Landscape genetics of wolverines (Gulo gulo): scale-dependent effects of bioclimatic, topographic, and anthropogenic variables

Abstract

<jats:title>Abstract</jats:title> <jats:p>Climate change can have particularly severe consequences for high-elevation species that are well-adapted to long-lasting snow conditions within their habitats. One such species is the wolverine, Gulo gulo, with several studies showing a strong, year-round association of the species with the area defined by persistent spring snow cover. This bioclimatic niche also predicts successful dispersal paths for wolverines in the contiguous United States, where the species shows low levels of genetic exchange and low effective population size. Here, we assess the influence of additional climatic, vegetative, topographic, and anthropogenic, variables on wolverine genetic structure in this region using a multivariate, multiscale, landscape genetic approach. This approach allows us to detect landscape-genetic relationships both due to typical, small-scale genetic exchange within habitat, as well as exceptional, long-distance dispersal among habitats. Results suggest that a combination of snow depth, terrain ruggedness, and housing density, best predict gene flow in wolverines, and that the relative importance of variables is scale-dependent. Environmental variables (i.e., isolation-by-resistance, IBR) were responsible for 79% of the explained variation at small scales (i.e., up to ~230 km), and 65% at broad scales (i.e., beyond ~420 km). In contrast, a null model based on only space (i.e., isolation-by-distance, IBD) accounted only for 17% and 11% of the variation at small and broad scales, respectively. Snow depth was the most important variable for predicting genetic structures overall, and at small scales, where it contributed 43% to the variance explained. At broad spatial scales, housing density and terrain ruggedness were most important with contributions to explained variation of 55% and 25%, respectively. While the small-scale analysis most likely captures gene flow within typical wolverine habitat complexes, the broad-scale analysis reflects long-distance dispersal across areas not typically inhabited by wolverines. These findings help to refine our understanding of the processes shaping wolverine genetic structure, which is important for maintaining and improving functional connectivity among remaining wolverine populations.</jats:p>

Do all roads lead to resistance? State road density is the main impediment to gene flow in a flagship species inhabiting a severely fragmented anthropogenic landscape

“…For forest and agricultural land, it was the opposite, as we hypothesized that areas with more forest or agriculture would provide less resistance to gene flow. The rescaled layers, all with a resolution of 100 m, were then transformed into actual resistance surfaces with values ranging between 100 (lowest resistance; this is simply the cell size) and 10,000 (highest resistance; 100 times the cell size) using the formula resistance = cellsize * 100 1 − relative landscape value (Balkenhol et al, 2020;Mateo-Sánchez et al, 2015a).…”

“…Approaches for assessing landscape effects on connectivity often use the concept of landscape resistance, which represents the willingness or ability of an organism to move through a particular environment (Zeller et al, 2012). Estimating landscape resistance is typically achieved by parameterizing the relative cost of environmental variables to movement and gene flow from empirical data, with lower resistance values indicating a higher probability of successfully moving through an area (Balkenhol et al, 2020;Zeller et al, 2017). Thus, connectivity planning based on landscape resistance should use actual data on gene flow or movement and such data should ideally be available for multiple, conservation relevant or umbrella species (Diniz et al, 2018;Meurant et al, 2018).…”

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

“…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).…”

Interior Alaska DoD training land wildlife habitat vulnerability to permafrost thaw, an altered fire regime, and hydrologic changes

“…This might have adverse effects in wood frog habitat, natural alpine environments, riparian vegetative cover, and other situations, including for terrestrial species with low vagility and poor dispersal capabilities. Retaining or restoring connectivity of these and other scarce ecotypes may be part of a general wildlife habitat conservation strategy (e.g., for wolverine; Balkenhol et al, 2020).…”