Reconstructing historical snow depth surfaces to evaluate changes in critical demographic rates and habitat components of snow‐dependent and snow‐restricted species

Abstract

Summary1. Climate change models consistently predict snow depth declines across the Northern Hemisphere. Snow depth has been linked to the demography of numerous species, and snow depth reduction is expected to affect the demography of some northern species. As many demographic studies depend on long-term population data that extend back beyond available sources of spatially continuous snow depth data, reliable hindcasting of snow depth surface maps is needed. 2. We developed a two-stage regression modelling approach to reconstructing historic snow depths using an existing and readily available spatiotemporal data set of daily meteorological variables across a large, heterogeneous landscape in North America from 1982 to 2003. The final model accounted for ecoregional differences and predicted snow depth as a function of elevation and total snowfall accumulation. 3. Model validations showed that the estimated snow depths were spatially and temporally accurate. 4. We demonstrate how to apply this model with ArcGIS to hindcast monthly and yearly sequences of high-resolution, spatially continuous surfaces of historic snow depths. We also illustrate the utility of the modelled snow depth surfaces for informing predictions of how changes in snow depth may influence the demography and habitats of snow-dependent and snow-restricted species by assessing the spatiotemporal stochasticity of snow conditions that potentially benefit wolverine Gulo gulo and the winter ranges, net energy costs and population growth of mule deer Odocoileus hemionus. 5. This model can be used to hindcast snow depth surfaces across similar regions; our simple twostage modelling approach can be applied to other regions where equivalent climate data are available. The resulting spatiotemporal snow depth surfaces estimated from this model can be linked to existing long-term wildlife population data sets to investigate the extent to which snow depth declines may regulate or limit populations.

Range-wide variation in the effect of spring snow phenology on Dall sheep population dynamics

“…Of the many environmental changes occurring in these regions, alterations to snow conditions may have the strongest impact on wildlife populations (Jones et al 2001). While warming could lead to earlier snowmelt, expected increases in precipitation in some areas may contribute to deeper snowpack and counteract this effect (IPCC 2013, Manning andGarton 2012). Greater snow accumulation can negatively affect survival and reproductive success of some species by directly reducing forage availability or increasing vulnerability to predation (Collins and Smith 1991, Telfer and Kelsall 1984, Post and Stenseth 1998.…”

“…Remote sensing provides information on snow cover extent because the unique spectral properties of snow make it readily distinguishable from other land cover types (Dozier et al 2009). Using remote sensing data to hindcast historic snow extent is a vital step in understanding northern species' responses to projected climate change (Manning and Garton 2012).…”

Recolonization following past persecution questions the importance of persistent snow cover as a range limiting factor for wolverines

“…In an influential study, Copeland et al (2010) correlated spatial data on wolverine den sites and spring snow cover (April 24–May 15) in North America and Fennoscandia, and suggested that the global distribution of wolverines is intrinsically linked to the persistence of spring snow cover. Subsequently, numerous other studies have reiterated and/or predicted that an expected decline in snow cover induced by global warming would result in a contraction of wolverine distribution, due to a reduction in suitable habitat and restricted connectivity (e.g., Barsugli et al, 2020; Bonamy et al, 2020; Ellis et al, 2013; Hof et al, 2012; Manning & Garton, 2011; McKelvey et al, 2011; Peacock, 2011; Schwartz et al, 2009, 2016). In turn, the design of potential corridors for wolverines has assumed that the presence of a late‐spring snowpack is crucial for the species (Dilkina et al, 2017), and the relation between snow and wolverines centers much of the discussion on the species' habitat requirements (Wolverine Science Panel, 2014).…”

Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic

“…Snow directly impacts climate through modulation of atmospheric circulation patterns via the snow-albedo feedback mechanism (Fletcher et al, 2009) and 40 atmospheric moisture budgets through its control on the amount of water available for evaporation (Callaghan et al, 2011). Thus, snow plays a fundamental role in controlling water availability, soil moisture, and temperature, affecting all components of Arctic ecosystem including vegetation (Evans et al, 1989;Schaefer and Messier, 1995;Scott and Rouse, 1995) animal populations (Forchhammer et al, 2008;Manning and Garton, 2012), microbial decomposition and carbon flux (Mauritz et al, 2017;Zona et al, 45 2016). The distribution of snow and timing of its melt is key to understanding how changes in hydrology, soil thermal regimes, and vegetation interact across the Arctic landscape (Jafarov et al, 2018).…”