Accounting for unobserved spatial variation in step selection analyses of animal movement via spatial random effects

Abstract

AbstractStep selection analysis (SSA) is a common framework for understanding animal movement and resource selection using telemetry data. Such data are, however, inherently autocorrelated in space, a complication that could impact SSA-based inference if left unaddressed. Accounting for spatial correlation is standard statistical practice when analyzing spatial data, and its importance is increasingly recognized in ecological models (e.g., species distribution models). Nonetheless, no framework yet exists to account for such correlation when analyzing animal movement using SSA.Here, we extend the popular methodIntegrated Step Selection Analysis(iSSA) by including aGaussian Field(GF) in the linear predictor to account for spatial correlation. For this, we use the Bayesian frameworkR-INLAand theStochastic Partial Differential Equations(SPDE) technique.We show through a simulation study that our method provides unbiased fixed effects estimates, quantifies their uncertainty well and improves the predictions. In addition, we demonstrate the practical utility of our method by applying it to three wolverine (Gulo gulo) tracks.Our method solves the problems of assuming spatially independent locations in the SSA framework. In addition, it offers new possibilities for making long-term predictions of habitat usage.