Process-focussed, multi-grain resource selection functions

• Our study focused on a novel technique to incorporate grain into resource selection.
• We developed “multi-grain resource selection functions”, or MRSFs.
• Habitat types were modelled using different grains of analysis.
• Habitat types were selected at different grains by white-tailed deer.
• MRSFs had lower AIC than models computed at a single scale of analysis.

Like most aspects of ecology, the process of habitat or resource selection scales in space as well as time. However, scaling questions have generally focused on extent including size of study area and home ranges that dictate availability of resources. Grain of analysis (size of resource units used) is generally restricted to questions of methodology as opposed to functional ecology. Most often, grain is adopted as a point, unit, or patch that is common in size to all habitat resources used and available; however, in the process of habitat selection, it is feasible that individual animals may opt to select for different resources at different grains. For example, animals may use units of vegetation association at a finer grain when feeding or resting compared to when moving through habitat. Here we introduce and evaluate the ‘multi-grain resource selection function’, or MRSF. We generated MRSFs for a case study of GPS-collared white-tailed deer (Odocoileus virginianus; n = 14) at Riding Mountain National Park, Manitoba, Canada. We created models across two seasons and extents and varied the radius around used and available points within which resource types were measured, and compared models to evaluate the relative importance of resource variables at different grains. We hypothesized that resource selection would vary with grain and that RSFs computed using multiple grains would be more predictive than models computed using a single grain as they better incorporate the space of influence on decision making in different habitat areas. We found that models of animals using grains of different sizes for different resource types were characterized by comparatively lower AIC scores. We conclude that scaling grain can and should be considered in models of resource selection, and that animals make decisions on resource selection at multiple grains. The MRSF, like analyses incorporating individual effects, density dependence, and functional responses, brings us closer to incorporating process, rather than only patterns, into the study of resource and habitat selection.