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dc.contributor.authorSweeney, David A.
dc.contributor.authorDeruiter, Stacy L.
dc.contributor.authorMcNamara-Oh, Ye Joo
dc.contributor.authorMarques, Tiago A.
dc.contributor.authorArranz, Patricia
dc.contributor.authorCalambokidis, John
dc.date.accessioned2019-04-08T10:30:01Z
dc.date.available2019-04-08T10:30:01Z
dc.date.issued2019-04-04
dc.identifier.citationSweeney , D A , Deruiter , S L , McNamara-Oh , Y J , Marques , T A , Arranz , P & Calambokidis , J 2019 , ' Automated peak detection method for behavioral event identification : detecting Balaenoptera musculus and Grampus griseus feeding attempts ' , Animal Biotelemetry , vol. 7 , 7 . https://doi.org/10.1186/s40317-019-0169-3en
dc.identifier.issn2050-3385
dc.identifier.otherPURE: 258517854
dc.identifier.otherPURE UUID: 2264f32e-c484-44e3-a852-22de8858bd84
dc.identifier.otherRIS: urn:9EFDBE33155731F0FAE15B2EFFAE89F5
dc.identifier.otherRIS: Sweeney2019
dc.identifier.otherORCID: /0000-0002-2581-1972/work/56861262
dc.identifier.otherScopus: 85063946899
dc.identifier.urihttp://hdl.handle.net/10023/17477
dc.descriptionThis project was supported by the U.S. Office of Naval Research (Grant No. N00014-16-1-3089). Tag deployments used here from the SOCAL-Behavioral Response Study were funded by the U.S. Office of Naval Research and Navy Living Marine Resources under multiple grants and contracts. Further funding was provided by the Kuipers Research Fellowship and the Calvin Alumni Association. TAM thanks partial support by CEAUL (funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013).en
dc.description.abstractThe desire of animal behaviorists for more flexible methods of conducting inter-study and inter-specific comparisons and meta-analysis of various animal behaviors compelled us to design an automated, animal behavior peak detection method that is potentially generalizable to a wide variety of data types, animals, and behaviors. We detected the times of feeding attempts by 12 Risso’s dolphins (Grampus griseus) and 36 blue whales (Balaenoptera musculus) using the norm-jerk (rate of change of acceleration) time series. The automated peak detection algorithm identified median true-positive rates of 0.881 for blue whale lunges and 0.410 for Risso’s dolphin prey capture attempts, with median false-positive rates of 0.096 and 0.007 and median miss rates of 0.113 and 0.314, respectively. Our study demonstrates that our peak detection method is efficient at automatically detecting animal behaviors from multisensor tag data with high accuracy for behaviors that are appropriately characterized by the data time series.
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofAnimal Biotelemetryen
dc.rightsCopyright © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectBlue whaleen
dc.subjectDetectionen
dc.subjectLungeen
dc.subjectNorm-jerken
dc.subjectPrey captureen
dc.subjectRisso's dolphinen
dc.subjectQH301 Biologyen
dc.subjectSignal Processingen
dc.subjectAnimal Science and Zoologyen
dc.subjectInstrumentationen
dc.subjectComputer Networks and Communicationsen
dc.subjectDASen
dc.subject.lccQH301en
dc.titleAutomated peak detection method for behavioral event identification : detecting Balaenoptera musculus and Grampus griseus feeding attemptsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
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.identifier.doihttps://doi.org/10.1186/s40317-019-0169-3
dc.description.statusPeer revieweden


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