• Login
    About WHOAS
    View Item 
    •   WHOAS Home
    • Woods Hole Oceanographic Institution
    • Biology
    • View Item
    •   WHOAS Home
    • Woods Hole Oceanographic Institution
    • Biology
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of WHOASCommunities & CollectionsBy Issue DateAuthorsTitlesKeywordsThis CollectionBy Issue DateAuthorsTitlesKeywords

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Semi-automated image analysis for the identification of bivalve larvae from a Cape Cod estuary

    Thumbnail
    View/Open
    0538.pdf (1.134Mb)
    Date
    2012-07
    Author
    Thompson, Christine M.  Concept link
    Hare, Matthew P.  Concept link
    Gallager, Scott M.  Concept link
    Metadata
    Show full item record
    Citable URI
    https://hdl.handle.net/1912/6629
    As published
    https://doi.org/10.4319/lom.2012.10.538
    DOI
    10.4319/lom.2012.10.538
    Abstract
    Machine-learning methods for identifying planktonic organisms are becoming well-established. Although similar morphologies among species make traditional image identification methods difficult for larval bivalves, species-specific shell birefringence patterns under polarized light permit identification by color and texture-based features. This approach uses cross-polarized images of bivalve larvae, extracts Gabor and color angle features from each image, and classifies images using a Support Vector Machine. We adapted this method, which was established on hatchery-reared larvae, to identify bivalve larvae from a series of field samples from a Cape Cod estuary in 2009. This method had 98% identification accuracy for four hatchery-reared species. We used a multiplex polymerase chain reaction (PCR) method to confirm field identifications and to compare accuracies to the software classifications. Image classification of larvae collected in the field had lower accuracies than both the classification of hatchery species and PCR-based identification due to error in visually classifying unknown larvae and variability in larval images from the field. A six-species field training set had the best correspondence to our visual classifications with 75% overall agreement and individual species agreements from 63% to 88%. Larval abundance estimates for a time-series of field samples showed good correspondence with visual methods after correction. Overall, this approach represents a cost- and time-saving alternative to molecular-based identifications and can produce sufficient results to address long-term abundance and transport-based questions on a species-specific level, a rarity in studies of bivalve larvae.
    Description
    Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography: Methods 10 (2012): 538-554, doi:10.4319/lom.2012.10.538.
    Collections
    • Biology
    Suggested Citation
    Limnology and Oceanography: Methods 10 (2012): 538-554
     
    All Items in WHOAS are protected by original copyright, with all rights reserved, unless otherwise indicated. WHOAS also supports the use of the Creative Commons licenses for original content.
    A service of the MBLWHOI Library | About WHOAS
    Contact Us | Send Feedback | Privacy Policy
    Core Trust Logo