Spatial capture–recapture with random thinning for unidentified encounters
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 mod...
Published in: | Ecology and Evolution |
---|---|
Main Authors: | , , , , |
Other Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
John Wiley & Sons
2021
|
Subjects: | |
Online Access: | http://hdl.handle.net/10261/250985 https://doi.org/10.1002/ece3.7091 https://doi.org/10.13039/501100006382 |
id |
ftcsic:oai:digital.csic.es:10261/250985 |
---|---|
record_format |
openpolar |
spelling |
ftcsic:oai:digital.csic.es:10261/250985 2024-02-11T10:09:21+01:00 Spatial capture–recapture with random thinning for unidentified encounters Jiménez, José Augustine, Ben C. Linden, Daniel W. Chandler, Richard Royle, J. Andrew Universidad de Oviedo Ministerio para la Transición Ecológica y el Reto Demográfico (España) 2021 http://hdl.handle.net/10261/250985 https://doi.org/10.1002/ece3.7091 https://doi.org/10.13039/501100006382 en eng John Wiley & Sons Publisher's version https://doi.org/10.1002/ece3.7091 Sí Ecology and Evolution 11(3): 1187-1198 (2021) http://hdl.handle.net/10261/250985 doi:10.1002/ece3.7091 2045-7758 http://dx.doi.org/10.13039/501100006382 33598123 open artículo http://purl.org/coar/resource_type/c_6501 2021 ftcsic https://doi.org/10.1002/ece3.709110.13039/501100006382 2024-01-16T11:13:49Z 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. 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. 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). 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. This research has received financial support from the Spanish Ministry for the Ecological Transition and the Demographic Challenge (Government of Spain). We are grateful to the Brown Bear Foundation (Fundación Oso Pardo), and Raquel Godinho (CIBIO-InBIO, ... Article in Journal/Newspaper Ursus arctos Digital.CSIC (Spanish National Research Council) Ecology and Evolution 11 3 1187 1198 |
institution |
Open Polar |
collection |
Digital.CSIC (Spanish National Research Council) |
op_collection_id |
ftcsic |
language |
English |
description |
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. 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. 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). 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. This research has received financial support from the Spanish Ministry for the Ecological Transition and the Demographic Challenge (Government of Spain). We are grateful to the Brown Bear Foundation (Fundación Oso Pardo), and Raquel Godinho (CIBIO-InBIO, ... |
author2 |
Universidad de Oviedo Ministerio para la Transición Ecológica y el Reto Demográfico (España) |
format |
Article in Journal/Newspaper |
author |
Jiménez, José Augustine, Ben C. Linden, Daniel W. Chandler, Richard Royle, J. Andrew |
spellingShingle |
Jiménez, José Augustine, Ben C. Linden, Daniel W. Chandler, Richard Royle, J. Andrew Spatial capture–recapture with random thinning for unidentified encounters |
author_facet |
Jiménez, José Augustine, Ben C. Linden, Daniel W. 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 & Sons |
publishDate |
2021 |
url |
http://hdl.handle.net/10261/250985 https://doi.org/10.1002/ece3.7091 https://doi.org/10.13039/501100006382 |
genre |
Ursus arctos |
genre_facet |
Ursus arctos |
op_relation |
Publisher's version https://doi.org/10.1002/ece3.7091 Sí Ecology and Evolution 11(3): 1187-1198 (2021) http://hdl.handle.net/10261/250985 doi:10.1002/ece3.7091 2045-7758 http://dx.doi.org/10.13039/501100006382 33598123 |
op_rights |
open |
op_doi |
https://doi.org/10.1002/ece3.709110.13039/501100006382 |
container_title |
Ecology and Evolution |
container_volume |
11 |
container_issue |
3 |
container_start_page |
1187 |
op_container_end_page |
1198 |
_version_ |
1790609203085180928 |