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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fttriple:oai:gotriple.eu:50|dedup_wf_001::53ab028b8806f5f5a71de91ac8938982 2023-05-15T15:49:46+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 2018-09-27 https://doi.org/10.5061/dryad.8g8r675 undefined unknown http://dx.doi.org/10.5061/dryad.8g8r675 https://dx.doi.org/10.5061/dryad.8g8r675 lic_creative-commons oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:118126 10.5061/dryad.8g8r675 oai:easy.dans.knaw.nl:easy-dataset:118126 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 re3data_____::r3d100000044 Life sciences medicine and health care Hidden Markov Models Viterbi algorithm capture-recapture hybridization Canis lupus envir geo Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2018 fttriple https://doi.org/10.5061/dryad.8g8r675 2023-01-22T17:23:13Z 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). ECE-2018-09-01063-CAPTUREMATRIXThe data are a capture matrix ... Dataset Canis lupus Unknown |
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Life sciences medicine and health care Hidden Markov Models Viterbi algorithm capture-recapture hybridization Canis lupus envir geo |
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Life sciences medicine and health care Hidden Markov Models Viterbi algorithm capture-recapture hybridization Canis lupus envir geo 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 Hidden Markov Models Viterbi algorithm capture-recapture hybridization Canis lupus envir geo |
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). ECE-2018-09-01063-CAPTUREMATRIXThe data are a capture matrix ... |
format |
Dataset |
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 |
2018 |
url |
https://doi.org/10.5061/dryad.8g8r675 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:118126 10.5061/dryad.8g8r675 oai:easy.dans.knaw.nl:easy-dataset:118126 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 re3data_____::r3d100000044 |
op_relation |
http://dx.doi.org/10.5061/dryad.8g8r675 https://dx.doi.org/10.5061/dryad.8g8r675 |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.5061/dryad.8g8r675 |
_version_ |
1766384789966815232 |