Project 3993: Gerlien Verhaegen, Emiliano Cimoli, Dhugal Lindsay. 2021. Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning. Biodiversity Data Journal. 9:e69374.
Specimen: Diplulmaris antarctica Maas, 1908 (UTAS:MCMEC2019_Diplulmaris_antarctica_a)
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Abstract

Background
Southern Ocean ecosystems are currently experiencing increased environmental changes and anthropogenic pressures, urging scientists to report on their biodiversity and biogeography. Two major taxonomically diverse and trophically important gelatinous zooplankton groups that have, however, stayed largely understudied until now are the cnidarian jellyfish and ctenophores. This data scarcity is predominantly due to many of these fragile, soft-bodied organisms being easily fragmented and/or destroyed with traditional net sampling methods. Progress in alternative survey methods including, for instance, optics-based methods is slowly starting to overcome these obstacles. As video annotation by human observers is both time-consuming and financially costly, machine learning techniques should be developed for the analysis of in situ/in aqua image-based datasets. This requires taxonomically accurate training sets for correct species identification and the present paper is the first to provide such data.

New information
In this study, we twice conducted three week-long in situ optics-based surveys of jellyfish and ctenophores found under the ice in the McMurdo Sound, Antarctica. Our study constitutes the first optics-based survey of gelatinous zooplankton in the Ross Sea and the first study to use in situ/in aqua observations to describe taxonomic, and some trophic and behavioral characteristics of gelatinous zooplankton from the Southern Ocean. Despite the small geographic and temporal scales of our study, we provided new undescribed morphological traits for all observed gelatinous zooplankton species (eight cnidarian and four ctenophore species). Three ctenophores and one leptomedusa likely represent undescribed species. Furthermore, along with the photography and videography, we prepared a Common Objects in Context (COCO) dataset, so that this study is the first to provide a taxonomist-ratified image training set for future machine learning algorithm development concerning Southern Ocean gelatinous zooplankton species.



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Article DOI: https://doi.org/10.3897/BDJ.9.e69374

Project DOI: 10.7934/P3993, http://dx.doi.org/10.7934/P3993
This project contains
  • 395 Media
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  • Creation Date:
    27 April 2021
  • Publication Date:
    14 August 2021
  • Project views: 6222

    Authors' Institutions

    • University of Tasmania

    • Japan Agency for Marine-Earth Science and Technology



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    Gerlien Verhaegen
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