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dc.contributor.authorBorchers, David L.
dc.contributor.authorMarques, Tiago A.
dc.identifier.citationBorchers , D L & Marques , T A 2017 , ' From distance sampling to spatial capture-recapture ' Advances in Statistical Analysis , vol. 101 , no. 4 , pp. 475-494 .
dc.identifier.otherPURE: 248517046
dc.identifier.otherPURE UUID: 75a12666-0ab2-404b-be07-d10f37a976ac
dc.identifier.otherScopus: 85009198588
dc.identifier.otherORCID: /0000-0002-2581-1972/work/56861279
dc.descriptionTAM thanks support by CEAUL (funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal, through the Project UID/MAT/00006/2013).en
dc.description.abstractDistance sampling and capture–recapture are the two most widely used wildlife abundance estimation methods. capture–recapture methods have only recently incorporated models for spatial distribution and there is an increasing tendency for distance sampling methods to incorporated spatial models rather than to rely on partly design-based spatial inference. In this overview we show how spatial models are central to modern distance sampling and that spatial capture–recapture models arise as an extension of distance sampling methods. Depending on the type of data recorded, they can be viewed as particular kinds of hierarchical binary regression, Poisson regression, survival or time-to-event models, with individuals’ locations as latent variables and a spatial model as the latent variable distribution. Incorporation of spatial models in these two methods provides new opportunities for drawing explicitly spatial inferences. Areas of likely future development include more sophisticated spatial and spatio-temporal modelling of individuals’ locations and movements, new methods for integrating spatial capture–recapture and other kinds of ecological survey data, and methods for dealing with the recapture uncertainty that often arise when “capture” consists of detection by a remote device like a camera trap or microphone.
dc.relation.ispartofAdvances in Statistical Analysisen
dc.rights© The Author(s) 2017. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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.en
dc.subjectDistance samplingen
dc.subjectSpatial capture-recaptureen
dc.subjectHierarchical modelen
dc.subjectPoisson processen
dc.subjectSurvival modelen
dc.subjectBinary regressionen
dc.subjectQA Mathematicsen
dc.subjectHA Statisticsen
dc.titleFrom distance sampling to spatial capture-recaptureen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
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.Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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