Landscape seasonality influences the resource selection of a snow-adapted forest carnivore, the Pacific marten

Abstract

Characterizing animal space-use and resource selection is central to effective conservation. In seasonally variable systems, animals may alter space-use to minimize risk, mediate physiological costs, and maintain access to resources. However, it is often unclear which environmental features influence space-use across seasons, and whether resource selection of non-migratory animals varies in seasonally snow-covered environments. We quantified space-use and scale-dependent resource selection of Pacific martens (Martes caurina) in northern California to evaluate the relative influence of abiotic (e.g., topography, weather) and biotic (e.g., forest structure) covariates on spatial ecology of martens in ecologically distinct seasons (i.e., snow-covered, snow-free). We obtained fine-scale location data from GPS-collared martens (n = 26) in the Cascade and Sierra Nevada mountain ranges in California, USA. We incorporated spatially explicit weather, topographic, and forest structure data in a scale-optimized, seasonal resource selection function framework to determine the relative importance of abiotic and biotic conditions during snow-covered and snow-free periods. During snow-free periods, martens selected for features associated with complex forest structure, including increasing stem basal area. Conversely, space-use was associated with dense forest structure and topographic features in snow-covered periods. Though the relative influence of abiotic and biotic covariates on resource selection varied by season, the scale at which these variables best explained space-use did not. Our results highlight seasonality and scale-dependence of resource selection by martens and emphasize the importance of understanding spatio-temporal responses of free-ranging animals to landscape heterogeneity. We suggest behavioral or ecological requirements that differ by season and scale may influence space-use and resource selection patterns, and, consequently, can inform conservation actions.