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

Type Article
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 IORCID9, Gomes Alessandra6, Handegard Nils OlavORCID10, Howell Kerry11, Jamet Cédric12, Kartveit Kyrre Heldal10, Moustahfid Hassan13, Parcerisas CleaORCID1, 7, Politikos DimitrisORCID14, Sauzède RaphaëlleORCID15, Sokolova MariaORCID16, Uusitalo LauraORCID17, 18, Van den bulcke Laure19, 20, Van helmond Aloysius T M21, Watson Jordan TORCID22, Welch Heather3, Beltran-Perez OscarORCID23, Chaffron Samuel24, 25, Greenberg David S26, Kühn Bernhard27, Kiko RainerORCID28, 29, Lo Madiop30, Lopes Rubens MORCID6, Möller Klas OveORCID31, Michaels William32, Pala Ahmet10, 33, Romagnan Jean-Baptiste34, Schuchert Pia35, Seydi Vahid36, Villasante Sebastian37, Malde KetilORCID10, 38, Irisson Jean-OlivierORCID28
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
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

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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Rubbens Peter, Brodie Stephanie, Cordier Tristan, Destro barcellos Diogo, Devos Paul, Fernandes-Salvador Jose A, Fincham Jennifer I, Gomes Alessandra, Handegard Nils Olav, Howell Kerry, Jamet Cédric, Kartveit Kyrre Heldal, Moustahfid Hassan, Parcerisas Clea, Politikos Dimitris, Sauzède Raphaëlle, Sokolova Maria, Uusitalo Laura, Van den bulcke Laure, Van helmond Aloysius T M, Watson Jordan T, Welch Heather, Beltran-Perez Oscar, Chaffron Samuel, Greenberg David S, Kühn Bernhard, Kiko Rainer, Lo Madiop, Lopes Rubens M, Möller Klas Ove, Michaels William, Pala Ahmet, Romagnan Jean-Baptiste, Schuchert Pia, Seydi Vahid, Villasante Sebastian, Malde Ketil, Irisson Jean-Olivier (2023). Machine learning in marine ecology: an overview of techniques and applications. Ices Journal Of Marine Science, 80(7), 1829-1853. Publisher's official version : , Open Access version :