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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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 |
_version_ |
1766401175214620672 |