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

Aug 2, 2026

Machine learning methods are reviewed to explain how they are used in marine ecology research and management. The article surveys major techniques, highlights typical data inputs such as species observations, environmental variables, and ecosystem measurements, and summarizes application patterns across modelling, prediction, classification, and decision support. Emphasis is placed on practical workflows, strengths and limitations, and how methodological choices affect interpretability and reliability. The review connects methodological development with ecological questions and provides guidance for applying machine learning responsibly in ocean studies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

Introduction

Overview of machine learning techniques

Common modelling and prediction approaches

Data sources and feature preparation

Applications in marine ecology

Species and ecosystem modelling

Environmental and ecological forecasting

Challenges, limitations, and best practices

Conclusion

ICES Journal of Marine Science, 2023, 80, 1829–1853 DOI: 10.1093/icesjms/fsad100 Advance access publication date: 3 August 2023 Review Article Machine learning in marine ecology: an overview of techniques and applications Peter Rubbens1,2 , Stephanie Brodie3 , Tristan Cordier4,5 , Diogo Destro Barcellos6 , Paul Devos7 , Jose A. Fernandes-Salvador8 , Jennifer I. Fincham 9 , Alessandra Gomes6 , Nils Olav Handegard 10 , Kerry Howell11 , Cédric Jamet12 , Kyrre Heldal Kartveit10 , Hassan Moustahfid13 , Clea Parcerisas 1,7 , Dimitris Politikos 14 , Raphaëlle Sauzède 15 , Maria Sokolova 16 , Laura Uusitalo 17,18 , Laure Van den Bulcke19,20 , Aloysius T. M. van Helmond21 , Jordan T. Watson 22,23 , Heather Welch3 , Oscar Beltran-Perez 24 , Samuel Chaffron25,26 , David S. Greenberg27 , Bernhard Kühn28 , Rainer Kiko 29,30 , Madiop Lo31 , Rubens M. Lopes 6 , Klas Ove Möller 32 , William Michaels33 , Ahmet Pala34,10 , Jean-Baptiste Romagnan35 , Pia Schuchert36 , Vahid Seydi37 , Sebastian Villasante38 , Ketil Malde 10,39 , and Jean-Olivier Irisson 29 ,* 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 8AZTI, 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 16Wageningen 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 21Wageningen University and Research, Wageningen Marine Research, 1976 CP IJmuiden, The Netherlands 22 Present affiliation: Pacific Islands Ocean Observing System, University of Hawai‘i at Mnoa, 1680 East West Road, POST 815, Honolulu HI 96822, USA 23Auke Bay Laboratory, National Oceanic and Atmospheric Administration, 17609 Pt. Lena Loop Rd., Juneau, AK 99801, USA 24 Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Seestrasse 15, 18119 Rostock, Germany 25 Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France 26 Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara Oceans GOSEE, F-75016 Paris, France 27 Institute of Coastal Systems, Helmholtz-Zentrum Hereon, Max-Planck-Straße 1, 21502 Ge

FAQs

What techniques does the review cover for machine learning in marine ecology?

The review summarizes key machine learning approaches and how they are typically structured for ecological tasks, including modelling and prediction pipelines.

Which kinds of data are commonly used in marine ecology applications?

Applications draw on ecological observations and measurements alongside environmental variables, with emphasis on preparing inputs that support reliable learning.

What challenges are highlighted when applying machine learning to marine ecology?

The document discusses limitations and practical concerns such as interpretability and reliability, showing how methodological choices influence ecological conclusions.

Aug 2, 2026

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