Machine learning in marine ecology: an overview of techniques and applications
| Type | : | ACL |
|---|---|---|
| Nature | : | Production scientifique |
| Au bénéfice du Laboratoire | : | Oui |
| Statut de publication | : | Publié |
| Année de publication | : | 2023 |
| Auteurs (38) | : | RUBBENS Peter BRODIE Stephanie CORDIER Tristan DESTRO BARCELLOS Diogo DEVOS Paul FERNANDES-SALVADOR Jose,a FINCHAM Jennifer,i GOMES Alessandra HANDEGARD N,o HOWELL Kerry JAMET Cédric KARTVEIT Kyrre,heldal MOUSTAHFID Hassan PARCERISAS Clea POLITIKOS Dimitris SAUZEDE Raphaelle 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 LOCRITANI M LOPES Rubens,m MÖLLER Klas,o MICHAELS William PALACIO-CASTRO Ana-maria ROMAGNAN Jean-baptiste SCHUCHERT Pia SEYDI Vahid VILLASANTE Sebastian MALDE Ketil IRISSON Jean-olivier |
| Revue scientifique | : | Ices Journal of Marine Science |
| Volume | : | |
| Fascicule | : | |
| Pages | : | |
| DOI | : | 10.1093/icesjms/fsad100 |
| URL | : | https://doi.org/10.1093/icesjms/fsad100 |
| 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. |
| Mots-clés | : | - |
| Commentaire | : | - |
| Tags | : | - |
| Fichier attaché | : | - |
| Citation | : |
Rubbens P, Brodie S, Cordier T, Destro Barcellos D, Devos P, Fernandes-Salvador JA, Fincham JI, Gomes A, Handegard NO, Howell K, Jamet C, Kartveit KH, Moustahfid H, Parcerisas C, Politikos D, Sauzede R, Sokolova M, Uusitalo L, Van den Bulcke L, Van Helmond ATM, Watson JT, Welch H, Beltran-Perez O, Chaffron S, Greenberg DS, Kühn B, Kiko R, Locritani M, Lopes RM, Möller KO, Michaels W, Palacio-Castro A-M, Romagnan J-B, Schuchert P, Seydi V, Villasante S, Malde K, Irisson J-O (2023) Machine learning in marine ecology: an overview of techniques and applications. Ices J Mar Sci | doi: 10.1093/icesjms/fsad100
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