Date Published: April 10, 2019
Publisher: Public Library of Science
Author(s): Ethan E. Berman, Nicholas C. Coops, Sean P. Kearney, Gordon B. Stenhouse, Bi-Song Yue.
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.
Snow dynamics are a key driver of the seasonal behaviors of a variety of wildlife species, through influencing resource availability and fitness costs [1–3]. In landscapes with harsh seasonal conditions, snow cover can dictate food quality and distribution, and along with cold temperatures can result in patterns of hibernation and migration. For hibernators, the accumulation of snow in the fall and ablation in the spring have been linked to both spatial and temporal denning patterns [4–5]. Snow distribution can also adversely influence energy costs, through increased difficulty moving through a deep snowpack  and by dictating the timing of spring vegetation emergence [7–9].
In this section we first describe the study area and provide details on grizzly bear telemetry data, core model covariates, and snow covariates. We then describe the iSSA modelling approach, beginning with the development of a core model built using covariates previously shown to influence habitat selection during spring (Table 1). Snow covariates were added to the core model in three configurations to assess whether the inclusion of snow improved model accuracy. The best fitting model was evaluated to determine average effects of individual snow covariates on probability of use by grizzly bears.
The average trends in the date of snow melt on the landscape are shown in Fig 3. Overall, snow in alpine environments melted the latest, whereas snow in montane and lower foothills melted earliest. The timing of snow melt at lower elevations, in upper foothills, lower foothills, and montane environments, fluctuated more year-to-year than at higher elevations in alpine and subalpine regions. Additionally, the years of 2010, 2015, and 2016 can be characterized as years with early snow melt in lower elevation areas.
This work has demonstrated the application of fine scale daily remote sensing data in evaluating a hypothesis relating snow cover variables to spring habitat selection and use of grizzly bears in Western Alberta. Based on the AIC tally and average AIC weights, the inclusion of fSCA improved our predictive model over both a core model and other models which contained more coarse spatial and temporal representations of snow on the landscape. Temporally dynamic covariates such as snow depth have previously been introduced into iSSA , however these were shown to demonstrate a weak or variable response, possibly due to low spatial resolution. The advancement of both the resolution and reliability of fine scale remote sensing datasets, such as daily fSCA values at 30 m spatial resolution, hold promise in investigating a range of hypotheses related to wildlife habitat selection, movement, and use. Future applications of this specific dataset include an analysis of grizzly bear denning location and date of den entry and emergence, which has been linked to snow dynamics and food availability .
Snow conditions are an integral habitat component for a number of species, including grizzly bears, wolverine , elk , deer , and caribou . The inclusion of fine scale snow data has potential to increase our understanding of the interaction between these species and the environment during key times of year. Through the use of iSSA, we have shown daily 30 m fractional snow covered area and annual days since snow melt to improve spring habitat selection models and establish relationships between grizzly bear use of the landscape and snow dynamics. Grizzly bears displayed a strong preference for use of locations with less snow cover in the spring, and areas where snow melted sooner. A better understanding of how bears use the landscape in relation to changing environmental and climatic variables can help resource managers and policy-makers to maintain a sustainable grizzly bear population in both the present as well as the future.