Spatial capture–recapture with random thinning for unidentified encounters
1. Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count...
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ftpubmed:oai:pubmedcentral.nih.gov:7863675 2023-05-15T18:42:13+02:00 Spatial capture–recapture with random thinning for unidentified encounters Jiménez, José C. Augustine, Ben Linden, Daniel W. B. Chandler, Richard Royle, J. Andrew 2020-12-08 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863675/ http://www.ncbi.nlm.nih.gov/pubmed/33598123 https://doi.org/10.1002/ece3.7091 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863675/ http://www.ncbi.nlm.nih.gov/pubmed/33598123 http://dx.doi.org/10.1002/ece3.7091 © 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Ecol Evol Original Research Text 2020 ftpubmed https://doi.org/10.1002/ece3.7091 2021-02-21T01:23:50Z 1. Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of “marked” and “unmarked” individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites. 2. Here we describe a “random thinning” SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE. 3. We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (Ursus arctos) density in Oriental Cantabrian Mountains (North Spain). 4. Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded. Text Ursus arctos PubMed Central (PMC) Ecology and Evolution 11 3 1187 1198 |
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Original Research Jiménez, José C. Augustine, Ben Linden, Daniel W. B. Chandler, Richard Royle, J. Andrew Spatial capture–recapture with random thinning for unidentified encounters |
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Original Research |
description |
1. Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of “marked” and “unmarked” individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites. 2. Here we describe a “random thinning” SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE. 3. We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (Ursus arctos) density in Oriental Cantabrian Mountains (North Spain). 4. Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded. |
format |
Text |
author |
Jiménez, José C. Augustine, Ben Linden, Daniel W. B. Chandler, Richard Royle, J. Andrew |
author_facet |
Jiménez, José C. Augustine, Ben Linden, Daniel W. B. Chandler, Richard Royle, J. Andrew |
author_sort |
Jiménez, José |
title |
Spatial capture–recapture with random thinning for unidentified encounters |
title_short |
Spatial capture–recapture with random thinning for unidentified encounters |
title_full |
Spatial capture–recapture with random thinning for unidentified encounters |
title_fullStr |
Spatial capture–recapture with random thinning for unidentified encounters |
title_full_unstemmed |
Spatial capture–recapture with random thinning for unidentified encounters |
title_sort |
spatial capture–recapture with random thinning for unidentified encounters |
publisher |
John Wiley and Sons Inc. |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863675/ http://www.ncbi.nlm.nih.gov/pubmed/33598123 https://doi.org/10.1002/ece3.7091 |
genre |
Ursus arctos |
genre_facet |
Ursus arctos |
op_source |
Ecol Evol |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863675/ http://www.ncbi.nlm.nih.gov/pubmed/33598123 http://dx.doi.org/10.1002/ece3.7091 |
op_rights |
© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1002/ece3.7091 |
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Ecology and Evolution |
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11 |
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3 |
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1187 |
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1198 |
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1766231833632047104 |