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Estimation of Population Size When Capture Probability Depends on Individual States

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Estimation of Population Size When Capture Probability Depends on Individual States. / Worthington, Hannah; McCrea, Rachel; King, Ruth et al.
In: Journal of Agricultural, Biological and Environmental Statistics, Vol. 24, No. 1, 15.03.2019, p. 154-172.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Worthington, H, McCrea, R, King, R & Griffiths, RA 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

APA

Worthington, H., McCrea, R., King, R., & Griffiths, R. A. (2019). Estimation of Population Size When Capture Probability Depends on Individual States. Journal of Agricultural, Biological and Environmental Statistics, 24(1), 154-172. https://doi.org/10.1007/s13253-018-00347-x

Vancouver

Worthington H, McCrea R, King R, Griffiths RA. Estimation of Population Size When Capture Probability Depends on Individual States. Journal of Agricultural, Biological and Environmental Statistics. 2019 Mar 15;24(1):154-172. Epub 2018 Dec 17. doi: 10.1007/s13253-018-00347-x

Author

Worthington, Hannah ; McCrea, Rachel ; King, Ruth et al. / Estimation of Population Size When Capture Probability Depends on Individual States. In: Journal of Agricultural, Biological and Environmental Statistics. 2019 ; Vol. 24, No. 1. pp. 154-172.

Bibtex

@article{b9a4c9041dc4465face74181ef7c08c6,
title = "Estimation of Population Size When Capture Probability Depends on Individual States",
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. Supplementary materials accompanying this paper appear online.",
keywords = "Abundance, Closed population, Individual heterogeneity, Transition probabilities",
author = "Hannah Worthington and Rachel McCrea and Ruth King and Griffiths, {Richard A.}",
year = "2019",
month = mar,
day = "15",
doi = "10.1007/s13253-018-00347-x",
language = "English",
volume = "24",
pages = "154--172",
journal = "Journal of Agricultural, Biological and Environmental Statistics",
issn = "1085-7117",
publisher = "Springer New York",
number = "1",

}

RIS

TY - JOUR

T1 - Estimation of Population Size When Capture Probability Depends on Individual States

AU - Worthington, Hannah

AU - McCrea, Rachel

AU - King, Ruth

AU - Griffiths, Richard A.

PY - 2019/3/15

Y1 - 2019/3/15

N2 - 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. Supplementary materials accompanying this paper appear online.

AB - 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. Supplementary materials accompanying this paper appear online.

KW - Abundance

KW - Closed population

KW - Individual heterogeneity

KW - Transition probabilities

U2 - 10.1007/s13253-018-00347-x

DO - 10.1007/s13253-018-00347-x

M3 - Journal article

VL - 24

SP - 154

EP - 172

JO - Journal of Agricultural, Biological and Environmental Statistics

JF - Journal of Agricultural, Biological and Environmental Statistics

SN - 1085-7117

IS - 1

ER -