Spatiotemporally variable snow properties drive habitat use of an Arctic mesopredator

Abstract

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.

Wolverines (Gulo gulo) in the Arctic: Revisiting distribution and identifying research and conservation priorities amid rapid environmental change

“…Arctic Canadian IK holders report that wolverine presence is predominantly determined by food availability, but that wolverines also associate broadly with rugged and rocky habitats, as well as higher elevations (Cardinal 2004 ). This trend aligns with Western science studies from the winter and spring in northern Alaska tundra, which have found that wolverine habitat selection is driven by terrain ruggedness, soil drainage, streams/rivers, and snow properties (Poley et al 2018 ; Glass et al 2021b ). At the occupancy level of selection (i.e., the placement of home ranges), wolverines select more rugged terrain and areas with better drained soils, possibly showing a preference for Arctic ground squirrel habitat (Poley et al 2018 ).…”

“…In Arctic Alaska, this results in higher occupancy in the mountains and foothills of the Brooks Range than the more northerly and less rugged coastal plain (Poley et al 2018 ). Within home ranges in Low Arctic Alaska during winter and spring, wolverines select strongly for streams and rivers, where they are observed hunting snowshoe hare and ptarmigan dwelling among the tall shrubs, as well as rugged terrain and deep, dense snow (Glass et al 2021b ).…”

Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

“…Additionally, it is challenging to measure these variables across broad spatial areas, or at the specific times when animals experience those snow conditions (e.g., during mid-winter episodic snowmelt; Pedersen et al, 2015). The collaborative development and application of project-specific, wildliferelevant snow variables are required to answer wildlifesnow research and management questions most effectively (Boelman et al, 2019) and will lead to a stronger understanding of the mechanistic links between animals and snow properties (Glass et al, 2021;Loe et al, 2020;Pedersen et al, 2021). Step 3 is further illustrated in Box 3: Ruffed grouse example.…”

“…The physical properties of snow, and their spatial distribution and temporal evolution, influence many ecological processes (Figure 1). For wildlife, snow properties can impact individuals by affecting movements and behaviors (Balkenhol et al, 2020; Berman et al, 2019; Boelman et al, 2017; Chimienti et al, 2020; Coady, 1974; Droghini & Boutin, 2018; Mahoney et al, 2018; Oliver et al, 2018; Oliver et al, 2020; Pedersen et al, 2021); predator–prey interactions (Horne et al, 2019; Nelson & Mech, 1986; Peers et al, 2020; Sirén et al, 2021); energetics related to foraging (Dumont et al, 2005; Fancy & White, 1985), locomotion (Fancy & White, 1987; Gurarie et al, 2019; Lundmark & Ball, 2008; Parker et al, 1984), and thermoregulation (Karniski, 2014; Pruitt Jr., 1957; Thompson III & Fritzell, 1988); forage accessibility (Hupp & Braun, 1989; Takatsuki et al, 1995; Visscher et al, 2006; White et al, 2009); as well as ground (Boelman et al, 2016) and subnivean habitat use (Bilodeau et al, 2013; Glass et al, 2021; Petty et al, 2015). Additionally, the effects of snow on individual survival (Hurley et al, 2017; Reinking et al, 2018; Shipley et al, 2020) and reproduction (Apollonio et al, 2013; Barnowe‐Meyer et al, 2011; Liston et al, 2016; Schmidt et al, 2019) can ultimately alter population‐level demographics (Apollonio et al, 2013; Berteaux et al, 2017; Boelman et al, 2019; Cosgrove et al, 2021; Desforges et al, 2021; Van de Kerk et al, 2018; Van de Kerk et al, 2020).…”

“…Further, these modeling tools can also incorporate field and remote sensing data to reproduce observations and create improved snow products using a data–model fusion approach. Previous studies (e.g., Boelman et al, 2019; Glass et al, 2021; Pedersen et al, 2021) have argued that a data–model fusion approach is increasingly necessary to produce the high‐quality, fit‐for‐purpose data products at appropriate spatial and temporal resolutions required to answer the wide range of wildlife–snow research and management questions. Data–model fusion systems can produce more accurate and sophisticated spatiotemporal snow distributions and evolutions than are possible with ground‐based, airborne, or satellite observational datasets, or models, alone (Boelman et al, 2019; Daly, 2006; Hedrick et al, 2015; Heilig et al, 2015; Stuefer et al, 2013).…”