A hierarchical machine learning framework for the analysis of large scale animal movement data
- Autores
- Torney, Colin J.; Morales, Juan Manuel; Husmeier, Dirk
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Background: In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. Methods: In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. Results: While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. Conclusions: Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.
Fil: Torney, Colin J.. University of Glasgow; Reino Unido
Fil: Morales, Juan Manuel. University of Glasgow; Reino Unido. 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: Husmeier, Dirk. University of Glasgow; Reino Unido - Materia
-
ANIMAL MOVEMENT
LARGE-SCALE DATA
MACHINE LEARNING - 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:11336/183698
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A hierarchical machine learning framework for the analysis of large scale animal movement dataTorney, Colin J.Morales, Juan ManuelHusmeier, DirkANIMAL MOVEMENTLARGE-SCALE DATAMACHINE LEARNINGhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Background: In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. Methods: In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. Results: While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. Conclusions: Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements.Fil: Torney, Colin J.. University of Glasgow; Reino UnidoFil: Morales, Juan Manuel. University of Glasgow; Reino Unido. 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: Husmeier, Dirk. University of Glasgow; Reino UnidoBioMed Central2021-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/183698Torney, Colin J.; Morales, Juan Manuel; Husmeier, Dirk; A hierarchical machine learning framework for the analysis of large scale animal movement data; BioMed Central; Movement Ecology; 9; 1; 12-2021; 1-112051-3933CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-021-00242-0info:eu-repo/semantics/altIdentifier/doi/10.1186/s40462-021-00242-0info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:40Zoai:ri.conicet.gov.ar:11336/183698instacron: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 09:41:40.622CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
A hierarchical machine learning framework for the analysis of large scale animal movement data |
title |
A hierarchical machine learning framework for the analysis of large scale animal movement data |
spellingShingle |
A hierarchical machine learning framework for the analysis of large scale animal movement data Torney, Colin J. ANIMAL MOVEMENT LARGE-SCALE DATA MACHINE LEARNING |
title_short |
A hierarchical machine learning framework for the analysis of large scale animal movement data |
title_full |
A hierarchical machine learning framework for the analysis of large scale animal movement data |
title_fullStr |
A hierarchical machine learning framework for the analysis of large scale animal movement data |
title_full_unstemmed |
A hierarchical machine learning framework for the analysis of large scale animal movement data |
title_sort |
A hierarchical machine learning framework for the analysis of large scale animal movement data |
dc.creator.none.fl_str_mv |
Torney, Colin J. Morales, Juan Manuel Husmeier, Dirk |
author |
Torney, Colin J. |
author_facet |
Torney, Colin J. Morales, Juan Manuel Husmeier, Dirk |
author_role |
author |
author2 |
Morales, Juan Manuel Husmeier, Dirk |
author2_role |
author author |
dc.subject.none.fl_str_mv |
ANIMAL MOVEMENT LARGE-SCALE DATA MACHINE LEARNING |
topic |
ANIMAL MOVEMENT LARGE-SCALE DATA MACHINE LEARNING |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Background: In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. Methods: In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. Results: While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. Conclusions: Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements. Fil: Torney, Colin J.. University of Glasgow; Reino Unido Fil: Morales, Juan Manuel. University of Glasgow; Reino Unido. 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: Husmeier, Dirk. University of Glasgow; Reino Unido |
description |
Background: In recent years the field of movement ecology has been revolutionized by our ability to collect high-accuracy, fine scale telemetry data from individual animals and groups. This growth in our data collection capacity has led to the development of statistical techniques that integrate telemetry data with random walk models to infer key parameters of the movement dynamics. While much progress has been made in the use of these models, several challenges remain. Notably robust and scalable methods are required for quantifying parameter uncertainty, coping with intermittent location fixes, and analysing the very large volumes of data being generated. Methods: In this work we implement a novel approach to movement modelling through the use of multilevel Gaussian processes. The hierarchical structure of the method enables the inference of continuous latent behavioural states underlying movement processes. For efficient inference on large data sets, we approximate the full likelihood using trajectory segmentation and sample from posterior distributions using gradient-based Markov chain Monte Carlo methods. Results: While formally equivalent to many continuous-time movement models, our Gaussian process approach provides flexible, powerful models that can detect multiscale patterns and trends in movement trajectory data. We illustrate a further advantage to our approach in that inference can be performed using highly efficient, GPU-accelerated machine learning libraries. Conclusions: Multilevel Gaussian process models offer efficient inference for large-volume movement data sets, along with the fitting of complex flexible models. Applications of this approach include inferring the mean location of a migration route and quantifying significant changes, detecting diurnal activity patterns, or identifying the onset of directed persistent movements. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12 |
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/11336/183698 Torney, Colin J.; Morales, Juan Manuel; Husmeier, Dirk; A hierarchical machine learning framework for the analysis of large scale animal movement data; BioMed Central; Movement Ecology; 9; 1; 12-2021; 1-11 2051-3933 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/183698 |
identifier_str_mv |
Torney, Colin J.; Morales, Juan Manuel; Husmeier, Dirk; A hierarchical machine learning framework for the analysis of large scale animal movement data; BioMed Central; Movement Ecology; 9; 1; 12-2021; 1-11 2051-3933 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://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-021-00242-0 info:eu-repo/semantics/altIdentifier/doi/10.1186/s40462-021-00242-0 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
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openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
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application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
BioMed Central |
publisher.none.fl_str_mv |
BioMed Central |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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