Titre :

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

Auteur(s) :

Rubbens, Peter [Auteur]
Brodie, Stephanie [Auteur]
Cordier, Tristan [Auteur]
Destro Barcellos, Diogo [Auteur]
Devos, Paul [Auteur]
Fernandes-Salvador, Jose [Auteur]
Fincham, Jennifer [Auteur]
Gomes, Alessandra [Auteur]
Handegard, Nils Olav [Auteur]
Howell, Kerry [Auteur]
Jamet, Cédric [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Kartveit, Kyrre Heldal [Auteur]
Moustahfid, Hassan [Auteur]
Parcerisas, Clea [Auteur]
Politikos, Dimitris [Auteur]
Sauzède, Raphaëlle [Auteur]
Sokolova, Maria [Auteur]
Uusitalo, Laura [Auteur]
van den Bulcke, Laure [Auteur]
van Helmond, Aloysius [Auteur]
Watson, Jordan [Auteur]
Welch, Heather [Auteur]
Beltran-Perez, Oscar [Auteur]
Chaffron, Samuel [Auteur]
Greenberg, David [Auteur]
Kühn, Bernhard [Auteur]
Kiko, Rainer [Auteur]
Lo, Madiop [Auteur]
Lopes, Rubens [Auteur]
Möller, Klas Ove [Auteur]
Michaels, William [Auteur]
Pala, Ahmet [Auteur]
Romagnan, Jean-Baptiste [Auteur]
Schuchert, Pia [Auteur]
Seydi, Vahid [Auteur]
Villasante, Sebastian [Auteur]
Malde, Ketil [Auteur]
Irisson, Jean-Olivier [Auteur]
Laboratoire d'océanographie de Villefranche [LOV]

Titre de la revue :

ICES JOURNAL OF MARINE SCIENCE

Pagination :

1829-1853

Éditeur :

Oxford University Press (OUP)

Date de publication :

2023-09-26

ISSN :

1054-3139

Discipline(s) HAL :

Planète et Univers [physics]/Océan, Atmosphère

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Collections :

• Laboratoire d'Océanologie et de Géosciences (LOG) - UMR 8187

Source :

Harvested from HAL

Date de dépôt :

2023-10-25T04:22:14Z