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dc.contributor.authorMcClintock, Brett Thomas
dc.contributor.authorRussell, Deborah Jill Fraser
dc.contributor.authorMatthiopoulos, Jason
dc.contributor.authorKing, Ruth
dc.identifier.citationMcClintock , B T , Russell , D J F , Matthiopoulos , J & King , R 2013 , ' Combining individual animal movement and ancillary biotelemetry data to investigate population-level activity budgets ' , Ecology , vol. 94 , no. 4 , pp. 838-849 .
dc.identifier.otherORCID: /0000-0002-1969-102X/work/49052047
dc.description.abstractRecent technological advances have permitted the collection of detailed animal location and ancillary biotelemetry data that facilitate inference about animal movement and associated behaviors. However, these rich sources of individual information, location, and biotelemetry data, are typically analyzed independently, with population-level inferences remaining largely post hoc. We describe a hierarchical modeling approach, which is able to integrate location and ancillary biotelemetry (e.g., physiological or accelerometer) data from many individuals. We can thus obtain robust estimates of (1) population-level movement parameters and (2) activity budgets for a set of behaviors among which animals transition as they respond to changes in their internal and external environment. Measurement error and missing data are easily accommodated using a state-space formulation of the proposed hierarchical model. Using Bayesian analysis methods, we demonstrate our modeling approach with location and dive activity data from 17 harbor seals (Phoca vitulina) in the United Kingdom. Based jointly on movement and diving activity, we identified three distinct movement behavior states: resting, foraging, and transit, and estimated population-level activity budgets to these three states. Because harbor seals are known to dive for both foraging and transit (but not usually for resting), we compared these results to a similar population level analysis utilizing only location data. We found that a large proportion of time steps were mischaracterized when behavior states were inferred from horizontal trajectory alone, with 33% of time steps exhibiting a majority of dive activity assigned to the resting state. Only 1% of these time steps were assigned to resting when inferred from both trajectory and dive activity data using our integrated modeling approach. There is mounting evidence of the potential perils of inferring animal behavior based on trajectory alone, but there fortunately now exist many flexible analytical techniques for extracting more out of the increasing wealth of information afforded by recent advances in biologging technology.
dc.subjectAnimal location dataen
dc.subjectMovement modelen
dc.subjectState-space modelen
dc.subjectSwitching behavioren
dc.subjectQ Scienceen
dc.titleCombining individual animal movement and ancillary biotelemetry data to investigate population-level activity budgetsen
dc.typeJournal articleen
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. Scottish Oceans Instituteen
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. Sea Mammal Research Uniten
dc.description.statusPeer revieweden

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