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dc.contributor.authorShimadzu, Hideyasu
dc.contributor.authorFoster, Scott D
dc.contributor.authorDarnell, Ross
dc.date.accessioned2016-04-28T09:30:04Z
dc.date.available2016-04-28T09:30:04Z
dc.date.issued2016-09
dc.identifier.citationShimadzu , H , Foster , S D & Darnell , R 2016 , ' Imperfect observations in ecological studies ' , Environmental and Ecological Statistics , vol. 23 , no. 3 , pp. 337-358 . https://doi.org/10.1007/s10651-016-0342-2en
dc.identifier.issn1352-8505
dc.identifier.otherPURE: 240936839
dc.identifier.otherPURE UUID: b94afa96-8b2f-4b58-9919-361c3cde90e6
dc.identifier.otherScopus: 84963995231
dc.identifier.otherWOS: 000382017300001
dc.identifier.urihttps://hdl.handle.net/10023/8691
dc.description.abstractEvery ecological data set is the result of sampling the biota at sampling locations. Such samples are rarely a census of the biota at the sampling locations and so will inherently contain biases. It is crucial to account for the bias induced by sampling if valid inference on biodiversity quantities is to be drawn from the observed data. The literature on accounting for sampling effects is large, but most are dedicated to the specific type of inference required, the type of analysis performed and the type of survey undertaken. There is no general and systematic approach to sampling. Here, we explore the unification of modelling approaches to account for sampling. We focus on individuals in ecological communities as the fundamental sampling element, and show that methods for accounting for sampling at the species level can be equated to individual sampling effects. Particular emphasis is given to the case where the probability of observing an individual, when it is present at the site sampled, is less than one. We call these situations ‘imperfect observations’. The proposed framework is easily implemented in standard software packages. We highlight some practical benefits of this formal framework: the ability of predicting the true number of individuals using an expectation that conditions on the observed data, and designing appropriate survey plans accounting for
dc.format.extent22
dc.language.isoeng
dc.relation.ispartofEnvironmental and Ecological Statisticsen
dc.rights© The Author(s) 2016 Open Access 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.en
dc.subjectCompound distributionsen
dc.subjectDetection probabilityen
dc.subjectEcological modellingen
dc.subjectMarine surveysen
dc.subjectSamplingen
dc.subjectSpecies Distribution Models (SDMs)en
dc.subjectQH301 Biologyen
dc.subjectNDASen
dc.subjectSDG 14 - Life Below Wateren
dc.subject.lccQH301en
dc.titleImperfect observations in ecological studiesen
dc.typeJournal articleen
dc.contributor.sponsorEuropean Research Councilen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.identifier.doihttps://doi.org/10.1007/s10651-016-0342-2
dc.description.statusPeer revieweden
dc.identifier.grantnumber250189en


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