Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods

Detalhes bibliográficos
Autor(a) principal: Cheoo, Gisela Vitória
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/39109
Resumo: Tese de mestrado em Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2019
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spelling Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methodsBaleia-da-GronelândiaEstimação de densidadeCaptura-recaptura espacialmente explícitaSensores fixosAcústica passivaTeses de mestrado - 2019Domínio/Área Científica::Ciências Naturais::MatemáticasTese de mestrado em Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2019Management and conservation of wildlife populations is a major concern. Population density is a key ecological variable when making adequate decisions about them. A variety of methods can be used for estimating density. Capture-recapture (CR, also known as mark- recapture) methods are a popular choice, but ignoring the spatial component of captures has historically led to problems with resulting inferences on abundance. Spatially explicit capture- recapture (SECR) methods use the spatial information to solve two key problems of classical CR: defining a precise study area where captures occur over and reducing un modeled heterogeneity in capture probabilities. Arrays of Directional Autonomous Sea floor Acoustic Recorders (DASARs) recorded calls from the Bearing-Chukchi-Beaufort (BCB) population of bowhead whales during the autumn migration. The available passive acoustic data set was collected over 5 sites (with 3–13 sensors per site) and 8 years (2007–2014), and then processed via both automated and manual procedures. The automated procedure involved computer-processing by a multi-stage detection, classification and localization algorithm. In the manual procedure, calls were detected and classified by trained staff who manually listened to the recordings and examined spectrograms. The resulting manual data presents some pitfalls for density estimation, including non-independence among sensors caused by human intervention. The non-independence leads to an excess of calls being detected in all DASARs on a site. Data from the automated procedure does not suffer the non-independence issue, but the amount of ’singletons’ is approximately 15 times higher than in the manual data. ’Singletons’ are calls detected exclusively in one sensor and we assume they mostly comprise false positives. False positives are sounds classified as coming from the species of interest, but in reality are something else. Considering only automated data from 2013 and 2014, several approaches were performed to solve the excess of singletons. Density estimation with a standard SECR analysis was conducted according to the following approaches: i)ignoring the singletons problem and analyzing all calls; ii) removing the singletons; and iii) discarding a proportion of 1 – p false positives from the singletons. Simulated results were compared to verify the best approach. We also discuss a new approach by developing a SECR likelihood function that accommodates truncation of certain acoustic cues, specifically singletons. We have laid foundations for the analysis of this data set, but there are other possible research avenues to explore. Our next steps would include embedding additional information (like received levels and bearing angle) in the SECR formulation.Marques, Tiago AndréThomas, LenRepositório da Universidade de LisboaCheoo, Gisela Vitória2019-07-15T14:50:35Z201920182019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/39109TID:202259870enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:37:22Zoai:repositorio.ul.pt:10451/39109Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:52:52.458058Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
title Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
spellingShingle Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
Cheoo, Gisela Vitória
Baleia-da-Gronelândia
Estimação de densidade
Captura-recaptura espacialmente explícita
Sensores fixos
Acústica passiva
Teses de mestrado - 2019
Domínio/Área Científica::Ciências Naturais::Matemáticas
title_short Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
title_full Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
title_fullStr Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
title_full_unstemmed Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
title_sort Estimation of bowhead whale (Balaena mysticetus) population density using spatially explicit capture-recapture (SECR) methods
author Cheoo, Gisela Vitória
author_facet Cheoo, Gisela Vitória
author_role author
dc.contributor.none.fl_str_mv Marques, Tiago André
Thomas, Len
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Cheoo, Gisela Vitória
dc.subject.por.fl_str_mv Baleia-da-Gronelândia
Estimação de densidade
Captura-recaptura espacialmente explícita
Sensores fixos
Acústica passiva
Teses de mestrado - 2019
Domínio/Área Científica::Ciências Naturais::Matemáticas
topic Baleia-da-Gronelândia
Estimação de densidade
Captura-recaptura espacialmente explícita
Sensores fixos
Acústica passiva
Teses de mestrado - 2019
Domínio/Área Científica::Ciências Naturais::Matemáticas
description Tese de mestrado em Bioestatística, Universidade de Lisboa, Faculdade de Ciências, 2019
publishDate 2018
dc.date.none.fl_str_mv 2018
2019-07-15T14:50:35Z
2019
2019-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/39109
TID:202259870
url http://hdl.handle.net/10451/39109
identifier_str_mv TID:202259870
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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