Machine learning in marine ecology: an overview of techniques and applications

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dc.contributor.authorRubbens, Peter
dc.contributor.authorBrodie, Stephanie
dc.contributor.authorCordier, Tristan
dc.contributor.authorDestro Barcellos, Diogo
dc.contributor.authorDevos, Paul
dc.contributor.authorFernandes-Salvador, Jose A.
dc.contributor.authorFincham, Jennifer I.
dc.contributor.authorGomes, Alessandra
dc.contributor.authorHandegard, Nils Olav
dc.contributor.authorHowell, Kerry
dc.contributor.authorJamet, Cédric
dc.contributor.authorKartveit, Kyrre Heldal
dc.contributor.authorMoustahfid, Hassan
dc.contributor.authorParcerisas, Clea
dc.contributor.authorPolitikos, Dimitris
dc.contributor.authorSauzède, Raphaëlle
dc.contributor.authorSokolova, Maria
dc.contributor.authorUusitalo, Laura
dc.contributor.authorVan den Bulcke, Laure
dc.contributor.authorvan Helmond, Aloysius T. M.
dc.contributor.authorWatson, Jordan T.
dc.contributor.authorWelch, Heather
dc.contributor.authorBeltran-Perez, Oscar
dc.contributor.authorChaffron, Samuel
dc.contributor.authorGreenberg, David S.
dc.contributor.authorKühn, Bernhard
dc.contributor.authorKiko, Rainer
dc.contributor.authorLo, Madiop
dc.contributor.authorLopes, Rubens M.
dc.contributor.authorMöller, Klas Ove
dc.contributor.authorMichaels, William
dc.contributor.authorPala, Ahmet
dc.contributor.authorRomagnan, Jean-Baptiste
dc.contributor.authorSchuchert, Pia
dc.contributor.authorSeydi, Vahid
dc.contributor.authorVillasante, Sebastian
dc.contributor.authorMalde, Ketil
dc.contributor.authorIrisson, Jean-Olivier
dc.contributor.departmentFisheries and Aquatic Ecosystems
dc.date.accessioned2023-09-18T14:22:12Z
dc.date.available2023-09-18T14:22:12Z
dc.date.issued2023-08-03
dc.descriptionPublication history: Accepted - 26 May 2023; Published - 3 August 2023.
dc.description.abstractMachine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.
dc.description.sponsorshipAll authors acknowledge the support of ICES through the Working group on Machine Learning in Marine Science (WGMLEARN).
dc.identifierhttps://hdl.handle.net/20.500.12518/579
dc.identifier.citationRubbens, P., Brodie, S., Cordier, T., Destro Barcellos, D., Devos, P., Fernandes-Salvador, J.A., Fincham, J.I., Gomes, A., Handegard, N.O., Howell, K., Jamet, C., Kartveit, K.H., Moustahfid, H., Parcerisas, C., Politikos, D., Sauzède, R., Sokolova, M., Uusitalo, L., Van den Bulcke, L., van Helmond, A.T.M., Watson, J.T., Welch, H., Beltran-Perez, O., Chaffron, S., Greenberg, D.S., Kühn, B., Kiko, R., Lo, M., Lopes, R.M., Möller, K.O., Michaels, W., Pala, A., Romagnan, J.-B., Schuchert, P., Seydi, V., Villasante, S., Malde, K. and Irisson, J.-O. (2023) ‘Machine learning in marine ecology: an overview of techniques and applications’, ICES Journal of Marine Science. Edited by C. Whidden. Oxford University Press (OUP). Available at: https://doi.org/10.1093/icesjms/fsad100.
dc.identifier.issn1054-3139
dc.identifier.issn1095-9289 (electronic)
dc.identifier.urihttps://doi.org/10.1093/icesjms/fsad100
dc.language.isoen
dc.publisherOxford Univerity Press
dc.rights© The Author(s) 2023. Published by Oxford University Press on behalf of International Council for the Exploration of the Sea. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.subjectacoustics
dc.subjectecology
dc.subjectimage
dc.subjectmachine learning
dc.subjectomics
dc.subjectprofiles
dc.subjectremote sensing
dc.subjectreview
dc.titleMachine learning in marine ecology: an overview of techniques and applications
dc.typeArticle
dcterms.dateAccepted2023-05-26
dcterms.dateSubmitted2022-09-29
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