Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals

Autores
Ruiz Suarez, Sofia Helena; Leos Barajas, Vianey; Alvarez Castro, Ignacio; Morales, Juan Manuel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.
Fil: Ruiz Suarez, Sofia Helena. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Leos Barajas, Vianey. North Carolina State University; Estados Unidos
Fil: Alvarez Castro, Ignacio. Universidad de la República; Uruguay
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Materia
ANIMAL BEHAVIOUR
COMPUTATIONAL BIOLOGY
MOVEMENT ECOLOGY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/108780

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network_name_str CONICET Digital (CONICET)
spelling Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervalsRuiz Suarez, Sofia HelenaLeos Barajas, VianeyAlvarez Castro, IgnacioMorales, Juan ManuelANIMAL BEHAVIOURCOMPUTATIONAL BIOLOGYMOVEMENT ECOLOGYhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.Fil: Ruiz Suarez, Sofia Helena. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Leos Barajas, Vianey. North Carolina State University; Estados UnidosFil: Alvarez Castro, Ignacio. Universidad de la República; UruguayFil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaPeerJ2020-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/108780Ruiz Suarez, Sofia Helena; Leos Barajas, Vianey; Alvarez Castro, Ignacio; Morales, Juan Manuel; Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals; PeerJ; PeerJ; 8; 2-2020; 1-232167-8359CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://peerj.com/articles/8452info:eu-repo/semantics/altIdentifier/doi/10.7717/peerj.8452info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:34:41Zoai:ri.conicet.gov.ar:11336/108780instacron: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 09:34:41.828CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
title Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
spellingShingle Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
Ruiz Suarez, Sofia Helena
ANIMAL BEHAVIOUR
COMPUTATIONAL BIOLOGY
MOVEMENT ECOLOGY
title_short Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
title_full Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
title_fullStr Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
title_full_unstemmed Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
title_sort Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals
dc.creator.none.fl_str_mv Ruiz Suarez, Sofia Helena
Leos Barajas, Vianey
Alvarez Castro, Ignacio
Morales, Juan Manuel
author Ruiz Suarez, Sofia Helena
author_facet Ruiz Suarez, Sofia Helena
Leos Barajas, Vianey
Alvarez Castro, Ignacio
Morales, Juan Manuel
author_role author
author2 Leos Barajas, Vianey
Alvarez Castro, Ignacio
Morales, Juan Manuel
author2_role author
author
author
dc.subject.none.fl_str_mv ANIMAL BEHAVIOUR
COMPUTATIONAL BIOLOGY
MOVEMENT ECOLOGY
topic ANIMAL BEHAVIOUR
COMPUTATIONAL BIOLOGY
MOVEMENT ECOLOGY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.
Fil: Ruiz Suarez, Sofia Helena. Universidad Nacional de Rosario. Facultad de Ciencias Económicas y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Fil: Leos Barajas, Vianey. North Carolina State University; Estados Unidos
Fil: Alvarez Castro, Ignacio. Universidad de la República; Uruguay
Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
description The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.
publishDate 2020
dc.date.none.fl_str_mv 2020-02
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/11336/108780
Ruiz Suarez, Sofia Helena; Leos Barajas, Vianey; Alvarez Castro, Ignacio; Morales, Juan Manuel; Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals; PeerJ; PeerJ; 8; 2-2020; 1-23
2167-8359
CONICET Digital
CONICET
url http://hdl.handle.net/11336/108780
identifier_str_mv Ruiz Suarez, Sofia Helena; Leos Barajas, Vianey; Alvarez Castro, Ignacio; Morales, Juan Manuel; Using approximate Bayesian inference for a “steps and turns” continuous-time random walk observed at regular time intervals; PeerJ; PeerJ; 8; 2-2020; 1-23
2167-8359
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://peerj.com/articles/8452
info:eu-repo/semantics/altIdentifier/doi/10.7717/peerj.8452
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv PeerJ
publisher.none.fl_str_mv PeerJ
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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