Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorStevenson, Ben C.
dc.contributor.authorvan Dam-Bates, Paul
dc.contributor.authorYoung, Callum K.Y.
dc.contributor.authorMeasey, John
dc.date.accessioned2021-11-30T00:36:28Z
dc.date.available2021-11-30T00:36:28Z
dc.date.issued2021-03
dc.identifier.citationStevenson , B C , van Dam-Bates , P , Young , C K Y & Measey , J 2021 , ' A spatial capture-recapture model to estimate call rate and population density from passive acoustic surveys ' , Methods in Ecology and Evolution , vol. 12 , no. 3 , pp. 432-442 . https://doi.org/10.1111/2041-210X.13522en
dc.identifier.issn2041-210X
dc.identifier.otherPURE: 270957564
dc.identifier.otherPURE UUID: 11fbf06e-16cf-42f5-a3f6-c1ff67b08896
dc.identifier.otherRIS: urn:8949C14C0EFB6EB2D07B33E229814B07
dc.identifier.otherScopus: 85096939002
dc.identifier.otherWOS: 000594050100001
dc.identifier.urihttps://hdl.handle.net/10023/24431
dc.descriptionThis work was funded by an EPSRC/NERC PhD grant (No. EP/1000917/1), by the EPSRC through a Doctoral Fellowship Prize, and by the Royal Society of New Zealand through Marsden Grant 19-UOA-211. JM thanks the DSI-NRF Centre of Excellence for Invasion Biology. Funding for the frog survey was received from the National Geographic Society/Waitt Grants Program (No. W184-11).en
dc.description.abstract1. Spatial capture‐recapture (SCR) models are commonly used to estimate animal population density from detections and subsequent redetections of individuals across space. In particular, acoustic SCR models deal with detections of animal vocalisations across an array of acoustic detectors. Previously published acoustic SCR methods either estimate call density (calls per unit space per unit time) rather than animal density itself, require an independently estimated call rate to estimate animal density, or discard data from all but one detected call from each individual. 2. In this manuscript, we develop a new spatial capture‐recapture model that estimates both call rate and animal density from the acoustic survey alone, without requiring an independently estimated call rate. Our approach therefore alleviates the need for the additional field work of physically locating and monitoring individual animals. We illustrate our method and compare it to an existing approach using a simulation study and an application to data collected on an acoustic survey of the visually cryptic Cape peninsula moss frog Arthroleptella lightfooti. 3. In the context of our acoustic survey, our calling animal density estimator has low bias, good precision, and confidence intervals with appropriate coverage, yielding results that are consistent with previous studies of the same species. 4. Our method can obtain accurate and precise estimates of animal density while eliminating the fieldwork burden associated with separately estimating call rate. We discuss how the development of our model’s likelihood reveals a clear path to further extensions, which may incorporate features such as animal movement processes and uncertain individual identification.
dc.language.isoeng
dc.relation.ispartofMethods in Ecology and Evolutionen
dc.rightsCopyright © 2020 British Ecological Society. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1111/2041-210X.13522.en
dc.subjectAnimal densityen
dc.subjectAnuransen
dc.subjectAutomated recording systemsen
dc.subjectCall densityen
dc.subjectDetection functionen
dc.subjectPassive acoustic monitoringen
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subjectNISen
dc.subject.lccQH301en
dc.titleA spatial capture-recapture model to estimate call rate and population density from passive acoustic surveysen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.identifier.doihttps://doi.org/10.1111/2041-210X.13522
dc.description.statusPeer revieweden
dc.date.embargoedUntil2021-11-30


This item appears in the following Collection(s)

Show simple item record