Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely...
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ftdans:oai:easy.dans.knaw.nl:easy-dataset:118126 2023-07-02T03:31:54+02:00 Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier 2019-02-07T17:14:19.000+01:00 http://nbn-resolving.org/urn:nbn:nl:ui:13-1u-w0qa https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:118126 unknown doi:10.5061/dryad.8g8r675/1 doi:10.1002/ece3.4819 http://nbn-resolving.org/urn:nbn:nl:ui:13-1u-w0qa doi:10.5061/dryad.8g8r675 https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:118126 OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf Life sciences medicine and health care 2019 ftdans https://doi.org/10.5061/dryad.8g8r675/110.1002/ece3.481910.5061/dryad.8g8r675 2023-06-13T13:34:05Z Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals). Other/Unknown Material Canis lupus Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen) |
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Open Polar |
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Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen) |
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ftdans |
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topic |
Life sciences medicine and health care |
spellingShingle |
Life sciences medicine and health care Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations |
topic_facet |
Life sciences medicine and health care |
description |
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals). |
author |
Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier |
author_facet |
Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier |
author_sort |
Santostasi, Nina Luisa |
title |
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations |
title_short |
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations |
title_full |
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations |
title_fullStr |
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations |
title_full_unstemmed |
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations |
title_sort |
data from: use of hidden markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations |
publishDate |
2019 |
url |
http://nbn-resolving.org/urn:nbn:nl:ui:13-1u-w0qa https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:118126 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_relation |
doi:10.5061/dryad.8g8r675/1 doi:10.1002/ece3.4819 http://nbn-resolving.org/urn:nbn:nl:ui:13-1u-w0qa doi:10.5061/dryad.8g8r675 https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:118126 |
op_rights |
OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf |
op_doi |
https://doi.org/10.5061/dryad.8g8r675/110.1002/ece3.481910.5061/dryad.8g8r675 |
_version_ |
1770271347027476480 |