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dc.contributor.author
Beyer, Hawthorne L.
dc.contributor.author
Morales, Juan Manuel
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dc.contributor.author
Murray, Dennis
dc.contributor.author
Fortin, Marie Josee
dc.date.available
2016-07-26T19:31:01Z
dc.date.issued
2013-05
dc.identifier.citation
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
dc.identifier.issn
2041-210X
dc.identifier.uri
http://hdl.handle.net/11336/6697
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Clasiffication Accuracy
dc.subject
Correlated Random Walk
dc.subject
Global Positioning System
dc.subject
Mechanistic Movement Modelling
dc.subject.classification
Estadística y Probabilidad
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dc.subject.classification
Matemáticas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-07-22T18:51:44Z
dc.journal.volume
4
dc.journal.number
5
dc.journal.pagination
433-441
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Hoboken
dc.description.fil
Fil: Beyer, Hawthorne L.. University Of Toronto; Canadá. University Of Queensland; Australia
dc.description.fil
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentina
dc.description.fil
Fil: Murray, Dennis. Trent University. Department of Biology; Canadá
dc.description.fil
Fil: Fortin, Marie Josee. University Of Toronto; Canadá
dc.journal.title
Methods in Ecology and Evolution
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12026/abstract
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/10.1111/2041-210X.12026
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1111/2041-210X.12026
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