alberta. Alberta Sustainable
Resource Development, Fish and Wildlife Division, and Alberta Conservation
Association, Wildlife Status Report No. 37, Edmonton, Alberta. 43 pp.
Krefting, L.W., and E.I. Roe. 1949. The role of some birds and mammals in seed germination.
Ecological Monographs 19, 270-286.
Mace, R.D., J.S. Waller, T.L. Manley, L.J. Lyon, and H. Zuuring. 1996. Relationships among
grizzly bears, roads and habitat in the Swan Mountains, Montana. Journal of Applied
Ecology 33, 1395-1404.
Mace, R.D., J.S. Waller, T.L. Manley, K. Ake, and W.T. Wittinger. 1999. Landscape evaluation
of grizzly bear habitat in western Montana. Conservation Biology 13, 367-377.
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.
Manly, B.F.J., L.L. McDonald, D.L. Thomas, T.L. McDonald, and W.P. Erickson. 2002.
Resource selection by animals: Statistical design and analysis for field studies. 2nd
Edition.
Matson, D.J., and T. Merrill. 2002. Extirpations of grizzly bears in the contiguous United States,
1850-2000. Conservation Biology 16, 1123-1136.
McLellan, B.N. 1998. Maintaining viability of brown bears along the southern fringe of their
distribution. Ursus 10, 607-611.
McLellan, B.N., and F.W. Hovey. 2002. Habitats selected by grizzly bears in a multiple use
landscape. Journal of Wildlife Management 65, 92-99.
Nielsen, S.E., M.S. Boyce, G.B. Stenhouse, and R.H.M. Munro. 2002a. Modeling grizzly bear
habitats in the Yellowhead Ecosystem of Alberta: Taking autocorrelation seriously. Ursus
13, 45-56.
Nielsen, S.E., M.S. Boyce, and G.B. Stenhouse. 2002b. Grizzly bear habitat model coefficients:
Comparison and estimates for NES and FMF Products. A report for the IRM NESALCES grizzly bear modeling project. 21 pp.
Nielsen, S.E., M.S. Boyce, G.B. Stenhouse, and R.H.M. Munro. 2003a. Development and testing
of phenologically driven grizzly bear habitat models. Ecoscience 10, 1-10.
Nielsen, S.E., S. Herrero, M.S. Boyce, B. Benn, M.L. Gibeau, S. Jevons, R.D. Mace. 2003b.
Modelling the spatial distribution of human-caused grizzly bear mortalities in the Central
Rockies Ecosystem of Canada. Manuscript, University of Alberta, 33 pp.
Otis, D.L., and G.C. White. 1999. Autocorrelation of location estimates and the analysis of
radiotracking data. Journal of Wildlife Management 63, 1039-1044.
SARS–ACLES Report
11
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Schooley, R.L., 1994. Annual variation in habitat selection: patterns concealed by pooled data.
Journal of Wildlife Management, 58: 367-374.
Tardiff, S.E., and J.A. Stanford. 1998. Grizzly bear digging: effects on subalpine meadow plants
in relation to mineral nitrogen availability. Ecology 79, 2219-2228.
Welch, C.A., J. Keay, K.C. Kendall, and C.T. Robbins. 1997. Constraints on frugivory by bears.
Ecology 78, 1105-1119.
Woodruffe., R. 2000. Predators and people: using human densities to interpret declines of large
carnivores. Animal Conservation 3, 165-173.
SARS–ACLES Report
12
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Table 1. ALCES landcover categories provided and used for modeling grizzly bear habitat
selection and mortality. Masked categories are those that were either too rare to model or
considered non-habitat a priori (Town/City).
ALCES Original Name Polygons Reclassified ALCES
Alpine
711
Alpine
Ag Rural Residnc
685
Ag Rural Residnc
Annual Crop
521
Annual Crop
Forage Crop
2341
Forage Crop
Douglas Fir
463
Douglas Fir
Forb
365
Forb
Forest Shrub
9967
Forest Shrub
Grassland
13097
Grassland
Grasslands
2900
Hardwood
11472 Hardwood
Lentic Large
19
Lentic Large
Lentic Med
143
Lentic Medium
Lentic Medium
293
Lentic Small
1117
Lentic Small
Lotic
136
Lotic
Major Road
576
Major Road
Mixedwood
33845
Mixedwood
Black Spruce
5722
Black Spruce
White Spruce
25251
White Spruce
Pine
50192
Pine
Industrial Plant
83
Industrial Plant
Pipeline
497
Pipeline
Recreational
118
Recreational
Surface Mine
2
Surface Mine/Pit
Surface Mines
4
Surface Pits
124
Wellsites
840
Wellsites
Rock/Ice
3114
Rock/Ice
Beach
565
Beach
Moss/Lichen
1
Moss/Lichen
Town/City
29
Town/City
SARS–ACLES Report
Final ALCES Class
Alpine
Masked Class
Crop / Agricultural / Rural
Douglas fir
Forb
Forest Shrub
Grassland
Hardwood
Lentic / Lotic
Major Road
Mixedwood
Spruce
Pine
Recreation / Anthropogenic
Rock / Ice
X
X
X
13
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Table 2. Grizzly bear VHF radiotelemetry data provided by M. Gibeau and used for assessing
habitat selection. Bear number, total observations, and size of home range from
minimum convex polygons (MCP) are given.
Bear # Observations MCP (km2)
10
70
1,821
11
33
1,159
13
123
1,146
14
22
733
15
152
1,275
16
28
671
17
56
108
18
147
293
23
14
3,366
24
123
400
26
117
366
27
21
32
28
53
311
30
137
334
31
31
72
32
83
286
33
144
778
34
22
1,359
35
37
242
36
109
574
37
133
1,102
39
28
109
40
96
329
41
108
384
42
59
780
SARS–ACLES Report
Bear # Observations MCP (km2)
44
21
894
45
43
723
46
118
478
47
104
268
49
10
51
51
23
1,861
52
71
1,149
53
26
305
54
37
622
55
38
214
56
57
507
57
87
490
59
69
249
60
45
188
61
47
297
62
84
297
63
35
425
64
40
181
65
36
139
66
43
295
68
14
1,586
69
13
562
70
19
89
72
10
131
14
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Table 3. Sampling points by landcover category (ALCES) used for contrasting available
(random) and use (radiotelemetry) locations in habitat selection models of grizzly bears.
RSF ratio is simply the frequency of use over available samples. All available samples
were generated using random points within MCP home ranges.
ALCES landcover
Alpine
Crop/agriculture/rural
Douglas fir
Forb
Forest shrub
Grassland
Hardwood
Lentic/lotic
Major road
Mixedwood
Pine
Recreation/anthropogenic
Rock/ice
Spruce
Total
SARS–ACLES Report
Available
1,120
44
26
8
1,430
1,196
133
234
72
2,695
5,323
26
7,674
7,041
27,022
Use
88
1
2
2
272
190
19
15
13
290
652
1
244
975
2,764
Total RSF ratio
1,208
0.77
45
0.22
28
0.75
10
2.44
1,702
1.86
1,386
1.55
152
1.40
249
0.63
85
1.77
2,985
1.05
5,975
1.20
27
0.38
7,918
0.31
8,016
1.35
29,786
15
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Table 4. Estimated habitat selection coefficients for ALCES variables. Bold font indicates
significance at the P = 0.05-level. Pine was used as the reference category (indicator
coding) for estimates of landcover categories. Robust standard errors are based on
estimates of variance across individual animals, not samples.
Variable
Road density
Other linear density
Alpine
Crop/agriculture/rural
Douglas fir
Forb
Forest shrub
Grassland
Hardwood
Lentic/lotic
Major road
Mixedwood
Recreation/anthropogenic
Rock/ice
Spruce
SARS–ACLES Report
Coef.
-0.009
0.045
-0.369
-1.510
-0.287
0.920
0.502
0.357
-0.017
-0.777
0.347
-0.076
-0.990
-1.249
0.194
Robust
Std. Err.
0.068
0.030
0.290
1.143
0.925
0.684
0.202
0.177
0.470
0.437
0.403
0.215
1.056
0.267
0.148
z
-0.13
1.49
-1.27
-1.32
-0.31
1.34
2.48
2.01
-0.04
-1.78
0.86
-0.36
-0.94
-4.68
1.31
P
0.899
0.136
0.203
0.187
0.756
0.179
0.013
0.044
0.971
0.076
0.389
0.722
0.348
0.000
0.190
95 % Confidence Interval
Lower
Upper
-0.143
0.125
-0.014
0.104
-0.936
0.199
-3.750
0.731
-2.101
1.527
-0.421
2.260
0.105
0.898
0.009
0.705
-0.939
0.905
-1.634
0.080
-0.443
1.137
-0.497
0.345
-3.060
1.080
-1.773
-0.726
-0.096
0.484
16
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Table 5. Estimated mortality coefficients for ALCES variables for random and mortality
comparisons. Pine was used as the reference category (indicator coding) for estimates of
landcover categories. Standard errors were bootstrapped from 199 re-samplings of the
data.
Variable
Road density
Other linear density
Alpine
Crop / agriculture / rural
Forb
Forest shrub
Grassland
Hardwood
Lentic / lotic
Major road
Mixedwood
Recreation / anthropogenic
Rock / ice
Spruce
SARS–ACLES Report
Coef.
0.495
0.543
-13.850
-0.445
0.494
0.952
0.111
-2.186
1.042
1.379
-0.093
0.558
-1.149
-0.039
Bootstrap
Std. Err.
z
0.056
8.89
0.045
11.98
10.435
-1.33
0.520
-0.86
7.887
0.06
0.323
2.95
0.362
0.31
8.641
-0.25
0.459
2.27
2.428
0.57
0.319
-0.29
1.952
0.29
1.115
-1.03
0.271
-0.14
95 % Confidence Interval
Lower
Upper
P
<0.001
0.386
0.604
<0.001
0.454
0.632
0.184
-34.301
6.602
0.392
-1.465
0.575
0.950
-14.964
15.951
0.003
0.320
1.584
0.760
-0.598
0.820
0.800
-19.123
14.751
0.023
0.142
1.942
0.570
-3.380
6.137
0.770
-0.718
0.531
0.775
-3.267
4.384
0.303
-3.335
1.037
0.886
-0.570
0.492
17
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Table 6. Estimated mortality coefficients for ALCES variables for radiotelemetry and mortality
comparisons. Pine was used as the reference category (indicator coding) for estimates of
landcover categories. Standard errors were bootstrapped from 199 re-samplings of the
data.
Variable
Road density
Other linear density
Alpine
Crop/agriculture/rural
Forb
Forest shrub
Grassland
Hardwood
Lentic/lotic
Major road
Mixedwood
Recreation/anthropogenic
Rock/ice
Spruce
SARS–ACLES Report
Coef.
0.681
0.314
-13.971
2.281
1.339
0.985
1.045
-0.293
1.229
0.755
0.411
2.081
-0.123
-0.261
Bootstrap
Std. Err.
0.073
0.052
10.761
5.056
8.644
0.281
0.338
7.746
0.483
2.224
0.308
7.594
1.496
0.241
z
9.28
6.03
-1.30
0.45
0.15
3.50
3.09
-0.04
2.54
0.34
1.33
0.27
-0.08
-1.09
95 % Confidence Interval
Lower
Upper
P
<0.001
0.538
0.825
<0.001
0.212
0.417
0.194
-35.062
7.121
0.652
-7.628
12.190
0.877
-15.604
18.281
<0.001
0.434
1.536
0.002
0.382
1.708
0.970
-15.474
14.888
0.010
0.282
2.175
0.734
-3.605
5.114
0.183
-0.194
1.015
0.784
-12.803
16.965
0.935
-3.055
2.809
0.278
-0.733
0.211
18
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Figure 1. Location of secondary study area (red dashed boundary) within the Southern Alberta
Regional Strategy (SARS) that was used for assessing grizzly bear habitat selection and
mortality.
SARS–ACLES Report
19
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
1.0
0.0
0.9
0.1
0.2
0.8
0.3
0.4
Exposure
0.7
0.5
0.6
0.6
0.5
0.7
0.4
0.9
0.3
1.0
0.8
0.2
0.1
Re 0.0
lat
ive 0.8
Pr
o 0.6
(R bab
SF ilit 0.4
-ri y o
sk f M
)
o
1.0
e
nc
e
r
r
0.6
u
cc n)
O
0.4 of
tio
y elec
t
i
l
i
s
0.2
ab itat
b
o
Pr ab
0.0
0.0 ve F-h
i
lat (RS
e
R
0.8
rta 0.2
lity
Figure 2. A graphic of exposure [P(A∩B)] relating to mortality risk and habitat selection.
Exposure should be a useful metric for comparing the impacts of landscape change. The
risk model refers to that which compares mortality locations with radiotelemetry
locations, while the RSF habitat selection model is that which compares radiotelemetry
locations with random available locations. Exposure is maximized for locations of both
high risk and high probability of occurrence and minimized for areas where either of the
two functions approach zero.
SARS–ACLES Report
20
Nielsen & Boyce 2003
Grizzly Bear Habi tat Selection & Mortality Coefficients
Figure 3. The region of the study area where grizzly bear habitat selection models were derived
and tested based on home ranges (brown MCP home ranges depicted) of radiocollared
grizzly bears. Extrapolations of habitats outside of this region were not tested and
therefore should be considered provisional.
SARS–ACLES Report
21