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Title: Machine learning in marine ecology: an overview of techniques and applications
Authors: Rubbens, Peter; Brodie, Stephanie; Cordier, Tristan; Destro Barcellos, Diogo; Devos, Paul; Fernandes, Jose A.; Fincham, I, Jennifer; Gomes, Alessandra; Handegard, Nils Olav and Howell, Kerry; Jamet, Cedric; Kartveit, Kyrre Heldal and Moustahfid, Hassan; Parcerisas, Clea; Politikos, Dimitris and Sauzede, Raphaelle; Sokolova, Maria; Uusitalo, Laura; Van den Bulcke, Laure; van Helmond, Aloysius T. M.; Watson, Jordan T. and Welch, Heather; Beltran-Perez, Oscar; Chaffron, Samuel and Greenberg, David S.; Kuehn, Bernhard; Kiko, Rainer; Lo, Madiop and Lopes, Rubens M.; Moeller, Klas Ove; Michaels, William and Pala, Ahmet; Romagnan, Jean-Baptiste; Schuchert, Pia; Seydi, Vahid; Villasante, Sebastian; Malde, Ketil; Irisson, Jean-Olivier
Citation: ICES JOURNAL OF MARINE SCIENCE, 2023, 80, 1829-1853
Abstract: Machine 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 \& SIM;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.
Issue Date: 2023
Type: Review; Early Access
DOI: 10.1093/icesjms/fsad100
ISSN: 1054-3139
E-ISSN: 1095-9289
Funder: Swiss National Science Foundation [31003A\_179125]
European Research Council [818449 AGENSI]
Horizon Europe programme [101094924]
project H2020 FutureMARES [869300]
project H2020 SusTunTech [869342]
CRIMAC centre - Research Council of Norway [309512]
Mission Atlantic project - European Union's Horizon 2020 Research and Innovation Programme [862428]
European Union's H2020 programme [7553521]
European's Maritime and Fisheries Fund
Danish Fisheries Agency [33112-I-19-076]
Fully Documented Fisheries - European Maritime and Fisheries Fund (EMFF)
Sand Fund of the Federal Public Service Economy
H2020 project AtlantECO [862923]
CNPq, Brazil [315033/2021-5]
French National Research Agency [ANR-19-MPGA-0012]
Heisenberg programme of the German Science Foundation [469175784]
NOAA [NA21OAR4310254]
IFREMER Scientific Direction project DEEP
Norwegian Ministry of Trade, Industry and Fisheries
Belmont Forum project WWWPIC [ANR-018-BELM-0003 01]
Agence Nationale de la Recherche (ANR) [ANR-19-MPGA-0012] Funding Source: Agence Nationale de la Recherche (ANR)
Horizon Europe - Research Infrastructures (RIS) [101094924] Funding Source: Horizon Europe - Research Infrastructures (RIS)
Appears in Publication types:Artículos científicos

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