Journal
CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES
Volume 80, Issue 3, Pages 451-467Publisher
CANADIAN SCIENCE PUBLISHING
DOI: 10.1139/cjfas-2022-0105
Keywords
broadband acoustics; inversion; machine learning; autonomous surface vehicle; zooplankton
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Zooplankton and ichthyoplankton in oceans can be detected as horizontal sound scattering layers (SSLs). However, quantifying their composition and density is subject to sampling biases. This study used autonomous hydroacoustic surveys and trawl sampling to investigate the epipelagic fauna in northern Norway. The inverse method applied to autonomous acoustic surveys can improve density estimates by diminishing avoidance biases and increasing the spatio-temporal resolution of ship-based surveys.
Throughout all oceans, aggregations of zooplankton and ichthyoplankton appear as horizontal sound scattering layers (SSLs) when detected with active acoustic techniques. Quantifying the composition and density of these layers is prone to sampling biases. We conducted a net and trawl survey of the epipelagic fauna in northern Norway (70 degrees N) in June 2018 while an au-tonomous surface vehicle equipped with a broadband echosounder (283-383 kHz) surveyed the same region. Densities from the autonomous hydroacoustic survey were calculated using forward estimates from the relative density from the net and trawl, and inversion estimates with statistical data-fitting. All four methods (net, trawl, acoustic forward and inverse methods) identified that copepods dominated the epipelagic SSL, while pteropods, amphipods and fish larvae were present in low densi-ties. The density estimates calculated with the inverse method were higher for mobile zooplankton, such as euphausiid larvae, than with the other methods. We concluded that the inverse method applied to broadband autonomous acoustic surveys can improve density estimates of epipelagic organisms by diminishing avoidance biases and increasing the spatio-temporal reso-lution of ship-based surveys.
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