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dc.contributor.authorKidney, Darren
dc.contributor.authorRawson, Benjamin M.
dc.contributor.authorBorchers, David Louis
dc.contributor.authorStevenson, Ben
dc.contributor.authorMarques, Tiago A.
dc.contributor.authorThomas, Len
dc.date.accessioned2016-05-20T12:30:06Z
dc.date.available2016-05-20T12:30:06Z
dc.date.issued2016-05-19
dc.identifier.citationKidney , D , Rawson , B M , Borchers , D L , Stevenson , B , Marques , T A & Thomas , L 2016 , ' An efficient acoustic density estimation method with human detectors applied to gibbons in Cambodia ' PLoS One , vol. 11 , no. 5 , e0155066 , pp. 1-16 . DOI: 10.1371/journal.pone.0155066en
dc.identifier.issn1932-6203
dc.identifier.otherPURE: 242358477
dc.identifier.otherPURE UUID: edb57631-789d-40ba-a8a3-ccf6f70baced
dc.identifier.otherScopus: 84982104066
dc.identifier.urihttp://hdl.handle.net/10023/8842
dc.descriptionD. Kidney was supported by an Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Grant studentship (EPSRC grant EP/P505097/1). B. Stevenson was supported by a studentship jointly funded by the University of St Andrews and EPSRC, through the National Centre for Statistical Ecology (EPSRC grant EP/I000917/1).en
dc.description.abstractSome animal species are hard to see but easy to hear. Standard visual methods for estimating population density for such species are often ineffective or inefficient, but methods based on passive acoustics show more promise. We develop spatially explicit capture-recapture (SECR) methods for territorial vocalising species, in which humans act as an acoustic detector array. We use SECR and estimated bearing data from a single-occasion acoustic survey of a gibbon population in northeastern Cambodia to estimate the density of calling groups. The properties of the estimator are assessed using a simulation study, in which a variety of survey designs are also investigated. We then present a new form of the SECR likelihood for multi-occasion data which accounts for the stochastic availability of animals. In the context of gibbon surveys this allows model-based estimation of the proportion of groups that produce territorial vocalisations on a given day, thereby enabling the density of groups, instead of the density of calling groups, to be estimated. We illustrate the performance of this new estimator by simulation. We show that it is possible to estimate density reliably from human acoustic detections of visually cryptic species using SECR methods. For gibbon surveys we also show that incorporating observers' estimates of bearings to detected groups substantially improves estimator performance. Using the new form of the SECR likelihood we demonstrate that estimates of availability, in addition to population density and detection function parameters, can be obtained from multi-occasion data, and that the detection function parameters are not confounded with the availability parameter. This acoustic SECR method provides a means of obtaining reliable density estimates for territorial vocalising species. It is also efficient in terms of data requirements since it only requires routine survey data. We anticipate that the low-tech field requirements will make this method an attractive option in many situations where populations can be surveyed acoustically by humans.en
dc.format.extent16en
dc.language.isoeng
dc.relation.ispartofPLoS Oneen
dc.rights© 2016 Kidney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectGE Environmental Sciencesen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectNDASen
dc.subject.lccGEen
dc.subject.lccQAen
dc.subject.lccQH301en
dc.titleAn efficient acoustic density estimation method with human detectors applied to gibbons in Cambodiaen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
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. Centre for Higher Education Researchen
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. Pure Mathematicsen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0155066
dc.description.statusPeer revieweden


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