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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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 |
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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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1784254870809739264 |