Abstract
The two dominant approaches for the analysis of species-habitat associations in animals 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 [RSFs]). We explain this fundamental incompatibility, and propose a solution by introducing to the animal movement field a novel use for the well-known family of Markov chain Monte Carlo (MCMC) algorithms. By design, the step selection rules of MCMC lead to a steady-state distribution that coincides with a given underlying function: the target distribution. We therefore propose an analogy between the movements of an animal and the movements of an MCMC sampler, to guarantee convergence of the step selection rules to the parameters underlying the population's utilization distribution. We introduce a rejection-free MCMC algorithm, the local Gibbs sampler, that better resembles real animal movement, and discuss the wide range of biological assumptions that it can accommodate. We illustrate our method with simulations on a known utilization distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler give rise to the correct RSF. Using simulated data, we demonstrate how this framework can be used to estimate resource selection and movement parameters.
Highlights
Understanding how animals use a landscape in response to its habitat composition is a crucial question in pure and applied ecology
We have presented a versatile class of models of animal movement, for which the steady-state distribution of locations is proportional to the same resource selection functions (RSF) that influences short-term movement
Our approach reconciles the resource selection and step selection approaches to the analysis of space use data
Summary
Understanding how animals use a landscape in response to its habitat composition is a crucial question in pure and applied ecology. Potts et al (2014a) described a numerical method to compute the utilization distribution given in Eq 4, as it generally has no closed form expression Those approaches are useful to predict space use from SSFs, but they do not allow the steadystate distribution of locations to be modeled in a simple parametric form, as in Eq 2. We verify that the distribution of simulated locations corresponds to the correct RSF, and we present a proof-of-concept analysis to demonstrate the potential of the method for estimating resource selection coefficients and parameters of the movement process from telemetry data
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