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dc.contributor.authorMichelot, Théo
dc.contributor.authorBlackwell, Paul G.
dc.contributor.authorChamaillé-Jammes, Simon
dc.contributor.authorMatthiopoulos, Jason
dc.identifier.citationMichelot , T , Blackwell , P G , Chamaillé-Jammes , S & Matthiopoulos , J 2019 , ' Inference in MCMC step selection models ' , Biometrics , vol. Early View .
dc.identifier.otherPURE: 262738854
dc.identifier.otherPURE UUID: 90416273-5b71-44d2-871b-ae3d3bd3b143
dc.identifier.otherRIS: urn:81DC4D9DD95D594CECA704DAA8A1DBED
dc.identifier.otherScopus: 85076749288
dc.identifier.otherWOS: 000501325300001
dc.descriptionTM was funded by the Leverhulme Trust, award number DS-2014-081. SCJ was supported by the grant ANR-16-CE02-0001-01 of the French Agence Nationale de la Recherche.en
dc.description.abstractHabitat selection models are used in ecology to link the spatial distribution of animals to environmental covariates and identify preferred habitats. The most widely used models of this type, resource selection functions, aim to capture the steady‐state distribution of space use of the animal, but they assume independence between the observed locations of an animal. This is unrealistic when location data display temporal autocorrelation. The alternative approach of step selection functions embed habitat selection in a model of animal movement, to account for the autocorrelation. However, inferences from step selection functions depend on the underlying movement model, and they do not readily predict steady‐state space use. We suggest an analogy between parameter updates and target distributions in Markov chain Monte Carlo (MCMC) algorithms, and step selection and steady‐state distributions in movement ecology, leading to a step selection model with an explicit steady‐state distribution. In this framework, we explain how maximum likelihood estimation can be used for simultaneous inference about movement and habitat selection. We describe the local Gibbs sampler, a novel rejection‐free MCMC scheme, use it as the basis of a flexible class of animal movement models, and derive its likelihood function for several important special cases. In a simulation study, we verify that maximum likelihood estimation can recover all model parameters. We illustrate the application of the method with data from a zebra.
dc.rightsCopyright © 2019 The International Biometric Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectAnimal movementen
dc.subjectLocal Gibbs sampleren
dc.subjectMarkov chain Monte Carloen
dc.subjectMCMC step selectionen
dc.subjectResource selection functionen
dc.subjectStep selection functionen
dc.subjectUtilization distributionen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.titleInference in MCMC step selection modelsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.description.statusPeer revieweden

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