Spatial capture–mark–resight estimation of animal population density

Summary Sightings of previously marked animals can extend a capture–recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood‐based non‐spa...

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Published in:Biometrics
Main Authors: Efford, Murray G., Hunter, Christine M.
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2017
Subjects:
Online Access:http://dx.doi.org/10.1111/biom.12766
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fbiom.12766
https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12766
id crwiley:10.1111/biom.12766
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spelling crwiley:10.1111/biom.12766 2023-12-03T10:29:29+01:00 Spatial capture–mark–resight estimation of animal population density Efford, Murray G. Hunter, Christine M. 2017 http://dx.doi.org/10.1111/biom.12766 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fbiom.12766 https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12766 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Biometrics volume 74, issue 2, page 411-420 ISSN 0006-341X 1541-0420 Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability journal-article 2017 crwiley https://doi.org/10.1111/biom.12766 2023-11-09T13:45:34Z Summary Sightings of previously marked animals can extend a capture–recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood‐based non‐spatial models, and some spatial versions fitted by Markov chain Monte Carlo sampling. As implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre‐marked animals is known. We develop a suite of likelihood‐based spatial mark–resight models that either include the marking phase (“capture–mark–resight” models) or require a known distribution of marked animals (narrow‐sense “mark–resight”). The new models sacrifice some information in the covariance structure of the counts of unmarked animals; estimation is by maximizing a pseudolikelihood with a simulation‐based adjustment for overdispersion in the sightings of unmarked animals. Simulations suggest that the resulting estimates of population density have low bias and adequate confidence interval coverage under typical sampling conditions. Further work is needed to specify the conditions under which ignoring covariance results in unacceptable loss of precision, or to modify the pseudolikelihood to include that information. The methods are applied to a study of ship rats Rattus rattus using live traps and video cameras in a New Zealand forest, and to previously published data. Article in Journal/Newspaper Rattus rattus Wiley Online Library (via Crossref) New Zealand Biometrics 74 2 411 420
institution Open Polar
collection Wiley Online Library (via Crossref)
op_collection_id crwiley
language English
topic Applied Mathematics
General Agricultural and Biological Sciences
General Immunology and Microbiology
General Biochemistry, Genetics and Molecular Biology
General Medicine
Statistics and Probability
spellingShingle Applied Mathematics
General Agricultural and Biological Sciences
General Immunology and Microbiology
General Biochemistry, Genetics and Molecular Biology
General Medicine
Statistics and Probability
Efford, Murray G.
Hunter, Christine M.
Spatial capture–mark–resight estimation of animal population density
topic_facet Applied Mathematics
General Agricultural and Biological Sciences
General Immunology and Microbiology
General Biochemistry, Genetics and Molecular Biology
General Medicine
Statistics and Probability
description Summary Sightings of previously marked animals can extend a capture–recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood‐based non‐spatial models, and some spatial versions fitted by Markov chain Monte Carlo sampling. As implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre‐marked animals is known. We develop a suite of likelihood‐based spatial mark–resight models that either include the marking phase (“capture–mark–resight” models) or require a known distribution of marked animals (narrow‐sense “mark–resight”). The new models sacrifice some information in the covariance structure of the counts of unmarked animals; estimation is by maximizing a pseudolikelihood with a simulation‐based adjustment for overdispersion in the sightings of unmarked animals. Simulations suggest that the resulting estimates of population density have low bias and adequate confidence interval coverage under typical sampling conditions. Further work is needed to specify the conditions under which ignoring covariance results in unacceptable loss of precision, or to modify the pseudolikelihood to include that information. The methods are applied to a study of ship rats Rattus rattus using live traps and video cameras in a New Zealand forest, and to previously published data.
format Article in Journal/Newspaper
author Efford, Murray G.
Hunter, Christine M.
author_facet Efford, Murray G.
Hunter, Christine M.
author_sort Efford, Murray G.
title Spatial capture–mark–resight estimation of animal population density
title_short Spatial capture–mark–resight estimation of animal population density
title_full Spatial capture–mark–resight estimation of animal population density
title_fullStr Spatial capture–mark–resight estimation of animal population density
title_full_unstemmed Spatial capture–mark–resight estimation of animal population density
title_sort spatial capture–mark–resight estimation of animal population density
publisher Wiley
publishDate 2017
url http://dx.doi.org/10.1111/biom.12766
https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fbiom.12766
https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12766
geographic New Zealand
geographic_facet New Zealand
genre Rattus rattus
genre_facet Rattus rattus
op_source Biometrics
volume 74, issue 2, page 411-420
ISSN 0006-341X 1541-0420
op_rights http://onlinelibrary.wiley.com/termsAndConditions#vor
op_doi https://doi.org/10.1111/biom.12766
container_title Biometrics
container_volume 74
container_issue 2
container_start_page 411
op_container_end_page 420
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