Data from: State-switching continuous-time correlated random walks

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datacite.RelatedIdentifierhttps://doi.org/10.1111/2041-210X.13154
datacite.RelatedIdentifier.relatedIdentifierTypeDOI
datacite.RelatedIdentifier.relationTypeIsSupplementTo
dc.contributor.authorMcConnell, Bernie J.
dc.date.accessioned2019-02-28T17:51:30Z
dc.date.available2019-02-28T17:51:30Z
dc.date.issued2019-02-28
dc.date.submitted2019
dc.description.abstract(1) 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.identifier.doidoi:10.5441/001/1.m7j2263r
dc.identifier.urihttps://datarepository.movebank.org/handle/10255/move.844
dc.language.isoeng
dc.relationMethods in Ecology and Evolution
dc.relation.haspartdoi:10.5441/001/1.m7j2263r/1
dc.relation.haspartdoi:10.5441/001/1.m7j2263r/2
dc.relation.isreferencedbydoi:10.1111/2041-210X.13154
dc.relation.isreferencedbydoi:10.1111/oik.01810
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectHalichoerus grypus
dc.subjectanimal movement
dc.subjectanimal tracking
dc.subjectgrey seal
dc.subjectHalichoerus grypus
dc.subjectNorth Sea
dc.subjectstate-space model
dc.titleData from: State-switching continuous-time correlated random walks
dc.typeArticle
dspace.entity.typeData package
dwc.ScientificNameHalichoerus grypus
mdr.animal.count1
mdr.citation.BibTex
@misc{001/1_m7j2263r,
  title = {Data from: State-switching continuous-time correlated random walks},
  author = {McConnell, BJ},
  year = {2019},
  URL = {http://dx.doi.org/10.5441/001/1.m7j2263r},
  doi = {doi:10.5441/001/1.m7j2263r},
  publisher = {Movebank data repository}
}
mdr.citation.CSE
McConnell BJ. 2019. Data from: State-switching continuous-time correlated random walks. Movebank Data Repository. https://doi.org/10.5441/001/1.m7j2263r
mdr.citation.RIS
TY  - DATA
ID  - doi:10.5441/001/1.m7j2263r
T1  - Data from: State-switching continuous-time correlated random walks
AU  - McConnell, Bernie J.
Y1  - 2019/02/28
KW  - Halichoerus grypus
KW  - animal behavior
KW  - animal movement
KW  - animal tracking
KW  - grey seal
KW  - Halichoerus grypus
KW  - North Sea
KW  - state-space model
KW  - Halichoerus grypus
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.m7j2263r
DO  - doi:10.5441/001/1.m7j2263r
ER  -
mdr.journal.titleMethods in Ecology and Evolution
mdr.location.count2535
mdr.study.id654043458
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