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Estimation of population size when capture probability depends on individual states

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Worthington2018_Article_EstimationOfPopulationSizeWhen.pdf (807.0Kb)
Date
03/2019
Author
Worthington, Hannah
McCrea, Rachel
King, Ruth
Griffiths, Richard
Keywords
Abundance
Closed population
Individual heterogeneity
Transition probabilities
GE Environmental Sciences
QA Mathematics
QH301 Biology
T-NDAS
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Abstract
We develop a multi-state model to estimate the size of a closed population from capture–recapture studies. We consider the case where capture–recapture data are not of a simple binary form, but where the state of an individual is also recorded upon every capture as a discrete variable. The proposed multi-state model can be regarded as a generalisation of the commonly applied set of closed population models to a multi-state form. The model allows for heterogeneity within the capture probabilities associated with each state while also permitting individuals to move between the different discrete states. A closed-form expression for the likelihood is presented in terms of a set of sufficient statistics. The link between existing models for capture heterogeneity is established, and simulation is used to show that the estimate of population size can be biased when movement between states is not accounted for. The proposed unconditional approach is also compared to a conditional approach to assess estimation bias. The model derived in this paper is motivated by a real ecological data set on great crested newts, Triturus cristatus.
Citation
Worthington , H , McCrea , R , King , R & Griffiths , R 2019 , ' Estimation of population size when capture probability depends on individual states ' , Journal of Agricultural, Biological and Environmental Statistics , vol. 24 , no. 1 , pp. 154-172 . https://doi.org/10.1007/s13253-018-00347-x
Publication
Journal of Agricultural, Biological and Environmental Statistics
Status
Peer reviewed
DOI
https://doi.org/10.1007/s13253-018-00347-x
ISSN
1085-7117
Type
Journal article
Rights
© The Author(s) 2018. 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.
Description
Funding: Carnegie Trust for the Universities of Scotland, UK Engineering and Physical Sciences Research Council (EP/10009171/1), UK Natural Environment Research Council (NE/J018473/1)
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  • University of St Andrews Research
URL
https://link.springer.com/article/10.1007%2Fs13253-018-00347-x#SupplementaryMaterial
URI
http://hdl.handle.net/10023/16735

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