On the estimation of animal density from spatial capture-recapture data

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dc.contributor.advisor Stevenson, B en
dc.contributor.author Young, Callum en
dc.date.accessioned 2018-07-12T00:16:34Z en
dc.date.issued 2018 en
dc.identifier.uri http://hdl.handle.net/2292/37444 en
dc.description Full text is available to authenticated members of The University of Auckland only. en
dc.description.abstract Spatial capture-recapture (SCR) methods can estimate the density of animal populations. SCR contains elements of both capture-recapture, and distance sampling methods. Data are obtained through repeated detections of individuals by detectors at known locations, allowing the incorporation of the detection function in the SCR model. Naturally, individuals whose home ranges are centred nearer to a detector have a greater probability of being detected. Data obtained from SCR surveys are commonly presented as capture histories, which may contain either counts of detections, or binary indications of a detection (or non-detection). As counts can be converted into binary data, either model may be fitted to SCR data. Some advocate fitting models to the binary data, as incorrectly assuming the underlying statistical (count) distribution produces biased estimates; others suggest modelling the full counts, as the magnitudes of the counts provide supplementary information over and above that of the binary capture histories. We introduce the “scr” package for R, and describe its main features. A simulation study is performed to assess the performance of each model fitted to data from various underlying distributions. We show that both models give very similar inferences in all cases, regardless of the model type or true distribution. Additionally, the inference appears to be appropriate, even when the data are significantly overdispersed. Existing methods cannot sufficiently model acoustically detected data without making a number of assumptions that are often violated in practice. We thus present a new model circumventing the issues present in existing methods, whilst improving on them such that there may be a reduction in survey effort and cost. We further extend the application of this new model to situations where clustering of individuals’ activity centres creates dependence problems with the data, and describe how our model accounts for this lack of independence. en
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof Masters Thesis - University of Auckland en
dc.relation.isreferencedby UoA99265081410902091 en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights Restricted Item. Available to authenticated members of The University of Auckland. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/nz/ en
dc.title On the estimation of animal density from spatial capture-recapture data en
dc.type Thesis en
thesis.degree.discipline Statistics en
thesis.degree.grantor The University of Auckland en
thesis.degree.level Masters en
dc.rights.holder Copyright: The author en
pubs.elements-id 747669 en
pubs.record-created-at-source-date 2018-07-12 en

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http://creativecommons.org/licenses/by-nc-sa/3.0/nz/ Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-sa/3.0/nz/


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