Linking resource selection and step selection models for habitat preferences in animals

摘要

The research community working on species-habitat associations in animals iscurrently facing a paralysing methodological conundrum, because its twodominant analytical approaches have been shown to reach divergent conclusions.Models fitted from the viewpoint of an individual (step selection functions),once scaled up, do not agree with models fitted from a population viewpoint(resource selection functions). We explain this fundamental incompatibility,and propose a solution by introducing to the animal movement field a novel usefor the well-known family of Markov chain Monte Carlo (MCMC) algorithms. Bydesign, the step selection rules of MCMC lead to a steady-state distributionthat coincides with a given underlying function: the target distribution. Wetherefore propose an analogy between the movements of an animal and themovements of an MCMC sampler, to guarantee convergence of the step selectionrules to the parameters underlying the population's utilisation distribution.We introduce a rejection-free MCMC algorithm, the local Gibbs sampler, thatbetter resembles real animal movement, and discuss the wide range of biologicalassumptions that it can accommodate. We illustrate our method with simulationson a known utilisation distribution, and show theoretically and empiricallythat locations simulated from the local Gibbs sampler arise from the correctresource selection function.