The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths

Autores
Beyer, Hawthorne L.; Morales, Juan Manuel; Murray, Dennis; Fortin, Marie Josee
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
1. Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes from which patterns of animal space use arise in heterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states. 2. We use stochastic simulations of twomovementmodels to quantify how behavioural state movement characteristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states frommovement paths. 3. Classification accuracy was contingent upon the degree of separation between the distributions that characterize the states (e.g. step length and turn angle distributions) and the relative frequency of the Behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0%when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was uncorrelated with path length, but the variance in classification accuracy was inversely related to path length. 4. Importantly, we find that classification accuracy can be predicted based on the separation between distributions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose (Alces alces). 5. We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data.
Fil: Beyer, Hawthorne L.. University Of Toronto; Canadá. University Of Queensland; Australia
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentina
Fil: Murray, Dennis. Trent University. Department of Biology; Canadá
Fil: Fortin, Marie Josee. University Of Toronto; Canadá
Materia
Clasiffication Accuracy
Correlated Random Walk
Global Positioning System
Mechanistic Movement Modelling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/6697

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network_name_str CONICET Digital (CONICET)
spelling The effectiveness of Bayesian state-space models for estimating behavioural states from movement pathsBeyer, Hawthorne L.Morales, Juan ManuelMurray, DennisFortin, Marie JoseeClasiffication AccuracyCorrelated Random WalkGlobal Positioning SystemMechanistic Movement Modellinghttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/11. Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes from which patterns of animal space use arise in heterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states. 2. We use stochastic simulations of twomovementmodels to quantify how behavioural state movement characteristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states frommovement paths. 3. Classification accuracy was contingent upon the degree of separation between the distributions that characterize the states (e.g. step length and turn angle distributions) and the relative frequency of the Behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0%when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was uncorrelated with path length, but the variance in classification accuracy was inversely related to path length. 4. Importantly, we find that classification accuracy can be predicted based on the separation between distributions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose (Alces alces). 5. We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data.Fil: Beyer, Hawthorne L.. University Of Toronto; Canadá. University Of Queensland; AustraliaFil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; ArgentinaFil: Murray, Dennis. Trent University. Department of Biology; CanadáFil: Fortin, Marie Josee. University Of Toronto; CanadáWiley2013-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6697Beyer, Hawthorne L.; Morales, Juan Manuel; Murray, Dennis; Fortin, Marie Josee; The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths; Wiley; Methods in Ecology and Evolution; 4; 5; 5-2013; 433-4412041-210Xenginfo:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12026/abstractinfo:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/doi/10.1111/2041-210X.12026info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:38Zoai:ri.conicet.gov.ar:11336/6697instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:41:38.721CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
title The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
spellingShingle The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
Beyer, Hawthorne L.
Clasiffication Accuracy
Correlated Random Walk
Global Positioning System
Mechanistic Movement Modelling
title_short The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
title_full The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
title_fullStr The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
title_full_unstemmed The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
title_sort The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
dc.creator.none.fl_str_mv Beyer, Hawthorne L.
Morales, Juan Manuel
Murray, Dennis
Fortin, Marie Josee
author Beyer, Hawthorne L.
author_facet Beyer, Hawthorne L.
Morales, Juan Manuel
Murray, Dennis
Fortin, Marie Josee
author_role author
author2 Morales, Juan Manuel
Murray, Dennis
Fortin, Marie Josee
author2_role author
author
author
dc.subject.none.fl_str_mv Clasiffication Accuracy
Correlated Random Walk
Global Positioning System
Mechanistic Movement Modelling
topic Clasiffication Accuracy
Correlated Random Walk
Global Positioning System
Mechanistic Movement Modelling
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 1. Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes from which patterns of animal space use arise in heterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states. 2. We use stochastic simulations of twomovementmodels to quantify how behavioural state movement characteristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states frommovement paths. 3. Classification accuracy was contingent upon the degree of separation between the distributions that characterize the states (e.g. step length and turn angle distributions) and the relative frequency of the Behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0%when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was uncorrelated with path length, but the variance in classification accuracy was inversely related to path length. 4. Importantly, we find that classification accuracy can be predicted based on the separation between distributions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose (Alces alces). 5. We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data.
Fil: Beyer, Hawthorne L.. University Of Toronto; Canadá. University Of Queensland; Australia
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentina
Fil: Murray, Dennis. Trent University. Department of Biology; Canadá
Fil: Fortin, Marie Josee. University Of Toronto; Canadá
description 1. Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes from which patterns of animal space use arise in heterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states. 2. We use stochastic simulations of twomovementmodels to quantify how behavioural state movement characteristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states frommovement paths. 3. Classification accuracy was contingent upon the degree of separation between the distributions that characterize the states (e.g. step length and turn angle distributions) and the relative frequency of the Behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0%when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was uncorrelated with path length, but the variance in classification accuracy was inversely related to path length. 4. Importantly, we find that classification accuracy can be predicted based on the separation between distributions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose (Alces alces). 5. We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data.
publishDate 2013
dc.date.none.fl_str_mv 2013-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/6697
Beyer, Hawthorne L.; Morales, Juan Manuel; Murray, Dennis; Fortin, Marie Josee; The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths; Wiley; Methods in Ecology and Evolution; 4; 5; 5-2013; 433-441
2041-210X
url http://hdl.handle.net/11336/6697
identifier_str_mv Beyer, Hawthorne L.; Morales, Juan Manuel; Murray, Dennis; Fortin, Marie Josee; The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths; Wiley; Methods in Ecology and Evolution; 4; 5; 5-2013; 433-441
2041-210X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12026/abstract
info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/doi/10.1111/2041-210X.12026
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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