Search and foraging behaviors from movement data: a comparison of methods

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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 J.
dc.contributor.funderMarine Renewable Energy Ireland
dc.contributor.funderIrish Research Council
dc.contributor.funderNatural Environment Research Council
dc.contributor.funderScience Foundation Ireland
dc.date.accessioned2018-07-30T10:30:25Z
dc.date.available2018-07-30T10:30:25Z
dc.date.issued2018
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.en
dc.description.sponsorshipNatural Environment Research Council (IRF NE/M017990/1, NE/H007466/1); Irish Research Council (GOIPG/2016/503)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBennison, A., Bearhop, S., Bodey, T. W., Votier, S. C., Grecian, W. J., Wakefield, E. D., Hamer, K. C. and Jessopp, M. (2018) 'Search and foraging behaviors from movement data: A comparison of methods', Ecology and Evolution, 8(1), pp. 13-24. doi: 10.1002/ece3.3593en
dc.identifier.doi10.1002/ece3.3593
dc.identifier.endpage24
dc.identifier.issn2045-7758
dc.identifier.issued1
dc.identifier.journaltitleEcology and Evolutionen
dc.identifier.startpage13
dc.identifier.urihttps://hdl.handle.net/10468/6510
dc.identifier.volume8
dc.language.isoenen
dc.publisherJohn Wiley & Sons Inc.en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Supplement/12/RC/2302s/IE/Marine Renewable Energy Ireland (MaREI) - EU Grant Manager/
dc.relation.urihttps://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.3593
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.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBehavioren
dc.subjectFirst passage timeen
dc.subjectHidden Markov modelsen
dc.subjectKernel densityen
dc.subjectk-meansen
dc.subjectMachine learningen
dc.subjectMovementen
dc.subjectState-space modelsen
dc.subjectTelemetryen
dc.titleSearch and foraging behaviors from movement data: a comparison of methodsen
dc.typeArticle (peer-reviewed)en
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