Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations
Abstract 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 i...
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crwiley:10.1002/ece3.4819 2024-06-02T08:05:01+00:00 Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier Agence Nationale de la Recherche 2019 http://dx.doi.org/10.1002/ece3.4819 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.4819 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.4819 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Ecology and Evolution volume 9, issue 2, page 744-755 ISSN 2045-7758 2045-7758 journal-article 2019 crwiley https://doi.org/10.1002/ece3.4819 2024-05-03T11:09:48Z Abstract 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). Article in Journal/Newspaper Canis lupus Wiley Online Library Ecology and Evolution 9 2 744 755 |
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Wiley Online Library |
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crwiley |
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English |
description |
Abstract 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). |
author2 |
Agence Nationale de la Recherche |
format |
Article in Journal/Newspaper |
author |
Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier |
spellingShingle |
Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations |
author_facet |
Santostasi, Nina Luisa Ciucci, Paolo Caniglia, Romolo Fabbri, Elena Molinari, Luigi Reggioni, Willy Gimenez, Olivier |
author_sort |
Santostasi, Nina Luisa |
title |
Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations |
title_short |
Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations |
title_full |
Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations |
title_fullStr |
Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations |
title_full_unstemmed |
Use of hidden Markov capture–recapture models to estimate abundance in the presence of uncertainty: Application to the estimation of prevalence of hybrids in animal populations |
title_sort |
use of hidden markov capture–recapture models to estimate abundance in the presence of uncertainty: application to the estimation of prevalence of hybrids in animal populations |
publisher |
Wiley |
publishDate |
2019 |
url |
http://dx.doi.org/10.1002/ece3.4819 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.4819 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.4819 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
Ecology and Evolution volume 9, issue 2, page 744-755 ISSN 2045-7758 2045-7758 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/ece3.4819 |
container_title |
Ecology and Evolution |
container_volume |
9 |
container_issue |
2 |
container_start_page |
744 |
op_container_end_page |
755 |
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1800749751946182656 |