Model-informed classification of broadband acoustic backscatter from zooplankton in an in situ mesocosm

Abstract

Classification of zooplankton to species with broadband echosounder data could increase the taxonomic resolution of acoustic surveys and reduce the dependence on net and trawl samples for ‘ground truthing’. Supervised classification with broadband echosounder data is limited by the acquisition of validated data required to train machine learning algorithms (‘classifiers’). We tested the hypothesis that acoustic scattering models could be used to train classifiers for remote classification of zooplankton. Three classifiers were trained with data from scattering models of four Arctic zooplankton groups (copepods, euphausiids, chaetognaths, and hydrozoans). We evaluated classifier predictions against observations of a mixed zooplankton community in a submerged purpose-built mesocosm (12 m3) insonified with broadband transmissions (185–255 kHz). The mesocosm was deployed from a wharf in Ny-Ålesund, Svalbard, during the Arctic polar night in January 2022. We detected 7722 tracked single targets, which were used to evaluate the classifier predictions of measured zooplankton targets. The classifiers could differentiate copepods from the other groups reasonably well, but they could not differentiate euphausiids, chaetognaths, and hydrozoans reliably due to the similarities in their modelled target spectra. We recommend that model-informed classification of zooplankton from broadband acoustic signals be used with caution until a better understanding of in situ target spectra variability is gained.

Broadband acoustical scattering in coastal environments: application to gelatinous organisms

“…An important advancement in acous�cal oceanography was the development broadband techniques which not only led to improvements in the spa�al resolu�on of acous�cal measurements (Lavery et al, 2017;Stanton et al, 2012), but also enabled received signals to be interpreted in frequency space for more complex analyses including discrimina�ng between types of scaterers and obtaining addi�onal informa�on about the size and other physical proper�es of scaterers (e.g Agersted et al, 2021;Basset et al, 2018Basset et al, , 2020Benoit-Bird and Waluk, 2020;Blanluet et al, 2019;Coter et al, 2021;Dunn et al, 2023;Forland et al, 2014;Kubilius et al, 2020;Lavery et al, 2007Lavery et al, , 2010Nesse et al, 2009;Stanton, 1996;Stanton et al, 1993Stanton et al, , 1994Stanton et al, , 1998Stanton et al, , 2003Chu, 2000, 2004).…”