LAUSR

Habitat-selection analyses are often used to link environmental covariates, measured within some spatial domain of assumed availability, to animal location data that are assumed to be independent. Step-selection functions (SSFs) relax this independence assumption, by using a conditional model that explicitly acknowledges the spatiotemporal dynamics of the availability domain and hence the temporal dependence among successive locations. However, it is not clear how to produce an SSF-based map of the expected utilization distribution. Here, we used SSFs to analyze virtual animal movement data generated at a fine spatiotemporal scale and then rarefied to emulate realistic telemetry data. We then compared two different approaches for generating maps from the estimated regression coefficients. First, we considered a naive approach that used the coefficients as if they were obtained by fitting an unconditional model. Second, we explored a simulation-based approach, where maps were generated using stochastic simulations of the parameterized step-selection process. We found that the simulation-based approach always outperformed the naive mapping approach and that the latter overestimated home-range size and underestimated local space-use variability. Differences between the approaches were greatest for complex landscapes and high sampling rates, suggesting that the simulation-based approach, despite its added complexity, is likely to offer significant advantages when applying SSFs to real data.