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dc.contributor.authorHersh, Taylor A.
dc.contributor.authorGero, Shane
dc.contributor.authorRendell, Luke
dc.contributor.authorWhitehead, Hal
dc.date.accessioned2021-05-31T15:30:04Z
dc.date.available2021-05-31T15:30:04Z
dc.date.issued2021-05-29
dc.identifier.citationHersh , T A , Gero , S , Rendell , L & Whitehead , H 2021 , ' Using identity calls to detect structure in acoustic datasets ' , Methods in Ecology and Evolution , vol. Early View . https://doi.org/10.1111/2041-210X.13644en
dc.identifier.issn2041-210X
dc.identifier.otherPURE: 274358578
dc.identifier.otherPURE UUID: 2feaf8aa-8ed4-4b3a-bcd0-429625a64a1c
dc.identifier.otherRIS: urn:863B909014F6C47736640F73A0C7995D
dc.identifier.otherORCID: /0000-0002-1121-9142/work/95041840
dc.identifier.otherScopus: 85106676927
dc.identifier.otherWOS: 000655875900001
dc.identifier.urihttp://hdl.handle.net/10023/23286
dc.description.abstract1.  Acoustic analyses can be powerful tools for illuminating structure within and between populations, especially for cryptic or difficult to access taxa. Acoustic repertoires are often compared using aggregate similarity measures across all calls of a particular type, but specific group identity calls may more clearly delineate structure in some taxa. 2.  We present a new method-the identity call method-that estimates the number of acoustically distinct subdivisions in a set of repertoires and identifies call types that characterize those subdivisions. The method uses contaminated mixture models to identify call types, assigning each call a probability of belonging to each type. Repertoires are hierarchically clustered based on similarities in call type usage, producing a dendrogram with 'identity clades' of repertoires and the (identity calls) that best characterize each clade. We validated this approach using acoustic data from sperm whales, grey-breasted wood-wrens, and Australian field crickets, and ran a suite of tests to assess parameter sensitivity. 3.  For all taxa, the method detected diagnostic signals (identity calls) and structure (identity clades; sperm whale subpopulations, wren subspecies, and cricket species) that were consistent with past research. Some datasets were more sensitive to parameter variation than others, which may reflect real uncertainty or biological variability in the taxa examined. We recommend that users perform comparative analyses of different parameter combinations to determine which portions of the dendrogram warrant careful versus confident interpretation. 4.  The presence of group-characteristic identity calls does not necessarily mean animals perceive them as such. Fine scale experiments like playbacks are a key next step to understand call perception and function. This method can help inform such studies by identifying calls that may be salient to animals and are good candidates for investigation or playback stimuli. For cryptic or difficult to access taxa with group-specific calls, the identity call method can aid managers in quantifying behavioural diversity and/or identifying putative structure within and between populations, given that acoustic data can be inexpensive and minimally invasive to collect.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rightsCopyright © 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.en
dc.subjectBioinformaticsen
dc.subjectCommunity ecologyen
dc.subjectConservationen
dc.subjectDiversityen
dc.subjectPopulation ecologyen
dc.subjectQH301 Biologyen
dc.subject3rd-DASen
dc.subject.lccQH301en
dc.titleUsing identity calls to detect structure in acoustic datasetsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Centre for Social Learning & Cognitive Evolutionen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews.Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews.Bioacoustics groupen
dc.contributor.institutionUniversity of St Andrews.Marine Alliance for Science & Technology Scotlanden
dc.identifier.doihttps://doi.org/10.1111/2041-210X.13644
dc.description.statusPeer revieweden


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