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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/6697
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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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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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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