OD44B:
Machine Learning in Biological Oceanography I Posters


Session ID#: 28080

Session Description:
Recent technological advances in instrumentation and computing have allowed scientists across all disciplines to collect an unprecedented amount of data. Biological oceanographers in particular are now faced with vast datasets that stymie traditional analysis methods. Scientists are increasingly leveraging machine learning (ML) techniques to process and analyze these information rich datasets. ML algorithms are designed to learn from one dataset to make accurate predictions about a new, independent one. While specific application domains might be quite different, the ML approaches used for analysis are often very similar. This session therefore aims to (1) identify new ML methods or applications, (2) examine overlap in disciplines applying similar ML techniques, (3) facilitate discussion and interdisciplinary collaborations among ML practitioners in the ocean science community, and (4) identify gaps and specific needs for oceanographers using ML. The session chairs welcome any submission detailing work on ML methods for ecological data analysis and inference in aquatic systems. The session is intended to have a broad scope and we invite abstracts from diverse fields such as imaging, acoustics, genomics, and modeling.
Primary Chair:  Eric Coughlin Orenstein, Scripps Institution of Oceanography, La Jolla, CA, United States
Co-chairs:  Jessica Y. Luo, National Center for Atmospheric Research, Climate and Global Dynamics, Boulder, CO, United States, John Burns, University of Hawai'i, Hawai'i Institute of Marine Biology, Papaikou, HI, United States and Ludwig Houegnigan, Polytechnic University of Catalonia, Barcelona Tech, ETSEIB, Barcelona, Spain
Moderators:  John Burns, University of Hawai'i, Hawai'i Institute of Marine Biology, Papaikou, HI, United States and Ludwig Houegnigan, Polytechnic University of Catalonia, ETSEIB, Barcelona, Spain
Student Paper Review Liaison:  John Burns, University of Hawai'i, Hawai'i Institute of Marine Biology, Papaikou, HI, United States
Index Terms:

4858 Population dynamics and ecology [OCEANOGRAPHY: BIOLOGICAL AND CHEMICAL]
4894 Instruments, sensors, and techniques [OCEANOGRAPHY: BIOLOGICAL AND CHEMICAL]
9820 Techniques applicable in three or more fields [GENERAL OR MISCELLANEOUS]
Cross-Topics:
  • F - Fisheries

Abstracts Submitted to this Session:

Applying Image Recognition to Enhance Fisheries Management Capabilities (302033)
Tzofi Malki Klinghoffer1, Caleb Perez2, Robert Vincent3, Paris Perdikaris3 and Chryssostomos Chryssostomidis3, (1)University of Alabama, Department of Computer Science, Tuscaloosa, AL, United States, (2)University of Washington Seattle, Department of Bioengineering, Seattle, WA, United States, (3)MIT Sea Grant, Cambridge, MA, United States
Automated Ocean Front Delineation from SST Image Products (305986)
David Lewis1, Richard L Crout2, Richard W Gould Jr3, Jason Jolliff4, Sean McCarthy1 and Dara D H Cadden5, (1)Naval Research Laboratory, Stennis Space Center, MS, United States, (2)John C. Stennis Space Center, Stennis Space Center, MS, United States, (3)Naval Research Lab., Ocenography, Stennis Space Center, MS, United States, (4)Naval Research Lab Stennis Space Center, Stennis Space Center, MS, United States, (5)Naval Oceanographic Office, Stennis Space Center, MS, United States
Heuristic Classification of Hydrophone Recordings at the Meso-American Reef (307251)
Daniel Wolff, University of North Carolina Greensboro, Greensboro, NC, United States, Patricia Gray, University of North Carolina Greensboro, NC, United States and Rafael de la Parra, Ch'ooj Ajauil AC, Mexico
Fast and Automatic Classification of Corals from Underwater Images (320685)
Ying MA1, Bing Ouyang2, Jose Principe3, John Reed2 and Stephanie Farrington, (1)University of Florida, Gainesville, FL, United States, (2)Florida Atlantic University, Harbor Branch Oceanographic Institute, Fort Pierce, FL, United States, (3)University of Florida, FL, United States
Identification of Microbial Biosignatures in Ship Microbiomes (321657)
Stephen Monell Techtmann, Ryan B Ghannam, Timothy M Butler and Matthew Brenemann, Michigan Technological University, Biological Sciences, Houghton, MI, United States
A workflow to enable insights into plankton ecology from large image datasets: Automated analysis, classification, and quantification of taxon-resolved abundance and biomass from Imaging FlowCytobot (322457)
Anya M Waite1, Joe Futrelle1, Louis W. Kilfoyle2 and Emily Peacock1, (1)Woods Hole Oceanographic Institution, Woods Hole, MA, United States, (2)Brown University, Providence, RI, United States
Automatic fish species classification with deep learning (323650)
Xiaopeng Cai1,2, Longzhuang Li1 and Philippe Tissot2, (1)Texas A&M University Corpus Christi, Department of Computing Sciences, Corpus Christi, TX, United States, (2)Texas A&M University Corpus Christi, Conrad Blucher Institute, Corpus Christi, TX, United States
Factors Influencing Ontogenetic Diet Shifts of Young of the Year Pollock: Predicting Fish Length from Diet Composition (323659)
Nissa Cooper Ferm1, Lauren Rogers2 and Matthew T. Wilson2, (1)NOAA/NMFS/ Lynker Technologies- Under Contract to Alaska Fisheries Science Center, Resource Assessment and Conservation Engineering Division, Seattle, WA, United States, (2)NOAA, Alaska Fisheries Science Center, Seattle, WA, United States
Exploiting laboratory images to improve automated classification performance on in situ plankton image data (324027)
Eric Coughlin Orenstein1, Kevin T Le2, Pedro Maravilha Morgado3, Paul L Roberts1, Melissa Carter1, Peter J. S. Franks1, Jules S Jaffe1, Nuno M. Vasconcelos2, William Chen2 and Students of UCSD-ECE191: Senior Capstone Design , (1)Scripps Institution of Oceanography, La Jolla, CA, United States, (2)University of California San Diego, Electrical and Computer Engineering, La Jolla, CA, United States, (3)University of California San Diego, Electrical and Computer Engineering, San Diego, CA, United States
Patterns of occurrence in marine diatom chains (324081)
Kasia M Kenitz, Eric Coughlin Orenstein, Paul L Roberts, Peter J. S. Franks, Jules S Jaffe and Andrew David Barton, Scripps Institution of Oceanography, La Jolla, CA, United States

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