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dc.contributor.authorPatterson, Toby A.
dc.contributor.authorParton, Alison
dc.contributor.authorLangrock, Roland
dc.contributor.authorBlackwell, Paul G.
dc.contributor.authorThomas, Len
dc.contributor.authorKing, Ruth
dc.date.accessioned2018-07-03T23:34:31Z
dc.date.available2018-07-03T23:34:31Z
dc.date.issued2017-10
dc.identifier.citationPatterson , T A , Parton , A , Langrock , R , Blackwell , P G , Thomas , L & King , R 2017 , ' Statistical modelling of individual animal movement : an overview of key methods and a discussion of practical challenges ' , Advances in Statistical Analysis , vol. 101 , no. 4 , pp. 399-438 . https://doi.org/10.1007/s10182-017-0302-7en
dc.identifier.issn1863-8171
dc.identifier.otherPURE: 250563358
dc.identifier.otherPURE UUID: 69d91346-993c-45a7-9532-9e2fee3862f9
dc.identifier.otherScopus: 85021805163
dc.identifier.otherORCID: /0000-0002-7436-067X/work/35609706
dc.identifier.otherWOS: 000412949200004
dc.identifier.urihttp://hdl.handle.net/10023/14877
dc.description.abstractWith the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry among biostatisticians. New approaches of ever greater complexity are continue to be added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyse individual animal movement data. Specifically, we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of individual animal movement. The paper concludes by offering some general observations on the direction of statistical analysis of animal movement. There is a trend in movement ecology towards what are arguably overly complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based on mainstream statistical practice. Additionally, some analysis methods developed within the ecological community ignore fundamental properties of movement data, potentially leading to misleading conclusions about animal movement. Corresponding approaches, e.g. based on Lévy walk-type models, continue to be popular despite having been largely discredited. We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity that are usable by general ecologists, but grounded in well-developed statistical practice and efficient to fit to large data sets.
dc.format.extent40
dc.language.isoeng
dc.relation.ispartofAdvances in Statistical Analysisen
dc.rights© 2017, Crown Copyright. 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 link.springer.com / https://doi.org/10.1007/s10182-017-0302-7en
dc.subjectHidden Markov modelen
dc.subjectMeasurement erroren
dc.subjectOrnstein–Uhlenbeck processen
dc.subjectState-space modelen
dc.subjectStochastic differential equationen
dc.subjectTime seriesen
dc.subjectQA Mathematicsen
dc.subjectEconomics and Econometricsen
dc.subjectAnalysisen
dc.subjectApplied Mathematicsen
dc.subjectModelling and Simulationen
dc.subjectStatistics and Probabilityen
dc.subjectSocial Sciences (miscellaneous)en
dc.subjectNDASen
dc.subject.lccQAen
dc.titleStatistical modelling of individual animal movement : an overview of key methods and a discussion of practical challengesen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews.Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.1007/s10182-017-0302-7
dc.description.statusPeer revieweden
dc.date.embargoedUntil2018-07-04
dc.identifier.urlhttps://arxiv.org/abs/1603.07511en


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