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Impact of an eddy dipole of the Mozambique channel on mesopelagic organisms, highlighted by multifrequency backscatter classification

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  • Pavanee Annasawmy
  • Gildas Roudaut
  • Anne Lebourges Dhaussy

Abstract

The impact of a cyclonic (C), an anticyclonic (AC) eddy and transition zone (TZ), which is the area between the two eddies, on acoustic groups representing various mesopelagic organisms, was investigated using a semi-supervised multifrequency classification approach (hereafter, Escore algorithm). The Escore algorithm involved selecting regions of interest (ROIs) within multifrequency (18, 38, 70, and 120 kHz) echograms and classifying into four clusters or echo-classes using Sv differences (Sv18-38, Sv70-38, and Sv120-38). Acoustic densities and diel vertical migration strength varied between the AC, C, and TZ according to the frequency. The vertical stratification of temperature, salinity and fluorescence within the oceanographic structures had varied influences on the vertical structure of each echo-class which represent zooplankton-like organisms, small and large fish with swimbladders, and small and large siphonophores with pneumatophores. The echo-classes within the C were influenced by surface fluorescence, whereas in the AC and TZ, the echo-classes were influenced by deeper fluorescence and strong EKE. Our study provides new insights into the environmental variables within mesoscale and sub-mesoscale features impacting different groups of mesopelagic communities in the Indian Ocean.

Suggested Citation

  • Pavanee Annasawmy & Gildas Roudaut & Anne Lebourges Dhaussy, 2024. "Impact of an eddy dipole of the Mozambique channel on mesopelagic organisms, highlighted by multifrequency backscatter classification," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-28, September.
  • Handle: RePEc:plo:pone00:0309840
    DOI: 10.1371/journal.pone.0309840
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    References listed on IDEAS

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    1. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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