Spatiotemporally variable snow properties drive habitat use of an Arctic mesopredator

Climate change is rapidly altering the composition and availability of snow, with implications for snow-affected ecological processes, including reproduction, predation, habitat selection, and migration. How snowpack changes influence these ecological processes is mediated by physical snowpack properties, such as depth, density, hardness, and strength, each of which is in turn affected by climate change. Despite this, it remains difficult to obtain meaningful snow information relevant to the ecological processes of interest, precluding a mechanistic understanding of these effects. This problem is acute for species that rely on particular attributes of the subnivean space, for example depth, thermal resistance, and structural stability, for key life-history processes like reproduction, thermoregulation, and predation avoidance. We used a spatially explicit snow evolution model to investigate how habitat selection of a species that uses the subnivean space, the wolverine, is related to snow depth, snow density, and snow melt on Arctic tundra. We modeled these snow properties at a 10 m spatial and a daily temporal resolution for 3 years, and used integrated step selection analyses of GPS collar data from 21 wolverines to determine how these snow properties influenced habitat selection and movement. We found that wolverines selected deeper, denser snow, but only when it was not undergoing melt, bolstering the evidence that these snow properties are important to species that use the Arctic snowpack for subnivean resting sites and dens. We discuss the implications of these findings in the context of climate change impacts on subnivean species.