Relative probability of predator-scat occurrence
We developed RSFs for predators (Manly et al., 2002), where ‘used’
samples were the locations of predator-specific scats along transect
lines and ‘available’ samples were random locations within a 50-m buffer
on each side of the line in a ratio of 1 scat:10 random points. We used
an exponential RSF design fitted using logistic regression. We did not
enter correlated variables (r >
|0.6|) into the same model. We used model selection
and Akaike’s Information Criterion corrected for small sample sizes
(AICc) to identify the best-supported RSF for each
species using a criterion of four ∆AIC points to distinguish between
competing models.
We used terrain, landcover, and anthropogenic features expected to
influence predator occurrence (Nielsen et al., 2002; Whittington et al.,
2005; Knopff et al., 2014).We measured landcover as cover type derived
from TM Landsat Imagery, vegetation “greenness” from the Normalized
Difference Vegetation Index (NDVI), forest fires ≤ 14 years old
(Hebblewhite, 2006), and cutblocks ≤ 20 years since harvest (Visscher &
Merrill, 2009) as mean proportion of 30 x 30-m pixels within a 1.3-km
radius (5.3-km2) buffer around a scat or random
location. This buffer size reflected the average daily movement of black
bears the shortest distance of all the predator species (see Appendix
S1, Supporting Information). Forest edge was based on a 30-m buffer of
conifer or mixed-deciduous forest with any other landcover type. We
derived terrain features (slope, elevation, and terrain ruggedness) from
a 30-m Digital Elevation Model. Locations were designated as within 30-m
or farther than 30-m of a trail. Forest edge, proximity to roads,
off-highway vehicle (motorized) trails, and waterways were measured as
the shortest distance (km) to the nearest feature or as density (km
km-2) within the buffer. We compared the predictions
of scat-based RSF of wolves and bears to a telemetry-based RSF at 1,000
random points. Scat-based RSF values increased as telemetry-based values
increased, but was not strictly linear (Appendix S2, Supporting
Information).
To include a numeric component, we weighted the wolf RSF by a
probability density function (PDF) based on kill rates derived from
annual, wolf pack size of collared packs (Hebblewhite & Merrill, 2007;
Berg, 2019), and weighted the RSF by the annual PDF in proportion of
scats collected in each year. We weighted the grizzly bear RSF by
estimates of grizzly bear densities, which were 2.4 times higher within
BNP compared to outside of BNP ( Whittington & Sawaya, 2015; Government
of Alberta, 2016). We smoothed the PDF values along the BNP border using
a 12.9-km moving window, the size corresponding to the average home
range for local grizzly bears (Nielsen et al., 2002). RSFs of cougars
and coyotes were not numerically weighted because no numeric indices
were available.