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

Michelot, T., Blackwell, P. G. and Matthiopoulos, J. (2019) Linking resource selection and step selection models for habitat preferences in animals. Ecology, 100(1), e02452. (doi: 10.1002/ecy.2452) (PMID:30047993)

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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.

Item Type:Articles
Additional Information:Funding: Leverhulme Trust. Grant Number: DS‐2014‐081.
Glasgow Author(s) Enlighten ID:Matthiopoulos, Professor Jason
Authors: Michelot, T., Blackwell, P. G., and Matthiopoulos, J.
College/School:College of Medical Veterinary and Life Sciences > Institute of Biodiversity Animal Health and Comparative Medicine
Journal Name:Ecology
ISSN (Online):1939-9170
Published Online:26 July 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Ecology 100(1):e02452
Publisher Policy:Reproduced under a Creative Commons License

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