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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/108780
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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 |
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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 |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |