IS12B:
Moving Beyond Technology: How Quantitative Image-Based Methods Help Reveal Ocean Ecology I

Session ID#: 93276

Session Description:
Over the past few decades, numerous methods (high-resolution optical or acoustic imaging) have been developed to image organisms and marine snow. These methods are typically used to detect, measure, and enumerate organisms and particles from samples, or directly in situ. The increasing use of imaging systems has permitted description of planktonic communities with unprecedented levels of spatial and temporal resolution, together with their linkages with biogeochemical cycles and environmental constraints.

However, beyond the technological challenge of obtaining high quality images for scientific purposes, there is a need to scale up such measurements to obtain globally standardized observations, aided by advanced Machine Learning methods for image classification.

These developments will bring significant challenges for the coming years: 1) management,  integration, and cross-calibration of the massive data flow originating from a network of different instruments; 2) automation of the classification of objects into taxonomic/morphological categories of scientific interest and import; 3) processing of data in near-real time, in particular for sensors embedded in autonomous platforms; 4) provision of collaborative pathways to visualize, annotate, quality control, and share the resulting data; and 5) integration of such data with existing multidisciplinary environmental sciences databases.

 

We invite presentations focusing on imaging and Machine Learning methods, but extending beyond pure technological challenges, in order to provide insights into ecological and biogeochemical processes in the ocean environment.

Co-Sponsor(s):
  • ME - Marine Ecology and Biodiversity
  • OB - Ocean Biology and Biogeochemistry
Index Terms:

1942 Machine learning [INFORMATICS]
4855 Phytoplankton [OCEANOGRAPHY: BIOLOGICAL]
4890 Zooplankton [OCEANOGRAPHY: BIOLOGICAL]
4894 Instruments, sensors, and techniques [OCEANOGRAPHY: BIOLOGICAL]
Primary Chair:  Fabien Lombard, Sorbonne University, Laboratoire d'Oceanographie de Villefranche sur Mer, Villefranche Sur Mer, France
Co-chairs:  Mark D Ohman, Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, United States, Heidi M Sosik, Woods Hole Oceanographic Institution, Woods Hole, MA, United States and Anya M Waite, Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada
Primary Liaison:  Fabien Lombard, Sorbonne University, Laboratoire d'Oceanographie de Villefranche sur Mer, Villefranche Sur Mer, France
Moderators:  Mark D Ohman, University of California San Diego, Scripps Institution of Oceanography, La Jolla, CA, United States and Heidi M Sosik, Woods Hole Oceanographic Institution, Woods Hole, MA, United States
Student Paper Review Liaison:  Fabien Lombard, Sorbonne University, Laboratoire d'Oceanographie de Villefranche sur Mer, Villefranche Sur Mer, France

Abstracts Submitted to this Session:

Characterizing zooplankton vertical distribution in the Sargasso Sea using an Underwater Vision Profiler (639011)
Joshua Stone, University of South Carolina, Biological Sciences, Columbia, United States, Brendan D. Turley, University of South Carolina, School of the Earth, Ocean, and Environment, Columbia, SC, United States and Ryan R Rykaczewski, NOAA Pacific Islands Fisheries Science Center, Honolulu, United States
'Community Composition and Size Distribution of Mesozooplankton in the Upper Kilometer of the Global Ocean' (643050)
Yawouvi Soviadan, Laboratoire de Géosciences Environnementales, Université de Lomé-Togo, Lomé, Togo; Laboratoire d'Océanographie de Villefranche sur Mer, Villefranche sur Mer, France and Lars Stemmann, Laboratoire d’Océanographie de Villefranche (LOV), UMR 7093, Sorbonne Université, Villefranche sur mer, France
Typology of Plankton Communities seen by In Situ Imaging, from the Epi to the Mesopelagic Layers of the Global Ocean (646523)
Thelma Panaiotis1, Marcel Babin2, Tristan Biard3, Francois Carlotti4, Laurent Coppola1, Lionel Guidi1, Helena Hauss5, Lee Karp-Boss6, Rainer Kiko7, Fabien Lombard1, Andrew M. P. McDonnell8, Marc Picheral9, Andreas Rogge10,11, Anya M Waite12, Jean Olivier Irisson1 and Lars Stemmann1, (1)Laboratoire d'Océanographie de Villefranche (LOV), UMR 7093, Sorbonne Université, Villefranche-sur-Mer, France, (2)Takuvik Joint International Laboratory, Université Laval & CNRS, Québec, QC, Canada, (3)Laboratoire d'Océanologie et de Géosciences (LOG), UMR 8187, Université du Littoral Côte d'Opale, Wimereux, France, (4)CNRS - Aix Marseille University, Mediterranean Institute of Oceanography, Marseille, France, (5)NORCE Norwegian Research Centre, Bergen, Norway, (6)University of Maine, School of Marine Sciences, Orono, United States, (7)GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany, (8)University of Alaska Fairbanks, Fairbanks, United States, (9)Laboratoire d’Océanographie de Villefranche (LOV), UMR 7093, Sorbonne Université, Villefranche-sur-Mer, France, (10)Institute for Ecosystem Research, Kiel University, Kiel, Germany, (11)Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research, Section Benthopelagic Processes, Bremerhaven, Germany, (12)Ocean Frontier Institute, Dalhousie University, Halifax, NS, Canada
Morphological diversity increases with oligotrophy along a zooplankton time series (646202)
Jean-Olivier Irisson1, Caroline Cailletton1, Corinne Desnos2, Laetitia Jalabert2, Amanda Elineau2, Lars Stemmann3 and Sakina-Dorothée Ayata3, (1)Sorbonne Université, Laboratoire d'Océanographie de Villefranche (LOV), Villefranche sur mer, France, (2)Sorbonne Université, Institut de la Mer de Villefranche (IMEV), France, (3)Sorbonne Université, Laboratoire d'Océanographie de Villefranche (LOV), France
High-resolution time series of plankton to understand trait-based community and food-web dynamics (651295)
Ewa Merz1, Jules S Jaffe2, Paul L Roberts3, Peter D Isles1, Marta Reyes1, Thomas Lormier1, Thea Kozakiewicz1, Nelson Stevens1, Stuart Dennis1 and Francesco Pomati1, (1)EAWAG Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland, (2)Scripps Institution of Oceanography, La Jolla, CA, United States, (3)Monterey Bay Aquarium Research Institute, Moss Landing, CA, United States
Quantitative Imaging Flow Cytometry Reveals Pigment-Based Overestimation of Diatoms and Microplankton in the North Atlantic (643992)
Alison Chase, University of Maine, Orono, ME, United States, Nils Haëntjens, University of Maine, School of Marine Sciences, Orono, ME, United States, Emmett Culhane, Yale University, New Haven, CT, United States, Sasha Jane Kramer, University of California Santa Barbara, Santa Barbara, United States, Emmanuel Boss, University of Maine, Orono, United States and Lee Karp-Boss, University of Maine, School of Marine Sciences, Orono, United States
Megafauna community assessment with cameras: Platform, annotator and methodology comparison (650864)
Timm Schoening1, Autun Purser2, Daniel Langenkämper3, Inken Suck1, James Taylor4, Daphne Cuvelier5, Lidia Lins6, Erik Simon-Lledó7, Yann Marcon8, Daniel Jones9, Tim W Nattkemper3, Kevin Köser1, Martin Zurowietz3, Jose Nuno Gomez-Pereira10 and Jens Greinert11, (1)GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, (2)Alfred-Wegener-Institute Helmholtz-Centrum for Polar and Marine Research, Bremerhaven, Germany, (3)Bielefeld University, Germany, (4)RIKEN Advanced Institute for Computational Sciences, Kobe, Japan, (5)MARE - Marine and environmental sciences centre / IMAR / Okeanos, University of the Acores, Portugal, (6)Ghent University, Gent, Belgium, (7)University of Southampton, National Oceanography Centre, United Kingdom, (8)Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research Bremerhaven, Bremerhaven, Germany, (9)University of Southampton, National Oceanography Centre, Southampton, United Kingdom, (10)Naturalist, Portugal, (11)GEOMAR Helmholtz Centre for Ocean Research Kiel, Marine Geosystems - DeepSea Monitoring, Kiel, Germany