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

2023; Oxford University Press; Volume: 80; Issue: 7 Linguagem: Inglês

10.1093/icesjms/fsad100

1095-9289

Peter Rubbens, Stephanie Brodie, Tristan Cordier, Diogo Destro Barcellos, Paul Devos, José A. Fernandes, Jennifer I. Fincham, Alessandra Rodrigues Gomes, Nils Olav Handegard, Kerry L. Howell, Cédric Jamet, Kyrre Heldal Kartveit, Hassan Moustahfid, Clea Parcerisas, Dimitris V. Politikos, Raphaëlle Sauzède, Maria Sokolova, Laura Uusitalo, Laure Van den Bulcke, A.T.M. van Helmond, Jordan T. Watson, Heather Welch, Óscar Darío Beltrán Pérez, Samuel Chaffron, David S. Greenberg, Bernhard Kühn, Rainer Kiko, M. W. Lo, Rubens M. Lopes, Klas Ove Möller, William Michaels, Ahmet Pala, Jean‐Baptiste Romagnan, Pia Schuchert, Vahid Seydi, Sebastián Villasante, Ketil Malde, Jean‐Olivier Irisson,

Coral and Marine Ecosystems Studies

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.