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dc.contributor.authorMichelot, Théo
dc.contributor.authorBlackwell, Paul G.
dc.date.accessioned2020-02-14T00:35:06Z
dc.date.available2020-02-14T00:35:06Z
dc.date.issued2019-02-14
dc.identifier.citationMichelot , T & Blackwell , P G 2019 , ' State-switching continuous-time correlated random walks ' , Methods in Ecology and Evolution , vol. Early View . https://doi.org/10.1111/2041-210X.13154en
dc.identifier.issn2041-210X
dc.identifier.otherPURE: 257793631
dc.identifier.otherPURE UUID: 8547b27a-9dd4-4526-b605-b43220c97769
dc.identifier.otherRIS: urn:664E987A52CD74519B8116A89189F335
dc.identifier.otherScopus: 85061594679
dc.identifier.urihttp://hdl.handle.net/10023/19461
dc.descriptionTM was supported by the Centre for Advanced Biological Modelling at the University of Sheffield, funded by the Leverhulme Trust, award number DS-2014-081. The grey seal telemetry data were provided by the Sea Mammal Research Unit (SMRU), University of St Andrews; their collection was conducted under UK Home Office Licence (60/3303) and supported by funding from the Natural Environment Research Council to SMRU.en
dc.description.abstract1.Continuous‐time models have been developed to capture features of animal movement across temporal scales. In particular, one popular model is the continuous‐time correlated random walk, in which the velocity of an animal is formulated as an Ornstein‐Uhlenbeck process, to capture the autocorrelation in the speed and direction of its movement. In telemetry analyses, discrete‐time state‐switching models (such as hidden Markov models) have been increasingly popular to identify behavioural phases from animal tracking data. 2.We propose a multistate formulation of the continuous‐time correlated random walk, with an underlying Markov process used as a proxy for the animal's behavioural state process. We present a Markov chain Monte Carlo algorithm to carry out Bayesian inference for this multistate continuous‐time model. 3.Posterior samples of the hidden state sequence, of the state transition rates, and of the state‐dependent movement parameters can be obtained. We investigate the performance of the method in a simulation study, and we illustrate its use in a case study of grey seal (Halichoerus grypus) tracking data. 4.The method we present makes use of the state‐space model formulation of the continuous‐time correlated random walk, and can accommodate irregular sampling frequency and measurement error. It will facilitate the use of continuous‐time models to estimate movement characteristics and infer behavioural states from animal telemetry data.
dc.language.isoeng
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rightsCopyright © 2019 The Authors. Methods in Ecology and Evolution © 2019 British Ecological Society. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1111/2041-210X.13154en
dc.subjectAnimal movementen
dc.subjectContinuous timeen
dc.subjectMultistate modelen
dc.subjectOrnstein–Uhlenbeck processen
dc.subjectRandom walken
dc.subjectState-space modelen
dc.subjectQH301 Biologyen
dc.subjectQA Mathematicsen
dc.subjectDASen
dc.subject.lccQH301en
dc.subject.lccQAen
dc.titleState-switching continuous-time correlated random walksen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.identifier.doihttps://doi.org/10.1111/2041-210X.13154
dc.description.statusPeer revieweden
dc.date.embargoedUntil2020-02-14


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