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dc.contributor.authorMcClintock, Brett Thomas
dc.contributor.authorKing, Ruth
dc.contributor.authorThomas, Len
dc.contributor.authorMatthiopoulos, Jason
dc.contributor.authorMcConnell, Bernie J
dc.contributor.authorMorales, Juan
dc.date.accessioned2012-05-18T00:05:44Z
dc.date.available2012-05-18T00:05:44Z
dc.date.issued2012-08
dc.identifier.citationMcClintock , B T , King , R , Thomas , L , Matthiopoulos , J , McConnell , B J & Morales , J 2012 , ' A general discrete-time modeling framework for animal movement using multistate random walks ' Ecological Monographs , vol 82 , no. 3 , pp. 335-349 . DOI: 10.1890/11-0326.1en
dc.identifier.issn0012-9615
dc.identifier.otherPURE: 5051569
dc.identifier.otherPURE UUID: 5fc1e8aa-1a9d-4f14-a341-e8457c2cc217
dc.identifier.otherScopus: 84865794640
dc.identifier.urihttp://hdl.handle.net/10023/2605
dc.description.abstractRecent developments in animal tracking technology have permitted the collection of detailed data on the movement paths of individuals from many species. However, analysis methods for these data have not developed at a similar pace, largely due to a lack of suitable candidate models, coupled with the technical difficulties of fitting such models to data. To facilitate a general modeling framework, we propose that complex movement paths can be conceived as a series of movement strategies among which animals transition as they are affected by changes in their internal and external environment. We synthesize previously existing and novel methodologies to develop a general suite of mechanistic models based on biased and correlated random walks that allow different behavioral states for directed (e.g., migration), exploratory (e.g., dispersal), area-restricted (e.g., foraging), and other types of movement. Using this “tool-box” of nested model components, multi-state movement models may be custom-built for a wide variety of species and applications. As a unified state-space modeling framework, it allows the simultaneous investigation of numerous hypotheses about animal movement from imperfectly observed data, including time allocations to different movement behavior states, transitions between states, the use of memory or navigation, and strengths of attraction (or repulsion) to specific locations. The inclusion of covariate information permits further investigation of specific hypotheses related to factors driving different types of movement behavior. Using reversible jump Markov chain Monte Carlo methods to facilitate Bayesian model selection and multi-model inference, we apply the proposed methodology to real data by adapting it to the natural history of the grey seal (Halichoerus grypus) in the North Sea. Although previous grey seal studies tended to focus on correlated movements, we found overwhelming evidence that bias towards haul-out or foraging locations better explained seal movement than simple or correlated random walks. Posterior model probabilities also provided evidence that seals transition among directed, area-restricted, and exploratory movements associated with haul-out, foraging, and other behaviors. With this intuitive framework for modeling and interpreting animal movement, we believe the development and application of bespoke movement models will become more accessible to ecologists and non-statisticians.en
dc.format.extent15en
dc.language.isoeng
dc.relation.ispartofEcological Monographsen
dc.rightsThis is the author's final version of this article. The definitive version is published in Ecological Monographs, (c) 2012 the Ecological Society of Americaen
dc.subjectAnimal location dataen
dc.subjectBiased correlated random walken
dc.subjectMovement modelen
dc.subjectState-space modelen
dc.subjectSwitching behavioren
dc.subjectTelemetryen
dc.subjectQA Mathematicsen
dc.subjectQH Natural historyen
dc.subject.lccQAen
dc.subject.lccQHen
dc.titleA general discrete-time modeling framework for animal movement using multistate random walksen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.identifier.doihttps://doi.org/10.1890/11-0326.1
dc.description.statusPeer revieweden


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