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Machine learning in marine ecology: An overview of techniques and applications
Rubbens, P.; Brodie, S.; Cordier, T.; Barcellos, D.D.; Devos, P.; Fernandes-Salvador, J.A.; Fincham, J.I.; Gomes, A.; Handegard, N.O.; Howell, K.; Jamet, C.; Kartveit, K.H.; Moustahfid, H.; Parcerisas, C.; Politikos, D.; Sauzède, R.; Sokolova, M.; Uusitalo, L.; Van den Bulcke, L.; van Helmond, A.T.M.; Watson, J.T.; Welch, H.; Beltran-Perez, O.; Chaffron, S.; Greenberg, D.S.; Kühn, B.; Kiko, R.; Lo, M.; Lopes, R.M.; Möller, K.O.; Michaels, W.; Pala, A.; 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./J. Cons. int. Explor. Mer 80(7): 1829-1853. https://dx.doi.org/10.1093/icesjms/fsad100
In: ICES Journal of Marine Science. Academic Press: London. ISSN 1054-3139; e-ISSN 1095-9289
Peer reviewed article  
Available in  Authors 
    Vlaams Instituut voor de Zee: Open access 392797 [ download pdf ]
Keywords
    Ecology
    Machine learning
    Physics > Acoustics
    Profiles
    Remote sensing
    Marine/Coastal
Author keywords
    image, omics, review
Project Top | Authors 
  • Marine Soundscapes in Shallow Water: Automated Tools for Characterization and Analysis
Authors  Top 
  • Rubbens, P.
  • Brodie, S.
  • Cordier, T.
  • Barcellos, D.D.
  • Devos, P.
  • Fernandes-Salvador, J.A.
  • Fincham, J.I.
  • Gomes, A.
  • Handegard, N.O.
  • Howell, K.
  • Jamet, C.
  • Kartveit, K.H.
  • Moustahfid, H.
  • Parcerisas, C.
  • Politikos, D.
  • Sauzède, R.
  • Sokolova, M.
  • Uusitalo, L.
  • Van den Bulcke, L.
  • van Helmond, A.T.M.
  • Watson, J.T.
  • Welch, H.
  • Beltran-Perez, O.
  • Chaffron, S.
  • Greenberg, D.S.
  • Kühn, B.
  • Kiko, R.
  • Lo, M.
  • Lopes, R.M.
  • Möller, K.O.
  • Michaels, W.
  • Pala, A.
  • Romagnan, J.-B.
  • Schuchert, P.
  • Seydi, V.
  • Villasante, S.
  • Malde, K.
  • Irisson, J.-O.
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.
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