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dc.contributor.authorLangrock, Roland
dc.contributor.authorHopcraft, Grant
dc.contributor.authorBlackwell, Paul
dc.contributor.authorGoodall, Victoria
dc.contributor.authorKing, Ruth
dc.contributor.authorNiu, Mu
dc.contributor.authorPatterson, Toby
dc.contributor.authorPedersen, Martin
dc.contributor.authorSkarin, Anna
dc.contributor.authorSchick, Robert Schilling
dc.identifier.citationLangrock , R , Hopcraft , G , Blackwell , P , Goodall , V , King , R , Niu , M , Patterson , T , Pedersen , M , Skarin , A & Schick , R S 2014 , ' Modelling group dynamic animal movement ' , Methods in Ecology and Evolution , vol. 5 , no. 2 , pp. 190-199 .
dc.identifier.otherPURE: 43921402
dc.identifier.otherPURE UUID: 9f6085ff-0270-4eea-9f6c-621a13690d2d
dc.identifier.otherScopus: 84893961321
dc.identifier.otherWOS: 000331402000011
dc.description.abstract1). Group dynamics are a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognized, particularly in order to differentiate the role of social forces in individual movement from environmental factors. However, to date, practical statistical methods, which can include group dynamics in animal movement models, have been lacking. 2). We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the group's centre, and an individual-level model, such that each individual makes its movement decisions relative to the group centroid. The basic idea is framed within the flexible class of hidden Markov models, extending previous work on modelling animal movement by means of multistate random walks. 3). While in simulation experiments parameter estimators exhibit some bias in non-ideal scenarios, we show that generally the estimation of models of this type is both feasible and ecologically informative. 4). We illustrate the approach using real movement data from 11 reindeer (Rangifer tarandus). Results indicate a directional bias towards a group centroid for reindeer in an encamped state. Though the attraction to the group centroid is relatively weak, our model successfully captures group-influenced movement dynamics. Specifically, as compared to a regular mixture of correlated random walks, the group dynamic model more accurately predicts the non-diffusive behaviour of a cohesive mobile group. 5). As technology continues to develop, it will become easier and less expensive to tag multiple individuals within a group in order to follow their movements. Our work provides a first inferential framework for understanding the relative influences of individual versus group-level movement decisions. This framework can be extended to include covariates corresponding to environmental influences or body condition. As such, this framework allows for a broader understanding of the many internal and external factors that can influence an individual's movement.
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rights© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society.en
dc.subjectBehavioural stateen
dc.subjectHidden Markov modelen
dc.subjectMaximum likelihooden
dc.subjectRandom walken
dc.titleModelling group dynamic animal movementen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
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. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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