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

Peter Rubbens, Stephanie Brodie, Tristan Cordier, Diogo Destro Barcellos, Paul Devos, Jose Fernandes-Salvador, Jennifer Fincham, Alessandra Gomes, Nils Olav Handegard, Kerry Howell, Cédric Jamet, Kyrre Heldal Kartveit, Hassan Moustahfid, Clea Parcerisas, Dimitris Politikos, Raphaëlle Sauzède, Maria Sokolova, Laura Uusitalo, Laure van den Bulcke, Aloysius T.M. van HelmondJordan Watson, Heather Welch, Oscar Beltran-Perez, Samuel Chaffron, David Greenberg, Bernhard Kühn, Rainer Kiko, Madiop Lo, Rubens Lopes, Klas Ove Möller, William Michaels, Ahmet Pala, Jean-Baptiste Romagnan, Pia Schuchert, Vahid Seydi, Sebastian Villasante, Ketil Malde, Jean-Olivier Irisson*, Christopher Whidden (Editor)

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

57 Citations (Scopus)

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.
Original languageEnglish
Article numberfsad100
Pages (from-to)1829-1853
JournalICES Journal of Marine Science
Volume80
Issue number7
Early online date3 Aug 2023
DOIs
Publication statusPublished - 26 Sept 2023

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