Machine learning in marine ecology: an overview of techniques and applications
| Type | Article | ||||||||||||
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| Date | 2023-09 | ||||||||||||
| Language | English | ||||||||||||
| Author(s) | Rubbens Peter1, 2, Brodie Stephanie3, Cordier Tristan4, 5, Destro barcellos Diogo6, Devos Paul7, Fernandes-Salvador Jose A8, Fincham Jennifer I 9, Gomes Alessandra6, Handegard Nils Olav10, Howell Kerry11, Jamet Cédric12, Kartveit Kyrre Heldal10, Moustahfid Hassan13, Parcerisas Clea1, 7, Politikos Dimitris14, Sauzède Raphaëlle15, Sokolova Maria16, Uusitalo Laura17, 18, Van den bulcke Laure19, 20, Van helmond Aloysius T M21, Watson Jordan T22, Welch Heather3, Beltran-Perez Oscar23, Chaffron Samuel24, 25, Greenberg David S26, Kühn Bernhard27, Kiko Rainer28, 29, Lo Madiop30, Lopes Rubens M6, Möller Klas Ove31, Michaels William32, Pala Ahmet10, 33, Romagnan Jean-Baptiste34, Schuchert Pia35, Seydi Vahid36, Villasante Sebastian37, Malde Ketil10, 38, Irisson Jean-Olivier28 |
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| Affiliation(s) | 1 : Flanders Marine Institute (VLIZ) , 8400 Oostende , Belgium 2 : Kytos BV , Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium 3 : Institute of Marine Science, University of California Santa Cruz , Santa Cruz, CA 95064 , USA 4 : Department of Genetics and Evolution, University of Geneva , 1205 Geneva , Switzerland 5 : NORCE Climate, NORCE Norwegian Research Centre AS, Bjerknes Centre for Climate Research , Jahnebakken 5, 5007 Bergen , Norway 6 : Oceanographic Institute, University of São Paulo , Praça do Oceanográfico, 191, 05508-120, São Paulo , Brazil 7 : Department of Information Technology, Research group WAVES, Ghent University , Tech Lane Ghent Science Park, 126, B-9058 Gent , Belgium 8 : AZTI, Marine Research, Basque Research and Technology Alliance (BRTA). Txatxarramendi Ugartea z/g , 48395 Sukarrieta , Spain 9 : Cefas , Pakefield Road, Lowestoft, Suffolk NR33 0HT , UK 10 : Institute of Marine Research , Nykirkekaien 1, 5005 Bergen , Norway 11 : School of Biological and Marine Sciences, University of Plymouth , Drake Circus, Plymouth PL4 8AA , UK 12 : Université du Littoral Côte d'Opale, CNRS, Univ. Lille, IRD, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences , F-62930 Wimereux , France 13 : National Oceanic and Atmospheric Administration, US Integrated Ocean Observing System , Silver Spring, MD 20910 , USA 14 : Institute of Marine Biological Resources and Inland, Hellenic Centre for Marine Research , 16452 Argyroupoli , Greece 15 : Sorbonne Université, CNRS, Institut de la Mer de Villefranche, FR3761 , F-06230 Villefranche-Sur-Mer , France 16 : Wageningen University and Research , Droevendaalsesteeg 1, Building 107, 6708 PB Wageningen , The Netherlands 17 : Finnish Environment Institute , Latokartanonkaari 11, FI-00790 Helsinki , Finland 18 : Natural Resources Institute Finland (Luke) , Latokartanonkaari 9, FI-00790 Helsinki , Finland 19 : Flanders Research Institute for Agriculture, Fisheries and Food, Marine Research , Jacobsenstraat 1, 8400 Ostend , Belgium 20 : Department of Data Analysis and Mathematical Modelling—Knowledge-based Systems Research Group, University of Ghent , Coupure Links 653, 9000 Gent , Belgium 21 : Wageningen University and Research, Wageningen Marine Research , 1976 CP IJmuiden , The Netherlands 22 : Auke Bay Laboratory, National Oceanic and Atmospheric Administration , 17609 Pt. Lena Loop Rd., Juneau, AK 99801 , USA 23 : Leibniz Institute for Baltic Sea Research Warnemünde (IOW) , Seestrasse 15, 18119 Rostock , Germany 24 : Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004 , F-44000 Nantes , France 25 : Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE , F-75016 Paris , France 26 : Institute of Coastal Systems, Helmholtz-Zentrum Hereon , Max-Planck-Straße 1, 21502 Geesthacht , Germany 27 : Johann Heinrich von Thünen Institute of Sea Fisheries , Herwigstraße 31, 27572 Bremerhaven , Germany 28 : Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, LOV , F-06230 Villefranche-sur-Mer , France 29 : GEOMAR Helmholtz Centre for Ocean Research Kiel , 24148 Kiel , Germany 30 : Aix Marseille Univ., Univ. Toulon, CNRS, IRD, Mediterranean Institute of Oceanography , F-13009 Marseille , France 31 : Institute of Carbon Cycles, Helmholtz-Zentrum Hereon , Max-Planck-Straße 1, 21502 Geesthacht , Germany 32 : NOAA, National Marine Fisheries Service, Office of Science and Technology , Silver Spring, MD 20910 , USA 33 : Department of Mathematics, University of Bergen , Allégaten 41, 5007 Bergen , Norway 34 : DECOD (Ecosystem Dynamics and Sustainability), IFREMER, INRAe, Institut-Agro-Agrocampus Ouest, rue de L'île d'Yeu , 44311 Nantes Cedex 3 , France 35 : Agri-Food & Biosciences Institute (AFBI), Environment and Marine Sciences Division , 18a Newforge Lane, Belfast BT9 5PX , UK 36 : Centre for Applied Marine Science, Bangor University , Menai Bridge LL59 5AB , UK 37 : EqualSea Lab-Cross-Research in Environmental Technologies (CRETUS), Department of Applied Economics, University of Santiago de Compostela , Santiago de Compostela 15782 , Spain 38 : Department of Informatics, University of Bergen , Allégaten 41, 5007 Bergen , Norway |
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| Source | Ices Journal Of Marine Science (1054-3139) (Oxford University Press (OUP)), 2023-09 , Vol. 80 , N. 7 , P. 1829-1853 | ||||||||||||
| DOI | 10.1093/icesjms/fsad100 | ||||||||||||
| WOS© Times Cited | 1 | ||||||||||||
| Keyword(s) | acoustics, ecology, image, machine learning, omics, profiles, remote sensing, review | ||||||||||||
| 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 ∼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. |
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