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Imperfect observations in ecological studies
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dc.contributor.author | Shimadzu, Hideyasu | |
dc.contributor.author | Foster, Scott D | |
dc.contributor.author | Darnell, Ross | |
dc.date.accessioned | 2016-04-28T09:30:04Z | |
dc.date.available | 2016-04-28T09:30:04Z | |
dc.date.issued | 2016-09 | |
dc.identifier.citation | Shimadzu , 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/s10651-016-0342-2 | en |
dc.identifier.issn | 1352-8505 | |
dc.identifier.other | PURE: 240936839 | |
dc.identifier.other | PURE UUID: b94afa96-8b2f-4b58-9919-361c3cde90e6 | |
dc.identifier.other | Scopus: 84963995231 | |
dc.identifier.other | WOS: 000382017300001 | |
dc.identifier.uri | https://hdl.handle.net/10023/8691 | |
dc.description.abstract | Every 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.extent | 22 | |
dc.language.iso | eng | |
dc.relation.ispartof | Environmental and Ecological Statistics | en |
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.subject | Compound distributions | en |
dc.subject | Detection probability | en |
dc.subject | Ecological modelling | en |
dc.subject | Marine surveys | en |
dc.subject | Sampling | en |
dc.subject | Species Distribution Models (SDMs) | en |
dc.subject | QH301 Biology | en |
dc.subject | NDAS | en |
dc.subject | SDG 14 - Life Below Water | en |
dc.subject.lcc | QH301 | en |
dc.title | Imperfect observations in ecological studies | en |
dc.type | Journal article | en |
dc.contributor.sponsor | European Research Council | en |
dc.description.version | Publisher PDF | en |
dc.contributor.institution | University of St Andrews. School of Biology | en |
dc.identifier.doi | https://doi.org/10.1007/s10651-016-0342-2 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | 250189 | en |
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