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

TL;DR

This paper introduces a novel method linking individual-based step selection models with population-level resource selection functions using a Markov chain Monte Carlo approach, ensuring consistent habitat preference estimates.

Contribution

It proposes a new MCMC-based framework, the local Gibbs sampler, to unify step and resource selection models, addressing their previous incompatibility.

Findings

The local Gibbs sampler accurately recovers known utilisation distributions.

Simulations show the method estimates resource selection and movement parameters effectively.

The approach guarantees convergence to the true underlying habitat preferences.

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). 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 a MCMC sampler, to guarantee convergence of the step selection rules to the parameters underlying the population's utilisation 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 utilisation distribution, and show theoretically and empirically that locations simulated from the local Gibbs sampler give rise to the correct resource selection function. Using simulated data, we demonstrate how this framework can be used to estimate resource selection and movement parameters.