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dc.contributor.authorBennison, Ashley
dc.contributor.authorBearhop, Stuart
dc.contributor.authorBodey, Thomas W.
dc.contributor.authorVotier, Stephen C.
dc.contributor.authorGrecian, W. James
dc.contributor.authorWakefield, Ewan D.
dc.contributor.authorHamer, Keith C.
dc.contributor.authorJessopp, Mark
dc.identifier.citationBennison , A , Bearhop , S , Bodey , T W , Votier , S C , Grecian , W J , Wakefield , E D , Hamer , K C & Jessopp , M 2018 , ' Search and foraging behaviors from movement data : a comparison of methods ' , Ecology and Evolution , vol. 8 , no. 1 , pp. 13-24 .
dc.identifier.otherPURE: 251712555
dc.identifier.otherPURE UUID: 616f7db4-6aae-41f1-8ccb-2848c8b37839
dc.identifier.otherScopus: 85034808955
dc.identifier.otherORCID: /0000-0002-6428-719X/work/39487848
dc.identifier.otherWOS: 000419483200002
dc.descriptionhis study was funded by NERC Standard Grant NE/H007466/1. AB is funded by the Irish Research Council (Project ID: GOIPG/2016/503). MJ is funded under Marine Renewable Energy Ireland (MaREI), The SFI Centre for Marine Renewable Energy Research (12/RC/2302), and EW is funded by the NERC (IRF NE/M017990/1).en
dc.description.abstractSearch behavior is often used as a proxy for foraging effort within studies of animal movement, despite it being only one part of the foraging process, which also includes prey capture. While methods for validating prey capture exist, many studies rely solely on behavioral annotation of animal movement data to identify search and infer prey capture attempts. However, the degree to which search correlates with prey capture is largely untested. This study applied seven behavioral annotation methods to identify search behavior from GPS tracks of northern gannets (Morus bassanus), and compared outputs to the occurrence of dives recorded by simultaneously deployed time-depth recorders. We tested how behavioral annotation methods vary in their ability to identify search behavior leading to dive events. There was considerable variation in the number of dives occurring within search areas across methods. Hidden Markov models proved to be the most successful, with 81% of all dives occurring within areas identified as search. k-Means clustering and first passage time had the highest rates of dives occurring outside identified search behavior. First passage time and hidden Markov models had the lowest rates of false positives, identifying fewer search areas with no dives. All behavioral annotation methods had advantages and drawbacks in terms of the complexity of analysis and ability to reflect prey capture events while minimizing the number of false positives and false negatives. We used these results, with consideration of analytical difficulty, to provide advice on the most appropriate methods for use where prey capture behavior is not available. This study highlights a need to critically assess and carefully choose a behavioral annotation method suitable for the research question being addressed, or resulting species management frameworks established.
dc.relation.ispartofEcology and Evolutionen
dc.rights© 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en
dc.subjectFirst passage timeen
dc.subjectHidden Markov modelsen
dc.subjectKernel densityen
dc.subjectMachine learningen
dc.subjectState-space modelsen
dc.subjectQH301 Biologyen
dc.subjectEcology, Evolution, Behavior and Systematicsen
dc.subjectNature and Landscape Conservationen
dc.titleSearch and foraging behaviors from movement data : a comparison of methodsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.description.statusPeer revieweden

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