Data from: Grizzly bear response to fine spatial and temporal scale spring snow cover in Western Alberta
Berman, Ethan E.; Coops, Nicholas C.; Kearney, Sean P.; Stenhouse, Gordon B. (2019), Data from: Grizzly bear response to fine spatial and temporal scale spring snow cover in Western Alberta, Dryad, Dataset, https://doi.org/10.5061/dryad.66gc768
Snow dynamics influence seasonal behaviors of wildlife, such as denning patterns and habitat selection related to the availability of food resources. Under a changing climate, characteristics of the temporal and spatial patterns of snow are predicted to change, and as a result, there is a need to better understand how species interact with snow dynamics. This study examines grizzly bear (Ursus arctos) spring habitat selection and use across western Alberta, Canada. Made possible by newly available fine-scale snow cover data, this research tests a hypothesis that grizzly bears select for locations with less snow cover and areas where snow melts sooner during spring (den emergence to May 31st). Using Integrated Step Selection Analysis, a series of models were built to examine whether snow cover information such as fractional snow covered area and date of snow melt improved models constructed based on previous knowledge of grizzly bear selection during the spring. Comparing four different models fit to 62 individual bear-years, we found that the inclusion of fractional snow covered area improved model fit 60% of the time based on Akaike Information Criterion tallies. Probability of use was then used to evaluate grizzly bear habitat use in response to snow and environmental attributes, including fractional snow covered area, date since snow melt, elevation, and distance to road. Results indicate grizzly bears select for lower elevation, snow-free locations during spring, which has important implications for management of threatened grizzly bear populations in consideration of changing climatic conditions. This study is an example of how fine spatial and temporal scale remote sensing data can be used to improve our understanding of wildlife habitat selection and use in relation to key environmental attributes.