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Machine learning techniques to characterize functional traits of plankton from image data
Orenstein, E.C.; Ayata, S.-D.; Maps, F.; Becker, E.C.; Benedetti, F.; Biard, T.; de Garidel-Thoron, T.; Ellen, J.S.; Ferrario, F.; Giering, S.L.C.; Guy-Haim, T.; Hoebeke, L.; Iversen, M.H.; Kiørboe, T.; Lalonde, J.-F.; Lana, A.; Laviale, M.; Lombard, F.; Lorimer, T.; Martini, S.; Meyer, A.; Möller, K.O.; Niehoff, B.; Ohman, M.D.; Pradalier, C.; Romagnan, J.-B.; Schröder, S.-M.; Sonnet, V.; Sosik, H.M.; Stemmann, L.S.; Stock, M.; Terbiyik Kurt, T.; Valcárcel-Pérez, N.; Vilgrain, L.; Wacquet, G.; Waite, A.M.; Irisson, J.-O. (2022). Machine learning techniques to characterize functional traits of plankton from image data. Limnol. Oceanogr. 67(8): 1647-1669. https://dx.doi.org/10.1002/lno.12101
In: Limnology and Oceanography. American Society of Limnology and Oceanography: Waco, Tex., etc. ISSN 0024-3590; e-ISSN 1939-5590, more
Peer reviewed article  
Available in  Authors 
Keyword
    Marine/Coastal
Authors  Top 
  • Orenstein, E.C.
  • Ayata, S.-D.
  • Maps, F.
  • Becker, E.C.
  • Benedetti, F.
  • Biard, T.
  • de Garidel-Thoron, T.
  • Ellen, J.S.
  • Ferrario, F.
  • Giering, S.L.C.
  • Guy-Haim, T.
  • Hoebeke, L., more
  • Iversen, M.H.
  • Kiørboe, T., more
  • Lalonde, J.-F.
  • Lana, A.
  • Laviale, M.
  • Lombard, F.
  • Lorimer, T.
  • Martini, S.
  • Meyer, A.
  • Möller, K.O.
  • Niehoff, B.
  • Ohman, M.D.
  • Pradalier, C.
  • Romagnan, J.-B.
  • Schröder, S.-M.
  • Sonnet, V.
  • Sosik, H.M.
  • Stemmann, L.S.
  • Stock, M., more
  • Terbiyik Kurt, T.
  • Valcárcel-Pérez, N.
  • Vilgrain, L.
  • Wacquet, G.
  • Waite, A.M.
  • Irisson, J.-O.
Abstract
    Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
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