Spatiotemporal variation in resource selection: insights from the American marten (Martes americana).

Behavioral and genetic adaptations to spatiotemporal variation in habitat conditions allow species to maximize their biogeographic range and persist over time in dynamic environments. An understanding of these local adaptations can be used to guide management and conservation of populations over broad extents encompassing diverse habitats. This understanding is often achieved by identifying covariates related to species' occurrence in multiple independent studies conducted in relevant habitats and seasons. However, synthesis across studies is made difficult by differences in the model covariates evaluated and analytical frameworks employed. Furthermore, inferences may be confounded by spatiotemporal variation in which habitat attributes are limiting to the species' ecological requirements. In this study, we sought to quantify spatiotemporal variation in resource selection by the American marten (Martes americana) in forest ecosystems of the Pacific Northwest, USA. We developed resource selection functions for both summer and winter based on occurrence data collected in mesic and xeric forest habitats. Use of a consistent analytical framework facilitated comparisons. Habitat attributes predicting marten occurrence differed strongly between the two study areas, but not between seasons. Moreover, the spatial scale over which covariates were calculated greatly influenced their predictive power. In the mesic environment, marten resource selection was strongly tied to riparian habitats, whereas in the xeric environment, marten responded primarily to canopy cover and forest fragmentation. These differences in covariates associated with marten occurrence reflect differences in which factors were limiting to marten ecology in each study area, as well as local adaptations to habitat variability. Our results highlight the benefit of controlled meta-replication studies in which analyses of multiple study areas and seasons at varying spatial scales are integrated into a single framework.