Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species

International audience The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative moveme...

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Published in:Applied Sciences
Main Authors: Peng, Chengbin, Duarte, Carlos, Costa, Daniel, Guinet, Christophe, Harcourt, Robert, Hindell, Mark, Mcmahon, Clive, Muelbert, Monica, Thums, Michele, Wong, Ka-Chun, Zhang, Xiangliang
Other Authors: Universidade de Lisboa = University of Lisbon (ULISBOA), Universidade Estadual de Feira de Santana Bahia =State University of Feira de Santana (UEFS), Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS), Department of Biological Sciences, Macquarie University, Macquarie University, Department of Biological Sciences, Antartic Wildlife Research Unit, School of Zoology, University of Tasmania, Institute for Marine and Antarctic Studies Hobart (IMAS), University of Tasmania Hobart, Australia (UTAS), Universidade Federal do Rio Grande, Australian Institute of Marine Science Perth (AIMS Perth), Australian Institute of Marine Science (AIMS), King Abdullah University of Science and Technology (KAUST)
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2019
Subjects:
Online Access:https://hal.science/hal-02746653
https://doi.org/10.3390/app9142935
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spelling ftunivnantes:oai:HAL:hal-02746653v1 2023-05-15T16:05:16+02:00 Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species Peng, Chengbin Duarte, Carlos Costa, Daniel, Guinet, Christophe Harcourt, Robert Hindell, Mark Mcmahon, Clive, Muelbert, Monica Thums, Michele Wong, Ka-Chun Zhang, Xiangliang Universidade de Lisboa = University of Lisbon (ULISBOA) Universidade Estadual de Feira de Santana Bahia =State University of Feira de Santana (UEFS) Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC) Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS) Department of Biological Sciences, Macquarie University Macquarie University, Department of Biological Sciences Antartic Wildlife Research Unit School of Zoology, University of Tasmania Institute for Marine and Antarctic Studies Hobart (IMAS) University of Tasmania Hobart, Australia (UTAS) Universidade Federal do Rio Grande Australian Institute of Marine Science Perth (AIMS Perth) Australian Institute of Marine Science (AIMS) King Abdullah University of Science and Technology (KAUST) 2019-07 https://hal.science/hal-02746653 https://doi.org/10.3390/app9142935 en eng HAL CCSD MDPI info:eu-repo/semantics/altIdentifier/doi/10.3390/app9142935 hal-02746653 https://hal.science/hal-02746653 doi:10.3390/app9142935 ISSN: 2076-3417 Applied Sciences https://hal.science/hal-02746653 Applied Sciences, 2019, 9 (14), pp.2935. ⟨10.3390/app9142935⟩ marine animal movement analysis recurrent neural networks representative patterns [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2019 ftunivnantes https://doi.org/10.3390/app9142935 2023-03-08T04:18:18Z International audience The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research. Article in Journal/Newspaper Elephant Seal Southern Elephant Seal Université de Nantes: HAL-UNIV-NANTES Applied Sciences 9 14 2935
institution Open Polar
collection Université de Nantes: HAL-UNIV-NANTES
op_collection_id ftunivnantes
language English
topic marine animal movement analysis
recurrent neural networks
representative patterns
[SDE]Environmental Sciences
spellingShingle marine animal movement analysis
recurrent neural networks
representative patterns
[SDE]Environmental Sciences
Peng, Chengbin
Duarte, Carlos
Costa, Daniel,
Guinet, Christophe
Harcourt, Robert
Hindell, Mark
Mcmahon, Clive,
Muelbert, Monica
Thums, Michele
Wong, Ka-Chun
Zhang, Xiangliang
Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
topic_facet marine animal movement analysis
recurrent neural networks
representative patterns
[SDE]Environmental Sciences
description International audience The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research.
author2 Universidade de Lisboa = University of Lisbon (ULISBOA)
Universidade Estadual de Feira de Santana Bahia =State University of Feira de Santana (UEFS)
Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC)
Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)
Department of Biological Sciences, Macquarie University
Macquarie University, Department of Biological Sciences
Antartic Wildlife Research Unit
School of Zoology, University of Tasmania
Institute for Marine and Antarctic Studies Hobart (IMAS)
University of Tasmania Hobart, Australia (UTAS)
Universidade Federal do Rio Grande
Australian Institute of Marine Science Perth (AIMS Perth)
Australian Institute of Marine Science (AIMS)
King Abdullah University of Science and Technology (KAUST)
format Article in Journal/Newspaper
author Peng, Chengbin
Duarte, Carlos
Costa, Daniel,
Guinet, Christophe
Harcourt, Robert
Hindell, Mark
Mcmahon, Clive,
Muelbert, Monica
Thums, Michele
Wong, Ka-Chun
Zhang, Xiangliang
author_facet Peng, Chengbin
Duarte, Carlos
Costa, Daniel,
Guinet, Christophe
Harcourt, Robert
Hindell, Mark
Mcmahon, Clive,
Muelbert, Monica
Thums, Michele
Wong, Ka-Chun
Zhang, Xiangliang
author_sort Peng, Chengbin
title Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
title_short Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
title_full Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
title_fullStr Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
title_full_unstemmed Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
title_sort deep learning resolves representative movement patterns in a marine predator species
publisher HAL CCSD
publishDate 2019
url https://hal.science/hal-02746653
https://doi.org/10.3390/app9142935
genre Elephant Seal
Southern Elephant Seal
genre_facet Elephant Seal
Southern Elephant Seal
op_source ISSN: 2076-3417
Applied Sciences
https://hal.science/hal-02746653
Applied Sciences, 2019, 9 (14), pp.2935. ⟨10.3390/app9142935⟩
op_relation info:eu-repo/semantics/altIdentifier/doi/10.3390/app9142935
hal-02746653
https://hal.science/hal-02746653
doi:10.3390/app9142935
op_doi https://doi.org/10.3390/app9142935
container_title Applied Sciences
container_volume 9
container_issue 14
container_start_page 2935
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